مجله سامانه های سطوح آبگیر باران
سال دهم شماره 2 (پیاپی 33، تابستان 1401)

Effective and less expensive implementation of plans, protection of projects, and future restoration of structures require the participation of stakeholders in all stages of study, implementation, monitoring, evaluation, and maintenance. Therefore, identifying and classifying indicators and sub-indices that affect people's participation and promoting them in the implementation of watershed and aquifer projects is very important. To this end, the objective of this research is to comparative evaluation of factors affecting people's non-participation from the point of view of watershed residents and also experts in the Asadli Watershed, Bojnord. In this research, factors affecting people's non-participation in watershed projects were classified in the form of four economic, social, education-extension, and design-implementation indicators and 15 sub-indices. In the following, from the point of view of experts (19 people) and stakeholders (31 people), the indicators and sub-indices affecting people's non-participation in watershed management projects were prioritized using the Fuzzy Hierarchical Analysis Process (FHAP) and the Friedman Test. The reliability measurement of the questionnaire was determined via Cronbach's alpha test. The obtained Cronbach's alpha value (= α 0.752) showed that the measuring tool (questionnaire) has good reliability. The results showed that from the point of view of experts and stakeholders, economic indicators and design-implementation have a greater role in people's non-participation than other indicators in the region under investigation. Experts and local societies were of the same opinion regarding the factors affecting non-participation in watershed projects in some cases, including in the field of "tribe and local people differences", "impossibility of issuing exploitation rights", "lack of indigenous knowledge of local people in the field of watershed management", and ”late return of watershed projects". A noteworthy point is the relative importance of the sub-index "lack of attention to local forces in the implementation of the project" from the point of view of both groups (rank 4 and 5 according to local societies and experts, respectively). The ranking results of the sub-indices from the perspective of experts and rural societies showed that the "lack of specific laws to support people's participation in watershed management projects" is the most important and also the sub-index "concentration of decision-making power in the center" is of great importance. However, the biggest difference in the importance of the factors from the perspective of two groups of experts and local communities is related to the sub-indices of "ignoring people's interests as a direct economic incentive" and” lack of consultation with local societies in project needs assessment".

In recent years, the world has faced many challenges. Among the natural phenomena, the flood effect has attracted a lot of attention due to its adverse effects. More than half of the destruction and damage caused by global flooding occurs in the Asian continent, which causes loss of life, damage to infrastructure, and creates conditions of panic in communities. Increasing resilience is critical for urban systems to cope with and minimize the risks of flooding. This issue is certainly more important in Iran because the increase in the frequency of floods is a significant concern for many regions of this country. In Iran, despite the existence of much research, this issue has not yet been fully covered by the scientific community. Therefore, the current research is planned with the aim of measuring flood vulnerability in the 26 watersheds of Ardabil Province based on 19 criteria and three different factors. To calculate the criteria, the meteorological and hydrological data, shape files, accessible GIS maps, statistical yearbooks, and previous studies were used as well. In addition, Shannon's entropy was used for criteria weighting and the VIKOR method was used for watershed ranking based on the exposure, sensitivity, resilience, and flood vulnerability. The results showed that Mashiran, Firozabad, and Gilandeh watersheds in terms of exposure factor, and Hir, Barogh, and Lai watersheds in terms of sensitivity factor had the highest ranks. Moreover, in terms of resilience factor, Polesoltani, Hajahmedkandi, and Barogh watersheds are highly resilient against floods respectively with ranks of 1 to 3. The results of flood vulnerability ranking also showed that Doost Bigloo, Mashiran, and Samian watersheds are the most vulnerable with ranks 1, 2, and 3.

