Journal of Hydrology最新文献

{"title":"The study of the method for the Φ values across the entire regional range of α in the frequency calculation of the P-Ⅲ distribution","authors":"Song Qifeng ,&nbsp;Chen Xi ,&nbsp;Zhang Zhicai","doi":"10.1016/j.jhydrol.2025.133360","DOIUrl":"10.1016/j.jhydrol.2025.133360","url":null,"abstract":"<div><div>The frequency computation of the P-Ⅲ distribution plays a pivotal role in various applications such as rainfall estimation, flood forecasting, and ecological flow analysis across various countries. The determination of Φ values stands as the crux of this computation. Traditionally, Φ values are derived through interpolation from a table, which introduces computational inaccuracies due to the complexity involved. To address this, an improved high-precision numerical integration algorithm has been developed. This enhanced method segments the integration interval into three distinct parts. It employs a series expansion, substitution, and continued fraction techniques to perform numerical integration across these segments, thereby obtaining Φ values for α values in the range just above zero to 100. It resolves the issue of slow convergence at the critical point x = α + 1. For α values between 100 and 1600, the distribution function is meticulously constructed, and Φ values are calculated using both the improved high-precision numerical integration algorithm and the constructed distribution function. It tackles the data overflow issue when α is greater than 100.When α exceeds 1600, an approximate function method is utilized to determine Φ values. By employing this comprehensive approach, a full-range calculation method for Φ values of the P-Ⅲ distribution is established. The accuracy of this method is validated by comparing its results with established truth values, revealing that the method’s calculations match the truth values to three decimal places. The method not only boasts high accuracy but also exhibits swift computational speed, making it a viable alternative to traditional Φ value tables and improving the accuracy of the calculation of the P-III distribution frequency curve in the field of hydrology.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133360"},"PeriodicalIF":5.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115223","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":"Corrigendum to “Soil moisture deficits triggered by increasing compound drought and heat events during the growing season in Arid Central Asia” [J. Hydrol. 660 (2025) 133397]","authors":"Qian Wang ,&nbsp;Changchun Xu ,&nbsp;Juncheng Long ,&nbsp;Qiyue Zhang ,&nbsp;Yu Luo ,&nbsp;Lin Li","doi":"10.1016/j.jhydrol.2025.133525","DOIUrl":"10.1016/j.jhydrol.2025.133525","url":null,"abstract":"","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"658 ","pages":"Article 133525"},"PeriodicalIF":5.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166760","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":"Synergistic estimates of global 4-day 500 m gross primary production, evapotranspiration, and ecosystem water use efficiency from satellite data","authors":"Yifei Sun ,&nbsp;Ronglin Tang ,&nbsp;Lingxiao Huang ,&nbsp;Meng Liu ,&nbsp;Yazhen Jiang ,&nbsp;Zhao-Liang Li","doi":"10.1016/j.jhydrol.2025.133506","DOIUrl":"10.1016/j.jhydrol.2025.133506","url":null,"abstract":"<div><div>Gross primary production (GPP) and evapotranspiration (ET) are essential components of global carbon and water cycles, respectively, while the ratio of GPP to ET, also known as ecosystem water use efficiency (WUE), reflects the trade-off between carbon gain and water loss in terrestrial ecosystems. Simultaneous estimates of GPP, ET, and WUE from satellite data with high accuracies are highly challenging due to negligence or inadequate representation of co-variation of GPP and ET in current models. This study develops a novel and practical model for Synergistic estimates of global 4-day 500 m gross primary Production, Evapotranspiration, and ecosystem water use Efficiency (SynPEE), by combining the multivariable convolutional neural network (MCNN) and a synthesis of in-situ observations at 314 globally distributed sites, satellite remote sensing datasets, and ERA5-land reanalysis datasets from 2000 to 2020. The newly proposed SynPEE model is prominently superior in (1) explicitly considering the synergistic relationship among the GPP, ET, and WUE; (2) achieving high-accuracy estimations of GPP, ET, and WUE simultaneously; and (3) avoiding the outliers of WUE estimates that are commonly found in the un-synergistic models. Validated against