This study aims to present a process for hydrological model exploration for selecting an appropriate model compatible with the modeling objectives. The process consists of three stages: (1) initial choice based on the modeling objectives; (2) model selection based on intercomparison among underlying conceptualizations of the models; and (3) final model selection based on influencing criteria such as availability of the model software and documentation, and availability of appropriate data. As an applied example, the process was used to find an appropriate model for a project to evaluate water supply and demand under climate and land use change scenarios in the Gorgan‐rud River Basin, Iran. The criteria affecting the final choice of a hydrological model were classified into three categories: (1) criteria related to the model, (2) criteria related to the model user, and (3) criteria related to the study area.
The KINEROS2 model was utilized for runoff simulation in the Dehgin catchment situated in the Hormozgan province of Iran. A parameter allocation procedure was used in lieu of parameter optimization. After parameter allocation, the model was able to adequately simulate hydrographs associated with high‐magnitude peak discharge events with the efficiency values between 0.011–0.83 for Nash–Sutcliffe and 0.36–0.98 for Kling–Gupta, but the model did not accurately simulate hydrographs corresponding to low‐magnitude peak discharge events. Although calibration after parameter allocation improved model performance with respect to the simulation of low‐magnitude discharge events, numerical values of the hydraulic conductivity and net capillary pressure as the most sensitive model parameters did not agree with parameters known to be reasonable in the region. So that the value of hydraulic conductivity was decreased from 61 to 55 mm/h in channels and from 3.7 to 1.7 mm/h in planes. The new values are physically reasonable but are not approximately the same as physical values associated with the regional and environmental context of the Dehgin catchment. In this case, the values of the evaluation criteria were obtained between −2.5 and 0.78 for Nash–Sutcliffe and 0.17 and 0.98 for Kling–Gupta. The results of using the HydroPSO package in R to automated calibration of the model, with the value of Nash–Sutcliffe between −0.63 and 0.43, indicated that autocalibration without intelligent and deliberate selection of parameters cannot accurately represent hydrological processes, and therefore should be avoided. Also, the results show that an understanding of the catchment environmental conditions and appropriate allocation of parameters is initially more effective as a first step of the modeling process and thereby contributes to a first‐order characterization of environmental conditions in the catchment.
Analyzing land use/land cover change is a fundamental tool for evaluating the environmental consequences of human activities. This research was conducted to detect and predict likely land use changes in the Gorganrud River basin, Iran, and to estimate past and future population growth as a driving force in land use change and degradation. First, land use maps for 1999, 2009, and 2017 were prepared. Then, the likely land use changes for 2030 and 2040 were predicted using the Land Change Modeler (LCM) in TERRSET software. Results indicate that the percentage of changes in agricultural and residential areas, bare lands, and semi‐dense forests from 1999 to 2017 were +4.2, +0.62, +1.76, and +3.15, respectively, while the percentage of changes in rangelands, dense forests, and water bodies were −8.7, −0.37, and −0.63. Analysis of changes from 2017 to 2040 indicates that the percentage of changes in croplands, dense forests, and bare lands may reach −4.42, −2.35, and −2.74, respectively. Conversely, the area of rangelands, semi‐dense forests, water bodies, and residential areas would likely increase by +7.78%, +1.02%, +0.04%, and + 0.7%, respectively. The population density in 2011 and 2016 was 94 and 97 persons/km2, respectively, whereas the 5‐year population growth rate was 3.5%. Better conservation practices to prevent deforestation and inappropriate growth of residential areas, in line with forest replantation to prevent the conversion of semi‐dense forests into rangelands, are some of the management strategies required in the study area. Population control and redistribution are other prescribed actions based on the research findings.
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