The lack of water in arid and semi-arid areas has become a serious crisis, endangering living conditions. Sloping lands in the geographical and natural areas of Central Province have always been critical areas as sources of sediment production and erosion. Meanwhile, these areas provide valuable capacity for productive employment and conservation of water and soil resources through the construction of rain gardens in sloping lands with the help of rain catchment systems in the province. For this purpose, the amount and volume of annual rainfall, flood depth and discharge for different return periods, slope map, distribution of slope classes to the surface, and Curve number (CN) of the watershed for different return periods were obtained. Then, the development potential of rainfed gardens in the low yielding and sloping rainfed fields of Kabodkamar watershed in Adramand village was analyzed with the help of rain catchment systems. The results showed that, according to the slope and rocky nature of the area, the annual rainfall, depth and volume of runoff, and retention coefficient, the form of waterways, hydrological coefficients of the sub-watersheds, the area has the potential to use for rain catchment surface systems. In addition, considering the habitat and agro-ecological capacity of the region, water and soil resources, as well as the topography and geometry of the mentioned lands, the development of rainfed gardens is considered a key way to increase production and create employment along with ensuring the sustainability of the ecosystem in the region.

In recent decades, the ever-increasing population, human needs, and changes in the consumption patterns of societies require the planning and management of water resources more than ever. Among the management strategies of underground water resources, we can point out the compensation of these resources through artificial nutrition and exploitation according to the capacity of aquifers. The studied area is located in the Qorve watershed with an area of ​​232.17 km2. In this research, which was carried out in Qorve plain in Kurdistan province, nine parameters of the slope, land use, the thickness of unsaturated alluvium, hydraulic conductivity, distance from the river, geology, electrical conductivity, aquifer transfer capability, and vegetation were investigated. The research method was based on the weighted-linear combination (WLC) method. The weights entered in this method were extracted by the AHP model in Super Decisions software. The results of this method showed that about 23.4% of the total area of ​​the plain and 54.4 km2 have a great value for feeding, and about 19% of the total area of ​​the plain is considered unsuitable.

In this study, a model was developed to determine the optimal irrigation schedule for the Qazvin Plain in such a way that economic productivity is maximized with the amount of water available and the area under cultivation. The AquaCrop plant growth model was used to determine crop performance in different irrigation programs. The plant growth model was connected to the MATLAB programming environment. Ants’ optimization algorithm was used to determine the optimal irrigation schedule. Products, the volume of different water, depth of irrigation water, and irrigation cycle were included as decision options. Finally, after determining the best parameters of ACO, the best irrigation program was determined by considering five different scenarios in terms of irrigation depth and frequency and crop cultivation priority. In order for the irrigation program to be generalizable in water deficit conditions, an irrigation deficit from 5% to 35% was considered in different stages of growth. If we want to have the same frequency and depth of water for all crops (scenario of constant irrigation frequency and constant irrigation depth) with the goal of maximum economic efficiency, the best irrigation schedule is frequency of 9 days with a depth of 86 mm. And if the goal is maximum water productivity, the best irrigation schedule is 8 days with a depth of 78 mm. In total, among all scenarios, the best irrigation program with the aim of maximum economic productivity was related to the second scenario with variable irrigation frequency of 7 and 8 days. The optimal water depth for autumn crops was 40 and 52 mm and for spring crops was 84 mm. In addition, the priority of planting was determined in the order of tomato, potato, fodder corn, beans, wheat, barley, rapeseed, seed corn, and sugar beet. In case of a decrease in the amount of available water, the best low-irrigation program for crops was determined in such a way as to create maximum economic productivity.

Regional Flood Frequency Analysis (RFFA) is an efficient method in flood discharge estimation in ungauged watersheds or with short-term statistics. The purpose of the present study is to evaluate the hybrid method in RFFA and maximum flood discharge estimation in the Namak Lake watershed, Iran. For this purpose, 16 hydrometric stations were selected. The selected hydrometric stations were then divided into five homogeneous areas based on watershed area. Three stations from different homogeneous regions were selected and used for validating the RFFA model, and with remaining 13 stations, the hybrid equations were estimated at 2, 5, 10, 25, 50, and 100-year return periods. The results showed that the accuracy of the RFFA models in the mentioned return periods are acceptable with the agreement index (d) more than 0.8. Root Mean Square Error (RMSE) in high return periods is also more than in low return periods. So that the lowest RMSE in the two-year return period was equal to 1.55 m3 s-1 and the highest in the 100-year return period was estimated at 14.64 m3 s-1.