Water Resources Research最新文献

{"title":"Spatial Hydrographs of River Flow and Their Analysis for Peak Event Detection in the Context of Satellite Sampling","authors":"Arnaud Cerbelaud, Cédric H. David, Sylvain Biancamaria, Jeffrey Wade, Manu Tom, Renato Prata de Moraes Frasson, George H. Allen, Hana Thurman, Denis Blumstein","doi":"10.1029/2024wr038444","DOIUrl":"https://doi.org/10.1029/2024wr038444","url":null,"abstract":"The study of river dynamics has long relied on the analysis of traditional in situ hydrographs. This graphical representation of temporal variability at a given location is so ubiquitous that the mere term “hydrograph” is widely recognized as a time series. While such a “temporal hydrograph” is well suited for in situ data analysis, it fails to represent hydrologic variability across space at a given time; a perspective that characterizes satellite-based hydrologic observations. Here we argue that the concept of “spatial hydrograph” should be the focus of its own dedicated scrutiny. We build “space series” of river discharge and present their analysis in the context of peak flow event detection. We propose the use of peak event spatial coverage, referred to as “length”, as an analog to event duration. Our analysis is performed in the Mississippi basin using a dense in situ network. We reveal that peak flow events range in length from around 75 to 1,800 km with a median (mean) value of 330 (520) km along the basin's largest rivers. Our analysis also suggests that spatial sampling needs to be a factor of 4 (2) finer in resolution than peak flow lengths to detect 81% ± 13% (70% ± 20%) of events and to estimate their length within 84% ± 3% (67% ± 12%) median accuracy. We evaluate the connection between temporal and spatial scales of peak flows and show that events with longer durations also affect larger extents. We finally discuss the implications for the design of satellite missions concerned with capturing floods across space.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853188","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 Non-Sigmoidal-Curve-Dependent Dynamic Threshold Method Improves Precipitation Phase Partitioning in the Northern Hemisphere","authors":"Lina Liu, Liping Zhang, Qin Zhang, Gangsheng Wang, Zhiling Zhou, Xiao Li, Zhenyu Tang","doi":"10.1029/2024wr038636","DOIUrl":"https://doi.org/10.1029/2024wr038636","url":null,"abstract":"Given the significant impact of precipitation phase transitions on water and energy balances, accurate phase partitioning is essential for hydrological modeling. Many commonly used precipitation phase partitioning methods (PPMs) rely on sigmoidal curve assumptions to determine thresholds, leading to biased partitioning results. Here we developed a non-sigmoidal-curve-dependent dynamic threshold method (NSDT) to establish time-varying and spatially varying thresholds for classifying precipitation into rain, snow, and sleet in the Northern Hemisphere. The NSDT avoids curve-fitting errors by directly calculating thresholds from snowfall and rainfall frequency curves. In this method, relative humidity and elevation are the two most influential variables to precipitation phase, and single-threshold and dual-threshold strategies are employed separately across different relative humidity ranges. The results show that station thresholds derived from NSDT have marked spatial variability. Furthermore, the NSDT performs well and robustly, with accuracy exceeding 80% over the wet-bulb temperature range [−10°C, 10°C] at each elevation range, relative humidity subinterval, and sub-time period. The NSDT outperforms six commonly used PPMs, especially at high elevations. Regarding the wet-bulb temperature range of [−4°C, 4°C], NSDT exhibits accuracy improvements ranging from 1.0% to 11.8% (0.4%–14.5%) across all elevation (relative humidity) subintervals compared to other PPMs. Overall, the NSDT method developed herein improves precipitation phase partitioning, which is expected to enhance the simulation accuracy of land surface models and hydrological models and provide a theoretical basis for a more accurate understanding of hydrological processes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"40 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846515","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 Multi-Resolution Deep-Learning Surrogate Framework for Global Hydrological Models","authors":"B. Droppers, M. F. P. Bierkens, N. Wanders","doi":"10.1029/2024wr037736","DOIUrl":"https://doi.org/10.1029/2024wr037736","url":null,"abstract":"Global hydrological models are important decision support tools for policy making in today's water-scarce world as their process-based nature allows for worldwide water resources assessments under various climate-change and socio-economic scenarios. Although efforts are continuously being made to improve water resource assessments, global hydrological model computational demands have dramatically increased and calibrating them has proven difficult. To address these issues, deep-learning approaches have gained prominence in the hydrological community, in particular the development of deep-learning surrogates. Nevertheless, the development of deep-learning global hydrological model surrogates remains limited, as most surrogate frameworks only focus on natural water states and fluxes at a single spatial resolution. Therefore, we introduce a global hydrological model surrogate framework that integrates spatially distributed runoff routing, including lake outflow and reservoir operation, includes human activities, such as water abstractions, and can scale across spatial resolutions. To test our framework, we develop a deep-learning surrogate for the PCRaster Global Water Balance (PCR-GLOBWB) global hydrological model. Our surrogate performed well when compared to the model outputs, with a median Kling-Gupta Efficiency of 0.50, while predictions were at least an order of magnitude faster. Moreover, the multi-resolution surrogate performed similarly to several single-resolution surrogates, indicating limited trade-offs between the surrogate's broad spatial applicability and its performance. Model surrogates are a promising tool for the global hydrological modeling community, given their potential benefits in reducing computational demands and enhancing calibration. Accordingly, our framework provides an excellent foundation for the community to create their own multi-scale deep-learning global hydrological model surrogates.