Journal of Hydrology最新文献

{"title":"Dual-model LSTM-Transformer framework with hydrodynamic adaptability for TN/TP predictions in tidal reaches","authors":"Zenghui Li ,&nbsp;Xiaopeng Li ,&nbsp;Zhihao Wang ,&nbsp;Zihao Li ,&nbsp;Peng Wang ,&nbsp;Futian Ren ,&nbsp;Qiannan Duan ,&nbsp;Xiaowei Lu ,&nbsp;Lei Huang","doi":"10.1016/j.jhydrol.2025.134231","DOIUrl":"10.1016/j.jhydrol.2025.134231","url":null,"abstract":"<div><div>The accurate prediction of total nitrogen (TN) and total phosphorus (TP) concentrations is crucial for mitigating eutrophication and supporting sustainable water resource management in tidal estuaries. However, the highly non-stationary dynamics of TN/TP concentrations—characterized by time-varying patterns driven by complex river-tide interactions and biogeochemical cycling—pose significant prediction challenges. While deep learning models like LSTM and Transformer have advanced water quality forecasting, their standalone applications struggle to simultaneously capture long-term dependencies, cross-variable relationships, and hydrodynamic drivers in tidal systems. To address these limitations, we developed a framework that integrates LSTM with Transformer (LSTM-Transformer, LT) and iTransformer (LSTM-iTransformer, LIT), incorporating river discharge (RD), tidal level (TL), rainfall, water temperature (Temp), and PH as key input features. Evaluated across nine sites spanning 600 km of the Yangtze River tidal reach, the dual-model framework demonstrates superior performance over baseline models (LSTM, Transformer, CNN-LSTM), achieving an average RMSE reduction of 32.6 % (range: 16.7–57.1 %). The LT—combining LSTM’s temporal memory with Transformer’s global attention—excels in river-dominated and tide-dominated zones. Meanwhile, the LIT, enhanced through inverted feature embedding, outperforms in transitional zones by resolving short-term variability and cross-parameter interactions. SHAP analysis quantitatively validates hydrodynamic drivers (RD and TL) as critical predictive factors. This study provides a robust modeling solution for TN/TP prediction in tidal reaches, addressing the urgent need for accurate forecasting to enable early pollution warnings and informed water management decisions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134231"},"PeriodicalIF":6.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045910","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":"Sensors prioritisation for hydrological forecasting based on interpretable machine learning","authors":"Andrea Menapace ,&nbsp;André Ferreira Rodrigues ,&nbsp;Daniele Dalla Torre ,&nbsp;Michele Larcher ,&nbsp;Manuel Herrera ,&nbsp;Bruno Brentan","doi":"10.1016/j.jhydrol.2025.134015","DOIUrl":"10.1016/j.jhydrol.2025.134015","url":null,"abstract":"<div><div>The digitalisation of the hydrological sector introduces new challenges related to IoT network implementation, extensive data management, and real-time analysis while offering significant opportunities to improve hydrological forecasts. Reliable information is crucial for managing hydrogeological risks and optimising water usage, particularly in the current era of climate change, marked by frequent and severe extreme events such as intense precipitation and prolonged droughts. This study aims to enhance short-term hydrological predictions by prioritising sensors based on interpretable machine learning. We propose an evaluation framework that involves tuning machine learning-based hydrological models for different horizons, applying leave-one-out cross-validation to simulate sensor failures and evaluate their significance, and defining sensor priority levels. Conducted in the South Tyrol watershed (northern Italy), this study uses data from streamflow gauges and weather stations. The results show that specific sensors significantly impact forecasting accuracy, and prioritisation improves the reliability of hydrological predictions. These findings highlight the importance of maintaining critical sensors and provide a data-driven methodology for optimising resource allocation in monitoring system maintenance, ultimately enhancing the robustness of hydrological forecasting and risk mitigation strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134015"},"PeriodicalIF":6.