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Showing 1 - 20 of 355 results

A Case Study of an Exceptional Atmospheric River and Explosively Deepening Cyclone Over the US Central Plains in March 2019

Zou, Xun

;

Cordeira, Jason M.

2025 | Journal of Geophysical Research: Atmospheres, 130(1) | PDF

In March 2019, a strong atmospheric river (AR) originating from the Gulf of Mexico transported abundant moisture inland and fueled a record‐breaking bomb cyclone in Colorado, resulting in widespread winter weather hazards across several states. Experimental model simulations and trajectory analysis indicate that mid‐tropospheric latent heat release ...

03/20/2026

Title : A Case Study of an Exceptional Atmospheric River and Explosively Deepening Cyclone Over the US Central Plains in March 2019 Personal Author(s) : Zou, Xun;Cordeira, Jason M.;Bartlett, Samuel M.;Ralph, F. Martin; Published Date : 2025 Source : Journal of Geophysical Research: Atmospheres, 130(1) DOI : https://doi.org/10.1029/2024JD042309 Journal Title : Journal of Geophysical Research: Atmospheres Zou, Xun et al. (2025). A Case Study of an Exceptional Atmospheric River and Explosively Deepening Cyclone Over the US Central Plains in March 2019. 130(1). https://doi.org/10.1029/2024JD042309 Zou, Xun et al. "A Case Study of an Exceptional Atmospheric River and Explosively Deepening Cyclone Over the US Central Plains in March 2019" 130, no. 1 (2025), https://doi.org/10.1029/2024JD042309 Zou, Xun et al. "A Case Study of an Exceptional Atmospheric River and Explosively Deepening Cyclone Over the US Central Plains in March 2019" vol. 130, no. 1, 2025, https://doi.org/10.1029/2024JD042309 Export RIS Citation Information. TY - AU - Zou, Xun AU - Cordeira, Jason M. AU - Bartlett, Samuel M. AU - Ralph, F. Martin AB - In March 2019, a strong atmospheric river (AR) originating from the Gulf of Mexico transported abundant moisture inland and fueled a record‐breaking bomb cyclone in Colorado, resulting in widespread winter weather hazards across several states. Experimental model simulations and trajectory analysis indicate that mid‐tropospheric latent heat release (LHR) coincided with the strength of the warm conveyor belt and played a key role in the deepening of the cyclone. The LHR promoted the generation of a lower tropospheric positive potential vorticity (PV) anomaly and a stronger low‐level cyclonic circulation, enhancing the cyclone, low‐level jet stream, and associated water vapor transport. Additionally, it generated an upper tropospheric negative PV anomaly and strong upper‐level anticyclonic circulation, influencing the structure of the trough‐ridge couplet and the associated Rossby wave. Reductions in the initial intensity of the AR and disallowing LHR both weakened the cyclone. However, disallowing LHR significantly disturbed the synoptic‐scale structure of the storm and embedded Rossby wave, resulting in a stronger impact. Thus, the reduction of diabatic PV generation, under the influence of AR activities, was crucial in the explosive intensification of the cyclone. Additionally, the reductions in AR intensity and the magnitude of cyclone weakening did not show a linear relationship. Few studies have explored interactions between ARs and continental cyclones, and this paper highlights the need for further research on AR‐associated extreme weather events inland. AN - noaa:72822 DO - https://doi.org/10.1029/2024JD042309 IS - 1 J2 - Journal of Geophysical Research: Atmospheres PB - American Geophysical Union (AGU) PY - 2025 T2 - Journal of Geophysical Research: Atmospheres T2 - Journal of Geophysical Research: Atmospheres, 130(1) TI - A Case Study of an Exceptional Atmospheric River and Explosively Deepening Cyclone Over the US Central Plains in March 2019 VL - 130 UR - https://repository.library.noaa.gov/view/noaa/72822 ER -
Decoding the Hysteretic Behaviour of Hydraulic Variables in Lowland Rivers Using Multivariate Monitoring Approaches

Muste, Marian ;

Kim, Kyeongdong

2025 | Hydrological Processes, 39(1) | PDF

This paper demonstrates that the multivariate monitoring methods are capable to underpin the systematic investigation of the hysteretic behaviour occurring during gradually‐varied flows. For this purpose, we present simultaneous measurements of stage, index velocity and free‐surface slope acquired continuously with high‐frequency sampling instrumen ...

03/20/2026

Title : Decoding the Hysteretic Behaviour of Hydraulic Variables in Lowland Rivers Using Multivariate Monitoring Approaches Personal Author(s) : Muste, Marian;Kim, Kyeongdong;Kim, Dongsu;Fleit, Gábor; Published Date : 2025 Source : Hydrological Processes, 39(1) DOI : https://doi.org/10.1002/hyp.70008 Journal Title : Hydrological Processes Muste, Marian et al. (2025). Decoding the Hysteretic Behaviour of Hydraulic Variables in Lowland Rivers Using Multivariate Monitoring Approaches. 39(1). https://doi.org/10.1002/hyp.70008 Muste, Marian et al. "Decoding the Hysteretic Behaviour of Hydraulic Variables in Lowland Rivers Using Multivariate Monitoring Approaches" 39, no. 1 (2025), https://doi.org/10.1002/hyp.70008 Muste, Marian et al. "Decoding the Hysteretic Behaviour of Hydraulic Variables in Lowland Rivers Using Multivariate Monitoring Approaches" vol. 39, no. 1, 2025, https://doi.org/10.1002/hyp.70008 Export RIS Citation Information. TY - AU - Muste, Marian AU - Kim, Kyeongdong AU - Kim, Dongsu AU - Fleit, Gábor AB - This paper demonstrates that the multivariate monitoring methods are capable to underpin the systematic investigation of the hysteretic behaviour occurring during gradually‐varied flows. For this purpose, we present simultaneous measurements of stage, index velocity and free‐surface slope acquired continuously with high‐frequency sampling instruments deployed at several river gaging sites exposed to different storm magnitudes. The experimental evidence reveals intrinsic features of unsteady open‐channel flow mechanics that are hinted by pertinent governing equations but rarely substantiated with in situ measurements. The illustrations are intentionally made for fluvial waves propagating in lowland rivers where the relationships among flow variables are most likely displaying hysteretic phasing in the progression of the hydraulic variables and loops in their relationships. The presented measurements highlight that: (a) the hysteretic behaviour is apparent in both time‐independent and time‐dependent graphical representations of any two of the hydraulic variables; (b) the severity of the hysteresis is commensurate with the geomorphic, hydraulic and hydrological characteristics of the measurement site; and (c) there are flow monitoring paradigms that can more accurately track changes of the flow variables during gradually‐varied flows than those currently used in practice. Also discussed are research needs for advancing the understanding of the mechanisms underlying the movement and storage of water in the lowland river environments as well as for increasing the accuracy of streamflow monitoring, modelling and forecasting. AN - noaa:72821 DO - https://doi.org/10.1002/hyp.70008 IS - 1 J2 - Hydrological Processes PB - Wiley PY - 2025 T2 - Hydrological Processes T2 - Hydrological Processes, 39(1) TI - Decoding the Hysteretic Behaviour of Hydraulic Variables in Lowland Rivers Using Multivariate Monitoring Approaches VL - 39 UR - https://repository.library.noaa.gov/view/noaa/72821 ER -
Toward Automated Scientific Discovery in Hydrology: The Opportunities and Dangers of AI Augmented Research Frameworks

Eythorsson, Darri

;

Clark, Martyn

2025 | Hydrological Processes, 39(1) | PDF

This commentary explores the potential of artificial intelligence (AI) to transform hydrological modelling workflows. We introduce a prototype AI‐assisted framework called INDRA (Intelligent Network for Dynamic River Analysis) that leverages a multi‐agent architecture composed of specialised large language models (LLMs) to assist in model conceptua ...

