Estimation of rainfall distribution for the southwestern region of Saudi Arabia

Abo-Monasar, Amin; Al-Zahrani, Muhammad A.

doi:10.1080/02626667.2013.872788

Cited by 21 publications

(15 citation statements)

References 25 publications

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“…In these two periods, the stimulated streamflow was underestimated or overestimated by the four methods. This situation could be explained by the fact that when extreme rainfalls occurred, i.e., higher or lower rainfall than the surrounding stations, the methods either overestimated or underestimated average rainfall because the estimation methods were affected by the surrounding rainfall stations [55]. In addition, we concluded that the response between precipitation and streamflow, as estimated by the SWAT, was not obvious in the light rain periods (dry periods) and prominent in heavy rain period (wet period).…”

Section: Analysis Of Runoff Process By Spatial Interpolation Of Precimentioning

confidence: 99%

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

Cheng

Wang

Engel

et al. 2017

Water

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Accurate assessment of spatial and temporal precipitation is crucial for simulating hydrological processes in basins, but is challenging due to insufficient rain gauges. Our study aims to analyze different precipitation interpolation schemes and their performances in runoff simulation during light and heavy rain periods. In particular, combinations of different interpolation estimates are explored and their performances in runoff simulation are discussed. The study was carried out in the Pengxi River basin of the Three Gorges Basin. Precipitation data from 16 rain gauges were interpolated using the Thiessen Polygon (TP), Inverse Distance Weighted (IDW), and Co-Kriging (CK) methods. Results showed that streamflow predictions employing CK inputs demonstrated the best performance in the whole process, in terms of the Nash-Sutcliffe Coefficient (NSE), the coefficient of determination (R 2 ), and the Root Mean Square Error (RMSE) indices. The TP, IDW, and CK methods showed good performance in the heavy rain period but poor performance in the light rain period compared with the default method (least sophisticated nearest neighbor technique) in Soil and Water Assessment Tool (SWAT). Furthermore, the correlation between the dynamic weight of one method and its performance during runoff simulation followed a parabolic function. The combination of CK and TP achieved a better performance in decreasing the largest and lowest absolute errors compared to any single method, but the IDW method outperformed all methods in terms of the median absolute error. However, it is clear from our findings that interpolation methods should be chosen depending on the amount of precipitation, adaptability of the method, and accuracy of the estimate in different rain periods.

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Section: Analysis Of Runoff Process By Spatial Interpolation Of Precimentioning

confidence: 99%

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

Cheng

Wang

Engel

et al. 2017

Water

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“…With respect to Iran, for some regions with a high correlation between precipitation and elevation, regression models turned out to be the best [4,[14][15][16][17], whereas in most regions, Kriging and Co-Kriging gave more accurate results compared with the other methods [15,16,20]. Similarly, despite some studies describing IDW as the best method [45][46][47], many studies emphasize the high accuracy of geostatistical methods (Kriging and Co-Kriging) for precipitation estimation for other regions as well, with Co-Kriging using the auxiliary elevation variable often showing the highest accuracy [9,12,[48][49][50][51][52][53][54][55]. Similar to in the above-mentioned studies, also during this study an acceptable accuracy of (Ordinary and Universal) Co-Kriging, using the auxiliary elevation variable, was found.…”

Section: Discussionmentioning

confidence: 99%

A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Aalijahan

Khosravichenar

2021

Atmosphere

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The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

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“…Mean catchment rainfall can be calculated by several techniques, such as Thiessen polygon, isohyetal, polynomial, geostatistical, inverse distance weighted, multi-quadratic interpolation, and kriging [31,32]. This study referred to Tsou et al [33] who used the Thiessen polygon method to calculate the mean catchment rainfall.…”

Section: Rainfall Datamentioning

confidence: 99%

Sensitivity Analysis on the Rising Relation between Short-Term Rainfall and Groundwater Table Adjacent to an Artificial Recharge Lake

Hsieh

Liu

Chung

et al. 2019

Water

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This study aimed to determine the highly sensitive variables for a groundwater simulation model adjacent to an artificial recharge lake (ARL) using short-term rainfall events. The model was established using an artificial neural network (ANN) with rainfall events. Normalized rainfall, rainfall intensity, and groundwater data were selected as model variables. The coefficient of determination (R2) was used for model performance assessment. Finally, a sensitivity analysis (SA) was conducted to evaluate the importance of each model input. The study results indicated that the R2 of the ANN model ranged between 0.759 and 0.914. The SA showed that the rainfall was more sensitive than rainfall intensity in the study area. Based on the SA results and relevant geological characteristics, it was observed that the rainfall of past 1-day, past 2-day, and past 3-day responded faster than the other variables to the wells near the river and the ARL. In addition, the past 2-day rainfall was highly sensitive to the groundwater table; this may be due to the fact that the well screen location was above sea level as observed in Wells 1, 2, and 6. The results indicate that the groundwater table variation is response-related to the distance from the wells to the river and the ARL, and the rainfall time-lag. This SA study is helpful to researchers wishing to study related ARL efficiency issues.

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Estimation of rainfall distribution for the southwestern region of Saudi Arabia

Cited by 21 publications

References 25 publications

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Sensitivity Analysis on the Rising Relation between Short-Term Rainfall and Groundwater Table Adjacent to an Artificial Recharge Lake

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