in-situ observations by a spatial 10-fold cross-validation scheme, the SynPEE model was proven to overall outperform the un-synergistic models (CNN_IN) constructed for separate estimates of GPP, ET and WUE. Moreover, the SynPEE model also showed much better performances than four state-of-the-art RS products, i.e., BESSv2, PMLv2, FLUXCOM, and MODIS. Furthermore, the spatio-temporal patterns of the 8-day and yearly GPP, ET and WUE estimates by the SynPEE model were generally consistent with those of the four state-of-the-art products. The SynPEE model has great potential of generating time-series products of high-accuracy global GPP, ET and WUE, which is promising to enhance our understanding of land–atmosphere interactions of carbon and water, thus better serving for terrestrial carbon and water management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133506"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084687","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":"Explainable transfer learning for subsurface soil moisture prediction","authors":"Sheng Ye ,&nbsp;Yifan Chai ,&nbsp;Jiyu Li ,&nbsp;Jingkai Wang ,&nbsp;Xuebin Deng ,&nbsp;Qihua Ran","doi":"10.1016/j.jhydrol.2025.133473","DOIUrl":"10.1016/j.jhydrol.2025.133473","url":null,"abstract":"<div><div>Soil moisture is a crucial component of hydrologic processes, influencing runoff generation, land–atmosphere interactions, and water resource management. Deep learning models have been implemented to predict soil moisture with existing large datasets. Transfer learning allows these models to be pre-trained in data-rich regions and applied to data-sparse areas to reduce reliance on local data. However, the mechanisms of how the prior knowledge is transferred and adapted to new regions remain unclear. In this study, a long short-term memory (LSTM) network was trained to predict subsurface soil moisture from surface data in the Yellow River Basin (YRB), and then transferred to an in-situ measurement site at a humid headwater catchment, Jianpinggou (JPG). Despite the significant differences, the transferred model outperformed the model trained with local data only, and showed further improvement after fine-tuning with a small number of in-situ measurements. Explainable artificial intelligence method, the SHapley Additive exPlanations (SHAP) was used to interpret the models developed during the transfer learning process. The SHAP values showed that, as the model transferred from arid to humid region, the influence of evapotranspiration-related factors decreased significantly, the impacts of precipitation also stopped rising with amount, while the impact of surface soil moisture increased. It suggests that the dominant mechanism has changed from infiltration and evaporation due to climate forcing in arid/semi-arid area to fluxes within soil matrix in humid spot. Our results demonstrate that interpretating models during the transfer learning process uncovers the evolution in model rationale, enhancing the validation of model’s physical reliability, revealing the shift in underlying mechanisms across climates and spatial scales, which could also provide more detailed correlations for causal analysis in future work.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133473"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115224","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 novel assessment framework for analyzing current implementation levels of sewerage and stormwater management components","authors":"Selami Kiliç ,&nbsp;Mahmut Firat ,&nbsp;Mahmud Güngör","doi":"10.1016/j.jhydrol.2025.133478","DOIUrl":"10.1016/j.jhydrol.2025.133478","url":null,"abstract":"<div><div>Sewerage and stormwater systems are quite important in terms of conveying wastewater to treatment plants, reducing the risk of flooding in heavy rains, and human and environmental health in the urban water cycle. The data should be measured accurately and regularly and appropriate methods should be applied for sewerage and stormwater management (SSM). In this study, a novel assessment framework and target definition model is proposed to evaluate the quality of the data and the current implementation level of the practices in SSM. The proposed system includes a total of 54 components covering SSM practices. A unique scoring system is proposed that allows the components to be scored between 0 and 5. The current status of each component is classified gradually (inadequate, poor, insufficient, moderate, good and excellent) with this scoring system. The developed system is tested in three utilities in Turkey. Utility I is better than other utilities in terms of data quality and implementation level of practices. Moreover, utility II scores are better than utility III scores. This framework provides a comprehensive and holistic assessment of the SSM. It also enables the identification of weaknesses and strengths, the creation of a roadmap for effective and sustainable SSM and monitoring the efficiency of the applied methods.