Abstract. Severe water erosion occurs during extreme storm events. Such an exceedingly severe storm occurred in Zhengzhou in central China on 20 July 2021 (the 7.20 storm). The magnitude and frequency of occurrence of this storm event were examined in terms of how erosive it was. To contextualize this extreme event, hourly rainfall data from 2420 automatic meteorological stations in China from 1951 to 2021 were analyzed to (1) characterize the spatial and temporal distribution of the rainfall amount and rainfall erosivity of the 7.20 storm, (2) evaluate the average recurrence interval of the maximum daily and event rainfall erosivity, and (3) establish the geographical distribution of the maximum daily and event rainfall erosivity in China. The center of the 7.20 storm moved from southeast to northwest in Henan Province, and the most intense period of rainfall occurred in the middle and late stages of the storm. Zhengzhou Meteorological Station happened to be aligned with the center of the storm, with a maximum daily rainfall of 552.5 mm and a maximum hourly rainfall intensity of 201.9 mm h−1. The average recurrence intervals of the maximum daily rainfall erosivity (43 354±1863 MJ mm ha−1 h−1) and the maximum event rainfall erosivity (58 874±2351 MJ mm ha−1 h−1) were estimated to be about 19 200 and 53 700 years, respectively, assuming the log-Pearson type-III distribution, and these were the maximum rainfall erosivities ever recorded among 2420 meteorological stations in mainland China up to 2022. The 7.20 storm suggests that the most erosive of storms does not necessarily occur in the wettest places in southern China, and these can occur in mid-latitude around 35∘ N with a moderate mean annual rainfall of 566.7 mm in Zhengzhou.
{"title":"The most extreme rainfall erosivity event ever recorded in China up to 2022: the 7.20 storm in Henan Province","authors":"Yuanyuan Xiao, S. Yin, Bofu Yu, Conghui Fan, Wenting Wang, Yun Xie","doi":"10.5194/hess-27-4563-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4563-2023","url":null,"abstract":"Abstract. Severe water erosion occurs during extreme storm events. Such an exceedingly severe storm occurred in Zhengzhou in central China on 20 July 2021 (the 7.20 storm). The magnitude and frequency of occurrence of this storm event were examined in terms of how erosive it was. To contextualize this extreme event, hourly rainfall data from 2420 automatic meteorological stations in China from 1951 to 2021 were analyzed to (1) characterize the spatial and temporal distribution of the rainfall amount and rainfall erosivity of the 7.20 storm, (2) evaluate the average recurrence interval of the maximum daily and event rainfall erosivity, and (3) establish the geographical distribution of the maximum daily and event rainfall erosivity in China. The center of the 7.20 storm moved from southeast to northwest in Henan Province, and the most intense period of rainfall occurred in the middle and late stages of the storm. Zhengzhou Meteorological Station happened to be aligned with the center of the storm, with a maximum daily rainfall of 552.5 mm and a maximum hourly rainfall intensity of 201.9 mm h−1. The average recurrence intervals of the maximum daily rainfall erosivity (43 354±1863 MJ mm ha−1 h−1) and the maximum event rainfall erosivity (58 874±2351 MJ mm ha−1 h−1) were estimated to be about 19 200 and 53 700 years, respectively, assuming the log-Pearson type-III distribution, and these were the maximum rainfall erosivities ever recorded among 2420 meteorological stations in mainland China up to 2022. The 7.20 storm suggests that the most erosive of storms does not necessarily occur in the wettest places in southern China, and these can occur in mid-latitude around 35∘ N with a moderate mean annual rainfall of 566.7 mm in Zhengzhou.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"21 4","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139168820","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 : 2023-12-20DOI: 10.5194/hess-27-4467-2023
Nkosinathi D Kaptein, Colin S. Everson, A. Clulow, Michele Lynn Toucher, I. Germishuizen
Abstract. Pine plantations are the dominant species currently planted within the South African commercial forestry industry. Improvements in bio-economy markets for dissolving wood pulp products have seen an expansion in fast-growing Eucalyptus plantations due to their higher productivity rates and better pulping properties than pine. This has raised concerns regarding the expansion of Eucalyptus plantations and how they will affect water resources as they have been reported to have higher water use (quantified using transpiration rates) than pine. We measured transpiration rates (mm yr−1), diameter at breast height (quantified as quadratic mean diameter, Dq, m) and leaf area index of an 8-year-old Eucalyptus grandis × Eucalyptus nitens clonal hybrid (GN) and a 20-year-old Pinus elliottii. Transpiration rates were measured for two