Water Resources Research最新文献

{"title":"Enhancing Streamflow Reanalysis Across the Conterminous US Leveraging Multiple Gridded Precipitation Data Sets","authors":"Ganesh R. Ghimire, Shih-Chieh Kao, Sudershan Gangrade","doi":"10.1029/2024wr038256","DOIUrl":"https://doi.org/10.1029/2024wr038256","url":null,"abstract":"Streamflow observations, essential for various water resource applications, are often unavailable at critical locations in need. Although different models have been proposed to enhance streamflow predictability at ungauged locations, the challenge extends beyond model fidelity. Differences in meteorologic forcing data sets, precipitation in particular, can significantly affect the accuracy of hydrologic predictions. This challenge intensifies across regions characterized by diverse hydro-climatological and geographical conditions, such as in the conterminous US (CONUS) where a single precipitation product struggles to consistently replicate observed hydrographs, particularly peak flow dynamics. To enhance streamflow predictions, we utilize a VIC-RAPID hydrologic modeling framework driven by multiple commonly used meteorological forcing data sets, such as Daymet, PRISM, ST4, AORC, and their hybrids and create multiple sets of 40-year (1980–2019) hourly, daily, and monthly streamflow reanalysis, Dayflow Version 2, for 2.7 million river reaches across the CONUS. Most forcings lead to skillful streamflow performance, except for ST4 in the mountainous west, where severe radar blockage adversely affects the accuracy. The evaluation using over 6,000 hourly stream gauges shows that hourly AORC and ST4 lead to improved annual peak flow performance over Daymet—driven streamflow (Dayflow V1), particularly in smaller basins, highlighting the value of high temporal resolution forcings in hydrologic predictions. Compared with other benchmark data sets like National Water Model V3.0, AORC-driven VIC-RAPID exhibits improved regional streamflow performance, with comparable peak flow representation. We envision that multi-forcing streamflow reanalysis data can inform regions in need of forcing data enhancement, diagnose hydrologic model performance, and benefit diverse water resource applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"9 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benefits of Calibrating a Global Hydrological Model for Regional Analyses of Flood and Drought Projections: A Case Study of the Yangtze River Basin","authors":"Fang Zhao, Ning Nie, Yang Liu, Congrui Yi, Luca Guillaumot, Yoshihide Wada, Peter Burek, Mikhail Smilovic, Katja Frieler, Matthias Buechner, Jacob Schewe, Simon N. Gosling","doi":"10.1029/2024wr037153","DOIUrl":"https://doi.org/10.1029/2024wr037153","url":null,"abstract":"Uncalibrated global hydrological models are primarily used to inform projections of flood and drought changes under global warming and their impacts, but it remains unclear how model calibration might benefit these projections. Using the Yangtze River Basin as a case study, we compare projected changes in flood and drought frequencies and their impacts—area, population, and gross domestic product affected—at various warming levels, from uncalibrated and calibrated simulations with the Community Water Model. These projections are driven by 10 General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6, within the Inter-Sectoral Impact Model Intercomparison Project framework. Calibration significantly improves simulated discharge, yet the impact of calibration under climate change on projected increases in flood frequency and their associated impacts is minor, in contrast to its notable role in drought projections. We further quantify the relative contribution of GCMs, emission scenarios, and calibration approaches to the projected impacts, finding that GCMs primarily drive projected flood changes, while emission scenarios and calibration contribute more significantly to the variance in drought projections after 2050. The differing sensitivities to calibration are attributed to the dominance of extreme precipitation in flood generation and the influence of long-term evapotranspiration trends on drought occurrence. The findings imply that future projections of relative changes in flood frequency and risks based on uncalibrated hydrological models are likely still quite reliable for warm and humid regions. However, careful calibration and model improvement is crucial for enhancing the reliability of future drought impact assessments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"92 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143666582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STREAM-Sat: A Novel Near-Realtime Quasi-Global Satellite-Only Ensemble Precipitation Dataset","authors":"Kaidi Peng, Daniel B. Wright, Yagmur Derin, Samantha H. Hartke, Zhe Li, Jackson Tan","doi":"10.1029/2023wr036756","DOIUrl":"https://doi.org/10.1029/2023wr036756","url":null,"abstract":"Satellite-based precipitation observations can provide near-global coverage with high spatiotemporal resolution in near-realtime. Their utility, however, is hindered by oftentimes large uncertainties that vary substantially in space and