مجله سامانه های سطوح آبگیر باران
سال دهم شماره 4 (پیاپی 35، زمستان 1401)

The current research was conducted with the aim of evaluating the effects of the flood spreading project on the aquifer from the perspective of the villagers living on the edge of the project. The flood spreading plan on the aquifer has been implemented since 1995 on the level of 1259 ha of land in Tangestan city in Bushehr province in the form of the Kausar 3 research and extension training station. The agricultural and garden lands or pastures used by them are located in the boundaries or on the sidelines of the project. Therefore, the statistical population of the research included 1,600 households in the villages on the edge of the flood spreading project on the Ahram Bushehr aquifer. The dependent variable of the project is the effects of the aquifer project from the perspective of the residents of the villages located on the edge of the project, which includes the effects of the project in terms of social, economic, public services, and hydrological characteristics. The above indicators were divided into sub-indicators in the questionnaire. To prioritize effective indicators and sub-indicators from the point of view of the beneficiaries (Villagers on the edge of the plan). A questionnaire of the type of qualitative ordinal variables and corresponding to the Likert scale with 28 items was used as a measurement tool. After measuring the validity and reliability of the questionnaire, a survey was conducted among the residents of the watershed. To rate effective indicators and sub-indicators, Friedman's test for two-way analysis of variance through rating, and also the comparison of the average rating of different groups was used. The findings of the research showed that the indicator of hydrological characteristics had the greatest impact on the implementation of the aquifer project, and the economic aspects of the implementation of the flood water spreading project on the aquifer are in second place. The implementation of flood spreading has had the least effect on the social field in the lives of the villagers on the edge of the project. Furthermore, from the point of view of local communities, the sub-indicator of "reduction of flood damage to village lands and gardens" has the most importance and the sub-indicator of "disagreement and conflict between the villagers of the region" has the least effect on the flood water spreading plan on the Bushehr Ahram aquifer.

Ecosystem services are fundamental for human well-being and are the basis of rural livelihoods, particularly for poor people. Rainwater harvesting can serve as an opportunity to enhance ecosystem productivity, thereby improving livelihoods, human well-being, and economies. Rainwater harvesting has been shown to create synergies between landscape management and human well-being. These synergies are particularly obvious when rainwater harvesting improves rainfed agriculture, is applied in watershed management, and when rainwater harvesting interventions address household water supplies in urban and rural areas. Rainwater harvesting has often been a neglected opportunity in water resource management: only water from surface and ground water sources is conventionally considered. Managing rainfall will also present new management opportunities, including rainwater harvesting. Improved water supply, enhanced agricultural production, and sustainable ecosystem services can be attained through adoption of rainwater harvesting with relatively low investments over fairly short time spans (5-10 years). Rainwater harvesting is a coping strategy in variable rainfall areas. In the future climate change will increase rainfall variability and evaporation, and population growth will increase demand on ecosystem services, in particular for water. Rainwater harvesting will become a key intervention in adaptation and reducing vulnerabilities. Moreover, in addition to increasing public knowledge about the ecosystem function of these systems, basic steps should be taken in the audit, evaluation and application of local knowledge of rainwater harvesting in the country.

Quantifying land use change dynamics is critical in tackling environmental and socio-economic challenges such as climate change in recent years. This study takes Siahkal County in Guilan Province as the research subject and analyzes the land use changes in two different years: 2000 and 2021, and predicts the change in 2031. We carried out land use change analysis using LANDSAT-7 ETM+ and LANDSAT-8 OLI multitemporal data pertaining to the years 2000 and 2021. For land use data extraction, a pixel-based digital classification using an ISODATA algorithm with a high Kappa index of 0.97 was applied to Landsat images. In addition, the MOLUSCE plugin in QGIS software was used to model land use change for 2000-2021, produce a transition probability matrix, and detect the future of land use for 2042 by using Cellular Automata Simulation. During 2000-2021, the result showed that the areas under agricultural areas, bare, built-up, plantation, shrubland, and woodland classes are found to increase with growth rates of about 31%, 39%, 67%, 88%, 20%, and 5%, respectively. The area under forest, grassland, and water bodies is found to decrease with decrease rates of about 22%, 16%, and 18%, respectively. Mountainous meadows did not change significantly. In terms of projected land use, the result also indicates fluctuations in land use change, especially for built-up land, showing a steady increase over time. We saw evidence of the local expansion of forest plantations, but the continuous decrease of natural forests may negatively impact the natural environment and landscape patterns regionally. In conclusion, the results of monitoring and modeling land use changes can be seen as a warning to managers, policy-makers, and planners. It is also advisable to use more data to analyze the impacts of land use changes on landscape patterns in future studies.