consecutive hydrological years (2019/20 and 2020/21) using a heat ratio sap-flow method, calibrated against a lysimeter. In the 2019/20 year, annual transpiration for P. elliottii exceeded GN by 28 %, while for the 2020/21 hydrological year, there was no significant difference between the transpiration of the two species, despite a 17 % and 21 % greater leaf area index for P. elliottii than GN in 2019/20 and 2020/21 measurement years respectively. Quadratic mean diameter increments were statistically similar (p > 0.05) in 2019/20, whereas the 2020/21 year produced significant differences (p<0.05). Tree transpiration is known to be influenced by climatic variables; therefore, a random forest regression model was used to test the level of influence between tree transpiration and climatic parameters. The soil water content, solar radiation and vapour pressure deficit were found to highly influence transpiration, suggesting these variables can be used in future water-use modelling studies. The profile water content recharge was influenced by rainfall events. After rainfall and soil profile water recharge, there was a rapid depletion of soil water by the GN trees, while the soil profile was depleted more gradually at the P. elliottii site. As a result, trees at the GN site appeared to be water stressed (reduced stem diameters and transpiration), suggesting that there was limited access to alternative water source (such as groundwater). The study concluded that previous long-term paired catchment studies indicate that eucalypts use more water than pine; however, periods of soil water stress and reduced transpiration observed in this study must be accommodated in hydrological models. Long-term total soil water balance studies are recommended in the same region to understand the long-term impact of commercial plantations on water resources.
{"title":"Transpiration rates from mature Eucalyptus grandis × E. nitens clonal hybrid and Pinus elliottii plantations near the Two Streams Research Catchment, South Africa","authors":"Nkosinathi D Kaptein, Colin S. Everson, A. Clulow, Michele Lynn Toucher, I. Germishuizen","doi":"10.5194/hess-27-4467-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4467-2023","url":null,"abstract":"Abstract. Pine plantations are the dominant species currently planted within the South African commercial forestry industry. Improvements in bio-economy markets for dissolving wood pulp products have seen an expansion in fast-growing Eucalyptus plantations due to their higher productivity rates and better pulping properties than pine. This has raised concerns regarding the expansion of Eucalyptus plantations and how they will affect water resources as they have been reported to have higher water use (quantified using transpiration rates) than pine. We measured transpiration rates (mm yr−1), diameter at breast height (quantified as quadratic mean diameter, Dq, m) and leaf area index of an 8-year-old Eucalyptus grandis × Eucalyptus nitens clonal hybrid (GN) and a 20-year-old Pinus elliottii. Transpiration rates were measured for two consecutive hydrological years (2019/20 and 2020/21) using a heat ratio sap-flow method, calibrated against a lysimeter. In the 2019/20 year, annual transpiration for P. elliottii exceeded GN by 28 %, while for the 2020/21 hydrological year, there was no significant difference between the transpiration of the two species, despite a 17 % and 21 % greater leaf area index for P. elliottii than GN in 2019/20 and 2020/21 measurement years respectively. Quadratic mean diameter increments were statistically similar (p > 0.05) in 2019/20, whereas the 2020/21 year produced significant differences (p<0.05). Tree transpiration is known to be influenced by climatic variables; therefore, a random forest regression model was used to test the level of influence between tree transpiration and climatic parameters. The soil water content, solar radiation and vapour pressure deficit were found to highly influence transpiration, suggesting these variables can be used in future water-use modelling studies. The profile water content recharge was influenced by rainfall events. After rainfall and soil profile water recharge, there was a rapid depletion of soil water by the GN trees, while the soil profile was depleted more gradually at the P. elliottii site. As a result, trees at the GN site appeared to be water stressed (reduced stem diameters and transpiration), suggesting that there was limited access to alternative water source (such as groundwater). The study concluded that previous long-term paired catchment studies indicate that eucalypts use more water than pine; however, periods of soil water stress and reduced transpiration observed in this study must be accommodated in hydrological models. Long-term total soil water balance studies are recommended in the same region to understand the long-term impact of commercial plantations on water resources.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"24 3","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139169399","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 : 2023-12-20DOI: 10.5194/hess-27-4579-2023
