Journal of Hydrology最新文献

{"title":"A physically-informed long short-term memory-based tool for predicting extensive droughts in the distant future","authors":"Ali Ghaffari ,&nbsp;Shrouq Abuismail ,&nbsp;Y.C. Ethan Yang ,&nbsp;Maryam Rahnemoonfar","doi":"10.1016/j.jhydrol.2025.133316","DOIUrl":"10.1016/j.jhydrol.2025.133316","url":null,"abstract":"<div><div>Agricultural drought is a specific type of drought that impacts agricultural activities and crop yield by lower precipitation and shortages in soil water content. Developing a drought prediction tool is crucial as it can aid farmers and authorities in devising mitigation strategies like crop rotation and deficit irrigation. We developed a long-term, large-scale drought prediction tool solely based on remote-sensing data where drought intensity was measured by an enhanced combined drought index (ECDI) that utilized a weighted summation of four climatic variables: precipitation, temperature, Normalized Differenced Vegetation Index, and soil moisture. The State of Texas in the US is selected as our case study area. We trained a Long-Short Term Memory network with past 21 years of training data to predict the four climatic variables and calculated ECDI for the next 12 months. For model evaluation, we compared results of predicted droughts from ECDI to actual drought events based on SPI-3 (Standardized Precipitation Index with a three-month time scale). Results showed that ECDI and SPI exhibit similar spatial distribution of droughts but with different intensities. We also compared ECDI/SPI values to US Drought Monitor (USDM) maps which show experts’ assessments of conditions related to dryness and drought. ECDI results were similar to USDM in case of drought extent but yielded different intensities. Results of this study showed that remote sensing data can be successfully used to predict future agricultural droughts for a longer period (12 months) and for a large-scale area to assist farmers and policymakers with designing mitigation measures.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133316"},"PeriodicalIF":5.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844930","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 differentiable hydrologic modeling with interpretable forcing fusion","authors":"Kamlesh Sawadekar ,&nbsp;Yalan Song ,&nbsp;Ming Pan ,&nbsp;Hylke Beck ,&nbsp;Rachel McCrary ,&nbsp;Paul Ullrich ,&nbsp;Kathryn Lawson ,&nbsp;Chaopeng Shen","doi":"10.1016/j.jhydrol.2025.133320","DOIUrl":"10.1016/j.jhydrol.2025.133320","url":null,"abstract":"<div><div>Atmospheric forcings for hydrologic models often contain significant errors, but traditional modifications only employ bias correction or distributional transformations based on rainfall measurements. Deep learning could fuse multiple datasets for improved hydrologic modeling, but is difficult to interpret. Here we introduce a “differentiable” data fusion framework where a neural network is pre-trained to provide parameters a process-based hydrologic model while a second network is trained to weigh multiple forcings (Daymet, NLDAS, and Maurer) for a fused precipitation input to the combined model. The fused precipitation data greatly improved streamflow simulation performance (both low flow and high flow, but especially high flow). Applying adaptive weights to a single forcing did not yield improvements. Overall, the fusion placed a higher weight on Daymet, and slightly lower weights on NLDAS and Maurer. NLDAS’s weights increased in the humid eastern US while Maurer’s increased in mountainous regions. The fused precipitation had similar means and large-magnitude event performance to Daymet. However, it exhibited higher correlation with station-based precipitation than any individual forcing or their simple average, and had close to the smallest bias for large storms. Pre-training the parameterization network based on the best-performing single forcing (Daymet) yielded better results than those based on the average of forcings. Overall, the differentiable hydrologic model offers a generic hydrology-informed fusion method to improve streamflow prediction.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133320"},"PeriodicalIF":5.