03/20/2026

Title : Toward Automated Scientific Discovery in Hydrology: The Opportunities and Dangers of AI Augmented Research Frameworks Personal Author(s) : Eythorsson, Darri;Clark, Martyn; Published Date : 2025 Source : Hydrological Processes, 39(1) DOI : https://doi.org/10.1002/hyp.70065 Journal Title : Hydrological Processes Eythorsson, Darri and Clark, Martyn (2025). Toward Automated Scientific Discovery in Hydrology: The Opportunities and Dangers of AI Augmented Research Frameworks. 39(1). https://doi.org/10.1002/hyp.70065 Eythorsson, Darri and Clark, Martyn "Toward Automated Scientific Discovery in Hydrology: The Opportunities and Dangers of AI Augmented Research Frameworks" 39, no. 1 (2025), https://doi.org/10.1002/hyp.70065 Eythorsson, Darri and Clark, Martyn "Toward Automated Scientific Discovery in Hydrology: The Opportunities and Dangers of AI Augmented Research Frameworks" vol. 39, no. 1, 2025, https://doi.org/10.1002/hyp.70065 Export RIS Citation Information. TY - AU - Eythorsson, Darri AU - Clark, Martyn AB - This commentary explores the potential of artificial intelligence (AI) to transform hydrological modelling workflows. We introduce a prototype AI‐assisted framework called INDRA (Intelligent Network for Dynamic River Analysis) that leverages a multi‐agent architecture composed of specialised large language models (LLMs) to assist in model conceptualization, configuration, execution, and interpretation. INDRA integrates with CONFLUENCE, a comprehensive modelling framework, to provide context‐aware guidance and automation throughout the modelling process. We discuss the opportunities and dangers of AI‐augmented research frameworks, emphasising the importance of maintaining human oversight while harnessing AI's potential to enhance efficiency, reproducibility, and scientific understanding. We argue that AI‐assisted workflows could democratise advanced hydrological modelling, enabling researchers worldwide to address critical water resources challenges, particularly in understudied regions. While acknowledging potential biases and risks, we advocate for responsible AI integration to catalyse a new paradigm in hydrological science. AN - noaa:72820 DO - https://doi.org/10.1002/hyp.70065 IS - 1 J2 - Hydrological Processes PB - Wiley PY - 2025 T2 - Hydrological Processes T2 - Hydrological Processes, 39(1) TI - Toward Automated Scientific Discovery in Hydrology: The Opportunities and Dangers of AI Augmented Research Frameworks VL - 39 UR - https://repository.library.noaa.gov/view/noaa/72820 ER -
Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings

Tang, Guoqiang

;

Clark, Martyn P.

2025 | Bulletin of the American Meteorological Society, 106(1), E146-E166 | PDF

Quantifying hydrologic modeling uncertainties originating from meteorological uncertainties is crucial yet unexplored on the global scale. This study integrates a novel ensemble meteorological dataset with process-based hydrologic models, assessing the impact of precipitation and temperature uncertainties across approximately three million subbasin ...

03/20/2026

Title : Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings Personal Author(s) : Tang, Guoqiang;Clark, Martyn P.;Knoben, Wouter J. M.;Liu, Hongli;Gharari, Shervan;Arnal, Louise;Wood, Andrew W.;Newman, Andrew J.;Freer, Jim;Papalexiou, Simon Michael; Published Date : 2025 Source : Bulletin of the American Meteorological Society, 106(1), E146-E166 DOI : https://doi.org/10.1175/BAMS-D-24-0007.1 Journal Title : Bulletin of the American Meteorological Society Tang, Guoqiang et al. (2025). Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings. 106(1). https://doi.org/10.1175/BAMS-D-24-0007.1 Tang, Guoqiang et al. "Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings" 106, no. 1 (2025), https://doi.org/10.1175/BAMS-D-24-0007.1 Tang, Guoqiang et al. "Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings" vol. 106, no. 1, 2025, https://doi.org/10.1175/BAMS-D-24-0007.1 Export RIS Citation Information. TY - AU - Tang, Guoqiang AU - Clark, Martyn P. AU - Knoben, Wouter J. M. AU - Liu, Hongli AU - Gharari, Shervan AU - Arnal, Louise AU - Wood, Andrew W. AU - Newman, Andrew J. AU - Freer, Jim AU - Papalexiou, Simon Michael AB - Quantifying hydrologic modeling uncertainties originating from meteorological uncertainties is crucial yet unexplored on the global scale. This study integrates a novel ensemble meteorological dataset with process-based hydrologic models, assessing the impact of precipitation and temperature uncertainties across approximately three million subbasins globally. We introduce two metrics to identify uncertainty hotspots: one tracing the uncertainty propagation from inputs to model outputs and the other measuring the uncertainty magnitude relative to hydrologic climatology (i.e., ratio between uncertainty and climate average). Our findings reveal different uncertainty responses across hydrologic variables to the combined precipitation and temperature uncertainties. For routed river streamflow, uncertainty propagation is strong in tropical rain forests and Europe (except the Scandinavian Peninsula) but weak in the deserts, which is partly attributed to the regional differences in baseflow ratios. In contrast, both uncertainty metrics indicate low streamflow uncertainties in cryosphere areas and downstream areas of major rivers. The substantial modeling uncertainties observed, particularly in the Southern Hemisphere and less developed regions, underscore the need to improve global spatial meteorological datasets. AN - noaa:72819 DO - https://doi.org/10.1175/BAMS-D-24-0007.1 IS - 1 J2 - Bulletin of the American Meteorological Society PB - American Meteorological Society PY - 2025 T2 - Bulletin of the American Meteorological Society T2 - Bulletin of the American Meteorological Society, 106(1), E146-E166 TI - Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings VL - 106 UR - https://repository.library.noaa.gov/view/noaa/72819 ER -
Do land models miss key soil hydrological processes controlling soil moisture memory?

Farmani, Mohammad A. ;

Behrangi, Ali

2025 | Hydrology and Earth System Sciences, 29(2), 547-566 | PDF

Soil moisture memory (SMM), which refers to how long a perturbation in soil moisture (SM) can last, is critical for understanding climatic, hydrological, and ecosystem interactions. Most land surface models (LSMs) tend to overestimate surface soil moisture and its persistency (or SMM), sustaining spuriously large soil surface evaporation during dry ...

03/20/2026

Title : Do land models miss key soil hydrological processes controlling soil moisture memory? Personal Author(s) : Farmani, Mohammad A.;Behrangi, Ali;Gupta, Aniket;Tavakoly, Ahmad;Geheran, Matthew;Niu, Guo-Yue; Published Date : 2025 Source : Hydrology and Earth System Sciences, 29(2), 547-566 DOI : https://doi.org/10.5194/hess-29-547-2025 Journal Title : Hydrology and Earth System Sciences Farmani, Mohammad A. et al. (2025). Do land models miss key soil hydrological processes controlling soil moisture memory?. 29(2). https://doi.org/10.5194/hess-29-547-2025 Farmani, Mohammad A. et al. "Do land models miss key soil hydrological processes controlling soil moisture memory?" 29, no. 2 (2025), https://doi.org/10.5194/hess-29-547-2025 Farmani, Mohammad A. et al. "Do land models miss key soil hydrological processes controlling soil moisture memory?" vol. 29, no. 2, 2025, https://doi.org/10.5194/hess-29-547-2025 Export RIS Citation Information. TY - AU - Farmani, Mohammad A. AU - Behrangi, Ali AU - Gupta, Aniket AU - Tavakoly, Ahmad AU - Geheran, Matthew AU - Niu, Guo-Yue AB - Soil moisture memory (SMM), which refers to how long a perturbation in soil moisture (SM) can last, is critical for understanding climatic, hydrological, and ecosystem interactions. Most land surface models (LSMs) tend to overestimate surface soil moisture and its persistency (or SMM), sustaining spuriously large soil surface evaporation during dry-down periods. We attempt to answer a question: do LSMs miss or misrepresent key hydrological processes controlling SMM? We use a version of Noah-MP with advanced hydrology that explicitly represents preferential flow and surface ponding and provides optional schemes of soil hydraulics. We test the effects of these processes, which are generally missed by most LSMs in SMM. We compare SMMs computed from various Noah-MP configurations against that derived from the Soil Moisture Active Passive (SMAP) L3 soil moisture and in situ measurements from the International Soil Moisture Network (ISMN) from the years 2015 to 2019 over the contiguous United States (CONUS). The results suggest that (1) soil hydraulics plays a dominant role and the Van Genuchten hydraulic scheme reduces the overestimation of the long-term surface SMM produced by the Brooks–Corey scheme, which is commonly used in LSMs; (2) explicitly representing surface ponding enhances SMM for both the surface layer and the root zone; and (3) representing preferential flow improves the overall representation of soil moisture dynamics. The combination of these missing schemes can significantly improve the long-term memory overestimation and short-term memory underestimation issues in LSMs. We suggest that LSMs for use in seasonal-to-subseasonal climate prediction should, at least, adopt the Van Genuchten hydraulic scheme. AN - noaa:72818 DO - https://doi.org/10.5194/hess-29-547-2025 IS - 2 J2 - Hydrology and Earth System Sciences PB - Copernicus GmbH PY - 2025 T2 - Hydrology and Earth System Sciences T2 - Hydrology and Earth System Sciences, 29(2), 547-566 TI - Do land models miss key soil hydrological processes controlling soil moisture memory? VL - 29 UR - https://repository.library.noaa.gov/view/noaa/72818 ER -
Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation

Velásquez, Nicolás

;

Krajewski, Witold F.