Andre Peters, S. Iden, W. Durner
Abstract. To model water, solute, and energy transport in porous media, it is essential to have accurate information about the soil hydraulic properties (SHPs), i.e., the water retention curve (WRC) and the soil hydraulic conductivity curve (HCC). It is important to have reliable data to parameterize these models, but equally critical is the selection of appropriate SHP models. While various expressions for the WRC are frequently compared, the capillary conductivity model proposed by Mualem (1976a) is widely used but rarely compared to alternatives. The objective of this study was to compare four different capillary bundle models in terms of their ability to accurately predict the HCC without scaling the conductivity function by a measured conductivity value. The four capillary bundle models include two simple models proposed by Burdine (1953) and Alexander and Skaggs (1986), which assume a bundle of parallel capillaries with tortuous flow paths, and two more sophisticated models based on statistical cut-and-random-rejoin approaches, namely those proposed by Childs and Collis-George (1950) and the aforementioned model of Mualem (1976a). To examine how the choice of the WRC parameterization affects the adequacy of different capillary bundle models, we utilized four different capillary saturation models in combination with each of the conductivity prediction models, resulting in 16 SHP model schemes. All schemes were calibrated using 12 carefully selected data sets that provided water retention and hydraulic conductivity data over a wide saturation range. Subsequently, the calibrated models were tested and rated by their ability to predict the hydraulic conductivity of 23 independent data sets of soils with varying textures. The statistical cut-and-random-rejoin models, particularly the Mualem (1976a) model, outperformed the simpler capillary bundle models in terms of predictive accuracy. This was independent of the specific WRC model used. Our findings suggest that the widespread use of the Mualem model is justified.
{"title":"Prediction of absolute unsaturated hydraulic conductivity – comparison of four different capillary bundle models","authors":"Andre Peters, S. Iden, W. Durner","doi":"10.5194/hess-27-4579-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4579-2023","url":null,"abstract":"Abstract. To model water, solute, and energy transport in porous media, it is essential to have accurate information about the soil hydraulic properties (SHPs), i.e., the water retention curve (WRC) and the soil hydraulic conductivity curve (HCC). It is important to have reliable data to parameterize these models, but equally critical is the selection of appropriate SHP models. While various expressions for the WRC are frequently compared, the capillary conductivity model proposed by Mualem (1976a) is widely used but rarely compared to alternatives. The objective of this study was to compare four different capillary bundle models in terms of their ability to accurately predict the HCC without scaling the conductivity function by a measured conductivity value. The four capillary bundle models include two simple models proposed by Burdine (1953) and Alexander and Skaggs (1986), which assume a bundle of parallel capillaries with tortuous flow paths, and two more sophisticated models based on statistical cut-and-random-rejoin approaches, namely those proposed by Childs and Collis-George (1950) and the aforementioned model of Mualem (1976a). To examine how the choice of the WRC parameterization affects the adequacy of different capillary bundle models, we utilized four different capillary saturation models in combination with each of the conductivity prediction models, resulting in 16 SHP model schemes. All schemes were calibrated using 12 carefully selected data sets that provided water retention and hydraulic conductivity data over a wide saturation range. Subsequently, the calibrated models were tested and rated by their ability to predict the hydraulic conductivity of 23 independent data sets of soils with varying textures. The statistical cut-and-random-rejoin models, particularly the Mualem (1976a) model, outperformed the simpler capillary bundle models in terms of predictive accuracy. This was independent of the specific WRC model used. Our findings suggest that the widespread use of the Mualem model is justified.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"303 ","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139170662","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 : 2023-12-20DOI: 10.5194/hess-27-4529-2023
Yuhang Zhang, Aizhong Ye, B. Analui, P. Nguyen, S. Sorooshian, K. Hsu, Yuxuan Wang
Abstract. Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing.