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851885","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":"Exacerbating hydrological extremes in China’s large reservoir drainage areas","authors":"Xinyu Li ,&nbsp;Kaiwen Wang ,&nbsp;Changming Liu ,&nbsp;Gang Zhao ,&nbsp;Zhouyuqian Jiang ,&nbsp;Qiuyu Luo ,&nbsp;Guan Wang ,&nbsp;Dan Zhang ,&nbsp;Jiamiao Yu ,&nbsp;Xiaomang Liu","doi":"10.1016/j.jhydrol.2025.133297","DOIUrl":"10.1016/j.jhydrol.2025.133297","url":null,"abstract":"<div><div>Reservoirs are vital infrastructure for mitigating hydrological extremes, providing water during droughts, and reducing risks associated with floods. Under a warming climate, increasing hydrological extremes in upstream catchments threaten water supply sustainability and dam security. However, the evolution and drivers of these extremes are still poorly understood due to limited precise drainage boundary data. To address this gap, we combine a delineation algorithm with manual adjustments according to recorded drainage areas, creating the most comprehensive publicly available inventory of 907 large Chinese reservoirs, each with a storage capacity exceeding 0.1 km³. By integrating delineated boundaries with an observation-based China natural runoff dataset, we find nearly 40 % of reservoirs face more intense and frequent droughts, jeopardizing their role in supporting regional water transfer projects. Additionally, nearly 60 % experience worsening pluvial conditions, putting reservoirs in the northwest, northeast, and lower Yangtze regions under flood control and coordination pressures. These intensifying hydrological extremes strongly correlate with climate variability modes, while their variations are further influenced by climate change, widespread greening, and other external factors. Given reservoirs’ essential role in human water use, this study highlights the urgent need to understand the effects of climate and landscape changes to advance sustainable water resource management and safeguard water security.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133297"},"PeriodicalIF":5.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854419","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":"Time-lapse ensemble-based electrical resistivity tomography to monitor water flow from managed aquifer recharge operations","authors":"Mauricio Arboleda-Zapata ,&nbsp;Gordon Osterman ,&nbsp;Xinyan Li ,&nbsp;Salini Sasidharan ,&nbsp;Helen E. Dahlke ,&nbsp;Scott A. Bradford","doi":"10.1016/j.jhydrol.2025.133282","DOIUrl":"10.1016/j.jhydrol.2025.133282","url":null,"abstract":"<div><div>Various managed aquifer recharge strategies, such as drywells, are being used in the California Central Valley (CCV) to replenish groundwater resources that have been depleted by over-pumping, especially during droughts. Drywell technology allows recharge water to bypass shallow impermeable layers and possible contaminated soils near the land surface. Understanding water flow in the vadose zone is crucial for assessing the performance of drywells regarding the amount of water that reaches the groundwater table and the fate of solutes. In this study, we demonstrate the applicability of time-lapse electrical resistivity tomography (TL-ERT) for imaging the water flow and subsequent aquifer recharge at a drywell site in the CCV with a thick (67–72 m) vadose zone. Additionally, TL-ERT results were compared to point-scale observations from a collocated monitoring well. To invert our TL-ERT data sets, geostatistical constraints were applied to favor layered models as expected due to the alluvial deposits in the study area. By considering different correlation lengths, an ensemble of resistivity model solutions was generated per time-step instead of a single model solution (as typically performed). Model differences between the mean model of the baseline data set and the models from the subsequent time steps allowed us to image the wetting front development until reaching the regional aquifer, a perched water table, and flush of salts that were otherwise not visible or blurred when using single model solutions from standard deterministic TL-ERT inversion approaches.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133282"},"PeriodicalIF":5.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844931","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":"Estimating rainfall intensity from surveillance audio: A hybrid model-data-driven framework","authors":"Xing Wang ,&nbsp;Kun Zhao ,&nbsp;Haiqin Chen ,&nbsp;Ang Zhou ,&nbsp;Jiuwei Zhao ,&nbsp;Shuaiyi Shi ,&nbsp;Thomas Glade","doi":"10.1016/j.jhydrol.2025.133295","DOIUrl":"10.1016/j.jhydrol.2025.133295","url":null,"abstract":"<div><div>Rainfall produces one of the most recognizable and variable sounds in nature. Audio data collected by widespread surveillance cameras provide a continuous record of rainfall events, which offers a potential opportunity for high spatiotemporal