Tongge Jing, Yi Zeng, Nufang Fang, Wei Dai, Zhihua Shi
The study of sediment-riverflow interactions during discrete hydrological events is vital for enhancing our understanding of the hydrological cycle. Hysteresis analysis, relying on high-resolution, continuous monitoring of suspended sediment concentration (SSC) and discharge (Q) data, is an effective tool for investigating complex hydrological events. It captures differing sediment dynamic at the same discharge level, which results from the asynchrony between the hydrograph and sediment graph during different phases of the event. However, there has been no comprehensive review systematically addressing the utility and significance of hysteresis analysis in soil and water management. This review synthesizes findings from over 500 global studies, providing a detailed examination of current research. We trace the development and application of hysteresis analysis in hydrology, illustrating its role in classifying and characterizing events, as well as uncovering sediment sources and transport mechanisms. Furthermore, hysteresis analysis has proven effective in identifying critical hydrological events, offering valuable insights for targeted watershed management. Our spatiotemporal analysis of global hysteresis research shows that over 70% of studies are located in semi-arid and Mediterranean climate zones, with an increasing focus on alpine and tropical regions due to climate change. This review also highlights critical limitations, including the scarcity of high-resolution data, inconsistent use of quantitative indices, and limited integration of hysteresis patterns into predictive hydrological approaches. Future research should focus on developing region-specific hydrological models that incorporate hysteresis dynamics, along with standardizing methodologies to apply hysteresis analysis across diverse climatic and geomorphic settings.
{"title":"A Review of Suspended Sediment Hysteresis","authors":"Tongge Jing, Yi Zeng, Nufang Fang, Wei Dai, Zhihua Shi","doi":"10.1029/2024wr037216","DOIUrl":"https://doi.org/10.1029/2024wr037216","url":null,"abstract":"The study of sediment-riverflow interactions during discrete hydrological events is vital for enhancing our understanding of the hydrological cycle. Hysteresis analysis, relying on high-resolution, continuous monitoring of suspended sediment concentration (SSC) and discharge (Q) data, is an effective tool for investigating complex hydrological events. It captures differing sediment dynamic at the same discharge level, which results from the asynchrony between the hydrograph and sediment graph during different phases of the event. However, there has been no comprehensive review systematically addressing the utility and significance of hysteresis analysis in soil and water management. This review synthesizes findings from over 500 global studies, providing a detailed examination of current research. We trace the development and application of hysteresis analysis in hydrology, illustrating its role in classifying and characterizing events, as well as uncovering sediment sources and transport mechanisms. Furthermore, hysteresis analysis has proven effective in identifying critical hydrological events, offering valuable insights for targeted watershed management. Our spatiotemporal analysis of global hysteresis research shows that over 70% of studies are located in semi-arid and Mediterranean climate zones, with an increasing focus on alpine and tropical regions due to climate change. This review also highlights critical limitations, including the scarcity of high-resolution data, inconsistent use of quantitative indices, and limited integration of hysteresis patterns into predictive hydrological approaches. Future research should focus on developing region-specific hydrological models that incorporate hysteresis dynamics, along with standardizing methodologies to apply hysteresis analysis across diverse climatic and geomorphic settings.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"35 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905206","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}
Arezoo RafieeiNasab, Michael N. Fienen, Nina Omani, Ishita Srivastava, Aubrey L. Dugger
The WRF-Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open-source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF-Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, in particular we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.