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"108 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841894","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":"Tidal Response of Groundwater in an Anisotropic Leaky Aquifer","authors":"Guoliang Wang, Chi-Yuen Wang, Yan Zhang, Jiangwei Zhang, Xiuyu Liang","doi":"10.1029/2024wr038851","DOIUrl":"https://doi.org/10.1029/2024wr038851","url":null,"abstract":"Groundwater tidal response analysis is a valuable tool for monitoring leakage in groundwater systems, yet the interpretation of this response has often been incomplete. Notably, the impact of anisotropic aquifer permeability on tidal response has not been addressed in existing models. This study presents an analytical model to examine the effect of anisotropy on the tidal response of an aquifer overlain by a semi-confined aquitard with finite storage. After verifying our model against previous models and numerical simulations, we fund: (a) At high vertical aquifer conductivity and aquitard leakage, the amplitude ratio of the tidal response is small, and the phase shift is positive, making our solution closely align with the existing leaky aquifer model. (b) As the vertical aquifer conductivity decreases, the amplitude ratio increases and the phase shift decreases and becomes negative at relatively low leakage, similar to that of a confined aquifer. (c) When the vertical aquifer conductivity is smaller relative to the horizontal one, the existing leaky aquifer model tends to underestimate the amplitude and overestimate the phase shift. (d) The aquitard storage has a significant effect on the tidal response of the aquifer when the aquitard leakage is large, but a negligible impact when the vertical aquifer conductivity is small. Applying our model to field data from four monitoring wells in the North China Plain, we find that when the shale content in the aquifer reaches 40.09%, our anisotropic model more effectively fits the observed phase shift compared to the existing leaky aquifer model.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"122 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841895","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":"Classifying Flash Flood Disasters From Disaster-Prone Environments to Support Mitigation Measures","authors":"Xiaoyan Zhai, Yongyong Zhang, Yongqiang Zhang, Ronghua Liu, Changjun Liu, Xiaoxiang Zhang, Yuehong Chen, Xiekang Wang, Nigel Wright","doi":"10.1029/2024wr037389","DOIUrl":"https://doi.org/10.1029/2024wr037389","url":null,"abstract":"Spatiotemporal heterogeneities in climatic, physiographic, and socio-economic environments cause complex and varied formation mechanisms in flash flood disasters. However, previous studies were usually conducted at event or catchment scale in specific environments. Investigation on disaster formation mechanisms in climatic, physiographic, and socio-economic environments with different combinations and quantities at large scale is not available, which further affects the decision-making of mitigation measures. Our study develops a type-based analytical framework of flash flood disasters and their causes from disaster-prone environments using ten-fold multivariate analysis including cluster analysis, analysis of similarities, and ordination analysis. Application of this framework to environment factors and losses of 37,332 disaster events across China revealed three disaster-prone environment types, contributing 55.5% ± 0.3%, 55.9% ± 0.3%, and 50.9% ± 0.2% to variations in disaster attributes, respectively. The events with low disaster intensities (24.6%) in undeveloped northwestern China were governed by short rainfall, low retention capacity, and low prevention investments, and their mitigation focused on afforestation and construction of rainfall and flash flood monitoring systems. Those with high disaster intensities (38.5%) in developed and disturbed central and southeastern China were interpreted by frequent intense rainfall and good flood prevention infrastructures, and their mitigation prioritized development of flash flood forecasting warning models, and grain for green, etc. Those with intermediate disaster intensities (36.9%) in undeveloped southwestern and central China were shaped by frequent short intense rainfall and steep rivers, and their mitigation required satellites or radars in alpine regions, multi-disaster prevention technology development, and dam construction.