Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, Kay Shelton
Abstract Fluvial flood forecasting systems increasingly couple river discharge to a flood map library or a real-time hydrodynamic model to provide forecast flood maps to humanitarian agencies. The forecast flood maps can be linked to potential impacts to inform forecast-based financing schemes. We investigate a new application of scale-selective verification by evaluating three flood forecasting systems. Two simulation library systems, Flood Foresight (30 m) and GloFAS Rapid Flood Mapping (1,000 m) and one hydrodynamically modelled system, the Bangladesh Flood Forecasting and Warning Centre (FFWC) Super Model (300 m), all made predictions of flooding extent at different spatial scales (grid lengths, in brackets) for the Jamuna River flood, Bangladesh, July 2020. The flood maps are validated against synthetic-aperture-radar-derived observations of flooding using a scale-selective approach that can compare directly across different spatial scales. At short forecast lead times, the Super Model outperforms the other systems. Near to the Bangladesh border, the trans-boundary benefits of the two global systems are evident. We find that scale-selective methods can quantify the skill of systems operating at different spatial scales so that the benefits and limitations can be evaluated. Multi-system comparison of flood maps is important for improving impact-based forecasts and ensuring funds and response activities are appropriately targeted.
{"title":"A multi-system comparison of forecast flooding extent using a scale-selective approach","authors":"Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, Kay Shelton","doi":"10.2166/nh.2023.025","DOIUrl":"https://doi.org/10.2166/nh.2023.025","url":null,"abstract":"Abstract Fluvial flood forecasting systems increasingly couple river discharge to a flood map library or a real-time hydrodynamic model to provide forecast flood maps to humanitarian agencies. The forecast flood maps can be linked to potential impacts to inform forecast-based financing schemes. We investigate a new application of scale-selective verification by evaluating three flood forecasting systems. Two simulation library systems, Flood Foresight (30 m) and GloFAS Rapid Flood Mapping (1,000 m) and one hydrodynamically modelled system, the Bangladesh Flood Forecasting and Warning Centre (FFWC) Super Model (300 m), all made predictions of flooding extent at different spatial scales (grid lengths, in brackets) for the Jamuna River flood, Bangladesh, July 2020. The flood maps are validated against synthetic-aperture-radar-derived observations of flooding using a scale-selective approach that can compare directly across different spatial scales. At short forecast lead times, the Super Model outperforms the other systems. Near to the Bangladesh border, the trans-boundary benefits of the two global systems are evident. We find that scale-selective methods can quantify the skill of systems operating at different spatial scales so that the benefits and limitations can be evaluated. Multi-system comparison of flood maps is important for improving impact-based forecasts and ensuring funds and response activities are appropriately targeted.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135718558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Groundwater availability is one of the key anxieties in most semi-arid regions of Ethiopia. The purpose of this study was to investigate the groundwater potential zone map of the alluvial plain of Gambela. The study applied analytic hierarchy process (AHP) models with four different machine learning algorithms: random forest classifier (RFC), gradient boosting classifier (GBC), decision tree classifier (DTC), and K-neighbor classifier (KNC). The features that are used as predictors include geology, geomorphology, slope, soil, lineament density, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic roughness index (TRI), and rainfall. The final output of the groundwater potential zone was classified as low, moderate, high, and very high potential zones. The authentication through receiver operating curve (ROC) shows 78.2, 93.4, 92.5, 72.4, and 87.7% values of area under the curve (AUC) for AHP, RFC, GBC, DTC, and KNC, respectively. The results show that RFC and GBC are the best GWPZ map estimator. The study also shows that rainfall and geomorphology are the primary factors influencing the GWPZ. The outcome might promote improved management alternatives in other areas of the country with a comparable climate.
{"title":"Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia","authors":"Tesema Kebede Seifu, Kidist Demessie Eshetu, Tekalegn Ayele Woldesenbet, Taye Alemayehu, Tenalem Ayenew","doi":"10.2166/nh.2023.083","DOIUrl":"https://doi.org/10.2166/nh.2023.083","url":null,"abstract":"Groundwater availability is one of the key anxieties in most semi-arid regions of Ethiopia. The purpose of this study was to investigate the groundwater potential zone map of the alluvial plain of Gambela. The study applied analytic hierarchy process (AHP) models with four different machine learning algorithms: random forest classifier (RFC), gradient boosting classifier (GBC), decision tree classifier (DTC), and K-neighbor classifier (KNC). The features that are used as predictors include geology, geomorphology, slope, soil, lineament density, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic roughness index (TRI), and rainfall. The final output of the groundwater potential zone was classified as low, moderate, high, and very high potential zones. The authentication through receiver operating curve (ROC) shows 78.2, 93.4, 92.5, 72.4, and 87.7% values of area under the curve (AUC) for AHP, RFC, GBC, DTC, and KNC, respectively. The results show that RFC and GBC are the best GWPZ map estimator. The study also shows that rainfall and geomorphology are the primary factors influencing the GWPZ. The outcome might promote improved management alternatives in other areas of the country with a comparable climate.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135814943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding the drivers of flooding is essential for flood disaster prevention. However, conventional flood prediction methods are hindered by their reliance on local discharge data, which can be constrained by limited spatial resolution. To address this limitation, we present a machine learning model that can categorize floods without requiring discharge data during inference. We first use circular statistics to calculate the relative importance of three candidate flood-generating mechanisms. Global land areas are classified into three primary categories and eight sub-categories based on the proportion of relative importance. A random forest model is then applied to identify the flood types by assuming that the discharge data is unavailable. The findings from circular statistics highlight that globally, soil moisture excess is the most influential driver of floods followed by extreme precipitation and snowmelt, with an average relative importance of 0.535, 0.387, and 0.078, respectively. The RF model performs well in resembling the three primary flood categories with an accuracy of 0.701 and a F1-score of 0.692 in 10-fold cross-validation. The trained gridded-based model provides a swift and efficient approach for analyzing flood mechanisms, even in limited discharge scenarios, allowing for rapid insights.