{"title":"Ensemble Methods for Parameter Estimation of WRF-Hydro","authors":"Arezoo RafieeiNasab, Michael N. Fienen, Nina Omani, Ishita Srivastava, Aubrey L. Dugger","doi":"10.1029/2024wr038048","DOIUrl":"https://doi.org/10.1029/2024wr038048","url":null,"abstract":"The WRF-Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open-source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF-Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, in particular we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"41 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902234","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}
Mariana Madruga de Brito, Jan Sodoge, Heidi Kreibich, Christian Kuhlicke
In July 2021, Germany experienced its costliest riverine floods in history, with over 189 fatalities and a staggering €33 billion in damages. Following this event, news outlets widely disseminated information on the flood's aftermath. Here, we demonstrate how newspaper data can be instrumental in the assessment of flood socioeconomic impacts often overlooked by conventional methods. Using natural language processing tools on 26,113 newspaper articles, we estimate the cascading impacts of the 2021 flood on various sectors and critical infrastructure, including water contamination, mental health, and tourism. Our results revealed severe and lasting impacts in the Ahr Valley, even months after the event. At the same time, we identified smaller-scale yet widespread impacts across Germany, which are typically overlooked by existing impact databases. Our approach advances current research by systematically examining indirect and intangible flood consequences over large areas. This underscores the value of leveraging complementary text data to provide a more comprehensive picture of flood impacts.
{"title":"Comprehensive Assessment of Flood Socioeconomic Impacts Through Text-Mining","authors":"Mariana Madruga de Brito, Jan Sodoge, Heidi Kreibich, Christian Kuhlicke","doi":"10.1029/2024wr037813","DOIUrl":"https://doi.org/10.1029/2024wr037813","url":null,"abstract":"In July 2021, Germany experienced its costliest riverine floods in history, with over 189 fatalities and a staggering €33 billion in damages. Following this event, news outlets widely disseminated information on the flood's aftermath. Here, we demonstrate how newspaper data can be instrumental in the assessment of flood socioeconomic impacts often overlooked by conventional methods. Using natural language processing tools on 26,113 newspaper articles, we estimate the cascading impacts of the 2021 flood on various sectors and critical infrastructure, including water contamination, mental health, and tourism. Our results revealed severe and lasting impacts in the Ahr Valley, even months after the event. At the same time, we identified smaller-scale yet widespread impacts across Germany, which are typically overlooked by existing impact databases. Our approach advances current research by systematically examining indirect and intangible flood consequences over large areas. This underscores the value of leveraging complementary text data to provide a more comprehensive picture of flood impacts.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902231","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}
As one of the largest contiguous karst landscapes in the world, southwest China has experienced severe soil erosion because of its frequent climate extremes, special hydrogeology, shallow and discontinuous soil, steep topography, and inappropriate land use. Furthermore, the construction of dams in recent decades has rendered the relationship between runoff and sediment discharge increasingly complex. However, the joint probability distributions and joint return periods of runoff-sediment discharge relationship are still not clear. The objective of this study was to investigate the synchronous-asynchronous probabilities and return periods of rich-poor combinations of annual runoff and sediment discharge using a bivariate copula function to assess the risk of soil erosion in four selected karst watersheds in southwest China. Results showed that sediment discharge has declined significantly in all watersheds except Liujiang, and annual runoff and sediment discharge were significantly positively correlated in all watersheds. The optimal marginal distribution and the best copula function of annual runoff and sediment discharge are not identical for each watershed. The synchronous and asynchronous probabilities of annual runoff and sediment discharge are close to 1:1 in the Wujiang watershed. The asynchronous probability is much higher for the combination of less runoff with more sediment discharge (r < s) than for the combination of more runoff with less sediment discharge (r > s) in Nanpanjiang. Therefore, the risk of soil erosion may be higher in the Wujiang and Nanpanjiang watersheds. The joint return periods of runoff-sediment discharge were concentrated in less than 5 years during the historical period. These return periods can provide data references for designing the scale of water resources projects and help in better soil erosion control in the future. This study could be a technical reference for identifying the non-stationarity of the multivariate relationship between runoff and sediment discharge in karst regions.
