Frekvensanalyse av ikke-stationære hydrologiske ekstremer for å redusere flomrisiko i et forandrende klima

• Xie, Kun; Li, Lu; Chen, Hua; Mayer, Stephanie; Dobler, Andreas & Xu, Chong-Yu [Vis alle 7 forfattere av denne artikkelen] (2025). Enhanced evaluation of hourly and daily extreme precipitation in Norway from convection-permitting models at regional and local scales. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 29(8), s. 2133–2152. doi: 10.5194/hess-29-2133-2025. Fulltekst i vitenarkiv Vis sammendrag

  Convection-permitting regional climate models (CPRCMs) have demonstrated enhanced capability in capturing extreme precipitation compared to regional climate models (RCMs) with convection parameterization schemes. Despite this, a comprehensive understanding of their added values in terms of daily or hourly extremes, especially at the local scale, remains limited. In this study, we conduct a thorough comparison of daily and hourly extreme precipitation from the HARMONIE Climate (HCLIM) model at 3 km resolution (HCLIM3) and 12 km resolution (HCLIM12) across Norway's diverse landscape, divided into five regions, using both gridded and in situ observations. Our main focus is on investigating the added value of CPRCMs (i.e., HCLIM3) compared to RCMs (i.e., HCLIM12) for extreme precipitation from regional to local scales and on quantifying to what extent CPRCMs can reproduce the orographic effect on extreme precipitation at both daily and hourly scales. We find that HCLIM3 matches observations better than HCLIM12 for daily and hourly extreme precipitation across most grid points in Norway, while HCLIM12 underestimates the extremes, especially for hourly extremes. At the regional scale, HCLIM3 captures the maximum 1 d precipitation (RX1 d) and the maximum 1 h precipitation (Rx1 h) more accurately across most regions and seasons, with some exceptions. Specifically, for daily extremes, it shows larger summer biases in the east, south and west, as well as return levels biases in the east; for hourly extremes, larger biases are observed in the summer and in the west compared to HCLIM12. Besides this, for the local scale, HCLIM3 also outperforms HCLIM12 in most regions and seasons, except for a slightly larger summer bias in terms of daily extremes in the south and west. Overall, HCLIM3 consistently demonstrates added value in simulating daily extremes in the middle and northern regions at both regional and local scales, as well as in simulating hourly extremes at all 10 stations, compared with HCLIM12. Both HCLIM3 and HCLIM12 capture the seasonality of daily extremes well, while HCLIM3 performs better for the hourly extremes, accurately representing their frequency and intensity. Additionally, both models capture the reverse orographic effect of Rx1 h at the regional scale, with no added value seen in HCLIM3, while, at the local scale, HCLIM3 shows added value compared to HCLIM12 in representing the reverse orographic effect of Rx1 d in all seasons except summer. This study highlights the importance of more realistic CPRCMs in providing reliable insights into the characteristics of precipitation extremes across Norway's five regions. Such information is crucial for effective adaptation management to mitigate severe hydro-meteorological hazards, especially for the local extremes.

• Xiong, Bin; Zheng, Shuchen; Ma, Qiumei; Fu, Chun; Wen, Tianfu & He, Zhongzheng [Vis alle 8 forfattere av denne artikkelen] (2024). Robustness of design flood estimates under nonstationary conditions: parameter sensitivity perspective. Stochastic environmental research and risk assessment (Print). ISSN 1436-3240. 38, s. 2297–2314. doi: 10.1007/s00477-024-02680-9. Fulltekst i vitenarkiv Vis sammendrag

  The changing frequency of flooding in global watersheds, driven by various human and natural factors like land use/cover changes and global warming, necessitates innovative approaches in flood frequency analysis and risk assessment. Nonetheless, the reliability of nonstationary frequency analysis models remains a concern given challenges in accurately measuring the uncertainty introduced by these methods and the impact on design flood values. In this study, deviation-based differential sensitivity indices, including single-parameter (SDDSI) and entire-parameter (EDDSI) measures were developed to assess the influence of parameter uncertainty in nonstationary models using Bayesian statistics and "equivalent reliability" nonstationary design. The Weihe River, the largest tributary of the Yellow River which is experiencing both climate change and heavy impact of human activities, is chosen to be the study area to investigate the impact of precipitation change and land use change on nonstationary flood frequency. Results show that in the One-At-A-Time (OAT) sensitivity analysis under a small uncertainty scenario (SUS) for parameter inputs, the shape parameter stands out as the most influential factor (SDDSI_SUS = 0.347) affecting the 100-year design flood in the Stationary Generalized Extreme Value (SGEV) model. For the Non-Stationary GEV (NGEV) models, the influence of this parameter is less pronounced, with SDDSI_SUS values of 0.095 and 0.093 for the SSP126 and SSP585 scenarios, respectively. Instead, attention turns to the regression coefficient of the grassland area, associated with the GEV scale parameter. In global sensitivity analysis under the posterior uncertainty scenario (PUS) for parameter inputs, the EDDSI_PUS values for SGEV, NGEV_SSP126, and NGEV_SSP585 models were 0.52, 1.41, and 1.30, respectively, inferring heightened sensitivity of NGEV models to perturbations from entire parameters. It is anticipated that incorporating additional evidence, such as historical flood data, is essential for accurate nonstationary hydrological design to mitigating the influence of parameter uncertainty. The sensitivity indices in this study provide significant insights for assessing the robustness of nonstationary hydrological design in flood risk management and applications.

• Zhu, Yongwei; Jiang, Shanhu; Ren, Liliang; Yan, Denghua; Cui, Hao & Wang, Menghao [Vis alle 7 forfattere av denne artikkelen] (2024). A Systematic Feedback Assessment Framework to Identify the Impact of Climate Change and Ecological Restoration on Water Yield Patterns. Water resources management. ISSN 0920-4741. 38, s. 3179–3195. doi: 10.1007/s11269-024-03808-w. Fulltekst i vitenarkiv
• Xiang, Xin; Guo, Shenglian; Cui, Zhen; Wang, Le & Xu, Chong-Yu (2024). Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method. Journal of Hydrology. ISSN 0022-1694. 642. doi: 10.1016/j.jhydrol.2024.131867. Fulltekst i vitenarkiv Vis sammendrag

  The advent of deep learning techniques has shown promising improvement in flood forecasting accuracy which are crucial for operating reservoir and mitigating flood damages. However, the practical application of typical deep learning models, such as the Long Short-Term Memory network (LSTM), is hindered by their high complexity, computational cost, and lack of interpretability. This study introduces an Explainable Convolutional Neural Network (ECNN) model that can effectively capture inter-variable relationships and temporal dynamics to address these challenges. The proposed ECNN model was applied to the Lushui basin in China and compared with benchmark models. The interpretability of the ECNN model was analyzed using the Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our findings demonstrated that the ECNN model exhibits exceptional performance in flood forecast tasks, particularly for long-term forecast horizons. Furthermore, precipitation and inflow play a decisive role in flood forecasting with the watershed-specific flood characteristics. The ECNN model is highly promising in real-time flood forecasting with ingenious parallel structure, remarkable accuracy, and interpretability.

• Lan, Tian; Du, Xinyue; Li, Weijing; Zhong, Hongbo; Zhong, Siqi & Ren, Congfeng [Vis alle 8 forfattere av denne artikkelen] (2024). Characterizing anthropogenic disturbance for sustainable hydrological regimes based on Information Theory. Journal of Hydrology X. 649. doi: 10.1016/j.jhydrol.2024.132449. Fulltekst i vitenarkiv Vis sammendrag

  Anthropogenic activities pose significant threats to hydrological regimes, leading to water resource exhaustion through intensive streamflow consumption. To identify regions where anthropogenic activities have a significant impact on streamflow under conditions of non-stationarity and non-linearity, this study developed the Anthropogenic Disturbance for Sustainable Hydrological Regimes (ADS) index using Information Theory. First, an identification framework is proposed to identify and verify the intricate connections between anthropogenic factors and streamflow based on multiple techniques. Then, Mutual Information theory was used to quantify the anthropogenic disturbances on streamflow caused by specific anthropogenic factors. A nested weighting scheme based on the Information Gain Ratio was further designed to determine the composite effects of anthropogenic activities on hydrological sustainability and to determine their positive or negative impacts through trend detection. The proposed index and framework are applied to case studies of the Wei River Basin in China and the Illinois River Basin in the USA. The results show that anthropogenic factors such as impervious surface ratio (ISR) and nighttime light (NTL) have significant temporal and spatial associations with streamflow variations, playing a critical role in shaping hydrological regimes. In the Wei River Basin, anthropogenic disturbances are increasing under non-stationary conditions, with ADS values ranging from 0.3 to 0.4. In contrast, only the Du Page sub-basin in the Illinois River Basin shows a similar increasing trend, while other sub-basins exhibit a decreasing trend, with ADS values ranging from −0.2 to −0.5. The comparative analysis of the two river basins suggests that differences in anthropogenic disturbances may be attributed to their respective economic development stages. The ADS index also reveals spatial heterogeneity in the Illinois River Basin, highlighting higher disturbances in economically developed northeastern areas compared to the less disturbed, vegetation-rich southwestern regions. Overall, the ADS index effectively maps the complex nonlinear interactions between anthropogenic activities and streamflow. Utilizing the insights from this index, targeted management strategies are recommended to achieve sustainable water resource utilization.

• Dai, Minglong; Xiong, Bin; Li, Lingqi & Xu, Chong-Yu (2024). Review and Reflection on the Development History of Hydroelectric Power in Norway. Journal of Water Resources Research. ISSN 2166-6024. 13(2), s. 145–157. doi: 10.12677/jwrr.2024.132017. Fulltekst i vitenarkiv
• Jiang, Shanhu; Du, Shuping; Ren, Liliang; Gong, Xinglong; Yan, Denghua & Yuan, Shanshui [Vis alle 9 forfattere av denne artikkelen] (2024). What control the spatial patterns and predictions of runoff response over the contiguous USA? Journal of Geographical Sciences. ISSN 1009-637X. 34(7), s. 1297–1322. doi: 10.1007/s11442-024-2249-4. Fulltekst i vitenarkiv
• Zhang, Shaobo; Zhou, Zuhao; Peng, Peiyi & Xu, Chong-Yu (2024). A New Framework for Estimating and Decomposing the Uncertainty of Climate Projections. Journal of Climate. ISSN 0894-8755. 37(2), s. 365–384. doi: 10.1175/JCLI-D-23-0064.1. Fulltekst i vitenarkiv
• Liu, Yangyi; Chen, Jie; Xiong, Lihua & Xu, Chong-Yu (2024). Integrating heterogeneous information for modeling non-stationarity of extreme precipitation in the Yangtze River Basin. Journal of Hydrology. ISSN 0022-1694. 645. doi: 10.1016/j.jhydrol.2024.132159. Fulltekst i vitenarkiv
• Li, Tongfang; Lan, Tian; Zhang, Hongbo; Sun, Jing; Xu, Chong-Yu & Chen, Yongqin David (2024). Identifying the possible driving mechanisms in Precipitation-Runoff relationships with nonstationary and nonlinear theory approaches. Journal of Hydrology. ISSN 0022-1694. 639. doi: 10.1016/j.jhydrol.2024.131535. Fulltekst i vitenarkiv
• Xu, Yuanhao; Lin, Kairong; Hu, Caihong; Chen, Xiaohong; Zhang, Jingwen & Xiao, Mingzhong [Vis alle 7 forfattere av denne artikkelen] (2024). Uncovering the Dynamic Drivers of Floods Through Interpretable Deep Learning. Earth's Future. 12(10). doi: 10.1029/2024EF004751. Fulltekst i vitenarkiv Vis sammendrag

  The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black-box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak-sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash-Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision-making process, three primary flood-inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation-dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data-driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics.

• He, Yanfeng; Guo, Shenglian; Zhou, Yanlai; Zhu, Di; Chen, Hua & Xiong, Lihua [Vis alle 8 forfattere av denne artikkelen] (2024). Boosting hydropower generation of mixed reservoirs for reducing carbon emissions by using a simulation–optimization framework. Hydrology Research. ISSN 1998-9563. 55(2), s. 144–160. doi: 10.2166/nh.2023.181. Fulltekst i vitenarkiv Vis sammendrag

  The optimization operation of reservoir seasonal Flood-Limited Water Levels (FLWLs) can counterbalance the hydropower generation and flood prevention in the flood season. This study proposes a multi-objective optimization operation model to optimize the reservoir seasonal FLWLs for enhancing synergies of hydropower generation and flood prevention. The integration of the Non-dominated Sorting Genetic Algorithm-II and a simulation-optimization framework is applied for optimizing the joint operation of reservoirs meanwhile achieving the Pareto solutions to reduce computation complexity and time. And then, the Technique for Order of Preference by Similarity to Ideal Solution is utilized to identify the best seasonal FLWL scheme grounded on multi-criteria decision-making analysis. The mixed reservoirs located in the upstream Yangtze River of China constitute the case study. The results showed that: compared with the annual FLWL scheme, the proposed seasonal FLWL schemes without increasing flood prevention risk could facilitate the joint operation of the mixed reservoirs to achieve 868 million kW·h (5.1% improvement) in average hydroelectricity production during the flood season, meanwhile reducing 681 million kg in carbon emissions accordingly. The results support that the proposed methods can boost hydropower production to benefit China's national tactics in accomplishing peak carbon dioxide emissions before 2030.

• Lin, Kangling; Sheng, Sheng; Chen, Hua; Zhou, Yanlai; Luo, Yuxuan & Xiong, Lihua [Vis alle 8 forfattere av denne artikkelen] (2023). Exploring an intelligent adaptation method of hydrological model parameters for flood simulations based on the light gradient-boosting machine. Journal of Hydrology. ISSN 0022-1694. 626. doi: 10.1016/j.jhydrol.2023.130340. Fulltekst i vitenarkiv Vis sammendrag

  Traditional hydrological modeling methods use a set of parameters to simulate flood processes with complex causes and variable intensity, which can easily lead to parameter instability. To address the problem of parameter instability, this study proposes an approach integrating the hydrological model with Intelligent Adaptation Parameters (IAP), whose intelligent adaptation relationship is established by the light gradient-boosting machine (LightGBM) based on individual calibration parameters by each flood event and flood characteristics including flood-caused rainstorm information and initial soil moisture. A widely used hydrological model, Xin 'anjiang (XAJ) model, is chosen to be integrated with IAP (XAJ-IAP) in this study, which has a relatively complex structure and a total of 15 model parameters. The obtained findings demonstrate that: (1) recalibrating the sensitive runoff concentration and separation parameters with a single flood leads to a notable enhancement in simulation accuracy, while simultaneously considering the model's physical significance; (2) the XAJ overestimates large floods and underestimates small floods. Compared with the XAJ, the XAJ-IAP has a better rain-flood response relationship and simulation accuracy for floods of different magnitudes, solving the problem of parameter instability that exists in XAJ; and (3) evaluated in terms of information gain, sensitive parameters contribute the most to the establishment of the intelligent adaptation relationship in the LightGBM compared to flood-caused rainstorm information and initial soil moisture, indicating that sensitive parameters are the most important input features of the LightGBM. It can be concluded that the intelligent adaptation system can not only solve the problem of parameter instability that exists when traditional hydrological models simulate complex and changeable floods, but also further reveal the relationship between the model and floods.

