Locating hotspots and assessing sediment accumulation are crucial aspects of water body management. The primary aim of this study was to examine sediment yield in the Hamesa watershed utilizing the Soil and Water Assessment Tool (SWAT) model and to propose best management practices. The basin was divided into 15 sub-basins, with 103 hydrological response units at the outlet of the Hamesa watershed. Simulation was conducted using meteorological and spatial data. Monthly streamflow and sediment data were calibrated for the period from 2000 to 2010 and validated for the period from 2011 to 2015 using the SWAT Uncertainty Calibration Program Sequential Uncertainty Fit (SUFI-2). Model performance was assessed using metrics including the coefficient of determination (R2), Nash–Sutcliffe model efficiency, observation standard deviation ratio, and percentage bias, which demonstrated very good results in both calibration and validation periods. The average annual sediment production in the Hamesa watershed was estimated at 9,800 t/year. Nine out of 50 affected sub-basins were categorized as producing moderate to very high sediment content (4.54–12.82 t/ha/year) and were chosen for sediment reduction scenarios. This study will play a significant role in managing impacted watersheds affected by soil erosion.
{"title":"Predicting sediment yield and locating hotspot areas in the Hamesa watershed of Ethiopia for effective watershed management","authors":"Fikru Damte Darota, Habitamu Bogale Borko, Chansler Dagnachew Adinew, Muluneh Legesse Edamo","doi":"10.2166/wcc.2024.648","DOIUrl":"https://doi.org/10.2166/wcc.2024.648","url":null,"abstract":"\u0000 \u0000 Locating hotspots and assessing sediment accumulation are crucial aspects of water body management. The primary aim of this study was to examine sediment yield in the Hamesa watershed utilizing the Soil and Water Assessment Tool (SWAT) model and to propose best management practices. The basin was divided into 15 sub-basins, with 103 hydrological response units at the outlet of the Hamesa watershed. Simulation was conducted using meteorological and spatial data. Monthly streamflow and sediment data were calibrated for the period from 2000 to 2010 and validated for the period from 2011 to 2015 using the SWAT Uncertainty Calibration Program Sequential Uncertainty Fit (SUFI-2). Model performance was assessed using metrics including the coefficient of determination (R2), Nash–Sutcliffe model efficiency, observation standard deviation ratio, and percentage bias, which demonstrated very good results in both calibration and validation periods. The average annual sediment production in the Hamesa watershed was estimated at 9,800 t/year. Nine out of 50 affected sub-basins were categorized as producing moderate to very high sediment content (4.54–12.82 t/ha/year) and were chosen for sediment reduction scenarios. This study will play a significant role in managing impacted watersheds affected by soil erosion.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"25 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226778","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}
Hadjer Keria, E. Bensaci, Asma Zoubiri, Zineb Ben Si Said
Drought is expected to increase in water bodies due to climate change. Monitoring long-term changes in wetlands is crucial for identifying fluctuations and conserving biodiversity. In this study, we assessed the long-term variability of remote sensing indicators in 25 watershed areas in Algeria known for their significant biodiversity. We employed two statistical methods, namely linear regression and the Mann–Kendall (MK) test, to capture long-term fluctuations by integrating data from various sources, including Modis and Landsat satellite data. A time-series dataset spanning 22 years was developed, consisting of the following indicators: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), and land surface temperature (LST). We evaluated the relationships between these variables. The results indicated that NDVI exhibited a stronger temporal response compared to EVI, NDWI, and NDMI. Additionally, negative associations between NDVI and LST confirmed the impact of drought and plant stress on vegetation in the study areas (R2 = 0.109–R2 = 0.5701). The NDMI results pointed to water stress in the water bodies, showing a significant decreasing trend. The results from the MK trend analysis underscored the importance of NDVI and highlighted its strong association with EVI, NDWI, and NDMI. Understanding the dynamics of vegetation and water stress has become crucial for ecosystem forecasts.