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046430","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":"Water vapor sorption behavior of shale organic matter with various types and maturation","authors":"Jiajun Fu ,&nbsp;Ke Xu ,&nbsp;Yeping Ji ,&nbsp;Xuezhe Wang ,&nbsp;Yiquan Ma ,&nbsp;Mehdi Ostadhassan ,&nbsp;Zhejun Pan ,&nbsp;Duo Wang ,&nbsp;Bo Liu ,&nbsp;Yubing Ke ,&nbsp;Mengdi Sun","doi":"10.1016/j.jhydrol.2025.134223","DOIUrl":"10.1016/j.jhydrol.2025.134223","url":null,"abstract":"<div><div>To investigate the water vapor adsorption (WVA) behavior of shale organic matter (OM), we conducted a series of WVA experiments integrated with gas physisorption, Fourier transform infrared spectroscopy (FTIR), and small-angle neutron scattering (SANS) on six OM samples with varying types (I, Ⅱ, and Ⅲ) and maturities (R_o: 1.21–3.56 %). The adsorption process was analyzed using the Dent and Freundlich models. Results indicate that capillary condensation accounts for more than 50% of the total WVA capacity at RH 0.95. Layered adsorption is primarily governed by pore surface properties: Type Ⅲ OM, contains carboxyl groups, exhibits the strongest adsorption strength, whereas overmature Type I OM, despite exhibiting rough pore surfaces and abundant adsorption sites, shows comparatively weak adsorption strength. Pore volume (PV) and specific surface area (SSA) provide the spatial basis for adsorption. Moreover, the presence of strongly hydrophilic functional groups (particularly carboxyl) and enhanced pore connectivity can extend the effective adsorption pore size range, further facilitating WVA. Among the OM types, Type I exhibits the greatest pore development and connectivity but lacks hydrophilic functional groups, while Type Ⅲ shows the opposite characteristics. Maturity also exerts a significant influence on both pore structure and surface properties. These findings highlight the coupling effects of pore structure and surface properties in controlling WVA and fill the research gap into the adsorption behavior of shale OM with different types and maturities.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134223"},"PeriodicalIF":6.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027401","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":"Herbaceous species richness enhances soil hydraulic conductivity by increasing soil pore connectivity","authors":"Huaqing Liu ,&nbsp;Xiaodong Gao ,&nbsp;Long Ma ,&nbsp;Heng Liu ,&nbsp;Xining Zhao","doi":"10.1016/j.jhydrol.2025.134218","DOIUrl":"10.1016/j.jhydrol.2025.134218","url":null,"abstract":"<div><div>Soil pore characteristics play a crucial role in determining soil hydrological processes. Plant diversity is recognized as a key factor in regulating soil hydraulic conductivity. However, the mechanisms by which plant diversity influencing soil pore characteristics and, consequently, hydraulic conductivity remain unclear. This study investigated the effects of herbaceous plant species richness on soil pore structure and its influence on both saturated and near-saturated hydraulic conductivity. A controlled experiment was conducted with four levels of herbaceous plant species richness (monoculture to four-species mixtures), coupled with soil pore analysis using X-ray computed tomography and measurements of saturated and near-saturated hydraulic conductivity. Our results showed that increasing plant diversity significantly increased soil porosity, particularly at plant richness levels of three and four species. This increase was primarily driven by an increase in connected porosity and a decrease in isolated porosity. Furthermore, the fractal dimension of both total pores and connected pores increased with increasing species richness. We found significant positive correlations between both near-saturated and saturated hydraulic conductivity and several pore characteristics including total porosity, connected porosity, connected pores, the fractal dimension, total biopores volume, and the specific characteristic of biopores. In-depth analysis indicated that saturated hydraulic conductivity was positively influenced by species richness, both directly through increased connected porosity and indirectly through changes in biopore volume, shape factor, and equivalent diameter. Additionally, species richness enhanced near-saturated hydraulic conductivity by increasing connected porosity and the shape factor of biopores. Our findings highlight the importance of plant diversity in regulating soil pore structure and enhancing hydraulic conductivity, contributing to a better understanding of the coupled plant-soil-hydrology processes and suggesting implications for sustainable ecosystem management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134218"},"PeriodicalIF":6.3,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096097","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":"Two-dimensional modeling of the impact of surface coverage on the evaporation and hydrodynamics of a shallow tropical reservoir","authors":"Iran Eduardo Lima Neto","doi":"10.1016/j.jhydrol.2025.134217","DOIUrl":"10.1016/j.jhydrol.2025.134217","url":null,"abstract":"<div><div>The effect of physical, chemical and/or biological covers on the reduction of evaporation in tanks, ponds and natural water bodies has long been studied. However, little is known about the impact of such devices on the hydrodynamics of reservoirs, specially under highly variable inflow conditions, as observed in tropical regions. The present study applied a two-dimensional model (CE-QUAL-W2) for simulating the impact of surface coverage (SC) from 0 to 100 % on the evaporation and hydrodynamics of a shallow tropical reservoir located in Fortaleza, Brazil, characterized by a high interannual and seasonal inflow variability. The effect of the placement and type of coverage was also tested. The model represented well the interannual and seasonal variations in evaporation, as well as the impact of SC, as compared to previous studies. Dimensionless correlations were proposed to predict the effect of SC on the reduction of evaporation (up to 91 %), surface temperature (up to 17 %) and horizontal velocity (up to 26 %). Seasonal variations in evaporation from the wet to the dry period ranged from an increase of 25 % to a decrease of 26 %, as surface coverage (SC) varied from zero (SC = 0) to full coverage (SC = 100 %). The results also indicated that the residence time (RT) increased with SC (up to 19 %) due to reduced temperature and horizontal velocity. A dimensionless correlation was also obtained to relate RT to SC. Additionally, the impact of SC on RT increased from wet to dry years (up to 16 %). Finally, the results showed that partial SC at shallower depths reduced evaporation (up to 6 %) compared to the other cases of surface cover placement and type. The 2D modeling approach and dimensionless correlations proposed in the present study not only advanced in the knowledge of evaporation and hydrodynamics of shallow reservoirs, but also serve as a practical tool to predict the potential impacts of physical, chemical and/or biological barriers on the water balance and water quality of such ecosystems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134217"},"PeriodicalIF":6.3,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009299","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 role of root-zone soil moisture in delaying vegetation responses to drought: Comparative insights from karst and non-karst areas","authors":"Yeye Liu,&nbsp;Jinjiao Lian,&nbsp;Yunpeng Nie,&nbsp;Kelin Wang,&nbsp;Hongsong Chen","doi":"10.1016/j.jhydrol.2025.134216","DOIUrl":"10.1016/j.jhydrol.2025.134216","url":null,"abstract":"<div><div>Vegetation loss occurs when soil moisture falls below a critical threshold, above which vegetation can withstand drought stress. Root-zone soil moisture (SM_root) is crucial for enhancing drought resistance and sustaining ecosystem stability, yet its role in shaping drought thresholds and how these thresholds differ between karst and non-karst regions remains poorly understood. Here, we quantified drought thresholds (T_SM) from both surface soil moisture (SM_surf) and SM_root across contrasting lithologies to evaluate how geological settings modulates vegetation drought responses. We found pronounced spatial heterogeneity in T_SM, with higher values in the peak forest plain (14.7th percentile) and basins, reflecting heightened atmospheric demand and greater drought vulnerability, whereas lower values in the karst plateau and gorge (10.3rd percentile) indicated stronger drought resistance. T_SM exhibited a significant increasing trend during 2001–2018, implying a gradual decline in drought resistance. SM_root-based thresholds (11.3rd percentile) were consistently lower than those derived from SM_surf (12.9th percentile), highlighting the role of SM_root in delaying the onset of drought stress. Karst ecosystems exhibited lower T_SM than non-karst systems, especially in croplands and forests, indicating that vegetation in karst landscapes operates under drier conditions yet maintains higher drought tolerance. However, projections under future climate scenarios reveal greater drought risks in karst ecosystems, particularly under strong warming. Furthermore, climatic controls on T_SM also differed between lithologies: in non-karst regions, thresholds responded coherently to plant-available water, whereas in karst regions, correlations with plant-available water alternated between positive and negative. This contrasting behavior suggests that karst vegetation flexibly shifts between shallow soil moisture and deeper bedrock water. These findings underscore the critical role of SM_root in mitigating drought impacts and reveal that geological settings shape ecosystem drought resilience under changing climates.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134216"},"PeriodicalIF":6.