{"title":"Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations","authors":"Yuhang Zhang, Aizhong Ye, B. Analui, P. Nguyen, S. Sorooshian, K. Hsu, Yuxuan Wang","doi":"10.5194/hess-27-4529-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4529-2023","url":null,"abstract":"Abstract. Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"112 ","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139170895","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}
Abstract. In water-scarce arid areas, the water cycle is affected by urban development and natural river changes, and urbanization has a profound impact on the hydrological system of the basin. Through an ecohydrological observation system established in the Shiyang River basin in the inland arid zone, we studied the impact of urbanization on the water cycle of the basin using isotope methods. The results showed that urbanization significantly changed the water cycle process in the basin and accelerated the rainfall-runoff process due to the increase in urban land area, and the mean residence time (MRT) of river water showed a fluctuating downward trend from upstream to downstream and was shortest in the urban area in the middle reaches, and the MRT was mainly controlled by the landscape characteristics of the basin. In addition, our study showed that river water and groundwater isotope data were progressively enriched from upstream to downstream due to the construction of metropolitan landscape dams, which exacerbated evaporative losses of river water and also strengthened the hydraulic connection between groundwater and river water around the city. Our findings have important implications for local water resource management and urban planning and provide important insights into the hydrologic dynamics of urban areas.
{"title":"Effects of urbanization on the water cycle in the Shiyang River basin: based on a stable isotope method","authors":"Rui Li, Guofeng Zhu, Siyu Lu, Liyuan Sang, Gaojia Meng, Longhu Chen, Yinying Jiao, Qinqin Wang","doi":"10.5194/hess-27-4437-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4437-2023","url":null,"abstract":"Abstract. In water-scarce arid areas, the water cycle is affected by urban development and natural river changes, and urbanization has a profound impact on the hydrological system of the basin. Through an ecohydrological observation system established in the Shiyang River basin in the inland arid zone, we studied the impact of urbanization on the water cycle of the basin using isotope methods. The results showed that urbanization significantly changed the water cycle process in the basin and accelerated the rainfall-runoff process due to the increase in urban land area, and the mean residence time (MRT) of river water showed a fluctuating downward trend from upstream to downstream and was shortest in the urban area in the middle reaches, and the MRT was mainly controlled by the landscape characteristics of the basin. In addition, our study showed that river water and groundwater isotope data were progressively enriched from upstream to downstream due to the construction of metropolitan landscape dams, which exacerbated evaporative losses of river water and also strengthened the hydraulic connection between groundwater and river water around the city. Our findings have important implications for local water resource management and urban planning and provide important insights into the hydrologic dynamics of urban areas.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"201 ","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139174255","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 : 2023-12-14DOI: 10.5194/hess-27-4385-2023
Diego Araya, P. Mendoza, Eduardo Muñoz-Castro, J. McPhee
Abstract. Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability.