{"title":"Long-term dynamics of remote sensing indicators to monitor the dynamism of ecosystems in arid and semi-arid areas: contributions to sustainable resource management","authors":"Hadjer Keria, E. Bensaci, Asma Zoubiri, Zineb Ben Si Said","doi":"10.2166/wcc.2024.409","DOIUrl":"https://doi.org/10.2166/wcc.2024.409","url":null,"abstract":"\u0000 \u0000 Drought is expected to increase in water bodies due to climate change. Monitoring long-term changes in wetlands is crucial for identifying fluctuations and conserving biodiversity. In this study, we assessed the long-term variability of remote sensing indicators in 25 watershed areas in Algeria known for their significant biodiversity. We employed two statistical methods, namely linear regression and the Mann–Kendall (MK) test, to capture long-term fluctuations by integrating data from various sources, including Modis and Landsat satellite data. A time-series dataset spanning 22 years was developed, consisting of the following indicators: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), and land surface temperature (LST). We evaluated the relationships between these variables. The results indicated that NDVI exhibited a stronger temporal response compared to EVI, NDWI, and NDMI. Additionally, negative associations between NDVI and LST confirmed the impact of drought and plant stress on vegetation in the study areas (R2 = 0.109–R2 = 0.5701). The NDMI results pointed to water stress in the water bodies, showing a significant decreasing trend. The results from the MK trend analysis underscored the importance of NDVI and highlighted its strong association with EVI, NDWI, and NDMI. Understanding the dynamics of vegetation and water stress has become crucial for ecosystem forecasts.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"27 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229084","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}
The decision-making process of wind–photovoltaic–hydropower systems involves knowledge from many fields. Influenced by the knowledge level of the decision-maker and the attribute information of the scheme set, there exists a certain uncertainty in the indicator weights. In view of this, this paper proposes a stochastic multi-criteria decision-making framework for scheduling of wind–photovoltaic–hydropower systems, which overcomes the difficulty of uncertainty in indicator weights or even completely unknown information about indicator weights at the time of decision-making. The Stochastic Multi-criteria Acceptability Analysis (SMAA) theory and the VIKOR model are introduced, and the proposed SMAA–VIKOR model makes the indicator weight space explicit. The study shows that the proposed SMAA–VIKOR model can overcome the obstacle of decision-makers’ lack of information on indicator weights. The ranking acceptability indicators calculated by the model show a more obvious trend of advantages and disadvantages, which gives full confidence to the decision-making group to formulate a plan to be implemented. It breaks through the bottleneck of group decision-making, which is difficult to make effective decisions due to the condition of incomplete information, and enriches the library of stochastic multi-criteria decision-making methods for the scientific formulation of scheduling schemes of wind–photovoltaic–hydropower systems under uncertainty conditions.
