Vrushti C. Kantharia, D. Mehta, Vijendra Kumar, Mohamedmaroof P. Shaikh, Shivendra Jha
Rainfall is the major component of the hydrologic cycle and it is the primary source of runoff. The main purpose of this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data at three different depths (5 cm, 100 cm and bedrock) for the Damanganga basin. The length of the data for the study period 1983–2022 is 39 years. The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized. The results were compared considering a range of model performance indicators as correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE) coefficient. The model application results shows that soil moisture at bedrock gives more precise value of daily discharge with (R2) and NSE value as 0.9936 and 0.9981, respectively, as compared to the soil moisture at a depth of 5 and 100 cm. The better results obtained for the measurement of soil moisture in the deeper soil layer are consistent with the hydrological behavior anticipated for the analyzed catchment, where the root-zone soil layer is the driver of the runoff response rather than the surface observations. This study can be helpful to hydrologists in selecting appropriate rainfall–runoff models.
{"title":"Rainfall–runoff modeling using an Adaptive Neuro-Fuzzy Inference System considering soil moisture for the Damanganga basin","authors":"Vrushti C. Kantharia, D. Mehta, Vijendra Kumar, Mohamedmaroof P. Shaikh, Shivendra Jha","doi":"10.2166/wcc.2024.143","DOIUrl":"https://doi.org/10.2166/wcc.2024.143","url":null,"abstract":"\u0000 Rainfall is the major component of the hydrologic cycle and it is the primary source of runoff. The main purpose of this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data at three different depths (5 cm, 100 cm and bedrock) for the Damanganga basin. The length of the data for the study period 1983–2022 is 39 years. The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized. The results were compared considering a range of model performance indicators as correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE) coefficient. The model application results shows that soil moisture at bedrock gives more precise value of daily discharge with (R2) and NSE value as 0.9936 and 0.9981, respectively, as compared to the soil moisture at a depth of 5 and 100 cm. The better results obtained for the measurement of soil moisture in the deeper soil layer are consistent with the hydrological behavior anticipated for the analyzed catchment, where the root-zone soil layer is the driver of the runoff response rather than the surface observations. This study can be helpful to hydrologists in selecting appropriate rainfall–runoff models.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734867","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 changes in river flow is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. By using the LARS-WG statistical downscaling model, the outputs of the general circulation model of the sixth report, including the ACCESS-ESM1 and BCC-CSM-MR models, under the SSP5.8.5 and SSP2.4.5 release scenarios. A more accurate spatial scale and daily precipitation and temperature time series were obtained for the studied area during the period of 2015–2043. Then the IHACRES rainfall-runoff model was calibrated in the study area. Based on the fit statistics in the calibration and validation stages, the overall performance of the developed model was evaluated as satisfactory. The calibrated hydrological model was driven by rainfall data and reduced air temperature to predict the effect of climate change on the output of the studied basin. The study showed that the studied basin has more rainfall (on average, 20.8% in the ACCESS-ESM1 model and 33.2% in the BCC-CSM2-MR model). The flow rate of the main river in the ACCESS-ESM1 model will decrease by 15% compared to the base period, and in the BCC-CSM2-MR model, it will increase by 16% compared to the base period.
