Chong-Yu Xu

Xu, C-Y , Gong, L., Jiang, T., Chen, D. and Singh, VP, 2006. Analysis of spatial distribution and temporal trend of reference evapotranspiration in Changjiang (Yangtze River) catchment. Journal of Hydrology 327, 81-93.

Xu, C-Y, Tunemar, L., Chen, Y.D. and Singh, V.P., 2006. Evaluation of seasonal and spatial variations of conceptual hydrological model sensitivity to precipitation data errors. Journal of Hydrology 324, 80-93.

Xu, C-Y, Widén, E. and Halldin, S., 2005. Modelling hydrological consequences of climate change - progress and challenges. Advances in Atmospheric Sciences 22(6), 789-797.

Xu, C-Y and D Chen, 2005. Comparison of seven models for estimation of evapotranspiration and groundwater recharge using lysimeter measurement data in Germany. Hydrological Processes, 19, 3717-3734.

Xu, C-Y , Gong, L., Jiang, T. and Chen, D., 2006. Decreasing reference evapotranspiration in a warming climate – a case of Changjiang (Yangtze River) catchment during 1970-2000. Advances in Atmospheric Sciences 23(4), 513-520.

Xu, C-Y and V P Singh, 2005. Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions. Journal of Hydrology, 308: 105-121. (In TOP 25 Hottest Articles within J Hydrol!)

Widén-Nilsson, E., Gong, L., Halldin, S. and Xu, C-Y, 2009 Model performance and parameter behavior for varying time aggregations and evaluation criteria in the WASMOD-M global water-balance model, Water Resources Research, 45, W05418, doi:10.1029/2007WR006695.

Widén, E., Halldin, S. and Xu, C-Y, 2007. Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation. Journal of Hydrology, 340, 105-118.

Gong, L, Widén-Nilsson, E., Halldin, S. and Xu, C-Y, 2009. Large-scale runoff routing with an aggregated network-response function. Journal of Hydrology, 368, 237–250.

Li, L., Xia, J., Xu, C-Y, Singh, VP, 2010. Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models. Journal of Hydrology 390, 210–221. (In TOP 25 Hottest Articles of the season!)

Jin, X-L, Xu, C-Y, Zhang, Q., Singh, VP., 2010. Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. Journal of Hydrology, 383, 147-155. (In TOP 25 Hottest Articles of the season!)

Chen, H, Guo, S.L., Xu, C-Y, Singh, V.P., 2007. Historical temporal trends of hydro-climatic variables and runoff response to climate change and their relevance in water resource management in the Hanjiang basin. Journal of Hydrology, 344, 171– 184. (In TOP 25 Hottest Articles within J Hydrol!)

Zhang, Q., Xu, C-Y, Chen, Y.D., 2008. Spatial and temporal variability of extreme precipitation during 1960-2005 in the Yangtze River basin and possible association with large-scale circulation. Journal of Hydrology, 353, 215-227, 10.1016/j.jhydrol.2007.11.023. (In TOP 25 Hottest Articles within J Hydrol!)

Du, JK, Xie, H., Hu, YJ., Xu, YP, and Xu, C-Y, 2009. Development and testing of a new storm runoff routing approach based on time variant spatially distributed travel time method. Journal of Hydrology, 369, 44-54. (In TOP 25 Hottest Articles of the season!)

Jiang, T., Chen, Y.D., Xu, C-Y, Chen, X.H., Chen, X., Singh, V.P., 2007. Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China. Journal of Hydrology, 336, 316– 333 (In TOP 25 Hottest Articles within J Hydrol!)

Du, J.K, Xie, S.P., Xu, Y.P. and Xu, C-Y, V.P. Singh, 2007. Development and testing of a simple physically-based distributed rainfall-runoff model for storm runoff simulation in humid forested basins. Journal of Hydrology, 336, 334– 346 (In TOP 25 Hottest Articles within J Hydrol!)

• Wang, Heng; Wang, Fuqiang; Xu, Chong-Yu; Zhao, Heng; Huang, Shengzhi & Yang, Huan [Vis alle 9 forfattere av denne artikkelen] (2026). Spatiotemporal evolution and concentration of global meteorological drought from a three-dimensional object-based perspective. Journal of Hydrology. ISSN 0022-1694. 680. doi: 10.1016/j.jhydrol.2026.136473. Fulltekst i vitenarkiv
• Barna, Danielle Marie; Engeland, Kolbjørn; Kneib, Thomas; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2026). Regional flood frequency analysis at multiple durations: A comparison of duration consistency in quantile and parameter regression techniques. Hydrology Research. ISSN 1998-9563. 57(5). doi: 10.1016/j.hydrch.2026.100002. Fulltekst i vitenarkiv
• Huang, Yali; Chen, Hua; Mosavi, Amirhosein; Liu, Yang; Hu, Zhaoyang & Xu, Chong-Yu (2026). Probabilistic multi-step forecasting of estuarine salinity intrusion for ecohydrological risk assessment. Journal of Hydrology. ISSN 0022-1694. 674. doi: 10.1016/j.jhydrol.2026.135475. Fulltekst i vitenarkiv
• Du, Shuping; Jiang, Shanhu; Ren, Liliang; Cui, Hao; Zhu, Yongwei & He, Miao [Vis alle 8 forfattere av denne artikkelen] (2026). Widespread increase in root zone water use exacerbates water stress in cultivated regions. Agricultural and Forest Meteorology. ISSN 0168-1923. 384. doi: 10.1016/j.agrformet.2026.111188. Fulltekst i vitenarkiv
• Gui, Xu; Ma, Qiumei; Li, Jiqing; Xiong, Lihua & Xu, Chong-Yu (2026). Reconstructing Reservoir Water Level-Area-Storage Volume Curve using Multi-Source Satellite Imagery and Intelligent Classification Algorithms. Water resources management. ISSN 0920-4741. 39, s. 5339–5358. Fulltekst i vitenarkiv Vis sammendrag

  The water level-area-storage volume (Z-A-V) relationship serves as the cornerstone of reservoir operations, governing water allocation, flood mitigation, and power generation. Sedimentation-induced capacity alterations can progressively degrade Z-A-V accuracy, yet systematic curve updates remain inadequately implemented across developing nations. Traditional reconstruction approaches face inherent limitations due to resource-intensive requirements including costly field surveys and data scarcity. Emerging satellite remote sensing technologies show transformative potential for dynamic reservoir monitoring, though their application in Z-A-V curve updating still requires substantive exploration. This study evaluates the capability of multi-source satellite imagery, including optical data from Landsat 8 and Sentinel-2, and synthetic aperture radar (SAR) data from Sentinel-1, for accurately reconstructing the Z-A-V curve. To extract high-accuracy reservoir surface extents, two advanced algorithms–Random Forest Classification (RFC) and Otsu thresholding–are applied to the optimal and SAR imagery, respectively, to delineate water and non-water pixels. The suitability of the satellite-derived Z-A-V curve is further assessed by estimating the storage capacity loss due to sedimentation accumulation and comparing these estimates with that derived from design curve. Using the Hongjiadu Reservoir in the upper reach of the Wujiang River, China, as a case study, the results show that: (1) all three satellite datasets accurately extract the reservoir surface areas, achieving average accuracies of 96%, 89%, and 88% for Sentinel-1, Landsat 8, and Sentinel-2, respectively; (2) the Z-A curve reconstructed from Sentinel-1 SAR imagery achieves the highest accuracy with and the coefficient of determination (R²) > 0.99 and root mean square error (RMSE) < 1; (3) the storage capacity loss estimated from Sentinel-1-derived Z-V curve (0.065 billion m³) closely aligns with the sedimentation-based estimate (0.077 billion m³), outperforming other imagery sources. This study demonstrates the significant potential of integrating high-quality satellite imagery and intelligent algorithms to enable frequent, cost-effective, and large-scale updates to reservoir Z-A-V curves, bridging a crucial gap in adaptability evaluation of multi-source remote sensing-based Z-A-V characteristic reconstructions.

• Tang, Senlin; Zhang, Qiang; Gong, Xintong; Xu, Chong-Yu; Singh, Vijay P. & Sun, Fubao [Vis alle 9 forfattere av denne artikkelen] (2026). Emergent constraints reveal underprediction of future global water availability under anthropogenic forcing. Global and Planetary Change. ISSN 0921-8181. 257. doi: 10.1016/j.gloplacha.2025.105252. Fulltekst i vitenarkiv
• Zhu, Yongwei; Jiang, Shanhu; Ren, Liliang; Guo, Jianying; Tang, Pengcheng & Xu, Chong-Yu (2025). Unraveling Interactive Effects of Climate, Hydrology, and CO2 on Ecological Drought with Interpretable Machine Learning. Forests. 16(8). doi: 10.3390/f16081325. Fulltekst i vitenarkiv Vis sammendrag

  As the risk of drought increases due to climate change, understanding ecological drought has become increasingly important for ensuring water resource security and carbon balance. However, most current ecological drought assessments rely on meteorological or hydrological indicators, which may not accurately reflect changes in the eco-physiological status of ecosystems. Therefore, this study establishes an ecological drought assessment framework using solar-induced chlorophyll fluorescence (SIF) as an indicator to examine its interpretable responses to climate–hydrology–environmental variables. The framework was tested across China’s nine major river basins and different ecosystems. Results show that SIF increased in 80.0% of China’s areas, with 60.9% showing significant increases (p < 0.05). Forest ecosystems experienced the lowest frequency of ecological drought but showed increasing duration and intensity, while grassland ecosystems had the highest frequency but decreasing duration and intensity. LightGBM machine learning analysis revealed that surface soil moisture (SMs), temperature (Tm), root-zone soil moisture (SMrz), and CO2 were the main factors influencing ecological drought, with SMs and Tm contributing to over 66.1% of ecological drought. The SMs-Tm interaction alleviated ecological drought under low-temperature and high-humidity conditions but initially intensified then alleviated ecological drought under high-temperature and high-humidity conditions. The SMs-CO2 interaction promoted ecological drought at high or low CO2 concentrations but alleviated it at moderate concentrations.

• Zhu, Di; Wang, Dawei; Zhou, Yanlai; Chen, Hua; Lin, Fanqi & Xu, Chong-Yu [Vis alle 7 forfattere av denne artikkelen] (2025). Unlocking hydropower potential: a collaborative framework for optimizing cascade reservoir drawdown. Sustainable Energy Technologies and Assessments. ISSN 2213-1388. 82. doi: 10.1016/j.seta.2025.104493. Fulltekst i vitenarkiv Vis sammendrag

  Collaborative drawdown operation is a critical yet underexplored strategy for optimizing hydropower production in cascade reservoir systems during dry seasons. While traditional studies often overlook the distinct contributions of annual runoff and drawdown storage, this study introduces a novel, data-driven framework that integrates three core modules: optimal hydropower production, runoff–storage–hydropower relationship modeling, and drawdown operation rule extraction. Applied to seven cascade reservoirs in the Yalong River, China, the framework identifies how drawdown dynamics influence energy output and supports the formulation of operational rules. Results show that the proposed approach increases total hydropower production by 8.45 billion kWh—an 8.69 % improvement—and reduces carbon emissions by 6.63 billion kg, in comparison to the standard operation policy (SOP). The multiple linear regression analysis reveals that drawdown storage contributes more substantially to energy generation than annual runoff. Furthermore, adaptive bandwidth kernel density estimation uncovers characteristic drawdown patterns, with upstream reservoirs initiating drawdown earlier than downstream ones—patterns that recur across multiple years. This study offers a scientifically robust and practically applicable method for extracting drawdown operation rules, advancing the precision and efficiency of hydropower scheduling. The findings provide critical insights and technical support for decision-makers managing large-scale reservoir systems under increasing hydrological uncertainty.

• Zhang, Ying; Zhang, Zengxin; Zhang, Qi; Zhang, Xingnan; Xu, Yang & Liu, Xiaoyang [Vis alle 8 forfattere av denne artikkelen] (2025). Cumulative and offsetting effects of Streamflow Response to Climate change and Large Reservoir Group in the Jinsha River Basin, China. Journal of Hydrology Regional Studies. 60. doi: 10.1016/j.ejrh.2025.102480. Fulltekst i vitenarkiv Vis sammendrag

  Study Region Jinsha River Basin (JRB), situated in the upper sections of the Yangtze River basin, China Study Focus The Jinsha River Basin (JRB), renowned as the world's largest hydropower base. In this study, we proposed a novel approach for separating the reservoir's influence on streamflow and the response mechanisms in diverse watersheds. We integrated XGBoost (the eXtreme Gradient Boosting), CNN-LSTM (Convolutional Neural Networks, Long Short-Term Memory) and Informer with 6 hydrological stations data for streamflow reconstruction. Traditional hydrological model SWAT (Soil and Water Assessment Tool) served as a benchmark. New Hydrological Insights for the Region Reservoir groups were exerting average 40.7 % influence on streamflow after 2010, altering the streamflow with maximum 20 % in the upstream, 42 % midstream and 60 % downstream in August in JRB. Also, reservoir regulation significantly shifted the seasonal streamflow pattern (especially low water season) in the JRB. Under extreme wet (SDI≥2) in flood season (June-November), the maximum reservoir regulated streamflow offsetting varied between 14.3 % and 28.6 % (2000–4000 m3/s out of Qmax=14000 m3/s). Conversely, under extreme drought (SDI≤-2) in the low water season, reservoir regulated streamflow accumulation accounted for 25–44.4 % (100–800 m3/s out of Qmax=1800 m3/s). Moreover, the XGBoost model can provide a new way for streamflow reconstruction and forecast, particularly in the quantification of the reservoir groups under climate change. Reservoir storage efficiency was more obvious in the midstream, especially after 2015 (with normalized difference (Sreconstruction – Sobservation)> 0 more frequent), while released more in the downstream after 2010 (with normalized difference<0 more frequent). The streamflow reconstruction simulation had a stronger correlation with the catchment streamflow than large-scale atmospheric circulation indices. In summary, our study demonstrates models forecast reliability in streamflow reconstruction under climate change and reservoirs, providing a new reasoning approach for extreme streamflow event early warning resulting from large reservoir operations.

