مجله سامانه های سطوح آبگیر باران
سال یازدهم شماره 2 (پیاپی 37، تابستان 1402)

Uncontrolled floods always cause extensive financial and human losses in cities. Flood control is one of the most important goals of urban management. In this regard, an attempt has been made to investigate the effect of delayed ponds on the flood control of the Eghbal Floodway located east of Mashhad. East Eghbal Floodway, with a sub-catchment of about 13850 ha, is one of the most significant floodways of the developing Mashhad metropolis, including eight major floodways and 213 sub-catchments with many urban facilities. To evaluate and simulate the flood path, the precipitation pattern was calculated using different methods, including the concentration time of each sub-catchment. The most extended length of the stream, the uniform distribution of catchment sensitivity, periodic block, and peak flood flow caused by critical rainfall with a 50-year return period were calculated at the outlet. Finally, according to the topography of the area and available places using two, three, and four detention ponds to consider 11 different scenarios (economic-hydraulic), the peak flow of the flood decreased, and the time of peak flow increased. By evaluating and comparing different scenarios, scenario number 11 (using four detention ponds reduces the peak flood discharge by 48%) was the best hydraulic scenario. In addition, scenario number 4 (using two detention ponds reduces peak discharge by 24%) was the best economic scenario. A dry trapezoidal catchment within the network with two outlets, a lower opening, and a rectangular overflow was modeled in SWMM5.0. Geographic Information System (GIS) and SWMM5.0 rainfall–runoff simulation have been used to determine the physical component of sub-catchments.

The excessive use of water and soil resources, especially in arid and semi-arid regions, causes increased soil erosion, followed by the appearance of destructive floods, the emergence of dust, and intensifying the phenomenon of desertification. Therefore, in order to prevent the environmental problems of these areas, there is a need for efficient management of their water and soil resources. The rainwater catchment systems are one of the strong points of dry and desert areas, which have a high ability to develop these areas. This study investigated the effect of rain catchment systems on the physical and chemical characteristics of rainwater catchment systems soil in the Mil Nadir area of Sistan. For this purpose, six soil samples were taken from the rainwater catchment systems and the control area and their physical and chemical properties were measured. The results of this study showed that there is a significant difference of 1% between the average percentage of clay, silt, and sand in the soil of the rainwater catchment systems areas compared to the control area (P<0.01). With the increase in the percentage of clay and silt, the soil texture from class Loamy-sandy in the control area was changed to loamy class in rainwater catchment systems, which caused a 28% increase in soil moisture compared to the control area. In the examination of soil chemical properties, the findings indicate that the amount of acidity, electrical conductivity, sodium, and sodium absorption ratio significantly (P<0.01) in the soil of rainwater catchment systems decreased, but the amount of organic matter, nitrogen, and potassium increased. The effect of rain catchment surface systems has improved the physico-chemical properties of the soil, due to the existing environmental crises and the acceleration of the desertification process in this region. The use of these catchment systems in the Sistan Region provides suitable conditions for improvement. It provides ecological and environmental protection of these areas.

One of the landscape management approaches is the construction of bridges along the rivers. On the other hand, the bridge scouring is a serious damage to river engineering as the main source of water and sustaining planet life. Accordingly, in this research, using field data, the accuracy of empirical methods, genetic algorithm (GA), least squares support vector machine (LSSVM), and combined method were compared in estimating scour depth of simple bridge piers. In the GA method, a number of empirical relationships were modified and the results of these modified relationships were compared with the measured scour values. In the LSSVM method, through the input of different independent parameters, model training was performed, and scour depth was predicted. In the combined method, using the LSSVM model from combining the results of different individual relations, the scour depth of the bridge piers was estimated. The results showed that modified relationships by genetic algorithm and LSSVM model have higher accuracy than empirical methods. Also, if only the parameters used in the empirical relationships are included as input parameters to the LSSVM model, the modified relationships have less error than the LSSVM model. The statistical evaluation criteria of RMSE, E, R2, and NSE for the best state of the combined method were 0.4 m, 49%, 0.88, and 0.58 respectively in the training stage and 0.52 m, 50%, 0.7, and 0.38 respectively in the test stage. In general, the combined method estimates scouring depth with higher accuracy than other methods.