{"title":"Effects of climate change on streamflow in the Dez Basin of Iran using the IHACRES model based on the CMIP6 model","authors":"M. Goodarzi, M. J. Abedi, Majid Niazkar","doi":"10.2166/wcc.2024.571","DOIUrl":"https://doi.org/10.2166/wcc.2024.571","url":null,"abstract":"\u0000 Understanding the changes in river flow is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. By using the LARS-WG statistical downscaling model, the outputs of the general circulation model of the sixth report, including the ACCESS-ESM1 and BCC-CSM-MR models, under the SSP5.8.5 and SSP2.4.5 release scenarios. A more accurate spatial scale and daily precipitation and temperature time series were obtained for the studied area during the period of 2015–2043. Then the IHACRES rainfall-runoff model was calibrated in the study area. Based on the fit statistics in the calibration and validation stages, the overall performance of the developed model was evaluated as satisfactory. The calibrated hydrological model was driven by rainfall data and reduced air temperature to predict the effect of climate change on the output of the studied basin. The study showed that the studied basin has more rainfall (on average, 20.8% in the ACCESS-ESM1 model and 33.2% in the BCC-CSM2-MR model). The flow rate of the main river in the ACCESS-ESM1 model will decrease by 15% compared to the base period, and in the BCC-CSM2-MR model, it will increase by 16% compared to the base period.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736973","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}
Pablo Morales-Rico, Jessica Ramos-Diaz, Estefani Mendoza-León, Francisco Silva-Olmedo, Frédéric Thalasso
Measuring greenhouse gas emissions from wastewater treatment plants is of utmost importance in the context of climate change. However, due to their variability and complexity, it is a particularly challenging task in aerated reactors. The current methods involve capturing gas emissions from the water surface, measuring gas flow rates, and determining the concentration of the emitted gas at that location. Our study proposes a new, more efficient method that eliminates the need for gas flow rate measurements and additional equipment. The proposed technique uses a gas analyzer and a specially designed floating chamber to measure the transient trend of gas concentration within the chamber from the moment it is deployed to when it reaches a new steady state. Our research shows that this method accurately determines methane and carbon dioxide emissions from aerated reactors and potentially other gases emitted in wastewater treatment plants. It is cost effective, versatile, and simplifies the measurement process. This method facilitates the assessment of greenhouse gas emissions in wastewater treatment plants. Our findings are backed by comprehensive testing in the aeration tanks of a full-scale activated sludge plant, across diverse conditions, including fine- and coarse-bubble aeration.
{"title":"A simplified open flux chamber method for the measurement of greenhouse gas emissions from activated sludge reactors","authors":"Pablo Morales-Rico, Jessica Ramos-Diaz, Estefani Mendoza-León, Francisco Silva-Olmedo, Frédéric Thalasso","doi":"10.2166/wcc.2024.580","DOIUrl":"https://doi.org/10.2166/wcc.2024.580","url":null,"abstract":"\u0000 \u0000 Measuring greenhouse gas emissions from wastewater treatment plants is of utmost importance in the context of climate change. However, due to their variability and complexity, it is a particularly challenging task in aerated reactors. The current methods involve capturing gas emissions from the water surface, measuring gas flow rates, and determining the concentration of the emitted gas at that location. Our study proposes a new, more efficient method that eliminates the need for gas flow rate measurements and additional equipment. The proposed technique uses a gas analyzer and a specially designed floating chamber to measure the transient trend of gas concentration within the chamber from the moment it is deployed to when it reaches a new steady state. Our research shows that this method accurately determines methane and carbon dioxide emissions from aerated reactors and potentially other gases emitted in wastewater treatment plants. It is cost effective, versatile, and simplifies the measurement process. This method facilitates the assessment of greenhouse gas emissions in wastewater treatment plants. Our findings are backed by comprehensive testing in the aeration tanks of a full-scale activated sludge plant, across diverse conditions, including fine- and coarse-bubble aeration.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740441","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}
Aida Hosseini Baghanam, V. Nourani, Mohammad Bejani, Chang-Qing Ke
This study examines two downscaling techniques, convolutional neural networks (CNNs) and feedforward neural networks for predicting precipitation and temperature, alongside statistical downscaling model as a benchmark model. The daily climate predictors were extracted from the European Center for Medium-range Weather Forecast (ECMWF) ERA5 dataset spanning from 1979 to 2010 for Tabriz city, located in the northwest of Iran. The biases in precipitation data of ERA5 predictors were corrected through the empirical quantile mapping method. Also, two nonlinear predictor screening methods, random forest and mutual information were employed, alongside linear correlation coefficient. While these methods facilitate identification of dominant regional climate change drivers, it is essential to consider their limitations, such as sensitivity to parameter settings, assumptions about data relationships, potential biases in handling redundancy and correlation, challenges in generalizability across datasets, and computational complexity. Evaluation results indicated that CNN, when applied without predictor screening, achieves coefficient of determination of 0.98 for temperature and 0.71 for precipitation. Ultimately, future projections were employed under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and concluded that the most increase in temperature by 2.9 °C and decrease in precipitation by 3.5 mm may occur under SSP5-8.5.