{"title":"Stochastic multi-criteria decision-making for scheduling of wind–photovoltaic–hydropower systems","authors":"Weifeng Liu, Yu Zhang, Xigang Xing, Xuning Guo, Rui Ma, Jieyu Li, Yunling Li","doi":"10.2166/wcc.2024.531","DOIUrl":"https://doi.org/10.2166/wcc.2024.531","url":null,"abstract":"\u0000 The decision-making process of wind–photovoltaic–hydropower systems involves knowledge from many fields. Influenced by the knowledge level of the decision-maker and the attribute information of the scheme set, there exists a certain uncertainty in the indicator weights. In view of this, this paper proposes a stochastic multi-criteria decision-making framework for scheduling of wind–photovoltaic–hydropower systems, which overcomes the difficulty of uncertainty in indicator weights or even completely unknown information about indicator weights at the time of decision-making. The Stochastic Multi-criteria Acceptability Analysis (SMAA) theory and the VIKOR model are introduced, and the proposed SMAA–VIKOR model makes the indicator weight space explicit. The study shows that the proposed SMAA–VIKOR model can overcome the obstacle of decision-makers’ lack of information on indicator weights. The ranking acceptability indicators calculated by the model show a more obvious trend of advantages and disadvantages, which gives full confidence to the decision-making group to formulate a plan to be implemented. It breaks through the bottleneck of group decision-making, which is difficult to make effective decisions due to the condition of incomplete information, and enriches the library of stochastic multi-criteria decision-making methods for the scientific formulation of scheduling schemes of wind–photovoltaic–hydropower systems under uncertainty conditions.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"89 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232434","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}
M. Rafiee, Sattar Rad, Mehdi Mahbod, Masih Zolghadr, Ravi Prakash Tripathi, H. M. Azamatulla
Precipitation, as one of the most significant parameters in hydrological simulations, is often difficult accessible in countries, such as Iran, due to an inadequate number of rain gauge stations. Remote sensing has provided an alternative source using a specific spatial and temporal resolution in rainfall estimation throughout an area. In this study, the effectiveness of the Hydrologic Engineering Center-Hydrologic Modeling System runoff rainfall simulation model was evaluated using the Global Precipitation Measurement (GPM) Mission satellite and rain gauge station precipitation data. The model was calibrated and validated using five flood event data of a hydrometric station at the outlet of the Bashar basin. Most important flood parameters including peak discharge (QP), flood volume (V) and time of concentration (TC) were used to evaluate and compare the application of satellite and ground station data in the model using various statistical indices. The accuracy of QP and V estimations by using rain gauge data was higher than those obtained by satellite data. However, the difference between mean relative error (MRE) in QP estimation was less than 1% (9.9 and 10.6% for rain gauge and satellite data, respectively). Conversely, higher accuracies were met for TC estimation using satellite (with MRE 9.1 and 10.2% for GPM and rain gauge data, respectively).
{"title":"Modeling run-off flow hydrographs using remote sensing data: an application to the Bashar basin, Iran","authors":"M. Rafiee, Sattar Rad, Mehdi Mahbod, Masih Zolghadr, Ravi Prakash Tripathi, H. M. Azamatulla","doi":"10.2166/wcc.2024.378","DOIUrl":"https://doi.org/10.2166/wcc.2024.378","url":null,"abstract":"\u0000 Precipitation, as one of the most significant parameters in hydrological simulations, is often difficult accessible in countries, such as Iran, due to an inadequate number of rain gauge stations. Remote sensing has provided an alternative source using a specific spatial and temporal resolution in rainfall estimation throughout an area. In this study, the effectiveness of the Hydrologic Engineering Center-Hydrologic Modeling System runoff rainfall simulation model was evaluated using the Global Precipitation Measurement (GPM) Mission satellite and rain gauge station precipitation data. The model was calibrated and validated using five flood event data of a hydrometric station at the outlet of the Bashar basin. Most important flood parameters including peak discharge (QP), flood volume (V) and time of concentration (TC) were used to evaluate and compare the application of satellite and ground station data in the model using various statistical indices. The accuracy of QP and V estimations by using rain gauge data was higher than those obtained by satellite data. However, the difference between mean relative error (MRE) in QP estimation was less than 1% (9.9 and 10.6% for rain gauge and satellite data, respectively). Conversely, higher accuracies were met for TC estimation using satellite (with MRE 9.1 and 10.2% for GPM and rain gauge data, respectively).","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"78 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234136","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}
Developing accurate flood forecasting models is necessary for flood control, water resources and management in the Mahanadi River Basin. In this study, convolutional neural network (CNN) is integrated with random forest (RF) and support vector regression (SVR) for making a hybrid model (CNN–RF and CNN–SVR) where CNN is used as feature extraction technique while RF and SVR are used as forecasting models. These hybrid models are compared with RF, SVR, and artificial neural network (ANN). The influence of training–testing data division on the performance of hybrid models has been tested. Hyperparameter sensitivity analyses are performed for forecasting models to select the best value of hyperparameters and to exclude the nonsensitive hyperparameters. Two hydrological stations (Kantamal and Kesinga) are selected as case studies. Results indicated that CNN–RF model performs better than other models for both stations. In addition, it is found that CNN has improved the accuracy of RF and SVR models for flood forecasting. The results of the training–testing division show that both models’ performance is better at 50–50% data division. Validation results show that both models are not overfitting or underfitting. Results demonstrate that CNN–RF model can be used as a potential model for flood forecasting in river basins.