{"title":"Joint Probability Analysis of the Rich-Poor Runoff and Sediment Discharge in Karst Watersheds","authors":"Jiayin Yao, Xingxiu Yu, Zhenwei Li, Shilei Peng, Xianli Xu","doi":"10.1029/2024wr038300","DOIUrl":"https://doi.org/10.1029/2024wr038300","url":null,"abstract":"As one of the largest contiguous karst landscapes in the world, southwest China has experienced severe soil erosion because of its frequent climate extremes, special hydrogeology, shallow and discontinuous soil, steep topography, and inappropriate land use. Furthermore, the construction of dams in recent decades has rendered the relationship between runoff and sediment discharge increasingly complex. However, the joint probability distributions and joint return periods of runoff-sediment discharge relationship are still not clear. The objective of this study was to investigate the synchronous-asynchronous probabilities and return periods of rich-poor combinations of annual runoff and sediment discharge using a bivariate copula function to assess the risk of soil erosion in four selected karst watersheds in southwest China. Results showed that sediment discharge has declined significantly in all watersheds except Liujiang, and annual runoff and sediment discharge were significantly positively correlated in all watersheds. The optimal marginal distribution and the best copula function of annual runoff and sediment discharge are not identical for each watershed. The synchronous and asynchronous probabilities of annual runoff and sediment discharge are close to 1:1 in the Wujiang watershed. The asynchronous probability is much higher for the combination of less runoff with more sediment discharge (r < s) than for the combination of more runoff with less sediment discharge (r > s) in Nanpanjiang. Therefore, the risk of soil erosion may be higher in the Wujiang and Nanpanjiang watersheds. The joint return periods of runoff-sediment discharge were concentrated in less than 5 years during the historical period. These return periods can provide data references for designing the scale of water resources projects and help in better soil erosion control in the future. This study could be a technical reference for identifying the non-stationarity of the multivariate relationship between runoff and sediment discharge in karst regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902233","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}
Youjiang Shen, Dai Yamazaki, Yadu Pokhrel, Gang Zhao
Accurate reservoir representation in large-scale river models remains challenging owing to limited access to data on reservoir operations. We contribute to model development by introducing a global machine-learning based flood storage capacity (FSC) data set and a satellite-based target storage reservoir operation scheme (SBTS). The FSC data set for 1,178 flood control reservoirs is constructed using multiple reservoir attributes and reported FSC data. Integrating these FSCs into SBTS enables its global applicability with generic formulations of reservoir zoning. Then, we develop SBTS by introducing monthly median values of satellite storage data as target storage parameters. With these seasonal patterns as constrains, improvements in simulation results are achieved. When simulated with observed inflow, SBTS performed significantly better (median Kling-Gupta efficiency values of 0.52 and 0.17 for outflow and storage simulations among 289 reservoirs), compared to the previous reservoir operation scheme with linearly interpolated target storage parameter (0.41 and −0.19). Compared to two existing global schemes without seasonal target storages, SBTS demonstrates improved performance for many reservoirs whose inflow seasonal pattern is more regular. When coupled with a global river model, it improved discharge simulations across 293 downstream gauges, with overall performance, peak, and low flow improving at 40%, 21%, and 35% of gauges, respectively, compared to simulations without reservoirs. However, reservoir simulations do not improve notably due to the biases in simulated inflow to reservoirs. We demonstrated that machine-learning FSC and satellite observations help improve reservoir parameterizations, and found that improvements in other aspects of river modeling are essential for accurately reproducing discharge patterns.
