Pub Date : 2024-07-26DOI: 10.5194/hess-28-3347-2024
Huy Dang, Y. Pokhrel
Abstract. Flow regimes in major global river systems are undergoing rapid alterations due to unprecedented stress from climate change and human activities. The Mekong River basin (MRB) was, until recently, among the last major global rivers relatively unaltered by humans, but this has been changing alarmingly in the last decade due to booming dam construction. Numerous studies have examined the MRB's flood pulse and its alterations in recent years. However, a mechanistic quantification at the basin scale attributing these changes to either climatic or human drivers is lacking. Here, we present the first results of the basin-wide changes in natural hydrological regimes in the MRB over the past 8 decades and the impacts of dams in recent decades by examining 83 years (1940–2022) of river regime characteristics simulated by a river–floodplain hydrodynamic model that includes 126 major dams in the MRB. Results indicate that, while the Mekong River's flow has shown substantial decadal trends and variabilities, the operation of dams in recent years has been causing a fundamental shift in the seasonal volume and timing of river flow and extreme hydrological conditions. Even though the dam-induced impacts have been small so far and most pronounced in areas directly downstream of major dams, dams are intensifying the natural variations in the Mekong's mainstream wet-season flow. Further, the additional 65 dams commissioned since 2010 have exacerbated drought conditions by substantially delaying the MRB's wet-season onset, especially in recent years (e.g., 2019 and 2020), when the natural wet-season durations are already shorter than in normal years. Further, dams have shifted by up to 20 % of the mainstream annual volume between the dry and wet seasons in recent years. While this has a minimal impact on the MRB's annual flow volume, the flood occurrence in many major areas of Tonlé Sap and the Mekong Delta has been largely altered. This study provides critical insights into the long-term hydrological variabilities and impacts of dams on the Mekong River's flow regimes, which can help improve water resource management in light of intensifying hydrological extremes.
{"title":"Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes","authors":"Huy Dang, Y. Pokhrel","doi":"10.5194/hess-28-3347-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3347-2024","url":null,"abstract":"Abstract. Flow regimes in major global river systems are undergoing rapid alterations due to unprecedented stress from climate change and human activities. The Mekong River basin (MRB) was, until recently, among the last major global rivers relatively unaltered by humans, but this has been changing alarmingly in the last decade due to booming dam construction. Numerous studies have examined the MRB's flood pulse and its alterations in recent years. However, a mechanistic quantification at the basin scale attributing these changes to either climatic or human drivers is lacking. Here, we present the first results of the basin-wide changes in natural hydrological regimes in the MRB over the past 8 decades and the impacts of dams in recent decades by examining 83 years (1940–2022) of river regime characteristics simulated by a river–floodplain hydrodynamic model that includes 126 major dams in the MRB. Results indicate that, while the Mekong River's flow has shown substantial decadal trends and variabilities, the operation of dams in recent years has been causing a fundamental shift in the seasonal volume and timing of river flow and extreme hydrological conditions. Even though the dam-induced impacts have been small so far and most pronounced in areas directly downstream of major dams, dams are intensifying the natural variations in the Mekong's mainstream wet-season flow. Further, the additional 65 dams commissioned since 2010 have exacerbated drought conditions by substantially delaying the MRB's wet-season onset, especially in recent years (e.g., 2019 and 2020), when the natural wet-season durations are already shorter than in normal years. Further, dams have shifted by up to 20 % of the mainstream annual volume between the dry and wet seasons in recent years. While this has a minimal impact on the MRB's annual flow volume, the flood occurrence in many major areas of Tonlé Sap and the Mekong Delta has been largely altered. This study provides critical insights into the long-term hydrological variabilities and impacts of dams on the Mekong River's flow regimes, which can help improve water resource management in light of intensifying hydrological extremes.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"18 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.5194/hess-28-3327-2024
Athanasios Tsiokanos, Martine Rutten, R. J. van der Ent, R. Uijlenhoet
Abstract. In July 2021, extreme precipitation caused devastating flooding in Germany, Belgium and the Netherlands, particularly in the Geul River catchment. Such precipitation extremes had not been previously recorded and were not expected to occur in summer. This contributed to poor flood forecasting and, hence, extensive damage. Climate change was mentioned as a potential explanation for these unprecedented events. However, before such a statement can be made, we need a better understanding of the drivers of floods in the Geul and their long-term variability, which are poorly understood and have not been recently examined. In this paper, we use an event-based approach to identify the dominant flood drivers in the Geul. We also employ (1) a multi-temporal trend analysis to investigate their temporal variability and (2) a novel methodology to detect the dominant direction of any trend. Results suggest that extreme 24 h precipitation alone is typically insufficient to cause floods. The joint probability of extreme and prolonged rainfall combined with wet initial conditions (compound event) determines the chances of flooding. Flood-producing precipitation shows a consistent increase in the winter half-year, a period in which more than 70 % of extremely high flows have historically occurred. While no consistent trend patterns are evident in the majority of precipitation and extreme flow trends in the summer half-year, an increasing direction is visible in the recent past.