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020261","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":"Comparison and application of machine learning, deep learning, and statistical analysis methods in estuarine saltwater intrusion forecasting","authors":"Fang Yang ,&nbsp;Huazhi Zou ,&nbsp;Qi Tang ,&nbsp;Lei Zhu ,&nbsp;Wenping Gong ,&nbsp;Zhongyuan Lin","doi":"10.1016/j.jhydrol.2025.134213","DOIUrl":"10.1016/j.jhydrol.2025.134213","url":null,"abstract":"<div><div>Accurate forecasting of estuarine saltwater intrusion is critical for water resource management, yet comprehensive comparisons of artificial intelligence (AI) methods remain limited. This study evaluates two machine learning models—random forest (RF) and support vector machine (SVM); three deep learning models—backpropagation neural network (BP), ELMAN neural network (ENN), and long short-term memory neural network (LSTM); a statistical method (SM); and a hybrid model combining SM and LSTM (C-SL). When sufficient training data were available, LSTM outperformed other methods, achieving coefficients of determination (R²) of 0.82, 0.58, and 0.46 for forecast lead times of 1, 3, and 7 days, respectively. The C-SL model further improved accuracy, increasing R² by 50 % and Nash-Sutcliffe efficiency (NSE) by 54.8 %, while reducing mean squared error (MSE) and root mean squared error (RMSE) by 27.5 % and 22.9 %, respectively. Notably, C-SL mitigated accuracy loss under limited data conditions, demonstrating robust reliability.. Seasonal analysis revealed that declining river discharge in the Modaomen Waterway shifted the estuary from highly stratified to partially mixed, causing fluctuations (0–5 days) in the lag between peak saltwater intrusion and the minimum daily maximum tidal range. A typical 3-day lag during the fortnightly tidal cycle reduced forecasting accuracy in the early dry season across all models. These findings guide model selection based on data availability and seasonal dynamics, offering practical insights for saltwater intrusion mitigation amid increasing extreme drought events.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134213"},"PeriodicalIF":6.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045800","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":"Influence of artificially-induced biocrust development on soil matrix infiltration: insights from a long-term field experiment and random forest modeling","authors":"Mingquan Zhao ,&nbsp;Vincent J.M.N.L. Felde ,&nbsp;Peng Liu ,&nbsp;Xinwen Xu ,&nbsp;Ling Xia ,&nbsp;Li Wu ,&nbsp;Shubin Lan","doi":"10.1016/j.jhydrol.2025.134211","DOIUrl":"10.1016/j.jhydrol.2025.134211","url":null,"abstract":"<div><div>Soil water availability is crucial for ecosystem sustainability in arid and semi-arid regions, making an understanding of soil infiltration processes essential for effective water management. This study investigated the impact of artificially-induced biocrusts, an innovative desertification mitigation and ecosystem restoration strategy, on soil matrix infiltration in the Qubqi Desert. Specifically, we focused on the development of artificially-induced biocrusts and compared the matrix infiltration dynamics of cyanobacteria- vs. moss-dominated biocrust communities after 16 and 22 years of restoration. Field matrix infiltration tests (54 tests total) were randomly conducted using a Mini Disk Infiltrometer (MDI) at –5 cm tension, and the results showed distinct patterns in soil matrix infiltration across different stages of biocrust restoration. As biocrusts formed and developed, the initial infiltration rate (IIR), steady infiltration rate (SIR), and sorptivity (S) all exhibited a decreasing trend, with moss-dominated biocrusts showing a more pronounced reduction compared to cyanobacteria-dominated biocrusts. In the early restoration stages, state transitions in the biocrust community (e.g., initial biocrust formation and succession from cyanobacteria- to moss-dominated types) caused distinct changes in soil hydrological properties, whereas infiltration changes stabilized during later stages as biocrust communities reached a relatively steady state. Key factors influencing soil matrix infiltration characteristics were identified, including biocrust thickness, chlorophyll-a (Chl-a) content, and dissolved organic carbon (DOC). Random forest modeling further verified these variables as critical predictors of biocrust matrix infiltration, achieving high predictive accuracy (R² &gt; 0.95). The findings underscore the potential of monitoring these factors to assess the impact of biocrust restoration (e.g., after cyanobacteria inoculation) on regional hydrological cycles. Moreover, our findings demonstrate that the formation and development of artificially-induced biocrusts significantly alter soil structure and infiltration behavior, potentially promoting preferential flow pathways and enhancing soil water retention, particularly during the early stages of restoration. Overall, these findings suggest artificially-induced biocrusts as a practical strategy for sustainable land management in regions that are at risk of desertification and provides a predictive framework for assessing their eco-hydrological impacts at large-scales.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134211"},"PeriodicalIF":6.