{"title":"Improving the statistical downscaling performance of climatic parameters with convolutional neural networks","authors":"Aida Hosseini Baghanam, V. Nourani, Mohammad Bejani, Chang-Qing Ke","doi":"10.2166/wcc.2024.592","DOIUrl":"https://doi.org/10.2166/wcc.2024.592","url":null,"abstract":"\u0000 This study examines two downscaling techniques, convolutional neural networks (CNNs) and feedforward neural networks for predicting precipitation and temperature, alongside statistical downscaling model as a benchmark model. The daily climate predictors were extracted from the European Center for Medium-range Weather Forecast (ECMWF) ERA5 dataset spanning from 1979 to 2010 for Tabriz city, located in the northwest of Iran. The biases in precipitation data of ERA5 predictors were corrected through the empirical quantile mapping method. Also, two nonlinear predictor screening methods, random forest and mutual information were employed, alongside linear correlation coefficient. While these methods facilitate identification of dominant regional climate change drivers, it is essential to consider their limitations, such as sensitivity to parameter settings, assumptions about data relationships, potential biases in handling redundancy and correlation, challenges in generalizability across datasets, and computational complexity. Evaluation results indicated that CNN, when applied without predictor screening, achieves coefficient of determination of 0.98 for temperature and 0.71 for precipitation. Ultimately, future projections were employed under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and concluded that the most increase in temperature by 2.9 °C and decrease in precipitation by 3.5 mm may occur under SSP5-8.5.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740604","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}
S. Mousavi, Naghmeh Mobarghaee Dinan, Saeed Ansarifard, Golnaz Darvishi, F. Borhani, Amir Naghibi
This study aimed to examine how global CO2 changes affect atmospheric CO2 (XCO2) concentrations in Iran from 2015 to 2020. XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) satellite and CO2 surface flux data from the Copernicus Atmosphere Monitoring Service were analyzed. Monthly and annual XCO2 and surface flux values were compared. Over the 6 years, XCO2 in Iran increased steadily by 12.66 ppm, mirroring global rises. However, Iran's CO2 surface flux decreased, with slight increases in anthropogenic emissions but decreased natural and total fluxes. Monthly patterns of XCO2 and surface flux exhibited variations, with XCO2 reaching its zenith in spring and dipping to its lowest point during summer, while surface flux attained its peak during the summer months. The results reveal a significant discrepancy between Iran's surface CO2 flux and atmospheric XCO2 trends. While Iran's anthropogenic emissions increased barely from 2015–2020, its natural and total CO2 fluxes decreased. However, XCO2 increased steadily over this period, indicating the dominant impact of global rather than local factors on Iran's CO2 levels. The research emphasizes the critical need for a coordinated international effort, utilizing satellite monitoring data, to implement science-based policies that mitigate escalating global CO2 emissions. Curbing worldwide greenhouse gas.
{"title":"Assessing the impact of global carbon dioxide changes on atmospheric fluctuations in Iran through satellite data analysis","authors":"S. Mousavi, Naghmeh Mobarghaee Dinan, Saeed Ansarifard, Golnaz Darvishi, F. Borhani, Amir Naghibi","doi":"10.2166/wcc.2024.702","DOIUrl":"https://doi.org/10.2166/wcc.2024.702","url":null,"abstract":"\u0000 \u0000 This study aimed to examine how global CO2 changes affect atmospheric CO2 (XCO2) concentrations in Iran from 2015 to 2020. XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) satellite and CO2 surface flux data from the Copernicus Atmosphere Monitoring Service were analyzed. Monthly and annual XCO2 and surface flux values were compared. Over the 6 years, XCO2 in Iran increased steadily by 12.66 ppm, mirroring global rises. However, Iran's CO2 surface flux decreased, with slight increases in anthropogenic emissions but decreased natural and total fluxes. Monthly patterns of XCO2 and surface flux exhibited variations, with XCO2 reaching its zenith in spring and dipping to its lowest point during summer, while surface flux attained its peak during the summer months. The results reveal a significant discrepancy between Iran's surface CO2 flux and atmospheric XCO2 trends. While Iran's anthropogenic emissions increased barely from 2015–2020, its natural and total CO2 fluxes decreased. However, XCO2 increased steadily over this period, indicating the dominant impact of global rather than local factors on Iran's CO2 levels. The research emphasizes the critical need for a coordinated international effort, utilizing satellite monitoring data, to implement science-based policies that mitigate escalating global CO2 emissions. Curbing worldwide greenhouse gas.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746674","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}
Izhar Ahmad, Muhammad Waseem, Sadaquat Hussain, M. Leta
This study examines Islamabad's landscape changes over four decades, attributing land degradation to shifts in land use and cover. Using Landsat imagery from 1980 to 2023, it analyzes urban growth in five categories. By employing the normalized difference vegetation index (NDVI) and normalized difference built-up index, it notes built-up areas expanding to 61% by 2023, agricultural land contraction, and fluctuating forest cover. Water bodies and bare land decrease significantly. With high accuracy values, NDVI fluctuates from +0.4523 in 1980 to +0.1596 in 2010, rebounding to +0.4422. Fluctuations in barren soil, vegetation, and built-up areas potentially contribute to temperature and rainfall changes. The study explores LULC and land surface temperature correlation. Surveyed respondents (755) express concerns about environmental changes, anticipating reduced rainfall and increased drought. Valuable for sustainable development goals, the study informs policy formulation for effective urban planning and land use control.