2025 | Journal of Hydrometeorology, 26(2), 169-184 | PDF

Hydrological models and quantitative precipitation estimation (QPE) are critical elements of flood forecasting systems. Both are subject to considerable uncertainties. Quantifying their relative contribution to the forecasted streamflow and flood uncertainty has remained challenging. Past work documented in the literature focused on one of these el ...

03/20/2026

Title : Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation Personal Author(s) : Velásquez, Nicolás;Krajewski, Witold F.;Seo, Bong-Chul; Published Date : 2025 Source : Journal of Hydrometeorology, 26(2), 169-184 DOI : https://doi.org/10.1175/JHM-D-24-0047.1 Journal Title : Journal of Hydrometeorology Velásquez, Nicolás et al. (2025). Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation. 26(2). https://doi.org/10.1175/JHM-D-24-0047.1 Velásquez, Nicolás and Krajewski, Witold F. and Seo, Bong-Chul "Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation" 26, no. 2 (2025), https://doi.org/10.1175/JHM-D-24-0047.1 Velásquez, Nicolás et al. "Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation" vol. 26, no. 2, 2025, https://doi.org/10.1175/JHM-D-24-0047.1 Export RIS Citation Information. TY - AU - Velásquez, Nicolás AU - Krajewski, Witold F. AU - Seo, Bong-Chul AB - Hydrological models and quantitative precipitation estimation (QPE) are critical elements of flood forecasting systems. Both are subject to considerable uncertainties. Quantifying their relative contribution to the forecasted streamflow and flood uncertainty has remained challenging. Past work documented in the literature focused on one of these elements separately from the other. With this in mind, we present a systematic approach to assess the impact of QPE uncertainty in streamflow forecasting. Our approach explores the operational Iowa Flood Center (IFC) hydrological model performance after altering two radar-based QPE products. We ran the Hillslope Link Model (HLM) for Iowa between 2015 and 2020, altering the Multi-Radar/Multi-Sensor (MRMS) system and the specific attenuation-based (IFCA) IFC radar-derived product with a multiplicative error term. We assessed the forecasting system performance at 112 USGS streamflow gauges using the altered QPE products. Our results suggest that addressing rainfall uncertainty has the potential for much-improved flood forecasting spatially and seasonally. We identified spatial patterns linking prediction improvements to the radar’s location and the magnitude of rainfall. Also, we observed seasonal trends suggesting underestimations during the cold season (October–April). The patterns for different radar products are generally similar but also show some differences, implying that the QPE algorithm plays a role. This study’s results are a step toward separating modeling and QPE uncertainties. Future work involving larger areas and different hydrological and error models is essential to improve our understanding of the impact of QPE uncertainty. AN - noaa:72817 DO - https://doi.org/10.1175/JHM-D-24-0047.1 IS - 2 J2 - Journal of Hydrometeorology PB - American Meteorological Society PY - 2025 T2 - Journal of Hydrometeorology T2 - Journal of Hydrometeorology, 26(2), 169-184 TI - Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation VL - 26 UR - https://repository.library.noaa.gov/view/noaa/72817 ER -
Hydrologic information systems: An introductory overview

Jones, Amber Spackman ;

Horsburgh, Jeffery S.

2025 | Environmental Modelling & Software, 185, 106308 | PDF

Hydrologic Information Systems (HIS) integrate hardware and software to support collection, management, and sharing of hydrologic observations data. Successful HIS facilitate hydrologic monitoring, scientific investigation, watershed management, and communication of hydrologic conditions. Furthermore, HIS support the day-to-day data operations that ...

03/20/2026

Title : Hydrologic information systems: An introductory overview Personal Author(s) : Jones, Amber Spackman;Horsburgh, Jeffery S.; Published Date : 2025 Source : Environmental Modelling & Software, 185, 106308 DOI : https://doi.org/10.1016/j.envsoft.2024.106308 Journal Title : Environmental Modelling & Software Jones, Amber Spackman and Horsburgh, Jeffery S. (2025). Hydrologic information systems: An introductory overview. 185https://doi.org/10.1016/j.envsoft.2024.106308 Jones, Amber Spackman and Horsburgh, Jeffery S. "Hydrologic information systems: An introductory overview" 185 (2025), https://doi.org/10.1016/j.envsoft.2024.106308 Jones, Amber Spackman and Horsburgh, Jeffery S. "Hydrologic information systems: An introductory overview" vol. 185, 2025, https://doi.org/10.1016/j.envsoft.2024.106308 Export RIS Citation Information. TY - AU - Jones, Amber Spackman AU - Horsburgh, Jeffery S. AB - Hydrologic Information Systems (HIS) integrate hardware and software to support collection, management, and sharing of hydrologic observations data. Successful HIS facilitate hydrologic monitoring, scientific investigation, watershed management, and communication of hydrologic conditions. Furthermore, HIS support the day-to-day data operations that are essential to organizations that monitor hydrologic systems. As an introductory overview of HIS, this paper reviews the history of HIS development and identifies and describes key components. Based on past HIS literature, patterns emerged for universal and generic HIS functionality and components. The main data pools are collection/acquisition, operational storage, and sharing/publication/dissemination with data flux occurring between pools. Persistent and contemporary challenges for HIS are identified, and examples of current and emerging HIS are described in the context of how they are addressing these challenges. Opportunities remain for coordinated community efforts to address outstanding barriers, advance HIS, and further enable hydrology. AN - noaa:72816 DO - https://doi.org/10.1016/j.envsoft.2024.106308 J2 - Environmental Modelling & Software PB - Elsevier BV PY - 2025 T2 - Environmental Modelling & Software T2 - Environmental Modelling & Software, 185, 106308 TI - Hydrologic information systems: An introductory overview VL - 185 UR - https://repository.library.noaa.gov/view/noaa/72816 ER -
Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model

Magoffin, Rachel Huber ;

Hales, Riley C.

2025 | Hydrology, 12(3), 43 | PDF

Effective decision-making in water resource management requires timely and reliable streamflow information. This study demonstrates how the GEOGLOWS Hydrologic Model, River Forecast System (RFS), can generate actionable hydrologic status products, focusing on a case study in the Nile River Basin. Through collaboration with stakeholders at the Nile ...

03/20/2026

Title : Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model Personal Author(s) : Magoffin, Rachel Huber;Hales, Riley C.;Nelson, E. James;Wara, Calvince;Williams, Gustavious P.;South, Andrew;Challa, Zeleke K.; Published Date : 2025 Source : Hydrology, 12(3), 43 DOI : https://doi.org/10.3390/hydrology12030043 Journal Title : Hydrology Magoffin, Rachel Huber et al. (2025). Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model. 12(3). https://doi.org/10.3390/hydrology12030043 Magoffin, Rachel Huber et al. "Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model" 12, no. 3 (2025), https://doi.org/10.3390/hydrology12030043 Magoffin, Rachel Huber et al. "Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model" vol. 12, no. 3, 2025, https://doi.org/10.3390/hydrology12030043 Export RIS Citation Information. TY - AU - Magoffin, Rachel Huber AU - Hales, Riley C. AU - Nelson, E. James AU - Wara, Calvince AU - Williams, Gustavious P. AU - South, Andrew AU - Challa, Zeleke K. AB - Effective decision-making in water resource management requires timely and reliable streamflow information. This study demonstrates how the GEOGLOWS Hydrologic Model, River Forecast System (RFS), can generate actionable hydrologic status products, focusing on a case study in the Nile River Basin. Through collaboration with stakeholders at the Nile Basin Initiative (NBI), we identify key information needs and apply standardized low flow calculations, including the Standardized Streamflow Index (SSI) and the 95th percentile (Q95) threshold, to assess stream conditions. Additionally, we apply the World Meteorological Organization’s (WMO) Hydrologic Status and Outlook System (HydroSOS) method for streams and generate the associated HydroSOS-styled graphs and maps. We present the hydrologic status products in a customized web application for stakeholders in the Nile Basin. We discuss how RFS can be applied globally to provide hydrologic information. AN - noaa:72815 DO - https://doi.org/10.3390/hydrology12030043 IS - 3 J2 - Hydrology PB - MDPI AG PY - 2025 T2 - Hydrology T2 - Hydrology, 12(3), 43 TI - Hydrologic Decision Support in the Nile Basin: Creating Status Products from the GEOGLOWS Hydrologic Model VL - 12 UR - https://repository.library.noaa.gov/view/noaa/72815 ER -
Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model

Li, Donghui

;

Vora, Anav

2024 | JAWRA Journal of the American Water Resources Association, 61(1) | PDF

This study investigates whether improvements could be achieved for the United States National Water Model (NWM) by using a data‐driven reservoir operation simulation algorithm, that is, the generic data‐driven reservoir operation model (GDROM), to replace the existing reservoir representation in NWM for reservoir routing in streamflow simulation. T ...

