Kristen Joy Valseth

• Harris, M.A.; Diehl, T.H.; Sanisaca, Sanisaca Gorman; Galanter, A.E.; Lombard, M.A. & Skinner, K.D. [Vis alle 11 forfattere av denne artikkelen] (2025). Automating physics-based models to estimate thermoelectric-power water use. Environmental Modelling & Software. ISSN 1364-8152. 185. doi: 10.1016/j.envsoft.2024.106265. Fulltekst i vitenarkiv Vis sammendrag

  Thermoelectric (TE) power plants withdraw more water than any other sector of water use in the United States and consume water at rates that can be significant especially in water-stressed regions. Historical TE water-use data have been inconsistent, incomplete, or discrepant, resulting in an increased research focus on improving the accuracy and availability of TE water-use data using modeling approaches. This paper describes and benchmarks new code that was developed to automate and update a physics-based TE water use model that was previously published. Utilizing the automated physics-based model, monthly TE-power water withdrawal and consumption were calculated for a total of 1341 TE power plants for the 2008–2020 historical reanalysis. The updated and automated physics-based thermoelectric-power water-use model provides spatially and temporally relevant TE water-use estimates that are consistent, reproducible, transparent, and can be generated efficiently for water-using, utility-scale TE-power plants across conterminous United States (CONUS).

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• Valseth, Kristen Joy; Valnes, Lars Magnus; Silantyeva, Olga; Lappegard, Gaute & Mardal, Kent-Andre (2025). CAMELS-Nordic, a large-scale hydrometeorological and catchment properties dataset for Norway and Sweden. doi: https:/www.hydrologiraadet.no/wp-content/uploads/2025/08/AbstractsLillehammer2025_210825.pdf. Fulltekst i vitenarkiv Vis sammendrag

  Historically, hydrologic studies have focused on one or a small number of basins. In many cases, these studies were limited by data availability and computational resources. In the last 20 years, the availability of large hydrological datasets, such as gridded meteorological data sets and streamflow timeseries, and increased computing resources have empowered large-sample hydrology studies. Having accessible and high-quality large datasets available to the science community facilitates the evaluation of hydrologic processes and prediction questions. To support modeling and climate research efforts in the Nordics the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies)-Nordic was collected and processed into a coherent dataset for the entirety of Norway and Sweden. CAMELS-Nordic combines not only meteorological and hydrological, but also topography, climate, streamflow, land cover, and soil data with python package to update time series automatically where possible. The development of the data package takes advantage of high-quality and freely available data from various Norwegian, Swedish, and European agencies. It includes: (1) daily forcing data (e.g. observations, interpolations, and modeled data) for catchments located in Norway and Sweden; (2) daily streamflow data; (3) digital elevation model; (4) catchment properties (size, location, elevation, and catchment files); (5) landcover; and (6) soil type data. Dataset time series span 1980 to 2022.

• Valseth, Kristen Joy; Valnes, Lars Magnus; Lappegard, Gaute; Silantyeva, Olga & Mardal, Kent-Andre (2025). Development of CAMELS-Nordic, a large-scale hydrometeorological and catchment properties dataset for Norway and Sweden. doi: 10.5194/egusphere-egu25-10411. Fulltekst i vitenarkiv Vis sammendrag

  Historically, hydrologic studies have focused on one or a small number of basins, in many cases these studies were limited by data availability and computational resources. The increased availability of large hydrological datasets in the last 20 years, such as gridded meteorological data sets and streamflow timeseries, and increased computing resources have empowered large-sample hydrology studies having accessible and high-quality large datasets available to the science community to facilitate the evaluation of hydrologic processes and prediction questions. To support modeling and climate research efforts in the Nordics the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies)-Nordic was collected and processed from multiple sources and databases into a coherent dataset for the entirety of Norway and Sweden. CAMELS-Nordic combines not only meteorological and hydrological, but also topography, climate, streamflow, land cover, soil, and geology data with python tools to update time series automatically. The development of the data package takes advantage of high-quality and freely available data from various Norwegian, Swedish, and European agencies.  It includes: (1) daily forcing data (e.g. observations, interpolations, and modeled data) for catchments located in Norway and Sweden; (2) daily streamflow data; (3) digital elevation model; (4) catchment properties (size, location, elevation, and catchment files); (5) landcover; and (6) soil type data. Dataset time series span 1980 to 2022.

Se alle arbeider i NVA