{"title":"Comparison of machine learning models for flood forecasting in the Mahanadi River Basin, India","authors":"Sanjay Sharma, Sangeeta Kumari","doi":"10.2166/wcc.2024.517","DOIUrl":"https://doi.org/10.2166/wcc.2024.517","url":null,"abstract":"\u0000 \u0000 Developing accurate flood forecasting models is necessary for flood control, water resources and management in the Mahanadi River Basin. In this study, convolutional neural network (CNN) is integrated with random forest (RF) and support vector regression (SVR) for making a hybrid model (CNN–RF and CNN–SVR) where CNN is used as feature extraction technique while RF and SVR are used as forecasting models. These hybrid models are compared with RF, SVR, and artificial neural network (ANN). The influence of training–testing data division on the performance of hybrid models has been tested. Hyperparameter sensitivity analyses are performed for forecasting models to select the best value of hyperparameters and to exclude the nonsensitive hyperparameters. Two hydrological stations (Kantamal and Kesinga) are selected as case studies. Results indicated that CNN–RF model performs better than other models for both stations. In addition, it is found that CNN has improved the accuracy of RF and SVR models for flood forecasting. The results of the training–testing division show that both models’ performance is better at 50–50% data division. Validation results show that both models are not overfitting or underfitting. Results demonstrate that CNN–RF model can be used as a potential model for flood forecasting in river basins.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"68 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140236945","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}
Nierji Reservoir is the largest and most important water conservancy project in the Nenjiang River Basin. A thorough understanding of variations in streamflow and the driving factors of the Nierji Reservoir Basin (NERB) is crucial, but there are still gaps. In this paper, the annual streamflow data of Nierji Reservoir from 1898 to 2013 were applied to detect the changing trend and abruptions using the Mann–Kendall method. Additionally, a Back Propagation-Artificial Neural Network (BP-ANN) model was developed to explore the relationships between the streamflow and its influencing factors and further quantify the relative contribution of each factor to the streamflow change. The results revealed that the annual streamflow of NERB significantly increased from 1898 to 2013 but declined during 1988–2013. Human activities were found to be the primary driver of streamflow decrease during 1988–2013, accounting for nearly 75% of the total change. Specifically, GDP had the largest influence, contributing 32% to the overall variation. Forest area, precipitation, and cultivated area had smaller contributions of 25, 23, and 18%, respectively. Temperature was found to have the least impact, with a relative contribution of 2%. This study provides valuable insights into water resources management in the Nenjiang River Basin, benefiting both agriculture and ecology.