resolution rainfall estimation. However, surveillance audio (SA)<span><span>¹</span></span> often contains complicated environmental noise that challenges the characterisation of rainfall and makes it difficult to obtain rainfall information from SA data. This study proposes a hybrid model-data-driven framework for the numerical estimation of rain intensity based on SA. The framework is implemented in two steps: 1) a convolutional neural network (CNN) and long short-term memory (LSTM) were used to learn the frequency and temporal characteristics of rain sound, respectively, and a novel parallel neural network (PNN) was constructed to determine rain categories (e.g., light, moderate, and heavy) or the categories of rain intensities, which enabled a coarse-grained rain intensity estimation. 2) Subsequently, the Root-Mean-Square Energy (RMS-Energy) of the audio clip was employed as the indicator, and a fine-grained rainfall intensity numerical calculation model based on SA data was built. Experimental results reveal that the PNN achieves optimal performance compared to some existing relevant models, indicating that the proposed PNN can effectively determine the rain category from urban SA data. Moreover, observation from real-world surveillance scenarios demonstrates that our method achieves an average relative error of 8.01%–25.68% in the cumulative rainfall estimation. This research sheds light on building a new low-cost and high-resolution rainfall observation network based on the existing surveillance camera recourses and providing valuable support to the current rainfall observation networks.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133295"},"PeriodicalIF":5.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850423","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":"Surface-temperature silica springs of the eastern Great Artesian Basin – Hydrogeology and hydrochemistry","authors":"J.A. Webb ,&nbsp;R.J. Fensham ,&nbsp;B. Laffineur","doi":"10.1016/j.jhydrol.2025.133311","DOIUrl":"10.1016/j.jhydrol.2025.133311","url":null,"abstract":"<div><div>An unusual north–south line of ∼ 50 springs along the eastern margin of the Eromanga Basin, northeastern Australia, discharge from the outcrop margin of the Hutton Sandstone aquifer, which is folded so the springs are fed by easterly groundwater flow, in contrast to the dominant westwards flow within the Eromanga Basin. This means that the effective recharge area of the Hutton Sandstone in this region is less than previously estimated. The springs occur as pools which represent water-table windows, with groundwater ‘streams’ flowing from one side to the other along subhorizontal joints within a near-surface silcrete layer developed on the Hutton Sandstone. The springs are recharged through fractures in the silcrete, feeding laminar groundwater flow through the Hutton Sandstone until its outcrop terminates. At this point flow transfers into the overlying silcrete as concentrated pathways probably localised along broad, shallow troughs in the silcrete beneath surface drainage lines. The springs are surrounded by white siliceous precipitates with a groundmass of intergrown amorphous silica and kaolinite; this may have been allophane originally. Most silica springs are geothermal, yet the eastern Alice Tableland springs have surface temperatures. The elevated dissolved Si levels in these springs are due to dissolution of relatively soluble silica microcrystallites within the silcrete through which the spring water flows. The lack of calcite precipitation from the springs reflects the low Ca concentrations of the groundwater, probably due to strong Ca uptake by plants.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133311"},"PeriodicalIF":5.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837847","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":"A review of machine learning, remote sensing, and statistical methods for reservoir water quality assessment","authors":"Mohammad Reza Nikoo ,&nbsp;Abrar Al Aamri ,&nbsp;Talal Etri ,&nbsp;Ghazi Al-Rawas","doi":"10.1016/j.jhydrol.2025.133323","DOIUrl":"10.1016/j.jhydrol.2025.133323","url":null,"abstract":"<div><div>Water reservoirs perform a number of essential functions, including water supply, flood control, hydropower generation, and agricultural and industrial support. In order to meet specific standards, the reservoir water quality needs to be protected. Because of human activities, including industrial discharges and agricultural runoff, reservoir’s water quality deteriorates. Deforestation and