{"title":"Improving Global Reservoir Parameterizations by Incorporating Flood Storage Capacity Data and Satellite Observations","authors":"Youjiang Shen, Dai Yamazaki, Yadu Pokhrel, Gang Zhao","doi":"10.1029/2024wr037620","DOIUrl":"https://doi.org/10.1029/2024wr037620","url":null,"abstract":"Accurate reservoir representation in large-scale river models remains challenging owing to limited access to data on reservoir operations. We contribute to model development by introducing a global machine-learning based flood storage capacity (FSC) data set and a satellite-based target storage reservoir operation scheme (SBTS). The FSC data set for 1,178 flood control reservoirs is constructed using multiple reservoir attributes and reported FSC data. Integrating these FSCs into SBTS enables its global applicability with generic formulations of reservoir zoning. Then, we develop SBTS by introducing monthly median values of satellite storage data as target storage parameters. With these seasonal patterns as constrains, improvements in simulation results are achieved. When simulated with observed inflow, SBTS performed significantly better (median Kling-Gupta efficiency values of 0.52 and 0.17 for outflow and storage simulations among 289 reservoirs), compared to the previous reservoir operation scheme with linearly interpolated target storage parameter (0.41 and −0.19). Compared to two existing global schemes without seasonal target storages, SBTS demonstrates improved performance for many reservoirs whose inflow seasonal pattern is more regular. When coupled with a global river model, it improved discharge simulations across 293 downstream gauges, with overall performance, peak, and low flow improving at 40%, 21%, and 35% of gauges, respectively, compared to simulations without reservoirs. However, reservoir simulations do not improve notably due to the biases in simulated inflow to reservoirs. We demonstrated that machine-learning FSC and satellite observations help improve reservoir parameterizations, and found that improvements in other aspects of river modeling are essential for accurately reproducing discharge patterns.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887947","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}
Accurate daily streamflow estimates are crucial for water resources management. Yet, many regions lack high-temporal-resolution data due to limited monitoring infrastructure, often relying on monthly aggregates or intermittent observations. Predicting streamflow in these sparsely sampled watersheds remains challenging. This study proposes a deep learning-based approach using Long Short-Term Memory, leveraging its inherent advantages in learning long-term dependencies within hydrological variables and processes to enhance streamflow predictions in sparsely sampled watersheds. The approach was evaluated for simulating daily flow patterns from monthly aggregated and monthly or weekly intermittent observations in two contrasting hydrological settings: near-natural and human-influenced watersheds. Results showed that the proposed approach reliably predicts daily flows from monthly aggregates with a median Nash-Sutcliffe efficiency (NSE) of 0.61 for near-natural and 0.48 for human-influenced watersheds. The proposed approach performed even better for daily flow predictions from monthly or weekly intermittent observation, achieving a median NSE of 0.70 and 0.55 for near-natural and human-influenced watersheds, respectively. The proposed approach remained robust across different seasons and hydrological regimes, with a median percentage bias of ±5%, except in arid regions. Moreover, data sensitivity analysis indicated that data from wet seasons were crucial for improving model predictions and that weekly data could yield results comparable to daily observations. Overall, this study demonstrates that the deep learning-based approach offers a robust and accurate representation of daily streamflow patterns from aggregated or intermittent observations, providing valuable hydrological insights and promising solutions for improving water resource management in regions with limited monitoring infrastructures.
{"title":"Deep Learning-Based Approach for Enhancing Streamflow Prediction in Watersheds With Aggregated and Intermittent Observations","authors":"Nikunj K. Mangukiya, Ashutosh Sharma","doi":"10.1029/2024wr037331","DOIUrl":"https://doi.org/10.1029/2024wr037331","url":null,"abstract":"Accurate daily streamflow estimates are crucial for water resources management. Yet, many regions lack high-temporal-resolution data due to limited monitoring infrastructure, often relying on monthly aggregates or intermittent observations. Predicting streamflow in these sparsely sampled watersheds remains challenging. This study proposes a deep learning-based approach using Long Short-Term Memory, leveraging its inherent advantages in learning long-term dependencies within hydrological variables and processes to enhance streamflow predictions in sparsely sampled watersheds. The approach was evaluated for simulating daily flow patterns from monthly aggregated and monthly or weekly intermittent observations in two contrasting hydrological settings: near-natural and human-influenced watersheds. Results showed that the proposed approach reliably predicts daily flows from monthly aggregates with a median Nash-Sutcliffe efficiency (NSE) of 0.61 for near-natural and 0.48 for human-influenced watersheds. The proposed approach performed even better for daily flow predictions from monthly or weekly intermittent observation, achieving a median NSE of 0.70 and 0.55 for near-natural and human-influenced watersheds, respectively. The proposed approach remained robust across different seasons and hydrological regimes, with a median percentage bias of ±5%, except in arid regions. Moreover, data sensitivity analysis indicated that data from wet seasons were crucial for improving model predictions and that weekly data could yield results comparable to daily observations. Overall, this study demonstrates that the deep learning-based approach offers a robust and accurate representation of daily streamflow patterns from aggregated or intermittent observations, providing valuable hydrological insights and promising solutions for improving water resource management in regions with limited monitoring infrastructures.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888990","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}
