Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132206
Huan Xu , Hao Wang , Pan Liu
Catchment classification based on hydrological similarity helps to understand the control factors of hydrological behavior. However, the relationship between hydrological behavior and its influencing factors has been unclear in Mainland of China because long-term and widely-distributed flow data is unavailable. Thus, this study intends to identify control factors of hydrological behavior in China’s basins by using classification. Gauged basins are clustered into several classes using the fuzzy c-means method based on flow signatures, which quantify catchment hydrological behavior. The classification and regression tree is employed to learn from cluster results and then obtain classes of basins without observed flow. Correlation methods are used to analyze the influence of basin signatures on flow signatures, while the difference significance test is applied to the hydrological behavior diversity between clusters from classification and regression tree. Results show that China’s basins are divided into five clusters, with low flow signatures more distinguishing classes than high flow signatures. It confirms that climate factors dominate hydrological behavior. However, soil is also an important control factor found in this study, which is rare in others. These findings help to understand hydrological behavior in China and reveal its control factors.
{"title":"Identifying control factors of hydrological behavior through catchment classification in Mainland of China","authors":"Huan Xu , Hao Wang , Pan Liu","doi":"10.1016/j.jhydrol.2024.132206","DOIUrl":"10.1016/j.jhydrol.2024.132206","url":null,"abstract":"<div><div>Catchment classification based on hydrological similarity helps to understand the control factors of hydrological behavior. However, the relationship between hydrological behavior and its influencing factors has been unclear in Mainland of China because long-term and widely-distributed flow data is unavailable. Thus, this study intends to identify control factors of hydrological behavior in China’s basins by using classification. Gauged basins are clustered into several classes using the fuzzy c-means method based on flow signatures, which quantify catchment hydrological behavior. The classification and regression tree is employed to learn from cluster results and then obtain classes of basins without observed flow. Correlation methods are used to analyze the influence of basin signatures on flow signatures, while the difference significance test is applied to the hydrological behavior diversity between clusters from classification and regression tree. Results show that China’s basins are divided into five clusters, with low flow signatures more distinguishing classes than high flow signatures. It confirms that climate factors dominate hydrological behavior. However, soil is also an important control factor found in this study, which is rare in others. These findings help to understand hydrological behavior in China and reveal its control factors.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132206"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663518","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}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132204
Yubin Zhang , Xiaoqun Wang , Tianyu Feng , Jijian Lian , Pingping Luo , Madhab Rijal , Wentao Wei
In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.
{"title":"Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge","authors":"Yubin Zhang , Xiaoqun Wang , Tianyu Feng , Jijian Lian , Pingping Luo , Madhab Rijal , Wentao Wei","doi":"10.1016/j.jhydrol.2024.132204","DOIUrl":"10.1016/j.jhydrol.2024.132204","url":null,"abstract":"<div><div>In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132204"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530719","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}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132160
Christina Papadaki , Pantelis Mitropoulos , Yiannis Panagopoulos , Elias Dimitriou
Establishment of hydrological criteria that could serve as guidelines for addressing intermittency is not an easy task. However, efforts in the last years are yielding promising advancements in this direction. Scientists have been working to unravel the complexities of intermittency dynamics. In this study, we aimed to investigate the characteristics of naturally intermittent water systems by exploring the occurrence of no-flow events. A hydrological model was employed to generate streamflow data. Our analysis encompassed a thorough examination of nineteen flow regime metrics, estimated across 2064 subbasins, with 190 of these meeting the criterion for intermittency. A custom Python library was deployed to automate the quantification of no-flow events, aligning with the concept of critical thresholds. Using the probabilistic t-distributed Stochastic Neighbor Embedding technique to capture complex patterns, three clusters were emerged. The first one was characterized by a low probability of no-flow events and a small number of no-flow events per year, the second cluster was abundant in no-flow events and demonstrated a tendency towards longer annual recession time scales. The third cluster stands out due to the significant variance in the duration of no-flow events. Concerning the time variability of the no-flow events, we concluded that they predominantly occurred during August. Both long- and short-term quantification of no-flow events should be under consideration so as to harmonize the naturally intermittent waterways with the water use requirements and the potential consequences of not meeting them. Future research should prioritize the investigation of hydroecology and ecohydrology in relation to streamflow dynamics and ecosystem interactions. By doing so, we can elevate our comprehension of how intermittent water systems function and their significance within the broader ecological context.
