Improving Streamflow Predictions in the Arid Southwestern United States Through Understanding of Baseflow Generation Mechanisms.
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| Název: | Improving Streamflow Predictions in the Arid Southwestern United States Through Understanding of Baseflow Generation Mechanisms. |
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| Autoři: | Farmani, Mohammad A., Tavakoly, Ahmad, Behrangi, Ali, Qiu, Yuan, Gupta, Aniket, Jawad, Muhammad, Sohi, Hossein Yousefi, Zhang, Xueyan, Geheran, Matthew, Niu, Guo‐Yue |
| Zdroj: | Water Resources Research; Oct2025, Vol. 61 Issue 10, p1-26, 26p |
| Témata: | HYDROLOGIC models, GROUNDWATER flow, MACHINE learning, METEOROLOGICAL precipitation analysis, SOIL permeability, WATER management, ARID regions, STEADY-state flow |
| Geografický termín: | UNITED States, WEST (U.S.) |
| Abstrakt: | Understanding the factors controlling baseflow (groundwater discharge) is critical for improving streamflow predictions in the arid southwestern United States. We used an enhanced version of the Noah‐MP land surface model with advanced hydrological process options and the Routing Application for Parallel computation of Discharge (RAPID) to examine the impacts of process representation, soil hydraulic parameters, and precipitation data sets on baseflow production and streamflow skill. Model experiments combined multiple configurations of hydrological processes, soil parameters, and three gridded precipitation products: NLDAS‐2, Integrated Multi‐satellite Retrievals for GPM Final, and NOAA AORC. RAPID was used to route Noah‐MP‐simulated runoff and generate daily streamflow at 390 U.S. Geological Survey (USGS) gauges. The modeled baseflow index (BFI) was compared with USGS‐derived BFI. Results show that (a) soil water retention curve model plays a dominant role, with the Van‐Genuchten hydraulic scheme reducing the overestimated BFI produced by the Brooks‐Corey, (b) hydraulic parameters (Van‐Genuchten parameters and hydraulic conductivity) strongly affect streamflow prediction, a machine learning‐based Van‐Genuchten parameters captures the USGS BFI, showing a better performance than the optimized National Water Model (NWM) by a median Kling‐Gupta Efficiency of 21%, and (c) incorporating a ponding depth threshold into the land surface models that increases infiltration is preferred. Overall, models with more physically realistic hydrologic representations show a better performance in modeling BFI and thus a better skill in streamflow predictions than the optimized NWM in the dry southwestern river basins. These findings can guide future studies in selecting reliable schemes and data sets (before calibration) to achieve better streamflow predictions as well as water resource projections. Plain Language Summary: Predicting river flow is crucial for managing water in the dry regions of the Western United States, where water is scarce and weather patterns can vary a lot. Large‐scale models like the NOAA National Water Model try to predict streamflow across the country, but they often perform poorly in arid areas. This study looks at one possible reason: how underground water, or baseflow, is represented in the models. Baseflow is especially important in dry regions because it's the main source of river water during long dry periods. We used a detailed computer model called Noah‐MP and tested different ways to simulate how water moves and is stored in the soil, using three different weather data sources. We compared the model's predictions with real river flow measurements from 390 U.S. Geological Survey sites over several decades. We found that: (a) The way soil absorbs and moves water has a big impact on how accurate the predictions are, (b) Models that use smart techniques like machine learning to better estimate soil properties tend to perform better, (c) How the model handles pooling water on the soil surface also affects predictions, especially in dry areas. Key Points: Soil hydraulics significantly affects groundwater recharge and baseflow generation and streamflow over the southwestern USSurface ponding instead of surface runoff enhances baseflow generation and baseflow indexA machine learning‐based soil water retention curve data set significantly enhances streamflow prediction skill [ABSTRACT FROM AUTHOR] |
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| Databáze: | Biomedical Index |
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