{"title":"Flood drivers and trends: a case study of the Geul River catchment (the Netherlands) over the past half century","authors":"Athanasios Tsiokanos, Martine Rutten, R. J. van der Ent, R. Uijlenhoet","doi":"10.5194/hess-28-3327-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3327-2024","url":null,"abstract":"Abstract. In July 2021, extreme precipitation caused devastating flooding in Germany, Belgium and the Netherlands, particularly in the Geul River catchment. Such precipitation extremes had not been previously recorded and were not expected to occur in summer. This contributed to poor flood forecasting and, hence, extensive damage. Climate change was mentioned as a potential explanation for these unprecedented events. However, before such a statement can be made, we need a better understanding of the drivers of floods in the Geul and their long-term variability, which are poorly understood and have not been recently examined. In this paper, we use an event-based approach to identify the dominant flood drivers in the Geul. We also employ (1) a multi-temporal trend analysis to investigate their temporal variability and (2) a novel methodology to detect the dominant direction of any trend. Results suggest that extreme 24 h precipitation alone is typically insufficient to cause floods. The joint probability of extreme and prolonged rainfall combined with wet initial conditions (compound event) determines the chances of flooding. Flood-producing precipitation shows a consistent increase in the winter half-year, a period in which more than 70 % of extremely high flows have historically occurred. While no consistent trend patterns are evident in the majority of precipitation and extreme flow trends in the summer half-year, an increasing direction is visible in the recent past.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.5194/hess-28-3261-2024
Yongshin Lee, F. Pianosi, Andrés Peñuela, M. Rico‐Ramirez
Abstract. Recent advancements in numerical weather predictions have improved forecasting performance at longer lead times. Seasonal weather forecasts, providing predictions of weather variables for the next several months, have gained significant attention from researchers due to their potential benefits for water resources management. Many efforts have been made to generate seasonal flow forecasts (SFFs) by combining seasonal weather forecasts and hydrological models. However, producing SFFs with good skill at a finer catchment scale remains challenging, hindering their practical application and adoption by water managers. Consequently, water management decisions in both South Korea and numerous other countries continue to rely on worst-case scenarios and the conventional ensemble streamflow prediction (ESP) method. This study investigates the potential of SFFs in South Korea at the catchment scale, examining 12 reservoir catchments of varying sizes (ranging from 59 to 6648 km2) over the last decade (2011–2020). Seasonal weather forecast data (including precipitation, temperature and evapotranspiration) from the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) are used to drive the Tank model (conceptual hydrological model) to generate the flow ensemble forecasts. We assess the contribution of each weather variable to the performance of flow forecasting by isolating individual variables. In addition, we quantitatively evaluate the “overall skill” of SFFs, representing the probability of outperforming the benchmark (ESP), using the continuous ranked probability skill score (CRPSS). Our results highlight that precipitation is the most important variable in determining the performance of SFFs and that temperature also plays a key role during the dry season in snow-affected catchments. Given the coarse resolution of seasonal weather forecasts, a linear scaling method to adjust the forecasts is applied, and it is found that bias correction is highly effective in enhancing the overall skill. Furthermore, bias-corrected SFFs have skill with respect to ESP up to 3 months ahead, this being particularly evident during abnormally dry years. To facilitate future applications in other regions, the code developed for this analysis has been made available as an open-source Python package.