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133478"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098697","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 spatial–temporal variability of methane emissions in a montane headstream: implication of precipitation, morphology and microhabitat","authors":"Honglin Chen ,&nbsp;Xiaofeng Wang ,&nbsp;Jilong Wang ,&nbsp;Xianxiang Li ,&nbsp;Chen Jian ,&nbsp;Dongfeng Li ,&nbsp;Yuewei Zhang ,&nbsp;Yixin He","doi":"10.1016/j.jhydrol.2025.133534","DOIUrl":"10.1016/j.jhydrol.2025.133534","url":null,"abstract":"<div><div>Headstreams are significant sources of atmospheric methane (CH₄). However, the high spatial–temporal variability presents significant challenges for inventory estimation of CH₄ emissions from headstreams. Particularly in mountainous headstreams with high heterogeneity of morphology and microhabitats, the key factors controlling CH₄ emissions remain unclear. This study conducted monthly surveys of dissolved CH₄ concentration (dCH₄) and CH₄ flux (fCH₄) in a montane headstream basin in southwestern China at high spatial resolution. It focused on the synthetic effects of morphology, microhabitat, and nutrients on the spatial–temporal variability in CH₄ fluxes. The overall mean dCH₄ and fCH₄ in the selected headstream were 79.4 ± 80.4 nmol L^-1 and 1.24 ± 0.98 mmol m⁻² d^-1, respectively, indicating it acts as a moderate source of CH₄ emission. The dCH₄ and fCH₄ displayed opposite temporal patterns: high concentrations but low fluxes during the dry season, and low concentrations but high fluxes during the wet season. These temporal patterns are primarily dominated by enhanced turbulent degassing driven by seasonal precipitation. From a watershed perspective, both dCH₄ and fCH₄ increased progressively from upstream to downstream. Total carbon and organic carbon in waters can explain over 60 % of the spatial variation in fCH₄ either in dry or rainy seasons, indicating that carbon accumulation downstream may account for the watershed variation in CH₄ emissions. River width and slope can influence dCH₄ and fCH₄ indirectly <em>via</em> disturbing nutrient distribution. In addition, from a local perspective, microhabitats (deep pools, shallows, or rapids) and substrate types in the headstream intensified the local heterogeneity of fCH₄ by affecting water turbulence and nutrient distribution, leading to multiple spatial variations. Given the complex habitat conditions of mountainous streams, especially in the context of global climate change, incorporating these variables into future models will enhance our understanding of the roles of headstreams in the global carbon cycle.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133534"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071323","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":"Should we average rain gauge values to estimate throughfall? A research of canopy structure and throughfall spatial variability using LiDAR technology","authors":"Yupan Zhang ,&nbsp;Chenwei Chiu ,&nbsp;Yuichi Onda ,&nbsp;Takashi Gomi","doi":"10.1016/j.jhydrol.2025.133496","DOIUrl":"10.1016/j.jhydrol.2025.133496","url":null,"abstract":"<div><div>Throughfall (<span><math><mrow><mi>TF</mi></mrow></math></span>) is a significant component in hydrological studies of forest ecosystems that characterize subcanopy precipitation (<span><math><msub><mi>P</mi><mi>g</mi></msub></math></span>) inputs. Previous studies averaged the <span><math><mrow><mi>TF</mi></mrow></math></span> observations from rain gauges placed uniformly under the canopy. However, variations in canopy structure and the spatial distribution of canopy density led to measurement variability across locations, even in single-species plantation forests. 