Homa Salehabadi, David G. Tarboton, Kevin Wheeler, James Prairie, Rebecca Smith, Sarah Baker
Plausible future streamflow time series are essential for evaluating policies and management strategies in river basins and testing the operation of water resource systems. Relying solely on stationary historical data is not sufficient in a changing climate. However, uncertainty in the range of streamflow projections from General Circulation Models calls into question their direct use in water resources planning. An intermediate approach is needed to identify ensembles of streamflow time series based on well-defined assumptions that represent plausible future hydrologic conditions. This paper suggests multiple quantitative storylines of plausible future conditions, each matched with a representative streamflow ensemble to serve as inputs for planning models where, to account for uncertainty, plans or policies that are robust to a range of plausible futures are developed. Applying this approach in the Colorado River Basin we found that, while three storylines were well matched with existing ensembles, there was no suitable ensemble representing increasing variability around a declining mean. To address this gap, we developed a general method to create new streamflow ensembles that account for future changes by combining observed and paleo-reconstructed flows and adjusting the marginal distribution of the streamflow time series to incorporate the estimated decline in, and increasing variability of, future flow. The results are a set of quantitative storylines that justify a range of plausible future conditions, and a new warming-driven declining streamflow ensemble for use in Colorado River Basin scenario evaluation and decision-making representing the plausible increasing variability around a declining mean storyline.
{"title":"Developing Storylines of Plausible Future Streamflow and Generating a New Warming-Driven Declining Streamflow Ensemble: Colorado River Case Study","authors":"Homa Salehabadi, David G. Tarboton, Kevin Wheeler, James Prairie, Rebecca Smith, Sarah Baker","doi":"10.1029/2024wr038618","DOIUrl":"https://doi.org/10.1029/2024wr038618","url":null,"abstract":"Plausible future streamflow time series are essential for evaluating policies and management strategies in river basins and testing the operation of water resource systems. Relying solely on stationary historical data is not sufficient in a changing climate. However, uncertainty in the range of streamflow projections from General Circulation Models calls into question their direct use in water resources planning. An intermediate approach is needed to identify ensembles of streamflow time series based on well-defined assumptions that represent plausible future hydrologic conditions. This paper suggests multiple quantitative storylines of plausible future conditions, each matched with a representative streamflow ensemble to serve as inputs for planning models where, to account for uncertainty, plans or policies that are robust to a range of plausible futures are developed. Applying this approach in the Colorado River Basin we found that, while three storylines were well matched with existing ensembles, there was no suitable ensemble representing increasing variability around a declining mean. To address this gap, we developed a general method to create new streamflow ensembles that account for future changes by combining observed and paleo-reconstructed flows and adjusting the marginal distribution of the streamflow time series to incorporate the estimated decline in, and increasing variability of, future flow. The results are a set of quantitative storylines that justify a range of plausible future conditions, and a new warming-driven declining streamflow ensemble for use in Colorado River Basin scenario evaluation and decision-making representing the plausible increasing variability around a declining mean storyline.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"80 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887946","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}
Xuhui Shen, Jintao Liu, Xiaole Han, Hai Yang, Hu Liu, Feiyu Ni
In humid hilly regions, macropore preferential flow in soils dominates the distribution of event water, thereby influencing the generation and development of runoff. However, the mechanism of how soil functions on macropore drainage and matrix absorption remains poorly understood due to complex soil water dynamics in a multi-porosity subsurface network. In this study, based on the source-responsive method that divides the soil into source-responsive and diffusive domains, the allocation ratio of infiltrated water in macropores recharging the matrix were derived and it was coupled with PIHM (Penn State Integrated Hydrologic Model) as PIHM-SRM (PS). By simulating the soil moisture process at profile scale and the runoff process at catchment scale, it was found that the PS overcame the difficulty of most hydrologic models in describing the process of replenishing moisture in dry soil. This leads to more satisfactory performance for flood peaks at the outlet (CCC > 0.84) and soil moisture peaks at three profiles (CCC = 0.97) compared to original PIHM models. Moreover, the separate channel of film flow in the PS further improves the simulation accuracy of peak response speed in subsurface floods under rainstorms (TP > 40 mm). Additionally, sensitivity analysis shows that the storage-discharge capacity of soil profiles dominates torrential flood forecasting in humid headwaters when considering the influence of macropores. Finally, considering the parameter-predictive property in the PS, field-based parameterized strategies are vital for distributed catchment modeling. This will enable the PS to improve flash torrent predictions in headwaters and be applied at catchment scales.