• Huo, Ran; Chen, Hua; Li, Lu; Xu, Chong-Yu; Li, Jingjing & Hong, Si [Vis alle 8 forfattere av denne artikkelen] (2023). Flood variability in the upper Yangtze River over the last millennium—Insights from a comparison of climate-hydrological model simulated and reconstruction. Science China. Earth Sciences. ISSN 1674-7313. 66(3), s. 547–567. doi: 10.1007/s11430-022-1008-5. Fulltekst i vitenarkiv Vis sammendrag

  Understanding hydrological responses to rising levels of greenhouse gases are essential for climate and impact research. It is, however, often limited by a lack of long record of observational data to provide a basis for understanding the long-term behavior of the climate system. Integrating reconstructed data and (global climate and hydrological) model simulations will help us to better understand the variability of climate and hydrology over timescales ranging from decades to centuries. In this study, we proposed an integrated approach to study flood variability in the upper reach of the Yangtze River over the last millennium to the end of the 21st century. To accomplish this, we first drove hydrological models using the precipitation and temperature from four Global Climate Models (GCM), BCC-CSM1.1, MIROC, MRI-CGCM3, and CCSM4, to simulate daily discharge for the upper reach of the Yangtze River during the period of the last millennium (850–1849), historical period (1850–2005), and a future period (2006–2099). Then, we evaluated whether the modeled precipitation, temperature, and extreme discharge had statistical properties similar to those shown in the documented dry-wet periods, temperature anomalies, and paleoflood records. Finally, we explored the extreme discharge variability using model simulations. The results indicate that: (1) The MIROC-ESM model, differing from the other three GCM models, revealed positive temperature changes from the warm period (Medieval Climate Anomaly; MCA) to the cold period (Little Ice Age; LIA), while the temperature variability of the other models was similar to the records. (2) The BCC-CSM1.1 model performed better than the others regarding correlations between modeled precipitation and documented dry-wet periods. (3) Over most of the subbasins in the upper Yangtze River, the magnitude of extreme discharge in the BCC-CSM1.1 model results showed that there was a decrease from the MCA to the LIA period and an increase in the historical period relative to the cold period, while a future increase was projected by the four GCMs under the influence of climate change.

• Du, Shuping; Jiang, Shanhu; Ren, Liliang; Yuan, Shanshui; Yang, Xiaoli & Liu, Yi [Vis alle 8 forfattere av denne artikkelen] (2023). Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning. Science of the Total Environment. ISSN 0048-9697. 899. doi: 10.1016/j.scitotenv.2023.166422. Fulltekst i vitenarkiv Vis sammendrag

  Understanding of runoff response changes (RRC) is essential for water resource management decisions. However, there is a limited understanding of the effects of climate and landscape properties on RRC behavior. This study explored RRC behavior across controls and predictability in 1003 catchments in the contiguous United States (CONUS) using catchment classification and machine learning. Over 1000+ catchments are grouped into ten classes with similar hydrological behavior across CONUS. Indices quantifying RRC were constructed and then predicted within each class of the 10 classes and over the entire1000+ catchments using two machine learning models (random forest and CUBIST) based on 56 indicators of catchment attributes (CA) and 16 flow signatures (FS). This enabled the ranking of the important influential factors on RRC. We found that (i) CA/FS-based clusters followed the ecoregions over CONUS, and the impact of climate on RRC seemed to overlap with physiographic attributes; (ii) CUBIST outperforms the random forest model both within the cluster and over the whole domain, with a mean improvement of 39 % (depending on clusters) within clusters. Runoff sensitivity was better predicted than runoff changes; (iii) FS related to runoff ratio, average, and high flow are the most important for RRC, whereas climate (evaporation and aridity) is a secondary factor; and (iv) RRC patterns are substantial in the dominant factor space. High total changes and catchment characteristic-induced changes occurred mainly at 100°west longitude. The elasticity of climate and catchment characteristics was found to be high in spaces with high evaporation and low runoff ratios and low in spaces with low evaporation and high runoff ratios. Uncertainties existed in the number of catchments between clusters which was verified using a fuzzy clustering algorithm. We recommend that future research that clarifies the impact of uncertainty on hydrological or catchment behavior should be conducted.

• Xu, Wentao; Xiong, Lihua; Yan, Lei; Dong, Linyao; Jiang, Cong & Xu, Chong-Yu (2023). Estimating the scale parameter of the norming constants method in analysing nonstationary annual maximum floods. Hydrological Processes. ISSN 0885-6087. 37. doi: 10.1002/hyp.14797. Fulltekst i vitenarkiv
• Yang, Xue; Li, Fengnian; Qi, Wenyan; Zhang, Mengyuan; Yu, Chengxi & Xu, Chong-Yu (2023). Regionalization methods for PUB: a comprehensive review of progress after the PUB decade. Hydrology Research. ISSN 1998-9563. 54(7), s. 885–900. doi: 10.2166/nh.2023.027. Fulltekst i vitenarkiv Vis sammendrag

  Abstract This paper presents an updated review of model-dependent regionalization methods in hydrology since the PUB decade, incorporating new regions and methodological advancements. Two categories of regionalization methods are discussed: distance-based and regression-based, with various modification approaches. Several factors affecting the accuracy of regionalization performance are identified, including hydrological models, climate characteristics, data availability, and regionalization techniques. The review concludes that distance-based regionalization methods with an output averaging option from multiple donor catchments are the most statistically reliable, and a threshold of 0.5–0.8 for donor selection is optimal for performance. The parsimonious hydrological model is also recommended, particularly in data-limited contexts. Other insights include the effectiveness of the ensemble concept and limited impact of prior classification. Additionally, it is found that the general performance difference between climatic classes is larger than between methods, and regression-based methods may have large uncertainties in tropical regions. Overall, this study provides practical guidance for improving regionalization studies and advancing the field of hydrology.

• Yan, Lei; Zhang, Liying; Xiong, Lihua; Yan, Pengtao; Jiang, Cong & Xu, Wentao [Vis alle 10 forfattere av denne artikkelen] (2023). Flood Frequency Analysis Using Mixture Distributions in Light of Prior Flood Type Classification in Norway. Remote Sensing. 15(2). doi: 10.3390/rs15020401. Fulltekst i vitenarkiv Vis sammendrag

  The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions.

• Jiang, Shanhu; Cui, Hao; Ren, Liliang; Yan, Denghua; Yang, Xiaoli & Yuan, Shanshui [Vis alle 9 forfattere av denne artikkelen] (2023). Will China's Yellow River basin suffer more serious combined dry and wet abrupt alternation in the future? Journal of Hydrology. ISSN 0022-1694. 624. doi: 10.1016/j.jhydrol.2023.129871. Fulltekst i vitenarkiv Vis sammendrag

  At present, many studies have investigated the evolution characteristics, frequency estimation techniques, and prediction of droughts and floods, but a comprehensive understanding of the spatiotemporal evolution law and joint risk of these two composite extreme events is still lacking. The synergistic effect of dry and wet abrupt alternation (DWAA) events has more significant consequences than a single drought and flood event, and these events have a considerable impact on agriculture, ecology, and economy. Furthermore, owing to the intensification of global warming and climate change, the potential synchronisation between DWAA events and extreme precipitation in the future requires special attention. Therefore, it is necessary to establish a reliable framework to analyse the evolution characteristics and risks of composite extreme events (DWAA). In this study, a cascade modelling chain comprising climate model downscaling, non-uniform bias-correction technique, and model integration was developed to study the spatiotemporal evolution characteristics and combined risk impact of DWAA events. The proposed methodology was applied to the Yellow River Basin (YRB) in the historical period (1960–2014, Hist) and the future period (2021–2060, FUT1; and 2061–2100, FUT2) under the Shared Socioeconomic Pathway (SSP) 3–7.0 and 5–8.5 scenarios. The results show that: (1) The constructed cascade modelling chain has high simulation accuracy for precipitation, and can be used to analyse future DWAA events. (2) Considering the precipitation characteristics under SSP3-7.0 and SSP5-8.5 scenarios, the precipitation increases slightly in the near future FUT1 (about 5–10%), and increases significantly in the far future FUT2 (about 10–70%). (3) Following either scenario, the frequency of DWAA events is expected to decrease in the future compared to that in the Hist, but the average intensity is expected to increase compared to that in the Hist. (4) The joint risk of the impact area and intensity of future DWAA events of FUT2 is greater than that of FUT1.

• Lin, Kangling; Chen, Hua; Zhou, Yanlai; Sheng, Sheng; Luo, Yuxuan & Guo, Shenglian [Vis alle 7 forfattere av denne artikkelen] (2023). Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting. Science of the Total Environment. ISSN 0048-9697. 891. doi: 10.1016/j.scitotenv.2023.164494. Fulltekst i vitenarkiv Vis sammendrag

  Due to a small proportion of observations, reliable and accurate flood forecasts for large floods present a fundamental challenge to artificial neural network models, especially when the forecast horizons exceed the flood concentration time of a river basin. This study proposed for the first time a Similarity search-based data-driven framework, and takes the advanced Temporal Convolutional Network based Encoder-Decoder model (S-TCNED) as an example for multi-step-ahead flood forecasting. A total of 5232 hourly hydrological data were divided into two datasets for model training and testing. The input sequence of the model included hourly flood flows of a hydrological station and rainfall data (traced back to the previous 32 h) of 15 gauge stations, and the output sequence stepped into 1- up to 16-hour-ahead flood forecasts. A conventional TCNED model was also built for comparison purposes. The results demonstrated that both TCNED and S-TCNED could make suitable multi-step-ahead flood forecasts, while the proposed S-TCNED model not only could effectively mimic the long-term rainfall-runoff relationship but also could provide more reliable and accurate forecasts of large floods than the TCNED model even in extreme weather conditions. There is a significant positive correlation between the mean sample label density improvement and the mean Nash-Sutcliffe Efficiency (NSE) improvement of the S-TCNED over the TCNED at the long forecast horizons (13 h up to 16 h). Based on the analysis of the sample label density, it is found that the similarity search largely improves the model performance by enabling the S-TCNED model to learn the development process of similar historical floods in a targeted manner. We conclude that the proposed S-TCNED model that converts and associates the previous rainfall-runoff sequence with the forecasting runoff sequence under a similar scenario can enhance the reliability and accuracy of flood forecasts while extending the length of forecast horizons.

• Sheng, Sheng; Chen, Hua; Lin, Kangling; Zhou, Yanlai; Wang, Jinxing & Chen, Jie [Vis alle 9 forfattere av denne artikkelen] (2023). Enhancing runoff simulation precision in the critical zone through spatiotemporal interpolation of areal rainfall with matrix decomposition. Hydrological Processes. ISSN 0885-6087. 37(11). doi: 10.1002/hyp.15039. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Modelling hydrological process in the critical zone not only contributes to a better understanding of interactions across different Earth surface spheres but also holds significant practical implications for water resource management and disaster prevention. Rainfall‐runoff simulation in critical zones is particularly challenging due to the amalgamation of temporal and spatial complexity, rainfall variability, and data limitations. As a pivotal input variable of hydrological models, accurate estimation of areal rainfall is critical to successful runoff simulation. However, most estimation methods ignore temporal information, thereby increasing uncertainty in rainfall estimation and constraining the precision of rainfall‐runoff simulation. In this study, the matrix decomposition‐based estimation method (F‐SVD), which considers the spatial and temporal correlation of the rainfall process is employed to estimate areal rainfall. The superiority of the method in producing two‐dimensional rainfall information is evaluated through its application in runoff simulation with the Xin'anjiang model. The simulation results of selected flood events in the Jianxi basin in southeast China, spanning from 2009 to 2019, are compared with those of two widely used rainfall estimation methods, namely Arithmetical Mean (AM) and Thiessen Polygons (TP). The results show that (1) F‐SVD not only produces the highest Pearson correlation coefficient between rainfall and runoff series but also reduces the number of flood events with abnormal rainfall‐runoff relationships; (2) the Xin'anjiang model based on F‐SVD achieves the highest Nash‐Sutcliffe efficiency and lowest Relative Error, and performs best in simulating peak flow and its occurrence time as compared to AM and TP. This study contributes to a finer characterization of watershed rainfall distribution, enhancing the accuracy and sharpness of runoff simulation. It provides reliable data support for critical zone research and offers a scientific foundation for rationally allocating and managing water resources.

• Jiang, Shanhu; Liu, Yating; Wang, Menghao; Zhu, Yongwei; Cui, Hao & Du, Shuping [Vis alle 7 forfattere av denne artikkelen] (2023). Quantifying the impact of climate change and human activities on the eco-hydrological regimes of the Weihe River Basin, Northwest China. Hydrology Research. ISSN 1998-9563. 54(1), s. 49–64. doi: 10.2166/nh.2022.194. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Climate change and anthropogenic interventions have obviously altered the eco-hydrological regimes. A quantitative evaluation and attribution of the eco-hydrological alterations are urgently required. In this study, we evaluated the various attributions of eco-hydrological regimes in the Weihe River Basin (WRB). Firstly, the trends and change-point analysis of hydrological elements were examined, and the natural streamflow was reproduced based on the variable infiltration capacity model. Then, the most ecologically relevant hydrological indicators (ERHIs) were selected and combined with the eco-deficit and eco-surplus indicators to assess the degree of eco-hydrological regime alterations. Finally, the relative contributions to eco-hydrological alterations were quantified using the ‘simulated–observed comparison’ method. The results showed that (1) the streamflow of the WRB exhibited significant decreasing trends (p < 0.01), and a significant change point (p < 0.01) of the streamflow series was identified in 1990. (2) Seven representative indicators of hydrological alteration were selected as ERHIs. (3) During the human-induced period (1991–2017), human activities were the dominant factors in the eco-hydrological alterations as well as the variations of the ERHI indexes and the eco-deficit and eco-surplus metrics. Overall, the proposed framework may improve the understanding of the driving forces of eco-hydrological regime alterations under a changing environment.

• Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2023). Flexible and consistent Flood–Duration–Frequency modeling: A Bayesian approach. Journal of Hydrology. ISSN 0022-1694. 620. doi: 10.1016/j.jhydrol.2023.129448. Fulltekst i vitenarkiv Vis sammendrag

  Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models’ ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process.

• Wu, Juan; Zhang, Qi; Li, Yunliang; Xu, Chong-Yu & Ye, Xuchun (2023). Spatial-temporal variations of stage-area hysteretic relationships in large heterogeneous lake–floodplain systems. Journal of Hydrology. ISSN 0022-1694. 620. doi: 10.1016/j.jhydrol.2023.129507. Fulltekst i vitenarkiv Vis sammendrag

  The hysteretic relationship between the water level and the inundated area is one of the basic non-linear characteristics of lake hydrology. However, it is difficult to obtain this relationship accurately, especially for large floodplain lakes that exhibit time-varying boundaries with rapid water-level fluctuations. Taking the largest lake-floodplain system of the Yangtze River basin – Poyang Lake and its extremely productive wetland – as an example, we investigated the spatial–temporal variation of the stage-area hysteretic relationship in large heterogeneous lake-floodplain systems by adopting the Enhanced Spatial and Temporal Adaptive Reflection Fusion Model (ESTARFM) based on the observed water levels and reconstructed high spatial–temporal resolution inundation datasets using multi-source remote sensing data. The major results indicate that the inundation dynamics in the regions of the main lake and seasonal floodplain lakes are remarkably inconsistent. Concerning the inundation behavior of the river and lake-floodplain, a conceptual model was established to explain the formation mechanism of the counter-clockwise and clockwise stage-area hysteretic relationships in the Poyang lake–floodplain system. Further investigation revealed that seasonal lakes exist widely in floodplain settings and have a crucial impact on increasing the hysteresis of upstream stations and decreasing that of downstream stations. The magnitude and direction of the stage-area hysteretic relationships varied with time in a changing environment. This study extends the understanding of the complexity of hydrological behavior in large heterogeneous lake-floodplain systems, which is of vital importance for lake water resources and ecological management.