{"title":"Unveiling flood-generating mechanisms using circular statistics-based machine learning approach without the need for discharge data during inference","authors":"Zhi Zhang, Dagang Wang, Xinxin Wu, Yiwen Mei, Jianxiu Qiu, Jinxin Zhu","doi":"10.2166/nh.2023.058","DOIUrl":"https://doi.org/10.2166/nh.2023.058","url":null,"abstract":"Understanding the drivers of flooding is essential for flood disaster prevention. However, conventional flood prediction methods are hindered by their reliance on local discharge data, which can be constrained by limited spatial resolution. To address this limitation, we present a machine learning model that can categorize floods without requiring discharge data during inference. We first use circular statistics to calculate the relative importance of three candidate flood-generating mechanisms. Global land areas are classified into three primary categories and eight sub-categories based on the proportion of relative importance. A random forest model is then applied to identify the flood types by assuming that the discharge data is unavailable. The findings from circular statistics highlight that globally, soil moisture excess is the most influential driver of floods followed by extreme precipitation and snowmelt, with an average relative importance of 0.535, 0.387, and 0.078, respectively. The RF model performs well in resembling the three primary flood categories with an accuracy of 0.701 and a F1-score of 0.692 in 10-fold cross-validation. The trained gridded-based model provides a swift and efficient approach for analyzing flood mechanisms, even in limited discharge scenarios, allowing for rapid insights.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aikaterini Gkika, Efstratios A. Zacharis, Dimitrios N. Skikos, Efthymios L. Lekkas
Abstract Electricity infrastructures are critical lifeline systems that are designed to serve with a high degree of reliability the power supply of consumers under normal operating conditions and in case of common failures or expected disturbances. However, many recent weather-induced disasters have brought unprecedented challenges to the electricity networks, underlining that power systems remain unprepared to absorb disruptive large-scale and severe events. Worse still, it is expected that such climate hazards will take place at rising frequency and intensity rates due to climate change. The intensification of meteorological extremes will lead to higher losses and changes in transmission capacity, increasing the frequency and importance of material damage to the aging electric infrastructure, thus resulting in significant disruptions, cascading failures, and unpredictable power outages. This review paper presents real-life examples of different types of extreme weather incidents and their impacts on the distribution network in Greece, a country that is highly vulnerable because of its location, geomorphology, and the existing overhead network assets, highlighting lessons learned related to adaptation options and disaster management best practices. Literature review and benchmarking with other grid operators are also employed to explore resilience-enhancing technical capabilities, weatherproof solutions, and operational strategies on which policy-making initiatives should focus.
{"title":"Battling the extreme: lessons learned from weather-induced disasters on electricity distribution networks and climate change adaptation strategies","authors":"Aikaterini Gkika, Efstratios A. Zacharis, Dimitrios N. Skikos, Efthymios L. Lekkas","doi":"10.2166/nh.2023.067","DOIUrl":"https://doi.org/10.2166/nh.2023.067","url":null,"abstract":"Abstract Electricity infrastructures are critical lifeline systems that are designed to serve with a high degree of reliability the power supply of consumers under normal operating conditions and in case of common failures or expected disturbances. However, many recent weather-induced disasters have brought unprecedented challenges to the electricity networks, underlining that power systems remain unprepared to absorb disruptive large-scale and severe events. Worse still, it is expected that such climate hazards will take place at rising frequency and intensity rates due to climate change. The intensification of meteorological extremes will lead to higher losses and changes in transmission capacity, increasing the frequency and importance of material damage to the aging electric infrastructure, thus resulting in significant disruptions, cascading failures, and unpredictable power outages. This review paper presents real-life examples of different types of extreme weather incidents and their impacts on the distribution network in Greece, a country that is highly vulnerable because of its location, geomorphology, and the existing overhead network assets, highlighting lessons learned related to adaptation options and disaster management best practices. Literature review and benchmarking with other grid operators are also employed to explore resilience-enhancing technical capabilities, weatherproof solutions, and operational strategies on which policy-making initiatives should focus.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135307198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This study investigates the effectiveness of ponds as a nature-based solution (NBS) to concurrently ameliorate flood and drought impacts, emphasizing the need for an integrated response to multi-extreme hydrological events. We incorporate ponds into agricultural landscapes in the Bagmati River Basin of Nepal and assess their performance using the Soil and Water Assessment Tool (SWAT+). Six different scenarios are thoroughly explored to see how these interventions affect the main components of the water balance, such as surface run-off, lateral flow, percolation, and evapotranspiration. The spatial efficiency of the ponds, particularly in their immediate surroundings and downstream areas, has been proven to be a crucial factor in their overall efficacy in attenuating extremes, which increases with the size of the intervention area. Although the effects of ponds on floods and droughts are minor, they could be significantly magnified by a synergistic use of other NBS tactics, such as conservation tillage or soil conservation techniques. Future studies should establish the most appropriate sites and volumes for these interventions, as well as further investigate the possible advantages of several NBS, to optimize flood and drought management in the Bagmati River Basin and other similarly susceptible places.