{"title":"Climate change-induced spatiotemporal variations of land use land cover by using multitemporal satellite imagery analysis","authors":"Izhar Ahmad, Muhammad Waseem, Sadaquat Hussain, M. Leta","doi":"10.2166/wcc.2024.675","DOIUrl":"https://doi.org/10.2166/wcc.2024.675","url":null,"abstract":"\u0000 This study examines Islamabad's landscape changes over four decades, attributing land degradation to shifts in land use and cover. Using Landsat imagery from 1980 to 2023, it analyzes urban growth in five categories. By employing the normalized difference vegetation index (NDVI) and normalized difference built-up index, it notes built-up areas expanding to 61% by 2023, agricultural land contraction, and fluctuating forest cover. Water bodies and bare land decrease significantly. With high accuracy values, NDVI fluctuates from +0.4523 in 1980 to +0.1596 in 2010, rebounding to +0.4422. Fluctuations in barren soil, vegetation, and built-up areas potentially contribute to temperature and rainfall changes. The study explores LULC and land surface temperature correlation. Surveyed respondents (755) express concerns about environmental changes, anticipating reduced rainfall and increased drought. Valuable for sustainable development goals, the study informs policy formulation for effective urban planning and land use control.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140750684","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}
Climate change leads to altered spatial and temporal patterns of precipitation due to activating the hydrological cycle. This study analyzes the distribution of precipitation concentration over the Levant in 1970–2018 for the first time at various time scales. The precipitation concentration index (PCI) was calculated using a quality-controlled time series from 167 meteorological stations. Trends were calculated using a Mann–Kendall nonparametric test, and a nonparametric Mann–Whitney test was used to determine the statistical significance of the differences between the mean sub-periods. The Levant can be divided into three regions, a strongly irregular concentration in south Palestine and Jordan as well as east Jordan; an irregular concentration in center/north Palestine, north Jordan, and east Syria; and a moderately irregular concentration over the Syrian coast. The annual precipitation concentration index (PCI) for the Levant, Palestine, and Syria non-significantly increased by 0.33, 0.32, and 0.22 unit/decade, respectively. Significant increasing trends occurred in the northwest of the West Bank, with averages of 0.75 unit/decade. The seasonal rainfall distribution tends to be uniform in winter and spring, mainly for south Levant, whereas the annual rainfall tends to be more concentrated. The autumn PCI significantly decreased for the Levant, Palestine, and Syria by −0.46, −0.61, and −0.54 units/decade, respectively.