{"title":"Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling","authors":"Diego Araya, P. Mendoza, Eduardo Muñoz-Castro, J. McPhee","doi":"10.5194/hess-27-4385-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4385-2023","url":null,"abstract":"Abstract. Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability.\u0000","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"4 6","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139002640","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 : 2023-12-14DOI: 10.5194/hess-27-4409-2023
L. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, J. Fiddes, Y. Lei, P. Kraaijenbrink, Tamara Mathys, M. Langer, Simon Allen, W. Immerzeel
Abstract. Climate change modifies the water and energy fluxes between the atmosphere and the surface in mountainous regions such as the Qinghai–Tibet Plateau (QTP), which has shown substantial hydrological changes over the last decades, including rapid lake level variations. The ground across the QTP hosts either permafrost or is seasonally frozen, and, in this environment, the ground thermal regime influences liquid water availability, evaporation and runoff. Consequently, climate-induced changes in the ground thermal regime may contribute to variations in lake levels, but the validity of this hypothesis has yet to be established. This study focuses on the cryo-hydrology of the catchment of Lake Paiku (southern Tibet) for the 1980–2019 period. We process ERA5 data with downscaling and clustering tools (TopoSCALE, TopoSUB) to account for the spatial variability of the climate in our forcing data (Fiddes and Gruber, 2012, 2014). We use a distributed setup of the CryoGrid community model (version 1.0) to quantify thermo-hydrological changes in the ground during this period. Forcing data and simulation outputs are validated with data from a weather station, surface temperature loggers and observations of lake level variations. Our lake budget reconstruction shows that the main water input to the lake is direct precipitation (310 mm yr−1), followed by glacier runoff (280 mm yr−1) and land runoff (180 mm yr−1). However, altogether these components do not offset evaporation (860 mm yr−1). Our results show that both seasonal frozen ground and permafrost have warmed (0.17 ∘C per decade 2 m deep), increasing the availability of liquid water in the ground and the duration of seasonal thaw. Correlations with annual values suggest that both phenomena promote evaporation and runoff. Yet, ground warming drives a strong increase in subsurface runoff so that the runoff/(evaporation + runoff) ratio increases over time. This increase likely contributed to stabilizing the lake level decrease after 2010. Summer evaporation is an important energy sink, and we find active-layer deepening only where evaporation is limited. The presence of permafrost is found to promote evaporation at the expense of runoff, consistently with recent studies suggesting that a shallow active layer maintains higher water contents close to the surface. However, this relationship seems to be climate dependent, and we show that a colder and wetter climate produces the opposite effect. Although the present study was performed at the catchment scale, we suggest that this ambivalent influence of permafrost may help to understand the contrasting lake level variations observed between the south and north of the QTP, opening new perspectives for future investigations.
{"title":"Recent ground thermo-hydrological changes in a southern Tibetan endorheic catchment and implications for lake level changes","authors":"L. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, J. Fiddes, Y. Lei, P. Kraaijenbrink, Tamara Mathys, M. Langer, Simon Allen, W. Immerzeel","doi":"10.5194/hess-27-4409-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4409-2023","url":null,"abstract":"Abstract. Climate change modifies the water and energy fluxes between the atmosphere and the surface in mountainous regions such as the Qinghai–Tibet Plateau (QTP), which has shown substantial hydrological changes over the last decades, including rapid lake level variations. The ground across the QTP hosts either permafrost or is seasonally frozen, and, in this environment, the ground thermal regime influences liquid water availability, evaporation and runoff. Consequently, climate-induced changes in the ground thermal regime may contribute to variations in lake levels, but the validity of this hypothesis has yet to be established. This study focuses on the cryo-hydrology of the catchment of Lake Paiku (southern Tibet) for the 1980–2019 period. We process ERA5 data with downscaling and clustering tools (TopoSCALE, TopoSUB) to account for the spatial variability of the climate in our forcing data (Fiddes and Gruber, 2012, 2014). We use a distributed setup of the CryoGrid community model (version 1.0) to quantify thermo-hydrological changes in the ground during this period. Forcing data and simulation outputs are validated with data from a weather station, surface temperature loggers and observations of lake level variations. Our lake budget reconstruction shows that the main water input to the lake is direct precipitation (310 mm yr−1), followed by glacier runoff (280 mm yr−1) and land runoff (180 mm yr−1). However, altogether these components do not offset evaporation (860 mm yr−1). Our results show that both seasonal frozen ground and permafrost have warmed (0.17 ∘C