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027400","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":"Integrated probabilistic forecasting framework for long-term reservoir outflow through dynamic coupling of meteorological–hydrological–engineering processes","authors":"Jiaying Tan ,&nbsp;Bin Xu ,&nbsp;Jian Zhu ,&nbsp;Ping-an Zhong ,&nbsp;Ran Mo ,&nbsp;Jiangyuan Li ,&nbsp;Yuanheng Dong ,&nbsp;Xinman Qin ,&nbsp;Jiayi Jiang ,&nbsp;Huili Wang ,&nbsp;Lingwei Zhu","doi":"10.1016/j.jhydrol.2025.134214","DOIUrl":"10.1016/j.jhydrol.2025.134214","url":null,"abstract":"<div><div>To address challenges of uncertainty and complex multi processes coupled modeling in long-term streamflow forecasts after reservoir operation, this study proposed an integrated data–mechanism-driven framework for long-term probabilistic reservoir outflow forecasting. First, the Dempster–Shafer evidence theory identifies key predictors. Then, a combined quantile regression–convolutional neural network–bidirectional long short-term memory (QRCNN-BiLSTM) model is developed for probabilistic inflow forecasting. Thereafter, multi-quantile scenarios generated by inflow forecasts integrated with operation-related factors are used as input, and physical mechanisms are integrated in the loss functions. A LightGBM model containing reservoir operation knowledge forecasts probabilistic outflow, thereby realizing simulation prediction of natural runoff to regulated runoff. Applied to the Bengbu Sluice on the Huai River, the results were as follows. (1) The QRCNN-BiLSTM model reduced the root mean square error (RMSE) by 6.8 % and improved the Nash–Sutcliffe efficiency by 6.0 %, outperforming the CNN-BiLSTM benchmark model in the prediction of inflow. It showed a higher coverage rate (CR) and narrower average relative bandwidth (RB) compared to the QR neural network (QRNN) benchmark model, with a 24.8 % reduction in mistaken deviation (MD). (2) The LightGBM model outperformed the LSTM benchmark model, reducing the RMSE, continuous ranked probability score, and MD by 3.5 %, 6.0 %, and 7.5 %, respectively, while also achieving better CR and RB values in the prediction of outflow. (3) The integrated QRCNN-BiLSTM–LightGBM model outperformed the QRNN–LSTM model within 1–2-month lead time. The proposed framework offers a more accurate, reliable, and robust probabilistic forecasting solution for water resource optimization.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134214"},"PeriodicalIF":6.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027399","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":"Rainfall amount shapes the soil erosion and vegetation protection effectiveness in soil conservation","authors":"Xinyu Zhang ,&nbsp;Shouhong Zhang ,&nbsp;Fan Zhang ,&nbsp;Jingyi Shi ,&nbsp;Jingqiu Chen","doi":"10.1016/j.jhydrol.2025.134219","DOIUrl":"10.1016/j.jhydrol.2025.134219","url":null,"abstract":"<div><div>Rainfall is a primary driving force of soil and water loss. However, the impacts of increasing rainfall amount (RA) on slope soil erosion are obscure, calling for a detailed integrated assessment. In particular, the relationship between dynamic RA and vegetation protection effectiveness remains poorly understood. Therefore, this study conducted a <em>meta</em>-analysis of 40 studies, representing 1,630 rainfall events and the associated runoff (R) and soil loss (S), collected from runoff plots across various regions of China up to the year 2023. Among these, 68.59% of the rainfall events occurred in the northern rocky mountainous region and the Loess Plateau, while 72.58% were recorded on Semi-alfisols and Amorphic soil. The runoff plots included grassland, shrubland, forest, and bare land, with RA ranging from light rain to downpours. The results indicated that increasing RA significantly promoted soil erosion and increased the slope erosion sensitivity. The weighted average runoff (WR) and the weighted average soil loss (WS) produced by downpours reached 12.06 and 11.16 times those produced by light to moderate rain, respectively. The weighted average runoff coefficient (WRC) increased from 0.24 to 0.41 as RA rose, while the weighted average sediment coefficient (WSC) from torrential rain to downpours was higher than from light to heavy rain. The vegetation protection effectiveness was influenced by both vegetation type and RA. Vegetation was more effective in controlling S than R. The effectiveness of vegetation in reducing runoff declined with increasing RA. Its ability to reduce erosion initially decreased and then increased dfrom light to torrential rain. Forest significantly reduced soil erosion and inhibited the linear relationship between ‘ln(RA)-ln(R)-ln(S)’. Additionally, this study presents a rainfall event classification based on soil loss tolerance and emphasizes that vegetation protection effectiveness is not equivalent to soil loss tolerance, thereby providing scientific support for regional soil and water conservation.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"663 ","pages":"Article 134219"},"PeriodicalIF":6.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045908","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}