Ali Haghizadeh; Lila Ghasemi
Abstract
In recent years, the flood situation of headwaters of the Dez River in Lorestan Province has increased. This is due to various factors such as climate change, reduction of vegetation cover, and increase in construction in the riparian zone. In 2022, floods occurred several times in the Dez headwaters ...
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In recent years, the flood situation of headwaters of the Dez River in Lorestan Province has increased. This is due to various factors such as climate change, reduction of vegetation cover, and increase in construction in the riparian zone. In 2022, floods occurred several times in the Dez headwaters in Lorestan province. These floods caused significant damage to life and property. Global conceptual models have been developed for more than two decades and their effectiveness in simulating streamflow has been proven. In this study, simulation of runoff rainfall in Silakhor-Rahimabad watershed was done using three daily (GR4J), monthly (GR2M) and annual (GR1A) models. The Nash-Sutcliffe (Nash), root mean square error (RMSE), and bias criteria were used to evaluate the model performance during the calibration and validation periods. The obtained results were highly significant. The GR1A model has Nash coefficients of 86.1 and 71.7 in both calibration and validation periods, respectively, so this model has a very good performance. For the other two models, the GR2M model and the GR4J model, the Nash coefficients in the two calibration and validation periods are 76.7, 70.2 and 61.4, 86.2, respectively. These coefficients also indicate the good and very good performance of these models in rainfall-runoff simulation. However, considering the satisfactory performance of the two evaluation criteria, RMSE and Bias, in the GR1A model, it can be concluded that the GR1A model had a better performance in simulating rainfall-runoff. Finally, the obtained results indicate that the GR4J, GR2M and GR1A conceptual models are suitable models for simulating the streamflow in the Silakhor-Rahimabad watershed.
Maryam sadat Jaafarzadeh; Ali Haghizadeh; Iraj Vayskarami
Abstract
Agriculture is not only the largest user of groundwater resources throughout the world but also its economy is highly dependent on these sources. Thanks to having more effective parameters and subsequently more accurate results, the classification methods in many fields, such as sustainable agriculture ...
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Agriculture is not only the largest user of groundwater resources throughout the world but also its economy is highly dependent on these sources. Thanks to having more effective parameters and subsequently more accurate results, the classification methods in many fields, such as sustainable agriculture has been taken into consideration. Discriminant analysis models are more complex, more accurate and more efficient in comparison to modern methods. In current study, the areas with infiltration potential located in some parts of Khomein, Shazand, Azna, Aligudarz and Durood areas (Marboreh watershed) were went under investigation using the mixture discriminant analysis (MDA) model. For this purpose, the infiltration samples gathered by double ring test, with the environment-effecting layers on infiltration, were prepared and then introduced to R_studio, employed to run MDA. In order to assess the results, validation indices (ROC curve, CCI, TSS, Recall and Precision indices) were used. According to the results, 6.2, 6.1, 12.7, 13.3 and 15.9% of areas of Shazand, Khomein, Durood, Azna and Aligodarz respectively lie in highly potential infiltration, whereas 1.1 16.5, 14.3, 19.6 and 10.8% of those areas were found to have extremely potential infiltration. Most of these areas have sandy soil texture and Quaternary formations with agricultural and range land uses. The accuracy indices that obtained as 0.89%, 76.66, 0.53, 0.91% and 0.73%, witnessing the acceptance and excellence of model performance. The results of this study can be useful in the decision-making for managers and planners regarding to the groundwater recharge in accordance with urban and agricultural needs, because groundwater resources and ensuring their stability are the main factors for sustainable agriculture.