• Jain, Sharad K.; Xu, Chong-Yu & Zhou, Yanlai (2023). Change analysis of All India and regional rainfall data series at annual and monsoon scales. Hydrology Research. ISSN 1998-9563. 54(4), s. 606–632. doi: 10.2166/NH.2023.005. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Rainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have been applied. This paper presents a critical appraisal of past studies and methodologies for trend analysis. Results of trend analysis of Indian rainfall data are presented. Data for all of India and for five homogenous regions (North-West, Central North-East, North-East, West Central, and Peninsular India) for 1871–2016 were used. The Pettitt change point test, regression, Mann-Kendall (MK), and Wavelet Decomposition were used to study different aspects of changes. Results of the change point test showed that most rainfall series had change points around 1957–65, possibly due to large-scale land use, cultivation, irrigation, and industrial changes in this period. Generally, rainfall for most homogenous regions and sub-divisions show falling trends; some are statistically significant. Series was also decomposed by the wavelet method. Approximate and detailed components of some decomposed series showed a significant declining trend. This work has focused on the magnitude of rainfalls; trends in rainfall intensities are also important. It is necessary to establish denser observation networks to collect short-term data and analyze.

• Yan, Lei; Lu, Dongyang; Xiong, Lihua; Wang, Hongfeng; Luan, Qinghua & Jiang, Cong [Vis alle 11 forfattere av denne artikkelen] (2023). Derivation of nonstationary rainfall intensity-duration-frequency curves considering the impacts of climate change and urbanization. Urban Climate. 52. doi: 10.1016/j.uclim.2023.101701. Fulltekst i vitenarkiv Vis sammendrag

  Urban infrastructure traditionally relies on stationary rainfall intensity-duration-frequency (IDF) curves. However, this assumption is challenged by climate change and urbanization. Many studies tried to update IDF using time covariate which lacks physical significance. More importantly, the stationary (ST) design method is not applicable for nonstationary (NS) design where the distributions of extreme precipitation change over time. For the annual maximum precipitation (AMP) in Beijing, we utilized local factors (urbanization and temperature) and global factors (ENSO and EASM etc.) to develop NS models, with the average annual reliability method first employed to update the IDF curves. Short-duration (shorter than 6-h) AMP of most stations show upward trends, whereas the AMP with longer durations exhibits downward trends. The NS modeling reveals that the 18-h AMPs is mainly affected by global processes (ENSO and EASM). The predictive accuracy of the optimal NS model outperforms ST model by a remarkable 219% during the validation period. In addition, the ST design rainfall tends to overestimate rainfall for durations longer than 12-h. Interestingly, the gap between NS and ST design uncertainties diminishes as duration/return period expands. The above findings provide new insights about impacts of local and global physical processes on the variation of extreme rainfall.

• Cui, Hao; Jiang, Shanhu; Gao, Bin; Ren, Liliang; Xiao, Weihua & Wang, Menghao [Vis alle 8 forfattere av denne artikkelen] (2023). On method of regional non-stationary flood frequency analysis under the influence of large reservoir group and climate change. Journal of Hydrology. ISSN 0022-1694. 618. doi: 10.1016/j.jhydrol.2023.129255. Fulltekst i vitenarkiv Vis sammendrag

  Global climate change and reservoir regulations can alter the natural flow of rivers. Influenced by these two drivers, flood sequences may no longer satisfy the assumption of stationary, thereby making it difficult to accurately analysis flood frequency and to design water conservancy projects. Therefore, it is of great significance to analyse the non-stationary frequency of flood sequences in a changing environment. In this study, we proposed a method for conducting nonstationary flood frequency analysis caused by cascade reservoirs as well as the low-frequency climate indices. The proposed non-stationary model 2, with the explanatory variables of climate indices and modified reservoir index (MRI), was compared with the traditional stationary model and the widely used non-stationary model 1 with time as the explanatory variable. The study was conducted at six hydrological stations in the main stream and tributaries of the upper reaches of the Yangtze River in China (considered as the Three Gorges Reservoir Area). The results of the generalized additive model for location, scale and shape (GAMLSS) showed that the Akaike information criterion and Bayesian information criterion values of the proposed non-stationary model method 2 are smaller than those of the two comparison models. When the low-frequency South Oscillation Index is high or the Arctic Oscillation and North Pacific Oscillation are low, the stationary model underestimates the design value of flood quantiles compared with the non-stationary model 2. Compared with the non-stationary model 1, the MRI and low-frequency climate indices as the explanatory variables in model 2 can better describe the non-stationary characteristics of flood frequency and amplitude. In addition, the non-stationary model considering external physical factors can provide better prediction of future design flood compared with two traditional models.

• Zhou, Yanlai; Cui, Zhen; Lin, Kangling; Sheng, Sheng; Chen, Hua & Guo, Shenglian [Vis alle 7 forfattere av denne artikkelen] (2022). Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques. Journal of Hydrology. ISSN 0022-1694. 604. doi: 10.1016/j.jhydrol.2021.127255. Fulltekst i vitenarkiv Vis sammendrag

  Making accurate and reliable probability density forecasts of flood processes is fundamentally challenging for machine learning techniques, especially when prediction targets are outside the range of training data. Conceptual hydrological models can reduce rainfall-runoff modelling errors with efficient quasi-physical mechanisms. The Monotone Composite Quantile Regression Neural Network (MCQRNN) is used for the first time to make probability density forecasts of flood processes and serves as a benchmark model, whereas it confronts the drawbacks of overfitting and biased-prediction. Here we propose an integrated model (i.e. XAJ-MCQRNN) that incorporates Xinanjiang conceptual model (XAJ) and MCQRNN to overcome the phenomena of error propagation and accumulation encountered in multi-step-ahead flood probability density forecasts. We consider flood forecasts as a function of rainfall factors and runoff data. The models are evaluated by long-term (2009–2015) 3-hour streamflow series of the Jianxi River catchment in China and rainfall products of the European Centre for Medium-Range Weather Forecasts. Results demonstrated that the proposed XAJ-MCQRNN model can not only outperform the MCQRNN model but also prominently enhance the accuracy and reliability of multi-step-ahead probability density forecasts of flood process. Regarding short-term forecasts in testing stages at four horizons, the XAJ-MCQRNN model achieved higher Nash-Sutcliffe Efficiency but lower Root Mean Square Error values, while improving Coverage Ratio and Relative Bandwidth values in comparison to the MCQRNN model. Consequently, the improvement can benefit the mitigation of the impacts associated with uncertainties of extreme flood and rainfall events as well as promote the accuracy and reliability of flood forecasting and early warning.

• Qi, Wen-Yan; Chen, Jie; Li, Lu; Xu, Chong-Yu; Li, Jingjing & Xiang, Yiheng [Vis alle 7 forfattere av denne artikkelen] (2022). Regionalization of catchment hydrological model parameters for global water resources simulations. Hydrology Research. ISSN 1998-9563. 53(3), s. 441–466. doi: 10.2166/nh.2022.118. Fulltekst i vitenarkiv Vis sammendrag

  Parameter regionalization of hydrological models is one of the most commonly used methods for hydrological prediction over ungauged catchments. Although there were many regional studies, there is no clear conclusion on the best-performing regionalization method for global hydrological modelling. The objective of this study is to determine an appropriate global-scale regionalization scheme (GSRS) for global hydrological modelling. To this end, the performance of five regionalization methods with two different average options, two weighting approaches, and seven efficiency thresholds (i.e. Kling-Gupta efficiency (KGE) values to measure hydrological model performances) was compared over thousands of catchments based on four conceptual hydrological models. Results of nine global models from the Global Earth Observation for Integrated Water Resource Assessment (EartH2Observe) project were selected to validate the accuracy of GSRS in estimating global runoff. The results show that: (1) Spatial proximity method with the Inverse Distance Weighting method and the output average option offers the best regionalization result when using the KGE ≥ 0.5 as an efficiency threshold for all four hydrological models, (2) the regionalization-based global hydrological simulation schemes (RGHSs), i.e. the proposed GSRS combining with four hydrological models, consistently performs better than the nine global models from EartH2Observe project in the estimation of runoff for most catchments, with varying degrees of improvement in the median, upper and lower quartiles, and whiskers of each performance metric, and (3) the global long-term annual water resources estimated by RGHSs range between 42,592 and 46,810 km3/yr.

• Zhu, Di; Chen, Hua; Zhou, Yanlai; Xu, Xinfa; Guo, Shenglian & chang, fi-john [Vis alle 7 forfattere av denne artikkelen] (2022). Exploring a multi-objective cluster-decomposition framework for optimizing flood control operation rules of cascade reservoirs in a river basin. Journal of Hydrology. ISSN 0022-1694. 614. doi: 10.1016/j.jhydrol.2022.128602. Fulltekst i vitenarkiv Vis sammendrag

  Multi-objective flood control operation of cascade reservoirs is a vital issue in river basin management. However, traditional multi-objective approaches commonly provide one operation scheme only and fail to offer decision-makers with more Pareto-front options. This study explores a multi-objective cluster-decomposition framework for optimizing the flood control operation rules of cascade reservoirs in a river basin. The proposed framework involves a multi-objective optimization module, a cluster-decomposition module, and an evaluation and sorting module. The multi-objective cluster-decomposition framework simultaneously deals with three objectives: to minimize the flood peaks of flood control points (O1); to minimize the reservoir capacity used for flood control (O2); and to minimize the flood diversion volume of the flood detention area (O3). The complex flood control system composed of two cascade reservoirs, four navigation-power junctions, one flood detection area, and three flood control points in the Ganjiang River basin of China constitutes the case study. The results demonstrate that the proposed framework can significantly improve the comprehensive benefits of the cascade reservoirs, where the maximum reduction in objectives O1–O3 is 2071 m3/s (the improvement rate is 2.64 %), 219 million m3 (the improvement rate is 44.60 %), and 167 million m3 (the improvement rate is 78.13 %), respectively. Furthermore, in contrast to the traditional multi-attribute evaluation method, the proposed framework can effectively identify compromised decisions through a cluster-decomposition module, which provides beneficial trade-off guidance in making a sound decision upon Pareto-front options.

• Zhou, Yanlai; Zhu, Di; Chen, Hua; Guo, Shenglian; Xu, Chong-Yu & Chang, Fi-John (2022). Deep learning–based neural networks for day-ahead power load probability density forecasting. Environmental Science and Pollution Research. ISSN 0944-1344. 30, s. 17741–17764. doi: 10.1007/s11356-022-23305-0. Fulltekst i vitenarkiv Vis sammendrag

  Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utilization efficiency and raises environmental risks endangering public safety. Electrical load, however, has a dynamically fluctuating behavior with notoriously nonlinear hourly, daily, and seasonal patterns. Accurate and reliable load forecasting plays an important role in scheduling power generation processes and preventing electrical systems from overloading; nevertheless, such forecasting is fundamentally challenging, especially under highly variable power load and climate conditions. This study proposed a deep learning–based monotone composite quantile regression neural network (D-MCQRNN) model to extract the multiple non-crossing and nonlinear quantile functions while conquering the drawbacks of error propagation and accumulation encountered in multi-step-ahead probability density forecasting. The constructed models were assessed by an hourly power load series collected at the electric grid center of Henan Province in China in two recent years, along with the corresponding meteorological data collected at 16 monitoring stations. The results demonstrated that the proposed D-MCQRNN model could significantly alleviate the time-lag and biased-prediction phenomena and noticeably improve the accuracy and reliability of multi-step-ahead probability density forecasts on power load. Consequently, the proposed model can significantly reduce the risk and impact of overload faults and effectively promote energy utilization efficiency, thereby mitigating GHG emissions and moving toward cleaner energy production.

• Guo, Shuhui; Xiong, Lihua; Chen, Jie; Guo, Shenglian; Xia, Jun & Zeng, Ling [Vis alle 7 forfattere av denne artikkelen] (2022). Nonstationary regional flood frequency analysis based on the Bayesian method. Water resources management. ISSN 0920-4741. 37, s. 659–681. doi: 10.1007/s11269-022-03394-9. Fulltekst i vitenarkiv Vis sammendrag

  Most researches on regional flood frequency analysis (RFFA) have proved that the incorporation of hydrologic information (e.g., catchment attributes and flood records) from different sites in a region can provide more accurate flood estimation than using only the observed flood series at the site of concern. One kind of RFFA is based on the Bayesian method with prior information inferred from regional regression by using the generalized least squares (GLS) model, which is more flexible than other RFFA methods. However, the GLS model for regional regression is a stationary method and not suitable for coping with nonstationary prior information. In this study, in nonstationary condition, the Bayesian RFFA with the prior information inferred from regional regression by using the linear mixed effect (LME) model (i.e. a model that adds random effects to the GLS model) is investigated. Both the GLS-based and LME-based Bayesian RFFA methods have been applied to four hydrological stations within the Dongting Lake basin for comparison, and the results show that the performance of nonstationary LME-based Bayesian RFFA method is better than that of stationary GLS-based Bayesian RFFA method according to the deviance information criterion (DIC). Compared with the stationary GLS-based Bayesian RFFA method, changes in uncertainty of regression coefficients estimation of at-site flood distribution parameters are different from site to site by using the nonstationary LME-based Bayesian RFFA method. The use of nonstationary LME-based Bayesian RFFA method reduces design flood uncertainty, especially for the very small exceedance probability at the tail. This study extends the application of the Bayesian RFFA method to the nonstationary condition, which is helpful for nonstationary flood frequency analysis of ungauged sites.

• Wang, Menghao; Jiang, Shanhu; Ren, Liliang; Xu, Chong-Yu; Shi, Peng & Yuan, Shanshui [Vis alle 8 forfattere av denne artikkelen] (2022). Nonstationary flood and low flow frequency analysis in the upper reaches of Huaihe River Basin, China, using climatic variables and reservoir index as covariates. Journal of Hydrology. ISSN 0022-1694. 612. doi: 10.1016/j.jhydrol.2022.128266. Fulltekst i vitenarkiv Vis sammendrag

  Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design.

• Li, Jingjing; Chen, Hua; Xu, Chong-Yu; Li, Lu; Zhao, haoyuan & Huo, Ran [Vis alle 7 forfattere av denne artikkelen] (2022). Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling. Water. 14(5). doi: 10.3390/w14050797. Fulltekst i vitenarkiv Vis sammendrag

  Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters

• Cui, Zhen; Zhou, Yanlai; Guo, Shenglian; Wang, Jun & Xu, Chong-Yu (2022). Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure. Journal of Hydrology. ISSN 0022-1694. 609. doi: 10.1016/j.jhydrol.2022.127764. Fulltekst i vitenarkiv Vis sammendrag

  Accurate and reliable multi-step-ahead flood forecasting is beneficial for reservoir operation and water resources management. The Encoder-Decoder (ED) that can tackle sequence-to-sequence problems is suitable for multi-step-ahead flood forecasting. This study proposes a novel ED with an exogenous input (EDE) structure for multi-step-ahead flood forecasting. The exogenous input can be the outputs of process-based hydrological models. This study constructs four multi-step-ahead flood forecasting approaches, including the Xinanjiang (XAJ) hydrological model, the single-output long short-term memory (LSTM) neural network with recursive strategies, the recursive ED combined with the LSTM neural network (LSTM-RED), and the LSTM-EDE models. The performance of these four models is evaluated and compared by the long-term 3 h hydrologic data series of the Lushui and Jianxi basins in China. The results show that the LSTM-RED model that integrates recursive strategies into the training process of neural networks is more advantageous than the LSTM model. The proposed LSTM-EDE model can overcome the exposure bias problem, simplify its model structure, increase the computational efficiency in the validation process, and improve the multi-step-ahead flood forecasting accuracy, as compared to the LSTM-RED model.

