WATER RESOURCES RESEARCH水资源研究
WATER RESOURCES RESEARCH(英文缩写 WATER RESOUR RES),ISSN 0043-1397,eISSN 1944-7973,中文译名:水资源研究 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 6.159 | Q1 |
| 2022 | 5.400 | Q1 |
| 2023 | 4.600 | Q1 |
| 2024 | 5.000 | Q1 |
| 2025 | 6.300 | Q1 |
WATER RESOURCES RESEARCH 最新收录文献
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1. Remotely Sensed Surface Water Storage Shows Distinct Patterns From SWAT-Simulated Data.
1. 遥感地表水储量显示出与SWAT模拟数据不同的模式PMID:日期:2026-06-08[中文摘要] 量化与预测湖泊和湿地蓄水的下游效益需要流域水文模型，而这类模型通常使用静态数字高程模型（DEM）数据来参数化地形洼地中的地表水蓄水。利用外部数据集对模拟动态进行校准和验证并不常见，尤其是在大型流域尺度上，模型校准通常集中于观测流量。本研究开发并评估了一种基于遥感（RS）的新型数据集（Sentinel-1和Sentinel-2），用于验证上密西西比河流域（UMRB；约440,000 km²）水土评估工具（SWAT）模型的模拟估算值。结果表明，基于静态DEM的参数化以及仅基于流量的模型校准均不足以捕捉UMRB中的空间和时间动态。SWAT估算的平均蓄水量比遥感蓄水量高74%±122%（均值±标准差），其中SWAT在湿地丰富的子流域中低估了地表水蓄水，而在农业排水暗管子流域中则高估了蓄水。仅在38.8%的子流域中，SWAT蓄水量与遥感蓄水量的时间序列呈正相关。由于遥感蓄水量同样易受误差影响，研究将蓄水估算值与选定小型湿地的测深数据进行了比较。尽管遥感从范围到蓄水量的转换仍存在不确定性，但本文所提出的方法和数据集为改进SWAT及其他基于过程的水文模型中蓄水过程的参数化和校准提供了有前景的选择。进一步考虑这些蓄水过程可能有助于提高湿地丰富模型区域中模拟河川流量的准确性。
[英文摘要] Quantifying and projecting the downstream benefits of water stored in lakes and wetlands ( ) requires watershed hydrologic models, which often parameterize surface water storage in topographic depressions using static digital elevation model (DEM) data. Calibration and validation of modeled dynamics using external data sets is uncommon, particularly across major river basins, with model calibration typically focused on observed discharge. Here, we develop and assess a novel remote sensing-based (RS) data set (Sentinel-1 and Sentinel-2) for verifying simulated estimates from a Soil and Water Assessment Tool (SWAT) model of the Upper Mississippi River Basin (UMRB; ~440,000 km). Our results suggest that static DEM-based parameterization as well as model calibration based solely on discharge do not adequately capture spatial and temporal dynamics in the UMRB. Mean as estimated by SWAT was 74% ± 122% (mean ± standard deviation) higher than RS , where SW storage in SWAT was underestimated in wetland-rich subbasins and overestimated in agricultural, tile-drained subbasins. Time series of SWAT and RS were positively correlated in only 38.8% of subbasins. As RS is also vulnerable to error, storage estimates were compared to bathymetric data in select small wetlands. While uncertainty remains in the conversion from extent to storage for RS , the method and data set presented here are a promising option for improved parameterization and calibration of processes in SWAT and other process-based hydrologic models. Further consideration of these storage processes can potentially improve the accuracy of simulated streamflow in wetland-rich model domains.
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2. Evaluating the Impacts of Agriculture Conservation on Water Quantity and Quality Through Trend, Predictability, and Causality Analysis.
2. 通过趋势、可预测性和因果关系分析评估农业保护对水量和水质的影响PMID:日期:2026-04We investigated the potential of nature-based solutions within the context of agriculture conservation practices (ACPs) that impact watershed hydrology and water quality in Shell Creek, Nebraska, USA. This region serves as an example of a successful watershed conversion driven by the collaboration of local stakeholders and government agencies, which also offers valuable solutions for addressing future climate challenges. We utilized a high-resolution remote sensing data covering a wide range of attributes, including continuous land cover, winter vegetation health, and summer cropping, and supplemented it with hydrometeorological and water quantity and quality data. The implementation patterns of ACPs and changes in water quantity and quality were studied using trend analysis. The data were then analyzed using explainable Artificial Intelligence and causal inference techniques to understand how ACPs lead to changes in water quantity and quality attributes. In addition, we carried out flood frequency analysis to assess the impact of ACPs on extreme flows. The findings suggested that winter cover cropping has extensively increased over the past 30 years. The health of winter vegetation also significantly improved, although there was an intensification in summer cropping. In the context of water quantity, extreme flood events have become less frequent. Results also showed a noticeable decrease in turbidity, total suspended solids concentration, and total phosphorus concentration. However, these changes were not statistically significant. The overall findings suggest that different nature-based solutions have increased in recent decades and have contributed to improvements in water quality and reductions in flood frequency in the watershed.