03/20/2026

Title : Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model Personal Author(s) : Li, Donghui;Vora, Anav;Cai, Ximing; Published Date : 2024 Source : JAWRA Journal of the American Water Resources Association, 61(1) DOI : https://doi.org/10.1111/1752-1688.13244 Journal Title : JAWRA Journal of the American Water Resources Association Li, Donghui et al. (2024). Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model. 61(1). https://doi.org/10.1111/1752-1688.13244 Li, Donghui and Vora, Anav and Cai, Ximing "Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model" 61, no. 1 (2024), https://doi.org/10.1111/1752-1688.13244 Li, Donghui et al. "Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model" vol. 61, no. 1, 2024, https://doi.org/10.1111/1752-1688.13244 Export RIS Citation Information. TY - AU - Li, Donghui AU - Vora, Anav AU - Cai, Ximing AB - This study investigates whether improvements could be achieved for the United States National Water Model (NWM) by using a data‐driven reservoir operation simulation algorithm, that is, the generic data‐driven reservoir operation model (GDROM), to replace the existing reservoir representation in NWM for reservoir routing in streamflow simulation. The evaluations of NWM versus NWM + GDROM are conducted using 41‐year NWM retrospective simulation products during 1979 to 2020. The results show that NWM + GDROM exhibits significant improvement to NWM in both the entire Contiguous U.S. (CONUS) and various regions. It is found that the accuracy of reservoir inflow and storage values affects the NWM + GDROM improvement. When driven by observed storage (which is usually more accurate than modeled storage) or more accurately simulated inflow, NWM + GDROM produces more significant improvements in reservoir outflow simulations in all the study CONUS regions. Especially, since high flows are more accurately simulated by NWM than low flows, NWM + GDROM has larger improvement for the simulation of high flows than low flows. AN - noaa:72814 DO - https://doi.org/10.1111/1752-1688.13244 IS - 1 J2 - JAWRA Journal of the American Water Resources Association PB - Wiley PY - 2024 T2 - JAWRA Journal of the American Water Resources Association T2 - JAWRA Journal of the American Water Resources Association, 61(1) TI - Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model VL - 61 UR - https://repository.library.noaa.gov/view/noaa/72814 ER -
Impacts of Changing Temperatures on the Water Budget in the Great Salt Lake Basin

Affram, Grace

;

Othman, Jihad

2025 | Water, 17(3), 420 | PDF

Quantifying the water budget in the Great Salt Lake (GSL) basin is a nontrivial task, especially under a changing climate that contributes to increasing temperatures and a shift towards more rainfall and less snowfall. This study examines the potential impacts of temperature thresholds on the water budget in the GSL, emphasizing the influence on sn ...

03/20/2026

Title : Impacts of Changing Temperatures on the Water Budget in the Great Salt Lake Basin Personal Author(s) : Affram, Grace;Othman, Jihad;Morovati, Reza;Castellanos, Saddy Pineda;Khoshnoodmotlagh, Sajad;Dunn, Diana;Dority, Braedon;Diaz, Katherine Osorio;Ratterman, Cody;Zhang, Wei; Published Date : 2025 Source : Water, 17(3), 420 DOI : https://doi.org/10.3390/w17030420 Journal Title : Water Affram, Grace et al. (2025). Impacts of Changing Temperatures on the Water Budget in the Great Salt Lake Basin. 17(3). https://doi.org/10.3390/w17030420 Affram, Grace et al. "Impacts of Changing Temperatures on the Water Budget in the Great Salt Lake Basin" 17, no. 3 (2025), https://doi.org/10.3390/w17030420 Affram, Grace et al. "Impacts of Changing Temperatures on the Water Budget in the Great Salt Lake Basin" vol. 17, no. 3, 2025, https://doi.org/10.3390/w17030420 Export RIS Citation Information. TY - AU - Affram, Grace AU - Othman, Jihad AU - Morovati, Reza AU - Castellanos, Saddy Pineda AU - Khoshnoodmotlagh, Sajad AU - Dunn, Diana AU - Dority, Braedon AU - Diaz, Katherine Osorio AU - Ratterman, Cody AU - Zhang, Wei AB - Quantifying the water budget in the Great Salt Lake (GSL) basin is a nontrivial task, especially under a changing climate that contributes to increasing temperatures and a shift towards more rainfall and less snowfall. This study examines the potential impacts of temperature thresholds on the water budget in the GSL, emphasizing the influence on snowmelt, evapotranspiration (ET), and runoff under varying climate warming scenarios. Current hydrological models such as the Variable Infiltration Capacity (VIC) model use a universal temperature threshold to partition snowfall and rainfall across different regions. Previous studies have argued that there is a wide range of thresholds for partitioning rainfall and snowfall across the globe. However, there is a clear knowledge gap in quantifying water budget components in the Great Salt Lake (GSL) basin corresponding to varying temperature thresholds for separating rainfall and snowfall under the present and future climates. To address this gap, the study applied temperature thresholds derived from observation-based data available from National Center for Environmental Prediction (NCEP) to the VIC model. We also performed a suite of hydrological experiments to quantify the water budget of the Great Salt Lake basin by perturbing temperature thresholds and climate forcing. The results indicate that higher temperature thresholds contribute to earlier snowmelt, reduced snowpack, and lower peak runoff values in the early spring that are likely due to increased ET before peak runoff periods. The results show that the GSL undergoes higher snow water equivalent (SWE) values during cold seasons due to snow accumulation and lower values during warm seasons as increased temperatures intensify ET. Projected climate warming may result in further reductions in SWE (~71%), increased atmospheric water demand, and significant impacts on water availability (i.e., runoff reduced by ~20%) in the GSL basin. These findings underscore the potential challenges that rising temperatures pose to regional water availability. AN - noaa:72813 DO - https://doi.org/10.3390/w17030420 IS - 3 J2 - Water PB - MDPI AG PY - 2025 T2 - Water T2 - Water, 17(3), 420 TI - Impacts of Changing Temperatures on the Water Budget in the Great Salt Lake Basin VL - 17 UR - https://repository.library.noaa.gov/view/noaa/72813 ER -
Bankfull and Mean‐Flow Channel Geometry Estimation Through Machine Learning Algorithms Across the CONtiguous United States (CONUS)

Zarrabi, Reihaneh

;

McDermott, Riley

2025 | Water Resources Research, 61(2) | PDF

Widely adopted models for estimating hydraulic geometry attributes rely on simplistic power‐law equations, which can introduce inaccuracy due to their inability to capture spatial variability. This study introduces a new model for predicting channel geometry utilizing advanced tree‐based Machine Learning (ML) algorithms. The research enhances the q ...

