A new Budyko framework extension using time series SARIMAX model

Fathi, Mohamed M.; Awadallah, Ayman G.; Abdelbaki, Ahmed; Haggag, Mohammed

doi:10.1016/j.jhydrol.2019.01.037

Cited by 38 publications

(19 citation statements)

References 30 publications

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“…Literature reviews showed its performance, applicability, and simplicity compared to other models 36 due to requiring two parameters: the ability to keep moisture in the soil (X 1 ) and the water exchange coefficient (X 2 ). Monthly rainfall, runoff, and evapotranspiration are the only three meteorological and hydrological data necessary 35 , 37 . Total runoff hydrograph, soil moisture content, groundwater flow, etc., are the model results.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

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Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.

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Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

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show abstract

“…The GR2M model's advantage requires only two parameters: production store: X 1 (mm) and groundwater exchange rate (X 2 ). Additionally, it needs only three monthly meteorological and hydrological data, i.e., rainfall, runoff, and evapotranspiration [30,31]. The GR2M model results give runoff hydrograph and other elements such as soil moisture content, surface runoff, the groundwater flow.…”

Section: Gr2m Modelmentioning

confidence: 99%

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Ditthakit

Pinthong

Salaeh

et al. 2021

Water

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Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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“…The GR2M model's advantage is that it requires only two parameters: the ability to retain soil moisture (X1) and the exchange coefficient water (X2). Additionally, it needs only three monthly meteorological and hydrological data, i.e., rainfall, runoff, and evapotranspiration [28,29]. The GR2M model results give runoff hydrograph as well as other elements such as soil moisture content, surface runoff, the groundwater flow, etc.…”

Section: Gr2m Modelmentioning

confidence: 99%

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in The Southern Basin of Thailand

Ditthakit¹,

Pinthong²,

Salaeh³

et al. 2021

Preprint

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Accurate monthly runoff estimation is fundamental in water resources management, planning, and development, resulting in preventing and reducing water-related problems, such as flooding and drought. This article evaluates the performance of the monthly hydrological rainfall-runoff model, GR2M model, in Thailand's southern basins. The GR2M model requires only two parameters, and no prior research has been reported on its application in this region. The 37 runoff stations, which are distributively located in three sub-watersheds of Thailand's southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. Thornthwaite method was utilized for the determination of evapotranspiration. The model's performance was conducted using three statistical indices: Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OPI). The model's calibration results for 37 runoff stations gave the average of NSE, r, and OPI of 0.637, 0.825, and 0.757, and those values for verification of 0.465, 0.750, and 0.639, respectively. It indicated a model's acceptable performance and could apply the GR2M model for determining monthly runoff variation in this region. The spatial distribution of X1 and X2 values was conducted by using IDW method. It was susceptible to the X1 value and X2 value of approximately more than 0.90 gave the higher model's performance.

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A new Budyko framework extension using time series SARIMAX model

Cited by 38 publications

References 30 publications

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in The Southern Basin of Thailand

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