• Song, Jinbo; Zhang, Qiang; Wu, Wenhuan; Singh, VP; Shen, Zexi & Wang, Gang [Vis alle 7 forfattere av denne artikkelen] (2022). Amplifying Flood Risk Across the Lower Yellow River Basin, China, Under Shared Socioeconomic Pathways. Frontiers in Earth Science. 10. doi: 10.3389/feart.2022.900866. Fulltekst i vitenarkiv Vis sammendrag

  The ecological conservation and high-quality development of the Yellow River basin (YRB) has been adopted as a major national strategy of China. However, the YRB is still afflicted by floods. Here, we proposed a flood risk indicator using direct economic loss degree and improved the SNRD (station-based nonlinear regression downscaling model) and simulated extreme precipitation processes. The correlation coefficient of downscaled average extreme precipitation of CMORPH and CMIP6 data reached 0.817. Moreover, we evaluated flood risk and flood hazard across the YRB based on the sixth phase Coupled Model Intercomparison Project (CMIP6) data under different SSPs. Under SSP126, SSP245, and SSP585 scenarios, the areas with increasing flood hazards during 2015–2045 accounted for 65.2, 69.0, and 64.5% of the entire YRB. In the spatial pattern, flood hazards decreased from southeastern to northwestern parts of the YRB. When compared with the spatial pattern of flood risks in 2015, regions with high flood risks expanded slightly and regions with low flood risks shrunk slightly. Higher flood risks can be observed in the middle and lower YRB and particularly in the lower YRB. These findings are critical for the mitigation of flood risk across the YRB under warming climate.

• Zhou, Yanlai; Guo, Shenglian; Xu, Chong-Yu; Xiong, Lihua; Chen, Hua & Ngongondo, Cosmo S Abdul [Vis alle 7 forfattere av denne artikkelen] (2022). Probabilistic interval estimation of design floods under non-stationary conditions by an integrated approach. Hydrology Research. ISSN 1998-9563. 53(2), s. 259–278. doi: 10.2166/nh.2021.007. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Quantifying the uncertainty of non-stationary flood frequency analysis is very crucial and beneficial for planning and design of water engineering projects, which is fundamentally challenging especially in the presence of high climate variability and reservoir regulation. This study proposed an integrated approach that combined the Generalized Additive Model for Location, Scale and Shape parameters (GAMLSS) method, the Copula function and the Bayesian Uncertainty Processor (BUP) technique to make reliable probabilistic interval estimations of design floods. The reliability and applicability of the proposed approach were assessed by flood datasets collected from two hydrological monitoring stations located in the Hanjiang River of China. The precipitation and the reservoir index were selected as the explanatory variables for modeling the time-varying parameters of marginal and joint distributions using long-term (1954–2018) observed datasets. First, the GAMLSS method was employed to model and fit the time-varying characteristics of parameters in marginal and joint distributions. Second, the Copula function was employed to execute the point estimations of non-stationary design floods. Finally, the BUP technique was employed to perform the interval estimations of design floods based on the point estimations obtained from the Copula function. The results demonstrated that the proposed approach can provide reliable probabilistic interval estimations of design floods meanwhile reducing the uncertainty of non-stationary flood frequency analysis. Consequently, the integrated approach is a promising way to offer an indication on how design values can be estimated in a high-dimensional problem.

• Yang, Wushuang; Xia, Runliang; Chen, Hua; Wang, Min & Xu, Chong-Yu (2022). The impact of calibration conditions on the transferability of conceptual hydrological models under stationary and nonstationary climatic conditions. Journal of Hydrology. ISSN 0022-1694. 613. doi: 10.1016/j.jhydrol.2022.128310. Fulltekst i vitenarkiv Vis sammendrag

  Changing climatic conditions have changed the stationary rainfall-runoff relationships in many basins. In this context, the value of the model parameters will depend more on the selection of the calibration period, which directly affects the accuracy of runoff forecasting. However, systematic exploration and testing of the impact of calibration conditions on the transferability of hydrological models under stationary and nonstationary climatic conditions require more effort. The present study investigates the impact of four calibration conditions on model transferability, including the length of the calibration period, the length of the interval between the calibration and transfer periods, the difference in climate conditions as measured by the total rainfall amount between the calibration and transfer periods, and the difference in the similarity of rainfall processes between the calibration and transfer periods. Two catchments with stationary and nonstationary climatic conditions, and five models, including XAJ, HBV, IHACRES, SIMHYD and GR4J, are used in this study. The results show that (1) a longer calibration period is required for catchments with nonstationary rainfall-runoff relationships to achieve stable simulations; (2) with the increase in the length of the interval between the calibration and transfer periods, the transferability of the model decreases gradually, and the degree of reduction is greater for catchments with nonstationary climatic conditions and rainfall-runoff relationships; and (3) when forecasting future runoff under nonstationary rainfall-runoff relationships, the closeness of the total rainfall amount between the calibration and transfer periods is more important than the similarity in rainfall series between the calibration and transfer periods. This study provides insight into the impact of calibration conditions on the transferability of hydrological models in the context of climate change.

• Wang, Menghao; Jiang, Shanhu; Ren, Liliang; Xu, Chong-Yu; Wei, Linyong & Cui, Hao [Vis alle 9 forfattere av denne artikkelen] (2022). The Development of a Nonstationary Standardised Streamflow Index Using Climate and Reservoir Indices as Covariates. Water resources management. ISSN 0920-4741. 36, s. 1377–1392. doi: 10.1007/s11269-022-03088-2. Fulltekst i vitenarkiv Vis sammendrag

  Under current global change, the driving force of evolution of drought has gradually transitioned from a single natural factor to a combination of natural and anthropogenic factors. Therefore, widely used standardised drought indices based on assumption of stationarity are challenged and may not accurately assess characteristics of drought processes. In this study, a nonstationary standardised streamflow index (NSSI) that incorporates climate and reservoir indices as external covariates was developed to access nonstationary hydrological drought. The first step of the proposed approach is to apply methods of trend and change point analysis to assess the nonstationarity of streamflow series to determine type of streamflow regime, that is, the natural and altered regime. Then, different nonstationary models were constructed to calculate the NSSI by selecting climate indices as covariates for streamflow series with natural regime, and climate and reservoir indices as covariate for streamflow series with altered regime. Four stations in the upper reaches of the Huaihe River basin, China, were selected to examine the performance of the proposed NSSI. The results indicated that Dapoling (DPL), Changtaiguan (CTG), and Xixian (XX) stations had natural streamflow regimes, while the Nanwan (NW) station had an altered regime. The global deviances of the optimal nonstationary models were 17 (2.2%), 18 (2.9%), 26 (4.0%), and 22 (3.5%) less than those of stationary models for DPL, CTG, NW, and XX stations, respectively. Especially, for the NW station influenced by reservoir regulations, the frequency of slight drought and moderate drought of NSSI was 12.8% lower than and 13.1% greater than those of SSI, respectively. Overall, the NSSI that incorporates the influence of climate variability and reservoir regulations provided more reliable assessment of hydrological drought than the traditional SSI.

• Zhou, Yanlai; Ruan, Gusong; Xu, Chong-Yu; Xiong, Lihua; Jain, Sharad K. & Li, Lu (2022). Detection and Attribution of Norwegian Annual Precipitation Variability Related to Teleconnections. Earth and Space Science. 9(3). doi: 10.1029/2021ea001857. Fulltekst i vitenarkiv Vis sammendrag

  Detection and attribution of precipitation variability are fundamentally challenging, especially in the presence of complex nonlinear relationships between precipitation variability and large-scale teleconnections. The aim of this study is twofold. First, we identify abrupt changes and the trend and periodicity characteristics of long-term (1950–2019) annual precipitation series over the Norway mainland by using the Multiple Comparison Procedures, the Mann-Kendall test, and the Wavelet Analysis. Second, we interpret the variability characteristics found over five regions of Norway by exploring their relationships with teleconnections through the Maximal Information Coefficient. The results indicate that significant abrupt changes appeared in the mean (variance) value of annual precipitation series at 117 (49) out of 159 rainfall stations of five regions in Norway at a significance level of 0.05. The occurrences of change points varied from 1979 to 1984 in five regions of Norway. The mean and variance of the annual precipitation series increased by 32% and 16% at most, respectively, compared with those values before the change points. The first periodicity (spanning four to five decades) was the dominant periodic component and could be used to best characterize the Norwegian precipitation variability. Because the subtropical Azores High (subpolar Icelandic Low) moves toward (away from) Europe, the relationship between the annual precipitation series and the Scandinavian pattern (Atlantic Multidecadal Oscillation) series tended to be of an upward (downward) hook structure. The association of precipitation variability and teleconnections found in this study can pave the way for new possibilities with regard to detection and attribution of precipitation variability.

• Huo, Ran; Li, Lu; Engeland, Kolbjørn; Xu, Chong-Yu; Chen, Hua & Paasche, Øyvind [Vis alle 7 forfattere av denne artikkelen] (2022). Changing flood dynamics in Norway since the last millennium and to the end of the 21st century. Journal of Hydrology. ISSN 0022-1694. 613, s. 128331–128331. doi: 10.1016/j.jhydrol.2022.128331. Fulltekst i vitenarkiv Vis sammendrag

  With the recent warming trend over Europe and the Arctic, the Nordic regions have experienced more frequent and damaging extreme hydrological events which are anticipated to increase towards the end of the 21st century. Despite explicit trends, large variations have been observed across basins and regions when it comes to precipitation and floods hinting at a strong natural hydroclimatic variability that further complicates any assessment of potential future changes. In this study, we aim to better understand how climate variability links with the current extremes and future projections of floods in Norway in the context of the last millennium and the future. Specifically, we simulate over 1000 years (850–2099) daily discharge and floods at 34 catchments over five regions of Norway from the last millennium (including a warm period and a cold period; 850–1849) to the end of the 21st century by an ensemble model-chain method including four global climate models (GCMs), two bias-correction methods and two hydrological models. The modelling results show (i) all GCMs except MIROC-ESM simulate a higher mean temperature in Medieval Climate Anomaly (MCA) period than Little Ice Age (LIA) period, while simulated annual precipitation varies a lot in different GCMs and catchments, (ii) no significant change of flood characteristics during the last millennium from ensemble-mean results, (iii) in future, we will have an extraordinary shift in flood seasonality and generating processes, and flood frequency increase in most of the study catchments in Norway, and (iv) climate projections represent the largest contribution to overall uncertainty in the projected changes in hydrological extremes for most of the catchments.

• Xu, Chong-Yu (2021). Issues influencing accuracy of hydrological modeling in a changing environment. Water Science and Engineering. ISSN 1674-2370. 14(2), s. 167–170. doi: 10.1016/j.wse.2021.06.005. Fulltekst i vitenarkiv
• Shi, Xinyan; Chen, Jie; Gu, Lei; Xu, Chong-Yu; Chen, Hua & Zhang, Liping (2021). Impacts and socioeconomic exposures of global extreme precipitation events in 1.5 and 2.0 °C warmer climates. Science of the Total Environment. ISSN 0048-9697. 766. doi: 10.1016/j.scitotenv.2020.142665. Fulltekst i vitenarkiv
• Qi, Wenyan; Chen, Jie; Li, Lu; Xu, Chong-Yu; Xiang, Yi-Heng & Zhang, Shao-Bo [Vis alle 7 forfattere av denne artikkelen] (2021). Impact of the number of donor catchments and the efficiency threshold on regionalization performance of hydrological models. Journal of Hydrology. ISSN 0022-1694. 601. doi: 10.1016/j.jhydrol.2021.126680. Fulltekst i vitenarkiv
• Ma, Qiumei; Xiong, Lihua; Xu, Chong-Yu; Li, Rongrong; Ji, Changming & Zhang, Yanke (2021). Flood Wave Superposition Analysis Using Quantitative Matching Patterns of Peak Magnitude and Timing in Response to Climate Change. Water resources management. ISSN 0920-4741. 35, s. 2409–2432. doi: 10.1007/s11269-021-02837-z. Fulltekst i vitenarkiv
• Yan, Lei; Xiong, Lihua; Jiang, Cong; Zhang, Mengjie; Wang, Dong & Xu, Chong-Yu (2021). Updating intensity–duration–frequency curves for urban infrastructure design under a changing environment. WIREs Water. 8(3). doi: 10.1002/wat2.1519. Fulltekst i vitenarkiv Vis sammendrag

  The intensity and frequency of extreme precipitation have increased in many regions in the past century due to climate change. Many studies have revealed that short-duration extreme precipitations are likely to become more and more severe in many areas, thus raising a question on whether our urban infrastructures have been designed adequately to cope with these changes. Currently, Intensity–duration–frequency (IDF) curves, which summarize the relationships between the intensity and frequency of extreme precipitation for different durations, are recommended as a criterion for urban infrastructure design and stormwater management. However, climate change is thought to have invalidated the stationary assumption in deriving IDF curves, that is, current IDF curves could misevaluate future extreme precipitation in many cases. Therefore, it is necessary to update the current IDF curves by considering possible changes of extreme precipitation. In this review, we first summarize observed changes in urban short-duration extreme precipitation and explore the physical mechanisms associated with changes. Then, we introduce two major approaches for updating IDF curves, namely the covariate-based nonstationary IDF curves and climate-model-based IDF curves. Advances in these two updating approaches for IDF curves are the focus of this review. These include the investigation of physically-based covariates with nonstationary modeling of extreme precipitation; nonstationary precipitation design strategies; and the statistical downscaling and dynamic downscaling methods for projecting future short-duration precipitation. Finally, we summarize some future research challenges and opportunities on providing reliable projections of future short-duration extreme precipitation and better characterize the probabilistic behavior of short-duration extreme precipitation for IDF design.

• Jiang, Cong; Xiong, Lihua; Xu, Chong-Yu & Yan, Lei (2021). A river network-based hierarchical model for deriving flood frequency distributions and its application to the Upper Yangtze basin. Water Resources Research. ISSN 0043-1397. 57(8). doi: 10.1029/2020WR029374. Fulltekst i vitenarkiv Vis sammendrag

  In flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at-site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In this study, a river network-based hierarchical model is developed to estimate the FFDs in the Upper Yangtze basin by making full use of the hydrological reasoning information of both the flood dependence within the river network and reservoir regulation. Under this hierarchical model, a covariate analysis based on the generalized additive model for location, scale and shape is performed to obtain the conditional distribution of the interested flood variable given both its upstream flood variables and the reservoir index quantifying reservoir regulation; and then, the FFD of the interested flood variable is derived by combining its conditional distribution with the probability distribution of the upstream flood variables. The application to the Upper Yangtze basin indicates that the proposed hierarchical model suggests a satisfactory performance in FFD estimation. It is also found that the reservoir regulation, especially that of the Three Gorges Reservoir, is of great significance in reducing the flood magnitude in the basin. Compared to the conventional FFD estimation method that directly fits the assumed theoretical probability distributions to the at-site flood samples, the hierarchical model incorporating the flood dependence within the river network exhibits an advantage in capturing the effect of reservoir regulation on the floods as well as in reducing the uncertainty in flood quantile estimation.

• Qi, Wenyan; Chen, Jie; Xu, Chong-Yu & Wan, Yongjing (2021). Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations. Remote Sensing. 13(13). doi: 10.3390/rs13132574. Fulltekst i vitenarkiv Vis sammendrag

  Global gridded precipitations have been extensively considered as the input of hydrological models for runoff simulations around the world. However, the limitations of hydrologic models and the inaccuracies of the precipitation datasets could result in large uncertainty in hydrological forecasts and water resource estimations. Therefore, it is of great importance to investigate the hydrological value of a weighted combination of hydrological models driven by different precipitation datasets. In addition, due to the diversities of combination members and climate conditions, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the weighted combinations. This study undertakes a comprehensive analysis of various multimodel averaging methods and schemes (i.e., the combination of the members in averaging) to identify the most skillful and reliable multimodel averaging application. To achieve this, four hydrological models driven by six precipitation datasets were used as averaging members. The behaviors of 9 averaging methods and 11 averaging schemes in hydrological simulations were tested over 2277 watersheds distributed in different climate regions in the world. The results show the following: (1) The multi-input averaging schemes (i.e., members consist of one model driven by multiple precipitation datasets) generally perform better than the multimodel averaging schemes (i.e., members consist of multiple models driven by the same precipitation dataset) for each averaging method; (2) The use of multiple members can improve the averaging performances. Six averaging members are found to be necessary and advisable, since using more than six members only imrpoves the estimation results slightly, as compared with using all 24 members; (3) The advantage of using averaging methods for hydrological modeling is region dependent. The averaging methods, in general, produced the best results in the warm temperate region, followed by the snow and equatorial regions, while a large difference among various averaging methods is found in arid and arctic regions. This is mainly due to the different averaging methods being affected to a different extent by the poorly performed members in the arid and arctic regions; (4) the multimodel superensemble method (MMSE) is recommended for its robust and outstanding performance among various climatic regions.