time. This problem is particularly pronounced in regions which lack dense ground-based measurements to quantify or reduce such uncertainty. Since this uncertainty is, by definition, a random process, probabilistic representations are needed to advance their operational application. Ensemble methods, in which uncertainty is depicted via multiple realizations of precipitation fields, have been widely used in numerical weather and climate prediction, but rarely in satellite contexts. Creating such an ensemble dataset is challenging due to the complexity of observational uncertainties and the scarcity of “ground truth” to characterize them. In this study, we attempt to resolve these two challenges and propose the first quasi-global (covering all continental land masses within 50°N-50°S) satellite-only ensemble precipitation dataset (STREAM-Sat), derived entirely from NASA's Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) and GPM's radar-radiometer combined precipitation product (2B-CMB). No ground-based measurements are used to generate STREAM-Sat, and it is suitable for near-realtime use without extending the 4-hr latency and 0.1°, 30-min spatiotemporal resolution of IMERG Early. We compare STREAM-Sat against several precipitation datasets, including global satellite-based, rain gage-based, atmospheric reanalysis, and merged products. While our proposed approach faces some limitations and is not universally superior to the comparison datasets in all respects, it does hold relative advantages due to its unique combination of accuracy, resolution, rainfall spatiotemporal structure, latency, and utility in hydrologic and hazard applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"200 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to ‘Comment on “A Modular Framework for Modeling Unsaturation Soil Hydraulic Properties Over the Full Moisture Range” by Tobias Weber, Wolfgang Durner, Thilo Streck, and Efstathios Diamantopoulos’","authors":"Tobias K. D. Weber, Thilo Streck, Efstathios Diamantopoulos","doi":"10.1029/2023wr036901","DOIUrl":"https://doi.org/10.1029/2023wr036901","url":null,"abstract":"In Weber et al. (2019), https://doi.org/10.1029/2018wr024584 (hereafter W19), modeling soil hydraulic properties (SHP) was systematically framed and presented in a didactic, carefully thought-out approach. In doing so, the authors coined the term SHP model system/model framework, acknowledging the decade old research on effective modeling of the SHP. At the heart of the model an integral was formulated that links non-capillary saturation to capillary saturation, based on any given saturation function for the capillary part. This approach sparked the interest by Peters and Iden (2021), https://doi.org/10.1029/2020wr028397 who wrote a comment. In this reply, we providing a detailed perspective on the comment and scrutinize the opinions by Peters and Iden (2021), https://doi.org/10.1029/2020wr028397. Additionally, we use the opportunity to clarify terminology with respect to the distinction between “non-capillary” and “capillary” pore spaces. Further, the Brunswick model system presented in W19 has been implemented in the HYDRUS software suite (Diamantopoulos et al., 2024, https://doi.org/10.1002/vzj2.20326).","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"92 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Discharge and Suspended Sediments in the Yangtze River Tidal Reach Using Coastal Acoustic Tomography","authors":"Cong Xiao, Ze-Nan Zhu, Chuanzheng Zhang, Xiao-Hua Zhu, Yun Long Ma, Zhao-Jun Liu, Li Xin Wei, Ji Wen Zhong, Rui Zeng, Yuan Feng Ding","doi":"10.1029/2024wr037763","DOIUrl":"https://doi.org/10.1029/2024wr037763","url":null,"abstract":"Conventional methods of measuring water discharge and suspended sediment concentration (e.g., water sampling and moving acoustic Doppler current profiler [ADCP]) present challenges in large tidal rivers due to temporal and spatial constraints. This study introduces a novel approach to monitor water discharge and suspended sediment discharge (SSD) in large tidal rivers. Total water discharge and SSD exhibit notable variability in tidal rivers due to the river–tidal interactions; understanding this variability and its causes is essential for effective tidal river management. From June to November 2023, a field study was conducted at Nanjing (NJ) to continuously monitor water discharge, suspended sediment concentration (SSC), and SSD in the tidal reaches of the Yangtze River using coastal acoustic tomography (CAT). Total water discharge ranged from 8,765 to 43,356 m³/s, with a mean of 27,825 m³/s, while tidal discharge varied between −11,998 and 9,983 m³/s, with a mean of 69 m³/s. SSC ranged from 0.02 to 0.09 kg/m³, and SSD ranged from 110 to 3,823 kg/s. Tidal variations in SSC and SSD were within ±0.04 kg/m³ and −1,252 to 1,410 kg/s, respectively. Over short timescales, tides caused instantaneous fluctuations in velocity, water discharge, and SSD, with tides contributing −40% to instantaneous water discharge and SSD at NJ. Over seasonal timescales, no significant wet/dry variations were observed in water discharge, SSC, or SSD during a few months of 2023. Long-term CAT application (e.g., decades) is required to reveal trends in tidal river dynamics.