Arisara Nakburee, Sangam Shrestha, S. Mohanasundaram, Ho Huu Loc, Manisha Maharjan
Abstract Teleconnection events can influence normal regional weather patterns and affect weather forecast accuracy. To improve the forecast ability, the relationship between main teleconnections such as El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Madden–Julian Oscillation (MJO), and climate variables (rainfall, maximum and minimum surface temperature, vertical mixing ratio, and vertical maximum temperature) was established using lag correlation coefficient and t-test methods. The results reveal moderately significant correlations between El Niño, positive IOD and rainfall, and vertical mixing ratio, which can be associated with lower-than-usual rainfall. The coincidence between El Niño and positive IOD events can worsen drought. Even though the MJO and regional weather correlations were significant, the magnitude of correlation coefficients was negligible. In addition, the spatiotemporal distribution of ENSO shows that the strong El Niño has more influence on rainfall anomalies in the post-1980s. Since there are insufficient studies on the association between teleconnections and climate variables, especially vertical mixing ratio, our findings can benefit prediction development for teleconnection-induced regional climate anomalies for extreme events and water management preparations in northern and northeastern Thailand.
{"title":"Influences of teleconnections on climate variables in northern and northeastern Thailand","authors":"Arisara Nakburee, Sangam Shrestha, S. Mohanasundaram, Ho Huu Loc, Manisha Maharjan","doi":"10.2166/wcc.2023.120","DOIUrl":"https://doi.org/10.2166/wcc.2023.120","url":null,"abstract":"Abstract Teleconnection events can influence normal regional weather patterns and affect weather forecast accuracy. To improve the forecast ability, the relationship between main teleconnections such as El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Madden–Julian Oscillation (MJO), and climate variables (rainfall, maximum and minimum surface temperature, vertical mixing ratio, and vertical maximum temperature) was established using lag correlation coefficient and t-test methods. The results reveal moderately significant correlations between El Niño, positive IOD and rainfall, and vertical mixing ratio, which can be associated with lower-than-usual rainfall. The coincidence between El Niño and positive IOD events can worsen drought. Even though the MJO and regional weather correlations were significant, the magnitude of correlation coefficients was negligible. In addition, the spatiotemporal distribution of ENSO shows that the strong El Niño has more influence on rainfall anomalies in the post-1980s. Since there are insufficient studies on the association between teleconnections and climate variables, especially vertical mixing ratio, our findings can benefit prediction development for teleconnection-induced regional climate anomalies for extreme events and water management preparations in northern and northeastern Thailand.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135363804","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}
Abu Bakar Arshed, Mohammad Masood, Muhammad Awais Zafar, Ghulam Nabi, Mudassar Iqbal
Watershed management is necessary to conserve water resources because the watershed hydrological processes are more affected by climate and land use change, resulting in the problems of droughts, floods, soil erosion, etc. This study determined suitable alternatives that can ensure viable strategies for tackling the climate change impacts at the Soan River Basin (SRB). A framework was applied to assess the impacts of climate change and land use/cover change (LUCC) using the Soil and Water Assessment Tool (SWAT). A multi-criteria decision analysis (MCDA) was used to prioritize watershed management alternatives by comparing watershed management criteria and alternatives using the analytic hierarchy process (AHP). Framework findings showed a 69 and 31% decline in runoff, and a 58 and 42% increment in evapotranspiration (ET) due to climate change and LUCC, respectively. The top prioritized suitable alternatives were water harvesting structure (WHS) and vegetative cover (VC). Suitability analysis showed that 63.61 and 16.56% area of the SRB were moderately to highly suitable for WHS, respectively. For soil and water management, VC has been found suitable to moderately suitable for 72.68 and 26.75% of the basin area, respectively. So, there should be adoption of such measures which will assist in configuring the climate adaptive strategies.