{"title":"Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea","authors":"Yongshin Lee, F. Pianosi, Andrés Peñuela, M. Rico‐Ramirez","doi":"10.5194/hess-28-3261-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3261-2024","url":null,"abstract":"Abstract. Recent advancements in numerical weather predictions have improved forecasting performance at longer lead times. Seasonal weather forecasts, providing predictions of weather variables for the next several months, have gained significant attention from researchers due to their potential benefits for water resources management. Many efforts have been made to generate seasonal flow forecasts (SFFs) by combining seasonal weather forecasts and hydrological models. However, producing SFFs with good skill at a finer catchment scale remains challenging, hindering their practical application and adoption by water managers. Consequently, water management decisions in both South Korea and numerous other countries continue to rely on worst-case scenarios and the conventional ensemble streamflow prediction (ESP) method. This study investigates the potential of SFFs in South Korea at the catchment scale, examining 12 reservoir catchments of varying sizes (ranging from 59 to 6648 km2) over the last decade (2011–2020). Seasonal weather forecast data (including precipitation, temperature and evapotranspiration) from the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) are used to drive the Tank model (conceptual hydrological model) to generate the flow ensemble forecasts. We assess the contribution of each weather variable to the performance of flow forecasting by isolating individual variables. In addition, we quantitatively evaluate the “overall skill” of SFFs, representing the probability of outperforming the benchmark (ESP), using the continuous ranked probability skill score (CRPSS). Our results highlight that precipitation is the most important variable in determining the performance of SFFs and that temperature also plays a key role during the dry season in snow-affected catchments. Given the coarse resolution of seasonal weather forecasts, a linear scaling method to adjust the forecasts is applied, and it is found that bias correction is highly effective in enhancing the overall skill. Furthermore, bias-corrected SFFs have skill with respect to ESP up to 3 months ahead, this being particularly evident during abnormally dry years. To facilitate future applications in other regions, the code developed for this analysis has been made available as an open-source Python package.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"51 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.5194/hess-28-3281-2024
M. A. Bracalenti, O. Müller, Miguel A. Lovino, E. Berbery
Abstract. The Gran Chaco ecoregion is South America's largest remaining continuous stretch of dry forest. It has experienced intensive deforestation, mainly in the western part known as the Dry Chaco, resulting in the highest rate of dry-forest loss globally between 2000 and 2012. The replacement of natural vegetation with other land uses modifies the surface's biophysical properties, affecting heat and water fluxes and modifying the regional climate. This study examines land use and land cover changes (LULCCs) in the Dry Chaco from 2001 to 2015 and their effects on local and non-local climate and explores the potential impacts of future agricultural expansion in the region. To this end, Weather Research and Forecasting (WRF) model simulations are performed for two scenarios: the first one evaluates the observed land cover changes between 2001 and 2015 that covered 8 % of the total area of the Dry Chaco; the second scenario assumes an intensive agricultural expansion within the Dry Chaco. In both scenarios, deforestation processes lead to decreases in leaf area index (LAI), reductions in stomatal resistance, and increases in albedo, thus reducing the net surface radiation and, correspondingly, decreasing the turbulent fluxes, suggesting a decline in available energy in the boundary layer. The result is an overall weakening of the water cycle in the Dry Chaco and, most prominently, implying a reduction in precipitation. A feedback loop develops since dry soil absorbs significantly less solar radiation than moist soil. Finally, the simulations suggest that the Dry Chaco will intensify its aridity, extending drier and hotter conditions into the Humid Chaco.