{"title":"Modelling Infiltration Based on Source-Responsive Method for Improving Simulation of Rapid Subsurface Stormflow","authors":"Xuhui Shen, Jintao Liu, Xiaole Han, Hai Yang, Hu Liu, Feiyu Ni","doi":"10.1029/2024wr037487","DOIUrl":"https://doi.org/10.1029/2024wr037487","url":null,"abstract":"In humid hilly regions, macropore preferential flow in soils dominates the distribution of event water, thereby influencing the generation and development of runoff. However, the mechanism of how soil functions on macropore drainage and matrix absorption remains poorly understood due to complex soil water dynamics in a multi-porosity subsurface network. In this study, based on the source-responsive method that divides the soil into source-responsive and diffusive domains, the allocation ratio of infiltrated water in macropores recharging the matrix were derived and it was coupled with PIHM (Penn State Integrated Hydrologic Model) as PIHM-SRM (PS). By simulating the soil moisture process at profile scale and the runoff process at catchment scale, it was found that the PS overcame the difficulty of most hydrologic models in describing the process of replenishing moisture in dry soil. This leads to more satisfactory performance for flood peaks at the outlet (CCC > 0.84) and soil moisture peaks at three profiles (CCC = 0.97) compared to original PIHM models. Moreover, the separate channel of film flow in the PS further improves the simulation accuracy of peak response speed in subsurface floods under rainstorms (TP > 40 mm). Additionally, sensitivity analysis shows that the storage-discharge capacity of soil profiles dominates torrential flood forecasting in humid headwaters when considering the influence of macropores. Finally, considering the parameter-predictive property in the PS, field-based parameterized strategies are vital for distributed catchment modeling. This will enable the PS to improve flash torrent predictions in headwaters and be applied at catchment scales.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"5 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887948","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}
Anthropogenic perturbations have substantially altered riverine carbon cycling worldwide, exerting influences on dissolved carbon dioxide (CO2) and methane (CH4) dynamics at multiple levels. However, the magnitude and role of anthropogenic activities in modulating carbon emissions across entire river networks, as well as the influence of climatic controls, remain largely unresolved. Here, we explore the controlling factors of riverine CO2 and CH4 dynamics across 62 subtropical, monsoon-influenced streams and rivers through basin-wide seasonal measurements. We found that land use and aquatic metabolism played significant roles in regulating the spatial and temporal patterns of both gases. Increased nutrient levels and organic matter contributed to higher partial pressure of CO2 (pCO2) and CH4 (pCH4). Dissolved oxygen, stable carbon isotope of dissolved inorganic carbon, the proportion of impervious surface, catchment slope, and river width were the major predictors for pCO2. For pCH4, the major predictors were Chlorophyll a and water temperature, which influence organic matter availability and methanogenesis. Seasonal variations in pCO2 and pCH4 were strongly modulated by hydroclimatic conditions, with temperature markedly regulating river ecosystem metabolism. These findings highlight the likelihood of significant changes in riverine carbon emissions as climate changes and land use patterns evolve, thereby profoundly affecting the global carbon cycle.