{"title":"Effective management of the watershed in response to historical climate change using a GIS-based multi-criteria decision analysis (MCDA)","authors":"Abu Bakar Arshed, Mohammad Masood, Muhammad Awais Zafar, Ghulam Nabi, Mudassar Iqbal","doi":"10.2166/wcc.2023.215","DOIUrl":"https://doi.org/10.2166/wcc.2023.215","url":null,"abstract":"\u0000 \u0000 Watershed management is necessary to conserve water resources because the watershed hydrological processes are more affected by climate and land use change, resulting in the problems of droughts, floods, soil erosion, etc. This study determined suitable alternatives that can ensure viable strategies for tackling the climate change impacts at the Soan River Basin (SRB). A framework was applied to assess the impacts of climate change and land use/cover change (LUCC) using the Soil and Water Assessment Tool (SWAT). A multi-criteria decision analysis (MCDA) was used to prioritize watershed management alternatives by comparing watershed management criteria and alternatives using the analytic hierarchy process (AHP). Framework findings showed a 69 and 31% decline in runoff, and a 58 and 42% increment in evapotranspiration (ET) due to climate change and LUCC, respectively. The top prioritized suitable alternatives were water harvesting structure (WHS) and vegetative cover (VC). Suitability analysis showed that 63.61 and 16.56% area of the SRB were moderately to highly suitable for WHS, respectively. For soil and water management, VC has been found suitable to moderately suitable for 72.68 and 26.75% of the basin area, respectively. So, there should be adoption of such measures which will assist in configuring the climate adaptive strategies.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48812276","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 explored co-occurring climate scale changes across the physioclimatically heterogeneous Tapi River basin (TRB) for baseline (1991–2020) and future periods (2021–2100). We used a novel multivariate framework comprising multi-model ensembles of bias-corrected rainfall and temperature from 5 global climate models (CMIP-5), 12 climate indices (6 for each variable), and principal component analysis (PCA). The univariate assessment showed statistically significant warming of 1.1–1.8 °C (1.5–4.0 °C) under RCP-4.5 (RCP-8.5) scenarios. The Middle Tapi basin showed a substantial shift towards a wetter climate regime in the future. The multivariate assessment of spatially varying climate indices resulted in four significant principal components (PCs). The relative evaluation of these PCs showed that nearly 41.6% (47.0%) of the TRB is vulnerable to the transition of the current climatic patterns to the dry-warm (wet-warm) regime under RCP-8.5 (RCP-4.5) in the near (distant) future. On the optimistic side, under RCP-4.5 and RCP-8.5, 53.0 and 69.8% of the TRB displayed signs of uniform temporal distribution with wet rainfall regimes and profound warming towards the end of the 21st century, respectively. The study outcomes would help to devise policies for regional sustainability and adopt mitigation measures to enhance resiliency in a changing climate.
{"title":"Spatial varying and co-occurring future climate changes over a heterogeneous river basin: a multivariate approach","authors":"L. Gehlot, P. L. Patel, P. V. Timbadiya","doi":"10.2166/wcc.2023.206","DOIUrl":"https://doi.org/10.2166/wcc.2023.206","url":null,"abstract":"\u0000 \u0000 This study explored co-occurring climate scale changes across the physioclimatically heterogeneous Tapi River basin (TRB) for baseline (1991–2020) and future periods (2021–2100). We used a novel multivariate framework comprising multi-model ensembles of bias-corrected rainfall and temperature from 5 global climate models (CMIP-5), 12 climate indices (6 for each variable), and principal component analysis (PCA). The univariate assessment showed statistically significant warming of 1.1–1.8 °C (1.5–4.0 °C) under RCP-4.5 (RCP-8.5) scenarios. The Middle Tapi basin showed a substantial shift towards a wetter climate regime in the future. The multivariate assessment of spatially varying climate indices resulted in four significant principal components (PCs). The relative evaluation of these PCs showed that nearly 41.6% (47.0%) of the TRB is vulnerable to the transition of the current climatic patterns to the dry-warm (wet-warm) regime under RCP-8.5 (RCP-4.5) in the near (distant) future. On the optimistic side, under RCP-4.5 and RCP-8.5, 53.0 and 69.8% of the TRB displayed signs of uniform temporal distribution with wet rainfall regimes and profound warming towards the end of the 21st century, respectively. The study outcomes would help to devise policies for regional sustainability and adopt mitigation measures to enhance resiliency in a changing climate.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48708672","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 divided the total storage potential in a natural channel into the ice production volume and the water storage capacity volume. Thermal factors, hydraulic processes, topography, and ice formation were selected to derive a discriminant equation for freeze-up and break-up conditions in the Inner Mongolia Reach of the Yellow River. The trends observed from data for the freeze-up dates, break-up dates, and total frozen days from 2017 to 2020 conform to the principle that the river is gradually frozen from the downstream to the upstream and later thawed from the upstream to the downstream. The number of frozen days in the downstream is greater than in the upstream. Results indicate that freeze-up typically occurs when the proportion of ice in the channel is relatively high. Higher temperatures and greater discharges are required to facilitate the break-up of the river when the equilibrium ice thickness is greater. This study can provide a theoretical basis and framework for establishing an accurate freeze-up and break-up forecast model to prevent and mitigate ice-induced disasters.