{"title":"The agricultural expansion in South America's Dry Chaco: regional hydroclimate effects","authors":"M. A. Bracalenti, O. Müller, Miguel A. Lovino, E. Berbery","doi":"10.5194/hess-28-3281-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3281-2024","url":null,"abstract":"Abstract. The Gran Chaco ecoregion is South America's largest remaining continuous stretch of dry forest. It has experienced intensive deforestation, mainly in the western part known as the Dry Chaco, resulting in the highest rate of dry-forest loss globally between 2000 and 2012. The replacement of natural vegetation with other land uses modifies the surface's biophysical properties, affecting heat and water fluxes and modifying the regional climate. This study examines land use and land cover changes (LULCCs) in the Dry Chaco from 2001 to 2015 and their effects on local and non-local climate and explores the potential impacts of future agricultural expansion in the region. To this end, Weather Research and Forecasting (WRF) model simulations are performed for two scenarios: the first one evaluates the observed land cover changes between 2001 and 2015 that covered 8 % of the total area of the Dry Chaco; the second scenario assumes an intensive agricultural expansion within the Dry Chaco. In both scenarios, deforestation processes lead to decreases in leaf area index (LAI), reductions in stomatal resistance, and increases in albedo, thus reducing the net surface radiation and, correspondingly, decreasing the turbulent fluxes, suggesting a decline in available energy in the boundary layer. The result is an overall weakening of the water cycle in the Dry Chaco and, most prominently, implying a reduction in precipitation. A feedback loop develops since dry soil absorbs significantly less solar radiation than moist soil. Finally, the simulations suggest that the Dry Chaco will intensify its aridity, extending drier and hotter conditions into the Humid Chaco.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"47 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.5194/hess-28-3243-2024
G. Rodrigues, A. Brosinsky, Í. S. Rodrigues, G. Mamede, José Carlos de Araújo
Abstract. The potential effects of climatic changes on water resources are crucial to be assessed, particularly in dry regions such as north-east Brazil (1 million km2), where water supply is highly reliant on open-water reservoirs. This study analyses the impact of evaporation (by the Penman method) on water availability for four scenarios based on two regional climatic models (Eta-CanESM2 and Eta-MIROC5) using the Representative Concentration Pathways (RCPs) 4.5 and 8.5. We compared the water availability in the period of 2071–2100 with that of the historical period (1961–2005). The scenarios derived from the Eta-CanESM2 model indicate an increase in the dry-season evaporative rate (2 % and 6 %, respectively) by the end of the century. Unlike the above scenarios, the ones derived from the Eta-MIROC5 model both show a change in the dry-season evaporative rate of −2 %. Consequently, for a 90 % reliability level, the expected reservoir capacity to supply water with high reliability is reduced by 80 %. It is reasonable to state that both patterns of future evaporation in the reservoirs may prove to be plausible. Because model-based projections of climate impact on water resources can be quite divergent, it is necessary to develop adaptations that do not need quantitative projections of changes in hydrological variables but rather ranges of projected values. Our analysis shows how open-water reservoirs might be impacted by climate change in dry regions. These findings complement a body of knowledge on the estimation of water availability in a changing climate and provide new data on and insights into water management in reservoir-dependent drylands.
{"title":"Impact of reservoir evaporation on future water availability in north-eastern Brazil: a multi-scenario assessment","authors":"G. Rodrigues, A. Brosinsky, Í. S. Rodrigues, G. Mamede, José Carlos de Araújo","doi":"10.5194/hess-28-3243-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3243-2024","url":null,"abstract":"Abstract. The potential effects of climatic changes on water resources are crucial to be assessed, particularly in dry regions such as north-east Brazil (1 million km2), where water supply is highly reliant on open-water reservoirs. This study analyses the impact of evaporation (by the Penman method) on water availability for four scenarios based on two regional climatic models (Eta-CanESM2 and Eta-MIROC5) using the Representative Concentration Pathways (RCPs) 4.5 and 8.5. We compared the water availability in the period of 2071–2100 with that of the historical period (1961–2005). The scenarios derived from the Eta-CanESM2 model indicate an increase in the dry-season evaporative rate (2 % and 6 %, respectively) by the end of the century. Unlike the above scenarios, the ones derived from the Eta-MIROC5 model both show a change in the dry-season evaporative rate of −2 %. Consequently, for a 90 % reliability level, the expected reservoir capacity to supply water with high reliability is reduced by 80 %. It is reasonable to state that both patterns of future evaporation in the reservoirs may prove to be plausible. Because model-based projections of climate impact on water resources can be quite divergent, it is necessary to develop adaptations that do not need quantitative projections of changes in hydrological variables but rather ranges of projected values. Our analysis shows how open-water reservoirs might be impacted by climate change in dry regions. These findings complement a body of knowledge on the estimation of water availability in a changing climate and provide new data on and insights into water management in reservoir-dependent drylands.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.5194/hess-28-3305-2024
Rutong Liu, Jiabo Yin, Louise J. Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, Aliaksandr Volchak