{"title":"Addressing large scale patterns of no-flow events in rivers: An in-depth analysis with Achelous software","authors":"Christina Papadaki , Pantelis Mitropoulos , Yiannis Panagopoulos , Elias Dimitriou","doi":"10.1016/j.jhydrol.2024.132160","DOIUrl":"10.1016/j.jhydrol.2024.132160","url":null,"abstract":"<div><div>Establishment of hydrological criteria that could serve as guidelines for addressing intermittency is not an easy task. However, efforts in the last years are yielding promising advancements in this direction. Scientists have been working to unravel the complexities of intermittency dynamics. In this study, we aimed to investigate the characteristics of naturally intermittent water systems by exploring the occurrence of no-flow events. A hydrological model was employed to generate streamflow data. Our analysis encompassed a thorough examination of nineteen flow regime metrics, estimated across 2064 subbasins, with 190 of these meeting the criterion for intermittency. A custom Python library was deployed to automate the quantification of no-flow events, aligning with the concept of critical thresholds. Using the probabilistic t-distributed Stochastic Neighbor Embedding technique to capture complex patterns, three clusters were emerged. The first one was characterized by a low probability of no-flow events and a small number of no-flow events per year, the second cluster was abundant in no-flow events and demonstrated a tendency towards longer annual recession time scales. The third cluster stands out due to the significant variance in the duration of no-flow events. Concerning the time variability of the no-flow events, we concluded that they predominantly occurred during August. Both long- and short-term quantification of no-flow events should be under consideration so as to harmonize the naturally intermittent waterways with the water use requirements and the potential consequences of not meeting them. Future research should prioritize the investigation of hydroecology and ecohydrology in relation to streamflow dynamics and ecosystem interactions. By doing so, we can elevate our comprehension of how intermittent water systems function and their significance within the broader ecological context.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132160"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530731","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}
Pub Date : 2024-10-21DOI: 10.1016/j.jhydrol.2024.132237
Xihua Yang , John Young , Haijing Shi , Qinggaozi Zhu , Ian Pulsford , Greg Chapman , Leah Moore , Angela G Gormley , Richard Thackway , Tim Shepherd
Understanding the dynamics of sediment transport and deposition in natural landscapes is critical to developing cost-effective mitigation measures to control soil erosion and protect ecosystems. However, none of a single existing model can quantify sediment delivery ratio (SDR) and the impact factors such as vegetation and geomorphology, especially in a complex landscape. In this case study, we applied an integrated approach including the revised universal soil loss equation (RUSLE) and the index of connectivity (IC) to assess hillslope erosion and SDR, namely RUSLE-IC-SDR, across a complex landscape in the Lower Snowy River area, Australia. The RUSLE factors were derived from a high-resolution (2 m) digital elevation model (DEM), digital soil maps, high-resolution rainfall data and remotely sensed fractional vegetation cover. A seven-class landform classification was delineated from the high-resolution DEM using a fuzzy logic landform model (FLAG). We further examined the impacts of rainfall, vegetation cover and geomorphology on sediment dynamics and distribution across the study area. Field and laboratory data from 10 plot sites across the study area were collected and used for model validation. This case study showed that the RUSLE-IC-SDR approach can assess the overall sediment budget and the impacts of rainfall, vegetation cover and geomorphology across a complex landscape. Findings from this study can identify and track the areas likely to generate high sediment yield for developing ecological restoration, feral animal management and other catchment management measures.