{"title":"Discriminant analysis of the freeze-up and break-up conditions in the Inner Mongolia Reach of the Yellow River","authors":"Zhixing Hou, Jun Wang, Jueyi Sui, Guowei Li, Baosen Zhang, Liangguang Zhou","doi":"10.2166/wcc.2023.203","DOIUrl":"https://doi.org/10.2166/wcc.2023.203","url":null,"abstract":"Abstract This study divided the total storage potential in a natural channel into the ice production volume and the water storage capacity volume. Thermal factors, hydraulic processes, topography, and ice formation were selected to derive a discriminant equation for freeze-up and break-up conditions in the Inner Mongolia Reach of the Yellow River. The trends observed from data for the freeze-up dates, break-up dates, and total frozen days from 2017 to 2020 conform to the principle that the river is gradually frozen from the downstream to the upstream and later thawed from the upstream to the downstream. The number of frozen days in the downstream is greater than in the upstream. Results indicate that freeze-up typically occurs when the proportion of ice in the channel is relatively high. Higher temperatures and greater discharges are required to facilitate the break-up of the river when the equilibrium ice thickness is greater. This study can provide a theoretical basis and framework for establishing an accurate freeze-up and break-up forecast model to prevent and mitigate ice-induced disasters.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135250628","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 Climate change and water supply shortages are paramount global concerns. Drought, a complex and often underestimated phenomenon, profoundly affects various aspects of human life. Thus, early drought forecasting is crucial for strategic planning and water resource management. This study introduces a novel hybrid model, combining wavelet transform with the Autoregressive Integrated Moving Average (ARIMA) model, known as Wavelet ARIMA (W-ARIMA), to enhance drought prediction accuracy. We meticulously analyze monthly precipitation data from January 1970 to December 2019 in Kabul, Afghanistan, focusing on multiple time scales (SPI 3, SPI 6, SPI 9, SPI 12). Comparative assessment against the conventional ARIMA approach reveals the superior performance of our W-ARIMA model. Key statistical indicators, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), underscore the improvements achieved by the W-ARIMA model, notably in SPI 12 forecasting. Additionally, we evaluate performance using metrics like R-square, NSE, PBIAS, and KGE, consistently demonstrating the W-ARIMA model's superiority. This substantial enhancement highlights the innovative model's clear superiority in drought forecasting for Kabul, Afghanistan. Our research underscores the critical significance of this hybrid model in addressing the challenges posed by drought within the broader context of climate change and water resource management.