erosion in the upstream region exacerbate the problem, disrupting the ecology. A comprehensive management practice is necessary to maintain reservoir water quality in addition to changes in flow patterns, temperature changes, and nutrient enrichment. A number of methods have been employed, including Remote Sensing (RS) for spatial monitoring of environmental change, Machine Learning (ML) for estimation/predicting water quality, and Multivariate Statistical Analysis (MSA) that can identify relationships among water quality variables and patterns. By examining the strengths of these methods, it is possible to maximize the effectiveness of reservoir management. For instance, by understanding each method, it is possible to identify the optimal combination of techniques to achieve the best results. Furthermore, it addresses a wide range of challenges related to assessing water quality and ecosystem health. The use of one or more of these approaches will depend on the objectives, data characteristics, and resources available. Additionally, it can be used to identify and mitigate the risks associated with reservoir management. The articles in this review paper were limited to those published between 2000 and 2023, with a reasonable geographical distribution based on our literature search in the SCOPUS database.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133323"},"PeriodicalIF":5.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834618","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":"A crop-specific dynamic irrigation scheme in a regional land surface-hydrologic modeling framework for improving human water-use estimation and irrigation impact assessment","authors":"Qianya Yang ,&nbsp;Jianhui Wei ,&nbsp;Chuanguo Yang ,&nbsp;Huanghe Gu ,&nbsp;Jianyong Ma ,&nbsp;Ningpeng Dong ,&nbsp;Joël Arnault ,&nbsp;Patrick Laux ,&nbsp;Benjamin Fersch ,&nbsp;Shasha Shang ,&nbsp;Zhongbo Yu ,&nbsp;Harald Kunstmann","doi":"10.1016/j.jhydrol.2025.133322","DOIUrl":"10.1016/j.jhydrol.2025.133322","url":null,"abstract":"<div><div>Irrigation has a notable impact on the natural environment by changing the water and energy balance at the land surface and thereby altering atmospheric processes. Assessing these impacts and estimating irrigation water demand often involves using process-based models that incorporate the representation of irrigation practices. However, current irrigation schemes are primarily tailored to arid and semi-arid regions, and there is a research gap for humid multi-cropping rice regions. In response, this study introduces a Crop-specific Dynamic Irrigation (CDI) scheme, seamlessly integrated into the land surface-hydrologic model NOAH-HMS. This development enables the differentiation of irrigation practices for rice and non-rice crops, facilitating more accurate estimates of water demand for irrigation. The newly developed model is applied to an important cropping region in southern China, the Poyang Lake Basin (PLB), where the rice cultivation area accounts for over 60% of all crop cultivation. Compared to the widely used traditional Dynamic Irrigation (DI) scheme, integrating CDI into NOAH-HMS improves the model performance in simulating irrigation water amount over the PLB, with a mean relative error between 2007–2015 reduced by 39%, and a correlation coefficient increased by +0.26. The identified impacts on the surface water and energy balance are more pronounced at local scale, especially over the intensively irrigated areas. The performed interannual variability analysis demonstrates that our irrigation scheme CDI developed in this study allows to estimate irrigation water use under different drought conditions and has the applicability of mitigating risks of crop failures due to for example compound dry and hot. We conclude that our Crop-specific Dynamic Irrigation scheme is highly advantageous for multi-cropping rice regions and holds the potential for expansion into the fully coupled atmospheric-hydrologic systems with a more comprehensive representation of human activities.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133322"},"PeriodicalIF":5.