{"title":"Estimating sediment delivery ratio using the RUSLE-IC-SDR approach at a complex landscape: A case study at the Lower Snowy River area, Australia","authors":"Xihua Yang , John Young , Haijing Shi , Qinggaozi Zhu , Ian Pulsford , Greg Chapman , Leah Moore , Angela G Gormley , Richard Thackway , Tim Shepherd","doi":"10.1016/j.jhydrol.2024.132237","DOIUrl":"10.1016/j.jhydrol.2024.132237","url":null,"abstract":"<div><div>Understanding the dynamics of sediment transport and deposition in natural landscapes is critical to developing cost-effective mitigation measures to control soil erosion and protect ecosystems. However, none of a single existing model can quantify sediment delivery ratio (SDR) and the impact factors such as vegetation and geomorphology, especially in a complex landscape. In this case study, we applied an integrated approach including the revised universal soil loss equation (RUSLE) and the index of connectivity (IC) to assess hillslope erosion and SDR, namely RUSLE-IC-SDR, across a complex landscape in the Lower Snowy River area, Australia. The RUSLE factors were derived from a high-resolution (2 m) digital elevation model (DEM), digital soil maps, high-resolution rainfall data and remotely sensed fractional vegetation cover. A seven-class landform classification was delineated from the high-resolution DEM using a fuzzy logic landform model (FLAG). We further examined the impacts of rainfall, vegetation cover and geomorphology on sediment dynamics and distribution across the study area. Field and laboratory data from 10 plot sites across the study area were collected and used for model validation. This case study showed that the RUSLE-IC-SDR approach can assess the overall sediment budget and the impacts of rainfall, vegetation cover and geomorphology across a complex landscape. Findings from this study can identify and track the areas likely to generate high sediment yield for developing ecological restoration, feral animal management and other catchment management measures.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132237"},"PeriodicalIF":5.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663570","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}
Pub Date : 2024-10-21DOI: 10.1016/j.jhydrol.2024.132219
Taizheng Liu , Yuqing Zhang , Bin Guo , Shuming Zhang , Xin Li
Increased frequency and magnitude of compound droughts and heatwaves (CDH) under climate warming pose a severe threat to food production on cropland, biodiversity in forests and grasslands, as well as the health of urban populations. However, there is a lack of comprehensive assessments on the different land use types exposed to CDH events. In this study, we explored the changes in cropland, forest, grassland, urban, and bare land exposure to CDH frequency and magnitude (CDHMI) in China under different emission scenarios in the far-future (2070–2099) based on 12 model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and Land Use Harmonization Version 2 (LUH2) data. The results indicate that with global warming, China is expected to face more frequent and severe CDH events in the future, particularly under high-emission scenarios. Correspondingly, Cropland, forest, grassland, and bare land exposure to CDH frequency and CDHMI show significant upward trends during 2015–2099, increasing at greater rates in high emission scenarios. Although the urban exposure to CDH frequency and CDHMI is projected to decelerate or even decline after 2050, urban exposure to CDH frequency and CDHMI under high-emission scenario will still increase by 605.20% and 207.32% during the far-future period (2070–2099) compared to 1981–2010, respectively. Regionally, the substantial increase in cropland, forest, grassland, urban, and bare land exposure to CDH frequency and CDHMI is concentrated in Northwestern China and Southern China due to the significant rise in frequency and magnitude of CDH events in these areas. The conclusions underline the importance and urgency of taking effective measures to limit emissions and respond to climate change.
