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The relationships between a variety of hydro-meteorological variables and irrigation water use rates (WUR) were investigated in this study. Standardized Precipitation Index (SPI), Potential Evapotranspiration (PET), and Normalized Difference Vegetation Index (NDVI) were explored to identify the relationship with the WUR. The Yeongsan river basin, the agricultural land of which is mostly occupied by well-irrigated paddy, was used for the pilot study. Four different temporal scales of SPI-3, 6, 9, and 12 were tested, and PET was calculated using the Thornthwaite method. To calculate NDVI, the surface spectral reflectance data, which was acquired by Moderate Resolution Imaging Spectroradiometer (MODIS) equipped on the Terra satellite, were used. As a result, there was a statistically significant relationship between SPI9 and the WUR during drought periods in which negative values of SPI9 were obtained. The WUR was strongly correlated with both PET and NDVI. Compared with SPI, the variability of WUR in this study area was more sensitively affected by PET and NDVI, which can cause a potential lack of agricultural water supply. The finding of this study implies that SPI9, PET, and NDVI are the critical factors for predicting water withdrawal during drought conditions so that they can be used for irrigational water use management. Although a part of the findings of this study has been discussed by a few previous studies, this study is novel in that it quantifies the relationship between these factors using actual field observations of streamflow withdrawal for irrigation.
Jang Sung; Donghae Baek; Young Ryu; Seung Seo; Kee-Won Seong. Effects of Hydro-Meteorological Factors on Streamflow Withdrawal for Irrigation in Yeongsan River Basin. Sustainability 2021, 13, 4969 .
AMA StyleJang Sung, Donghae Baek, Young Ryu, Seung Seo, Kee-Won Seong. Effects of Hydro-Meteorological Factors on Streamflow Withdrawal for Irrigation in Yeongsan River Basin. Sustainability. 2021; 13 (9):4969.
Chicago/Turabian StyleJang Sung; Donghae Baek; Young Ryu; Seung Seo; Kee-Won Seong. 2021. "Effects of Hydro-Meteorological Factors on Streamflow Withdrawal for Irrigation in Yeongsan River Basin." Sustainability 13, no. 9: 4969.
This study proposed a deep learning-based model to estimate stream water-use rate (WUR) using precipitation (P) and potential evapotranspiration (PET). Correlations were explored to identify relationships among accumulated meteorological variables for various time durations (three-, four-, five-, and six-month cumulative) and WUR, which revealed that three-month cumulative meteorological variables and WUR were highly correlated. A deep belief network (DBN) based on iterating parameter tuning was developed to estimate WUR using P, PET, and antecedent stream water-use rate (DWUR). The training and validation periods were 2011–2016, and 2017–2019, respectively. The results showed that the PET-DWUR based model provided better performances in Nash–Sutcliff efficiency (NSE), root mean square error (RMSE), and determination coefficient (R2) than the P-PET-DWUR and P-DWUR models. The framework in this study can provide a forecast model for deficiencies of stream water use coupled with a weather forecast model.
Jang Hyun Sung; Young Ryu; Eun-Sung Chung. Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network. Water 2020, 12, 2700 .
AMA StyleJang Hyun Sung, Young Ryu, Eun-Sung Chung. Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network. Water. 2020; 12 (10):2700.
Chicago/Turabian StyleJang Hyun Sung; Young Ryu; Eun-Sung Chung. 2020. "Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network." Water 12, no. 10: 2700.
In order to enhance the streamflow forecast skill, seasonal/sub-seasonal streamflow forecasts can be post-processed by incorporating new information, such as climate signals. This study proposed a simple yet efficient approach, the “Bivar_update” model that utilizes bivariate climate forecast to update individual probabilities of the ensemble streamflow prediction. The Bayesian updating scheme is used to update the joint probability mass function derived from historic precipitation and temperature data sets. Thirty-five dam basins were used for the case study, and the modified Tank model was embedded into the ensemble streamflow prediction framework. The performance of the proposed approach was evaluated through a comparison with a reference streamflow forecast model, the “Univar_update” model, that reflects only precipitation forecast, in terms of deterministic and categorical streamflow forecast accuracy. For this purpose, multiple cases of probabilistic precipitation and temperature forecasts were synthetically generated. As a result, the Bivar_update model was able to decrease the errors in forecast under below-normal conditions. The improvements in forecasting skills were found for both measures; deterministic and categorical streamflow forecasts. Since the proposed Bivar_update model reflects both precipitation and temperature information, it can compensate low predictability especially under dry conditions in which the streamflow’s dependency on temperature increases.
Jang Hyun Sung; Young Ryu; Seung Beom Seo. Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction. Sustainability 2020, 12, 2905 .
AMA StyleJang Hyun Sung, Young Ryu, Seung Beom Seo. Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction. Sustainability. 2020; 12 (7):2905.
Chicago/Turabian StyleJang Hyun Sung; Young Ryu; Seung Beom Seo. 2020. "Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction." Sustainability 12, no. 7: 2905.