Water Resources Research最新文献

{"title":"Assessing Spatial Patterns of Carbon and Nutrient Dynamics in Catchments of Complex Topography","authors":"Taiqi Lian, Simone Fatichi, Manfred Stähli, Sara Bonetti","doi":"10.1029/2025wr040260","DOIUrl":"https://doi.org/10.1029/2025wr040260","url":null,"abstract":"The topography of a landscape regulates the spatial distribution of water and energy fluxes, which are main drivers of vegetation and soil carbon and nutrient dynamics. Despite the recognized role of topography in mediating such processes, quantifying and predicting the spatial distribution of carbon and nutrient fluxes and stocks in highly heterogeneous landscapes remains challenging. The main limitations stem from the prevalence of largely decoupled modeling approaches which fail to concurrently account for ecohydrological and biogeochemical processes as well as the lack of adequate frameworks describing the links among topography, water and energy balances, and soil biogeochemical dynamics. Here, we extend the capabilities of the mechanistic ecohydrological model Tethys‐Chloris‐Biogeochemistry (T&C‐BG) by including a soil carbon and nutrient routing module in the distributed model version. The newly developed T&C‐BG‐2D model is validated against long‐term hydrological and biogeochemical measurements from the Hafren catchment in Wales (UK) and the Erlenbach catchment in the Swiss pre‐Alps. The model successfully captures carbon and nutrient concentrations and dynamics in these catchments, with relative differences between simulated and observed median values of between −4% and −0.3% for dissolved organic carbon, and between 1% and 20% for ammonia. A sensitivity analysis in the Erlenbach basin suggests that elevation explains over 80% of the observed spatial patterns, followed by topographic wetness index (12.6%), aspect (2.9%), and curvature (2.1%). These findings underscore topography's critical role in shaping water, carbon, and nutrient dynamics, which cannot be reflected in plot‐scale simulations neglecting spatial interactions and topographic effects.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"118 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183244","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":"Distributed Hybrid Flood Modeling Framework: Integrating Physical Mechanisms With Deep Learning for Enhanced Efficiency and Accuracy","authors":"Miao He, Shanhu Jiang, Liliang Ren, Hao Cui, Shuping Du, Yongwei Zhu, Mingming Ren, Tianling Qin, Xiaoli Yang, Xiuqin Fang, Chong‐Yu Xu","doi":"10.1029/2025wr039932","DOIUrl":"https://doi.org/10.1029/2025wr039932","url":null,"abstract":"To address the limitations of process‐driven models in characterizing physical mechanisms and the interpretability challenges of data‐driven models in flood forecasting, this study proposes a distributed hybrid flood modeling (DHFM) framework that integrates physical mechanisms with deep learning. Differentiable diffusion wave (DW) and convolutional neural network (CNN) routing methods are introduced, which can be seamlessly integrated into the DHFM framework. A differentiable Muskingum (MK) routing method is also implemented as a benchmark. The Mishui Basin in China is selected as a case study to systematically evaluate the performance and interpretability of these three routing methods under both gauged and ungauged scenarios. Results show that the DHFM framework can effectively achieve physical parameterization across different sub‐basins. Compared to the lumped Xin'anjiang hydrological model, it achieve <jats:italic>s</jats:italic> higher accuracy in both daily streamflow and flood simulations, while also demonstrating favorable interpretability of the embedded neural network. Under gauged scenarios, the differentiable CNN method slightly outperforms DW in terms of performance and efficiency, and significantly surpasses MK. As the number of training stations increases, model performance tends to stabilize or decline. In ungauged scenarios, CNN performs well with sufficient training data (&gt;2 stations) but is sensitive to station selection, exhibiting a substantial performance drop with only one station. In contrast, DW and MK show greater stability. The differentiable CNN method shows potential for adaptively learning unit hydrographs based on channel attributes. The proposed DHFM framework not only enhances flood simulation accuracy but also provides novel perspectives for understanding the physical mechanisms underlying flood processes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"65 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181117","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":"Toward Climate‐Robust Rainfall Runoff Models: Development and Evaluation of Parameter Libraries That Produce Dependable Predictions Across Diverse Conditions","authors":"J. D. Hughes, S. S. H. Kim","doi":"10.1029/2025wr040385","DOIUrl":"https://doi.org/10.1029/2025wr040385","url":null,"abstract":"Determining rainfall runoff responses of catchments to unprecedented climate conditions is an issue which has largely eluded the hydrologic community for many years. Conceptual rainfall runoff models are used globally to predict runoff for regional water resources management and planning. However, obtaining parameter values suitable for future climate conditions requires approaches that consider conditions beyond historical periods. This paper takes advantage