Hossein Salehi; Saeid Gharechelou; Saeed Golian; Mohammad Reza Ranjbari; Emad Mahjoobi
Abstract
Simulation of runoff for long-term climatic studies is crucial for effective water resource management in a watershed. However, obtaining long-term input data can be challenging, especially in remote and inaccessible areas. Recently, long-term climatic precipitation data have proven to be highly efficient ...
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Simulation of runoff for long-term climatic studies is crucial for effective water resource management in a watershed. However, obtaining long-term input data can be challenging, especially in remote and inaccessible areas. Recently, long-term climatic precipitation data have proven to be highly efficient in various fields. In this study runoff was simulated in the Hableroud basin from 1992 to 1996 using three climatic rainfall data sources: APHRODITE, PERSIANN-CDR, and ERA5-Land, as well as interpolated rainfall data from rain gauge stations. The Variable Infiltration Capacity (VIC) model was employed to simulate runoff with Kling Gupta efficiency (KGE) as a objective function. To assessment the accuracy of precipitation data from each dataset, at the cell scale a network was developed by Inverse distance weighted (IDW) method. The results indicated that the APHRODITE dataset had the highest accuracy while PERSIANN-CDR had the lowest. The KGE for simulated daily runoff with IDW data was 0.78 during the calibration period and 0.76 during the validation period. Evaluating the simulated runoff using climatic precipitation data revealed that PERSIANN-CDR satellite precipitation data was less accurate in detecting precipitation amounts but performed better in simulating runoff. The KGE for this data on a daily scale was 0.64 during the calibration period and 0.77 during the validation period. The KGE for APHRODITE precipitation data, based on IDW data ranked second with values of 0.62 and 0.75 during the calibration and validation periods, respectively. ERA5-Land precipitation data, ranked third with a KGE of 0.50 during the calibration period and 0.66 during the validation period. These findings indicate that climatic precipitation data can be effectively utilized in watershed management studies with low cost and appropriate accuracy, particularly in basins lacking a regular network or long-term data availability.Additionally results demonstrated that the VIC hydrological model performed well in simulating daily and monthly runoff.
Ashkan Banikhedmat; hosein salehi; saeed golian; farshad koohian afzal; nazanin ezati boorestan
Abstract
Introduction
One of the methods for estimating the amount of runoff resulting from precipitation is the use of hydrological models. The SWAT model is one of the widely used tools for simulating the quantity and quality of water at the watershed level. This model is a conceptual model that is capable ...
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Introduction
One of the methods for estimating the amount of runoff resulting from precipitation is the use of hydrological models. The SWAT model is one of the widely used tools for simulating the quantity and quality of water at the watershed level. This model is a conceptual model that is capable of simulating large watersheds with different management scenarios. One of the major challenges of this model and many other hydrological models is the calibration of effective and sensitive parameters for estimating the amount of runoff. In general, calibration methods can be divided into two groups: manual and automatic. Manual calibration of a model requires the modeler to have a good understanding of the model's physics. On the other hand, due to the time-consuming nature, existing complexities and the development of new optimization algorithms, nowadays automatic calibration has gained more attention. Automatic calibration is based on three components: the objective function, the optimization algorithm, and the station information. The use of a single objective function in model calibration may lead to an increase in error in other aspects of the simulation. Scientific experience in single-objective calibration has shown that no single objective function, even with high efficiency, can accurately represent all the characteristics and properties of a watershed. Therefore, the use of an appropriate optimization algorithm to improve calibration results includes the use of multiple objective functions to identify a set of efficient solutions.
Materials and methods
The study area is located in the western part of Iran, in Kermanshah Province, with an area of 5467 square kilometers. The minimum and maximum elevations in the area are 1275 and 3360 meters, respectively. The average precipitation in the watershed is about 505 mm, with the highest rainfall occurring in the months of November and Decemeber, and the lowest rainfall in the months of Julay and August. The main rivers in this watershed are Mark, Gharehsoo, and Razavar. In this study, the SWAT rainfall-runoff model was calibrated using the NSGA-II algorithm under three calibration scenarios. For model calibration, the first scenario used the NSE objective function, which focuses on maximum flows. In the second scenario, to focus on minimum flows, the logarithmic transformation of the simulated and observed streamflow series was used, and the NSE efficiency coefficient was adopted as the objective function, represented as LogNSE. The third scenario was a combination of the first and second scenarios, where the non-concordant objective functions NSE and LogNSE were used simultaneously.
Results and discussion
The results of this study showed that based on the NSE evaluation index values (0.83, 0.74 and 0.83 for the first to third scenarios) and the model overestimation and examination of the flow graph in the first scenario, which showed a tendency towards higher flows, this scenario would be more efficient in estimating maximum flows. Additionally, considering the LogNSE evaluation index (0.69, 0.74 and 0.72 for the first to third scenarios), the second scenario with the LogNSE single objective performed better in minimum flows. However, the model constructed using two non-concordant objective functions aimed to achieve a balance and showed satisfactory performance in simultaneously estimating maximum and minimum flows.
Conclusion
In general, it can be concluded that if the objective of the study is to investigate maximum and minimum flows, such as flood or drought studies, single-objective algorithms will perform better. However, if the objective is to control the water balance and achieve satisfactory performance of a model in both maximum and minimum flows, a two-objective scenario with a non-concordant approach can yield better results compared to single-objective algorithms.