{"title":"Substantial increase in future land exposure to compound droughts and heatwaves in China dominated by climate change","authors":"Taizheng Liu , Yuqing Zhang , Bin Guo , Shuming Zhang , Xin Li","doi":"10.1016/j.jhydrol.2024.132219","DOIUrl":"10.1016/j.jhydrol.2024.132219","url":null,"abstract":"<div><div>Increased frequency and magnitude of compound droughts and heatwaves (CDH) under climate warming pose a severe threat to food production on cropland, biodiversity in forests and grasslands, as well as the health of urban populations. However, there is a lack of comprehensive assessments on the different land use types exposed to CDH events. In this study, we explored the changes in cropland, forest, grassland, urban, and bare land exposure to CDH frequency and magnitude (CDHMI) in China under different emission scenarios in the far-future (2070–2099) based on 12 model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and Land Use Harmonization Version 2 (LUH2) data. The results indicate that with global warming, China is expected to face more frequent and severe CDH events in the future, particularly under high-emission scenarios. Correspondingly, Cropland, forest, grassland, and bare land exposure to CDH frequency and CDHMI show significant upward trends during 2015–2099, increasing at greater rates in high emission scenarios. Although the urban exposure to CDH frequency and CDHMI is projected to decelerate or even decline after 2050, urban exposure to CDH frequency and CDHMI under high-emission scenario will still increase by 605.20% and 207.32% during the far-future period (2070–2099) compared to 1981–2010, respectively. Regionally, the substantial increase in cropland, forest, grassland, urban, and bare land exposure to CDH frequency and CDHMI is concentrated in Northwestern China and Southern China due to the significant rise in frequency and magnitude of CDH events in these areas. The conclusions underline the importance and urgency of taking effective measures to limit emissions and respond to climate change.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132219"},"PeriodicalIF":5.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554650","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}
Pub Date : 2024-10-21DOI: 10.1016/j.jhydrol.2024.132203
Zi’ang Ni , Qianqian Yang , Linwei Yue , Yanfei Peng , Qiangqiang Yuan
While ground meteorological stations provide accurate snow depth data, their limited spatial coverage results in observational gaps. Satellites offer long-term, large-scale observations, addressing these gaps. Existing snow depth retrieval algorithms mainly use passive microwave remote sensing data with a 25 km resolution, insufficient for capturing snow depth variability in mountainous areas. This paper introduces active microwave backscatter data and machine learning techniques for high-resolution snow depth estimation. We conducted a preliminary exploration of the relationship between Sentinel-1 backscatter coefficient and snow depth. Due to factors such as vegetation coverage and underlying soil properties, the relationship between and snow depth is complex and nonlinear. Consequently, six machine learning models were trained to learn this relationship using and auxiliary data as input features, with in-situ snow depth serving as the target variable. After extensive validation, the Extreme Random Trees (ERT) model was selected for its high accuracy and stability. Using the ERT model, we generated 500 m-resolution snow depth data for northern hemisphere mountains, then analyzed temporal snow depth variations and altitudinal stratification.
{"title":"Estimating high-resolution snow depth over the North Hemisphere mountains utilizing active microwave backscatter and machine learning","authors":"Zi’ang Ni , Qianqian Yang , Linwei Yue , Yanfei Peng , Qiangqiang Yuan","doi":"10.1016/j.jhydrol.2024.132203","DOIUrl":"10.1016/j.jhydrol.2024.132203","url":null,"abstract":"<div><div>While ground meteorological stations provide accurate snow depth data, their limited spatial coverage results in observational gaps. Satellites offer long-term, large-scale observations, addressing these gaps. Existing snow depth retrieval algorithms mainly use passive microwave remote sensing data with a 25 km resolution, insufficient for capturing snow depth variability in mountainous areas. This paper introduces active microwave backscatter data and machine learning techniques for high-resolution snow depth estimation. We conducted a preliminary exploration of the relationship between Sentinel-1 backscatter coefficient <span><math><mrow><msup><mrow><mi>σ</mi></mrow><mn>0</mn></msup></mrow></math></span> and snow depth. Due to factors such as vegetation coverage and underlying soil properties, the relationship between <span><math><mrow><msup><mrow><mi>σ</mi></mrow><mn>0</mn></msup></mrow></math></span> and snow depth is complex and nonlinear. Consequently, six machine learning models were trained to learn this relationship using <span><math><mrow><msup><mrow><mi>σ</mi></mrow><mn>0</mn></msup></mrow></math></span> and auxiliary data as input features, with in-situ snow depth serving as the target variable. After extensive validation, the Extreme Random Trees (ERT) model was selected for its high accuracy and stability. Using the ERT model, we generated 500 m-resolution snow depth data for northern hemisphere mountains, then analyzed temporal snow depth variations and altitudinal stratification.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132203"},"PeriodicalIF":5.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530716","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}