{"title":"Variations in the streamflow of the Nierji Reservoir Basin and quantification of the influencing factors","authors":"Chunxu Han, Fengping Li, Xiaolan Li, Sheng Wang, Yanhua Xu","doi":"10.2166/wcc.2024.652","DOIUrl":"https://doi.org/10.2166/wcc.2024.652","url":null,"abstract":"\u0000 \u0000 Nierji Reservoir is the largest and most important water conservancy project in the Nenjiang River Basin. A thorough understanding of variations in streamflow and the driving factors of the Nierji Reservoir Basin (NERB) is crucial, but there are still gaps. In this paper, the annual streamflow data of Nierji Reservoir from 1898 to 2013 were applied to detect the changing trend and abruptions using the Mann–Kendall method. Additionally, a Back Propagation-Artificial Neural Network (BP-ANN) model was developed to explore the relationships between the streamflow and its influencing factors and further quantify the relative contribution of each factor to the streamflow change. The results revealed that the annual streamflow of NERB significantly increased from 1898 to 2013 but declined during 1988–2013. Human activities were found to be the primary driver of streamflow decrease during 1988–2013, accounting for nearly 75% of the total change. Specifically, GDP had the largest influence, contributing 32% to the overall variation. Forest area, precipitation, and cultivated area had smaller contributions of 25, 23, and 18%, respectively. Temperature was found to have the least impact, with a relative contribution of 2%. This study provides valuable insights into water resources management in the Nenjiang River Basin, benefiting both agriculture and ecology.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"11 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140239134","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}
Hossein Ruigar, S. Emamgholizadeh, Saeid Gharechelou, Saeed Golian
Several factors, including natural and human-induced, can affect river discharge. This study aims to examine the influence of land use changes and climate change on the monthly average rainfall time series in the Talar River Basin, situated in northern Iran. To investigate the impact of human factors, namely land use change and point source operations, on monthly average streamflow, the DBEST method was used to detect any breakpoint in the streamflow time series caused by gradual changes in land use and climate. The SWAT model was used to simulate the basin at two stations, Kiakola and Shirghah, between 2001 and 2020. The land use maps were created for the years 2001 and 2019. Calibration and validation at the Kiakola station showed that the Nash–Sutcliffe model (NSE) had an efficiency of 0.8 and 0.76, respectively, while at the Shirghah station, the same values were 0.84 and 0.75. Findings revealed that human activities, specifically the combined impact of land use change and point source operations, had a 60% influence on the monthly average streamflow of the Talar River. Further showed that the combination of land use and harvesting played the most significant role in the basin's outflow on a monthly scale.
{"title":"Evaluating the impacts of anthropogenic, climate, and land use changes on streamflow","authors":"Hossein Ruigar, S. Emamgholizadeh, Saeid Gharechelou, Saeed Golian","doi":"10.2166/wcc.2024.664","DOIUrl":"https://doi.org/10.2166/wcc.2024.664","url":null,"abstract":"\u0000 Several factors, including natural and human-induced, can affect river discharge. This study aims to examine the influence of land use changes and climate change on the monthly average rainfall time series in the Talar River Basin, situated in northern Iran. To investigate the impact of human factors, namely land use change and point source operations, on monthly average streamflow, the DBEST method was used to detect any breakpoint in the streamflow time series caused by gradual changes in land use and climate. The SWAT model was used to simulate the basin at two stations, Kiakola and Shirghah, between 2001 and 2020. The land use maps were created for the years 2001 and 2019. Calibration and validation at the Kiakola station showed that the Nash–Sutcliffe model (NSE) had an efficiency of 0.8 and 0.76, respectively, while at the Shirghah station, the same values were 0.84 and 0.75. Findings revealed that human activities, specifically the combined impact of land use change and point source operations, had a 60% influence on the monthly average streamflow of the Talar River. Further showed that the combination of land use and harvesting played the most significant role in the basin's outflow on a monthly scale.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"75 S2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238482","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}
Étienne Guilpart, Vahid Espanmanesh, A. Tilmant, Marc-André Bourgault
Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability-impact-adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis.