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"124 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Surface Water and Ocean Topography Mission (SWOT) Prior Lake Database (PLD): Lake Mask and Operational Auxiliaries","authors":"Jida Wang, Claire Pottier, Cécile Cazals, Marjorie Battude, Yongwei Sheng, Chunqiao Song, Md Safat Sikder, Xiao Yang, Linghong Ke, Manon Delhoume, Marielle Gosset, Rafael Reis Alencar Oliveira, Manuela Grippa, Félix Girard, George H. Allen, Xiangtao Xu, Xiaolin Zhu, Sylvain Biancamaria, Laurence C. Smith, Jean-François Crétaux, Tamlin M. Pavelsky","doi":"10.1029/2023wr036896","DOIUrl":"https://doi.org/10.1029/2023wr036896","url":null,"abstract":"Lakes are among the most prevalent and predominant water repositories on the Earth's land surface. A primary objective of the Surface Water and Ocean Topography (SWOT) satellite mission is to monitor surface water elevation, area, and storage change in lakes globally. To meet this objective, prior information on lakes, such as locations and benchmark extents, is required to organize SWOT's KaRIn observations for computing lake storage variation over time. Here, we present the SWOT mission Prior Lake Database (PLD) to fulfill this requirement. This paper emphasizes the development of the “operational PLD,” which consists of (a) a high-resolution mask encompassing approximately 6 million lakes and reservoirs that meet the minimum size criterion of 1 ha, as defined in SWOT’s lake observation science goals, and (b) multiple operational auxiliaries that support the lake mask in generating SWOT's standard lake vector data products. We built the prior lake mask by harmonizing the UCLA Circa-2015 Global Lake Dataset and several state-of-the-art reservoir databases. Operational auxiliaries were produced from multi-theme geospatial data to provide essential information for PLD functionality, including lake catchments and influence areas, ice phenology, relationship with SWOT prior rivers, and spatiotemporal coverage by SWOT overpasses. Globally, over three quarters of the prior lakes are smaller than 10 ha. About 97% of the lakes, constituting half of the global lake area, are fully observed at least once per orbit cycle. The PLD will be recursively improved throughout the mission lifetime and serves as a critical framework for organizing, processing, and interpreting SWOT observations over lacustrine environments with fundamental significance to lake system science.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"16 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Competing Effects of Vegetation Greening-Induced Changes in Summer Evapotranspiration and Precipitation on Water Yield in the Yangtze River Basin Based on WRF Simulations","authors":"Guoshuai Liu, Weiguang Wang","doi":"10.1029/2024wr038663","DOIUrl":"https://doi.org/10.1029/2024wr038663","url":null,"abstract":"Remarkable vegetation greening has been observed in the Yangtze River Basin (YRB) during the past two decades, triggering noteworthy hydrological consequences. Previous studies have assessed the hydrological effect of vegetation greening but ignored the vegetation-precipitation feedbacks from land-atmosphere interactions. To address this knowledge gap, here we conduct coupled land-atmosphere model simulations prescribed with satellite vegetation observations to investigate how vegetation greening in the YRB affects regional hydrological cycles through vegetation physiological processes and biophysical feedbacks, with potentially competing effects on water yield (WY) by altering evapotranspiration (ET) and precipitation. Over the 2001–2020 period, the leaf area index in summer shows a significant increasing trend at a rate of 0.34 m² m⁻² decade⁻¹ (<i>P</i> &lt; 0.01). This vegetation greening causes a substantial rise in ET, primarily due to increased plant transpiration and canopy evaporation, along with reduced soil evaporation attributed to enhanced root water uptake and shading of the soil surface. Moreover, the modeled results indicate that vegetation greening is the key driver for the observed ET enhancement. In addition, vegetation greening induces increases in precipitation by modulating moisture flux convergence, which although statistically insignificant, provides considerable water to compensate for the enhanced ET. For the cumulative effects of vegetation greening from 2001 to 2020 at the basin scale, the increased precipitation (approximately, 101 mm) outpaces the increased water consumption (approximately, 93 mm), resulting in an insignificant effect on WY. Our findings underscore the importance of considering vegetation-precipitation feedbacks in evaluations of the hydrological response to natural or deliberate vegetation changes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"34 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inertial Flow-Driven Enhancement of Solute Mixing and Partitioning at Rough-Walled Fracture Intersections: Experimental and Numerical Investigations","authors":"Dahye Kim, In Wook Yeo","doi":"10.1029/2023wr035609","DOIUrl":"https://doi.org/10.1029/2023wr035609","url":null,"abstract":"This