Pub Date : 2024-10-21DOI: 10.1016/j.jhydrol.2024.132232
Wei Wei , Jiping Wang , Xufeng Wang , Yongze Song , Mohsen Sherif , Xiangyu Wang , Ashraf Dewan , Omri Y Ram , Peng Yan , Ting Liu , Dang Lu , Yongfan Guo , Yingqiang Li
Assessing the stability of terrestrial water storage (TWS) under drought conditions is critical for the sustainable development of water resources. In this study, we integrated surface temperature (ST), leaf area index (LAI), and precipitation (P) data from five different scenarios (History, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to develop a standardized temperature vegetation precipitation index (STVPI). The index was then utilized to monitor global drought conditions and investigate the stability of TWS to drought disaster. The results showed that STVPI can not only monitor meteorological drought, but also has a remarkable sensitivity and applicability to drought caused by sparse vegetation. Notably, 21.16% of the global land area will have a drought trend under the SSP1-2.6 scenario, while it will rise to 35.81% under the SSP5-8.5 scenario, which underscored the potential for an expansion of drought-affected regions worldwide as a result of ongoing global warming and escalating emissions. In addition, the results also found that the warm temperate and tropical regions at lower elevations have an advantage in maintaining the stability of TWS. Unfortunately, the stability of TWS to drought will decline in the western Sahara Desert, central China and northern United States in the future, where will face a serious water crisis. The research framework provides an important reference for deeply evaluating and scientifically allocating water resources under climate change.
{"title":"Assessing the stability of terrestrial water storage to drought based on CMIP6 forcing scenarios","authors":"Wei Wei , Jiping Wang , Xufeng Wang , Yongze Song , Mohsen Sherif , Xiangyu Wang , Ashraf Dewan , Omri Y Ram , Peng Yan , Ting Liu , Dang Lu , Yongfan Guo , Yingqiang Li","doi":"10.1016/j.jhydrol.2024.132232","DOIUrl":"10.1016/j.jhydrol.2024.132232","url":null,"abstract":"<div><div>Assessing the stability of terrestrial water storage (TWS) under drought conditions is critical for the sustainable development of water resources. In this study, we integrated surface temperature (ST), leaf area index (LAI), and precipitation (P) data from five different scenarios (History, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to develop a standardized temperature vegetation precipitation index (STVPI). The index was then utilized to monitor global drought conditions and investigate the stability of TWS to drought disaster. The results showed that STVPI can not only monitor meteorological drought, but also has a remarkable sensitivity and applicability to drought caused by sparse vegetation. Notably, 21.16% of the global land area will have a drought trend under the SSP1-2.6 scenario, while it will rise to 35.81% under the SSP5-8.5 scenario, which underscored the potential for an expansion of drought-affected regions worldwide as a result of ongoing global warming and escalating emissions. In addition, the results also found that the warm temperate and tropical regions at lower elevations have an advantage in maintaining the stability of TWS. Unfortunately, the stability of TWS to drought will decline in the western Sahara Desert, central China and northern United States in the future, where will face a serious water crisis. The research framework provides an important reference for deeply evaluating and scientifically allocating water resources under climate change.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132232"},"PeriodicalIF":5.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530781","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}
Pub Date : 2024-10-21DOI: 10.1016/j.jhydrol.2024.132235
Renjie Zhou , Quanrong Wang , Aohan Jin , Wenguang Shi , Shiqi Liu