{"title":"Using the precipitation concentration index for characterizing the rainfall distribution in the Levant","authors":"Ala A. M. Salameh","doi":"10.2166/wcc.2024.037","DOIUrl":"https://doi.org/10.2166/wcc.2024.037","url":null,"abstract":"\u0000 Climate change leads to altered spatial and temporal patterns of precipitation due to activating the hydrological cycle. This study analyzes the distribution of precipitation concentration over the Levant in 1970–2018 for the first time at various time scales. The precipitation concentration index (PCI) was calculated using a quality-controlled time series from 167 meteorological stations. Trends were calculated using a Mann–Kendall nonparametric test, and a nonparametric Mann–Whitney test was used to determine the statistical significance of the differences between the mean sub-periods. The Levant can be divided into three regions, a strongly irregular concentration in south Palestine and Jordan as well as east Jordan; an irregular concentration in center/north Palestine, north Jordan, and east Syria; and a moderately irregular concentration over the Syrian coast. The annual precipitation concentration index (PCI) for the Levant, Palestine, and Syria non-significantly increased by 0.33, 0.32, and 0.22 unit/decade, respectively. Significant increasing trends occurred in the northwest of the West Bank, with averages of 0.75 unit/decade. The seasonal rainfall distribution tends to be uniform in winter and spring, mainly for south Levant, whereas the annual rainfall tends to be more concentrated. The autumn PCI significantly decreased for the Levant, Palestine, and Syria by −0.46, −0.61, and −0.54 units/decade, respectively.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751423","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}
This study uses the Weather Research and Forecasting (WRF) model with five different cumulus parameterization schemes (CPSs) at a resolution of 30 km to simulate the summer (June, July, and August) extreme precipitation event in the Yellow River Basin (YRB) in 2018. The goal of this study is to investigate the sensitivity of extreme precipitation simulation in the YRB during the summer of 2018 to CPSs in the WRF model. The results show that all five CPSs were capable of approximately simulating the direction of the rain bands in the YRB during the summer of 2018, but the simulation results of all CPSs tended to overestimate the value of precipitation amount. Upon further evaluation using seven different methods, it was found that the Betts–Miller–Janjic (BMJ) scheme provided the best simulation of this event. The complex orography of the YRB has a significant influence on moisture transport. The WRF model may have overestimated the moisture flux, which could have contributed to the overestimation of precipitation. The summer extreme precipitation event in the YRB during 2018 may have been influenced by an influx of excessive moisture from the western boundary.
{"title":"Impact of cumulus parameterization schemes on summer extreme precipitation simulation in the Yellow River Basin: the 2018 case","authors":"Shihao Chen, Xing Lv, Baohui Men","doi":"10.2166/wcc.2024.681","DOIUrl":"https://doi.org/10.2166/wcc.2024.681","url":null,"abstract":"\u0000 \u0000 This study uses the Weather Research and Forecasting (WRF) model with five different cumulus parameterization schemes (CPSs) at a resolution of 30 km to simulate the summer (June, July, and August) extreme precipitation event in the Yellow River Basin (YRB) in 2018. The goal of this study is to investigate the sensitivity of extreme precipitation simulation in the YRB during the summer of 2018 to CPSs in the WRF model. The results show that all five CPSs were capable of approximately simulating the direction of the rain bands in the YRB during the summer of 2018, but the simulation results of all CPSs tended to overestimate the value of precipitation amount. Upon further evaluation using seven different methods, it was found that the Betts–Miller–Janjic (BMJ) scheme provided the best simulation of this event. The complex orography of the YRB has a significant influence on moisture transport. The WRF model may have overestimated the moisture flux, which could have contributed to the overestimation of precipitation. The summer extreme precipitation event in the YRB during 2018 may have been influenced by an influx of excessive moisture from the western boundary.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754081","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}
Juan Rivera, Malaëka Robo, Emilio Bianchi, Cristóbal Mulleady
Streamflow simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b) were analyzed to evaluate future changes in surface water resources over northern Patagonia, a region that contributes significantly to the total hydropower production of Argentina. Ten global hydrological models (GHMs), forced by four general circulation models, effectively capture the winter streamflow maximum in the Negro river basin. However, most of them face challenges in simulating the late-spring pulse due to a misrepresentation of temperature over the higher elevations of the Andes. We quantified the future streamflow evolution using a multi-model ensemble from a subset of the best-performing GHMs under the RCP2.6 and RCP6.0 emission scenarios for two temporal horizons. According to the multi-model ensemble, there is a projected decrease in the annual streamflow of the analyzed rivers, which is more important considering the RCP6.0 scenario during the late 21st century, reaching up to −40% relative to the 1979–2005 reference period. This reduction is attributed to the projected precipitation decline in the headwaters of the Negro river basin in response to changes in the surface pressure patterns. These results have implications for regional water authorities for the development of adaptation plans considering future demand projections.
