Managing basin-wide ecosystem services using the bankruptcy theory

Ashrafi, Saeed; Khoie, Mohammad Masoud Mohammadpour; Kerachian, Reza; Shafiee-Jood, Majid

doi:10.1016/j.scitotenv.2022.156845

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…Numerous studies have used the SWAT model to examine the impacts of various factors on streamflow and sediment loads [61], [62]. These factors include changes in land use land cover (LULC) [63], [64], [65], impacts of climate change [9], [66], [67], [68], improvements in ecosystem services [69], [70], [71], validation of satellite-based products [29], [59], [72], [73], [74], [75], [76], and pollution from agricultural chemicals [77].…”

Section: B Hydrological Model Swatmentioning

confidence: 99%

Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River basin Using CMIP6 Projections

Tran,

Do,

Nguyen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Quantifying the intensity and frequency of climatic extremes under the impacts of climate change is crucial for effective water resource management. In this study, we utilize the Soil and Water Assessment Tool (SWAT) hydrological model, robust indices, e.g., Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) as well as the Interquartile Range (IQR) method for a comprehensive analysis of the river flow response to future climate scenarios towards 2090. Four General Circulation Models (GCMs) under two Shared Socioeconomic Pathways (SSPs) have been used, including BCC-CSM2-MR, CanESM5, MIROC6, and MRI-ESM2-0. We aim to reveal the future impacts of extreme events and their potential consequences for local livelihoods and human well-being in the Srepok River basin-a major tributary of the Mekong River basin in Southeast Asia. Our findings include (1) a significant discrepancy between extreme events found with more flood events projected towards 2090; (2) a shift in precipitation patterns with an increase in intensity is observed; and (3) a correlation between climatic extremes and regional characteristics has been identified. This work provides valuable insights into the anticipated changes in climatic extremes under the impacts of climate change and serves as the scientific basis for stakeholders and decision-makers to develop adaptative strategies and sustainable plans to enhance the region's resilience.

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Section: B Hydrological Model Swatmentioning

confidence: 99%

Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River basin Using CMIP6 Projections

Tran,

Do,

Nguyen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Numerous studies have utilized the SWAT model to examine the impacts of various factors on streamflow and sediment loads (Ahmed et al, 2020). These factors include land use and land cover (LULC) changes (Anaba et al, 2017;Aryal et al, 2022;, the effects of climate change (Vo et al, 2018;Aslam et al, 2022;Shafeeque et al, 2023a;, improvements in ecosystem services (Ashrafi et al, 2022a;Arshad et al, 2022;Ashrafi et al, 2022b;Tapas et al, 2022), and the validation of satellite-based products (Arshad et al, 2021;Tran et al, 2022a;Noor et al, 2023).…”

Section: Semi-distributed Hydrological Model Swatmentioning

confidence: 99%

The role of reservoirs under the impacts of climate change on the Srepok River basin, Central Highlands of Vietnam

Tran,

Nguyen,

Grodzka-Łukaszewska

et al. 2023

Front. Environ. Sci.

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Forecasting streamflow is important for managing future water resources and environmental needs under the impacts of climate change. Moreover, quantifying the combined effects of future climate variations and human-made infrastructures, e.g., dams and reservoirs, poses a significant challenge. In this study, we used the Soil and Water Assessment Tool (SWAT) for a case study in the Srepok River Basin—a tributary of the Mekong River Basin. Here, we aim to reveal the impacts of various climate change scenarios and the effects of reservoir operations in this region. Our findings indicate that 1) the projected annual streamflow is anticipated to increase by a minimum of 9.2% (2046–2065) and could peak at an increase of 14.9% (2080–2099) under the highest greenhouse gas emissions, 2) Srepok 4, Srepok 3, and Buon Kuop demonstrate a higher capability for mitigating flood peaks and managing seasonal flow in the downstream floodplain, whereas Buon Tua Srah shows the least performance, and 3) reservoirs operated with annual regulation have more pronounced impacts than those regulated on a daily schedule. Our work provides i) a scientific foundation for regional stakeholders and decision-makers to develop sustainable strategies that address the combined effects of reservoir operation and future climate, and ii) it supports national authorities and officials in resolving conflicts related to transboundary rivers within the Mekong River Basin.

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“…Climate change has increased the importance of rainfall forecasting, because it has changed the duration and frequency of floods and increased their magnitude (Ashrafi, Kerachian, et al., 2022; Ashrafi, Khoie, et al., 2022). As a result of inundation, landslides, and debris flows in urban areas, heavy rainfall often leads to fatalities and significant financial damages (Seo et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Amini

Dolatshahi

Kerachian

2023

Water Resources Research

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Climate change has increased the importance of rainfall forecasting, because it has changed the duration and frequency of floods and increased their magnitude Ashrafi, Khoie, et al., 2022). As a result of inundation, landslides, and debris flows in urban areas, heavy rainfall often leads to fatalities and significant financial damages (Seo et al., 2014). Furthermore, overflow resulting from heavy rainfall is a primary contributor to the transport of water pollutants (Lintern et al., 2018). Various preventive measures have been developed, including flood warning systems, to alleviate damages caused by rainfall and enable urban flood management infrastructures to operate more efficiently. Therefore, accurate and reliable rainfall forecasting is essential in urban areas (Poornima & Pushpalatha, 2019). Reliable rainfall nowcasting is a challenging and complex undertaking since rainfall features are influenced by various variables, such as pressure, temperature, relative humidity, and wind speed (Govindaraju, 2000). Due to the difficulty in obtaining meteorological data and the complexity of estimating relationships among variables, physical models are not usually suitable for rainfall nowcasting. An effective alternative is to use empirical models to determine the relationship between inputs like temperature and relative humidity and outputs like rainfall (Nasseri et al., 2008). To ensure optimal performance for urban drainage infrastructures, rainfall nowcasting frameworks should be able to make accurate forecasts with different lead times. Researches on rainfall forecasting and its influencing factors have been conducted for decades, and many approaches have been proposed. There are various traditional forecasting models in the literature, which have been developed using classic time series algorithms such as linear regression, autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural networks (ANNs), numerical weather predictions (NWPs), and Fuzzy logic algorithm (

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Managing basin-wide ecosystem services using the bankruptcy theory

Cited by 13 publications

References 30 publications

Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River basin Using CMIP6 Projections

Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River basin Using CMIP6 Projections

The role of reservoirs under the impacts of climate change on the Srepok River basin, Central Highlands of Vietnam

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

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