• Yan, Lei; Xiong, Lihua; Ruan, Gusong; Zhang, Mengjie & Xu, Chong-Yu (2021). Design flood estimation with varying record lengths in Norway under stationarity and nonstationarity scenarios. Hydrology Research. ISSN 1998-9563. 52(6), s. 1596–1614. doi: 10.2166/nh.2021.026. Fulltekst i vitenarkiv Vis sammendrag

  In traditional flood frequency analysis, a minimum of 30 observations is required to guarantee the accuracy of design results with an allowable uncertainty, however, there has not been a recommendation for the requirement on the length of data in NFFA (nonstationary flood frequency analysis). Therefore, this study has been carried out with three aims: (i) to evaluate the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths; (ii) to examine the impacts of flood record lengths on the NS and ST design floods and associated uncertainties; and (iii) to recommend the probable requirements of flood record length in NFFA. To achieve these objectives, 20 stations with record length longer than 100 years in Norway were selected and investigated by using both GEV (generalized extreme value)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results indicate that the fitting quality and predictive capabilities of GEV-NS0 outperform those of GEV-ST models when record length is approximately larger than 60 years for most stations, and the stability of the GEV-ST and GEV-NS0 is improved as record lengths increase. Therefore, a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway.

• Yan, Lei; Xiong, Lihua; Luan, Qinghua; Jiang, Cong; Yu, Kunxia & Xu, Chong-Yu (2020). On the Applicability of the Expected Waiting Time Method in Nonstationary Flood Design. Water resources management. ISSN 0920-4741. 34, s. 2585–2601. doi: 10.1007/s11269-020-02581-w. Fulltekst i vitenarkiv Vis sammendrag

  Given a changing environment, estimating a flood magnitude corresponding to a desired return period considering nonstationarity is crucial for hydrological engineering designs. Four nonstationary design methods, namely expected waiting time (EWT), expected number of exceedances (ENE), equivalent reliability (ER), and average design life level (ADLL) have already been proposed in recent years. Among them, the EWT method needs to estimate design flood magnitudes by solving numerically. In addition, EWT requires estimating design quantiles for infinite lifespan, or extrapolation time (textra), to guarantee the convergence of the EWT solution under certain conditions. However, few studies have systematically evaluated pros and cons of the EWT method as to how to determine the textra and what kinds of misunderstandings on the applicability of the EWT method exist. In this study, we aim to provide the first investigation of various factors that influence the value of textra in the EWT method, and provide comprehensive comparison of the four methods from the perspectives of textra, design values and associated uncertainties. The annual maximum flood series (AMFS) of 25 hydrological stations, with increasing and decreasing trends, in Pearl River and Weihe River were chosen for illustrations. The results indicate that: (1) the textra of EWT is considerably affected by the trend of AMFS and the choice of extreme distributions. In other words, the textra of stations with increasing trends was significantly smaller than that of stations with decreasing trends, and the textra was also larger for distributions with heavier tail; (2) EWT produced larger design values than ENE for increasing trends, and both EWT and ENE yielded larger design values than ER and ADLL for higher return periods, while complete opposite results were obtained for decreasing trends.

• Zhou, Yanlai; Guo, Shenglian; Xu, Chong-Yu; Chang, Fi‐John & Yin, Jiabo (2020). Improving the Reliability of Probabilistic Multi‐Step‐Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network. Water. 12(2). doi: 10.3390/w12020578. Fulltekst i vitenarkiv Vis sammendrag

  It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved.

• Xiong, Bin; Xiong, Lihua; Guo, SL; Xu, Chong-Yu; Xia, Jun & Zhong, Yixuan [Vis alle 7 forfattere av denne artikkelen] (2020). Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River From 1470 to 2017. Water Resources Research. ISSN 0043-1397. 56(8). doi: 10.1029/2020WR027112. Fulltekst i vitenarkiv Vis sammendrag

  Censored data (CD) of floods, that is, the combination of systematic data (SD) and historical data, can help improve the robustness of flood frequency analysis, due to its temporal information expansion. However, in nonstationary flood frequency analysis, the approach to utilize the CD has rarely been investigated. In this study, a covariate‐based nonstationary flood frequency analysis framework based on various likelihood functions using the generalized extreme value (GEV) distribution was built to utilize the CD, with uncertainty considered. This framework was applied to the study of the annual maximum flood frequency of the Yichang gauging station 44 km downstream of the Three Gorges Dam over the period from 1470 to 2017. A summer precipitation anomaly and a reservoir index were used as covariates to explain the variation of the distribution parameters. The results show that for either the SD or CD, the nonstationary models are preferred to the stationary ones by the deviance information criterion, and these nonstationary models may prove to be practical in engineering application, due to the acceptable uncertainty range in flood quantiles derived from covariates. Compared to the stationary or nonstationary models based on the SD, the corresponding model based on the CD results in a higher posterior mean and a smaller posterior standard deviation for the shape parameter of the GEV distribution. It is concluded that the use of historical information under the nonstationary frequency analysis framework may be remarkable in reducing design flood uncertainty, especially for the very small exceedance probability at the tail.

• Yang, Wushuang; Chen, Hua; Xu, Chong-Yu; Huo, Ran; Chen, Jie & Guo, Shenglian (2020). Temporal and spatial transferabilities of hydrological models under different climates and underlying surface conditions. Journal of Hydrology. ISSN 0022-1694. 591. doi: 10.1016/j.jhydrol.2020.125276. Fulltekst i vitenarkiv Vis sammendrag

  Changing conditions of the climate and underlying surface have altered the rainfall-runoff relationships in many basins, greatly increasing additional challenges in the applicability of hydrological models for studying the hydrological response to those potential changes. However, systematic and simultaneous testing and comparing of both temporal and spatial transferabilities of different hydrological models under changing conditions have not received enough attention. The present study investigates the potential differences between temporal and spatial transferabilities of different hydrological models under different climatic and underlying surface conditions, which are synthesized from two basins in Southern China with 50-year historical records (1966–2015). The transferability of five hydrological models, i.e., XAJ, HBV, SIMHYD, IHACRES and GR4J, is investigated under the synthesised changing conditions by using a new evaluation method, proposed in this study. The results show that: (1) the proposed evaluation method is proved to be effective in evaluating the transferability of the models; (2) for temporal transferability under stationary condition, the five models show similar performances, but for spatial transferability, the performances of complex models (XAJ and HBV) are better than that of the simple model (GR4J); (3) the difference in underlying surface conditions in the target basin affects spatial transferability of the models; (4) hydrological models have much better transferability from dry to wet period than otherwise. This study provides an insight to test temporal and spatial transferabilities of hydrological models in the context of changing climate and underlying surface conditions.

• Lin, Kairong; Chen, Haiyan; Xu, Chong-Yu; Yan, Ping; Lan, Tian & Liu, Zhiyong [Vis alle 7 forfattere av denne artikkelen] (2020). Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm. Journal of Hydrology. ISSN 0022-1694. 584. doi: 10.1016/j.jhydrol.2020.124696. Fulltekst i vitenarkiv Vis sammendrag

  Flash floods are one of the most severe natural disasters throughout the world, and are responsible for sizeable social and economic losses, as well as countless injuries and death. Risk assessment, which identifies areas susceptible to flooding, has been shown to be an effective tool for managing and mitigating flash floods. The study aims to introduce the methods to determine the weights of the risk indices, and identify the different risk clusters. In this regard, we proposed a methodology for comprehensively assessing flash flood risk in a GIS environment, by the improved analytic hierarchy process (IAHP) method, and an integration of iterative self-organizing data (ISODATA) analysis and maximum likelihood (ISO-Maximum) clustering algorithm. The weight for each risk index is determined by the IAHP, which integrates the subjective characteristics with objective attributes of the assessment data. Based on the data mining technology, the integration of ISO-Maximum clustering algorithm derives a more reasonable classification. The Guangdong Province of China was selected for testing the proposed method’s applicability, and we used a receiver operating characteristics (ROC) curve approach to validate the modeling of the flash-flood risk distribution. The validation against the historical flash flood data indicates a high reliability of this method for comprehensive flash flood risk assessment. In order to verify the proposed method’s superiority, in addition, the technique for order performance by similarity to ideal solution (TOPSIS) and the weights-of-evidence (WE) methods are used for comparison with the IAHP and ISO-Maximum clustering algorithm method. Moreover, we analyzed and compared the regularity of flash floods in the rural and urban areas. This study not only provides a new approach for large-scale flash flood comprehensive risk assessment, but also assists researchers and local decision-makers in designing flash flood mitigation strategies.

• Bian, G.D.; Du, Jinkang; Song, Mingming; Zhang, Xueliang; Zhang, Xingqi & Li, Runjie [Vis alle 9 forfattere av denne artikkelen] (2020). Detection and attribution of flood responses to precipitation change and urbanization: a case study in Qinhuai River Basin, Southeast China. Hydrology Research. ISSN 1998-9563. 51(2), s. 351–365. doi: 10.2166/nh.2020.063. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Both flood magnitude and frequency might change under the changing environment. In this study, a procedure combining statistical methods, flood frequency analysis and attribution analysis was proposed to investigate the response of floods to urbanization and precipitation change in the Qinhuai River Basin, an urbanized basin located in Southeast China, over the period from 1986 to 2013. The Mann–Kendall test was employed to detect the gradual trend of the annual maximum streamflow and the peaks over threshold series. The frequency analysis was applied to estimate the changes in the magnitude and frequency of floods between the baseline period (1986–2001) and urbanization period (2002–2013). An attribution analysis was proposed to separate the effects of precipitation change and urbanization on flood sizes between the two periods. Results showed that: (1) there are significant increasing trends in medium and small flood series according to the Mann–Kendall test; (2) the mean and threshold values of flood series in the urbanization period were larger than those in the baseline period, while the standard deviation, coefficient of variation and coefficient of skewness of flood series were both higher during the baseline period than those during the urbanization period; (3) the flood magnitude was higher during the urbanization period than that during the baseline period at the same return period. The relative changes in magnitude were larger for small floods than for big floods from the baseline period to the urbanization period; (4) the contributions of urbanization on floods appeared to amplify with the decreasing return period, while the effects of precipitation diminish. The procedure presented in this study could be useful to detect the changes of floods in the changing environment and conduct the attribution analysis of flood series. The findings of this study are beneficial to further understanding interactions between flood behavior and the drivers, thereby improving flood management in urbanized basins.

• Wang, Huimin; Chen, Jie; Xu, Chong-Yu; Zhang, J.K. & Chen, Hua (2020). A framework to quantify the uncertainty contribution of GCMs over multiple sources in hydrological impacts of climate change. Earth's Future. 8. doi: 10.1029/2020EF001602. Fulltekst i vitenarkiv Vis sammendrag

  The quantification of climate change impacts on hydrology is subjected to multiple uncertainty sources. Large ensembles of hydrological simulations based on multimodel ensembles (MMEs) have been commonly applied to represent overall uncertainty of hydrological impacts. However, as increasing numbers of global climate models (GCMs) are being developed, how many GCMs in MMEs are sufficient to characterize overall uncertainty is not clear. Therefore, this study investigates the influences of GCM quantity on quantifying overall uncertainty and uncertainty contributions of multiple sources in hydrological impacts. Large ensembles of hydrological simulations are obtained through the permutation of 3 greenhouse gas emission scenarios, 22 GCMs, 6 downscaling techniques, 5 hydrological models (HMs), and 5 sets of HM parameters, which enables to decompose uncertainty components using analysis of variance. The influences of GCM quantity are investigated by repeatedly conducting uncertainty decomposition for hydrological simulations from subsets with different numbers of GCMs. The results show that GCMs are the leading uncertainty sources in evaluating changes in annual and peak streamflows, while for changes in low flow, other uncertainty sources except HM parameters also have large contributions to overall uncertainty. Furthermore, on the condition of using no more than five GCMs, there are large possibilities that the overall uncertainty and GCMs' uncertainty contribution are underestimated. Using around 10 GCMs can ensure that the median of different combinations generates similar uncertainty components as the whole ensemble. Therefore, it is recommended to use at least 10 GCMs in studies of climate change impacts on hydrology to thoroughly quantify uncertainty.

• Lan, Tian; Zhang, Hongbo; Xu, Chong-Yu; Singh, Vijay P. & Lin, Kairong (2020). Detection and attribution of abrupt shift in minor periods in human-impacted streamflow. Journal of Hydrology. ISSN 0022-1694. 584. doi: 10.1016/j.jhydrol.2020.124637. Fulltekst i vitenarkiv Vis sammendrag

  Understanding the long-term variability of streamflow and its response to human activities in water-limited areas is essential for socio-hydrologic models’ development. In this study, a framework for the detection and attribution of abrupt shift of minor periods in human-impacted streamflow is proposed. First, the most significant abrupt shift in human-impacted streamflow is detected using the Pettitt test and verified based on different statistical characteristics of streamflow series (trend, periodicity, and different quantiles) and main physical causes (main reservoirs operations). According to the breakpoint, the study period was divided into approximately natural sub-period and human-impacted sub-period. Interestingly, we found the “missing” of minor (2–4-year timescale) periods of the runoff records after abrupt shift points in the study cases. To investigate its mechanisms, we proposed an Improved Multivariate Fuzzy Mean Generating Function (IMFMGF) model to simulate the natural runoff in the human-impacted period and decomposed the observed runoff into natural runoff component and human-impacted changing runoff component. Then, the periodicity of these two components was compared based on Morlet wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD). Results showed that the minor periods’ wave crests and troughs of the above two components had excellent negative correspondence. The candidate mechanism is that the offsetting effects (i.e., the regular anthropogenic withdrawal or intake of water.) resulted in the disappearance of minor periods of the human-impacted observed, which can give more certain inputs into the prediction of non-stationary streamflow series.

• Ngongondo, Cosmo; Zhou, Yanlai & Xu, Chong-Yu (2020). Multivariate framework for the assessment of key forcing to Lake Malawi levels variations in non-stationary frequency analysis. Environmental Monitoring and Assessment. ISSN 0167-6369. 192. doi: 10.1007/s10661-020-08519-4. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test ( α = 0.05 level) for the first moment and the F test for the second moment ( α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.