Karst groundwater is a critical freshwater resource for numerous regions worldwide. Monitoring and predicting karst spring discharge is essential for effective groundwater management and the preservation of karst ecosystems. However, the high heterogeneity and karstification pose significant challenges to physics-based models in providing robust predictions of karst spring discharge. In this study, an interpretable multi-step hybrid deep learning model called selective EEMD-TFT is proposed, which adaptively integrates temporal fusion transformers (TFT) with ensemble empirical mode decomposition (EEMD) for predicting karst spring discharge. The selective EEMD-TFT hybrid model leverages the strengths of both EEMD and TFT techniques to learn inherent patterns and temporal dynamics from nonlinear and nonstationary signals, eliminate redundant components, and emphasize useful characteristics of input variables, leading to the improvement of prediction performance and efficiency. It consists of two stages: in the first stage, the daily precipitation data is decomposed into multiple intrinsic mode functions using EEMD to extract valuable information from nonlinear and nonstationary signals. All decomposed components, temperature and categorical date features are then fed into the TFT model, which is an attention-based deep learning model that combines high-performance multi-horizon prediction and interpretable insights into temporal dynamics. The importance of input variables will be quantified and ranked. In the second stage, the decomposed precipitation components with high importance are selected to serve as the TFT model’s input features along with temperature and categorical date variables for the final prediction. Results indicate that the selective EEMD-TFT model outperforms other sequence-to-sequence deep learning models, such as LSTM and single TFT models, delivering reliable and robust prediction performance. Notably, it maintains more consistent prediction performance at longer forecast horizons compared to other sequence-to-sequence models, highlighting its capacity to learn complex patterns from the input data and efficiently extract valuable information for karst spring prediction. An interpretable analysis of the selective EEMD-TFT model is conducted to gain insights into relationships among various hydrological processes and analyze temporal patterns.
{"title":"Interpretable multi-step hybrid deep learning model for karst spring discharge prediction: Integrating temporal fusion transformers with ensemble empirical mode decomposition","authors":"Renjie Zhou , Quanrong Wang , Aohan Jin , Wenguang Shi , Shiqi Liu","doi":"10.1016/j.jhydrol.2024.132235","DOIUrl":"10.1016/j.jhydrol.2024.132235","url":null,"abstract":"<div><div>Karst groundwater is a critical freshwater resource for numerous regions worldwide. Monitoring and predicting karst spring discharge is essential for effective groundwater management and the preservation of karst ecosystems. However, the high heterogeneity and karstification pose significant challenges to physics-based models in providing robust predictions of karst spring discharge. In this study, an interpretable multi-step hybrid deep learning model called selective EEMD-TFT is proposed, which adaptively integrates temporal fusion transformers (TFT) with ensemble empirical mode decomposition (EEMD) for predicting karst spring discharge. The selective EEMD-TFT hybrid model leverages the strengths of both EEMD and TFT techniques to learn inherent patterns and temporal dynamics from nonlinear and nonstationary signals, eliminate redundant components, and emphasize useful characteristics of input variables, leading to the improvement of prediction performance and efficiency. It consists of two stages: in the first stage, the daily precipitation data is decomposed into multiple intrinsic mode functions using EEMD to extract valuable information from nonlinear and nonstationary signals. All decomposed components, temperature and categorical date features are then fed into the TFT model, which is an attention-based deep learning model that combines high-performance multi-horizon prediction and interpretable insights into temporal dynamics. The importance of input variables will be quantified and ranked. In the second stage, the decomposed precipitation components with high importance are selected to serve as the TFT model’s input features along with temperature and categorical date variables for the final prediction. Results indicate that the selective EEMD-TFT model outperforms other sequence-to-sequence deep learning models, such as LSTM and single TFT models, delivering reliable and robust prediction performance. Notably, it maintains more consistent prediction performance at longer forecast horizons compared to other sequence-to-sequence models, highlighting its capacity to learn complex patterns from the input data and efficiently extract valuable information for karst spring prediction. An interpretable analysis of the selective EEMD-TFT model is conducted to gain insights into relationships among various hydrological processes and analyze temporal patterns.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132235"},"PeriodicalIF":5.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530898","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}
Pub Date : 2024-10-20DOI: 10.1016/j.jhydrol.2024.132207
Arlex Marin-Ramirez , David Tyler Mahoney , Brenden Riddle , Leonie Bettel , James F. Fox
Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.