• Xie, Kun; Li, Lu; Chen, Hua; Mayer, Stephanie; Sobolowski, Stefan Pieter & Xu, Chong-Yu (2025). Future Local‐Scale Extreme Precipitation Changes in Norway: A Convection‐Permitting Climate Model Perspective. Journal of Geophysical Research (JGR): Atmospheres. ISSN 2169-897X. 130(13). doi: 10.1029/2024jd042899. Fulltekst i vitenarkiv Vis sammendrag

  This study examines how extreme precipitation events in Norway could change in the future, focusing on both hourly and daily time scale. Using HCLIM regional climate models applied at 3 and 12 km horizontal resolutions, we investigate the change in precipitation intensity and frequency by the end of the 21st century. Our findings show that most regions of Norway are likely to experience more intense and frequent precipitation, particularly for hourly extremes. Simulations driven by GFDL-CM3 GCM shows a larger increase in extremes than those driven by EC-EARTH GCM with weaker warming and smaller increase in moisture. The seasonality of extreme precipitation in these models demonstrate no shift in most regions, except the southern and western regions. Notably, the high-resolution convection-permitting model projects a larger increase in extreme intensity but comparable increase in hourly and daily extreme event frequency than its coarser-resolution counterpart across most regions. This study highlights the need for high-resolution climate models to better understand how extreme precipitation might affect specific regions and locations in a warming world. Understanding these local changes is crucial to preparing for potential floods and managing water resources effectively.

• Wang, Xiao; Lan, Tian; Zhang, Hongbo; Ren, Chongfeng; Chen, Yongqin David & Xu, Chong-Yu (2025). Enhancing Hydrological Model Performance Through Clustering of Dynamic Catchment Characteristics and Parameter Discretization. Water Resources Research. ISSN 0043-1397. 61(10). doi: 10.1029/2024wr038783. Fulltekst i vitenarkiv Vis sammendrag

  Abstract A common structural deficiency in hydrological modeling is the inability to represent the temporal variations within the catchment due to an inadequate understanding of the physical mechanisms behind these processes, leading to generalized runoff simulations. To address model structural deficiencies, we propose a novel approach called Clustering of Dynamic Catchment Characteristics and Parameter Discretization (CDCC). This approach establishes a comprehensive index system describing the meteorological and land‐surface conditions of the catchment, using data mining techniques to extract dynamic characteristics from the index system and cluster the time series into sub‐periods with similar hydrological processes. Then the sub‐period calibration technique is used to couple the extracted catchment dynamics and hydrological models, with parameters discretized and calibrated in each sub‐period accordingly. The results show that the CDCC approach effectively identifies temporal variations in dominant hydrological processes using the Model Parameter Estimation Experiment data set. When applied in conjunction with the HYMOD model, the CDCC approach improves the median Nash‐Sutcliffe Efficiency from 0.55 to 0.67, while significantly enhancing model performance across different flow phases with reduced Root Mean Square Error. Although poor parameter responses to catchment dynamics may arise due to optimization algorithm convergence, parameter boundary constraints, equifinality, or model structure limitations, the dynamic parameter sets effectively capture temporal variations and improve the simulation of various flow phases. The CDCC method would enhance the structural flexibility of current hydrological models, extending their applicability across diverse hydrometeorological conditions and supporting more efficient water resource management under changing environment.

• Wang, Menghao; Jiang, Shanhu; Ren, Liliang; Yuan, Shanshui; Xu, Junzeng & Xu, Chong-Yu (2025). A novel time-varying threshold level method for evaluating non-stationary hydrological drought. Journal of Hydrology. ISSN 0022-1694. 662. doi: 10.1016/j.jhydrol.2025.133977. Fulltekst i vitenarkiv Vis sammendrag

  Affected by climate change and human activities, the evolution of drought has become more complex. Traditional drought assessment methods based on the assumption of stationarity are facing challenges. Currently, research on the non-stationary improvement of standardized drought indices (SDI) is relatively mature, while research on threshold level methods (TL) is still in its infancy. In this study, we propose a “deconstruct-reshape” framework to construct a time-varying threshold level (TLvar) method for assessing non-stationary hydrological droughts. Deconstruction refers to decomposing non-stationary streamflow series into periodic and trend terms. Then, the periodic terms are combined to generate a de-trend streamflow series to extract the threshold value. Reconstruction refers to the linear superposition of the extracted threshold and trend term to obtain the reconstructed TLvar. These two steps are the innovations of this study. Furthermore, the method was performed in 49 human-induced watersheds around the world. Kendall correlation analysis demonstrated that the correlation coefficients between the drought-flood series characterized by the TLvar method and the well-established time-varying parameter standardized streamflow index (SSIvar) were 0.642 to 0.858 on a 3-month cumulative scale and 0.736 to 0.965 on a 12-month cumulative scale. Meanwhile, a strong correlation has been identified between the drought-flood series characterized by SSIvar and TLvar and the Standardized Precipitation Index (SPI) series, particularly during periods of drought, in both small and large watersheds. In addition, the verification results based on historical drought events in the EM-DAT database demonstrated that the TLvar method can accurately capture historical drought events and reasonably assess their severity, avoiding the problems of underestimation or overestimation of drought events in traditional methods. Overall, the TLvar method proposed in this study is an effective method that can serve as an important complement to existing non-stationary drought assessment methods. This method could be applied with SSIvar to facilitate qualitative comparisons of drought severity and quantify streamflow deficit volumes in specific catchments, thereby achieving a comprehensive qualitative to quantitative assessment of non-stationary hydrological drought.

• Wang, Menghao; Jiang, Shanhu; Ren, Liliang; Cui, Hao; Yuan, Shanshui & Xu, Junzeng [Vis alle 7 forfattere av denne artikkelen] (2025). Attribution of streamflow and its seasonal variation to dual nature-society drivers using CMIP6 data and hydrological models. Journal of Hydrology. ISSN 0022-1694. 659. doi: 10.1016/j.jhydrol.2025.133314. Fulltekst i vitenarkiv Vis sammendrag

  Attributing changes in streamflow processes is crucial for water resource management as well as for understanding and mitigation of flood and drought risks. However, most existing attribution methods lack a unified approach to handle various causal variables, making them unsuitable for comprehensive attribution assessments. Therefore, this study proposed a framework to quantitatively attribute the impacts of natural and anthropogenic climate change, land use and cover change (LUCC), and human water withdrawal on streamflow and its seasonality. The framework consists of three steps: (1) bias correction of Coupled Model Intercomparison Project Phase 6 (CMIP6) data and construction of a dualistic nature-society water cycle model; (2) simulation of streamflow processes and identification of streamflow seasonality under different climate forcing and LUCC scenarios; and (3) quantitative attribution of streamflow evolution characteristics. The Weihe River Basin (WRB) in China has been selected as a case study area for the proposed attribution framework. The quantitative analysis indicates that natural and anthropogenic climate change, LUCC, and human water withdrawal account for 20.8%, 27.9%, 4.6%, and 46.7% of the decreasing trend in streamflow volume and –42.4%, –28.1%, –5.1%, and 175.6% of the weakening trend in streamflow seasonality in the WRB, respectively. These results suggest that human water withdrawal reduces streamflow and weakens its seasonality, while the other three factors contribute to streamflow reduction but enhance its seasonality. Overall, this study effectively distinguishes the impacts of anthropogenic and natural climate change on streamflow processes, thus providing a deep understanding of the influences of human-induced hydro-climate change.

• Tong, Shanlin; Chen, Jie; Xia, Rui; Li, Wenpan; Chen, Yan & Xu, Chong-Yu (2025). Quantifying ecosystem-human water trade-offs: A clean water scarcity perspective. Journal of Hydrology Regional Studies. 60. doi: 10.1016/j.ejrh.2025.102526. Fulltekst i vitenarkiv Vis sammendrag

  Study region Yangtze River basin. Study focus Water security is a long-standing concern in developing and developed countries, putting rigorous standards on the reliability of approaches applied to identity and access water scarcity. Previous methods for assessing water scarcity have primarily focused on the quantitative and qualitative needs of human water consumption, underestimating the spatiotemporal fluctuation in water demand of ecosystems and the impacts of ecological water scarcity. Here, we introduce a novel approach to estimate clean water stress index by integrating changes in ecosystem requirement and human demand. New hydrological insights for the region We calculated clean water stress index (CWSI) and distinguished different types of water scarcity in the Yangtze River basin. Results indicate an upward tendency in CWSI from 2010 to 2020, with severe deficiencies occurred in upstream regions. Nearly 9.52 % of grid cells were threatened by ecology-based water scarcity. The increase in environmental flow requirement exacerbated clean water scarcity, which was unevenly distributed across temporal and spatial dimensions. The weighted Theil’s index increased from 0.283 in 2010–0.417 in 2020. Scenario simulations show that the variations of clean water stress index were predominantly driven by natural factor, while human activity assumed a secondary role. Our proposed approach provides a novel perspective for elucidating the impacts of ecosystem and human on water security.

• Sun, Yixin; Zhang, Qiang; Wang, Gang; Xu, Chong-Yu; Singh, Vijay P. & Liu, null Da [Vis alle 9 forfattere av denne artikkelen] (2025). Dual effects of climate change and socioeconomic development on flood exposure in the Yellow River Basin, China. Journal of Hydrology Regional Studies. 60, s. 102504–102504. doi: 10.1016/j.ejrh.2025.102504. Fulltekst i vitenarkiv Vis sammendrag

  Study region The Yellow River Basin (YRB) in China. Study focus This research constructs and evaluates future flood-inundated urban land, population exposure, and GDP exposure in the YRB under various SSP-RCP scenarios. It also discusses the contributions of different factors to changes in exposure and quantifies the future flood control efficiency of flood protection measures. New hydrological insights for the region We find that provinces in the lower YRB have a higher proportion of flood-inundated urban land. By the end of the 21st century, the proportion of flood-inundated urban land of the cities in lower YRB is expected to increase. Meanwhile, the average population exposure in the YRB under different SSP scenarios will decrease by 6.22–47.23 %, while GDP exposure will increase by 68–72 % across all SSP scenarios. Compared to climate change, socio-economic development is the primary factor influencing flood exposure. Considering flood protection measures, the lower YRB exhibit higher flood control efficiency, while middle and upper YRB still face high exposure due to insufficient effectiveness of protection measures. Protective efficiency of existing flood control measures will further decline with climate change: by the end of the 21st century, only 25 % of cities in the YRB will effectively reduce population exposure, and 35 % will mitigate GDP exposure.

• Sun, Deliang; Wu, Xiaoqing; Zhang, Qiang; Woolway, R. Iestyn; Xu, Chong-Yu & Singh, Vijay P. [Vis alle 11 forfattere av denne artikkelen] (2025). Warming-induced spatial shifts in single and double cropping rice habitat suitability across China. Ecological Indicators. ISSN 1470-160X. 181. doi: 10.1016/j.ecolind.2025.114410. Fulltekst i vitenarkiv Vis sammendrag

  As a staple food for more than half of the global population, rice is crucial for world food security, especially in developing economies. However, there is a knowledge gap concerning the spatial extent of rice habitat suitability in China − the world’s largest rice producer – particularly regarding actual cultivation feasibility beyond purely climatic factors. Here, we assessed historical (2017–2020) and future (2021–2100) shifts in the suitability of single (SR) and double (DR) cropping rice systems, and established a research framework for rice suitability (Machine Learning-Ecological Niche Modeling frameworks, ML-ENM). We use the MaxEnt model to evaluate potential habitat suitability under natural constraints and the XGBoost algorithm to assess potential cultivation feasibility. The study reveals that the proposed framework that combines ecological niche modeling with agronomic feasibility analysis generates actionable insights for climate-resilient crop system design, with direct implications for global adaptation planning. The results showed that high-suitability areas for DR (11.1%) were concentrated in central and southern China, with suitability decreasing progressively toward concentric zones. In contrast, high-suitability areas for SR (11.3%) were more widely distributed across the Sichuan Basin, Yangtze River Basin, southern North China Plain, and Northeast Plain. Notably, the SR suitability was more climate-sensitive than DR. Warming drove DR expansion northward and westward, though with reduced suitability, whereas SR remained stable in the short term but shifted toward higher latitudes under prolonged warming. These findings provide spatially explicit insights for optimizing rice cultivation strategies, mitigating warming impacts, and enhancing climate resilience in China’s rice production.

• Ren, Mingming; Jiang, Shanhu; Ren, Liliang; Yan, Yiqi; Cui, Hao & Zhu, Yongwei [Vis alle 10 forfattere av denne artikkelen] (2025). Nonstationary spatiotemporal evolution of extreme flood and low flow affected by climate change and human activities in the Yellow River basin. Journal of Hydrology Regional Studies. 61. doi: 10.1016/j.ejrh.2025.102640. Fulltekst i vitenarkiv Vis sammendrag

  Study region The Yellow River Basin, China. Study focus Extreme weather events occur frequently under global change, and the assumption of stationary for hydrological series may no longer be valid. Therefore, we proposed a new framework based on a nonstationary statistical model that incorporates machine learning for detecting spatiotemporal variations of extreme events. New hydrological insights for the region The series of these events at most stations show nonstationary characteristics during both the base period and the change period. By optimizing and evaluating different types of nonstationary models based on the Generalized Additive Model for Location, Scale and Shape (GAMLSS), the model with the climate index (CI) and the human-induced index (HI) as covariates demonstrates superior applicability compared to the model using the CI and the reservoir index (RI). Furthermore, the higher probability of extreme flood and low flow were observed at Tangnaihai, while the lower probability of extreme low flow was identified at Huaxian. Extreme flood in the YRB show weak inter-station correlations with high spatial heterogeneity, especially between Tangnaihai and Huayuankou, while extreme low flow is generally well correlated except between Lanzhou and its downstream stations (Toudaoguai and Longmen) due to water withdrawals from irrigation districts. The results provide scientific basis for reservoir flood control, river ecological health and the safety and stability of power systems.