of data from 207 Australian catchments to determine model parameters that most closely produce expected rainfall runoff coefficients (ratio of runoff to rainfall) for a wide range of environmental conditions. This was done for two popular rainfall runoff models, GR4J and Sacramento. In a two‐step process, parameters were first selected that could adequately reproduce observed runoff coefficients across the 207 catchments. Acceptable parameter sets were stored in a library from which, in the second step, parameters were selected for each individual catchment according to various goodness‐of‐fit metrics. Performance of this calibration approach was compared with a classical optimization employed for each catchment (DELO—Differential Evolution Local Optimization). The study found performance trade‐offs using the parameter library based calibration compared to DELO for metrics such as Nash‐Sutcliffe Efficiency and percentage bias. The library‐based calibration exhibited behavior that more closely aligned with expectations under perturbed climate conditions, compared to DELO parameters. Results also showed tolerable estimates of rainfall runoff coefficient using DELO parameters at many sites when rainfall is reduced by no more than 25%. However, there is a high risk of under‐ or over‐estimating runoff coefficients at larger reductions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181120","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":"Spatiotemporal Variations of Terrestrial Water Storage and Driving Factors in the Water Towers of Northwest China Based on GRACE and Multi-Source Data Sets","authors":"Jiayuan Feng, Bingjie Li, Jinxi Song, Bin Tang, Myint Myint Nyein, Bawa Precious Tani","doi":"10.1029/2024wr039490","DOIUrl":"https://doi.org/10.1029/2024wr039490","url":null,"abstract":"Mountains represent important water towers and thus are the primary water suppliers for downstream areas. However, divergent trends in terrestrial water storage (TWS) in water towers and their driving factors have not been clarified. This study investigated water tower units (WTUs) in Northwest China (NWC) and used GRACE/GRACE-FO and multi-source remote sensing data to analyze the spatiotemporal changes in TWS over the past 20 years. Based on correlation analysis and structural equation modeling, the effects of precipitation (PRE), evapotranspiration (ET), temperature (TEM) and NDVI on TWS were revealed. The results showed that TWS decreased in the WTUs of the Junggar and Ili River basins while increasing in the Qaidam and Yellow River basins. Glacier retreat and snowmelt were the components that contributed the most to TWS changes in the WTUs of the Tarim, Junggar, Ili River, and Irtysh River basins, while lake expansion, groundwater, and soil moisture contributed the most in the remaining WTUs. Additionally, the dominant roles of climate and vegetation factors on TWS changes varied significantly among the WTUs. For example, glacial retreat and increased ET caused by climate warming and vegetation greening exacerbated the TWS reductions in WTUs of the Junggar and Ili River basins, whereas vegetation greening dominated TWS changes within the Yellow River basin by affecting groundwater storage and soil moisture. These findings are crucial for understanding changes in the hydrological cycle of water towers and optimizing future watershed water resource management strategies.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145153956","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":"Model Inputs and Data Requirements for Process-Based Stream Temperature Modeling in Regulated Peri-Alpine Rivers","authors":"David Dorthe, Michael Pfister, Stuart N. Lane","doi":"10.1029/2024wr038860","DOIUrl":"https://doi.org/10.1029/2024wr038860","url":null,"abstract":"Regulated rivers can experience sharp temperature variations induced by intermittent hydropower production (thermopeaking). To mitigate ecological impacts, dam operators need to assess the impacts of hydropeaking on stream temperature, and to test scenarios that might reduce them. While stream temperature modeling has been investigated in numerous studies, few have systematically assessed how integrated processes and their representation affect model performance, and models capable of capturing both sub-hourly variations and long-term thermal dynamics remain a challenge. Herein, a stream temperature model within the HEC-RAS platform was used to model the thermal regime of a regulated river in Switzerland, with a 10-min timestep over the annual time-scale and for a 22-km long reach; and for which we had installed a network of stream temperature sensors. While the initial model demonstrated an acceptable performance at the yearly scale (Mean Absolute Error: 0.78–2.10°C and Kling-Gupta Efficiency: 0.55–0.85), this was not the case at the daily or seasonal time-scales. Two model corrections were found to be crucial; (a) the correction of potential incoming solar radiation for local shading; and (b) the representation of the heat flux linked to water-sediment exchanges. With these two corrections, the annual performance improved (<i>MAE</i>: 0.48–0.83°C and <i>KGE</i>: 0.85–0.93) as did the daily and seasonal performance. Although physically based, the model required calibration, underscoring the importance of high-quality in situ temperature data. The resulting model proves effective for practical applications in hydropower