{"title":"Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models","authors":"Arlex Marin-Ramirez , David Tyler Mahoney , Brenden Riddle , Leonie Bettel , James F. Fox","doi":"10.1016/j.jhydrol.2024.132207","DOIUrl":"10.1016/j.jhydrol.2024.132207","url":null,"abstract":"<div><div>Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132207"},"PeriodicalIF":5.9,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530908","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}
Pub Date : 2024-10-20DOI: 10.1016/j.jhydrol.2024.132222
Hong Lv , Zening Wu , Xiaokang Zheng , Dengming Yan , Zhilei Yu , Wenxiu Shang
Flood-bearing bodies are urban components directly impacted and damaged by disasters. Current methods for attribute identification and diagnosis of flood-bearing bodies, relying on real-time monitoring, are inadequate for pre-disaster forecasting and lack comprehensiveness. To reduce the uncertainty associated with single data sources, a Dual Path Network (DPN) method was employed to extract feature vectors based on multi-source datasets. A meta-classifier was constructed by integrating five base learners using Stacking, optimized by Quantum Particle Swarm Optimization (QPSO)-enhanced Gaussian Process Regression, forming an ensemble learner for predicting urban spatial classification. Utilizing GIS proximity analysis functions, attributes of functional zones, spatial attributes of points of interest (POI), and flood loss were assigned to each flood-bearing body grid. By overlaying urban flood inundation maps, multi-attribute diagnosis of flood-bearing bodies was achieved. The Jinshui District of Zhengzhou, China, is selected as the study area. The results show: (1) Predictions of urban functional zone categories in four other districts of Zhengzhou showed an average accuracy rate of 78.5 % through random sampling point validation. The threshold effect of prediction accuracy at different scales was significant. (2) Simulated flood economic losses for recurrence intervals of 1 year, 5 years, 10 years, 20 years, 50 years, and 100 years exhibited an exponential growth trend. (3) The multiple flood-bearing attributes of each flooded grid can be diagnosed. Finally, the model was effectively verified by simulating and comparing historical data from the “7·20” flood event in Zhengzhou.
{"title":"Multi-attribute diagnosis of urban flood-bearing bodies based on integrated learning with Stacking–GPR–QPSO coupling","authors":"Hong Lv , Zening Wu , Xiaokang Zheng , Dengming Yan , Zhilei Yu , Wenxiu Shang","doi":"10.1016/j.jhydrol.2024.132222","DOIUrl":"10.1016/j.jhydrol.2024.132222","url":null,"abstract":"<div><div>Flood-bearing bodies are urban components directly impacted and damaged by disasters. Current methods for attribute identification and diagnosis of flood-bearing bodies, relying on real-time monitoring, are inadequate for pre-disaster forecasting and lack comprehensiveness. To reduce the uncertainty associated with single data sources, a Dual Path Network (DPN) method was employed to extract feature vectors based on multi-source datasets. A meta-classifier was constructed by integrating five base learners using Stacking, optimized by Quantum Particle Swarm Optimization (QPSO)-enhanced Gaussian Process Regression, forming an ensemble learner for predicting urban spatial classification. Utilizing GIS proximity analysis functions, attributes of functional zones, spatial attributes of points of interest (POI), and flood loss were assigned to each flood-bearing body grid. By overlaying urban flood inundation maps, multi-attribute diagnosis of flood-bearing bodies was achieved. The Jinshui District of Zhengzhou, China, is selected as the study area. The results show: (1) Predictions of urban functional zone categories in four other districts of Zhengzhou showed an average accuracy rate of 78.5 % through random sampling point validation. The threshold effect of prediction accuracy at different scales was significant. (2) Simulated flood economic losses for recurrence intervals of 1 year, 5 years, 10 years, 20 years, 50 years, and 100 years exhibited an exponential growth trend. (3) The multiple flood-bearing attributes of each flooded grid can be diagnosed. Finally, the model was effectively verified by simulating and comparing historical data from the “7·20” flood event in Zhengzhou.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132222"},"PeriodicalIF":5.9,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530779","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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