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851740","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":"Multi-scale dynamic spatiotemporal graph attention network for forecasting karst spring discharge","authors":"Renjie Zhou","doi":"10.1016/j.jhydrol.2025.133289","DOIUrl":"10.1016/j.jhydrol.2025.133289","url":null,"abstract":"<div><div>Karst aquifers are important groundwater resources that supply drinking water for approximately 25 % of the world’s population. Their complex hydrogeological structures, dual-flow regimes, and highly heterogeneous flow pose significant challenges for accurate hydrodynamic modeling and sustainable management. Traditional modeling approaches often struggle to capture the intricate spatial dependencies and multi-scale temporal patterns inherent in karst systems, particularly the interactions between rapid conduit flow and slower matrix flow. This study proposes a novel multi-scale dynamic graph attention network integrated with long short-term memory model (GAT-LSTM) to innovatively learn and integrate spatial and temporal dependencies in karst systems for forecasting spring discharge. The model introduces several innovative components: (1) graph-based neural networks with dynamic edge-weighting mechanism are proposed to learn and update spatial dependencies based on both geographic distances and learned hydrological relationships, (2) a multi-head attention mechanism is adopted to capture different aspects of spatial relationships simultaneously, and (3) a hierarchical temporal architecture is incorporated to process hydrological temporal patterns at both monthly and seasonal scales with an adaptive fusion mechanism for final results. These features enable the proposed model to effectively account for the dual-flow dynamics in karst systems, where rapid conduit flow and slower matrix flow coexist. The newly proposed model is applied to the Barton Springs of the Edwards Aquifer in Texas. The results demonstrate that it can obtain more accurate and robust prediction performance across various time steps compared to traditional temporal and spatial deep learning approaches. Based on the multi-scale GAT-LSTM model, a comprehensive ablation analysis and permutation feature important are conducted to analyze the relative contribution of various input variables on the final prediction. These findings highlight the intricate nature of karst systems and demonstrate that effective spring discharge prediction requires comprehensive monitoring networks encompassing both primary recharge contributors and supplementary hydrological features that may serve as valuable indicators of system-wide conditions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133289"},"PeriodicalIF":5.9,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837846","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":"Enhancement of the SWAT+ model for simulating paddy rice cultivation and irrigation management in agricultural watersheds","authors":"Jaehak Jeong ,&nbsp;Jeffery Arnold ,&nbsp;Seonggyu Park ,&nbsp;Ricardo Sorando ,&nbsp;Soon-Kun Choi ,&nbsp;Min-Kyeong Kim","doi":"10.1016/j.jhydrol.2025.133288","DOIUrl":"10.1016/j.jhydrol.2025.133288","url":null,"abstract":"<div><div>Paddy cultivation accounts for over two-thirds of global rice production and 21 % of agricultural irrigation. While SWAT+ shows potential for simulating hydrology in paddy-dominant watersheds, improvements are needed. This study enhances SWAT+ by introducing a process module to simulate paddy hydrology and irrigation management. Unlike conventional hydrologic settings for Hydrologic Response Units (HRUs), the paddy module developed in SWAT+ introduces hydrologic mass balance for standing water in paddy HRUs. To implement paddy management, including transplanting, puddling, paddy irrigation, and fertilizer application, new paddy-specific conditions and actions are incorporated into SWAT+ decision tables. Case studies in South Korea and Spain demonstrate significant improvements in streamflow prediction and irrigation volume estimation. The Potential EvapoTranspiration COefficient (PETCO) was the most sensitive parameter in both watersheds, while the PERcolation Coefficient (PERCO) was more influential in non-paddy areas with high percolation rates. The study highlights distinct water balance differences, with paddy fields exhibiting higher evapotranspiration (&gt;75 %) and surface runoff (&gt;175 %) than other land uses. Compared to the curve number method, the paddy module improved streamflow simulation, achieving NSE values of 0.7–0.84 and PBIAS within ±10 %, particularly capturing high flows during the growing season. These enhancements strengthen SWAT+’s applicability for paddy-dominant watersheds, offering valuable insights for agricultural hydrology research.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"659 ","pages":"Article 133288"},"PeriodicalIF":5.9,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844926","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}