per decade 2 m deep), increasing the availability of liquid water in the ground and the duration of seasonal thaw. Correlations with annual values suggest that both phenomena promote evaporation and runoff. Yet, ground warming drives a strong increase in subsurface runoff so that the runoff/(evaporation + runoff) ratio increases over time. This increase likely contributed to stabilizing the lake level decrease after 2010. Summer evaporation is an important energy sink, and we find active-layer deepening only where evaporation is limited. The presence of permafrost is found to promote evaporation at the expense of runoff, consistently with recent studies suggesting that a shallow active layer maintains higher water contents close to the surface. However, this relationship seems to be climate dependent, and we show that a colder and wetter climate produces the opposite effect. Although the present study was performed at the catchment scale, we suggest that this ambivalent influence of permafrost may help to understand the contrasting lake level variations observed between the south and north of the QTP, opening new perspectives for future investigations.\u0000","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"51 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139002711","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 : 2023-12-14DOI: 10.5194/hess-27-4369-2023
Hsing-Jui Wang, R. Merz, Soohyun Yang, S. Basso
Abstract. Floods are often disastrous due to underestimation of the magnitude of rare events. Underestimation commonly happens when the magnitudes of floods follow a heavy-tailed distribution, but this behavior is not recognized and thus neglected for flood hazard assessment. In fact, identifying heavy-tailed flood behavior is challenging because of limited data records and the lack of physical support for currently used indices. We address these issues by deriving a new index of heavy-tailed flood behavior from a physically based description of streamflow dynamics. The proposed index, which is embodied by the hydrograph recession exponent, enables inferring heavy-tailed flood behavior from daily flow records, even of short length. We test the index in a large set of case studies across Germany encompassing a variety of climatic and physiographic settings. Our findings demonstrate that the new index enables reliable identification of cases with either heavy- or non-heavy-tailed flood behavior from daily flow records. Additionally, the index suitably estimates the severity of tail heaviness and ranks it across cases, achieving robust results even with short data records. The new index addresses the main limitations of currently used metrics, which lack physical support and require long data records to correctly identify tail behaviors, and provides valuable information on the tail behavior of flood distributions and the related flood hazard in river basins using commonly available discharge data.
{"title":"Inferring heavy tails of flood distributions through hydrograph recession analysis","authors":"Hsing-Jui Wang, R. Merz, Soohyun Yang, S. Basso","doi":"10.5194/hess-27-4369-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4369-2023","url":null,"abstract":"Abstract. Floods are often disastrous due to underestimation of the magnitude of rare events. Underestimation commonly happens when the magnitudes of floods follow a heavy-tailed distribution, but this behavior is not recognized and thus neglected for flood hazard assessment. In fact, identifying heavy-tailed flood behavior is challenging because of limited data records and the lack of physical support for currently used indices. We address these issues by deriving a new index of heavy-tailed flood behavior from a physically based description of streamflow dynamics. The proposed index, which is embodied by the hydrograph recession exponent, enables inferring heavy-tailed flood behavior from daily flow records, even of short length. We test the index in a large set of case studies across Germany encompassing a variety of climatic and physiographic settings. Our findings demonstrate that the new index enables reliable identification of cases with either heavy- or non-heavy-tailed flood behavior from daily flow records. Additionally, the index suitably estimates the severity of tail heaviness and ranks it across cases, achieving robust results even with short data records. The new index addresses the main limitations of currently used metrics, which lack physical support and require long data records to correctly identify tail behaviors, and provides valuable information on the tail behavior of flood distributions and the related flood hazard in river basins using commonly available discharge data.\u0000","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"28 5","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139002734","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 : 2023-12-11DOI: 10.5194/hess-27-4335-2023
C. Michailovsky, Bert Coerver, M. Mul, Graham Jewitt