• Luo, Qi; Luo, Yuxuan; Zhou, Yanlai; Zhu, Di; Chang, Fi-John & Xu, Chong-Yu (2025). Reducing Carbon Emissions: A Multi-Objective Approach to the Hydropower Operation of Mega Reservoirs. Sustainability. 17(6). doi: 10.3390/su17062770. Fulltekst i vitenarkiv Vis sammendrag

  Optimizing the joint drawdown operation of mega reservoirs presents a significant opportunity to enhance the comprehensive benefits among hydropower output, water release, and carbon emission reduction. However, achieving the complementary drawdown operation of mega reservoirs while considering reservoir carbon emissions poses a notable challenge. In this context, this study introduces an innovative multi-objective optimization framework tailored for the joint drawdown operation of mega reservoirs. Firstly, a multi-objective optimization model, leveraging an intelligent evolutionary algorithm, is developed to minimize reservoir carbon emissions (Objective 1), maximize hydropower output (Objective 2), and maximize water release (Objective 3). Subsequently, a multi-criteria decision-making approach to search for the optimal scheme is employed. The proposed framework is applied to seven mega reservoirs within the Hanjiang River basin, China. The results show that the framework is effective in promoting comprehensive benefits, improving hydropower production by 8.3%, reservoir carbon emission reduction by 5.6%, and water release by 6.2% from the optimal solution under wet scenarios, compared to standard operation policies. This study not only provides a fresh perspective on the multi-objective drawdown operation of mega reservoirs but also offers valuable support to stakeholders and decision-makers in formulating viable strategic recommendations that take potential carbon emissions and advantages into account.

• Lu, Chang; Liu, Da; Zhang, Qiang; Xu, Chong-Yu; Li, Jianfeng & Wang, Gang [Vis alle 9 forfattere av denne artikkelen] (2025). Rising temperatures trigger warming-wetting conditions in the East River Basin, China. Journal of Hydrology Regional Studies. 60. doi: 10.1016/j.ejrh.2025.102577. Fulltekst i vitenarkiv Vis sammendrag

  Study region The East River Basin (ERB) is located at the junction of the Western Pacific Ocean, the South China Sea, and Eurasia. Study focus In 2011–2012, the ERB suffered severe droughts in all four seasons, so the wetting and/or drying tendency of the ERB had aroused widespread concern. Based on the ERA5 reanalysis and CMIP6 multi-model datasets, we used a Lagrangian trajectory model to unravel precipitation, temperature, and evaporation changes during the historical period 1981–2023 and predict wetting and/or drying tendencies (measured by precipitation minus evaporation, PME) during the future period 2024–2099. New hydrological insight for the region During the historical period, average annual precipitation decreased (-10.3 mm/a), evapotranspiration increased slightly (0.87 mm/a), and air temperature increased significantly (0.29°C/10a). We found that precipitation decreased over the northern and central ERB and evapotranspiration increased over the west to 115°E, while temperature increased over the eastern ERB, resulting in a warmer and drier state over the entire ERB. External water vapor input is the prime driver of precipitation change (accounting for 79 % of the total), mainly from the North Pacific Ocean (31 %) and the Indian Ocean (26 %). The net water vapor fluxes declined in the late 20th century and peaked in 2006, and then decreased rapidly until 2023, wherein internal water cycle contributed to 21 % of precipitation change. Under the 1.5°C warming scenario, ERB will be in a warming-drying state, while under higher warming levels (2°C, 3°C, 4°C warming scenarios), ERB will be in a warming-wetting tendency. These findings can provide an important reference for basin-scale water resources management and mitigation of water issues in the context of climate warming.

• Lin, Fanqi; Zhou, Yanlai; Ning, Zhihao; Liu, Pan; Chang, Fi-John & Xu, Chong-Yu (2025). Unlocking hydropower synergies: Multi-objective optimization drawdown operation of cascade reservoirs. Renewable Energy. ISSN 0960-1481. 247. doi: 10.1016/j.renene.2025.123097. Fulltekst i vitenarkiv
• Li, Shufei; Xia, Jun; Zhou, Yanlai; Xu, Chong-Yu & Chang, Fi-John (2025). Multi-objective optimization of seasonal emergency storage in cascade reservoirs for enhanced drought resilience and hydropower synergy. Journal of Water and Climate Change. ISSN 2040-2244. 16(10), s. 3046–3066. doi: 10.2166/wcc.2025.816. Fulltekst i vitenarkiv Vis sammendrag

  Optimizing the seasonal emergency storage capacity (SESC) of cascade reservoirs is crucial for enhancing water supply and hydropower generation (HG), particularly in the face of extreme drought events. However, this task becomes exceptionally challenging due to the complex interplay of spatiotemporal drought variability and the topographically intricate nature of the basin. This study presents a multi-objective optimization framework for enhancing SESC in cascade reservoirs to simultaneously improve HG and drought resilience. Using a dynamic drawdown strategy, the model incorporates spatiotemporal drought variability and reservoir constraints. Applied to a five-reservoir cascade on the Yangtze River, the model outperforms the standard operation policy (SOP), yielding an 8.7% increase in hydropower output, a 45.5% reduction in river drought duration, and a 4.5% improvement in low-flow conditions. The framework offers robust decision-making support for climate-adaptive water resource management.

• Li, Chao; Li, Guangshuai; Zhu, Bin; Chen, Xi; Xu, Chong-Yu & Zhang, Zengxin (2025). Investigating the Regulation Effects of Vegetation Restoration on Precipitation Patterns Over the Loess Plateau, China. International Journal of Climatology. ISSN 0899-8418. 45(11). doi: 10.1002/joc.70006. Fulltekst i vitenarkiv Vis sammendrag

  ABSTRACT The large‐scale vegetation restoration program implemented across China's Loess Plateau altered regional precipitation regimes by modifying biophysical processes in surface‐atmosphere interactions. This study integrated multi‐decadal satellite records (1982–2018) with WRF model simulations to reveal the biophysical mechanisms underlying vegetation‐mediated precipitation modulation. The results indicated that: (1) The response of precipitation to vegetation restoration on the Loess Plateau was not obvious between 1982 and 1999. However, precipitation began to trend upward after 2000, with arid regions experiencing higher multi‐year average precipitation compared to semi‐arid and semi‐humid regions. Nonetheless, the positive effect of vegetation restoration on regional precipitation started to diminish after 2015. (2) Vegetation restoration increased evapotranspiration, altering atmospheric humidity and air movement via changes in surface hydrothermal dynamics and energy conversion, thus boosting regional precipitation. Vegetation restoration contributed 3.68 mm/a to regional PRE between 2000 and 2018, accounting for about 31.32% of the PRE growth rate. (3) Vegetation restoration could compensate for the soil moisture consumption from increased evapotranspiration by increasing precipitation; however, after 2015, the beneficial effects of vegetation restoration on precipitation and soil moisture started to wane. These results indicated that vegetation restoration initiated a positive feedback loop, enhancing both soil moisture and regional precipitation. However, an excessive increase in vegetation might exacerbate regional water scarcity and pose a significant threat to the local ecology if precipitation and soil moisture did not increase proportionally. This finding held crucial implications for guiding vegetation restoration efforts on the Loess Plateau, optimising water resource management and promoting ecological sustainability.

• Lan, Tian; Li, Tongfang; Zhang, Hongbo; Wu, Jiefeng; Chen, Yongqin David & Xu, Chong-Yu (2025). Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 29(4), s. 924–924. doi: 10.5194/hess-29-903-2025. Fulltekst i vitenarkiv Vis sammendrag

  Abstract. The influence of climate change and anthropogenic activities on precipitation–runoff relationships (PRRs) has been widely discussed. Traditional models assuming stationary conditions can lead to inaccurate streamflow predictions. To address this issue, we propose a Driving index for changes in Precipitation–Runoff Relationships (DPRR), identified as key PRR influencers, involving climate forcing, groundwater, vegetation dynamics, and anthropogenic influences. According to the quantitative results of inputting the candidate driving factors into a holistic conceptual model, the possible process explanations for changes in the PRR were deduced. This framework is validated across five sub-basins in the Wei River basin. Moreover, non-stationary hydrological processes were initially detected, and the nonlinear correlations among the factors were assessed. The results show that baseflow emerges as the primary factor positively influencing the PRR (enhancing the PRR) but with high uncertainty. Potential evapotranspiration plays a dominant role in driving negative PRR changes in the sub-basins which are characterized by a semi-arid climate and minor human interference. Vegetation dynamics negatively influence the PRR, with driving levels correlating with the scale of soil and water conservation engineering, displaying lower uncertainty. Anthropogenic influences, represented by impervious surface ratio (ISR), night-time light (NTL), and population density (POP), exhibit varying driving levels, with ISR having the strongest and most direct impact, closely linked to urbanization processes and scales. The temporal dynamics of driving factors computed by dynamic DPRR generally correspond with hydrological regime shifts in non-stationary environments. The study's findings offer a comprehensive understanding of hydrological processes, enabling informed decision-making for the development of sustainable hydrological models.

• Lan, Tian; Du, Xinyue; Xie, Xue; Lin, Kairong; Zhang, Hongbo & Gong, Xinghui [Vis alle 8 forfattere av denne artikkelen] (2025). Exploring controls on precipitation-runoff dependencies: Implications for non-stationary and spatially heterogeneous analyses. Journal of Hydrology. ISSN 0022-1694. 659. doi: 10.1016/j.jhydrol.2025.133333. Fulltekst i vitenarkiv
• Huang, Kailin; Pénard, Lionel; Chen, Hua & Xu, Chong-Yu (2025). Measurement as functional minimization: An image-based method for river surface velocity and discharge estimation. Advances in Water Resources. ISSN 0309-1708. 206, s. 105160–105160. doi: 10.1016/j.advwatres.2025.105160. Fulltekst i vitenarkiv Vis sammendrag

  Hydrological monitoring establishes the foundation for scientific studies of water resources. The introduction of image-based hydrometry methods offers a non-intrusive and highly efficient approach for collecting discharge data from rivers. However, challenges persist for image-based hydrometry methods, such as partial observation, light reflection, unknown surface-to-depth coefficients, and unphysical velocity estimates requiring post-processing. In this study we propose a new image-based functional optimization method to address these issues. This Lagrangian multipliers formulation transforms river surface velocity and discharge estimation into the minimization of a distance in the functional space. The algorithm effectively handles partial, large scale, and high reflection observations, and the velocity field obtained through this method adheres to the physical governing equations. Three experiments were conducted across rivers of different scales, lighting, and weather conditions in France and China to demonstrate the applicability and robustness of the proposed algorithm. Through optimization of an objective functional, the results reveal that the proposed method can directly estimate river surface velocity and discharge from an image sequence using numerical model without the need for post-processing outliers or missing values. The study demonstrates that the proposed method has the potential to be widely deployed in different hydrological monitoring scenarios to improve hydrologic monitoring efficiency.

• Huang, Kailin; Pénard, Lionel; Chen, Hua & Xu, Chong-Yu (2025). Estimating river surface velocity and discharge through image-based data assimilation. Journal of Hydrology. ISSN 0022-1694. 660, s. 133167–133167. doi: 10.1016/j.jhydrol.2025.133167. Fulltekst i vitenarkiv Vis sammendrag

  Hydrological monitoring is important for the effective management of water resources, prevention of flooding, and advancement of scientific knowledge. Accurate estimation of river surface velocity is a fundamental aspect of hydrological monitoring; however, its estimation is often hindered by the presence of environmental or instrumental noise, the occurrence of outliers, and partial observations. Computational fluid dynamics (CFD) can improve surface velocity estimation to mitigate these issues; however, the parameters of the CFD model affect the estimated results. This study explores the possibility of using image-based data assimilation to improve the accuracy of river surface velocity and discharge estimation. Given cross-sectional bathymetry as a required input, a custom CFD model has been developed that could readily incorporate surface velocity observations. Initially, a Reynolds-averaged model has been constructed based on the section bathymetry. In the inference stage, the parameters are sampled and evaluated based on the proposed framework to investigate three scenarios: 1) how the bottom roughness height affects the estimation, 2) the effect of observation noise, and 3) the influence of partially observed data. The results demonstrate that the proposed algorithm can infer the two-dimensional cross-sectional velocity field from sparse surface velocity observations in different scenarios and improve the efficiency and robustness of image-based hydrometry. In the future, more complex scenarios and field studies could be considered to improve the assimilation framework.

• Huang, Kangkang; Zhou, Yanlai; Mei, Yadong; Chang, Fi-John & Xu, Chong-Yu (2025). Boosting Efficiency: Optimizing Pumped-Storage Power Station Operation by a Mixed Integer Linear Programming Approach. Energies. 18. doi: 10.3390/en18184977. Fulltekst i vitenarkiv Vis sammendrag

  The inherent variability and unpredictability of renewable energy output pose significant challenges to power grid stability. Pumped Storage Power Stations (PSPS) play a pivotal role in mitigating these challenges, enhancing the grid’s reliability and operational efficiency. This study proposes an advanced linear analytical method based on Mixed-Integer Linear Programming (MILP) to optimize the short-term scheduling of PSPSs. The goal is to simultaneously maximize the reduction in equivalent load fluctuations and improve power generation benefits. The model linearizes the objective functions, constraints, and decision variables, applying MILP to efficiently derive optimal dispatch solutions. Using the Heimifeng (HMF) PSPS in Hunan Province as a case study, data from four representative daily load scenarios in 2023 are employed to optimize both power output and pumping processes. The results highlight the following nonlinear, competitive relationship between load fluctuation improvements and power generation benefits: as power benefits increase the rate of improvement in load fluctuations tends to decrease. The optimal solutions demonstrate significant outcomes, with improvements exceeding 11.5% in equivalent load fluctuations across all scenarios and daily power benefits surpassing $41,100, reaching a peak of $55,700. This study introduces a robust linear analytical framework capable of simultaneously enhancing power benefits and stabilizing load fluctuations, thereby offering valuable technical support for decision-makers.

