مجله سامانه های سطوح آبگیر باران
سال نهم شماره 2 (پیاپی 29، تابستان 1400)

Soil infiltration is one of the most crucial parts for designing rainwater catchment systems and has an essential role in determining the area for rainwater harvesting. Using the Kamphorst field rain simulator, the infiltration rate of the suitable sites for the rainwater catchment systems has been determined. After selecting the suitable site, the soil was saturated, and the experiments were performed a day later. Simulation experiments have been conducted in two sites. The first site was Pishkamr city in Golestan province and the second site was near Sararood research station in Kermanshah province. The constant slope was at 12%, and the rain intensities were 33, 64, and 110 mm/h for 15 minutes in four repetitions. At the end of the experiment, the total volume of runoff was collected, and the infiltration rate was calculated. The results showed that with increasing rainfall intensity from 64 to 110 mm/h, the runoff volume of Pishkamr and Sararood increased 6.1 and 13.7 times, respectively. The infiltration rate in comparison with rain intensity showed that by increasing rainfall intensity from 64 to 110 mm/h, the infiltration rate increased 16.6% and 25.3% in the Pishkamr and Sararood, respectively. The comparison of the infiltration rate in the two sites also showed that the infiltration rate in the Sararood site was higher than the Pishkamr site. Therefore, with the knowledge of soil Infiltration, suitable rainwater catchment systems can be designed to supply water for plants.

This study aimed to evaluate and compare the estimated rainfall of GPM meteorological satellite with ground stations using non-parametric Kolmogorov-Smirnov in Gorganrood catchment, Golestan province. In this study, 16 stations were used with the statistical period of 20.03.2014 -20.03.2016. The daily rainfall data of the GPM satellite with a spatial resolution of 0.1 degrees were collected. Data analysis was performed in the MATLAB environment. The results showed that ​​in Bagh Salian, Dashte Shad, Ghochamz, Hagh-ol-Khaje, Zaringol stations, the P-value ​​were higher than the α significance level and were 0.83, 0.36, 0.11, 0.95, 0.33, respectively. Therefore, it showed that the difference between satellite precipitation data and ground station observations was not significant. The results also showed that (0.95) P-value was the highest correlation of GPM data with observational records in Hagh-ol-Khaje station. The lowest level of compliance with the value of 0.001 is related to Shirabad station. Therefore, to improve GPM satellite products, especially in watersheds with the wide spatial distribution and temporal variations of precipitation, calibration of satellite precipitation products should be on the agenda to improve their accuracy in measuring daily precipitation. The Kolmogorov-Smirnov test was performed for the first time to examine the correlation of daily GPM satellite data with observational data, and there was a relative correlation between satellite data and observed ground station records in all stations where the H0 hypothesis was accepted

The curve number (CN) method is one of the most common methods for estimating runoff in watersheds. The CN value depends on the soil infiltration, vegetation cover, and antecedent soil moisture content. According to the antecedent soil moisture content, three types of CN can be used: CN for dry soil moisture condition, CN for medium soil moisture condition, and CN for wet soil moisture condition. However, the determination of CN from the vegetation cover and soil infiltration needs high accuracy. In this study, 63 measured rainfall-runoff events in the Baghan watershed and 34 measured rainfall-runoff events in the Booshigan watershed were used for estimating runoff with seven conditions: 1) Calculating the CN value based on soil information and vegetation cover in different parts of the watershed. 2) Calibrating the surface storage coefficient of the runoff estimation equation by considering the medium condition for all events. 3) Calibrating the surface storage coefficient of the runoff estimation equation by considering the real soil moisture condition (dry or medium or wet) of each event. 4) Considering the average of observed CN in all measured rainfall-runoff events. 5 to 7) Calculating the relationship between the observed CN and measured rainfall as linear, power, and standard equation. For mentioned conditions, 48 measured rainfall-runoff events in the Baghan watershed and 26 measured rainfall-runoff events in the Booshigan watershed were used for calibrating the results, and the remained measured data in each watershed were used to evaluate the results. The results in two watersheds demonstrated that the linear, power, and standard conditions were better for runoff estimation. In the three mentioned conditions, the CN value depends on the measured rainfall data instead of using the soil infiltration information and vegetation cover of the watershed. Also, the results showed that it is not suitable to use the common method for determining the CN value (based on soil infiltration and vegetation cover of the watershed), and then estimating the runoff amount.