Abstract. Climate change influences the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. Although machine learning is increasingly employed for hydrological simulations, few studies have used it to project hydrological droughts, not to mention bivariate risks (referring to drought duration and severity) as well as their socioeconomic effects under climate change. We developed a cascade modeling chain to project future bivariate hydrological drought characteristics in 179 catchments over China, using five bias-corrected global climate model (GCM) outputs under three shared socioeconomic pathways (SSPs), five hydrological models, and a deep-learning model. We quantified the contribution of various meteorological variables to daily streamflow by using a random forest model, and then we employed terrestrial water storage anomalies and a standardized runoff index to evaluate recent changes in hydrological drought. Subsequently, we constructed a bivariate framework to jointly model drought duration and severity by using copula functions and the most likely realization method. Finally, we used this framework to project future risks of hydrological droughts as well as the associated exposure of gross domestic product (GDP) and population. Results showed that our hybrid hydrological–deep-learning model achieved > 0.8 Kling–Gupta efficiency in 161 out of the 179 catchments. By the late 21st century, bivariate drought risk is projected to double over 60 % of the catchments mainly located in southwestern China under SSP5-85, which shows the increase in drought duration and severity. Our hybrid model also projected substantial GDP and population exposure by increasing bivariate drought risks, suggesting an urgent need to design climate mitigation strategies for a sustainable development pathway.
{"title":"Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China","authors":"Rutong Liu, Jiabo Yin, Louise J. Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, Aliaksandr Volchak","doi":"10.5194/hess-28-3305-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3305-2024","url":null,"abstract":"Abstract. Climate change influences the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. Although machine learning is increasingly employed for hydrological simulations, few studies have used it to project hydrological droughts, not to mention bivariate risks (referring to drought duration and severity) as well as their socioeconomic effects under climate change. We developed a cascade modeling chain to project future bivariate hydrological drought characteristics in 179 catchments over China, using five bias-corrected global climate model (GCM) outputs under three shared socioeconomic pathways (SSPs), five hydrological models, and a deep-learning model. We quantified the contribution of various meteorological variables to daily streamflow by using a random forest model, and then we employed terrestrial water storage anomalies and a standardized runoff index to evaluate recent changes in hydrological drought. Subsequently, we constructed a bivariate framework to jointly model drought duration and severity by using copula functions and the most likely realization method. Finally, we used this framework to project future risks of hydrological droughts as well as the associated exposure of gross domestic product (GDP) and population. Results showed that our hybrid hydrological–deep-learning model achieved > 0.8 Kling–Gupta efficiency in 161 out of the 179 catchments. By the late 21st century, bivariate drought risk is projected to double over 60 % of the catchments mainly located in southwestern China under SSP5-85, which shows the increase in drought duration and severity. Our hybrid model also projected substantial GDP and population exposure by increasing bivariate drought risks, suggesting an urgent need to design climate mitigation strategies for a sustainable development pathway.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"105 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-24DOI: 10.5194/hess-28-3219-2024
A. Ekka, Yong Jiang, S. Pande, P. van der Zaag
Abstract. The construction of dams threatens the health of watershed ecosystems. The purpose of this study is to show how multiple dams in a basin can impact hydrological flow regimes and subsequently aquatic ecosystems that depend on river flows. The approach assesses the ecosystem services (ESs), including the tradeoffs between economic and ecological services due to altered flow regimes. It uses a previously developed model that integrates a landscape-based hydrological model with a reservoir operations model on a basin scale. The approach is novel because not only does it offer the analysis of alterations in ecosystem services on a daily scale when pre-dam data are unavailable but also allows for dams to be synthetically placed anywhere in the river network and the corresponding alterations in flow regimes to be simulated in a flexible manner. As a proof of concept, we analyse the economic and ecological performances of different spatial configuration of existing reservoirs instead of synthetically placed reservoirs in the upper Cauvery River basin in India. Such a study is timely and conducted for the first time, especially in light of calls to assess the cascade of reservoirs in India and regions elsewhere where pre-dam data are unavailable. The hydrological impact of different configurations of reservoirs is quantified using indicators of hydrologic alteration (IHAs). Additionally, the production of two major ecosystem services that depend on the flow regime of the river, as indicated by irrigated agricultural production and the normalized fish diversity index (NFDI), is estimated, and a tradeoff curve, i.e. a production possibility frontier, for the two services is established. Through the lens of the indices chosen for the ecosystem services, the results show that smaller reservoirs on lower-order streams are better for the basin economy and the environment than larger reservoirs. Cultivating irrigated crops of higher value can maximize the value of stored water and, with lower storage, generate a better economic value than cultivating lower-value crops while reducing hydrological alterations. The proposed approach, especially when simulating synthetic spatial configurations of reservoirs, can help water and river basin managers to understand the provision of ecosystem services in hydrologically altered basins, optimize dam operations, or even prioritize dam removals with a goal of achieving a balanced provision of ecosystem services.