• Hu, Lei; Zhang, Qiang; Wang, Gang; Xu, Chong-Yu; Singh, Vijay P. & Liu, Da [Vis alle 9 forfattere av denne artikkelen] (2025). Rainstorm-induced flood risks exacerbated in future by dual climatic-human impacts in the lower yellow river basin of China. Natural Hazards. ISSN 0921-030X. 121(18), s. 21859–21859. doi: 10.1007/s11069-025-07666-6. Fulltekst i vitenarkiv Vis sammendrag

  Human-induced warming is intensifying the water cycle, increasing the risk of extreme precipitation and rainstorm-induced floods (RIF), posing a challenge for the sustainable socioeconomic development of the Yellow River Basin (YRB). Future flood risks under the dual impact of climate change and urbanization are less than adequately understood. In this study, we selected 9 indicators to formulate an RIF risk evaluation Model based on hazard of disaster-inducing factors, sensitivity of disaster-prone environment, and vulnerability of disaster-bearing areas (HSVM). We combined the analytical hierarchy process (AHP) and the entropy weight (EW) method to estimate RIF risks during historical (1950–2014), near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) periods over the YRB under the SSP245, SSP585 scenarios. We found decreasing RIF hazard along the southeast to northwest direction, and moderate and high RIF hazard in the middle and lower YRB. Using the RIF risk during the historical period as the baseline, the RIF hazard increased by 8.34%-18.29% (11.66%-28.93%) from near to long term under the SSP245 (SSP585) scenarios. High vulnerability was identified in the middle and lower YRB, particularly over urbanized regions. Compared to the historical period, the vulnerability increased by 30.46%-79.39% (29.14%-93.18%) from near to long term under the SSP245 (SSP585) scenarios. Specifically, the RIF risks were precipitously high in the provinces of the middle and lower YRB, such as Shandong, Henan, and Shaanxi, and the RIF risk increased by 47.00%-91.50% (50.67%-169.51%) from near to long term under the SSP245 (SSP585) scenarios in the YRB. The increase in RIF risk over Henan and Shandong reached 58.02%-283.05% at different times under different scenarios in the future, which is higher than the historical levels of other provinces during the same period. This study highlighted RIF risks in the middle and lower YPR, calling for urgent mitigation measures for flood disaster reduction.

• He, Miao; Jiang, Shanhu; Ren, Liliang; Cui, Hao; Du, Shuping & Zhu, Yongwei [Vis alle 11 forfattere av denne artikkelen] (2025). Distributed Hybrid Flood Modeling Framework: Integrating Physical Mechanisms With Deep Learning for Enhanced Efficiency and Accuracy. Water Resources Research. ISSN 0043-1397. 61(10). doi: 10.1029/2025wr039932. Fulltekst i vitenarkiv Vis sammendrag

  Abstract To address the limitations of process‐driven models in characterizing physical mechanisms and the interpretability challenges of data‐driven models in flood forecasting, this study proposes a distributed hybrid flood modeling (DHFM) framework that integrates physical mechanisms with deep learning. Differentiable diffusion wave (DW) and convolutional neural network (CNN) routing methods are introduced, which can be seamlessly integrated into the DHFM framework. A differentiable Muskingum (MK) routing method is also implemented as a benchmark. The Mishui Basin in China is selected as a case study to systematically evaluate the performance and interpretability of these three routing methods under both gauged and ungauged scenarios. Results show that the DHFM framework can effectively achieve physical parameterization across different sub‐basins. Compared to the lumped Xin'anjiang hydrological model, it achieve s higher accuracy in both daily streamflow and flood simulations, while also demonstrating favorable interpretability of the embedded neural network. Under gauged scenarios, the differentiable CNN method slightly outperforms DW in terms of performance and efficiency, and significantly surpasses MK. As the number of training stations increases, model performance tends to stabilize or decline. In ungauged scenarios, CNN performs well with sufficient training data (>2 stations) but is sensitive to station selection, exhibiting a substantial performance drop with only one station. In contrast, DW and MK show greater stability. The differentiable CNN method shows potential for adaptively learning unit hydrographs based on channel attributes. The proposed DHFM framework not only enhances flood simulation accuracy but also provides novel perspectives for understanding the physical mechanisms underlying flood processes.

• Silantyeva, Olga; Huang, Shaochun & Xu, Chong-Yu (2025). Evaluating Flexible Configurations of the Shyft Hydrologic Model Framework Across Mainland Norway. Hydrology and Earth System Sciences Discussions (HESSD). ISSN 1812-2116. doi: 10.5194/egusphere-2025-4071. Fulltekst i vitenarkiv Vis sammendrag

  The development and application of numerous hydrological models have played an indispensable role in advancing our understanding of hydrological processes, improving forecasting capabilities, supporting the design and operation of water conservancy projects, and facilitating water resource assessments. However, due to the spatial heterogeneity and temporal variability of climate and basin characteristics, the inherent complexity of hydrological processes, and data limitations, hydrological modeling faces two major bottlenecks: first, no single model is universally applicable to all river basins; second, further improvement in simulation accuracy of existing fixed-structure models remain challenging. As a result, the emergence of hydrological modeling frameworks with flexible structures and configurable components represents the next generation in the model development. Shyft is one of such flexible modeling frameworks fulfilling the above-mentioned purpose. It is cross platform and open source, jointly developed by academic and industrial partners. The framework allows uncertainty analysis, streamflow simulations, and forecasting. Most evaluation efforts of the framework to date have focused on smaller basins, but there is also a need to benchmark model performance more comprehensively. Here, we present a public benchmark for discharge simulation for 109 catchments across mainland Norway. Five model configurations are evaluated containing two different evapotranspiration routines (Priestley-Taylor and Penman-Monteith), two runoff methods (Kirchner and HBV) and two snow modules (temperature-index and semi-physical). The models are calibrated with 10 variants of target goal functions: KGE-based family, consisting of KGE, LKGE, bcKGE, KGE_LKGE, KGE_bcKGE, and NSE-based family, with NSE, LNSE, bcNSE, NSE_LNSE, NSE_bcNSE. The simulations are divided into two major groups: without precipitation correction and with precipitation correction. The evaluation is performed from 1981 to 2020 (approx. 40 years) at a daily time step. Using KGE, NSE and percent bias (PBIAS) as main evaluation metrics, the model configurations are compared against each other and against climatological benchmarks. The results show that all selected models were able to beat both mean and median flow benchmarks for the majority of catchments in all the target goal function set ups. 89 % of catchments gain higher performance scores with precipitation correction, but the picture is mixed for different metrics and models. The KGE and NSE performance metrics reveal that models, which combine temperature-index snow-tiles model and Kirchner runoff (-STK), perform best, but require precipitation correction to improve PBIAS. The models, which have semi-physical gamma-snow routine (-GSK), show relatively low performance with KGE and NSE scores, especially in Mountain and Inland hydrological regimes, but have the lowest |PBIAS|if no precipitation correction is applied. Precipitation correction shows limited effect on the -GSK models, even deteriorating some of the scores. The model, which combines temperature index snow-tiles and HBV runoff instead of Kirchner (-STHBV), is the most sensitive to precipitation correction: it has the worst PBIAS score across all models without precipitation correction, but jumps to third place in all three metrics, if the correction is applied. The study highlights that KGE-based goal functions reduce PBIAS more than any of the NSE-based goal functions. The study confirms that logarithmic transformation on streamflows, both if LKGE and LNSE are used as target goal functions, generate parameter sets with majority of outliers (KGE scores lower than -0.41). This new benchmark has potential to help with diagnosing problems, improving algorithms and further development within hydrological part of Shyft. Modeling results are made publicly available for further investigation.

• He, Liying; Chen, Hua; Huang, Yu; Sheng, Sheng; Zhou, Nie & Xu, Chong-Yu (2025). Flow inversion method based on multi-feature optical remote sensing indicators. Dili Xuebao. ISSN 0375-5444. 80(11), s. 3041–3057. doi: 10.11821/dlxb202511012. Fulltekst i vitenarkiv
• Lai, Chenguang; Liao, Yanghao; Yu, Haijun; Wang, Zhaoli; Liao, Yaoxing & Yang, Bing [Vis alle 10 forfattere av denne artikkelen] (2025). Formation mechanism analysis and the prediction for compound flood arising from rainstorm and tide using explainable artificial intelligence. Journal of Environmental Management (JEM). ISSN 0301-4797. 388. doi: 10.1016/j.jenvman.2025.125858. Fulltekst i vitenarkiv
• Du, Shuping; Jiang, Shanhu; Ren, Liliang; Cui, Hao; Zhu, Yongwei & He, Miao [Vis alle 7 forfattere av denne artikkelen] (2025). Incorporating plant access to groundwater in global root zone storage capacity estimate. Agricultural and Forest Meteorology. ISSN 0168-1923. 374. doi: 10.1016/j.agrformet.2025.110807. Fulltekst i vitenarkiv Vis sammendrag

  Plant-available groundwater water is well-documented. However, the impact of groundwater flux on root zone storage capacity (Sr), the maximum water storage within the subsurface root zone available for plant transpiration, remains poorly understood. In this study, we present a more conservative, lower-bound global estimate of Sr, incorporating groundwater for the first time into the deficit-based calculation of Sr using a novel conceptual method that accounts for groundwater contribution fraction. Our findings reveal widespread plant reliance on groundwater, with a mean use of at least 20 mm, equivalent to a water volume of 1700 km³. In western United States, this use can exceed 100 mm. Distinct spatial patterns in Sr emerge globally, with higher values in mountainous forests and lower values in boreal grasslands. Comparisons with observed rooting depths confirm that the deficit-based method, when incorporating groundwater, effectively predicts underground root traits. Biotic and abiotic factors critically influence Sr values, with irrigation and topographic convergence exacerbating this reduction. Groundwater-dependent ecosystems rely heavily on root-zone water storage, utilizing an average of 2151 km³ of water. Despite inherent uncertainties in input data, our study provides the first systematic evaluation of groundwater’s role in shaping Sr estimates, offering key insights for water resource management and ecosystem sustainability.

• Xia, Tianyu; Zhou, Yanlai; Xu, Chong-Yu; Liu, Pan; Luo, Yuxuan & Chang, Fi John (2025). A Spatiotemporal Pathformer-Based Deep Learning Framework for Watershed Flood Forecasting. Water Resources Research. ISSN 0043-1397. 61(12). doi: 10.1029/2025WR040193. Fulltekst i vitenarkiv Vis sammendrag

  Effective flood forecasting is essential for implementing proactive flood management and risk reduction strategies. However, conventional artificial neural networks often fail to capture the complex spatiotemporal dependencies among hydrometeorological variables, resulting in system biases and time-lag errors, especially during extreme flood events. This study introduces a spatiotemporal Pathformer-based deep learning framework for multi-step-ahead flood forecasting that dynamically adapts to flood magnitude and duration. The model integrates a dual self-attention mechanism and adaptive path selection, enhancing its ability to model nonlinear rainfall-runoff relationships and long horizon dependencies. Using a case study in the Jianxi basin with 25,341 hydrometeorological records at a 3-hr resolution, the spatiotemporal Pathformer's performance was evaluated across 1- to 7-step forecast horizons. A comparative analysis with Long Short-Term Memory (LSTM) and Transformer models demonstrates the spatiotemporal Pathformer's superior predictive accuracy and stability. It improves nash-sutcliffe efficiency by 3.0% and 7.4%, increases Volume Efficiency by 3.4% and 9.6%, reduces root mean square error by 18.4% and 34.9%, and lowers mean absolute error by 17.5% and 36.1% compared to LSTM and Transformer, respectively. By effectively mitigating time-lag errors and prediction bottlenecks, the spatiotemporal Pathformer ensures robust and reliable forecasting, even during extreme flood events. The application of SHapley Additive exPlanations analysis increases the model's interpretability, transparency, and trustworthiness by revealing the key hydrometeorological drivers behind its predictions. These results establish the spatiotemporal Pathformer as an advanced solution for next-generation flood forecasting, with strong potential to improve real-world applications in flood prevention and water resource management.

• Nkhoma, Lusungu; Gelati, Emiliano; Xu, Chong-Yu & Tallaksen, Lena M. (2025). Evaluation of the performance of high-resolution global climate datasets over Malawi. Physics and Chemistry of the Earth. ISSN 1474-7065. 142. doi: 10.1016/j.pce.2025.104221. Fulltekst i vitenarkiv
• Ning, Zhihao; Zhou, Ying; Zhou, Yanlai; Xiong, Lihua; Xu, Chong-Yu & Chang, Fi John (2025). Optimizing multi-objective reservoir operations for carbon reduction, power generation, and ecological protection of mega cascade reservoirs. Journal of Cleaner Production. ISSN 0959-6526. 535. doi: 10.1016/j.jclepro.2025.147139. Fulltekst i vitenarkiv
• Zhang, Xiaoqi; Yang, Han; Xu, Jijun; Wang, Yongqiang; Liu, Pan & Xu, Chong-Yu (2025). Increasing the available water diversion volume of water source project through flood resource utilization: A case study of the middle route of the South-to-North water diversion project in China. Reliability Engineering & System Safety. ISSN 0951-8320. 253. doi: 10.1016/j.ress.2024.110530. Fulltekst i vitenarkiv Vis sammendrag

  Water supply operating rules are critical for guaranteeing the safety of the water supply in the source project and receiving areas and overcoming the uneven distribution of water resources among different regions. Previous studies have mainly focused on optimizing the water supply operating rules to balance multiple benefits under the current engineering design parameters. In this study, a framework is proposed in which the water supply operating rules for the resource reservoir are adapted through flood resource utilization. The correlations among seasonal floods are first analyzed by considering the influence of water diversion projects. The seasonal flood-limited water levels (FLWLs) are then re-designed in terms of a flood damage assessment index under the deduced most-probable seasonal floods. Finally, the water supply operating curves of the resource reservoir are optimized by adopting the recommended seasonal FLWLs. The middle route of the South-to-North Water Diversion Project in China is taken as a case study. The results show that the recommended seasonal FLWLs are 160.2 m in summer and 164.2 m in autumn, and that the annual average available water diversion volume can be increased by 0.52 billion m3 without increasing the flood risk. Compared with the current water supply operating rules, the optimal operating curves make the annual distribution of the available water diversion volume more uniform in different typical-year scenarios and reduce the surplus reservoir water. These findings are helpful for exploring the potential of water resource utilization.