Despite the high capacity of groundwater resources in confronting different stresses such as climate change, rapid population growth, and greater need for water resources, especially in arid and semi-arid countries such as Iran, the over-exploitation of these valuable resources have caused a negative balance of groundwater, upset the natural balance of aquifers, and created many problems in many parts of the country. In arid and semi-arid areas, artificial groundwater recharge can be used to balance the aquifer and restore groundwater resources. In the current study, after conducting a review on the artificial groundwater recharge methods as one of the effective ways to restore the aquifers, the artificial recharge schemes were examined with emphasis on national research projects. The literature review demonstrated that the artificial groundwater recharge schemes are primarily conducted by the floodwater spreading method in the country, which in most cases, acts as an effective process to balance aquifers. This approach may play a significant role in achieving sustainable water management in Iran due to the highest efficiency and lowest cost. The results showed that the type of aquifer, hydrogeological conditions, the type of geological formation, the slope of the region, and the economic conditions are the most important factors in selecting the artificial groundwater recharge method. The results also indicated that comprehensive studies are crucial to select the location and method of artificial groundwater recharge to achieve the desired results and successful efficiency of the project. Finally, the obstruction and therefore, the decrease of the infiltration rate were identified as the main problems in the implementation of the artificial groundwater recharge projects.

In this research, a plan for the intelligent design of dry landscaping has been proposed for urban green spaces, non-residential, and administrative units with a rainwater harvesting approach. In this plan, by using two infiltrate and non-infiltrate layers and designing a drainage network between the two layers, due to the appropriate slope of the area, rainwater runoff can be extracted. Also, to prevent using different drains with an economic justification approach, we have created systems to collect and direct rainwater to the exit point of the system and guide rainwater to the desired source. By implementing these systems, soil moisture, especially in the output range of each section, and the efficiency of this project will increase. Executing this project will reduce water consumption and increase efficient use of harvested water, and as a result, will have significant economic profitability and help conserving water resources. Daily rainfall, runoff, evaporation, and soil temperature were determined by the equipment. The results showed that with this design, runoff can be doubled, soil moisture of the region can be increased up to 5%, rainwater runoff can be stored to a large extent to be used for different purposes, and reduce the intensity of soil temperature and evaporation, which can reduce water consumption for green space irrigation. On the other hand, with the implementation of this project and the design of suitable artificial feeding systems, rainwater runoff can be intelligently directed to the groundwater aquifers. This positive environmental approach can play a crucial role in reducing land subsidence, especially in the plains of Iran.

Successive drought events as one of the most important environmental crises, along with population growth and uncontrolled water extraction, have led to an increase in the depth of groundwater, especially in arid and semi-arid regions. The purpose of this study is to investigate the geostatistical relationship between groundwater depth data and groundwater depth based on precipitation data in three observation wells located in Fars province. These wells were selected based on the PSO clustering technique. Thus, the three observation wells that were closest to the center of the calculated clusters were selected as the representative of the clusters. These wells are located in Karsia, Dolatabad, and Fatehabad regions for clusters 1 to 3, respectively. Monthly groundwater depth data has been used from 2003 to 2017. Kriging and cokriging methods were performed in the GS+ environment. In this research, precipitation data was used as an auxiliary variable. Furthermore, the models were selected based on the lowest RSS values ​​and the nearest R2 values, and the spatial structure ratio (C / C0 + C) to one. Accordingly, the selected models for the main variable (groundwater depth) in the first to third clusters are spherical, power, and linear, respectively, and for cross-variogram models (precipitation - groundwater depth) are all spherical. The results showed that in the validation and test stage, the Cokriging method has higher accuracy than the Kriging method. The test stage in kriging and cokriging methods for RMSE index are (0.92 and 0.41), (0.54 and 0.52), and (1.25 and 0.95) in 1, 2, and 3 clusters, respectively.