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"61 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837124","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":"Linking Surface Processes, Solute Generation, and CO2 Budgets Across Lithological and Land Cover Gradients in Rocky Mountain Watersheds","authors":"John R. Slosson, Isaac J. Larsen, Matthew J. Winnick, José M. Marmolejo-Cossío, Kenneth H. Williams","doi":"10.1029/2023wr036850","DOIUrl":"https://doi.org/10.1029/2023wr036850","url":null,"abstract":"Chemical weathering in mountain critical zones controls river chemistry and regulates long-term climate. Mountain landscapes contain diverse landforms created by geomorphic processes, including landslides, glacial moraines, and rock glaciers. These landforms generate unique flowpaths and water-rock interactions that modify water chemistry as precipitation is transformed to streamflow. Variations in lithology and vegetation also strongly control water chemistry. Prior work has shown that landslides generate increased dissolved solute concentrations in rapidly uplifting mountains. However, there is still uncertainty regarding the magnitude which different geomorphic processes and land cover variations influence solute chemistry across tectonic and climatic regimes. We measured ion concentrations in surface water from areas that drain a variety of landforms and across land cover gradients in the East River watershed, a tributary of the Colorado River. Our results show that landslides produce higher solute concentrations than background values measured in streams draining soil-mantled hillslopes and that elevated concentrations persist centuries to millennia after landslide occurrence. Channels with active bedrock incision also generate high solute concentrations, whereas solute concentrations in waters draining moraines and rock glaciers are comparable to background values. Solute fluxes from landslides and areas of bedrock incision are 1.6–1.8 times greater than nearby soil-mantled hillslopes. Carbonic acid weathering dominates surface water samples from watersheds with greater vegetation coverage. Geomorphically enhanced weathering generates hotspots for net CO₂ release or sequestration, depending on lithology, that are 1.5–3.5 times greater than background values, which has implications for understanding links among surface processes, chemical weathering, and carbon cycle dynamics in alpine watersheds.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"60 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831876","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":"Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring","authors":"Jie Song, Yujun Yi","doi":"10.1029/2024wr038271","DOIUrl":"https://doi.org/10.1029/2024wr038271","url":null,"abstract":"Estuarine wetlands, formed by the interaction of fluvial and tidal processes, exhibit complex spatiotemporal variations in soil moisture and salinity. Predicting soil moisture and salinity in estuarine wetlands is key for ecosystem management and assessing environmental impacts, while traditional methods have limitations in resolution and complexity. The elucidation of transport pattern and prediction of water and salt in estuarine wetland soils remain significant challenges. To address these challenges and improve our ability to predict and manage wetland soil properties, this study employs an in situ Internet of Things (IoT)-based monitoring network and a interpretable, cascaded machine learning model to predict these critical soil parameters. The IoT platform facilitates real-time and longitudinal tracking of soil volumetric moisture content, salinity, and groundwater depth in the Yellow River Delta salt marsh wetlands, and the high-fidelity monitoring data are used to build a two-stage machine learning model. Artificial Neural Networks, Support Vector Machines, Random Forests (RF), and Gradient Boosting Decision Trees (GBDT) were used to develop the soil moisture and salinity prediction models. The cascaded framework, in combination with a moisture and a salinity sub-model, which inspired by soil water and salt transport processes, was found to be an effective approach for capturing moisture-salinity dynamics. The Gradient Boosting Decision Tree (GBDT) algorithm predicted moisture best (<i>R</i>² = 0.846), while the GBDT-RF model predicted salinity best (<i>R</i>² = 0.875). To enhance model interpretability, SHAP (Shapley Additive exPlanations) analysis was applied, revealing that groundwater depth is the most significant positive driver of soil moisture, while water content is the dominant negative driver of soil salinity. These findings align with established eco-hydrological processes, validating the models' ability to capture physically meaningful relationships. Sensitivity analysis revealed critical groundwater depth thresholds that strongly influence soil moisture and salinity. Specifically, as the water table rises, soil moisture increases to saturation at −0.5 m. Salt accumulates rapidly at −0.8 m (27% soil moisture) and becomes stable and close to seawater salinity. With real-time in situ monitoring and the cascaded soil property prediction model, the method framework can accurately simulate and predict wetland soil moisture and salinity patterns, providing a valuable tool for monitoring and managing these vulnerable ecosystems and better understanding of wetland responses to environmental changes and supports evidence-based conservation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"558 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831879","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":"Foundation Models as Assistive Tools in Hydrometeorology: Opportunities, Challenges, and Perspectives","authors":"Lujia Zhang, Yurong Song, Hanzhe Cui, Mengqian Lu, Chenyue Li, Binhang Yuan, Bin Wang, Upmanu Lall, Jing Yang","doi":"10.1029/2024wr039553","DOIUrl":"https://doi.org/10.1029/2024wr039553","url":null,"abstract":"Most state-of-the-art AI applications in hydrometeorology are based