{"title":"How economically and environmentally viable are multiple dams in the upper Cauvery Basin, India? A hydro-economic analysis using a landscape-based hydrological model","authors":"A. Ekka, Yong Jiang, S. Pande, P. van der Zaag","doi":"10.5194/hess-28-3219-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3219-2024","url":null,"abstract":"Abstract. The construction of dams threatens the health of watershed ecosystems. The purpose of this study is to show how multiple dams in a basin can impact hydrological flow regimes and subsequently aquatic ecosystems that depend on river flows. The approach assesses the ecosystem services (ESs), including the tradeoffs between economic and ecological services due to altered flow regimes. It uses a previously developed model that integrates a landscape-based hydrological model with a reservoir operations model on a basin scale. The approach is novel because not only does it offer the analysis of alterations in ecosystem services on a daily scale when pre-dam data are unavailable but also allows for dams to be synthetically placed anywhere in the river network and the corresponding alterations in flow regimes to be simulated in a flexible manner. As a proof of concept, we analyse the economic and ecological performances of different spatial configuration of existing reservoirs instead of synthetically placed reservoirs in the upper Cauvery River basin in India. Such a study is timely and conducted for the first time, especially in light of calls to assess the cascade of reservoirs in India and regions elsewhere where pre-dam data are unavailable. The hydrological impact of different configurations of reservoirs is quantified using indicators of hydrologic alteration (IHAs). Additionally, the production of two major ecosystem services that depend on the flow regime of the river, as indicated by irrigated agricultural production and the normalized fish diversity index (NFDI), is estimated, and a tradeoff curve, i.e. a production possibility frontier, for the two services is established. Through the lens of the indices chosen for the ecosystem services, the results show that smaller reservoirs on lower-order streams are better for the basin economy and the environment than larger reservoirs. Cultivating irrigated crops of higher value can maximize the value of stored water and, with lower storage, generate a better economic value than cultivating lower-value crops while reducing hydrological alterations. The proposed approach, especially when simulating synthetic spatial configurations of reservoirs, can help water and river basin managers to understand the provision of ecosystem services in hydrologically altered basins, optimize dam operations, or even prioritize dam removals with a goal of achieving a balanced provision of ecosystem services.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"48 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.5194/hess-28-3191-2024
Francesco Serinaldi
Abstract. Statistics is often misused in hydro-climatology, thus causing research to get stuck on unscientific concepts that hinder scientific advances. In particular, neglecting the scientific rationale of statistical inference results in logical and operational fallacies that prevent the discernment of facts, assumptions, and models, thus leading to systematic misinterpretations of the output of data analysis. This study discusses how epistemological principles are not just philosophical concepts but also have very practical effects. To this aim, we focus on the iterated underestimation and misinterpretation of the role of spatio-temporal dependence in statistical analysis of hydro-climatic processes by analyzing the occurrence process of extreme precipitation (P) derived from 100-year daily time series recorded at 1106 worldwide gauges of the Global Historical Climatology Network. The analysis contrasts a model-based approach that is compliant with the well-devised but often neglected logic of statistical inference and a widespread but theoretically problematic test-based approach relying on statistical hypothesis tests applied to unrepeatable hydro-climatic records. The