{"title":"Anthropogenic and Hydroclimatic Controls on the CO2 and CH4 Dynamics in Subtropical Monsoon Rivers","authors":"Shuai Chen, Lishan Ran, Clément Duvert, Boyi Liu, Yongli Zhou, Xiankun Yang, Qianqian Yang, Yuxin Li, Si-Liang Li","doi":"10.1029/2024wr038341","DOIUrl":"https://doi.org/10.1029/2024wr038341","url":null,"abstract":"Anthropogenic perturbations have substantially altered riverine carbon cycling worldwide, exerting influences on dissolved carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) dynamics at multiple levels. However, the magnitude and role of anthropogenic activities in modulating carbon emissions across entire river networks, as well as the influence of climatic controls, remain largely unresolved. Here, we explore the controlling factors of riverine CO<sub>2</sub> and CH<sub>4</sub> dynamics across 62 subtropical, monsoon-influenced streams and rivers through basin-wide seasonal measurements. We found that land use and aquatic metabolism played significant roles in regulating the spatial and temporal patterns of both gases. Increased nutrient levels and organic matter contributed to higher partial pressure of CO<sub>2</sub> (<i>p</i>CO<sub>2</sub>) and CH<sub>4</sub> (<i>p</i>CH<sub>4</sub>). Dissolved oxygen, stable carbon isotope of dissolved inorganic carbon, the proportion of impervious surface, catchment slope, and river width were the major predictors for <i>p</i>CO<sub>2</sub>. For <i>p</i>CH<sub>4</sub>, the major predictors were Chlorophyll <i>a</i> and water temperature, which influence organic matter availability and methanogenesis. Seasonal variations in <i>p</i>CO<sub>2</sub> and <i>p</i>CH<sub>4</sub> were strongly modulated by hydroclimatic conditions, with temperature markedly regulating river ecosystem metabolism. These findings highlight the likelihood of significant changes in riverine carbon emissions as climate changes and land use patterns evolve, thereby profoundly affecting the global carbon cycle.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887949","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}
The color of lakes is an essential indicator of the local ecological state, and the corresponding changes can reflect the physical and biochemical processes of lakes. However, worldwide changes in lake color and their drivers remain largely unknown. Here, we analyze the long-term color distributions and changes of 67,579 lakes worldwide from 1984 to 2021 by utilizing 32 million consistent satellite observations. Blue lakes (<495 nm) were primarily located in high-latitude and high-elevation areas. Green lakes (495–560 nm) were more prevalent in densely populated middle-latitude regions, while most red and yellow colors (≥560 nm) were located in the Southern Hemisphere. Our findings reveal distinct temporal patterns of lake color changes, with the majority of global lakes shifted toward shorter wavelengths. This phenomenon is more common in Warm Temperate and Boreal zones. Lake color changes are closely linked to basin vegetation conditions, population, water volume change, and lake area. Our study provides essential references for monitoring the ecological status of global lakes, further supporting the sustainable development of water resources in the future.
{"title":"Satellite Observations Reveal Widespread Color Variations in Global Lakes Since the 1980s","authors":"Xiaoyi Shen, Chang-Qing Ke, Zheng Duan, Yu Cai, Haili Li, Yao Xiao","doi":"10.1029/2023wr036926","DOIUrl":"https://doi.org/10.1029/2023wr036926","url":null,"abstract":"The color of lakes is an essential indicator of the local ecological state, and the corresponding changes can reflect the physical and biochemical processes of lakes. However, worldwide changes in lake color and their drivers remain largely unknown. Here, we analyze the long-term color distributions and changes of 67,579 lakes worldwide from 1984 to 2021 by utilizing 32 million consistent satellite observations. Blue lakes (<495 nm) were primarily located in high-latitude and high-elevation areas. Green lakes (495–560 nm) were more prevalent in densely populated middle-latitude regions, while most red and yellow colors (≥560 nm) were located in the Southern Hemisphere. Our findings reveal distinct temporal patterns of lake color changes, with the majority of global lakes shifted toward shorter wavelengths. This phenomenon is more common in Warm Temperate and Boreal zones. Lake color changes are closely linked to basin vegetation conditions, population, water volume change, and lake area. Our study provides essential references for monitoring the ecological status of global lakes, further supporting the sustainable development of water resources in the future.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"63 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887243","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}
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