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3. Deep Learning Prediction and Interpretation of Riverine Nitrate Export Across the Mississippi River Basin.
PMID:日期:2025-08-09Excess riverine nitrate causes downstream eutrophication, notably in the Gulf of Mexico where hypoxia is linked to nutrient-rich discharge from the Mississippi River Basin (MRB). We developed a long short-term memory (LSTM) model using high-frequency sensor data from across the conterminous US to predict daily nitrate concentrations, achieving strong temporal validation performance (median KGE = 0.60). Spatial validation-or prediction in unmonitored basins-yielded lower performance for nitrate concentration (median KGE = 0.18). Nonetheless, spatial validation was crucial in quantifying the impact of current data gaps and guiding the model's targeted application to the MRB where spatial validation performance was stronger (median KGE = 0.34). Modeling results for the MRB from 1980 to 2022 showed relatively low riverine nitrate export (19 ± 4% of surplus), indicating large-scale retention of surplus nitrate within the MRB. Interannual nitrate yields varied significantly, especially in Midwestern states like Iowa, where wet-year export fractions (42 ± 24%) far exceeded dry year export (6 ± 6%), suggesting increased hydrologic connectivity and remobilization of legacy nitrogen. Further evidence of legacy nitrate remobilization was noted in a subset of Midwestern basins where, on occasion, annual surplus export fractions exceeded 100%. Interpretable Shapley values identified key spatial drivers influencing mean nitrate concentrations-tile drainage, roadway density, wetland cover-and quantitative, non-linear thresholds in their influence, offering management targets. This study leverages machine learning and aquatic sensing to provide improved spatiotemporal predictions and insights into nitrate drivers, thresholds, and legacy impacts, offering valuable information for targeted nutrient management strategies in the MRB.
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4. Dilute Species Transport During Generalized Newtonian Fluid Flow in Porous Medium Systems.
4. 广义牛顿流体在多孔介质系统中流动时的稀物质传输PMID:日期:2025-01-04Dilute species transport in generalized Newtonian fluids (GNFs) is typically described using explanatory empirical approaches assuming a traditional Fickian form, which is an approach that lacks predictive ability for systems and conditions not specifically investigated. Dilute species transport was investigated for a wide range of Cross and Carreau fluids flowing through a set of monodisperse and polydisperse sphere pack porous media. Both microscale and macroscale simulations were performed to demonstrate that GNF fluid flow can be predicted based upon Newtonian characterization of the media and rheological characterization of the fluid. Dilute species transport was shown to have a Fickian limit with dispersivity dependent on the porous media, fluid properties, and the flow rate in a nonlinear fashion. Dimensionless analysis and symbolic regression was used to deduce an explanatory and predictive function to describe dispersivity in terms of relevant system properties, enabling prediction of dilute species transport for GNFs flowing through porous media that does not require any non-Newtonian experiments or parameter estimation.
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5. Combining a Multi-Lake Model Ensemble and a Multi-Domain CORDEX Climate Data Ensemble for Assessing Climate Change Impacts on Lake Sevan.
5. 结合多湖泊模型集合与多区域CORDEX气候数据集合评估气候变化对塞万湖的影响PMID:日期:2024-11-22Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi-scenario, and multi GCM- RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature and substantial bottom warming , longer stratification periods (+55 days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems.
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6. Distribution and Characteristics of Blackwater Rivers and Streams of the Contiguous United States.
6. 美国本土黑水河流与溪流的分布及特征PMID:日期:2024-02-16Blackwater rivers and streams have stained or tea-colored water from tannins released by decaying plant matter. Natural conditions in these waters often differ from non-blackwater systems. For example, the pH and oxygen levels in waters can be very low, but completely natural. We examined an existing USEPA data set and found that blackwaters existed across the contiguous United States but were most common in the east. Water chemistry showed differences between blackwater and non-blackwater sites, but differences were not consistent across ecoregions making national scale generalizations difficult. Physical habitat data analysis did not show dramatic differences between blackwater and non-blackwater sites. Blackwater typically arises from streams that drain tannin-rich bogs/muskeg and wetlands, so as expected blackwater sites had a shorter Euclidean distance to wetlands than non-blackwater sites and existed in watersheds with a higher percentage of wetland habitat. Blackwaters in Northern and Temperate Plains tended to have higher acid neutralizing capacity, conductivity, and lower True Color; a visual color scale used for water purity. We posit that differences were because Color and Dissolved Organic Carbon at these sites were from buried wetland deposits rather than contemporary wetland habitats. Research needs that may increase our understanding and management of blackwaters include development of an operational definition that includes a classification framework and reference conditions for different blackwater types, identification of stressors and their associated dynamics that negatively impact blackwater systems, and development of data-driven, consistent, and repeatable assessment methods, including development of targets, protective of unique conditions in blackwater rivers and streams.