Land use change is an important global and local ecological trend and is one of the major challenges in the 21st century. The purpose of this study was to model land use changes in the Gahvareh region, Kermanshah province using the LCM model. This model has the ability to simulate several land use changes by utilizing and integrating Markov chain models, multi-layer perceptron neural network, logistic regression, and MLOP. In this research, Landsat 4, Landsat 5, and Landsat 8 satellite images were used to prepare land cover maps. After initial corrections, the images were classified using the Maximum likelihood method, and land use maps for the years 1986, 2000, and 2018 were prepared. After monitoring the changes of different land uses during two periods (1986-2000) and (2000-2018) and assessing the validation of the model, finally, the land use map of 2028 was predicted using the LCM model. The results showed that during the first and second calibration periods, 30% and 42% of forest land were reduced, 26 and 37 ha were added to agricultural land and 80% and 32% were added to residential land respectively. The most changes during the study period were the conversion of forest lands to pasture and thin forest (80%). Forecasts for the year 2028 showed that the dense forest would be destroyed and would be decreased in half in comparison to 2018. Evaluation of the accuracy of transmission potential modeling using artificial neural networks showed high accuracy in most of the scenarios.

The warming of the earth also affects the condition of other parts of the climate system and causes the phenomenon of climate change. Global warming increases the moisture capacity of the air and decreases the relative humidity of the air. The most common method of simulating climate parameters is to use the output data of atmospheric general circulation models. When the results of the models are evaluated on a much smaller scale, significant differences are created. But how can these differences be eliminated and models can be chosen that can provide a suitable background for the future of climate change. In this research, these differences are investigated and the accuracy of the results of climate models in predicting the relative humidity of the air is investigated using the validation method. In this regard, the relative humidity data of Dasht Naz synoptic station in Neka city was used on a daily basis with a statistical length of 25 years (1990-2014). Next, the relative humidity parameter data of the closest cell to the synoptic station was downloaded from the data of general atmospheric circulation models, the sixth report of the Intergovernmental Panel on Climate Change, in the common period (1990-2014) and extracted in Arc GIS 10.8 software. According to the results, ACCESS-ESM1-5, CNRM-CM6-1, CanESM5, IPSL-CM6A-LR, GISS-E2-1-G and MIROC6 models showed the highest correlation with observational data, but Except for two ACCESS-ESM1-5 and CanESM5 models, the other mentioned models had a high bias compared to the observational data. Therefore, ACCESS-ESM1-5 and CanESM5_ESM models are recommended among climate models to predict relative humidity in the future in the Neka region.

Remote sensing, as a powerful tool in meteorology, is able to cover the gaps in ground measurements and provide a uniform platform for spatial analysis. However, due to the different results obtained from the accuracy and performance of sensors in different regions, it is necessary to evaluate and validate their products in each region independently. This study, therefore, aims to evaluate the performance of the satellite precipitation products (SPPs) of PERSIANN, GPM, ERA5, and TRMM in a daily rainfall scale over the Razavi Khorasan Province, against the ground observations collected from the 19 rain gauges. In this regard, 113,880 images were called to extract daily rainfall data from the above four databases. To evaluate the SPPs, statistical indices including correlation coefficient (CC), root mean square of the errors (RMSE), and percentage of bias (PBias), Kline Gupta Efficiency (KGE), and Agreement Index (d) were used. Moreover, POD, FAR, and CSI classification indices were used to evaluate the accuracy of the data presented in the indication of days with rainfall events. The climate effect was also investigated by incorporating the change in latitude. Our results revealed better performance for the ERA5 dataset with 0.2≤ CC ≤ 0.68, 2≤ RMSE ≤ 3.7, and its performance indicators have KGE ≥ 0.26 and d ≥ 0.68. Regarding classification indices, ERA5 has POD and CSI higher than 0.68 and 0.3, respectively, and TRMM has relative superiority only in the FAR index. At the level of the studied area, the index analysis shows the better performance of the ERA5 database compared to other studied data. Based on the results of this research, the increase in elevation has improved some statistical indicators in some products, whereas, in some other cases, changes in elevation have a negative effect on the accuracy. In general, the ERA5 database at the point and regional scale are evaluated to have a more appropriate performance in estimating daily precipitation data, and its data can be used in meteorological and hydrological analysis.