• Du, Shuping; Jiang, Shanhu; Ren, Liliang; Zhu, Yongwei; Cui, Hao & He, Miao [Vis alle 7 forfattere av denne artikkelen] (2025). Seasonal patterns and hydrological regulations of root zone storage capacity across United States. Agricultural and Forest Meteorology. ISSN 0168-1923. 363. doi: 10.1016/j.agrformet.2025.110428. Fulltekst i vitenarkiv Vis sammendrag

  Root zone storage capacity (Sr) represents the maximum subsurface storage accessible to plant roots. It is primarily influenced by water availability and water demand, thus exhibiting temporal change in response to climate variations. Previous studies have primarily focused on the spatial patterns of Sr across local to global scales; however, there remains a limited understanding of its temporal patterns, particularly in relation to seasonal changes. This work explores the seasonal behavior of Sr for wet and dry seasons and the hydrological regulation of seasonal Sr. We propose a seasonal modeling framework based on apportionment entropy, which considers the phase difference between water and energy. Within this framework, the PDM-FLEX hydrological model, an integration of the probability distributed model (PDM) with the FLEX lumped model, was employed to calculate catchment-scale Sr for each season across 671 catchments in the contiguous United States. Results show that: i) this framework can effectively capture seasonal Sr, with wet season Sr (an average of 564 mm) generally being smaller than dry season Sr (an average of 820 mm) for most catchments. In the west, plants routinely access deep water, leading to comparable Sr for both wet and dry seasons. Incorporating seasonal Sr into the hydrological model can improve simulation performance across time scales; ii) dry season Sr is more responsive to hydroclimatic control compared to wet season Sr, as plants in arid climates are more sensitive to water accessibility; and iii) during the wet season, low Sr relative to precipitation leads to an unresponsive hydrological reaction. However, during the dry season, a routine correlation between Sr and precipitation produces responsive hydrological behavior. These findings indicate that plants seasonally adapt their root systems and that these seasonal variations in Sr would have significant hydrological implications.

• Zhou, Yanlai; Ning, Zhihao; Huang, Kangkang; Guo, Shenglian; Xu, Chong-Yu & Chang, Fi-John (2025). Sustainable energy integration: Enhancing the complementary operation of pumped-storage power and hydropower systems. Renewable and Sustainable Energy Reviews. ISSN 1364-0321. 210. doi: 10.1016/j.rser.2024.115175. Fulltekst i vitenarkiv Vis sammendrag

  Efficiently optimizing the joint operation of off-river pumped-storage power (PSP) and hydropower stations offers a substantial opportunity to enhance synergies in power generation, financial returns, and carbon emission reduction. However, integrating solar power, wind power, and hydropower poses challenges, notably in managing their intermittent nature. This study presents an innovative multi-objective optimization model designed specifically for the joint operation of off-river PSP and mass hydropower stations. The primary objectives of this model are to maximize hydropower benefits while minimizing residual load fluctuations, thus enabling the power grid's smooth accommodation of variable renewable energies. Utilizing the Multi-objective Salp Swarm Algorithm, this method effectively addresses the challenges posed by intermittent renewable energies. This approach improves hydropower output and reduces residual load fluctuations, fostering a synergistic relationship between off-river PSP and hydropower stations. Conducting a case study involving an off-river PSP station and 396 regulatable hydropower stations in China, this study utilized the practical operation scheme as a baseline. The findings underscore that the optimized operation scheme significantly enhances synergies, especially under the condition of reaching maximum power load. Compared to the baseline scheme, this approach demonstrates substantial improvements across vital metrics, including a 26 % increase in hydropower output, a 22 % reduction in residual load fluctuation, a 26 % boost in financial benefits, and a 25 % decrease in carbon emissions. By addressing load fluctuations and enhancing the integration of variable renewable energies, this study advances United Nations' sustainable development goal - affordable and clean energy, paving the way for resilient and eco-friendly energy systems.

• He, Miao; Jiang, Shanhu; Ren, Liliang; Cui, Hao; Du, Shuping & Zhu, Yongwei [Vis alle 10 forfattere av denne artikkelen] (2025). Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning. Journal of Hydrology. ISSN 0022-1694. 649. doi: 10.1016/j.jhydrol.2024.132440. Fulltekst i vitenarkiv Vis sammendrag

  Recently, differentiable modeling techniques have emerged as a promising approach to bidirectionally integrating neural networks and hydrologic models, achieving performance levels close to deep learning models while preserving the ability to output physical states and fluxes. However, there remains a lack of systematic exploration into the performance and physical interpretability of hybrid models that use neural networks to replace the runoff generation and routing processes in regionalized modeling. This research developed 12 regionalized hybrid models based on a differentiable parameter learning (DPL) framework, utilizing the Hydrologiska Byråns Vattenbalansavdelning (HBV) model as the foundational backbone. These hybrid models incorporate neural networks to replace the various physical processes within the runoff generation and routing modules. The publicly available CAMELS dataset is employed to evaluate the performance and interpretability of these hybrid models. The results show that while the median Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) coefficients for all hybrid models are lower than those of the purely data-driven regionalized long short-term memory neural network (LSTM) model (median NSE: 0.742, median KGE: 0.762), the best-performing hybrid model (median NSE: 0.731, median KGE: 0.761) approaches the LSTM model and has better physical interpretability. Embedding neural networks does not inherently guarantee improved performance and may, in some cases, even result in reduced performance. The degree of performance enhancement is not significantly correlated with the number of embedded neural networks. Compared to replacing the runoff generation process, substituting the routing process with neural networks yields more substantial performance improvements and enables the learning of different routing patterns based on the catchment’s static attributes. This study underscores the importance of reasonably balancing the location, complexity, and quantity of embedded neural networks to achieve a trade-off between model performance and interpretability in hybrid modeling. These insights contribute to advancing regionalized hybrid modeling development.

• Wang, Chengyun; Chen, Jie; Lee, Sung-Ching; Xiong, Lihua; Su, Tianhua & Lin, Qian [Vis alle 7 forfattere av denne artikkelen] (2025). Response and recovery times of vegetation productivity under drought stress: Dominant factors and relationships. Journal of Hydrology. ISSN 0022-1694. 655. doi: 10.1016/j.jhydrol.2025.132945. Fulltekst i vitenarkiv Vis sammendrag

  Drought, a recurring disturbance in water cycle, significantly impacts terrestrial ecosystems. Understanding the response and recovery times of ecosystems to drought is imperative for assessing drought impacts and unraveling ecological dynamics. However, the dominant factors influencing response and recovery times of vegetation productivity to drought remain unclear. This study first evaluated the response and recovery times of vegetation productivity to soil drought using gross primary production (GPP) from an event-based perspective in China. Random forest models were used to identify the dominant factors of response and recovery times, and the relationship between response and recovery times was also investigated. Results showed that the mean GPP response time was 3.29 months, increasing from humid southeast to arid northwest China during 1989–2019. The mean recovery time was 3.46 months, longer in humid eastern regions compared to arid western ones. GPP responded most promptly in spring drought and slowly in autumn and winter droughts, whereas recovery was quick in summer and autumn droughts, and delayed in spring and winter droughts. Moreover, a notable negative correlation existed between response and recovery times with a long response time associating with a fast recovery rate and a short response time indicating a long recovery time. Drought duration was the primary factor influencing response time, while soil moisture during the recovery period was the critical for recovery time. These findings suggest resilience strategies to enhance ecosystem adaptability by strengthening adaptive capacities in regions with prolonged recovery time and safeguarding regions with long response time and rapid recovery capacity.

• Lan, Tian; Zhang, Jiajia; Li, Huanhuan; Zhang, Hongbo; Gong, Xinghui & Sun, Jing [Vis alle 8 forfattere av denne artikkelen] (2025). Flow duration curve prediction: A framework integrating regionalization and copula model. Journal of Hydrology. ISSN 0022-1694. 647. doi: 10.1016/j.jhydrol.2024.132364. Fulltekst i vitenarkiv Vis sammendrag

  Flow Duration Curve (FDC) is an essential graphical tool for illustrating the variability of observed historical streamflow. Achieving an advanced understanding of the physical characteristics governing FDCs is crucial for enhancing predictions of FDCs in ungauged basins. However, this remains challenging due to the complex processes that control streamflow components and their interactions. To address this, a novel framework that integrates regionalization and process-based methods is proposed for predicting FDCs in ungauged basins. This framework implements a hydrological similarity-based regionalization method to estimate hydrological model parameters in ungauged basins, enhancing streamflow prediction reliability. It categorizes streamflow into four distinct components based on delayed flow separation: Short-delay, intermediate-delay, long-delay, and baseline-delay. These components are synthesized to construct the FDC, with their interdependencies modeled using a Vine copula structure. A Bayesian-based estimation technique for the copula function parameters is developed to further improve the precision of the predicted FDC. Applied to nine selected MOPEX ungauged basins, the framework demonstrated superior accuracy, especially for low to middle streamflow phases. Moreover, the framework exhibited superior simulation accuracy during the validation period, highlighting its substantial potential for future-oriented water resource management and planning strategies.

• Wang, Menghao; Jiang, Shanhu; Ren, Liliang; Xu, Junzeng; Yuan, Shanshui & Xu, Chong-Yu (2025). An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method. Journal of Hydrology Regional Studies. 59. doi: 10.1016/j.ejrh.2025.102329. Fulltekst i vitenarkiv Vis sammendrag

  Study region Weihe River basin (WRB) in northern China. Study focus In a changing environment, traditional drought assessment methods may not be applicable when assumptions of stationarity are violated. Accordingly, this study proposes a framework that incorporates the time-varying parameter standardized streamflow index (SSIvar) and threshold level method (TLvar) for the non-stationary hydrological drought assessment. Then, the SSIvar and TLvar methods are compared with time-invariant and transplantation parameter SSI (SSIinvar and SSItran) and TL (TLinvar and TLtran) to validate their effectiveness. New hydrological insights for the region Validation results showed that SSIvar has the highest Kendall correlation coefficients with standardized precipitation index (SPI) and soil moisture index (SSMI) at 0.81 and 0.78, respectively, outperforming SSIinvar (0.67and 0.62) and SSItran (0.68 and 0.63). The TLvar method behaves in the same way, indicating that the SSIvar and TLvar methods provide a more accurate assessment of non-stationary hydrological drought. Furthermore, the comparison results show that the mean duration and severity of hydrological drought in the WRB increased by 22.37 % and 13.72 % for SSIvar method and 34.69 % and 19.15 % for TLvar method from 1961–1990 to 1991–2020, respectively, revealing that hydrological drought in the WRB has aggravated over the past 30 years. Overall, the combined use of SSIvar and TLvar provides a comprehensive understanding of non-stationary drought, integrating qualitative (e.g., severity levels) and quantitative (e.g., streamflow deficits) measures.

• Zhang, Qiang; Wang, Danzhou; Feng, Anlan; Wang, Gang; Hu, Lei & Xu, Chong-Yu [Vis alle 7 forfattere av denne artikkelen] (2025). Improved non-stationary SPEI and its application in drought monitoring in China. Journal of Hydrology. ISSN 0022-1694. 652. doi: 10.1016/j.jhydrol.2025.132706. Fulltekst i vitenarkiv Vis sammendrag

  Drought index is the first step in drought monitoring and mitigation. However, nonstationarity in the hydrometeorological data series violates the assumption of stationarity in drought indices. Here we propose a nonstationary standardized precipitation-evapotranspiration index (NSPEIt). We adopt the Penalized Splines (PS) and Stochastic Partial Differential Equations (SPDE) to fit the water deficit (Dt), derive a time-varying log-logistic distribution, and develop two versions of NSPEIt, i.e. NSPEIt-PS and NSPEIt-SPDE. We find that hydrometeorological data series in the Qinghai-Tibet Plateau Region (QTR), East Asian Monsoon Region (EMR) are nonstationary. NSPEIt performed satisfactorily in drought monitoring for both stationary and nonstationary hydrometeorological series. Specifically, NSPEIt-PS had higher drought monitoring performance than NSPEIt-SPDE. We compared standardized precipitation index (SPI), self-calibrated Palmer Drought Severity Index (scPDSI), NSPEIt-PS, NSPEIt-SPDE, soil moisture (SM), and normalized difference vegetation index (NDVI) and found that NSPEIt-PS performed better than NSPEIt-SPDE in drought monitoring and other drought indices considered in this study. However, NSPEIt_SPDE performed better in meteorological drought monitoring but NSPEI_PS was better in agricultural drought monitoring. When compared to historical droughts in 2009, the existing drought indices considered in this study tended to underestimate and/or overestimate drought intensity, whereas NSPEIt-PS and NSPEIt-SPDE captured well droughts with higher intensity, closely describing the spatial evolution of historical meteorological droughts. Results of NSPEIt-PS and NSPEIt-SPDE indicated intensifying droughts in the QTR, and regions with intensifying droughts distributed sporadically in the Northwest Arid and semi-arid Region (NAR) and EMR. Besides, droughts with higher frequency, longer duration, and higher intensity can be monitored in southwest China, the Pearl River Basin and the Yellow River basin.

• Shi, Pengfei; Lyu, Kai; Li, Zhenya; Yang, Tao; Xu, Chong-Yu & Hao, Xiaobo [Vis alle 7 forfattere av denne artikkelen] (2025). A Novel Topography-Based Approach for Real-Time Flood Inundation Mapping. Water Resources Research. ISSN 0043-1397. 61(2). doi: 10.1029/2024WR037851. Fulltekst i vitenarkiv Vis sammendrag

  The occurrence frequency and catastrophe caused by flooding are increasing rapidly, highlighting the importance of real-time impact-based forecasting. However, traditional approaches primarily based on hydrodynamic models need large computational cost and generally fail to achieve real-time flood mapping, especially for large-scale watersheds. In this work, a novel, simple and convenient approach called Topography-based Flood Inundation Mapping (TOPFIM) is developed to achieve rapid and accurate flood mapping. TOPFIM is characterized by an adaptive river segmentation method and a dynamic inundation volume allocation approach adhering full water volume constraint. The proposed approach is applied to the upper reaches of the Le'an River basin, China, and HEC-RAS is employed as the benchmark for comparison. The results demonstrate that TOPFIM's simulation accuracy for inundation extent approaches that of hydrodynamic models, with an averaged critical success index of 0.83 and hit rate of 0.90 compared to HEC-RAS's simulation. Moreover, TOPFIM generates flood inundation mapping prediction within 10 s rather than hours required by conventional hydrodynamic models. It signifies a pivotal practical enhancement that has the potential to effectively preserve lives and protect assets in times of flood emergencies. Overall, as a simple and convenient tool, TOPFIM demonstrates its potential for real-time flood inundation mapping and risk analysis.