model-based approach highlights the actual impact of spatio-temporal dependence and a finite sample size on statistical inference, resulting in over-dispersed marginal distributions and biased estimates of dependence properties, such as autocorrelation and power spectrum density. These issues also affect the outcome and interpretation of statistical tests for trend detection. Overall, the model-based approach results in a theoretically coherent modeling framework where stationary stochastic processes incorporating the empirical spatio-temporal correlation and its effects provide a faithful description of the occurrence process of extreme P at various spatio-temporal scales. On the other hand, the test-based approach leads to theoretically unsubstantiated results and interpretations, along with logically contradictory conclusions such as the simultaneous equi-dispersion and over-dispersion of extreme P. Therefore, accounting for the effect of dependence in the analysis of the frequency of extreme P has a huge impact that cannot be ignored, and, more importantly, any data analysis can be scientifically meaningful only if it considers the epistemological principles of statistical inference such as the asymmetry between confirmatory and disconfirmatory empiricism, the inverse-probability problem affecting statistical tests, and the difference between assumptions and models.
摘要。统计学在水文气候学中经常被误用，从而导致研究陷入不科学的概念，阻碍科学进步。特别是，忽视统计推论的科学原理会导致逻辑和操作谬误，妨碍对事实、假设和模型的辨别，从而导致对数据分析结果的系统性误读。本研究讨论了认识论原则不仅是哲学概念，而且具有非常实际的影响。为此，我们通过分析全球历史气候学网络（Global Historical Climatology Network）1106 个全球测站记录的 100 年日时间序列得出的极端降水量（P）的发生过程，重点探讨在水文气候过程的统计分析中反复低估和误解时空依赖性的作用。该分析对比了一种基于模型的方法和一种基于检验的方法，前者符合精心设计但往往被忽视的统计推断逻辑，而后者则普遍存在但理论上有问题，依赖于对不可重复的水文气候记录进行统计假设检验。基于模型的方法突出了时空依赖性和有限样本量对统计推断的实际影响，导致边际分布过于分散，对自相关性和功率谱密度等依赖性属性的估计存在偏差。这些问题也会影响趋势检测统计检验的结果和解释。总体而言，基于模型的方法产生了一个理论上连贯的建模框架，其中包含了经验时空相关性及其影响的静态随机过程忠实地描述了不同时空尺度上极端 P 的发生过程。另一方面，基于检验的方法会导致理论上未经证实的结果和解释，以及逻辑上相互矛盾的结论，如极端 P 同时存在等离散和过离散。因此，在分析极端 P 的频率时考虑依赖性的影响具有不可忽视的巨大作用，更重要的是，任何数据分析只有考虑到统计推断的认识论原则，如证实经验主义和不证实经验主义之间的不对称性、影响统计检验的反概率问题以及假设和模型之间的差异，才具有科学意义。
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Pub Date : 2024-07-19DOI: 10.5194/hess-28-3161-2024
M. Khanam, Giulia Sofia, E. Anagnostou
Abstract. Flooding is predicted to become more frequent in the coming decades because of global climate change. Recent literature has highlighted the importance of river morphodynamics in controlling flood hazards at the local scale. Abrupt and short-term geomorphic changes can occur after major flood-inducing storms. However, there is still a widespread lack of ability to foresee where and when substantial geomorphic changes will occur, as well as their ramifications for future flood hazards. This study sought to gain an understanding of the implications of major storm events for future flood hazards. For this purpose, we developed self-organizing maps (SOMs) to predict post-storm changes in stage–discharge relationships, based on storm characteristics and watershed properties at 3101 stream gages across the contiguous United States (CONUS). We tested and verified a machine learning (ML) model and its feasibility to (1) highlight the variability of geomorphic responses to flood-inducing storms across various climatic and geomorphologic regions across CONUS and (2) understand the impact of these storms on the stage–discharge relationships at gaged sites as a proxy for changes in flood hazard. The established model allows us to select rivers with stage–discharge relationships that are more prone to change after flood-inducing storms, for which flood recurrence intervals should be revised regularly so that hazard assessment can be up to date with the changing conditions. Results from the model show that, even though post-storm changes in channel conveyance are widespread, the impacts on flood hazard vary across CONUS. The influence of channel conveyance variability on flood risk depends on various hydrologic, geomorphologic, and atmospheric parameters characterizing a particular landscape or storm. The proposed framework can serve as a basis for incorporating channel conveyance adjustments into flood hazard assessment.