• Li, Runjie; Du, Jinkang; Bian, Guodong; Wang, Yuefeng; Chen, Changchun & Zhang, Xueliang [Vis alle 12 forfattere av denne artikkelen] (2020). An Integrated Modelling Approach for Flood Simulation in the Urbanized Qinhuai River Basin, China. Water resources management. ISSN 0920-4741. 34, s. 3967–3984. doi: 10.1007/s11269-020-02614-4. Fulltekst i vitenarkiv Vis sammendrag

  The accurate simulation and prediction of flood response in urbanized basins remains a great challenge due to the spatial and temporal heterogeneities in land surface properties. We hereby propose an integrated modelling approach that consists of a semi-distributed conceptual hydrological model and a novel parameterization strategy. The modelling approach integrates the Xinanjiang (XAJ) model, Taihu Basin (TB) model, and Nash instantaneous unit hydrograph (IUH) into a framework. Model parameters are calibrated by optimizing their relationships with corresponding physical factors. The proposed modelling approach is applied in the Qinhuai River basin (QRB), China. The modelling approach shows satisfactory performance in flood simulation both for calibration and validation of flood events in the QRB. The approach has temporal and spatial prediction capability by using the established relationships between parameter values and physical factors. Robustness analysis reveals that the different sets of flood events used for parameter relationship calibration led to similar model performance. Numerical experiments show that impervious coverage poses strong influences on the model performance and needs to be considered in flood routing simulations for small- or medium-intensity flood events.

• Lan, Tian; Lin, Kairong; Xu, Chong-Yu; Tan, Xuezhi & Chen, Xiaohong (2020). Dynamics of hydrological-model parameters: mechanisms, problems and solutions. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 24, s. 1347–1366. doi: 10.5194/hess-24-1347-2020. Fulltekst i vitenarkiv Vis sammendrag

  Abstract. It has been demonstrated that the application of time-varying hydrological-model parameters based on dynamic catchment behavior significantly improves the accuracy and robustness of conventional models. However, the fundamental problems for calibrating dynamic parameters still need to be addressed. In this study, five calibration schemes for dynamic parameters in hydrological models were designed to investigate the underlying causes of poor model performance. The five schemes were assessed with respect to the model performance in different flow phases, the transferability of the dynamic parameters to different time periods, the state variables and fluxes time series, and the response of the dynamic parameter set to the dynamic catchment characteristics. Furthermore, the potential reasons for the poor response of the dynamic parameter set to the catchment dynamics were investigated. The results showed that the underlying causes of poor model performance included time-invariant parameters, “compensation” among parameters, high dimensionality and abrupt shifts in the parameters. The recommended calibration scheme exhibited good performance and overcame these problems by characterizing the dynamic behavior of the catchments. The main reason for the poor response of the dynamic parameter set to the catchment dynamics may be the poor convergence performance of the parameters. In addition, the assessment results of the state variables and fluxes and the convergence performance of the parameters provided robust indications of the dominant response modes of the hydrological models in different sub-periods or catchments with distinguishing catchment characteristics.

• Zhou, Yanlai; Chang, Fi‐John; Chang, Li-Chiu; Lee, Wei-De; Huang, Angela & Xu, Chong-Yu [Vis alle 7 forfattere av denne artikkelen] (2020). An advanced complementary scheme of floating photovoltaic and hydropower generation flourishing water-food-energy nexus synergies. Applied Energy. ISSN 0306-2619. 275. doi: 10.1016/j.apenergy.2020.115389. Fulltekst i vitenarkiv Vis sammendrag

  Hybrid hydropower and floating photovoltaic power generation has far-reaching effects on the intertwined water, food and energy (WFE) nexus, but the complementary operation is fundamentally challenging especially under high uncertainties of hydro-meteorological conditions. This study proposed an artificial intelligence-based WFE system-overarching solution driven by hybrid hydro-floating photovoltaic power generation for promoting nexus synergies. A multi-objective optimization model grounded upon the Grasshopper Optimization Algorithm was developed to simultaneously maximize hydro-floating photovoltaic power output, the ratio of water storage to reservoir capacity, and the ratio of water supply to water demand. The Shihmen Reservoir watershed and its WFE system in northern Taiwan constituted the case study. The results demonstrated that the proposed optimization model could significantly improve synergistic benefits of the WFE nexus by reaching 13%, 13.3% and 15.1% in water storage, food production and hydro-floating photovoltaic power output, respectively. The optimal tilt angles of floating photovoltaic installation would vary between −11.9° (Summer) and 44.3° (Winter). This study opens up new perspectives on green energy production expansion while stimulating WFE nexus synergies in support of policy-makers with feasible schemes on floating photovoltaic deployment in the interest of social sustainability. In consequence, new niches are exploited for floating photovoltaic deployment and give rise to impact mitigation concerning hydro-meteorological uncertainties on WFE nexus management.

• Yang, Han; Xiong, Lihua; Xiong, Bin; Zhang, Quan & Xu, Chong-Yu (2020). Separating runoff change by the improved Budyko complementary relationship considering effects of both climate change and human activities on basin characteristics. Journal of Hydrology. ISSN 0022-1694. 591. doi: 10.1016/j.jhydrol.2020.125330. Fulltekst i vitenarkiv Vis sammendrag

  Numerous efforts have been made to separate effects of climate change and human activities on long-term runoff change, including a commonly used method based on the Budyko complementary relationship (BCR) to deal with the non-closure problems often encountered in separating the observed runoff change into its contributed components. The BCR was derived by assuming the change of the Budyko parameter reflecting basin characteristics is related to human activities alone. However, some studies show that basin characteristics are related to both climate change and human activities in certain basins, where applying the BCR method could lead to wrong results. In the study, a more flexible form of the BCR, the improved Budyko complementary relationship (IBCR), is developed under the situation that basin characteristics may relate to climate change. A separation method based on the IBCR is also developed. In this IBCR method, the relationship of the basin characteristics parameter to climate and/or human factors is estimated by using a multi-year moving window. To identify the flexibility of the IBCR method relative to the BCR method, both methods were applied to the Weihe basin of China, whose basin characteristics have been found to be affected by both climate change and human activities. Compared with the IBCR method, the BCR method overestimates the climate effect, while underestimates the human effect on the runoff change in the Weihe basin. It is found that whether or not to incorporate the climate effect into the basin characteristics parameter has a significant impact on the runoff change separation results in Weihe basin. Overall, the IBCR method is more reasonable and flexible for separating the long-term runoff change into different components.

• Zhou, Yanlai; Guo, Shenglian; Xu, Chong-Yu; Chang, Fi‐John; Chen, Hua & Liu, Pan [Vis alle 7 forfattere av denne artikkelen] (2020). Stimulate hydropower output of mega cascade reservoirs using an improved Kidney Algorithm. Journal of Cleaner Production. ISSN 0959-6526. 244. doi: 10.1016/j.jclepro.2019.118613. Fulltekst i vitenarkiv Vis sammendrag

  The evolutionary algorithms can solve reservoir operation with a fast convergence rate whereas the major impediments in handling the joint operation of mega cascade reservoirs easily trigger the technical bottlenecks, i.e. trapping into a local optimum, instability and loss of good solutions. This study proposes a methodology that fuses three auxiliary strategies into the Kidney Algorithm (KA) to optimize the hydropower output for conquering the bottlenecks in the KA concerning the joint operation of six mega cascade reservoirs located in the Jin-Sha River basin in China. The proposed theme would contribute to the application of the state-of-the-art evolutionary algorithms in boosting the cleaner hydropower production of mega cascade reservoirs. The three auxiliary strategies are that: firstly, the exploration and exploitation strategy is employed to stimulate the movement of solutions to surmount technical drawback of trapping into a local optimum; secondly, the adaptive strategy is used to automatically adjust algorithm parameter values to overcome the instability problem; lastly, the elitism strategy is introduced to preserve the best solution at every epoch to avoid the loss of good solutions. Our methodology, without expanding or upgrading hydraulic infrastructures, can increase the hydropower production of the six mega cascade reservoirs by 7.8%, as compared with the standard operation policy. The hydropower production can reach 4.8 billion kW·h/year, which can decrease 3.77 billion kg/year in CO2 emission, and bring 217.44 million USD/year in hydropower benefits. The improved KA can considerably increase the reliability and resilience of hydropower output as well as largely decrease the vulnerability of hydropower output. The results suggest that our methodology can stimulate hydropower output to yield more benefits regarding cleaner production, carbon emission reduction and sustainability.

• Reynolds, J.E.; Halldin, Sven; Seibert, Jan; Xu, Chong-Yu & Grabs, Thomas (2020). Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios. Hydrological Sciences Journal. ISSN 0262-6667. 65(9), s. 1512–1524. doi: 10.1080/02626667.2020.1747619. Fulltekst i vitenarkiv Vis sammendrag

  Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts.

• Huo, Ran; Li, Lu; Chen, Hua; Xu, Chong-Yu; Chen, Jie & Guo, Shenglian (2020). Extreme Precipitation Changes in Europe from the Last Millennium to the End of the Twenty-First Century. Journal of Climate. ISSN 0894-8755. 34, s. 567–588. doi: 10.1175/JCLI-D-19-0879.1. Fulltekst i vitenarkiv Vis sammendrag

  In this study, we aim to better understand the current and future projections of precipitation extremes in Europe in the context of climatic variability over a long-term period from the last millennium to the end of the twenty-first century. The daily gridded precipitation data from five global climate models (GCMs) of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are chosen to investigate natural variability and precipitation extremes during the last millennium (850–1849), the historical (1850–2005) period, and two representative concentration pathway (RCP2.6 and RCP8.5) scenarios (2006–99). First, the seasonal and annual precipitation and extreme statistics from GCMs are evaluated using reconstruction and observation. Second, the spatial and temporal patterns of extreme precipitation from GCMs are investigated from the last millennium to the end of the twenty-first century. Meanwhile, the characteristic changes of extreme precipitation for the five regions of Europe are further analyzed. The results revealed the following: GCMs underestimate extreme precipitation and overestimate mean precipitation compared with the observations from European Climate Assessment and Dataset (ECA&D); the majority of Europe except southern Europe will most likely have large magnitude increases in the extreme precipitation and mean precipitation in the future under both RCP scenarios; there is no systematic change of precipitation extremes from the last millennium to the historical period from all GCMs; and larger magnitude increases are shown in 100- and 200-yr than in 5- and 10-yr return period precipitation from both RCP scenarios. In addition, short-duration extreme precipitation will most likely increase more than longer-duration extremes.

• Lan, Tian; Lin, Kairong; Xu, Chong-Yu; Liu, Zhiyong & Cai, Huayang (2020). A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 24, s. 5859–5874. doi: 10.5194/hess-24-5859-2020. Fulltekst i vitenarkiv Vis sammendrag

  Previous studies have shown that the seasonal dynamics of model parameters can compensate for structural defects of hydrological models and improve the accuracy and robustness of the streamflow forecast to some extent. However, some fundamental issues for improving model performance with seasonal dynamic parameters still need to be addressed. In this regard, this study is dedicated to (1) proposing a novel framework for seasonal variations of hydrological model parameters to improve model performance and (2) expanding the discussion on model results and the response of seasonal dynamic parameters to dynamic characteristics of catchments. The procedure of the framework is developed with (1) extraction of the dynamic catchment characteristics using current data-mining techniques, (2) subperiod calibration operations for seasonal dynamic parameters, considering the effects of the significant correlation between the parameters, the number of multiplying parameters, and the temporal memory in the model states in two adjacent subperiods on calibration operations, and (3) multi-metric assessment of model performance designed for various flow phases. The main finding is that (1) the proposed framework significantly improved the accuracy and robustness of the model; (2) however, there was a generally poor response of the seasonal dynamic parameter set to catchment dynamics. Namely, the dynamic changes in parameters did not follow the dynamics of catchment characteristics. Hence, we deepen the discussion on the poor response in terms of (1) the evolutionary processes of seasonal dynamic parameters optimized by global optimization, considering that the possible failure in finding the global optimum might lead to unreasonable seasonal dynamic parameter values. Moreover, a practical tool for visualizing the evolutionary processes of seasonal dynamic parameters was designed using geometry visualization techniques. (2) We also discuss the strong correlation between parameters considering that dynamic changes in one parameter might be interfered with by other parameters due to their interdependence. Consequently, the poor response of the seasonal dynamic parameter set to dynamic catchment characteristics may be attributed in part to the possible failure in finding the global optimum and strong correlation between parameters. Further analysis also revealed that even though individual parameters cannot respond well to dynamic catchment characteristics, a dynamic parameter set could carry the information extracted from dynamic catchment characteristics and improve the model performance.

• Li, Zhenya; Yang, Tao; Xu, Chong-Yu; Shi, Pengfei; Yong, Bin & Huang, Ching-Sheng [Vis alle 7 forfattere av denne artikkelen] (2020). Evaluating the area and position accuracy of surface water paths obtained by flow direction algorithms. Journal of Hydrology. ISSN 0022-1694. 583. doi: 10.1016/j.jhydrol.2020.124619. Fulltekst i vitenarkiv Vis sammendrag

  The surface water path (SWP) extracted from digital elevation model (DEM) by flow direction algorithms is widely employed to obtain a variety of topographic variables used in hydrological modeling. Accurate SWPs can facilitate understanding the underlying mechanisms of water movement on Earth’s surface. However, the accuracy of extracted SWPs by different flow direction algorithms has not been systematically studied. In this work, two indicators are developed to measure the area and position errors of extracted SWPs relative to theoretical SWPs on four synthetic surfaces representing typical terrains of natural watersheds. Based on the formulas of the synthetic surfaces, theoretical true SWP can be derived for any grid cell on the DEM discretized from the synthetic surfaces. Several widely used flow direction algorithms including three single flow direction (SFD) algorithms (i.e. D8, Rho8 and D8-LTD approaches) and three multiple flow direction (MFD) algorithms (i.e. FDFM, MFD-md and D∞ approaches) are implemented to extract SWPs. Results suggest that significant distinctions can be detected in SWPs extracted by different flow direction algorithms. The SWPs extracted by SFD algorithms are always one-dimensional non-dispersive lines because SFD algorithms allow only one flow direction at each grid cell. In contrast, the SWPs extracted by MFD algorithms show excessive artificial dispersion. The average area error of extracted SWPs ranges from 16.3% to 75.2% on different synthetic surfaces and the minimum is obtained by FDFM approach for all synthetic surfaces. The average position error falls in the range of 46.0% to 161.4%. The maximum is gained by D8 or FDFM approach, and the minimum by D8-LTD or D∞ approach. The cross compensation of SWP area induced by artificial dispersion leads to relatively high area accuracy but relatively low position accuracy of MFD algorithms. In addition, increasing DEM resolution without capturing more topographic variability can decrease the area and position accuracy due to error accumulation from more steps of flow direction calculation. Our findings provide a beneficial insight into applying SWP-derived topographic variables to hydrological modeling.

• Chen, Shilei; Xiong, Lihua; Ma, Qiumei; Kim, Jong-Suk; Chen, Jie & Xu, Chong-Yu (2020). Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method. Journal of Hydrology. ISSN 0022-1694. 589. doi: 10.1016/j.jhydrol.2020.125156. Fulltekst i vitenarkiv Vis sammendrag

  Merging gauge observation with a single original satellite-based precipitation product (SPP) is a common approach to generate spatial precipitation estimates. For the generation of high-quality precipitation maps, however, this common method has two drawbacks: (1) the spatial resolutions of original SPPs are still too coarse; and (2) a single SPP can’t capture the spatial pattern of precipitation well. To overcome these drawbacks, a two-step scheme consisting of downscaling and fusion was proposed to merge gauge observation with multiple SPPs. In both downscaling and fusion steps, the geographically weighted ridge regression (GWRR) method, which is a combination of the geographically weighted regression (GWR) method and the ridge regression method, is proposed and implemented to generate improved spatial precipitation estimates by overcoming the collinearity problem of the pure GWR method. The proposed two-step merging scheme was applied to Xijiang Basin of China for deriving daily precipitation estimates from the data of both gauge observation and four near real-time SPPs (i.e., TMPA-3B42RT, CMORPH, PERSIANN and GSMaP_NRT) during the period of 2010–2017. The results showed that: (1) the collinearity problem caused by GWR was not serious in downscaling but serious enough to prevent GWR from being directly used in the fusion; and (2) the proposed two-step merging scheme significantly improved the spatial resolution and accuracy of precipitation estimates over the original SPPs. Comparisons also showed that, in the second step (fusion) of the merging scheme, the use of multiple SPPs provided more reliable spatial precipitation estimates than using a single SPP.