Soil moisture is an important variable in climatic, hydrological, and ecological systems that link atmospheric processes to the earth's surface. The rainfall systems of the water year 2017-2018 abnormally caused more than normal rainfall in Fars province and caused major changes in the surface moisture of the soil. The purpose of this research is to analyze the spatial changes of soil moisture before and after heavy spring rains in Fars province using Downscale RADAR images. Using the backscatter bands of VV and VH polarizations as well as the incident angle band (𝜃) extracted from Sentinel 1 radar images and land use extracted from the MODIS sensor, a training layer was created. Furthermore, using the support vector machine algorithm, the downscaling soil moisture map was obtained. The results showed that the volumetric soil moisture with high resolution is between 0.18 and 0.38 in the rainy year and between 0.12 and 0.24 in the long term. An anomaly map showed that between 0.14 and 0.18 m3 increased soil moisture. The positive anomaly is more in the east and south of the province, and less humid areas have experienced a greater share of positive anomaly. Moran's index statistic with a value of 0.99 has also confirmed the spatial autocorrelation of soil moisture anomaly and clustering of moisture increase. In general, it can be concluded that by using the results of this method, it is possible to monitor areas with low or high soil moisture anomalies after different rainfalls and to improve the decision-making process.

Qanat is a technique for obtaining water and transferring it for various uses. Due to the widespread arid and semi-arid areas in Iran, Qanat is the most sustainable method to obtain underground water. Considering the role of the Qanat in providing water to dry areas in the current situation of the country, where the issue of water shortage and water resources management is very important. Due to the importance of the Qanat in supplying water to dry areas in the country, the issue of water shortage and water resource management is very important knowing and studying the Qanat is very important and necessary. The purpose of the present study is to investigate and prioritize the most important physical and human parameters affecting Qanat discharge using the Analytical Hierarchy Method (AHP) according to the opinion of experts in the Mashhad-Chenaran plain. In this regard, firstly, the factors affecting Qanat were identified and studied by reviewing the sources and using the opinions of experts specializing in the field of Qanat and underground water. Then, using the hierarchical analysis method in the Expert Choice software environment, the weight and importance of each of the different parameters were determined. The results of parameter prioritization using the hierarchical analysis method showed that physical parameters with a weight of 0.545 have more value in Qanat discharge than human parameters. Also, based on the results of the prioritization of the physical parameters effective on Qanat discharge, the parameters related to the characteristics of the aquifer with a weight of 0.497 have the most importance in Qanat discharge. Also, according to the results obtained for the prioritization of human indicators, the ownership parameter with a weight of 0.539 has the most importance in Qanat discharge.

Accurate estimation of water infiltration into the soil is important for the design of irrigation systems. Therefore, calibration of different models is necessary to estimate the permeability in different regions. The aim of the present study was to calibrate and evaluate the models of Kastiakov, Kastiakov modified, Philip, SCS, Novell, and Horton in different soil textures. To evaluate the accuracy of the models, error indicators including RMSE, MAE, MR, PE, and R2 were used. The models were calibrated using an Excel software solver. Evaluation of evaluation indicators showed that the Novell penetration model with average RMSE, MAE, MR, PE, and R2 equal to 0.021, 0.068, 0.999, 0.126, and 0.98, respectively, has better performance than the five models. The other is in the whole range of studies. In contrast, the analysis of the variance of different models showed no significant difference between the calculated and measured values. The performance of the models in different textures showed that Horton, Novell, Kastiakov, and Kastiakov models were less sensitive to changes in soil texture.