3D forest point clouds were reconstructed using a drone LiDAR system and a voxelization method was employed to compute the volume of leaves at rain gauges and individual tree scales to describe the canopy structure. Canopy saturation was assessed by observing changes in the proportion of direct <span><math><mrow><mi>TF</mi></mrow></math></span> from high-temporal-resolution rain gauge data and correlating these patterns with quantitative canopy structure, a volume-<span><math><mrow><mi>TF</mi></mrow></math></span> model was built to characterize the challenge of raindrops passing through the leaves. In addition, the <span><math><mrow><mi>TF</mi></mrow></math></span> conversion rate was corrected by considering the amount of canopy saturation and <span><math><mrow><mi>TF</mi></mrow></math></span> proportion changes before and after saturation. After inputting the volume and <span><math><msub><mi>P</mi><mi>g</mi></msub></math></span> parameters, the accuracy of the model was verified at the rain gauge scale with R² = 0.8239 – 0.9906 (n = 70). The modeled results showed that <span><math><mrow><mi>TF</mi><mo>/</mo><msub><mi>P</mi><mi>g</mi></msub></mrow></math></span> during the observation period was 53.24 %, which was slightly lower than the average of the 20 rain gauges over 265 rainfall events (63.21 %). Spatial variability significantly affected <span><math><mrow><mi>TF</mi></mrow></math></span> generation during changes in rain gauge location (coefficient of variation (<span><math><mrow><mi>Cv</mi></mrow></math></span>) = 0.64) compared with Pg (<span><math><mrow><mi>Cv</mi></mrow></math></span> = 0.3). The incorporation of saturation improved the model accuracy (RMSE; 3.69 mm &gt; 3.28 mm,). This suggests that the method of averaging the 20 rain gauges to estimate <span><math><mrow><mi>TF</mi></mrow></math></span> results in limited confidence owing to the spatial variability of the canopies. Our model quantifies the spatial variability of canopy and <span><math><mrow><mi>TF</mi></mrow></math></span>, contributing to the construction of more robust and precise hydrological models, effectively capturing stand heterogeneity and enabling targeted forest management practices.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133496"},"PeriodicalIF":5.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098700","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 runoff modelling through strengthened snowmelt and glacier module enhances runoff attribution in a large watershed in Central Asia","authors":"Jianan Yu ,&nbsp;Bing Gao ,&nbsp;Mingliang Li ,&nbsp;Peng Xiao","doi":"10.1016/j.jhydrol.2025.133528","DOIUrl":"10.1016/j.jhydrol.2025.133528","url":null,"abstract":"<div><div>Understanding runoff changes at the catchment scale is important for water resources management. Particularly, accurate attribution of runoff changes poses a significant challenge for water resources management in cold and arid regions due to complex cryospheric processes. This study improved the physically based distributed Geomorphology-Based Hydrological Model (GBHM) through modified snow module and supplement of glacier melt module. The improved model was validated using streamflow observations and applied to simulate natural runoff in Central Asia’s Balkhash Lake Basin (1955–2020). By comparing simulated natural runoff with observed records, we quantified climate change and human activities impacts across three periods: Human activities dominated runoff reductions during 1970–1986 (80.11%) and 1987–2002 (67.55%), climate change and human activities showed comparable influences during the period of 2003–2020 (43.22% vs 56.78%). Scenario simulations demonstrated limited hydrological effects from land use changes, with no consistent directional trend. The improved GBHM provides a practical tool for cold region hydrology studies, offering critical insights for sustainable water management in transboundary basins over arid and cold region.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133528"},"PeriodicalIF":5.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071321","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":"Dual-tracer analysis of stable isotopes and thermal signals to quantify groundwater residence times in karst rhythmic spring systems","authors":"Xulei Guo ,&nbsp;Mingming Luo ,&nbsp;Jingwen Li ,&nbsp;Yifan Chen ,&nbsp;Ye Kuang ,&nbsp;Cong Jiang ,&nbsp;Hong Zhou","doi":"10.1016/j.jhydrol.2025.133493","DOIUrl":"10.1016/j.jhydrol.2025.133493","url":null,"abstract":"<div><div>As a hydrogeological manifestation of siphon conduit-cavity coupling mechanisms in karst systems, the regulatory capacity of rhythmic spring structures on groundwater storage dynamics and their heterogeneous controls on residence time distributions remain insufficiently quantified. Focusing on the Chaoshuidong (CSD) karst rhythmic spring system in southern China, this study employs a dual-tracer framework integrating stable hydrogen–oxygen isotopes and thermal signatures with sinusoidal transfer