• 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.

• Xie, Kun; Li, Lu; Chen, Hua & Xu, Chong-Yu (2025). Assessing the performance of convection-permitting climate model in reproducing basin-scale hydrological extremes: A western Norway case study. Journal of Hydrology. ISSN 0022-1694. 656. doi: 10.1016/j.jhydrol.2025.132989. Fulltekst i vitenarkiv Vis sammendrag

  Convection-permitting regional climate models (CPRCMs) have been shown to improve the representation of extreme precipitation compared to coarser resolution regional climate models (RCMs). Their benefits for hydrological extremes, such as floods, remains uncertain. This study evaluates the performance of a 3-km resolution convection-permitting model (HCLIM3) against a coarser 12-km resolution climate model (HCLIM12) from the HARMONIE-Climate (HCLIM) model, focusing on precipitation, temperature, and floods in two basins over Western Norway: Røykenes basin (dominated by rainfall-generate flood) and Bulken basin (dominated by snowmelt-generate flood). In the study, we use both a physically-based, distributed Weather Research and Forecasting Model Hydrological system (WRF-Hydro) and a conceptual, lumped Hydrologiska Byråns Vattenbalansavdelning (HBV) model to assess flood simulations. The results show: (1) HCLIM3 better captures spatial variability of annual maximum 1-day and 1-hour precipitation compared to HCLIM12, but both HCLIM models exhibit cold biases which are more pronounced at lower elevation areas, particularly in HCLIM12. (2) HCLIM3-driven simulations do not show benefit in flood simulations across the two basins, except for severe flood peaks, compared to HCLIM12, the choice of hydrological model has a large impact on the results. The HBV model underestimates flood peaks and frequency, while WRF-Hydro more accurately simulates them in the Røykenes but overestimates them in the Bulken likely due to the biases of forcing data, particularly when driven by HCLIM3. The study concludes that CPRCMs improve the simulation of extreme precipitation and temperature but not show clear added value for flood simulations, especially in Bulken. This highlights the critical need for bias correction to ensure accurate flood predictions, even when driven by CPRCMs.

• Ning, Zhihao; Zhou, Yanlai; He, Juntao; Tang, Chun; Xu, Chong-Yu & Chang, Fi-John (2025). Balancing water, power, and carbon: A synergistic optimization framework for mega cascade reservoir operations. Renewable Energy. ISSN 0960-1481. 243. doi: 10.1016/j.renene.2025.122567. Fulltekst i vitenarkiv Vis sammendrag

  This study addresses the critical intersection of renewable energy production and carbon emission reduction in the context of intensified human activities and global climate change by proposing an innovative optimization framework for mega cascade reservoirs. Unlike traditional approaches that often prioritize hydropower output or carbon emissions, our framework uniquely integrates a multi-objective optimization model that simultaneously minimizes carbon emissions, mitigates flood risk, and maximizes hydropower output within the physical constraints of cascade reservoir operations. To assess the performance of various impoundment schemes, we apply the Technique for Order Preference by Similarity to Ideal Solution, demonstrating the model's versatility across different inflow scenarios using seven cascade reservoirs in the Yangtze River as case studies. Our findings reveal that, compared to practical operation scheme, the optimal scheme enhances hydropower output by 5.82 billion kW·h/a (5.32 %), increases water supply by 2.68 billion m³ (8.00 %), reduces carbon emissions by 17.31 GgC/a (14.66 %), and lowers carbon intensity by 0.63 kgCO2e/MW·h (15.22 %). This research advances theoretical frameworks for reservoir operations and offers practical implications for policymakers, enabling more informed decision-making to achieve sustainable development goals. The novel integration of water-carbon synergies within reservoir management contributes significantly to the discourse on sustainable energy systems and climate resilience.

• Cui, Hao; Jiang, Shanhu; Ren, Liliang; Qin, Tianling; Zhu, Qiuan & Fang, Xiuqin [Vis alle 7 forfattere av denne artikkelen] (2025). Identification of drought-flood abrupt alternation by a new non-stationary framework considering natural-social system impacts. Journal of Hydrology. ISSN 0022-1694. 654. doi: 10.1016/j.jhydrol.2025.132899. Fulltekst i vitenarkiv Vis sammendrag

  The drought-flood abrupt alternation (DFAA), defined as the phenomenon of flood (drought) following droughts (flood), magnifies the impact of individual flood and drought events yet has not been accurate assessment. Previous studies have used the stationary approach to identify DFAA events, where a fixed parameter is used to fit the SSI and then a threshold is determined to identify DFAA events. However, because changes in climate or human activities can induce significant variations in the underlying probability distributions of streamflow, the method’s usual assumption of stationarity should be questioned. In this study, a new non-stationary assessment framework considering natural-social systems is developed for identifying hydrological DFAA events. The framework was applied to the study of DFAA events at Huayuankou Station (HYK) in the Yellow River Basin (YRB) over the past 60 years (1961–2020). The main findings include: (1) The non-stationary test results demonstrate the emergence of non-stationary characteristics of streamflow at HYK station. The study period is divided into two periods, the base period (1961–1990) and the change period (1991–2020), based on the mutation test results. (2) the climate and anthropogenic factors screened based on the causal inference method can effectively improve the fitting performance of the non-stationary model of the streamflow at the HYK station, and better respond to the evolutionary characteristics under the changing environments of the extreme hydrological events; and a new sedimentation-based reservoir index (SDRI) considering reservoir siltation has been developed, which has less uncertainty than fitting reservoir index (RI) as a covariate. (3) The trends of DFAA events identified by non-stationary standardized streamflow index (NSSI) and standardized streamflow index (SSI) are consistent. However, the frequency and severity of DFAA events may be underestimated if the non-stationary characteristics of streamflow are not considered. (4) The frequency and severity of DFAA events at HYK station during the change period were higher than those during the base period. In conclusion, the above findings contribute to a better understanding of the effect of streamflow non-stationary on DFAA event identification and can provide valuable information for drought and flood management decisions in changing environments.

• Zhu, Yongwei; Jiang, Shanhu; Ren, Liliang; Yan, Yiqi; Zhu, Qiuan & Yang, Xiaoli [Vis alle 9 forfattere av denne artikkelen] (2025). Identifying cumulative transition effects of large-scale vegetation restoration on climate and hydrology via a dynamically separating framework. Agricultural and Forest Meteorology. ISSN 0168-1923. 366. doi: 10.1016/j.agrformet.2025.110494. Fulltekst i vitenarkiv Vis sammendrag

  Vegetation restoration is an important approach to improve ecosystems and address climate warming. However, there is significant debate regarding climate and hydrological impacts of large-scale vegetation restoration. This study proposes a framework for dynamically separating the cumulative climate and hydrological effects of vegetation restoration, offering a perspective on the seasonal and regional variations in these effects. The framework applies WRF-NoahMP land-atmosphere coupled model with various surface parameters to distinguish the seasonal climate and hydrological effects of the Grain for Green Program as well as the inconsistency in climate and hydrological effects between important grassland restoration and afforestation regions. The Middle Yellow River Basin, a region severely impacted by large-scale vegetation restoration, was selected to demonstrate the proposed approach. The results indicate that seasonal variations in albedo, fraction of vegetation cover, and leaf area index contribute to distinct seasonal patterns in the climate and hydrological effects of vegetation restoration in the study region, with maximum effects observed in summer. A significant shift in the cumulative climate and hydrological effects occurred around 2010 during 2001-2020. Compared to the afforestation region, the grassland restoration region showed significantly reduced land surface temperature and soil moisture, and enhanced evaporation and precipitation recycling (p < 0.05). Our study contributes to an efficient method for distinguishing the seasonal cumulative climate and hydrological effects of vegetation restoration, as well as the inconsistency in climate and hydrological effects resulting from afforestation and grassland restoration regions, providing insights to better implement vegetation restoration initiatives.

• Zhou, Nie; Chen, Hua; Xu, Chong-Yu; Liu, Bingyi & Yang, Jing (2025). Improving river surface flow velocity measurement by coupling adaptive flow direction search algorithm with space-time image velocimetry. Journal of Hydrology. ISSN 0022-1694. 653. doi: 10.1016/j.jhydrol.2025.132800. Fulltekst i vitenarkiv Vis sammendrag

  Space-time image velocimetry (STIV) is a video-based technique for measuring river surface flow velocities and is widely used owing to its simplicity, efficiency, and safety. However, a key limitation of traditional STIV is its reliance on a preset velocity measurement line, which can lead to significant errors when the actual flow direction deviates from this predefined line. To overcome this limitation, a novel adaptive flow direction search algorithm based on Hough transform is proposed. The algorithm dynamically adjusts the measurement line direction in real time based on the flow conditions at the measurement point, thereby enabling more accurate surface flow velocity measurements. The proposed method was verified using synthetic videos, and its applicability for surface flow velocity was confirmed through comparisons with the traditional fixed-direction method and an acoustic Doppler current profiler (ADCP) both in artificial channels and natural rivers. The results revealed that the proposed method effectively identified and adapted to different flow directions in synthetic videos with a mean absolute error less than 1.0°. Furthermore, with the adaptive flow direction search algorithm, the measurement accuracy of the surface flow velocity estimation was significantly improved compared with traditional fixed direction method, with the relative error in the cross-sectional average flow velocity being less than 5 % under both artificial channel and natural river conditions. The proposed adaptive flow direction search algorithm can help in enhancing the stability of the STIV technique under complex flow conditions, and provides more precise and reliable river surface flow velocity measurement.

• Xu, Hanbing; Wang, Xue; Zhou, Yanlai; Xia, Tianyu; Chang, Fi-John & Xu, Chong-Yu (2025). Enhancing multi-step-ahead algal bloom forecasts in river ecosystems by a hybrid recursive deep learning model. Hydrology Research. ISSN 1998-9563. 56(3), s. 260–278. doi: 10.2166/nh.2025.160. Fulltekst i vitenarkiv Vis sammendrag

  River algal blooms pose a significant environmental threat, necessitating accurate forecasts and timely warnings for effective prevention. This study proposes a novel hybrid model, combining an external recursive long short-term memory neural network based on encoder–decoder (RLSTM-ED) with a backpropagation (BP) neural network, denoted as RLSTM-ED-BP. A dataset comprising 34,992 hydrological, climatic, and water quality (4-hourly) observations from the Hanjiang River Basin in China was divided for model training and testing. Comparative analysis with an RLSTM baseline demonstrated that the RLSTM-ED-BP model enhanced the Nash–Sutcliffe coefficient (NSE) by more than 5% and reduced the root mean square error by over 10% during the 24-h forecast horizon. The RLSTM-ED-BP model yielded NSE and threat score values exceeding 0.95 and efficiently provided early warnings for algal bloom events. The model's enhanced performance contributes to the generalizability of deep learning approaches in addressing the critical environmental challenge of algal blooms.

• Huo, Ran; Li, Lu; Huang, Kailin; Chen, Hua; Guo, Chuncheng & Paasche, Øyvind [Vis alle 7 forfattere av denne artikkelen] (2025). Using deep learning to understand flood variability across the last millennium from GCM atmospheric variables in two contrasting catchments. Journal of Hydrology. ISSN 0022-1694. 654. doi: 10.1016/j.jhydrol.2025.132851. Fulltekst i vitenarkiv Vis sammendrag

  Understanding of historical flooding characteristics is conducive for predicting future floods and their characteristics. This study applies deep learning techniques to explore nonlinear long-term relationships between atmospheric variables simulated by the NorESM1-F model and river flow within two selected catchments, the Wujiang basin in Southern China and the Bulken basin in Western Norway. We investigate the feasibility of using atmospheric variables for long-term daily discharge simulations, especially in the context of cold-warm and dry-wet fluctuations over the past 1000 years. Our analysis delves into the changing patterns of atmospheric variables and their impact on discharge and flood patterns. The results indicate that (1) The deep state-space model could effectively simulate daily discharge at the catchment scale by incorporating relevant atmospheric variables of reanalysis data; (2) In our paleoclimate simulations, there is a noteworthy correlation between temperature and precipitation data from the NorESM1-F model over the past millennium with the reconstructed temperature and a proxy indicator for dry-wet conditions in the study basins; (3) Our investigation highlights differences in the simulation of solar1 and solar2, particularly in relation to climate variability associated with the Medieval Warm Period (MWP) and the Little Ice Age (LIA). We observe that, during the periods characterized by larger oscillations in precipitation and temperature, the frequency of floods tends to increase.

• Liu, Yangyi; Chen, Jie; Xiong, Lihua & Xu, Chong-Yu (2025). Runoff responses to Atlantic multidecadal and Pacific decadal oscillations in China: Insights from the last millennium simulations. Journal of Hydrology Regional Studies. 58. doi: 10.1016/j.ejrh.2025.102212. Fulltekst i vitenarkiv Vis sammendrag

  Study region China Study focus There is usually a teleconnection between large-scale climate indices and runoff. Among multiple large-scale climate indices, Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) have received increased attention due to their long periodicities and low-frequency variability. However, the short observations limit the study of runoff response to AMO and PDO. Taking Medieval Climate Anomaly (MCA) and Little Ice Age (LIA) as study periods and focusing on summer runoff in China, this study aims to investigate 1) the major time scales at which AMO/PDO affects runoff, and 2) the individual and coupled effects of AMO and PDO on runoff by using last millennium climate model simulations. New hydrological insights for the region AMO and PDO have stronger correlations with summer runoff at interdecadal time scales than at interannual time scales during MCA and LIA. For individual effects during MCA, the positive AMO phase can cause positive summer runoff anomalies in eastern China, northern China, and southern Qinghai-Tibet Plateau, while PDO has the opposite effect. The results during LIA are similar to those during MCA. For coupled effects during MCA, when the AMO phase is positive, the influence of PDO on runoff is much smaller than AMO. During LIA, the synergistic effects on summer runoff are shown by the in-phase AMO and PDO in southern China and the southern Qinghai-Tibet Plateau.