Abstract. Remote sensing (RS) data are becoming an increasingly important source of information for water resource management as they provide spatially distributed data on water availability and use. However, in order to guide appropriate use of the data, it is important to understand the impact of the uncertainties of RS data on water resource studies. Previous studies have shown that the degree of closure of the water balance from remote sensing data is highly variable across basins and that different RS products vary in their levels of accuracy depending on climatological and geographical conditions. In this paper, we analyzed the water-balance-derived runoff from global RS products for 931 catchments across the globe. We compared time series of runoff estimated through a simplified water balance equation using three precipitation (CHIRPS, GPM, and TRMM), five evapotranspiration (MODIS, SSEBop, GLEAM, CMRSET, and SEBS), and three water storage change (GRACE-CSR, GRACE-JPL, and GRACE-GFZ) RS datasets with monthly in situ discharge data for the period 2003–2016. Results were analyzed through the lens of 10 quantifiable catchment characteristics in order to investigate correlations between catchment characteristics and the quality of RS-based water balance estimates of runoff and whether specific products performed better than others under certain conditions. The median Nash–Sutcliffe efficiency (NSE) for all gauges and all product combinations was −0.02, and only 44.9 % of the time series reached a positive NSE. A positive NSE could be obtained for 73.7 % of stations with at least one product combination, while the overall best-performing product combination was positive for 58.4 % of stations. This confirms previous findings that the best-performing products cannot be globally established. When investigating the results by catchment characteristic, all combinations tended to show similar correlations between catchment characteristics and the quality of estimated runoff, with the exception of combinations using MODIS evapotranspiration, for which the correlation was frequently reversed. The combinations with the GPM precipitation product generally performed worse than the CHIRPS and TRMM data. However, this can be attributed to the fact that the GPM data are available at higher latitudes compared to the other products, where performance is generally poorer. When removing high-latitude stations, this difference was eliminated, and GPM and TRMM showed similar performance. The results show the highest positive correlation between highly seasonal rainfall and runoff NSE. On the other hand, increasing snow cover, altitude, and latitude decreased the ability of the RS products to close the water balance. The catchment's dominant climate zone was also found to be correlated with time series performance, with the tropical areas providing the highest (median NSE = 0.11) and arid areas the lowest (median NSE = −0.09) NSE values. No correlation was found b
{"title":"Investigating sources of variability in closing the terrestrial water balance with remote sensing","authors":"C. Michailovsky, Bert Coerver, M. Mul, Graham Jewitt","doi":"10.5194/hess-27-4335-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4335-2023","url":null,"abstract":"Abstract. Remote sensing (RS) data are becoming an increasingly important source of information for water resource management as they provide spatially distributed data on water availability and use. However, in order to guide appropriate use of the data, it is important to understand the impact of the uncertainties of RS data on water resource studies. Previous studies have shown that the degree of closure of the water balance from remote sensing data is highly variable across basins and that different RS products vary in their levels of accuracy depending on climatological and geographical conditions. In this paper, we analyzed the water-balance-derived runoff from global RS products for 931 catchments across the globe. We compared time series of runoff estimated through a simplified water balance equation using three precipitation (CHIRPS, GPM, and TRMM), five evapotranspiration (MODIS, SSEBop, GLEAM, CMRSET, and SEBS), and three water storage change (GRACE-CSR, GRACE-JPL, and GRACE-GFZ) RS datasets with monthly in situ discharge data for the period 2003–2016. Results were analyzed through the lens of 10 quantifiable catchment characteristics in order to investigate correlations between catchment characteristics and the quality of RS-based water balance estimates of runoff and whether specific products performed better than others under certain conditions. The median Nash–Sutcliffe efficiency (NSE) for all gauges and all product combinations was −0.02, and only 44.9 % of the time series reached a positive NSE. A positive NSE could be obtained for 73.7 % of stations with at least one product combination, while the overall best-performing product combination was positive for 58.4 % of stations. This confirms previous findings that the best-performing products cannot be globally established. When investigating the results by catchment characteristic, all combinations tended to show similar correlations between catchment characteristics and the quality of estimated runoff, with the exception of combinations using MODIS evapotranspiration, for which the correlation was frequently reversed. The combinations with the GPM precipitation product generally performed worse than the CHIRPS and TRMM data. However, this can be attributed to the fact that the GPM data are available at higher latitudes compared to the other products, where performance is generally poorer. When removing high-latitude stations, this difference was eliminated, and GPM and TRMM showed similar performance. The results show the highest positive correlation between highly seasonal rainfall and runoff NSE. On the other hand, increasing snow cover, altitude, and latitude decreased the ability of the RS products to close the water balance. The catchment's dominant climate zone was also found to be correlated with time series performance, with the tropical areas providing the highest (median NSE = 0.11) and arid areas the lowest (median NSE = −0.09) NSE values. No correlation was found b","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"13 S2","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138978913","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 : 2023-12-11DOI: 10.5194/hess-27-4355-2023