{"title":"Drought forecasting using W-ARIMA model with standardized precipitation index","authors":"Reza Rezaiy, Ani Shabri","doi":"10.2166/wcc.2023.431","DOIUrl":"https://doi.org/10.2166/wcc.2023.431","url":null,"abstract":"Abstract Climate change and water supply shortages are paramount global concerns. Drought, a complex and often underestimated phenomenon, profoundly affects various aspects of human life. Thus, early drought forecasting is crucial for strategic planning and water resource management. This study introduces a novel hybrid model, combining wavelet transform with the Autoregressive Integrated Moving Average (ARIMA) model, known as Wavelet ARIMA (W-ARIMA), to enhance drought prediction accuracy. We meticulously analyze monthly precipitation data from January 1970 to December 2019 in Kabul, Afghanistan, focusing on multiple time scales (SPI 3, SPI 6, SPI 9, SPI 12). Comparative assessment against the conventional ARIMA approach reveals the superior performance of our W-ARIMA model. Key statistical indicators, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), underscore the improvements achieved by the W-ARIMA model, notably in SPI 12 forecasting. Additionally, we evaluate performance using metrics like R-square, NSE, PBIAS, and KGE, consistently demonstrating the W-ARIMA model's superiority. This substantial enhancement highlights the innovative model's clear superiority in drought forecasting for Kabul, Afghanistan. Our research underscores the critical significance of this hybrid model in addressing the challenges posed by drought within the broader context of climate change and water resource management.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135299869","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}
Irrigation with recycled wastewater can reduce freshwater demand and improve soil fertility, but it can also increase CO2 emissions from soil and contribute to global warming. This study investigated whether biochar and mycorrhiza can reduce CO2 emissions and enhance soil quality in wastewater-irrigated turf. A factorial experiment was conducted with four levels of biochar (0, 0.5, 1, and 1.5%), two mycorrhiza (with and without), and two types of irrigation water (freshwater and recycled wastewater). Soil CO2 and H2O emissions, moisture and temperature, and chemical and physical properties were measured for 3 months. Biochar and mycorrhiza treatments significantly reduced CO2 emissions by 19.4–45.0% compared to the control treatment. The combination of biochar at a 1.5% level with mycorrhiza had the highest emission-reducing effect. Biochar and mycorrhiza treatments also reduced H2O emissions by 8.1–14.6%, increased soil organic matter, carbon, and total nitrogen, regulated soil EC and pH, and improved soil porosity and aggregate stability. The results suggest that biochar and mycorrhiza can be effective strategies to mitigate CO2 emissions and improve soil quality in wastewater irrigation. The combination of biochar with mycorrhiza can have synergistic benefits for soil carbon storage and conservation.
{"title":"Biochar and mycorrhiza enhance soil carbon storage and reduce CO2 emissions in wastewater-irrigated turf","authors":"U. Sahin, T. Cakmakci, C. Yerli","doi":"10.2166/wcc.2023.270","DOIUrl":"https://doi.org/10.2166/wcc.2023.270","url":null,"abstract":"\u0000 Irrigation with recycled wastewater can reduce freshwater demand and improve soil fertility, but it can also increase CO2 emissions from soil and contribute to global warming. This study investigated whether biochar and mycorrhiza can reduce CO2 emissions and enhance soil quality in wastewater-irrigated turf. A factorial experiment was conducted with four levels of biochar (0, 0.5, 1, and 1.5%), two mycorrhiza (with and without), and two types of irrigation water (freshwater and recycled wastewater). Soil CO2 and H2O emissions, moisture and temperature, and chemical and physical properties were measured for 3 months. Biochar and mycorrhiza treatments significantly reduced CO2 emissions by 19.4–45.0% compared to the control treatment. The combination of biochar at a 1.5% level with mycorrhiza had the highest emission-reducing effect. Biochar and mycorrhiza treatments also reduced H2O emissions by 8.1–14.6%, increased soil organic matter, carbon, and total nitrogen, regulated soil EC and pH, and improved soil porosity and aggregate stability. The results suggest that biochar and mycorrhiza can be effective strategies to mitigate CO2 emissions and improve soil quality in wastewater irrigation. The combination of biochar with mycorrhiza can have synergistic benefits for soil carbon storage and conservation.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45877545","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 Hydro-economic optimization models are common in hydropower reservoir modeling to aid system operators and planners. In these models, operations are driven by the economic value and constrained by the availability of water. The objective is to either minimize total costs or maximize total benefits. In this study, a hydro-economic optimization model for the integrated reservoir system of the Upper Euphrates Basin, with major tributaries providing water flow to the Euphrates River, is introduced. These model the 10 large-scale reservoirs of the basin with a total installed capacity of 3,255 MW. Water management and hydropower decision-making operations are evaluated with a piecewise linear programming algorithm in monthly time steps using a 45-year historical hydrology between 1971 and 2016. The model aims to maximize hydropower revenue over a long-term time horizon with energy prices varying by month. Reservoir storage and turbine release decisions are optimized for multiple hydropower plants connected in serial or parallel. Hydropower generation, revenue, reservoir storage, capacity ratios and generation reliability results are analyzed. Results show that these hydropower plants generate about 9,481 Gigawatt hour (GWh) of energy with an average turbine capacity use of 36% and obtain a revenue of 620 million $ per year.