study investigates the impact of the transition from viscous linear to inertial nonlinear flows on solute mixing and partitioning at rough-walled fracture intersections, using direct observations of flow dynamics and solute partitioning processes through microscopic particle image velocimetry. It is generally known that mixing at fracture intersections decreases when transport shifts from diffusion-dominated to advection-dominated processes, but this trend holds only in viscous linear flows. The experimental results conducted in this study reveal that in inertial flows, significant changes in flow structures occur at rough-walled fracture intersections, including the straightening and stretching of main streamlines and the formation of fully developed eddies. Fluid stretching and the formation of eddies contribute to advection-driven diffusive mixing. The straightened streamlines deliver solutes to the outflow leg along a direct path. More importantly, fully developed eddies generate spiral advective paths that reconnect to the main flow channels, enhancing solute redistribution at the intersection. Microscopic measurements and quantitative analyses show that flow nonlinearity—including the formation of eddies, along with enhanced flow straightening and stretching—contributes to increased flow heterogeneity and solute redistribution at fracture intersections. This phenomenon appears as an increase in “apparent” mixing at rough-walled fracture intersections.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"43 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Water Table Kinematic Conditions With Unsaturated Flow Insights","authors":"Jun-Hong Lin, Ying-Fan Lin","doi":"10.1029/2024wr038724","DOIUrl":"https://doi.org/10.1029/2024wr038724","url":null,"abstract":"Analytical models interpreting aquifer pumping test data often rely on water table kinematic conditions that assume instantaneous gravity drainage, leading to underestimation of specific yield during the drainage process. This study derives a new water table condition based on a coupled saturated-unsaturated flow model that fully accounts for both unsaturated and saturated flow dynamics. The new condition incorporates the hydraulic properties of the unsaturated zone, providing a more accurate representation of physical processes while maintaining mathematical tractability. Applied to a groundwater flow model for a pumping problem, the drawdown solution is derived using integral transformations. The proposed model is validated using field data from a series of pumping tests at the Boise Hydrogeophysical Research Site in Idaho. The results demonstrate that the new water table condition provides more reliable estimates of specific yield, effectively addressing the underestimation issue associated with existing models. Moreover, the model requires no additional empirical parameters, making it a practical tool for characterizing unconfined aquifer properties.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"55 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Robustness of the Explanatory Power of Machine Learning Models: Insights From a New Explainable AI Approach Using Sensitivity Analysis","authors":"Banamali Panigrahi, Saman Razavi, Lorne E. Doig, Blanchard Cordell, Hoshin V. Gupta, Karsten Liber","doi":"10.1029/2024wr037398","DOIUrl":"https://doi.org/10.1029/2024wr037398","url":null,"abstract":"Machine learning (ML) is increasingly considered the solution to environmental problems where limited or no physico-chemical process understanding exists. But in supporting high-stakes decisions, where the ability to <i>explain</i> possible solutions is key to their acceptability and legitimacy, ML can fall short. Here, we develop a method, rooted in formal <i>sensitivity analysis</i>, to uncover the primary drivers behind ML predictions. Unlike many methods for <i>explainable artificial intelligence</i> (XAI), this method (a) accounts for complex multi-variate distributional properties of data, common in environmental systems, (b) offers a global assessment of the input-output response surface formed by ML, rather than focusing solely on local regions around existing data points, and (c) is scalable and data-size independent, ensuring computational efficiency with large data sets. We apply this method to a suite of ML models predicting various water quality variables in a pilot-scale experimental pit lake. A critical finding is that subtle alterations in the design of some ML models (such as variations in random seed, functional class, hyperparameters, or data splitting) can lead to different interpretations of how outputs depend on inputs. Further, models from different ML families (decision trees, connectionists, or kernels) may focus on different aspects of the information provided by data, despite displaying similar predictive power. Overall, our results underscore the need to assess the explanatory robustness of ML models and advocate for using model ensembles to gain deeper insights into system drivers and improve prediction reliability.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"214 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}