Mehrad Rahimpour Asenjan, F. Brissette, J. Martel, R. Arsenault
Abstract. Efficient adaptation strategies to climate change require the estimation of future impacts and the uncertainty surrounding this estimation. Over- or underestimating future uncertainty may lead to maladaptation. Hydrological impact studies typically use a top-down approach in which multiple climate models are used to assess the uncertainty related to the climate model structure and climate sensitivity. Despite ongoing debate, impact modelers have typically embraced the concept of “model democracy”, in which each climate model is considered equally fit. The newer Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, with several models showing a climate sensitivity larger than that of Phase 5 (CMIP5) and larger than the likely range based on past climate information and understanding of planetary physics, have reignited the model democracy debate. Some have suggested that “hot” models be removed from impact studies to avoid skewing impact results toward unlikely futures. Indeed, the inclusion of these models in impact studies carries a significant risk of overestimating the impact of climate change. This large-sample study looks at the impact of removing hot models on the projections of future streamflow over 3107 North American catchments. More precisely, the variability in future projections of mean, high, and low flows is evaluated using an ensemble of 19 CMIP6 general circulation models (GCMs), 5 of which are deemed hot based on their global equilibrium climate sensitivity (ECS). The results show that the reduced ensemble of 14 climate models provides streamflow projections with reduced future variability for Canada, Alaska, the Southeast US, and along the Pacific coast. Elsewhere, the reduced ensemble has either no impact or results in increased variability in future streamflow, indicating that global outlier climate models do not necessarily provide regional outlier projections of future impacts. These results emphasize the delicate nature of climate model selection, especially based on global fitness metrics that may not be appropriate for local and regional assessments.
{"title":"Understanding the influence of “hot” models in climate impact studies: a hydrological perspective","authors":"Mehrad Rahimpour Asenjan, F. Brissette, J. Martel, R. Arsenault","doi":"10.5194/hess-27-4355-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4355-2023","url":null,"abstract":"Abstract. Efficient adaptation strategies to climate change require the estimation of future impacts and the uncertainty surrounding this estimation. Over- or underestimating future uncertainty may lead to maladaptation. Hydrological impact studies typically use a top-down approach in which multiple climate models are used to assess the uncertainty related to the climate model structure and climate sensitivity. Despite ongoing debate, impact modelers have typically embraced the concept of “model democracy”, in which each climate model is considered equally fit. The newer Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, with several models showing a climate sensitivity larger than that of Phase 5 (CMIP5) and larger than the likely range based on past climate information and understanding of planetary physics, have reignited the model democracy debate. Some have suggested that “hot” models be removed from impact studies to avoid skewing impact results toward unlikely futures. Indeed, the inclusion of these models in impact studies carries a significant risk of overestimating the impact of climate change. This large-sample study looks at the impact of removing hot models on the projections of future streamflow over 3107 North American catchments. More precisely, the variability in future projections of mean, high, and low flows is evaluated using an ensemble of 19 CMIP6 general circulation models (GCMs), 5 of which are deemed hot based on their global equilibrium climate sensitivity (ECS). The results show that the reduced ensemble of 14 climate models provides streamflow projections with reduced future variability for Canada, Alaska, the Southeast US, and along the Pacific coast. Elsewhere, the reduced ensemble has either no impact or results in increased variability in future streamflow, indicating that global outlier climate models do not necessarily provide regional outlier projections of future impacts. These results emphasize the delicate nature of climate model selection, especially based on global fitness metrics that may not be appropriate for local and regional assessments.\u0000","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"1 5","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980640","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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