• Lin, Kangling; Sheng, Sheng; Zhou, Yanlai; Liu, Feng; Li, Zhiyu & Chen, Hua [Vis alle 9 forfattere av denne artikkelen] (2020). The exploration of a Temporal Convolutional Network combined with Encoder-Decoder framework for runoff forecasting. Hydrology Research. ISSN 1998-9563. 51(5), s. 1136–1149. doi: 10.2166/nh.2020.100. Fulltekst i vitenarkiv
• Yang, Xue; Magnusson, Jan; Huang, Shaochun; Beldring, Stein & Xu, Chong-Yu (2020). Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions. Journal of Hydrology. ISSN 0022-1694. 582, s. 124357–124357. doi: 10.1016/j.jhydrol.2019.124357. Fulltekst i vitenarkiv Vis sammendrag

  Hydrological models have been widely used to predict runoff in regions with observed discharge data, and regionalization methods have been extensively discussed for providing runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003–2012). Great progress has been achieved in the field of regionalization in previous studies, in which different hydrological models have been coupled with various regionalization methods. However, different conclusions have been drawn due to the use of different hydrological models, regionalization methods, and study regions. In this study, we assessed the performance of the five most widely used regionalization methods (spatial proximity with parameter averaging option (SP-par), spatial proximity with output averaging option (SP-out), physical similarity with parameter averaging option (Phy-par), physical similarity with output averaging option (Phy-out), and regression methods (PCR)) and four daily rainfall-runoff models (GR4J, WASMOD, HBV and XAJ, with 6, 8, 13, and 17 parameters, respectively) at the same time. Our aim was to evaluate how the performance of the regionalization methods depends on (a) the selection of hydrological models, (b) nonstationary climate conditions, and (c) different climatic regions. This investigation used data from 86 independent catchments evenly distributed throughout Norway, covering three different climate zones (oceanic, continental and polar tundra) according to the Köppen-Geiger classification. The results showed that (a) the SP-out and Phy-out methods performed better than the SP-par and Phy-par for all the hydrological models, and the regression method performed worst in most cases; (b) the difference between the parameter averaging option and the output averaging option is positively related to the number of hydrological model parameters, i.e. the greater the number of parameters, the larger the difference between the two options; (c) the XAJ model with the greatest number of parameters produced the best results in most cases, and models with fewer parameters tend to produce similar performance for the different regionalization methods; (d) models with more parameters displayed larger declines in performance than those with fewer parameters for nonstationary conditions; and (e) clear differences in the performance of the regionalization methods exist among the three climatic regions. This study provides insight into the relationship between the complexity of hydrological models and regionalization methods in cold and seasonally snow-covered regions.

• Li, Wei; Chen, Jie; Li, Lu; Chen, Hua; Liu, Bingyi & Xu, Chong-Yu [Vis alle 7 forfattere av denne artikkelen] (2019). Evaluation and bias correction of S2S precipitation for hydrological extremes. Journal of Hydrometeorology. ISSN 1525-755X. 20(9), s. 1887–1906. doi: 10.1175/JHM-D-19-0042.1. Fulltekst i vitenarkiv
• Li, Xiang-Quan; Chen, Jie; Xu, Chong-Yu; Li, Lu & Chen, Hua (2019). Performance of post-processed methods in hydrological predictions evaluated by deterministic and probabilistic criteria. Water resources management. ISSN 0920-4741. 33(9), s. 3289–3302. doi: 10.1007/s11269-019-02302-y. Fulltekst i vitenarkiv Vis sammendrag

  Meteorological Ensemble Streamflow Prediction (ESP), which uses Ensemble Weather forecasts (EWFs) to drive hydrological models, is a useful methodology for extending forecast periods and to provide valuable uncertainty information to improve the operation of future water resources. However, raw EWFs are usually biased and under-dispersive and so cannot be directly used in ESP, leading to the development of several post-processing methods. The performance of these methods needs to be evaluated/compared in building ESP based on deterministic and probabilistic criteria. In addition, likely influencing factors also need to be identified. This study evaluated the performance of four state-of-the-art methods: the Generator-based Post-Processing (GPP) method, Extended Logistic Regression (ExLR), Bayesian Model Averaging (BMA) and Affine Kernel Dressing (AKD), using a simple bias correction (BC) method as a benchmark. The evaluation was carried out over four watersheds with different basin areas in the humid region of central-south China based on the weather reforecasts from the Global Ensemble Forecasting System (GEFS). The results show that the performance of the post-processing methods varies with the forecast variable (precipitation, or air temperature or streamflow), but all of them outperform the BC and GEFS. For the four post-processing methods, the advantage of the generator-based methods (GPP and ExLR) lies in their probabilistic performance, which outperforms the distribution-based methods (BMA and AKD) by about 10% in precipitation forecasts and about 20% in streamflow forecasts, while the distribution-based methods (BMA and AKD) are better at their deterministic performance for precipitation forecasts, with a benefit of about 15%. Meanwhile, the post-processing methods generally perform better for precipitation and streamflow forecasts, but worse for air temperature forecasts for a bigger basin compared to the distribution-based methods. The results of this study emphasize the importance of considering the uncertainty of post-processing methods in ESP.

• Xue, Yang; Magnusson, Jan & Xu, Chong-Yu (2019). Transferability of regionalization methods under changing climate. Journal of Hydrology. ISSN 0022-1694. 568, s. 67–81. doi: 10.1016/j.jhydrol.2018.10.030. Fulltekst i vitenarkiv
• Huang, Qingzhong; Zhang, Qiang; Xu, Chong-Yu; Li, Qin & Sun, Peng (2019). Terrestrialwater storage in China: Spatiotemporal pattern and driving factors. Sustainability. 11(23). doi: 10.3390/su11236646. Fulltekst i vitenarkiv Vis sammendrag

  China is the largest agricultural country with the largest population and booming socio-economy, and hence, remarkably increasing water demand. In this sense, it is practically critical to obtain knowledge about spatiotemporal variations of the territorial water storage (TWS) and relevant driving factors. In this study, we attempted to investigate TWS changes in both space and time using the monthly GRACE (Gravity Recovery and Climate Experiment) data during 2003–2015. Impacts of four climate indices on TWS were explored, and these four climate indices are, respectively, El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific decadal oscillation (PDO). In addition, we also considered the impacts of precipitation changes on TWS. We found significant correlations between climatic variations and TWS changes across China. Meanwhile, the impacts of climate indices on TWS changes were shifting from one region to another across China with different time lags ranging from 0 to 12 months. ENSO, IOD and PDO exerted significant impacts on TWS over 80% of the regions across China, while NAO affected TWS changes over around 40% of the regions across China. Moreover, we also detected significant relations between TWS and precipitation changes within 9 out of the 10 largest river basins across China. These results highlight the management of TWS across China in a changing environment and also provide a theoretical ground for TWS management in other regions of the globe.

• Hu, Chen; Xia, Jun; She, Dunxian; Xu, Chong-Yu; Zhang, Liping & Song, Zhihong [Vis alle 7 forfattere av denne artikkelen] (2019). A modified regional L-moment method for regional extreme precipitation frequency analysis in the Songliao River Basin of China. Atmospheric research. ISSN 0169-8095. 230. doi: 10.1016/j.atmosres.2019.104629. Fulltekst i vitenarkiv Vis sammendrag

  The regional frequency analysis (RFA) is a widely used method in analyzing the changes of extreme precipitation (EP). The uncertainties in the identification of homogeneous subregions and the selection of optimal regional frequency distributions can largely influence the results of the RFA. In this study, the fuzzy c-means method combined with the extended Xie-Benn index (FCXB) is used to help determine the optimal division of subregions. In addition, we introduce a new comprehensive index (CI) to overcome the shortcomings of present measures and reduce the uncertainty in regional frequency distribution selection. The changes of EP at 93 meteorological stations in the Songliao River Basin (SRB) during 1960 to 2016 is analyzed. The main results show that: (1) FCXB can effectively identify the optimum number of homogeneous subregions automatically, and the corresponding subregion division is proven to be reasonable and reliable; (2) compared with the single goodness-of-fit measure, the developed CI can reduce the uncertainties in distribution selection and determine the optimal regional distribution in a reliable way; (3) the estimated EP under different return periods both decrease from the south to the north of the SRB, which indicates the risk of high-intensity EP events in the southern SRB is relatively higher. These findings can provide technical support for local policymakers to formulate effective measures to lessen the damages of the EP on ecosystem and society.

• Ma, Qiumei; Xiong, Lihua; Xia, Jun; Xiong, Bin; Yang, Han & Xu, Chong-Yu (2019). A censored shifted mixture distribution mapping method to correct the bias of daily IMERG satellite precipitation estimates. Remote Sensing. 11(11). doi: 10.3390/rs11111345. Fulltekst i vitenarkiv Vis sammendrag

  Satellite precipitation estimates (SPE) provide useful input for hydrological modeling. However, hydrological modeling is frequently hindered by large bias and errors in SPE, inducing the necessity for bias corrections. Traditional distribution mapping bias correction of daily precipitation commonly uses Bernoulli and gamma distributions to separately model the probability and intensities of precipitation and is insufficient towards extremes. This study developed an improved distribution mapping bias correction method, which established a censored shifted mixture distribution (CSMD) as a transfer function when mapping raw precipitation to the reference data. CSMD coupled the censored shifted statistical distribution to jointly model both the precipitation occurrence probability and intensity with a mixture of gamma and generalized Pareto distributions to enhance extreme-value modeling. The CSMD approach was applied to correct the up-to-date SPE of Integrated Multi-satelliE Retrievals for Global Precipitation Measurement (GPM) with near-real-time “Early” run (IMERG-E) over the Yangtze River basin. To verify the hydrological response of bias-corrected IMERG-E, the streamflow of the Wujiang River basin was simulated using Ge´nie Rural with 6 parameters (GR6J) and Coupled Routing Excess Storage (CREST) models. The results showed that the bias correction using both BerGam (traditional bias correction combining Bernoulli with gamma distributions) and the improved CSMD could reduce the systematic errors of IMERG-E. Furthermore, CSMD outperformed BerGam in correcting overall precipitation (with the median of mean absolute errors of 2.46 mm versus 2.81 mm for CSMD and BerGam respectively, and the median of modified Nash–Sutcliffe efficiency of 0.39 versus 0.29) and especially in extreme values for uniform format and particular attention paid to extremes. In addition, the hydrological effect that CSMD correction exerted on IMERG-E, driving GR6J and CREST rainfall-runoff modeling, outperformed that of the BerGam correction. This study provides a promising integrated distribution mapping framework to correct the biased daily SPE, contributing to more reliable hydrological forecasts by informing accurate precipitation forcing.

• Jiang, Shanhu; Wang, Menghao; Ren, Liliang; Xu, Chong-Yu; Yuan, Fei & Liu, Yi [Vis alle 8 forfattere av denne artikkelen] (2019). A framework for quantifying the impacts of climate change and human activities on hydrological drought in a semiarid basin of Northern China. Hydrological Processes. ISSN 0885-6087. 33(7), s. 1075–1088. doi: 10.1002/hyp.13386. Fulltekst i vitenarkiv Vis sammendrag

  Climate change and human activities are two major driving forces affecting the hydrologic cycle, which further influence the stationarity of the hydrologic regime. Hydrological drought is a substantial negative deviation from the normal hydrologic conditions affected by these two phenomena. In this study, we propose a framework for quantifying the effects of climate change and human activities on hydrological drought. First, trend analysis and change‐point test are performed to determine variations of hydrological variables. After that, the fixed runoff threshold level method (TLM) and the standardized runoff index (SRI) are used to verify whether the traditional assessment methods for hydrological drought are applicable in a changing environment. Finally, two improved drought assessment methods, the variable TLM and the SRI based on parameter transplantation are employed to quantify the impacts of climate change and human activities on hydrological drought based on the reconstructed natural runoff series obtained using the variable infiltration capacity hydrologic model. The results of a case study on the typical semiarid Laohahe basin in North China show that the stationarity of the hydrological processes in the basin is destroyed by human activities (an obvious change‐point for runoff series is identified in 1979). The traditional hydrological drought assessment methods can no longer be applied to the period of 1980–2015. In contrast, the proposed separation framework is able to quantify the contributions of climate change and human activities to hydrological drought during the above period. Their ranges of contributions to hydrological drought calculated by the variable TLM method are 20.6–41.2% and 58.8–79.4%, and the results determined by the SRI based on parameter transplantation method are 15.3–45.3% and 54.7–84.7%, respectively. It is concluded that human activities have a dominant effect on hydrological drought in the study region. The novelty of the study is twofold. First, the proposed method is demonstrated to be efficient in quantifying the effects of climate change and human activities on hydrological drought. Second, the findings of this study can be used for hydrological drought assessment and water resource management in water‐stressed regions under nonstationary conditions.

• Chen, Ying; Xu, Chong-Yu; Chen, Xingwei; Xu, Youpeng; Yin, Yixing & Gao, Lu [Vis alle 7 forfattere av denne artikkelen] (2019). Uncertainty in simulation of land-use change impacts on catchment runoff with multi-timescales based on the comparison of the HSPF and SWAT models. Journal of Hydrology. ISSN 0022-1694. 573, s. 486–500. doi: 10.1016/j.jhydrol.2019.03.091. Fulltekst i vitenarkiv
• Yan, Lei; Xiong, Lihua; Ruan, Gusong; Xu, Chong-Yu; Yan, Pengtao & Liu, Pan (2019). Reducing uncertainty of design floods of two-component mixture distributions by utilizing flood timescale to classify flood types in seasonally snow covered region. Journal of Hydrology. ISSN 0022-1694. 574, s. 588–608. doi: 10.1016/j.jhydrol.2019.04.056. Fulltekst i vitenarkiv Vis sammendrag

  The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT-based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT-based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT-based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization.

• Zhou, Yanlai; Chang, Li-Chiu; Uen, Tin-Shuan; Guo, Shenglian; Xu, Chong-Yu & Chang, Fi-John (2019). Prospect for small-hydropower installation settled upon optimal water allocation: An action to stimulate synergies of water-food-energy nexus. Applied Energy. ISSN 0306-2619. 238, s. 668–682. doi: 10.1016/j.apenergy.2019.01.069. Fulltekst i vitenarkiv
• Liu, Jiajia; Zhou, Zuhao; Yan, Ziqi; Gong, Jiaguo; Jia, Yangwen & Xu, Chong-Yu [Vis alle 7 forfattere av denne artikkelen] (2019). A new approach to separating the impacts of climate change and multiple human activities on water cycle processes based on a distributed hydrological model. Journal of Hydrology. ISSN 0022-1694. 578. doi: 10.1016/j.jhydrol.2019.124096. Fulltekst i vitenarkiv
• Chen, Guiya; Zhao, Xiaofeng; Zhou, Yanlai; Guo, Shenglian; Xu, Chong-Yu & Chang, Fi-John (2019). Emergency Disposal Solution for Control of a Giant Landslide and Dammed Lake in Yangtze River, China. Water. 11(9). doi: 10.3390/w11091939. Fulltekst i vitenarkiv Vis sammendrag

  Although landslide early warning and post-assessment is of great interest for mitigating hazards, emergency disposal solutions for properly handling the landslide and dammed lake within a few hours or days to mitigate flood risk are fundamentally challenging. In this study, we report a general strategy to effectively tackle the dangerous situation created by a giant dammed lake with 770 million cubic meters of water volume and formulate an emergency disposal solution for the 25 million cubic meters of debris, composed of engineering measures of floodgate excavation and non-engineering measures of reservoirs/hydropower stations operation. Such a disposal solution can not only reduce a large-scale flood (10,000-year return period, 0.01%) into a small-scale flood (10-year return period, 10%) but minimize the flood risk as well, guaranteeing no death raised by the giant landslide.