• Liu, Yangyi; Chen, Jie; Xiong, Lihua & Xu, Chong-Yu (2025). Non-stationary flood frequency analysis by a new decomposition-based method considering external forcing and internal variability of the climate system. Journal of Hydrology. ISSN 0022-1694. 661. doi: 10.1016/j.jhydrol.2025.133560. Fulltekst i vitenarkiv Vis sammendrag

  Climate variability is driven by external forcing of climate change and internal variability of climate system. Extreme runoff has been affected by climate variability and exhibits non-stationary behaviors. However, the role of these two factors in runoff changes remains unclear. Therefore, this study aims to investigate future changes in extreme runoff represented by annual maximum 1-day runoff (AM1R) over China and to evaluate the impacts of external forcing and internal variability on these changes. A decomposition-based non-stationary frequency analysis method is proposed to estimate the frequency changes in extreme runoff events, which incorporates components of runoff influenced by external forcing and internal variability. Two shared socioeconomic pathways (i.e., SSP2-4.5 and SSP5-8.5) are selected for future (2015 ∼ 2100). The results show that the proposed decomposition-based non-stationary method performs comparably well with the covariate-based method. The differences between the median empirical probabilities calculated by the decomposition-based method and theoretical probabilities are within 0.4 % for all return periods. For changes in AM1R, the catchments with increased AM1R are more than those with decreased AM1R under the two scenarios. Moreover, the influence of external forcing on AM1R is stronger than internal variability in more than 50 % of catchments. For the frequency of extreme AM1R events, the return period corresponding to 50-yr AM1R in the historical period is projected to be shorter in at least 66 % of catchments under the two scenarios. This study indicates that extreme runoff events are likely to become more frequent in the future, which is important for flood prevention policies.

• Xiong, Bin; Zhuo, Yunqiang; Xu, Chong-Yu; Xiong, Lihua; Chen, Zeqiang & Tian, Yifei (2024). Analysis on the evolution and driving mechanism of low flow regime from 1956 to 2022 in Poyang Lake, China. Shuili Xuebao. ISSN 0559-9350. 55(3), s. 313–324. doi: 10.13243/j.cnki.slxb.20230329. Fulltekst i vitenarkiv
• Kong, Rui; Zhu, Bin; Zhang, Zengxin; Zhang, Ying; Huang, Richao & Chen, Xi [Vis alle 7 forfattere av denne artikkelen] (2024). Dissecting the characteristics and driver factors of potential vegetation water use efficiency in China. Science of the Total Environment. ISSN 0048-9697. 953. doi: 10.1016/j.scitotenv.2024.176174. Fulltekst i vitenarkiv
• Zhou, Yanlai; Xu, Hanbing; Xia, Tianyu; Xiong, Lihua; Chang, Li-Chiu & Chang, Fi-John [Vis alle 7 forfattere av denne artikkelen] (2024). Methane degassing in global river reservoirs and its impacts on carbon budgets and sustainable water management. Science of the Total Environment. ISSN 0048-9697. 957. doi: 10.1016/j.scitotenv.2024.177623. Fulltekst i vitenarkiv
• Zhou, Nie; Chen, Hua; Liu, Bingyi & Xu, Chong-Yu (2024). Enhanced river suspended sediment concentration identification via multimodal video image, optical flow, and water temperature data fusion. Journal of Environmental Management (JEM). ISSN 0301-4797. 367. doi: 10.1016/j.jenvman.2024.122048. Fulltekst i vitenarkiv
• 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.

• Li, Lingqi; Jiang, Enhui; Liu, Chang & Xu, Chong-Yu (2024). How does top-down water unified allocation and regulation decelerate water utilization? Insights from the Yellow River, China. Journal of Cleaner Production. ISSN 0959-6526. 447. doi: 10.1016/j.jclepro.2024.141402. Fulltekst i vitenarkiv
• 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.

• Liu, Yangyi; Chen, Jie; Xiong, Lihua; Liu, Dedi & Xu, Chong-Yu (2024). Long-term evolution and non-stationary behaviours of daily and subdaily precipitation extremes in China. International Journal of Climatology. ISSN 0899-8418. 44(4), s. 1087–1109. doi: 10.1002/joc.8373. Fulltekst i vitenarkiv
• Chen, Meng; Chen, Hua; Wu, Zeheng; Huang, Yu; Zhou, Nie & Xu, Chong-Yu (2024). A Review on the Video-Based River Discharge Measurement Technique. Sensors. 24(14). doi: 10.3390/s24144655. Fulltekst i vitenarkiv Vis sammendrag

  The hydrological monitoring of flow data is important for flood prevention and modern river management. However, traditional contact methods are increasingly struggling to meet the requirements of simplicity, accuracy, and continuity. The video-based river discharge measurement is a technique to monitor flow velocity without contacting the water body by using the image-recognition algorithms, which has been verified to have the advantages of full coverage and full automation compared with the traditional contact technique. In order to provide a timely summary of the available results and to inform further research and applications, this paper reviews and synthesizes the literature on the general implementation routes of the video-based river discharge measurement technique and the principles and advances of today’s popular image-recognition algorithms for velocity detection. Then, it discusses the challenges of image-recognition algorithms in terms of image acquisition conditions, parameter uncertainties, and complex meteorological and water environments. It is concluded that the performance of this technique can be improved by enhancing the robustness and accuracy of video-based discharge measurement algorithms, minimizing weather effects, and improving computational efficiency. Finally, future development directions for further perfecting this technique are outlined.

• Sun, Peng; Ge, Chenhao; Yao, Rui; Bian, Yaojin; Yang, Huilin & Zhang, Qiang [Vis alle 8 forfattere av denne artikkelen] (2024). Development of a nonstationary Standardized Precipitation Evapotranspiration Index (NSPEI) and its application across China. Atmospheric research. ISSN 0169-8095. 300. doi: 10.1016/j.atmosres.2024.107256. Fulltekst i vitenarkiv
• 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
• Lin, Zhixin; Wang, Qiang; Xu, Youpeng; Luo, Shuang; Zhou, Caiyu & Yu, Zhihui [Vis alle 7 forfattere av denne artikkelen] (2024). Soil moisture dynamics and associated rainfall-runoff processes under different land uses and land covers in a humid mountainous watershed. Journal of Hydrology. ISSN 0022-1694. 636. doi: 10.1016/j.jhydrol.2024.131249. Fulltekst i vitenarkiv
• Li, Shufei; Zhu, Di; Lin, Fanqi; Xia, Jun; Zhou, Yanlai & Chang, Fi-John [Vis alle 7 forfattere av denne artikkelen] (2024). Unlocking synergies of drawdown operation: Multi-objective optimization of reservoir emergency storage capacity. Journal of Environmental Management (JEM). ISSN 0301-4797. 368. doi: 10.1016/j.jenvman.2024.122148. Fulltekst i vitenarkiv
• Ren, Mingming; Jiang, Shanhu; Ren, Liliang; Weng, Baisha; Wang, Menghao & Cui, Hao [Vis alle 7 forfattere av denne artikkelen] (2024). A new non-stationary standardised streamflow index using the climate indices and the optimal anthropogenic indices as covariates in the Wei River Basin, China. Journal of Hydrology Regional Studies. 51. doi: 10.1016/j.ejrh.2023.101649. Fulltekst i vitenarkiv Vis sammendrag

  Study region Catchment area above the Huaxian station along the Wei River Basin, China. Study focus This study attempts to construct a new Non-stationary Standardized Streamflow Index (NSSI) applicable to the variable streamflow sequence of the Wei River Basin based on the climate index and the optimal anthropogenic index, and analyse the drought characteristics of the basin. The climate index is used to quantify climate change factors and three anthropogenic indices are used to quantify the factor of human activities, including the reservoir index, the human-induced index calculated based on the Variable Infiltration Capacity (VIC) hydrological model and the Long Short-Term Memory (LSTM) model machine learning approach, respectively. New hydrological insights for the region The human-induced index based on the LSTM model is more suitable for quantifying anthropogenic factors in the Wei River Basin. The NSSI performs better than the SSI in drought identification. The NSSI based on the LSTM model can capture more frequent severe drought and extreme drought events. The frequency of severe drought and extreme drought is higher in summer and autumn than in the others. The NSSI can better characterize the hydrological drought processes under a non-stationary condition, thus it can provide a more effective reference for regional drought assessment and related policy-making from the perspective of a changing environment.

• Tong, Shanlin; Chen, Jie & Xu, Chong-Yu (2024). A framework for evaluating the combined effects of water transfer and storage strategies on water stress alleviation. Ecological Indicators. ISSN 1470-160X. 166. doi: 10.1016/j.ecolind.2024.112542. Fulltekst i vitenarkiv
• Yang, Xue; Li, Fengnian; Li, Shi; Fu, Xiaohua; Luo, Jungang & Zuo, Ganggang [Vis alle 7 forfattere av denne artikkelen] (2024). How does the daily regulation hydropower station reduce the hydrological regime impact? A case study in upper Yellow River. Hydrology Research. ISSN 1998-9563. 55(6), s. 646–662. doi: 10.2166/nh.2024.031. Fulltekst i vitenarkiv Vis sammendrag

  Human activities, particularly the regulation of hydropower stations, have profoundly altered river flow patterns. While studies have extensively assessed the impact of large or multi-year regulated hydropower stations on hydrological regimes using indicators of hydrological alteration (IHA) and range of variability approach (RVA), the impact of daily regulation hydropower stations has received comparatively less attention. This study aims to evaluate the influence of daily regulation hydropower stations on hydrological regime changes, focusing on the upper Yellow River region in China. Using daily runoff data from 1954 to 2020 at the Guide station, the study compares the impacts of multi-year regulated (Longyangxia) and daily regulation (Laxiwa and Nina) hydropower stations. The Mann-Kendall test showed that 27 out of 32 Indicators of Hydrological Alteration (IHAs) had significant trends under Longyangxia operation, which reduced to 18 IHAs with the inclusion of daily regulation stations. The Range of Variability Approach (RVA) revealed that only 46.87% of IHAs exhibited high alteration from the natural regime when daily regulation was considered, down from 75.00% with Longyangxia alone. This suggests that daily regulation can mitigate the negative impacts of multi-year regulation, potentially enhancing the river's eco-hydrological health.

• 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
• Wang, Chengyun; Chen, Jie; Xiong, Lihua; Tong, Shanlin & Xu, Chong-Yu (2024). Trigger thresholds and their dynamics of vegetation production loss under different atmospheric and soil drought conditions. Science of the Total Environment. ISSN 0048-9697. 950. doi: 10.1016/j.scitotenv.2024.175116. 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
• Zhu, Yuxin; Zhou, Yanlai; Xu, Chong-Yu & Chang, Fi-John (2024). Optimizing complementary operation of mega cascade reservoirs for boosting hydropower sustainability. Sustainable Energy Technologies and Assessments. ISSN 2213-1388. 64. doi: 10.1016/j.seta.2024.103719. 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
• Feng, Jiajin; Li, Jun; Xu, Chong-Yu; Wang, Zhaoli; Zhang, Zhenxing & Wu, Xushu [Vis alle 10 forfattere av denne artikkelen] (2024). Viewing Soil Moisture Flash Drought Onset Mechanism and Their Changes Through XAI Lens: A Case Study in Eastern China. Water Resources Research. ISSN 0043-1397. 60(6). doi: 10.1029/2023WR036297. Fulltekst i vitenarkiv Vis sammendrag

  Soil moisture flash droughts often pose significant challenges to humans and ecosystems, with wide-ranging socioeconomic consequences. However, the underlying mechanisms of flash droughts and their changes remain unquantified. Taking China as a case study, we present a novel framework that combines machine learning with interpretable and cluster techniques to investigate flash drought mechanisms from 1980 to 2018. We first quantified the temporal contribution of drivers and further identified different mechanisms during drought onsets. We subsequently investigated the temporal changes in different mechanisms and classified drought event types. We identified four driving mechanism types triggering drought: Concurrent precipitation, Antecedent-concurrent precipitation, Antecedent temperature-concurrent precipitation, and Antecedent transpiration-concurrent precipitation. The total effects from vegetation transpiration contributed to around 50% of the impacts for mechanisms involving antecedent transpiration and concurrent precipitation, highlighting the non-neglectable role of vegetation water consumption in drought occurrences. Remarkably, about 60% of flash drought onsets exhibited close association with the antecedent anomalies, which contribute approximately 50% of overall effects, emphasizing the importance of the cumulative effects of drivers. Moreover, driving mechanisms associated with temperature and transpiration increased significantly over time, implying an elevated influence of these factors on droughts. Our classification of drought events reveals that nearly 70% of events were driven by at least two mechanisms, underscoring a complex time-varying pattern of driving factors during drought events. The proposed holistic framework not only sheds insight into the multifaceted mechanisms driving flash droughts within China but also extends its potential applicability to broader geographical contexts.