{"title":"Development of Upper Euphrates Basin hydro-economic model and hydropower generation optimization","authors":"Ayca Aytac, M. Cihat Tuna, Mustafa Sahin Dogan","doi":"10.2166/wcc.2023.377","DOIUrl":"https://doi.org/10.2166/wcc.2023.377","url":null,"abstract":"Abstract Hydro-economic optimization models are common in hydropower reservoir modeling to aid system operators and planners. In these models, operations are driven by the economic value and constrained by the availability of water. The objective is to either minimize total costs or maximize total benefits. In this study, a hydro-economic optimization model for the integrated reservoir system of the Upper Euphrates Basin, with major tributaries providing water flow to the Euphrates River, is introduced. These model the 10 large-scale reservoirs of the basin with a total installed capacity of 3,255 MW. Water management and hydropower decision-making operations are evaluated with a piecewise linear programming algorithm in monthly time steps using a 45-year historical hydrology between 1971 and 2016. The model aims to maximize hydropower revenue over a long-term time horizon with energy prices varying by month. Reservoir storage and turbine release decisions are optimized for multiple hydropower plants connected in serial or parallel. Hydropower generation, revenue, reservoir storage, capacity ratios and generation reliability results are analyzed. Results show that these hydropower plants generate about 9,481 Gigawatt hour (GWh) of energy with an average turbine capacity use of 36% and obtain a revenue of 620 million $ per year.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298985","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 Accurate runoff prediction is of great significance for flood prevention and mitigation, agricultural irrigation, and reservoir scheduling in watersheds. To address the strong non-linear and non-stationary characteristics of runoff series, a hybrid model of monthly runoff prediction, variational mode decomposition (VMD)–long short-term memory (LSTM)–Transformer, is proposed. Firstly, VMD is used to decompose the runoff series into multiple modal components, and the sample entropy of each modal component is calculated and divided into high-frequency and low-frequency components. The LSTM model is then used to predict the high-frequency components and the transformer to predict the low-frequency components. Finally, the prediction results are summed to obtain the final prediction results. The Mann–Kendall trend test method is used to analyze the runoff characteristics of the Miyun Reservoir, and the constructed VMD–LSTM–Transformer model is used to forecast the runoff of the Miyun Reservoir. The prediction results are compared and evaluated with those of VMD–LSTM, VMD–Transformer, empirical mode decomposition (EMD)–LSTM–Transformer, and empirical mode decomposition (EMD)–LSTM models. The results show that the Nash–Sutcliffe efficiency coefficient (NSE) value of this model is 0.976, mean absolute error (MAE) is 0.206 × 107 m3, mean absolute percentage error (MAPE) is 0.381%, and root mean squared error (RMSE) is 0.411 × 107 m3, all of which are better than other models, indicating that the VMD–LSTM–Transformer model has higher prediction accuracy and can be applied to runoff prediction in the actual study area.
{"title":"Monthly runoff prediction using the VMD-LSTM-Transformer hybrid model: a case study of the Miyun Reservoir in Beijing","authors":"Shaolei Guo, Yihao Wen, Xianqi Zhang, Haiyang Chen","doi":"10.2166/wcc.2023.257","DOIUrl":"https://doi.org/10.2166/wcc.2023.257","url":null,"abstract":"Abstract Accurate runoff prediction is of great significance for flood prevention and mitigation, agricultural irrigation, and reservoir scheduling in watersheds. To address the strong non-linear and non-stationary characteristics of runoff series, a hybrid model of monthly runoff prediction, variational mode decomposition (VMD)–long short-term memory (LSTM)–Transformer, is proposed. Firstly, VMD is used to decompose the runoff series into multiple modal components, and the sample entropy of each modal component is calculated and divided into high-frequency and low-frequency components. The LSTM model is then used to predict the high-frequency components and the transformer to predict the low-frequency components. Finally, the prediction results are summed to obtain the final prediction results. The Mann–Kendall trend test method is used to analyze the runoff characteristics of the Miyun Reservoir, and the constructed VMD–LSTM–Transformer model is used to forecast the runoff of the Miyun Reservoir. The prediction results are compared and evaluated with those of VMD–LSTM, VMD–Transformer, empirical mode decomposition (EMD)–LSTM–Transformer, and empirical mode decomposition (EMD)–LSTM models. The results show that the Nash–Sutcliffe efficiency coefficient (NSE) value of this model is 0.976, mean absolute error (MAE) is 0.206 × 107 m3, mean absolute percentage error (MAPE) is 0.381%, and root mean squared error (RMSE) is 0.411 × 107 m3, all of which are better than other models, indicating that the VMD–LSTM–Transformer model has higher prediction accuracy and can be applied to runoff prediction in the actual study area.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135255344","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}