functions and linear reservoir modeling. Hydrological time-series data encompassing precipitation, surface water, and groundwater phases were systematically collected to compute mean residence times (MRT) across distinct flow regimes, thereby elucidating structural controls imposed by siphon-conduit networks on aquifer response characteristics. Key findings demonstrate: (1) Methodological coherence in MRT estimates derived from three independent approaches, revealing shallow circulation pathways (29.5–60 d) deep circulation reservoirs with MRT greater than 198–213 days; (2) The observed approximately 40 day phase lag between thermal tracer peaks and stable isotope signatures demonstrates differential advective-conductive heat transfer mechanisms along hierarchically structured groundwater flow paths; (3) Through analysis of the peak temperatures during intermittent discharges at the CSD spring and the corresponding groundwater temperatures upstream of the flow path, the peak discharge during the siphon conduit-cavity controlled intermittent outflow was estimated to consist of approximately 10 % base flow and 90 % deep-circulating groundwater. Furthermore, the water residence time within the siphon conduit-cavity structure was determined to be approximately 279 days. This observation indicates that siphon cavities function as temporary storage and mixing reservoirs for groundwater, thereby prolonging flow duration. Furthermore, this finding underscores the efficacy of temperature tracing in quantifying the mixing ratios of diverse groundwater sources. This study contributes to an enhanced understanding of water cycling within complex karst basins and extends the application of environmental isotopes to groundwater research.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133493"},"PeriodicalIF":5.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098696","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":"Predicting hydrological drought indices using a hybrid data-driven model incorporating hydrological, geomorphological, and human activity impacts","authors":"Pin-Chun Huang","doi":"10.1016/j.jhydrol.2025.133491","DOIUrl":"10.1016/j.jhydrol.2025.133491","url":null,"abstract":"<div><div>This study presents a hybrid data-driven model to predict hydrological drought indices by integrating geomorphological, hydrological, and human activity factors. The model is trained using streamflow data simulated by the SWAT (Soil and Water Assessment Tool) and incorporates spatial zoning via Self-Organizing Map (SOM) networks to account for spatial variability across different zones. Each zone is trained independently using a ConvLSTM (Convolutional Long Short-Term Memory) model, which captures spatial and temporal information critical to hydrological time series data. Key input factors include geomorphological features such as drainage area, stream order, land cover, and hydrological and meteorological conditions like precipitation and evapotranspiration. Human activity factors, such as groundwater abstraction and industrial water consumption, are also integrated to reflect their impact on drought conditions. The trained model outputs two key hydrological drought indices, the standardized runoff index (SRI) and drought deficit volume, which are used to assess drought severity and further employed to calculate more metrics concerning drought termination. The hybrid model enhances drought prediction accuracy by leveraging the spatial and temporal dynamics of the watershed system without the additional use of a hydrological model. With a 30-day (1-month) prediction window, the model effectively captures temporal drought patterns while maintaining a balance between accuracy and computational efficiency. Furthermore, key evaluation metrics confirm the model’s accuracy and robustness. The Mean Relative Error (MRE) is less than 0.058, indicating minimal prediction error, while the Nash-Sutcliffe Efficiency (NSE) is greater than 0.905, demonstrating strong agreement with observed values. Additionally, the Pearson Correlation Coefficient (PCC) exceeds 0.976, highlighting a near-perfect correlation between predictions and actual data. These findings confirm the model’s reliability and effectiveness in drought prediction. These improvements provide valuable insights for efficient water resource management and drought impact mitigation.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"660 ","pages":"Article 133491"},"PeriodicalIF":5.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071326","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}