03/20/2026

Title : Bankfull and Mean‐Flow Channel Geometry Estimation Through Machine Learning Algorithms Across the CONtiguous United States (CONUS) Personal Author(s) : Zarrabi, Reihaneh;McDermott, Riley;Erfani, Seyed Mohammad Hassan;Cohen, Sagy; Published Date : 2025 Source : Water Resources Research, 61(2) DOI : https://doi.org/10.1029/2024wr037997 Journal Title : Water Resources Research Zarrabi, Reihaneh et al. (2025). Bankfull and Mean‐Flow Channel Geometry Estimation Through Machine Learning Algorithms Across the CONtiguous United States (CONUS). 61(2). https://doi.org/10.1029/2024wr037997 Zarrabi, Reihaneh et al. "Bankfull and Mean‐Flow Channel Geometry Estimation Through Machine Learning Algorithms Across the CONtiguous United States (CONUS)" 61, no. 2 (2025), https://doi.org/10.1029/2024wr037997 Zarrabi, Reihaneh et al. "Bankfull and Mean‐Flow Channel Geometry Estimation Through Machine Learning Algorithms Across the CONtiguous United States (CONUS)" vol. 61, no. 2, 2025, https://doi.org/10.1029/2024wr037997 Export RIS Citation Information. TY - AU - Zarrabi, Reihaneh AU - McDermott, Riley AU - Erfani, Seyed Mohammad Hassan AU - Cohen, Sagy AB - Widely adopted models for estimating hydraulic geometry attributes rely on simplistic power‐law equations, which can introduce inaccuracy due to their inability to capture spatial variability. This study introduces a new model for predicting channel geometry utilizing advanced tree‐based Machine Learning (ML) algorithms. The research enhances the quality of the extensive HYDRoacoustic data set supporting Surface Water Oceanographic Topography (HYDRoSWOT) through a proposed preprocessing method. Observations of bankfull and mean‐flow conditions at each gauge site are identified and extracted as target variables for model development. HYDRoSWOT‐extracted attributes, along with other predictors from various sources, such as National Hydrography Data set Plus (NHDPlusV2.1), are used to train and validate predictive models. The models achieve average R2 values of 0.85 for channel width and 0.69 for channel depth, demonstrating high accuracy in capturing spatial variability in hydraulic geometry attributes. Independent evaluations further test the models' performance in predicting reach‐averaged conditions at locations outside the training and testing data sets. The results show that the proposed model significantly outperforms existing regional hydraulic geometry relations, with accuracy improvements of 30% for bankfull width and 76% for bankfull depth. The proposed model is then utilized to generate channel width and depth under bankfull and mean‐flow conditions data set across approximately 2.7 million streams within NHDPlusV2.1 data set across the CONtiguous United State (CONUS). This data set is a valuable resource for water‐related sciences, including hydrology, geomorphology, flood modeling, water quality assessment, and flood management. AN - noaa:72812 DO - https://doi.org/10.1029/2024wr037997 IS - 2 J2 - Water Resources Research PB - American Geophysical Union (AGU) PY - 2025 T2 - Water Resources Research T2 - Water Resources Research, 61(2) TI - Bankfull and Mean‐Flow Channel Geometry Estimation Through Machine Learning Algorithms Across the CONtiguous United States (CONUS) VL - 61 UR - https://repository.library.noaa.gov/view/noaa/72812 ER -
Machine Learning for a Heterogeneous Water Modeling Framework

Frame, Jonathan M.

;

Araki, Ryoko

2025 | JAWRA Journal of the American Water Resources Association, 61(1) | PDF

This technical note describes recent efforts to integrate machine learning (ML) models, specifically long short‐term memory (LSTM) networks and differentiable parameter learning conceptual hydrological models (δ conceptual models), into the next‐generation water resources modeling framework (Nextgen) to enhance future versions of the U.S. National ...

03/20/2026

Title : Machine Learning for a Heterogeneous Water Modeling Framework Personal Author(s) : Frame, Jonathan M.;Araki, Ryoko;Bhuiyan, Soelem Aafnan;Bindas, Tadd;Rapp, Jeremy;Bolotin, Lauren;Deardorff, Emily;Liu, Qiyue;Haces‐Garcia, Francisco;Liao, Mochi;Frazier, Nels;Ogden, Fred L.; Published Date : 2025 Source : JAWRA Journal of the American Water Resources Association, 61(1) DOI : https://doi.org/10.1111/1752-1688.70000 Journal Title : JAWRA Journal of the American Water Resources Association Frame, Jonathan M. et al. (2025). Machine Learning for a Heterogeneous Water Modeling Framework. 61(1). https://doi.org/10.1111/1752-1688.70000 Frame, Jonathan M. et al. "Machine Learning for a Heterogeneous Water Modeling Framework" 61, no. 1 (2025), https://doi.org/10.1111/1752-1688.70000 Frame, Jonathan M. et al. "Machine Learning for a Heterogeneous Water Modeling Framework" vol. 61, no. 1, 2025, https://doi.org/10.1111/1752-1688.70000 Export RIS Citation Information. TY - AU - Frame, Jonathan M. AU - Araki, Ryoko AU - Bhuiyan, Soelem Aafnan AU - Bindas, Tadd AU - Rapp, Jeremy AU - Bolotin, Lauren AU - Deardorff, Emily AU - Liu, Qiyue AU - Haces‐Garcia, Francisco AU - Liao, Mochi AU - Frazier, Nels AU - Ogden, Fred L. AB - This technical note describes recent efforts to integrate machine learning (ML) models, specifically long short‐term memory (LSTM) networks and differentiable parameter learning conceptual hydrological models (δ conceptual models), into the next‐generation water resources modeling framework (Nextgen) to enhance future versions of the U.S. National Water Model (NWM). We address three specific methodology gaps of this new modeling framework: (1) assess model performance across many ungauged catchments, (2) diagnostic‐based model selection, and (3) regionalization based on catchment attributes. We demonstrate that an LSTM trained on CAMELS catchments can make large‐scale predictions with Nextgen across the New England region and match the average flow duration curve observed by stream gauges for streamflow with low exceedance probability (high flows), but diverges from the mean in high exceedance probability (low flows). We demonstrate improvements in peak flow predictions when using δ conceptual model, but results also suggest that performance increases may come at a cost of accurately representing hydrologic states within the conceptual model. We propose a novel approach using ML to predict the most performant mosaic modeling approach and demonstrate improved distributions of efficiency scores throughout the large sample of basins. Our findings advocate for the future development of ML capabilities within Nextgen for advancing operational hydrological modeling. AN - noaa:72811 DO - https://doi.org/10.1111/1752-1688.70000 IS - 1 J2 - JAWRA Journal of the American Water Resources Association PB - Wiley PY - 2025 T2 - JAWRA Journal of the American Water Resources Association T2 - JAWRA Journal of the American Water Resources Association, 61(1) TI - Machine Learning for a Heterogeneous Water Modeling Framework VL - 61 UR - https://repository.library.noaa.gov/view/noaa/72811 ER -
Multi‐Physics Data Assimilation Framework for Remotely Sensed Snowpacks to Improve Water Prediction

Shrestha, Prabhakar

;

Barros, Ana P.

2025 | Water Resources Research, 61(2) | PDF

Recent advances in remote sensing of snow using Synthetic Aperture Radar have shown the potential for retrievals of Snow Water Equivalent (SWE) at high spatial resolution with good accuracy. These data can be integrated with physically based models to reconstruct spatial heterogeneity and reduce uncertainty in quantifying SWE. In this study, we pre ...

03/20/2026

Title : Multi‐Physics Data Assimilation Framework for Remotely Sensed Snowpacks to Improve Water Prediction Personal Author(s) : Shrestha, Prabhakar;Barros, Ana P.; Published Date : 2025 Source : Water Resources Research, 61(2) DOI : https://doi.org/10.1029/2024WR037885 Journal Title : Water Resources Research Shrestha, Prabhakar and Barros, Ana P. (2025). Multi‐Physics Data Assimilation Framework for Remotely Sensed Snowpacks to Improve Water Prediction. 61(2). https://doi.org/10.1029/2024WR037885 Shrestha, Prabhakar and Barros, Ana P. "Multi‐Physics Data Assimilation Framework for Remotely Sensed Snowpacks to Improve Water Prediction" 61, no. 2 (2025), https://doi.org/10.1029/2024WR037885 Shrestha, Prabhakar and Barros, Ana P. "Multi‐Physics Data Assimilation Framework for Remotely Sensed Snowpacks to Improve Water Prediction" vol. 61, no. 2, 2025, https://doi.org/10.1029/2024WR037885 Export RIS Citation Information. TY - AU - Shrestha, Prabhakar AU - Barros, Ana P. AB - Recent advances in remote sensing of snow using Synthetic Aperture Radar have shown the potential for retrievals of Snow Water Equivalent (SWE) at high spatial resolution with good accuracy. These data can be integrated with physically based models to reconstruct spatial heterogeneity and reduce uncertainty in quantifying SWE. In this study, we present a Multi‐Physics Data Assimilation Framework (MPDAF) to improve operational water prediction by assimilating snow measurements/retrievals or microwave data. This framework is demonstrated over Grand Mesa, Colorado during NASA's SnowEx’17 campaign. To illustrate the potential benefit of satellite‐based time‐series of SAR measurements, we investigate the value of data assimilation (DA) with window lengths determined by potential satellite revisit times and anticipated estimation error models. Daily assimilation of integral quantities like snow depth showed dramatic improvement in predicted snow depth, SWE and in vertical profile of snow density. Independent evaluation against pit measurements shows that assimilation of SWE retrievals from airborne SnowSAR backscatter measurements substantially reduced bias in snow depth (from −22% to 0%) and SWE (from −19% to 3%), also recovering spatial heterogeneity not resolved by weather forecasts. Assimilation impacts both snowpack microphysics and the forward simulation of volume backscatter in X and Ku bands with dependence on snow depth changes. The uncertainty in the forward estimates of backscatter is consistent with the synthetic measurement uncertainty based on pit data, thus demonstrating that MPDAF preserves end‐to‐end physical consistency among assimilated retrievals and forward simulations of backscatter measurements critical for operational retrievals. AN - noaa:72810 DO - https://doi.org/10.1029/2024WR037885 IS - 2 J2 - Water Resources Research PB - American Geophysical Union (AGU) PY - 2025 T2 - Water Resources Research T2 - Water Resources Research, 61(2) TI - Multi‐Physics Data Assimilation Framework for Remotely Sensed Snowpacks to Improve Water Prediction VL - 61 UR - https://repository.library.noaa.gov/view/noaa/72810 ER -
Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human‐Water Systems