Tegegn Kassa Beyene, A. Agarwal, M. Jain, B. Yadav
This study investigates drought propagation from meteorological to hydrological and streamflow required to recover from drought in four sub-basins: Genale, Tekeze, Awash, and Baro basins of Ethiopia. Due to limited observed streamflow data, the soil moisture accounting and routing (SMAR) model was used to extend the streamflow data for each sub-basin from 1985 to 2017. Drought characteristics in terms of duration, severity, and onset/offset of drought and propagation time at different time scales are investigated using run theory and Pearson correlation, respectively. Two Archimedean copulas (Clayton and Gumbel) are used to identify the joint return period between drought duration and severity and the amount of streamflow required to recover from hydrological drought for each sub-basin. Our results revealed that drought frequency has increased over most sub-basins over the last two decades. The propagation time from meteorological drought to hydrological drought is shorter over the Tekeze sub-basin (1–3 months); however, Genale and Awash sub-basin show 3- to 6-month propagation time. The more extended propagation time is seen over the Baro sub-basin (6–9 months). The required amount of water for drought recovery estimation shows a linear relationship between the duration of the drought and the amount required.
{"title":"Investigation of the propagation of meteorological to hydrological drought and water required to recover from drought over Ethiopian basins","authors":"Tegegn Kassa Beyene, A. Agarwal, M. Jain, B. Yadav","doi":"10.2166/wcc.2023.024","DOIUrl":"https://doi.org/10.2166/wcc.2023.024","url":null,"abstract":"\u0000 \u0000 This study investigates drought propagation from meteorological to hydrological and streamflow required to recover from drought in four sub-basins: Genale, Tekeze, Awash, and Baro basins of Ethiopia. Due to limited observed streamflow data, the soil moisture accounting and routing (SMAR) model was used to extend the streamflow data for each sub-basin from 1985 to 2017. Drought characteristics in terms of duration, severity, and onset/offset of drought and propagation time at different time scales are investigated using run theory and Pearson correlation, respectively. Two Archimedean copulas (Clayton and Gumbel) are used to identify the joint return period between drought duration and severity and the amount of streamflow required to recover from hydrological drought for each sub-basin. Our results revealed that drought frequency has increased over most sub-basins over the last two decades. The propagation time from meteorological drought to hydrological drought is shorter over the Tekeze sub-basin (1–3 months); however, Genale and Awash sub-basin show 3- to 6-month propagation time. The more extended propagation time is seen over the Baro sub-basin (6–9 months). The required amount of water for drought recovery estimation shows a linear relationship between the duration of the drought and the amount required.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43557736","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}
Rishith Kumar Vogeti, K. Raju, D. Nagesh Kumar, Advani Manish Rajesh, S. V. Somanath Kumar, Yashraj Santosh Kumar Jha
Soil Water Assessment Tool (SWAT), Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and Hydrologic Simulation Program Fortran (HSPF) are explored for streamflow simulation of Lower Godavari Basin, India. The simulating ability of models is evaluated using four indicators. SWAT has shown exceptional simulating ability in calibration and validation compared to the other two. Accordingly, SWAT is used in the climate change framework using an ensemble of 13 Global Climate Models and 4 Shared Socioeconomic Pathways (SSPs). Three-time segments, near-future (2021–2046), mid-future (2047–2072), and far-future (2073–2099), are considered for analysis. Four SSPs show a substantial increase in streamflow compared to the historical period (1982–2020). These deviations range from 17.14 (in SSP245) to 28.35% (in SSP126) (near-future), 31.32 (SSP370) to 43.28% (SSP585) (mid-future), and 30.41 (SSP126) to 70.8% (SSP585) (far-future). Across all timescales covering 948 months, the highest projected streamflow observed in SSP126, SSP245, SSP370, and SSP585 were 4962.36, 6,108, 6,821, and 6,845 m3/s, respectively. Efforts are also made to appraise the influence of multi-model combinations on streamflow. The present study is expected to provide a platform for holistic decision-making, which helps develop efficient basin planning and management alternatives.
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