{"title":"Sensitivity of bias correction step on generating hydrological scenarios","authors":"Étienne Guilpart, Vahid Espanmanesh, A. Tilmant, Marc-André Bourgault","doi":"10.2166/wcc.2024.555","DOIUrl":"https://doi.org/10.2166/wcc.2024.555","url":null,"abstract":"\u0000 Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability-impact-adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"6 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241215","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}
Climate change poses significant challenges to water resources and streamflow in Ethiopia, a country highly dependent on agriculture and vulnerable to environmental shifts. This paper reviews the current state of knowledge on climate change impacts on streamflow in Ethiopia, emphasizing factors driving these changes and drawing insights from relevant studies. The analysis encompasses hydrological responses to climate change, including alterations in precipitation patterns, temperature fluctuations, and changes in water availability. Additionally, the study examines the impact of land use changes on streamflow dynamics. Comparative insights from neighboring countries and river basins further illuminate the broader regional implications of climate change on water resources According to the previous research reviewed in this paper, climate change, land use change, and increment in extreme events (drought) have affected the stream flow over the last decades. The findings underscore the urgent need for adaptive strategies and sustainable water management practices to mitigate the adverse effects of climate change on streamflow and ensure water security in Ethiopia and beyond.
{"title":"Hydro-meteorological response to climate change impact in Ethiopia: a review","authors":"K. Chanie","doi":"10.2166/wcc.2024.711","DOIUrl":"https://doi.org/10.2166/wcc.2024.711","url":null,"abstract":"\u0000 Climate change poses significant challenges to water resources and streamflow in Ethiopia, a country highly dependent on agriculture and vulnerable to environmental shifts. This paper reviews the current state of knowledge on climate change impacts on streamflow in Ethiopia, emphasizing factors driving these changes and drawing insights from relevant studies. The analysis encompasses hydrological responses to climate change, including alterations in precipitation patterns, temperature fluctuations, and changes in water availability. Additionally, the study examines the impact of land use changes on streamflow dynamics. Comparative insights from neighboring countries and river basins further illuminate the broader regional implications of climate change on water resources According to the previous research reviewed in this paper, climate change, land use change, and increment in extreme events (drought) have affected the stream flow over the last decades. The findings underscore the urgent need for adaptive strategies and sustainable water management practices to mitigate the adverse effects of climate change on streamflow and ensure water security in Ethiopia and beyond.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"16 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243181","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}
Ensemble modelling has become a significant technique in the field of machine learning, as it utilises the combined knowledge of multiple base models to improve the accuracy of predictions in different domains. Nevertheless, the effectiveness of ensemble predictions relies on the implementation of post-processing techniques that enhance and optimize the outputs of the ensemble. This study explores the domain of ensemble data post-processing, utilizing a machine learning-focused methodology to thoroughly assess and contrast a variety of post-processing methods. TIGGE Ensemble data from ECMWF and NCEP were used from 2010 to 2020. Research covers machine learning approaches post-processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, QM were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for post-processing performed exceptionally well with BS value of 0.10 and RPS value of 0.11 at all grid points for both methods. The ROC–AUC values for the cNLR and BMA methods were found to be 91.87 and 91.82%, respectively. The results show that improved post-processing techniques can be helpful to predict the flood in advance with accurate precision and warning.
{"title":"Performance evaluation and verification of post-processing methods for TIGGE ensemble data using machine learning approaches","authors":"Anant Patel, S. M. Yadav","doi":"10.2166/wcc.2024.563","DOIUrl":"https://doi.org/10.2166/wcc.2024.563","url":null,"abstract":"\u0000 \u0000 Ensemble modelling has become a significant technique in the field of machine learning, as it utilises the combined knowledge of multiple base models to improve the accuracy of predictions in different domains. Nevertheless, the effectiveness of ensemble predictions relies on the implementation of post-processing techniques that enhance and optimize the outputs of the ensemble. This study explores the domain of ensemble data post-processing, utilizing a machine learning-focused methodology to thoroughly assess and contrast a variety of post-processing methods. TIGGE Ensemble data from ECMWF and NCEP were used from 2010 to 2020. Research covers machine learning approaches post-processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, QM were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for post-processing performed exceptionally well with BS value of 0.10 and RPS value of 0.11 at all grid points for both methods. The ROC–AUC values for the cNLR and BMA methods were found to be 91.87 and 91.82%, respectively. The results show that improved post-processing techniques can be helpful to predict the flood in advance with accurate precision and warning.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"32 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243494","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}
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