{"title":"Evaluating the impact of ponds on flood and drought mitigation in the Bagmati River Basin, Nepal","authors":"Kabita Gautam, Gerald Corzo","doi":"10.2166/nh.2023.050","DOIUrl":"https://doi.org/10.2166/nh.2023.050","url":null,"abstract":"Abstract This study investigates the effectiveness of ponds as a nature-based solution (NBS) to concurrently ameliorate flood and drought impacts, emphasizing the need for an integrated response to multi-extreme hydrological events. We incorporate ponds into agricultural landscapes in the Bagmati River Basin of Nepal and assess their performance using the Soil and Water Assessment Tool (SWAT+). Six different scenarios are thoroughly explored to see how these interventions affect the main components of the water balance, such as surface run-off, lateral flow, percolation, and evapotranspiration. The spatial efficiency of the ponds, particularly in their immediate surroundings and downstream areas, has been proven to be a crucial factor in their overall efficacy in attenuating extremes, which increases with the size of the intervention area. Although the effects of ponds on floods and droughts are minor, they could be significantly magnified by a synergistic use of other NBS tactics, such as conservation tillage or soil conservation techniques. Future studies should establish the most appropriate sites and volumes for these interventions, as well as further investigate the possible advantages of several NBS, to optimize flood and drought management in the Bagmati River Basin and other similarly susceptible places.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135826861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aquifer thermal energy storage (ATES) systems offer reduced energy costs, lower carbon emissions, and increased energy resilience. The feasibility, however, depends on several factors and require usually optimization. We study an ATES system with injection and extraction wells (cf. graphical abstract). The purpose of the investigation was to calculate the recovery factor of an ATES system with a cyclic repetition of injection and pumping. In the paper, we discuss analytical and numerical radial solutions of differential equations for heat transport in water-saturated porous media. A similar solution was obtained for a 2-D-horizontal confined aquifer with a constant radial flow. Numerical solutions were derived by using a high-resolution Lagrangian approach suppressing spurious oscillations and artificial dispersion. The numerical solution and the analytical solutions give consistent results and match each other well. The solutions describe instantaneous and delayed heat transfer between fluid and solid, as well as time-varying water flow. In hydrological terms, these solutions are relevant for a wide range of problems where groundwater reservoirs are utilized for extraction and storage (namely, irrigation; water supply; geothermal extraction).
{"title":"Analytical and numerical solutions of radially symmetric aquifer thermal energy storage problems","authors":"Z. Birhanu, N. Kitterød, H. Krogstad, A. Kværnø","doi":"10.5194/HESS-2017-303","DOIUrl":"https://doi.org/10.5194/HESS-2017-303","url":null,"abstract":"\u0000 \u0000 Aquifer thermal energy storage (ATES) systems offer reduced energy costs, lower carbon emissions, and increased energy resilience. The feasibility, however, depends on several factors and require usually optimization. We study an ATES system with injection and extraction wells (cf. graphical abstract). The purpose of the investigation was to calculate the recovery factor of an ATES system with a cyclic repetition of injection and pumping. In the paper, we discuss analytical and numerical radial solutions of differential equations for heat transport in water-saturated porous media. A similar solution was obtained for a 2-D-horizontal confined aquifer with a constant radial flow. Numerical solutions were derived by using a high-resolution Lagrangian approach suppressing spurious oscillations and artificial dispersion. The numerical solution and the analytical solutions give consistent results and match each other well. The solutions describe instantaneous and delayed heat transfer between fluid and solid, as well as time-varying water flow. In hydrological terms, these solutions are relevant for a wide range of problems where groundwater reservoirs are utilized for extraction and storage (namely, irrigation; water supply; geothermal extraction).","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"38 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89050038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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