Water Quality Modeling of Mahabad Dam Watershed–Reservoir System under Climate Change Conditions, Using SWAT and System Dynamics

Nazari-Sharabian, Mohammad; Taheriyoun, Masoud; Ahmad, Sajjad; Karakouzian, Moses; Ahmadi, Azadeh

doi:10.3390/w11020394

Cited by 72 publications

(37 citation statements)

References 49 publications

(15 reference statements)

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“…Runoff forecasting plays an essential role in flood mitigation management, agricultural water management, water transportations, and other socio-economical activities closely related to water resources. Many approaches, including physics-based models [1][2][3] and data-driven models [4][5][6], have been developed during recent decades to obtain a precise prediction result of the runoff.…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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“…Jamali et al showed that lack of adaptation to climate variations leads to a considerable decrease in efficiency in Iran's Karkheh Hydropower in the future [51]. Some examples of a lack of insufficient climate assessment studies and improper managements in Iran are: damages caused by heavy rains; floods and damages on many houses, infrastructure, water and power supply; groundwater depletion; droughts and their impacts on food quality and quantity, economy, health, and people's livelihood; drying lakes; sand storms; and ecosystem degradation [52][53][54][55][56][57][58][59][60][61][62]. These issues indicate the necessity of implementing sustainable adaptation and mitigation plans, considering possible variations in climate.…”

Section: Resultsmentioning

confidence: 99%

Relationship between Sunspot Numbers and Mean Annual Precipitation: Application of Cross-Wavelet Transform—A Case Study

Nazari-Sharabian

Karakouzian

2020

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Observations show that the Sun, which is the primary source of energy for the Earth's climate system, is a variable star. In order to understand the influence of solar variability on the Earth's climate, knowledge of solar variability and solar-terrestrial interactions is required. Knowledge of the Sun's cyclic behavior can be used for future prediction purposes on Earth. In this study, the possible connection between sunspot numbers (SSN) as a proxy for the 11-year solar cycle and mean annual precipitation (MAP) in Iran were investigated, with the motivation of contributing to the controversial issue of the relationship between SSN and MAP. Nine locations throughout Iran were selected, representing different climatic conditions in the country. Cross-wavelet transform (XWT) analysis was employed to investigate the temporal relationship between cyclicities in SSN and MAP. Results indicated that a distinct 8-12-year correlation exists between the two time series of SSN and MAP, and peaks in precipitation mostly occur one to three years after the SSN maxima. The findings of this study can be beneficial for policymakers, to consider future potential droughts and wet years based on sunspot activities, so that water resources can be more properly managed.

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“…(1) The RCPs climate scenario Due to space limitations, the RCPs climate scenario was selected to output precipitation and temperature series, and the 2001 to 2017 rate was periodically divided, and 2018 to 2050 was the forecast period. The precipitation and temperature were statistically analyzed and simulated in the RCP 2.6, RCP 4.5 and RCP 8.5 (as 1, 2 and 3) scenarios through the calibrated and down-scaled GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M models (as a, b, c, d and e) from 2001 to 2017 [33]. If the data has uncertainty in the time of acquisition, or the time precision of the data is not met, then the validity of the spatio-temporal data cannot be explained, and reasonable reasoning cannot be made for the uncertain data in the data.…”

Section: Data Collection and Arrangementmentioning

confidence: 99%

Climate Change Impacts on Drought-Flood Abrupt Alternation and Water Quality in the Hetao Area, China

Yang

Weng

et al. 2019

Water

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Drought-flood abrupt alternation (DFAA) is an extreme hydrological phenomenon caused by meteorological anomalies. To combat the climate change, the watershed integrated management model—Soil and Water Assessment Tool model (SWAT)—was used to simulate DFAA, total nitrogen (TN) and total phosphorus (TP) from 1961 to 2050, based on measured precipitation data in the Hetao area and the downscaled Representative Concentration Pathways (RCPs) climate scenarios. In the future, the increase in temperature and the increase in extreme precipitation will aggravate the pollution of water bodies. Results indicate that the risk of water quality exceeding the standard will increase when DFAA happens, and the risk of water quality exceeding the standard was the greatest in the case of drought-to-flood events. Results also indicate that, against the backdrop of increasing temperature and increasing precipitation in the future, the frequency of long-cycle and short-cycle drought-flood abrupt alternation index (LDFAI, SDFAI) in the Hetao area will continue to decrease, and the number of DFAA situations will decrease. However, the zone of high-frequency DFAA situations will move westward from the eastern Ulansuhai Nur Lake, continuing to pose a risk of water quality deterioration in that region. These results could provide a basis for flood control, drought resistance and pollution control in the Hetao and other areas.

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Water Quality Modeling of Mahabad Dam Watershed–Reservoir System under Climate Change Conditions, Using SWAT and System Dynamics

Cited by 72 publications

References 49 publications

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Relationship between Sunspot Numbers and Mean Annual Precipitation: Application of Cross-Wavelet Transform—A Case Study

Climate Change Impacts on Drought-Flood Abrupt Alternation and Water Quality in the Hetao Area, China

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