Razavi, Saman

;

Duffy, Ashleigh

2025 | Water Resources Research, 61(2) | PDF

The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, is becoming the mantra of numerous initiatives aimed at tackling pressing water challenges. Yet, the transition from rhetoric to actual implementation is impeded by incongruence in semantics, methodologies, and discourse among d ...

03/20/2026

Title : Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human‐Water Systems Personal Author(s) : Razavi, Saman;Duffy, Ashleigh;Eamen, Leila;Jakeman, Anthony J.;Jardine, Timothy D.;Wheater, Howard;Hunt, Randall J.;Maier, Holger R.;Abdelhamed, Mohamed S.;Ghoreishi, Mohammad;Gupta, Hoshin;Döll, Petra;Moallemi, Enayat A.;Yassin, Fuad;Strickert, Graham;Nabavi, Ehsan;Mai, Juliane;Li, Yanping;Thériault, Julie M.;Wu, Wenyan;Pomeroy, John;Clark, Martyn P.;Ferguson, Grant;Gober, Patricia;Cai, Ximing;Reed, Maureen G.;Saltelli, Andrea;Elshorbagy, Amin;Sedighkia, Mahdi;Terry, Julie;Lindenschmidt, Karl‐Erich;Hannah, David M.;Li, Kailong;Asadzadeh, Masoud;Harvey, Natasha;Moradkhani, Hamid;Grimm, Volker; Published Date : 2025 Source : Water Resources Research, 61(2) DOI : https://doi.org/10.1029/2024WR038088 Journal Title : Water Resources Research Razavi, Saman et al. (2025). Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human‐Water Systems. 61(2). https://doi.org/10.1029/2024WR038088 Razavi, Saman et al. "Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human‐Water Systems" 61, no. 2 (2025), https://doi.org/10.1029/2024WR038088 Razavi, Saman et al. "Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human‐Water Systems" vol. 61, no. 2, 2025, https://doi.org/10.1029/2024WR038088 Export RIS Citation Information. TY - AU - Razavi, Saman AU - Duffy, Ashleigh AU - Eamen, Leila AU - Jakeman, Anthony J. AU - Jardine, Timothy D. AU - Wheater, Howard AU - Hunt, Randall J. AU - Maier, Holger R. AU - Abdelhamed, Mohamed S. AU - Ghoreishi, Mohammad AU - Gupta, Hoshin AU - Döll, Petra AU - Moallemi, Enayat A. AU - Yassin, Fuad AU - Strickert, Graham AU - Nabavi, Ehsan AU - Mai, Juliane AU - Li, Yanping AU - Thériault, Julie M. AU - Wu, Wenyan AU - Pomeroy, John AU - Clark, Martyn P. AU - Ferguson, Grant AU - Gober, Patricia AU - Cai, Ximing AU - Reed, Maureen G. AU - Saltelli, Andrea AU - Elshorbagy, Amin AU - Sedighkia, Mahdi AU - Terry, Julie AU - Lindenschmidt, Karl‐Erich AU - Hannah, David M. AU - Li, Kailong AU - Asadzadeh, Masoud AU - Harvey, Natasha AU - Moradkhani, Hamid AU - Grimm, Volker AB - The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, is becoming the mantra of numerous initiatives aimed at tackling pressing water challenges. Yet, the transition from rhetoric to actual implementation is impeded by incongruence in semantics, methodologies, and discourse among disciplinary scientists and societal actors. Here, we embrace “integrated modeling”—both quantitatively and qualitatively—as a vital exploratory instrument to advance such integration, providing a means to navigate complexity and manage the uncertainty associated with understanding, diagnosing, predicting, and governing human‐water systems. From this standpoint, we confront disciplinary barriers by offering seven focused reviews and syntheses of existing and missing links across the frontiers distinguishing surface and groundwater hydrology, engineering, social sciences, economics, Indigenous and place‐based knowledge, and studies of other interconnected natural systems such as the atmosphere, cryosphere, and ecosphere. While there are, arguably, no bounds to the pursuit of inclusivity in representing the spectrum of natural and human processes around water resources, we advocate that integrated modeling can provide a focused approach to delineating the scope of integration, through the lens of three fundamental questions: (a) What is the modeling “purpose”? (b) What constitutes a sound “boundary judgment”? and (c) What are the “critical uncertainties” and their compounding effects? More broadly, we call for investigating what constitutes <styled-content>warranted</styled-content> “systems complexity,” as opposed to <styled-content>unjustified</styled-content> “computational complexity” when representing complex natural and human‐natural systems, with careful attention to interdependencies and feedbacks, scaling issues, nonlinear dynamics and thresholds, hysteresis, time lags, and legacy effects. AN - noaa:72809 DO - https://doi.org/10.1029/2024WR038088 IS - 2 J2 - Water Resources Research PB - American Geophysical Union (AGU) PY - 2025 T2 - Water Resources Research T2 - Water Resources Research, 61(2) TI - Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human‐Water Systems VL - 61 UR - https://repository.library.noaa.gov/view/noaa/72809 ER -
Warmer Winters Drive Declines in Snowpack and Consequent Increases in Annual and Seasonal Runoff in a Headwater Region of the Northeastern United States

Hale, Kate ;

Schroth, Andrew

2025 | Hydrological Processes, 39(2) | PDF

In montane, snow‐affected regions of the United States, a warming climate threatens the timing and amount of future water delivery. It is expected that winter precipitation falling as snow will continue decreasing and the frequency of winter snowmelt events will continue increasing, with unknown impacts on the partitioning of water between evapotra ...