mitigation and river temperature management under complex flow regimes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"63 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140632","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":"Exploring the Potential of Remote Sensing‐Based River Temperature Tool for Improving Columbia River Reservoir Management Toward Fish Abundance Outcomes","authors":"G. K. Darkwah, Faisal Hossain","doi":"10.1029/2024wr039639","DOIUrl":"https://doi.org/10.1029/2024wr039639","url":null,"abstract":"The thermal condition of riverine ecosystems significantly influences fish survival and migration. Understanding the spatial relationship between water temperature and fish abundance requires a comprehensive spatiotemporal overview of river temperature. In this study, we used multi‐decadal spatiotemporal river temperature estimates from the Thermal History of Regulated Rivers (THORR) tool to explore the relationship between water temperature, fish abundance, and migration patterns. We demonstrated the potential of such a tool and the corresponding analyses to inform and improve reservoir management for fish abundance. Our assessment, based on the mass balance concept, considered the influx and efflux of migratory fish during the fall season in the Hanford Reach along the Columbia River to determine fish retention. We found that the proportion of fish leaving the reach increases with rising water temperatures. Although fish appear to travel faster at higher temperatures according to THORR‐based analyses, discharge‐focused dam operations upstream did not result in downstream cooling, thus failing to improve thermal conditions for fish in the downstream reach. A long‐term multi‐decadal trend showed a significant increase in fall water temperatures beyond stressful levels over the past decade. These findings underscore the critical need for balanced dam operations that consider both discharge and temperature requirements to ensure optimal conditions for fish survival and migration. The insights provided by the THORR tool not only enhance our understanding of riverine thermal dynamics but also offer a valuable resource for developing sustainable water management practices in regulated rivers where fish passage is critical. By leveraging THORR's capabilities, we can better plan ways to protect aquatic ecosystems, support biodiversity, and promote the resilience of fish populations amidst climate change impacts.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"73 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141025","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":"Quantifying Groundwater Response and Uncertainty in Beaver-Influenced Mountainous Floodplains Using Machine Learning-Based Model Calibration","authors":"Lijing Wang, Tristan Babey, Zach Perzan, Sam Pierce, Martin Briggs, Kristin Boye, Kate Maher","doi":"10.1029/2024wr039192","DOIUrl":"https://doi.org/10.1029/2024wr039192","url":null,"abstract":"Beavers (&lt;i&gt;Castor canadensis&lt;/i&gt;) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model-data integration workflow to address uncertainty in groundwater response to beaver-induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning-based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down-valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down-valley underflow, from 2&lt;span data-altimg=\"/cms/asset/3db32cdc-51a3-4073-8788-519bbfe1b9f6/wrcr70383-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"285\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70383-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-mi data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"percent sign\" data-semantic-type=\"punctuation\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:00431397:media:wrcr70383:wrcr70383-math-0001\" display=\"inline\" location=\"graphic/wrcr70383-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow&gt;&lt;mi data-semantic-=\"\" data-semantic-role=\"unknown\" data-semantic-speech=\"percent sign\" data-semantic-type=\"punctuation\"&gt;%&lt;/mi&gt;&lt;/mrow&gt;$%$&lt;/annotation&gt;&lt;/semantics&gt;&lt;/math&gt;&lt;/mjx-assistive-mml&gt;&lt;/mjx-container&gt; during dry pond periods to 20&lt;span data-altimg=\"/cms/asset/3bc84b16-2fea-47a4-a9aa-7b033e3ed11b/wrcr70383-math-0002.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"286\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70383-math-0002.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-mi data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"percent sign\" data-semantic-type=\"punctuation\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:00431397:media:wrcr70383:wrcr70383-math-0002\" display=\"inline\" location=\"graphic","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"94 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134261","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 Physics-Informed Deep Learning Method With Adaptively Weighted Loss for Modeling Soil Water Flows","authors":"Cunwen Li, Yan Zhu, Xiaoping Zhang, Lili Ju, Qiang Luo, Hui Feng","doi":"10.1029/2024wr039108","DOIUrl":"https://doi.org/10.1029/2024wr039108","url":null,"abstract":"Richards' equation, widely used to model soil water flows, presents numerical challenges due to the high nonlinearity of its constitutive relationships. The deep learning method with a physics-informed neural network (PINN) provides