on classic deep learning approaches. However, such approaches cannot automatically integrate multiple functions to construct a single intelligent agent, as each function is enabled by a separate model trained on independent data sets. Foundation models (FMs), which can process diverse inputs and perform different tasks, present a substantial opportunity to overcome this challenge. In this commentary, we evaluate how three state-of-the-art FMs, specifically GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, perform across four key task types in hydrometeorology: data processing, event diagnosis, forecast and prediction, and decision-making. The models perform well in the first two task types and offer valuable information for decision-makers but still face challenges in generating reliable forecasts. Moreover, this commentary highlights the concerns regarding the use of FMs: hallucination, responsibility, over-reliance, and openness. Finally, we propose that enhancing human-AI collaboration and developing domain-specific FMs could drive the future of FM applications in hydrometeorology. We also provide specific recommendations to achieve the perspectives.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"19 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827214","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":"Identifying and Quantifying the Impact of Climatic and Non-Climatic Drivers on River Discharge in Europe","authors":"Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui","doi":"10.1029/2024wr038220","DOIUrl":"https://doi.org/10.1029/2024wr038220","url":null,"abstract":"Our water resources have changed over the last century through a combination of water management evolution and climate change. Understanding and decomposing the drivers of discharge changes is essential to preparing and planning adaptive strategies. To separate the response of catchment dynamics between climate change-related and other factors in discharge observations, we propose a methodology to compare discharge observations to discharge from a physically based model. The novelty lies in the fact that, to keep the comparison pertinent despite systematic biases in physically based model outputs, we compare both systems using a common framework of interpretation, a parsimonious model, which allows us to isolate trends in catchment dynamics from trends due to average changes in annual climate variables. The modeled system stands as the reference to reproduce changes only due to evolving climate dynamics. Comparing it to the interpretation framework applied to the observation system highlights the effect of the non-modeled factors on catchment dynamics and discharge, such as human intervention in rivers and water uptakes. We show that over Europe, especially in the South, the dominant explanations for discharge trends are non-climatic factors. Still, in some catchments of Northern Europe, climate change seems to be the dominating driver of change. We hypothesize that the dominating non-climatic factors are irrigation development, groundwater pumping and other human water usage. These results show the importance of including non-climatic factors in physically based models to understand the main drivers of discharge better and accurately project future changes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"32 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823106","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":"Streamflow Monitoring at High Temporal Resolution Based on Non-Contact Instruments and Manually Surveyed Bathymetry in a River Prone to Bathymetric Shifts","authors":"G. Nord, S. Safdar, M. Hasanyar, K. O. Eze, R. Biron, G. Freche, H. Denis, C. Legout, A. Hauet, M. Esteves","doi":"10.1029/2024wr037536","DOIUrl":"https://doi.org/10.1029/2024wr037536","url":null,"abstract":"This study presents a proof of concept of a reliable methodology for monitoring streamflow in a dynamic river of the Alps prone to bathymetric changes using non-contact instruments. The method relies on water level and surface velocity radar monitoring, discharge measurements by Large-Scale Particle Image Velocimetry (LSPIV), and topographic surveys. A single proportional relation, stable under bathymetric changes, is established between maximum surface velocity (<i>V</i>_s,max) and bulk velocity (<i>U</i>_mean) using LSPIV measurements. The location of the maximum surface velocity is also shown to be relatively stable under bathymetric changes. The Isovel model, a theoretical approach which requires minimal information (i.e., bathymetry, water level and bed roughness) is also used to assess its capacity to predict the <i>V</i>_s,max–<i>U</i>_mean relation and the location of the maximum surface velocity. Such model could be useful for applying the method in the absence of LSPIV measurements in the future. The applicability of the method is finally tested over a 2.5-year data set. Discharge is calculated at a time step of 10 min by multiplying the bulk velocity and the wetted cross-sectional area. The results are compared to the specific discharge time series at the historical station located 2.5 km further upstream, which has a stage-discharge rating curve, to assess the credibility of the proposed method. Good agreement is generally observed when surface velocity is above 0.7 m/s, but accuracy decreases for lower velocities. A simplified uncertainty analysis estimates a 25% relative error on discharge calculated with the presented method.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"55 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823108","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}