03/20/2026

Title : Warmer Winters Drive Declines in Snowpack and Consequent Increases in Annual and Seasonal Runoff in a Headwater Region of the Northeastern United States Personal Author(s) : Hale, Kate;Schroth, Andrew;Shanley, James;Wemple, Beverley; Published Date : 2025 Source : Hydrological Processes, 39(2) DOI : https://doi.org/10.1002/hyp.70092 Journal Title : Hydrological Processes Hale, Kate et al. (2025). Warmer Winters Drive Declines in Snowpack and Consequent Increases in Annual and Seasonal Runoff in a Headwater Region of the Northeastern United States. 39(2). https://doi.org/10.1002/hyp.70092 Hale, Kate et al. "Warmer Winters Drive Declines in Snowpack and Consequent Increases in Annual and Seasonal Runoff in a Headwater Region of the Northeastern United States" 39, no. 2 (2025), https://doi.org/10.1002/hyp.70092 Hale, Kate et al. "Warmer Winters Drive Declines in Snowpack and Consequent Increases in Annual and Seasonal Runoff in a Headwater Region of the Northeastern United States" vol. 39, no. 2, 2025, https://doi.org/10.1002/hyp.70092 Export RIS Citation Information. TY - AU - Hale, Kate AU - Schroth, Andrew AU - Shanley, James AU - Wemple, Beverley AB - In montane, snow‐affected regions of the United States, a warming climate threatens the timing and amount of future water delivery. It is expected that winter precipitation falling as snow will continue decreasing and the frequency of winter snowmelt events will continue increasing, with unknown impacts on the partitioning of water between evapotranspiration and runoff, water quality, flooding, and drought. The northeastern United States represents a humid climate with uniform precipitation seasonality and a transient snowpack. Limited research on changing winter conditions and water availability has been conducted in the region, in part due to scarce observations. An observational network has been recently established (2022) to span a Summit‐to‐Shore (S2S) continuum in Vermont for improved understanding and characterisation of snowpack variability across the landscape. We leverage the S2S network alongside available multi‐decade records of meteorology, snow depth, and runoff to relate long‐term snowpack characteristics in Vermont to seasonal and annual runoff within the high‐elevation headwater Ranch Brook watershed (9.6 km2). In the last 57 years, average winter temperatures have increased by 2.6°C, snow season length has decreased by almost 3 weeks, average snow depth has decreased by 16%, and winter season rain‐on‐snow (ROS) event frequency has increased from 1 to 3.5 per year. In response, average daily winter runoff has increased, which is strongly related to increased annual runoff ratios (R2 = 0.70). Separating the 22‐year runoff record into water years with more versus less winter runoff revealed that years with more winter runoff corresponded to increased winter temperatures, 15% smaller snowpack, two times more ROS events, 52% more winter runoff, 31% larger annual runoff ratio, and increased summer rainfall variance. A steady decline in the regional snowpack and related impacts on downstream water resources may have implications for ecosystems and agricultural, industrial, and domestic water supply. AN - noaa:72808 DO - https://doi.org/10.1002/hyp.70092 IS - 2 J2 - Hydrological Processes PB - Wiley PY - 2025 T2 - Hydrological Processes T2 - Hydrological Processes, 39(2) TI - Warmer Winters Drive Declines in Snowpack and Consequent Increases in Annual and Seasonal Runoff in a Headwater Region of the Northeastern United States VL - 39 UR - https://repository.library.noaa.gov/view/noaa/72808 ER -
A Duration‐Over‐Threshold Model for Flood Frequency and Flow Regime Characterization

Lawson, K. S.

;

Underwood, Kristen L.

2025 | Water Resources Research, 61(2) | PDF

Proper characterization of river flow is essential for the development of structural and non‐structural measures to reduce flood damages, restore ecosystem functions, and manage environmental contaminants in riparian zones. The duration of flood events is an important feature that drives riverine processes and functions such as erosion, geomorphic ...

03/20/2026

Title : A Duration‐Over‐Threshold Model for Flood Frequency and Flow Regime Characterization Personal Author(s) : Lawson, K. S.;Underwood, Kristen L.;Diehl, Rebecca M.;Rizzo, Donna M.; Published Date : 2025 Source : Water Resources Research, 61(2) DOI : https://doi.org/10.1029/2023WR037022 Journal Title : Water Resources Research Lawson, K. S. et al. (2025). A Duration‐Over‐Threshold Model for Flood Frequency and Flow Regime Characterization. 61(2). https://doi.org/10.1029/2023WR037022 Lawson, K. S. et al. "A Duration‐Over‐Threshold Model for Flood Frequency and Flow Regime Characterization" 61, no. 2 (2025), https://doi.org/10.1029/2023WR037022 Lawson, K. S. et al. "A Duration‐Over‐Threshold Model for Flood Frequency and Flow Regime Characterization" vol. 61, no. 2, 2025, https://doi.org/10.1029/2023WR037022 Export RIS Citation Information. TY - AU - Lawson, K. S. AU - Underwood, Kristen L. AU - Diehl, Rebecca M. AU - Rizzo, Donna M. AB - Proper characterization of river flow is essential for the development of structural and non‐structural measures to reduce flood damages, restore ecosystem functions, and manage environmental contaminants in riparian zones. The duration of flood events is an important feature that drives riverine processes and functions such as erosion, geomorphic adjustment, habitat suitability, and nutrient and water quality dynamics. Despite this, most flood characterization methods focus on relating the magnitude of annual‐maximum discharges to frequency, without addressing the duration of flood events. We investigated event‐specific discharge‐duration dynamics at 33 USGS stream gages within the US state of Vermont. Building on the method of Feng et al., 2017, <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://doi.org/10.1061/(asce)he.1943-5584.0001573">https://doi.org/10.1061/(asce)he.1943‐5584.0001573</ext-link>, flood events from 15‐min discharge timeseries were extracted using an automated threshold method. A statistical model was fit at each gage for both frequency of discharge exceedance and conditional duration of discharge exceedance. This Duration‐Over‐Threshold model estimates the arrival rate of a discharge threshold, q, being exceeded for a given duration, d. Fitted model parameters were compared to basin and channel physiographical characteristics to develop regional regression equations and examine potential watershed processes underlying the duration dynamics. Model parameters summarizing event duration were best predicted by drainage area, mainstem slope, and soil depth/type. The regional regression equations enable design event estimation in ungaged catchments of the study region, which may be used to improve the predictive capacity of hydraulic and ecosystem models, outline a range of potential geomorphic trajectories, or inform emergency management plans and flood damage rating curves. AN - noaa:72807 DO - https://doi.org/10.1029/2023WR037022 IS - 2 J2 - Water Resources Research PB - American Geophysical Union (AGU) PY - 2025 T2 - Water Resources Research T2 - Water Resources Research, 61(2) TI - A Duration‐Over‐Threshold Model for Flood Frequency and Flow Regime Characterization VL - 61 UR - https://repository.library.noaa.gov/view/noaa/72807 ER -
Leveraging High‐Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin

Kemper, John T. ;

Underwood, Kristen L.

2025 | JAWRA Journal of the American Water Resources Association, 61(2) | PDF

As high‐frequency sensor networks increasingly enhance data‐driven models of water quality, process‐based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, ...

03/20/2026

Title : Leveraging High‐Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin Personal Author(s) : Kemper, John T.;Underwood, Kristen L.;Hamshaw, Scott D.;Davis, Dany;Siemion, Jason;Shanley, James B.;Schroth, Andrew W.; Published Date : 2025 Source : JAWRA Journal of the American Water Resources Association, 61(2) DOI : https://doi.org/10.1111/1752-1688.70011 Journal Title : JAWRA Journal of the American Water Resources Association Kemper, John T. et al. (2025). Leveraging High‐Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin. 61(2). https://doi.org/10.1111/1752-1688.70011 Kemper, John T. et al. "Leveraging High‐Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin" 61, no. 2 (2025), https://doi.org/10.1111/1752-1688.70011 Kemper, John T. et al. "Leveraging High‐Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin" vol. 61, no. 2, 2025, https://doi.org/10.1111/1752-1688.70011 Export RIS Citation Information. TY - AU - Kemper, John T. AU - Underwood, Kristen L. AU - Hamshaw, Scott D. AU - Davis, Dany AU - Siemion, Jason AU - Shanley, James B. AU - Schroth, Andrew W. AB - As high‐frequency sensor networks increasingly enhance data‐driven models of water quality, process‐based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient‐boosted decision tree algorithm (LightGBM) trained on 5+ years of high‐frequency monitoring data to forecast in‐stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM‐based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country. AN - noaa:72806 DO - https://doi.org/10.1111/1752-1688.70011 IS - 2 J2 - JAWRA Journal of the American Water Resources Association PB - Wiley PY - 2025 T2 - JAWRA Journal of the American Water Resources Association T2 - JAWRA Journal of the American Water Resources Association, 61(2) TI - Leveraging High‐Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin VL - 61 UR - https://repository.library.noaa.gov/view/noaa/72806 ER -
Development of a Novel Evaporation–Imbibition Method to Characterize the Hysteresis Properties of Unsaturated Soils

Nam, Byunghee ;

Poudyal, Bhanu

2025 | Journal of Hydrologic Engineering, 30(5) | PDF

The evaporation (EV) method is a widely used experimental technique to measure unsaturated soil hydraulic properties. This method characterizes how soil-water retention (SWR) and unsaturated hydraulic conductivity (UHC) values change with soil-water matric potential. Typically, unsaturated soils exhibit hysteretic behavior, where the soil moisture ...