a fresh perspective for solving this equation without prior knowledge of soil water constitutive relationship. However, existing PINN-based methods for Richards' equation are still significantly limited by the feasible soil types, and further developments are urgently needed to make the neural network models practically applicable to various types of soils. In this paper, we introduce a deep learning method, “PINN-AWL,” which simultaneously build the PINN model for prediction of soil water flows and establish the constitutive relationships between soil matric potential, soil water content and unsaturated hydraulic conductivity. An adaptively weighted loss is specially designed for the training process of this model. Specifically, the loss function is adjusted with self-adaptive weights at the training points in each iteration of training. This allows the proposed PINN-AWL to automatically focus more on regions where the solution is difficult to fit. The prediction accuracy and generalization ability of the PINN-AWL are thoroughly tested on various soils ranging from silty to sandy types. We also conduct studies to investigate the optimal structure and hyper-parameters used in the proposed method. The numerical results demonstrate that the proposed PINN-AWL significantly outperforms both the standard PINN and the monotonic PINN, especially on soils exhibiting strong nonlinearity in constitutive relationships, as indicated by larger “<i>n</i>” values in the van Genuchten-Mualem model.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134327","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 Storage and Release in Permafrost Catchments: Insights From Hydrometrics, End‐Member Mixing, and Water Age Characterization","authors":"Arsh S. Grewal, Ciaran J. Harman, Sean K. Carey","doi":"10.1029/2024wr038957","DOIUrl":"https://doi.org/10.1029/2024wr038957","url":null,"abstract":"Seasonality strongly influences hydrological and chemical transport in permafrost‐underlain mountain catchments. In spring, snowmelt delivers large volumes of water, but frozen ground limits infiltration, causing shallow flow pathways to quickly route water to streams. As thaw progresses, storage capacity increases, flow paths deepen, and previously frozen water becomes mobile. Changing storage capacity and activation of deeper flow paths can alter the degree of storage turnover and transit time distributions of outgoing fluxes. Here we characterize the storage and release of water in two headwater catchments underlain by continuous permafrost located in Tombstone Territorial Park in Yukon, Canada. Our objectives were to: (a) evaluate the rate, timing, and magnitude of all hydrological fluxes, (b) utilize Bayesian mixing analysis to partition runoff into rain and snow contributions, and (c) apply the StorAge Selection (SAS) framework to characterize water age dynamics in both catchments. Results show ∼400 mm of precipitation entered the basins, ∼45% as snow, which melted over 4 weeks. Evapotranspiration (ET) was roughly equal to discharge, increasing throughout the summer. Mixing results suggest nearly all (&gt;90%) of runoff during freshet was snow water in both catchments, indicating limited mixing with old water. In contrast, most of the rain left the basins as ET. The water balance and SAS framework highlight significant contributions from melting ground ice post‐freshet. Additionally, high flows resulted in a more uniform SAS function, indicating greater mixing of storage. ET was comprised of mainly young water, likely due to the high field capacity of organic soils and the shallow rooting of tundra vegetation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140633","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":"Drip Irrigation in Dryland Agriculture Controls Soil Water-Filled Pore Space and Reduces Greenhouse Gas Emissions: A Meta-Analysis","authors":"Liqiang Zhang, Zehang Zhao, Xingxu Wu, Yuhan Yang, Hongyu Wang, Zhengguo Cui, Qiuzhu Li, Jinhu Cui","doi":"10.1029/2024wr039388","DOIUrl":"https://doi.org/10.1029/2024wr039388","url":null,"abstract":"Drip irrigation (DI) could effectively reduce greenhouse gas (GHG) emissions from dryland agriculture, helping mitigate global warming. Here, we performed a meta-analysis to quantify the effects of dryland DI on GHG emissions under different climatic conditions, soil conditions, and agricultural management practices. The results showed that DI can reduce GHG emissions by decreasing the soil moisture content (i.e., water-filled pore space). The N₂O and CO₂ emissions decreased by 29.2% and 6.1%, respectively, and global warming potential decreased by 18.7%, but CH₄ emissions increased by 9.7%–14.0%. When the irrigation scheduling was higher than 70% and the nitrogen application was 180–300 kg ha⁻¹, shallow buried DI with water flow controlled below 2 L hr⁻¹ was the best strategy for emission reduction. In addition, compared with traditional irrigation methods, DI demonstrates greater long-term effectiveness in reducing N₂O and CO₂ emissions. We also found that greenhouse vegetable production combined with DI has great potential for reducing GHG emissions. This study provides evidence for the application of DI technology to reduce global dryland GHG emissions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"101 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140914","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}