{"title":"To what extent do flood-inducing storm events change future flood hazards?","authors":"M. Khanam, Giulia Sofia, E. Anagnostou","doi":"10.5194/hess-28-3161-2024","DOIUrl":"https://doi.org/10.5194/hess-28-3161-2024","url":null,"abstract":"Abstract. Flooding is predicted to become more frequent in the coming decades because of global climate change. Recent literature has highlighted the importance of river morphodynamics in controlling flood hazards at the local scale. Abrupt and short-term geomorphic changes can occur after major flood-inducing storms. However, there is still a widespread lack of ability to foresee where and when substantial geomorphic changes will occur, as well as their ramifications for future flood hazards. This study sought to gain an understanding of the implications of major storm events for future flood hazards. For this purpose, we developed self-organizing maps (SOMs) to predict post-storm changes in stage–discharge relationships, based on storm characteristics and watershed properties at 3101 stream gages across the contiguous United States (CONUS). We tested and verified a machine learning (ML) model and its feasibility to (1) highlight the variability of geomorphic responses to flood-inducing storms across various climatic and geomorphologic regions across CONUS and (2) understand the impact of these storms on the stage–discharge relationships at gaged sites as a proxy for changes in flood hazard. The established model allows us to select rivers with stage–discharge relationships that are more prone to change after flood-inducing storms, for which flood recurrence intervals should be revised regularly so that hazard assessment can be up to date with the changing conditions. Results from the model show that, even though post-storm changes in channel conveyance are widespread, the impacts on flood hazard vary across CONUS. The influence of channel conveyance variability on flood risk depends on various hydrologic, geomorphologic, and atmospheric parameters characterizing a particular landscape or storm. The proposed framework can serve as a basis for incorporating channel conveyance adjustments into flood hazard assessment.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"124 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.5194/hess-28-3133-2024
Mohamad El Gharamti, A. Rafieeinasab, James L. McCreight
Abstract. In the face of escalating instances of inland and flash flooding spurred by intense rainfall and hurricanes, the accurate prediction of rapid streamflow variations has become imperative. Traditional data assimilation methods face challenges during extreme rainfall events due to numerous sources of error, including structural and parametric model uncertainties, forcing biases, and noisy observations. This study introduces a cutting-edge hybrid ensemble and optimal interpolation data assimilation scheme tailored to precisely and efficiently estimate streamflow during such critical events. Our hybrid scheme uses an ensemble-based framework, integrating the flow-dependent background streamflow covariance with a climatological error covariance derived from historical model simulations. The dynamic interplay (weight) between the static background covariance and the evolving ensemble is adaptively computed both spatially and temporally. By coupling the National Water Model (NWM) configuration of the WRF-Hydro modeling system with the Data Assimilation Research Testbed (DART), we evaluate the performance of our hybrid prediction system using two impactful case studies: (1) West Virginia's flash flooding event in June 2016 and (2) Florida's inland flooding during Hurricane Ian in September 2022. Our findings reveal that the hybrid scheme substantially outperforms its ensemble counterpart, delivering enhanced streamflow estimates for both low and high flow scenarios, with an improvement of up to 50 %. This heightened accuracy is attributed to the climatological background covariance, mitigating bias and augmenting ensemble variability. The adaptive nature of the hybrid algorithm ensures reliability, even with a very small time-varying ensemble. Moreover, this innovative hybrid data assimilation system propels streamflow forecasts up to 18 h in advance of flood peaks, marking a substantial advancement in flood prediction capabilities.
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