• He, Miao; Jiang, Shanhu; Ren, Liliang; Cui, Hao; Qin, Tianling & Du, Shuping [Vis alle 9 forfattere av denne artikkelen] (2024). Streamflow prediction in ungauged catchments through use of catchment classification and deep learning. Journal of Hydrology. ISSN 0022-1694. 639. doi: 10.1016/j.jhydrol.2024.131638. Fulltekst i vitenarkiv
• Liu, Shuiqing; Zhou, Zuhao; Liu, Jiajia; Li, Jia; Jia, Yangwen & Wang, Hao [Vis alle 7 forfattere av denne artikkelen] (2024). Impact of climate change on water quality evolution in cold regions using a water–heat–nitrogen processes coupled model. Environmental Science and Pollution Research. ISSN 0944-1344. 31, s. 22395–22409. doi: 10.1007/s11356-024-32562-0. Fulltekst i vitenarkiv
• Xiong, Jinghua; Guo, Shenglian; Abhishek, .; Yin, Jiabo; Xu, Chong-Yu & Wang, Jun [Vis alle 7 forfattere av denne artikkelen] (2024). Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 28(8), s. 1873–1895. doi: 10.5194/hess-28-1873-2024. Fulltekst i vitenarkiv Vis sammendrag

  Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in ∼ 100 000 existing reservoirs. Here, we use the finest-spatiotemporal-resolution (1 d and 1 km) precipitation dataset from an ensemble of machine learning algorithms to present the spatial distribution of 1 d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find that the national PMP generally decreases from southeast to northwest and is typically dominated by the high variability of precipitation extremes in northern China and high intensity in southern China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing them with satellite and in situ results due to differences in spatial scales and computation methods. Interannual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land–atmosphere coupling effects, leads to a widespread increase (> 20 %) in PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emissions. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

• Li, Yanqian; Zhou, Yanlai; Luo, Yuxuan; Ning, Zhihao & Xu, Chong-Yu (2024). Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs. Energies. 17(21). doi: 10.3390/en17215485. Fulltekst i vitenarkiv
• Cui, Zhen; Guo, Shenglian; Chen, Hua; Liu, Dedi; Zhou, Yanlai & Xu, Chong-Yu (2024). Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 28(13), s. 2809–2829. doi: 10.5194/hess-28-2809-2024. Fulltekst i vitenarkiv Vis sammendrag

  The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study proposes the copula-based hydrological uncertainty processor BMA (CHUP-BMA) method by introducing a copula-based HUP in the framework of BMA to bypass the need for a normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of eight members (two forecast precipitation inputs; two advanced long short-term memory, LSTM, models; and two objective functions used to calibrate parameters) and is applied to the interval basin between the Xiangjiaba and Three Gorges Reservoir (TGR) dam sites. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168 h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy and that the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width and continuous ranked probability score metrics of the CHUP-BMA method are reduced by a maximum of 28.42 % and 17.86 % within all forecast horizons, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

• 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
• Zeng, Qiang; Zhao, Qiang; Luo, Yang-Tao; Ma, Shun-Gang; Kang, You & Li, Yu-Qiong [Vis alle 8 forfattere av denne artikkelen] (2024). Uncertainty estimation of hydrological modelling using gridded precipitation as model inputs in the Gandaki River Basin. Journal of Hydrology Regional Studies. 53. doi: 10.1016/j.ejrh.2024.101825. Fulltekst i vitenarkiv Vis sammendrag

  Study Region Four subbasins in the Gandaki River Basin in the southern Himalayan region. Study Focus In the southern Himalayan region, gridded precipitation datasets are often used to complement sparse gauge network observations in hydrological modelling for water resource assessment. However, the applicability and uncertainty of using these datasets in hydrological modelling remain to be evaluated. In this study, nine high spatial resolution gridded precipitation datasets were used to force a widely used hydrological model to simulate the daily runoff at four hydrological stations in this region. The accuracy of these datasets was evaluated, and the model calibration results and modelling uncertainty were analyzed. New Hydrological Insights for the Region Our analysis revealed large differences among the nine gridded precipitation datasets, and most datasets underestimated the annual precipitation in the low-elevation regions and overestimated that in the high Himalayas. Furthermore, through the adjustments of model parameters, the underestimations of precipitation by gridded datasets were compensated for by the overestimation of glacial melt. Therefore, the model performance was better, and the model uncertainty was lower in basins with higher glacial coverage. Notably, our findings indicated that the performances of gridded precipitation datasets were heterogeneous and that their direct use in hydrological modelling may result in unreasonable hydrological process variables; thus, these datasets should be evaluated and used with caution in water resource assessments in Himalayan regions.

• Tong, Shanlin; Xia, Rui; Chen, Jie; Li, Wenpan; Chen, Yan & Xu, Chong-Yu (2024). A diagnostic framework to reveal future clean water scarcity in a changing climate. Journal of Hydrology Regional Studies. 56. doi: 10.1016/j.ejrh.2024.102040. Fulltekst i vitenarkiv Vis sammendrag

  Study region The Yangtze River basin. Study focus Clean water scarcity is one of the major constraints for human sustainable development. Previous diagnosis of water scarcity has focus on fluctuations in the amount of water needed by humans, while ignoring the imbalance between water supply and demand that is exacerbated by changes in water quality under the context of climate change. This study proposes a diagnosis framework for clean water scarcity analysis under future climate projections by combining climate and socioeconomic projections, hydrological system, environmental requirement and human water withdrawal simulations, and water quality projections. This framework is demonstrated in analyzing the clean water scarcity for near (2031–2060) and far (2070–2099) futures. New hydrological insights for the region The results show that by the end of this century, the clean water stress index will decrease substantially, and the number of people potentially threatened by clean water scarcity is expected to decrease from 294.8 million to 159.1 million. Clean water scarcity is expected to ease as freshwater resources increase in the future. Based on scenario simulations, the results further reveal that mitigation measures for clean water scarcity are mainly affected by natural factors in near and far futures. These findings support that the proposed framework can provide a new perspective for assessing and explaining the multifaceted influences of climate change on clean water scarcity.

• Zhong, Sirui; He, Yanfeng; Guo, Shenglian; Xie, Yuzuo & Xu, Chong-Yu (2024). Design flood estimation of cascade reservoirs based on vine-copula flood regional composition. Journal of Hydrology Regional Studies. 56. doi: 10.1016/j.ejrh.2024.102071. Fulltekst i vitenarkiv Vis sammendrag

  Study region The study region is the Yalong River basin, the largest tributary of the Jinsha River in China. Study focus The most likely flood regional composition (MLFRC) method has been widely used to estimate the design flood of cascade reservoirs in the operation period, in which the Gumbel copula or t-copula is often selected to fit the theoretical joint distribution. However, the Gumbel copula is only suitable for 2 or 3 cascade reservoirs, while the t-copula has great uncertainty in parameter estimation. In this study, we introduce vine-copula into MLFRC framework to estimate design flood of cascade reservoirs, and comprehensively compare the universality of different copulas through statistical experiments and a case study on the cascade reservoirs in Yalong River basin. New hydrological insights for the region The results indicated that: (1) Vine-copula significantly outperforms Gumbel copula and t-copula in terms of fitting the theoretical joint distribution. (2) The vine-copula based MLFRC method can successfully solve the different flood regional composition schemes most accurately in the simulated scenarios. (3) Compared with the design floods in construction period, the design floods in operation period in the Yalong River basin decrease significantly due to the regulation of upstream reservoirs. The 1000-year design flood at basin outlet section decrease by about 40 %.

• Song, Ying; Yang, Tao; Li, Zhenya & Xu, Chong-Yu (2024). A complex synthetic surface for assessing flow direction algorithms based on total contributing area. Geomorphology. ISSN 0169-555X. 450. doi: 10.1016/j.geomorph.2024.109080. Fulltekst i vitenarkiv
• Xu, Wenxin; Chen, Jie; Corzo, Gerald; Xu, Chong-Yu; Zhang, Xunchang John & Xiong, Lihua [Vis alle 8 forfattere av denne artikkelen] (2024). Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions. Water Resources Research. ISSN 0043-1397. 60(2). doi: 10.1029/2023WR035618. Fulltekst i vitenarkiv Vis sammendrag

  This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model-simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) as the DL model, is applied to predict 1-, 3-, and 6-month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN-GRU models with great improvement in Kling-Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN-GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision-makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited.

• 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.

• Li, Xia; Zhou, Zuhao; Liu, Jiajia; Xu, Chong-Yu; Xia, Junqiang & Wang, Pengxiang [Vis alle 8 forfattere av denne artikkelen] (2024). Analysis and partitioning of terrestrial water storage in the Yellow River source region. Hydrological Processes. ISSN 0885-6087. 38(2). doi: 10.1002/hyp.15097. Fulltekst i vitenarkiv
• Zhong, Sirui; Guo, Shenglian; Wang, Yun; Wang, Heyu; Xie, Yuzuo & Xu, Chong-Yu (2024). A novel Joint Probability Density Difference Approach for assessing the alteration of hydrologic regime. River Research and Applications. ISSN 1535-1459. 40(5), s. 659–672. doi: 10.1002/rra.4260. Fulltekst i vitenarkiv Vis sammendrag

  The construction and operation of upstream reservoirs have significantly altered downstream hydrologic regime. Appropriate and quantifiable assessment method for the alteration of hydrologic regime is considerably vital and emergent for ecological protection and restoration. The Range of Variability Approach (RVA) and modified RVA methods have been widely used in practice to assess the hydrological alteration. However, these methods have failed to concurrently describe the distribution of indicator and morphological features in detail, which might inevitably lead to the misjudgment of alteration. This paper proposes a Joint Probability Density Difference Approach (JPDDA) method to address the major drawbacks of these previous methods with the introduction of Gaussian Kernel Density Estimation (KDE) and copula function. The annual average flow is selected as the reference variable to construct a proper joint probability density function between itself and other hydrological alteration indicators. The JPDDA method could describe the marginal distribution in detail through Gaussian KDE and also link the morphological features with copula function. Along with pervious methods, the hydrological alterations at Yichang hydrological station, Yangtze River are estimated based on the measured flow from 1949 to 2022. It is shown that the hydrologic regime has suffered from a moderate or even heavy alteration under the influence of massive upstream cascade reservoirs, and the JPDDA outperforms the other methods in terms of rationality and stability for practical assessments. Thus, the proposed JPDDA method is strongly advised to handle the hydrological alteration and could provide a reasonable reference for ecological operation.

• Zhu, Yuxin; Zhou, Yanlai; Wang, Yun; Ning, Zhihao; Guo, Shenglian & Chang, Fi-John [Vis alle 7 forfattere av denne artikkelen] (2024). Compromising power generation and flood control: Optimizing flood operating water levels in mega reservoirs. Sustainable Energy Technologies and Assessments. ISSN 2213-1388. 70. doi: 10.1016/j.seta.2024.103963. Fulltekst i vitenarkiv
• Luo, Yuxuan; Zhou, Yanlai; Xu, Hanbing; Chen, Hua; Chang, Fi-John & Xu, Chong-Yu (2024). Enhancing physically-based flood forecasts through fusion of long short-term memory neural network with unscented Kalman filter. Journal of Hydrology. ISSN 0022-1694. 641. doi: 10.1016/j.jhydrol.2024.131819. Fulltekst i vitenarkiv

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• Silantyeva, Olga; Huang, Shaochun & Xu, Chong-Yu (2026). Evaluating flexible process-based hydrological configurations against data-driven benchmarks: a large-sample study across 109 Norwegian catchments. doi: https:/acrobat.adobe.com/id/urn:aaid:sc:EU:15b5965a-cc8b-44c2-ba59-2523cca43137. Fulltekst i vitenarkiv Vis sammendrag

  Large-sample hydrology provides a rigorous basis for assessing model generality across diverse hydro-climatic conditions. We evaluate five configurations of the Shyft flexible modelling framework, varying evapotranspiration (Priestley–Taylor vs. Penman–Monteith), snow (temperature-index snow tiles vs. semi-physical gamma snow), and runoff response (Kirchner vs. HBV routines) across 109 Norwegian catchments spanning mountain, inland, atlantic, baltic, and transient hydrological regimes. Model skill is assessed using KGE and its components, percent bias (PBIAS), KGE on reciprocal flow, and interannual NSE, under ten calibration goal functions. Results are contextualised against the hydroBM benchmark ensemble (Knoben et al., 2024) and an LSTM baseline from the neuralhydrology package (Kratzert et al., 2022). One configuration, containing Penman–Monteith evapotranspiration, temperature-index snow, and Kirchner runoff, performs on par with the LSTM across all metrics. We attribute part of this competitiveness to the Kavetski–Kuczera (2007) smoothing algorithm, which mitigates calibration landscape discontinuities in a manner functionally analogous to gradient-based optimisation in neural network training. This highlights that fair comparison between process-based and data-driven models must account for the full complexity of their respective optimisation problems. The role of precipitation correction in modulating cross-regime performance is also discussed. This study establishes a reproducible baseline for process-based and hybrid model evaluation in the Norwegian context, and lays groundwork for integrating physics-informed constraints into data-driven approaches.

• Silantyeva, Olga; Huang, Shaochun & Xu, Chong-Yu (2026). Benchmarking flexible modelling framework Shyft across mainland Norway. doi: 10.5194/egusphere-egu26-22432. Fulltekst i vitenarkiv Vis sammendrag

  Developing hydrological models, which are process-aware and reliably transferable across diverse environments remains a challenge. We benchmark Shyft – an open-source, fully FAIR (findable, accessible, interoperable and reusable) flexible modeling framework, across 109 catchments in mainland Norway to evaluate how model structure, forcing uncertainty and calibration objective jointly shape streamflow simulation performance. We adopt large sample hydrology perspective to probe five models “stacks”, providing alternative process choices, such as evapotranspiration (Penman-Monteith vs Priestley-Taylor), snowmelt (temperature-index vs semiphysical) and runoff response (Kirchner vs HBV tank and soil) with multiple goal functions drawn from KlingGupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE), with and without catchment specific precipitation correction. We use a suite of evaluation metrics targeting bias, hydrograph dynamics, low flows and interannual variability. We move beyond crude mean-flow benchmarks toward simple climatological benchmarks, providing an objective context for model skill evaluation, given the seasonal nature of Norwegian catchments.The evaluation revealed that configurations containing temperature-index snow simulation and Kirchner runoff offer the greatest robustness and generality across all hydrological regimes. In terms of objective functions, KGEbased targets outperform NSE-based targets, with metric combining KGE and box-cox transformed KGE (KGE_bcKGE) identified as a promising generalist objective, which performs well across diverse metrics, including low-flow targeted (KGE(1/Q)) and interannual NSE. Furthermore, precipitation correction was found to be essential for improving performance in Mountain and Inland regimes, suggesting snow undercatch as a primary source of precipitation uncertainty. Among simple benchmarks, daily mean was found to be best predictor setting model expectations for future model intercomparisons in the region. Our results demonstrate the need for balance of structural adequacy, forcing uncertainty and equifinality.This project is supported by Norwegian Research Council NFR project 336621.

• Nkhoma, Lusungu Wezzie; Ngongondo, Cosmo; Xu, Chong-Yu; Gelati, Emiliano & Tallaksen, Lena M. (2024). Evaluation of the performance of high-resolution global climate datasets over Malawi. Fulltekst i vitenarkiv
• Lesteberg, Ola; Skaugen, Thomas & Xu, Chong-Yu (2025). Adding Value to Hydrological Simulations by In Situ Observations of Precipitation and Temperature. Universitetet i Oslo. Fulltekst i vitenarkiv

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