{"title":"Impact of climate change on the streamflow in northern Patagonia","authors":"Juan Rivera, Malaëka Robo, Emilio Bianchi, Cristóbal Mulleady","doi":"10.2166/wcc.2024.492","DOIUrl":"https://doi.org/10.2166/wcc.2024.492","url":null,"abstract":"\u0000 \u0000 Streamflow simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b) were analyzed to evaluate future changes in surface water resources over northern Patagonia, a region that contributes significantly to the total hydropower production of Argentina. Ten global hydrological models (GHMs), forced by four general circulation models, effectively capture the winter streamflow maximum in the Negro river basin. However, most of them face challenges in simulating the late-spring pulse due to a misrepresentation of temperature over the higher elevations of the Andes. We quantified the future streamflow evolution using a multi-model ensemble from a subset of the best-performing GHMs under the RCP2.6 and RCP6.0 emission scenarios for two temporal horizons. According to the multi-model ensemble, there is a projected decrease in the annual streamflow of the analyzed rivers, which is more important considering the RCP6.0 scenario during the late 21st century, reaching up to −40% relative to the 1979–2005 reference period. This reduction is attributed to the projected precipitation decline in the headwaters of the Negro river basin in response to changes in the surface pressure patterns. These results have implications for regional water authorities for the development of adaptation plans considering future demand projections.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140752989","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}
Soumia Achli, Terence Epule Epule, D. Dhiba, Wiam Salih, A. Chehbouni
In Morocco, the historical record depicts a situation characterized by increasing temperatures and diminishing precipitation, which often ends up in severe drought episodes. This research examines the vulnerability of wheat, barley, and maize to growing season temperature changes as well as socio-economic adaptive capacity proxies. This work uses a composite index of vulnerability that posits that the vulnerability index is a function of the exposure, sensitivity, and adaptive capacity indexes. FAOSTAT and Yield Gap Atlas data were used for the period 1991–2016 to calculate the sensitivity index. The World Bank Climate Portal provided the mean annual growing season temperature data used to compute the exposure index. The World Bank, figshare, and MPR archives were used to capture the proxies of adaptive capacity such as literacy and poverty rates. These findings indicate that wheat has the lowest vulnerability index and the greatest adaptive capacity index, while barley has the strongest vulnerability and lowest adaptive capacity indexes. Sub-nationally, the indices of vulnerability and the standardized growing season's temperature decreased northward. Northward, wheat records the lowest vulnerability and highest adaptive capacity, and the second highest standard growing season temperature.
{"title":"Vulnerability of maize, barley, and wheat yields to growing season temperature and socioeconomic indicators in Morocco","authors":"Soumia Achli, Terence Epule Epule, D. Dhiba, Wiam Salih, A. Chehbouni","doi":"10.2166/wcc.2024.498","DOIUrl":"https://doi.org/10.2166/wcc.2024.498","url":null,"abstract":"\u0000 \u0000 In Morocco, the historical record depicts a situation characterized by increasing temperatures and diminishing precipitation, which often ends up in severe drought episodes. This research examines the vulnerability of wheat, barley, and maize to growing season temperature changes as well as socio-economic adaptive capacity proxies. This work uses a composite index of vulnerability that posits that the vulnerability index is a function of the exposure, sensitivity, and adaptive capacity indexes. FAOSTAT and Yield Gap Atlas data were used for the period 1991–2016 to calculate the sensitivity index. The World Bank Climate Portal provided the mean annual growing season temperature data used to compute the exposure index. The World Bank, figshare, and MPR archives were used to capture the proxies of adaptive capacity such as literacy and poverty rates. These findings indicate that wheat has the lowest vulnerability index and the greatest adaptive capacity index, while barley has the strongest vulnerability and lowest adaptive capacity indexes. Sub-nationally, the indices of vulnerability and the standardized growing season's temperature decreased northward. Northward, wheat records the lowest vulnerability and highest adaptive capacity, and the second highest standard growing season temperature.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367366","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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