03/20/2026

Title : Development of a Novel Evaporation–Imbibition Method to Characterize the Hysteresis Properties of Unsaturated Soils Personal Author(s) : Nam, Byunghee;Poudyal, Bhanu;Kebede, Mahlet M.;Srinivasan, Venkatraman;Clement, T. Prabhakar; Published Date : 2025 Source : Journal of Hydrologic Engineering, 30(5) DOI : https://doi.org/10.1061/JHYEFF.HEENG-6472 Journal Title : Journal of Hydrologic Engineering Nam, Byunghee et al. (2025). Development of a Novel Evaporation–Imbibition Method to Characterize the Hysteresis Properties of Unsaturated Soils. 30(5). https://doi.org/10.1061/JHYEFF.HEENG-6472 Nam, Byunghee et al. "Development of a Novel Evaporation–Imbibition Method to Characterize the Hysteresis Properties of Unsaturated Soils" 30, no. 5 (2025), https://doi.org/10.1061/JHYEFF.HEENG-6472 Nam, Byunghee et al. "Development of a Novel Evaporation–Imbibition Method to Characterize the Hysteresis Properties of Unsaturated Soils" vol. 30, no. 5, 2025, https://doi.org/10.1061/JHYEFF.HEENG-6472 Export RIS Citation Information. TY - AU - Nam, Byunghee AU - Poudyal, Bhanu AU - Kebede, Mahlet M. AU - Srinivasan, Venkatraman AU - Clement, T. Prabhakar AB - The evaporation (EV) method is a widely used experimental technique to measure unsaturated soil hydraulic properties. This method characterizes how soil-water retention (SWR) and unsaturated hydraulic conductivity (UHC) values change with soil-water matric potential. Typically, unsaturated soils exhibit hysteretic behavior, where the soil moisture and hydraulic conductivity values can vary between wetting and drying cycles encountered under natural conditions. Unfortunately, one of the key limitations of the EV method is that it cannot be used to measure unsaturated soil hydraulic properties under wetting (imbibition) conditions. In this study, we proposed a new method, namely, the evaporation–imbibition (EVI) method, for characterizing the hysteresis behavior of unsaturated soils. The method uses a novel experimental approach for facilitating controlled imbibition from the bottom, which allows for measuring soil characteristics under wetting conditions. Furthermore, to improve measurement accuracy, in situ approaches are used for calibrating the tensiometer and soil moisture probes, and these methods are integrated as part of the EVI method. The performance of our EVI method was tested using two different soils for multiple wetting and drying cycles. Our results show that the EVI method was able to characterize the initial and main drying and wetting curves as well as the intermediate scanning curves. The proposed method is a promising approach that improves the overall capability of the traditional EV method for characterizing the hysteretic patterns of SWR and UHC functions. AN - noaa:72805 DO - https://doi.org/10.1061/JHYEFF.HEENG-6472 IS - 5 J2 - Journal of Hydrologic Engineering PB - American Society of Civil Engineers (ASCE) PY - 2025 T2 - Journal of Hydrologic Engineering T2 - Journal of Hydrologic Engineering, 30(5) TI - Development of a Novel Evaporation–Imbibition Method to Characterize the Hysteresis Properties of Unsaturated Soils VL - 30 UR - https://repository.library.noaa.gov/view/noaa/72805 ER -
AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning

Sajja, Ramteja

;

Pursnani, Vinay

2025 | IEEE Access, 13, 42401-42413 | PDF

Floodplain management is critical for mitigating flood risks and safeguarding communities. The FloodPlain Manager (FPM) certification is essential for professionals in this field, but current preparation methods often fall short in providing comprehensive, accessible, and engaging study resources. This research introduces a novel AI-assisted educat ...

03/20/2026

Title : AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning Personal Author(s) : Sajja, Ramteja;Pursnani, Vinay;Sermet, Yusuf;Demir, Ibrahim; Published Date : 2025 Source : IEEE Access, 13, 42401-42413 DOI : https://doi.org/10.1109/ACCESS.2025.3548591 Journal Title : IEEE Access Sajja, Ramteja et al. (2025). AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning. 13https://doi.org/10.1109/ACCESS.2025.3548591 Sajja, Ramteja et al. "AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning" 13 (2025), https://doi.org/10.1109/ACCESS.2025.3548591 Sajja, Ramteja et al. "AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning" vol. 13, 2025, https://doi.org/10.1109/ACCESS.2025.3548591 Export RIS Citation Information. TY - AU - Sajja, Ramteja AU - Pursnani, Vinay AU - Sermet, Yusuf AU - Demir, Ibrahim AB - Floodplain management is critical for mitigating flood risks and safeguarding communities. The FloodPlain Manager (FPM) certification is essential for professionals in this field, but current preparation methods often fall short in providing comprehensive, accessible, and engaging study resources. This research introduces a novel AI-assisted educational tool designed specifically for FPM certification preparation and training process. Leveraging advanced natural language processing and machine learning techniques, this tool offers personalized learning experiences, interactive question-and-answer sessions, and real-time feedback to aspiring floodplain managers. The system architecture integrates certification-specific content through a sophisticated document parsing process, ensuring relevance and accuracy. Evaluation of the tool, conducted through text similarity analysis, demonstrates its effectiveness in preparing candidates for the FPM certification exam. With 91.7% accuracy for open-ended questions and 95.12% for multiple-choice questions, the tool offers a personalized learning experience through dynamic flashcards and adaptive quizzes, highlighting its potential to enhance vocational training and exam readiness. This study underscores the transformative role of AI in professional education and suggests future directions for expanding the tool’s capabilities and application to other certifications. AN - noaa:72804 DO - https://doi.org/10.1109/ACCESS.2025.3548591 J2 - IEEE Access PB - Institute of Electrical and Electronics Engineers (IEEE) PY - 2025 T2 - IEEE Access T2 - IEEE Access, 13, 42401-42413 TI - AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning VL - 13 UR - https://repository.library.noaa.gov/view/noaa/72804 ER -
Global patterns in observed hydrologic processes

McMillan, Hilary

;

Araki, Ryoko

2025 | Nature Water, 3(4), 497-506 | PDF

To manage water resources and forecast river flows, hydrologists seek to understand how water moves from precipitation, through watersheds, into river channels. However, we lack fundamental information on the spatial distribution and physical controls on global hydrologic processes. This information is needed to provide theoretical support for larg ...

03/20/2026

Title : Global patterns in observed hydrologic processes Personal Author(s) : McMillan, Hilary;Araki, Ryoko;Bolotin, Lauren;Kim, Dong-Hyun;Coxon, Gemma;Clark, Martyn;Seibert, Jan; Published Date : 2025 Source : Nature Water, 3(4), 497-506 DOI : https://doi.org/10.1038/s44221-025-00407-w Journal Title : Nature Water McMillan, Hilary et al. (2025). Global patterns in observed hydrologic processes. 3(4). https://doi.org/10.1038/s44221-025-00407-w McMillan, Hilary et al. "Global patterns in observed hydrologic processes" 3, no. 4 (2025), https://doi.org/10.1038/s44221-025-00407-w McMillan, Hilary et al. "Global patterns in observed hydrologic processes" vol. 3, no. 4, 2025, https://doi.org/10.1038/s44221-025-00407-w Export RIS Citation Information. TY - AU - McMillan, Hilary AU - Araki, Ryoko AU - Bolotin, Lauren AU - Kim, Dong-Hyun AU - Coxon, Gemma AU - Clark, Martyn AU - Seibert, Jan AB - To manage water resources and forecast river flows, hydrologists seek to understand how water moves from precipitation, through watersheds, into river channels. However, we lack fundamental information on the spatial distribution and physical controls on global hydrologic processes. This information is needed to provide theoretical support for large-domain model simulations. Here, to address this issue, we present a global, searchable database of 400 research watersheds with published descriptions of dominant hydrologic flow pathways. This knowledge synthesis approach leverages decades of grant funding, fieldwork effort and local expertise. We use the database to test longstanding hypotheses about the roles of climate, biomes and landforms in controlling hydrologic processes. We show that aridity predicts the depth of water flow pathways and that terrain and biomes predict the prevalence of lateral flow pathways. These new data and search capabilities support efficient hypothesis testing to investigate emergent patterns that relate landscape organization to hydrologic function. AN - noaa:72803 DO - https://doi.org/10.1038/s44221-025-00407-w IS - 4 J2 - Nature Water PB - Springer Science and Business Media LLC PY - 2025 T2 - Nature Water T2 - Nature Water, 3(4), 497-506 TI - Global patterns in observed hydrologic processes VL - 3 UR - https://repository.library.noaa.gov/view/noaa/72803 ER -

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