• Yang, Han; Xiong, Lihua; Ma, Qiumei; Xia, Jun; Chen, Jie & Xu, Chong-Yu (2019). Utilizing satellite surface soil moisture data in calibrating a distributed hydrological model applied in humid regions through a multi-objective Bayesian hierarchical framework. Remote Sensing. 11(11). doi: 10.3390/rs11111335. Fulltekst i vitenarkiv Vis sammendrag

  The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration.

• Chen, Xiaohong; Ye, Changqing; Zhang, Jiaming; Xu, Chong-Yu; Zhang, Lijuan & Tang, Yihan (2019). Selection of an optimal distribution curve for non-stationary flood series. Atmosphere. 10(1). doi: 10.3390/atmos10010031. Fulltekst i vitenarkiv Vis sammendrag

  The stationarity assumption of hydrological processes has long been compromised by human disturbances in river basins. The traditional hydrological extreme-value analysis method, i.e., “extreme value theory” which assumes stationarity of the time series, needs to be amended in order to adapt to these changes. In this paper, taking the East River basin, south China as a case study, a framework was put forward for selection of a suitable distribution curve for non-stationary flood series by using the time-varying moments (TVM). Data used for this study are the annual maximum daily flow of 1954–2009 at the Longchuan, Heyuan and Boluo Stations in the study basin. Five types of distribution curves and eight kinds of trend models, for a combination of 40 models, were evaluated and compared. The results showed that the flood series and optimal distribution curves in the East River basin have been significantly impacted by a continuously changing environment. With the increase of the degree of human influence, the thinner tails of distributions are more suitable for fitting the observed flow data, and the trend models are changed from CP (mean and standard deviation fitted by parabolic trend model) to CL (mean and standard deviation fitted by linear trend model) from upstream to downstream of the catchment. The design flood flow corresponding to a return period of more than 10 years at the Longchuan, Heyuan and Boluo Stations was overestimated by more than 28.36%, 53.24% and 26.06%, respectively if the non-stationarity of series is not considered and the traditional method is still used for calculation. The study reveals that in a changing environment, more advanced statistical methods that explicitly account for the non-stationarity of extreme flood characteristics are required.

• Xiong, Bin; Xiong, Lihua; Xia, Jun; Xu, Chong-Yu; Jiang, Cong & Du, Tao (2019). Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 23(11), s. 4453–4470. doi: 10.5194/hess-23-4453-2019. Fulltekst i vitenarkiv Vis sammendrag

  Many studies have shown that downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally performed by comparing the pre-dam and post-dam frequencies of certain streamflow indicators, such as floods and droughts. In this study, a rainfall–reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under a framework of a covariate-based nonstationary flood frequency analysis using the Bayesian inference method. The RRCI is derived from a combination of both a reservoir index (RI) for measuring the effects of reservoir storage capacity and a rainfall index. More precisely, the OR joint (the type of possible joint events based on the OR operator) exceedance probability (OR-JEP) of certain scheduling-related variables selected out of five variables that describe the multiday antecedent rainfall input (MARI) is used to measure the effects of antecedent rainfall on reservoir operation. Then, the RI-dependent or RRCI-dependent distribution parameters and five distributions, the gamma, Weibull, lognormal, Gumbel, and generalized extreme value, are used to analyze the annual maximum daily flow (AMDF) of the Ankang, Huangjiagang, and Huangzhuang gauging stations of the Han River, China. A phenomenon is observed in which although most of the floods that peak downstream of reservoirs have been reduced in magnitude by upstream reservoirs, some relatively large flood events have still occurred, such as at the Huangzhuang station in 1983. The results of nonstationary flood frequency analysis show that, in comparison to the RI, the RRCI that combines both the RI and the OR-JEP resulted in a much better explanation for such phenomena of flood occurrences downstream of reservoirs. A Bayesian inference of the 100-year return level of the AMDF shows that the optimal RRCI-dependent distribution, compared to the RI-dependent one, results in relatively smaller estimated values. However, exceptions exist due to some low OR-JEP values. In addition, it provides a smaller uncertainty range. This study highlights the necessity of including antecedent rainfall effects, in addition to the effects of reservoir storage capacity, on reservoir operation to assess the reservoir effects on downstream flood frequency. This analysis can provide a more comprehensive approach for downstream flood risk management under the impacts of reservoirs.

• Wang, Hui-Min; Chen, Jie; Xu, Chong-Yu; Chen, Hua; Guo, Shenglian & Xie, Ping [Vis alle 7 forfattere av denne artikkelen] (2019). Does the weighting of climate simulations result in a better quantification of hydrological impacts? Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 23(10), s. 4033–4050. doi: 10.5194/hess-23-4033-2019. Fulltekst i vitenarkiv Vis sammendrag

  With the increase in the number of available global climate models (GCMs), pragmatic questions come up in using them to quantify climate change impacts on hydrology: is it necessary to unequally weight GCM outputs in the impact studies, and if so, how should they be weighted? Some weighting methods have been proposed based on the performances of GCM simulations with respect to reproducing the observed climate. However, the process from climate variables to hydrological responses is nonlinear, and thus the assigned weights based on performances of GCMs in climate simulations may not be correctly translated to hydrological responses. Assigning weights to GCM outputs based on their ability to represent hydrological simulations is more straightforward. Accordingly, the present study assigns weights to GCM simulations based on their ability to reproduce hydrological characteristics and investigates their influences on the quantification of hydrological impacts. Specifically, eight weighting schemes are used to determine the weights of GCM simulations based on streamflow series simulated by a lumped hydrological model using raw or bias-corrected GCM outputs. The impacts of weighting GCM simulations are investigated in terms of reproducing the observed hydrological regimes for the reference period (1970–1999) and quantifying the uncertainty of hydrological changes for the future period (2070–2099). The results show that when using raw GCM outputs to simulate streamflows, streamflow-based weights have a better performance in reproducing observed mean hydrograph than climate-variable-based weights. However, when bias correction is applied to GCM simulations before driving the hydrological model, the streamflow-based unequal weights do not bring significant differences in the multi-model ensemble mean and uncertainty of hydrological impacts, since bias-corrected climate simulations become rather close to observations. Thus, it is likely that using bias correction and equal weighting is viable and sufficient for hydrological impact studies.

• Jiang, C.; Xiong, L.H.; Yan, L.; Dong, J.F. & Xu, Chong-Yu (2019). Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 23(3), s. 1683–1704. doi: 10.5194/hess-23-1683-2019. Fulltekst i vitenarkiv Vis sammendrag

  Abstract. Multivariate hydrologic design under stationary conditions is traditionally performed through the use of the design criterion of the return period, which is theoretically equal to the average inter-arrival time of flood events divided by the exceedance probability of the design flood event. Under nonstationary conditions, the exceedance probability of a given multivariate flood event varies over time. This suggests that the traditional return-period concept cannot apply to engineering practice under nonstationary conditions, since by such a definition, a given multivariate flood event would correspond to a time-varying return period. In this paper, average annual reliability (AAR) was employed as the criterion for multivariate design rather than the return period to ensure that a given multivariate flood event corresponded to a unique design level under nonstationary conditions. The multivariate hydrologic design conditioned on the given AAR was estimated from the nonstationary multivariate flood distribution constructed by a dynamic C-vine copula, allowing for time-varying marginal distributions and a time-varying dependence structure. Both the most-likely design event and confidence interval for the multivariate hydrologic design conditioned on the given AAR were identified to provide supporting information for designers. The multivariate flood series from the Xijiang River, China, were chosen as a case study. The results indicated that both the marginal distributions and dependence structure of the multivariate flood series were nonstationary due to the driving forces of urbanization and reservoir regulation. The nonstationarities of both the marginal distributions and dependence structure were found to affect the outcome of the multivariate hydrologic design.

• Zhang, Qiang; Shen, Zexi; Xu, Chong-Yu; Sun, Peng; Hu, Pan & He, Chunyang (2019). A new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application. Science of the Total Environment. ISSN 0048-9697. 690, s. 1048–1067. doi: 10.1016/j.scitotenv.2019.06.310. Fulltekst i vitenarkiv Vis sammendrag

  Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been widely used in studies of climate changes related to scenarios at global and regional scales. However, CMIP5 outputs cannot be used directly in analysis of climate changes due to coarse spatial resolution. Here, we proposed a new statistical downscaling method for the downscaling practice of the CMIP5 outputs, i.e. Bias-corrected and station-based Non-linear Regression Downscaling method based on Randomly-Moving Points (BNRD). And up to now, there are only two global downscaled CMIP5 precipitation datasets, i.e. NASA daily downscaled CMIP5 precipitation product and BCSD-based (Bias Correction Spatial Disaggregation) monthly downscaled CMIP5 precipitation product available online, which are both based on BCSD downscaling method. Hence, we evaluated downscaling performance of BNRD by comparing it with the downscaled CMIP5 outputs using the BCSD method in this current study. The results indicate that: (1) during the period for development of the model (1964–2005), the error between downscaled CMIP5 precipitation and GPCC ranges between −50 mm–50 mm at monthly scale. When compared to BCSD-downscaled CMIP5 precipitation, BNRD-downscaled CMIP5 precipitation well reduces errors and avoids underestimation and overestimation of GPCC by BCSD-downscaled CMIP5 precipitation; (2) during period for verification of the downscaling models (2006–2013), the maximum (182 mm), minimum (15 mm) and average (68 mm) RMSEs between BNRD-downscaled CMIP5 precipitation and GPCC are all lower than those between BCSD-downscaled CMIP5 precipitation and GPCC at continental scales. Besides, from the average precipitation viewpoint, BNRD-downscaled CMIP5 precipitation is in higher correlation (around 0.75) with GPCC than BCSD-downscaled CMIP5 precipitation under RCP4.5 and RCP8.5 scenarios at continental scales; (3) BNRD resolved the negative relation to GPCC in the areas near equator, including north part of the South America, southern Africa, northern Australia. In all, BNRD downscaling method developed in this study performs better in describing GPCC changes in both space and time when compared to BCSD and can be used for downscaling practice of CMIP5 and even potentially CMIP6 precipitation outputs over the globe.

• Xiong, Feng; Guo, Shenglian; Liu, Pan; Xu, Chong-Yu; Zhong, Yixuan & Yin, Jiabo [Vis alle 7 forfattere av denne artikkelen] (2019). A general framework of design flood estimation for cascade reservoirs in operation period. Journal of Hydrology. ISSN 0022-1694. 577. doi: 10.1016/j.jhydrol.2019.124003. Fulltekst i vitenarkiv Vis sammendrag

  The hydrological regimes of downstream reservoirs have been significantly altered due to the operation and regulation of upstream cascade reservoirs. The original design flood quantiles, namely “design flood in construction period”, do not consider anthropogenic impacts in reservoir operation period, and have led to enormous conflicts between flood control and conservation. In this study, the “design flood and flood limited water level in operation period” are defined for practical application. We establish a general framework to measure the spatiotemporal pattern of streamflow and to estimate design floods of cascade reservoirs in operation period. The multivariate t-copula and a genetic algorithm strategy are proposed to solve the curse of dimensionality encountered in the derivation of most likely regional composition. The Jinsha River and Yalong River cascade reservoir system in China, which consists of 13 large reservoirs with the total storage capacity of 74.06 billion m3 and hydropower capacity of 71.47 GW, is selected as a case study. Results indicate that: (1) The curse of dimensionality can be well addressed by applying multivariate t-copula to build high dimensional joint distribution and using the genetic algorithm to achieve the most likely regional composition. (2) Compared with the design floods in construction period, the design floods of downstream reservoirs in operation period have been significantly reduced due to the upstream reservoir regulation. The 1000-year design peak flood discharge, 3-day, 7-day and 30-day flood volumes of Xiangjiaba reservoir decrease by 38.7%, 37.4%, 34.2% and 13.8%, respectively. (3) The flood limited water level of these reservoirs can be raised without increasing flood control risks in operation period. The cascade reservoirs in the Jinsha River and Yalong River can generate 3.28 billion kW h more hydropower (or increase 4.3%) annually during flood season.

• Reynolds, J.E.; Halldin, S.; Seibert, J.; Xu, Chong-Yu & Grabs, T. (2019). Robustness of flood-model calibration using single and multiple events. Hydrological Sciences Journal. ISSN 0262-6667. doi: 10.1080/02626667.2019.1609682. Fulltekst i vitenarkiv Vis sammendrag

  Lack of discharge data for model calibration is challenging for flood prediction in ungauged basins. Since establishment and maintenance of a permanent discharge station is resource demanding, a possible remedy could be to measure discharge only for a few events. We tested the hypothesis that a few flood-event hydrographs in a tropical basin would be sufficient to calibrate a bucket-type rainfall–runoff model, namely the HBV model, and proposed a new event-based calibration method to adequately predict floods. Parameter sets were chosen based on calibration of different scenarios of data availability, and their ability to predict floods was assessed. Compared to not having any discharge data, flood predictions improved already when one event was used for calibration. The results further suggest that two to four events for calibration may considerably improve flood predictions with regard to accuracy and uncertainty reduction, whereas adding more events beyond this resulted in small performance gains.

• Zhang, X.Q.; Liu, P.; Xu, Chong-Yu; Gong, Y.; Cheng, L. & He, S.K. (2019). Real-time reservoir flood control operation for cascade reservoirs using a two-stage flood risk analysis method. Journal of Hydrology. ISSN 0022-1694. 577. doi: 10.1016/j.jhydrol.2019.123954. Fulltekst i vitenarkiv
• Wang, Jialing; Chen, Hua; Xu, Chong-Yu; Zeng, Qiang; Wang, Qingjing & Kim, Jong-Suk [Vis alle 8 forfattere av denne artikkelen] (2018). Tracking the error sources of spatiotemporal differences in TRMM accuracy using error decomposition method. Hydrology Research. ISSN 1998-9563. 49(6), s. 1960–1976. doi: 10.2166/nh.2018.191. Fulltekst i vitenarkiv

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• Li, Jingjing; Chen, Hua; Xu, Chong-Yu; Li, Lu; Zhao, haoyuan & Huo, Ran [Vis alle 7 forfattere av denne artikkelen] (2022). Correction: Li et al. Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling. Water 2022, 14, 797. Water. 14(11). doi: 10.3390/w14111714. Fulltekst i vitenarkiv Vis sammendrag

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• Wan, Yongjing; Chen, Jie; Xie, Ping; Xu, Chong-Yu & Li, Daiyuan (2021). Evaluation of climate model simulations in representing the precipitation non-stationarity by considering observational uncertainties. Fulltekst i vitenarkiv
• Qi, Wenyan; Chen, Jie; Li, Lu; Xu, Chong-Yu; Xiang, Yiheng & Zhang, Shaobo [Vis alle 7 forfattere av denne artikkelen] (2021). Impact of the number of donor catchments and the efficiency threshold on regionalization performance of hydrological models. Fulltekst i vitenarkiv
• Wang, Weiguang; Chen, Lu & Xu, Chong-Yu (2021). Editorial: Hydrological Modeling in Water Cycle Processes. Water. 13(14). doi: 10.3390/w13141882. Fulltekst i vitenarkiv
• Zhou, Yanlai; Xu, Chong-Yu; Ngongondo, Cosmo & Li, Lu (2021). Detection, attribution and frequency analysis of non-stationary flood peaks in 32 big rivers worldwide. Fulltekst i vitenarkiv
• Li, Hong; Xu, Chong-Yu; Sæthun, Nils Roar; Braskerud, Bent Christen & Volden, Erland Per (2019). Identification of floodways by four approaches. Fulltekst i vitenarkiv

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