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7. Discharge, Groundwater Gradients, and Streambed Micro-Topography Control the Temporal Dynamics of Transient Storage in a Headwater Reach.
7. 排放、地下水位梯度与河床微地形对源头河段瞬态储存时间动态的控制作用PMID:日期:2023-07-10Contradictory interpretations of transient storage modeling (TSM) results of past studies hamper the understanding of how hydrologic conditions control solute transport in streams. To address this issue, we conduct 30 instantaneous tracer experiments in the Weierbach stream, Luxembourg. Using an iterative modeling approach, we calibrate TSM parameters and assess their identifiability across various hydrologic conditions. Near-stream groundwater monitoring wells and LIDAR scans of the streambed are used to evaluate the area of the hyporheic zone and of the submerged sediments for each experiment. Our findings show that increasing discharge enhances parameters interaction requiring more samples of TSM parameters to obtain identifiable results. Our results also indicate that transient storage at the study site is influenced by in-stream and hyporheic exchange processes during low discharge, likely due to the hyporheic zone's large extent and the relatively low water level compared to the size of slate fragments on the streambed. However, as discharge increases, in-stream storage zones become part of the advective channel and the lower localized stream water losses to the adjacent groundwater suggests a decrease of the hyporheic exchange on transient storage. The results obtained were utilized to generate a hydrograph for the study site illustrating the dynamic evolution of in-stream and hyporheic storage with varying discharge, providing insights into the expected influence of different transient storage processes prior to tracer experiments. Overall, our study enhances the understanding of the role of the hyporheic area and in-stream storage zones in transient storage and helps estimate TSM parameters more accurately.
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8. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance.
PMID:日期:2023-06-05Building accurate rainfall-runoff models is an integral part of hydrological science and practice. The variety of modeling goals and applications have led to a large suite of evaluation metrics for these models. Yet, hydrologists still put considerable trust into visual judgment, although it is unclear whether such judgment agrees or disagrees with existing quantitative metrics. In this study, we tasked 622 experts to compare and judge more than 14,000 pairs of hydrographs from 13 different models. Our results show that expert opinion broadly agrees with quantitative metrics and results in a clear preference for a Machine Learning model over traditional hydrological models. The expert opinions are, however, subject to significant amounts of inconsistency. Nevertheless, where experts agree, we can predict their opinion purely from quantitative metrics, which indicates that the metrics sufficiently encode human preferences in a small set of numbers. While there remains room for improvement of quantitative metrics, we suggest that the hydrologic community should reinforce their benchmarking efforts and put more trust in these metrics.
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9. Six Decades of Hindsight Into Yesa Reservoir (Central Spanish Pyrenees): River Flow Dwindles as Vegetation Cover Increases and Mediterranean Atmospheric Dynamics Take Control.
9. 对Yesa水库（西班牙中部比利牛斯山脉）的60年后见：随着植被覆盖率的增加和地中海大气动力学的控制，河流流量减少PMID:日期:2023-01-10River discharge has experienced diverse changes in the last decades due to modification of hydrological patterns, anthropogenic intervention, re-vegetation or annual and interannual climatic and atmospheric fluctuations. Assessing the recent changes in river discharge and understanding the main drivers of these changes is thus extremely important from theoretical and applied points of view. More specifically, here we want to draw attention toward the impacts of streamflow changes on reservoir storage and operation. We describe the hydrological dynamics of the Yesa reservoir draining catchment, located in the central Spanish Pyrenees, and characterize the reservoir operation modes over the last 60 years (1956-2020). We analyze concurrent climatic (precipitation, air temperature, drought index), atmospheric mechanisms, land cover (Normalized Different Vegetation Index) and discharge (inlet and outlet of Yesa reservoir) time-series. By using the wavelet transform methodology, we detect historical breakpoints in the hydrological dynamics at different time-scales. Distinctive periods are thus identified. More regular seasonal flows characterized the catchment's dynamics during the first decades of the study period, while the last decades were characterized by a high inter-annual variability. These changes are primarily attributed to the natural re-vegetation process that the catchment experienced. Furthermore, we related changes in atmospheric circulation with a decline of the long-term discharge temporal features. This research contributes to the understanding of long-term river discharge changes and helps to improve the reservoir management practices.
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10. Automatic Regionalization of Model Parameters for Hydrological Models.
PMID:日期:2022-12-27Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters "saturated hydraulic conductivity" and "field capacity," which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step toward automatic TF estimation of model parameters for distributed models.