index-based assessment

This research aims to analyze the spatial water footprint of agricultural products in four categories of blue, green, gray, and white water in Khiavchai sub-watersheds. To this end, the meteorological data (monthly rainfall and temperature) were used as input data for the CROPWAT program. The Google Earth images were used to map the land use areas and estimate the area of agricultural and rangelands. It was found that apples, sour cherries, cherries, walnuts, and grapes are the dominant crops produced in the orchard area. The four types of virtual waters of agricultural products were analyzed in four land uses, i.e., orchards, rainfed agriculture, irrigated agriculture, and rangelands. The total water consumed by the apple product is more than other products in all sub-watersheds and the amount of virtual water of walnut is also high in most sub-watersheds. The total consumption of blue, green, white, and gray water in sour cherry, cherry, and grape was the same and was evaluated as less than the other two products. In addition, the average amount of blue water for rainfed agriculture was estimated as 2788.85 m3 t-1, among which lentils and chickpeas had the higher blue water among the rainfed crops. According to the results, sub-watersheds 7 and 14 located in western parts of the study area had the highest of virtual water. In sub-watershed 7, a large part of its area is used for rangeland and only a small part is related to rainfed agriculture, and all area of sub-watershed 14 is used for rangeland. Therefore, the appropriate cultivation pattern can be determined based on the amount of virtual water and its spatial changes.

Ecological quality is an inclusive measurement unit of the elements, configuration, and performance of an ecosystem in the temporal and spatial scales. In addition, assessing ecological quality could provide valuable information for experts and managers in the first step of directed planning and comprehensive watershed management. Therefore, the current research was conducted to calculate the ecological quality index (EQI) in one of the upland watersheds of Yamchi Dam, which is not without the effect of human interference.

Nir Watershed as a study area is located in the southwestern part of Ardabil Province. To carry out the current research, first, the essential variables of ecological quality assessment (normalized difference vegetation index (NDVI), fractional vegetation coverage (FVC), leaf area index (LAI), net primary production (NPP), moisture index (IM), land surface temperature (LST), soil erosion, ecological integrity index, and runoff retention index) were collected and calculated. The variables were chosen to represent the three functions of ecosystems maintaining, supporting, and regulating. After standardization, weighting of the variables was done using the advanced multi-criteria, i.e., Projection Pursuit Model (PPM). Afterward, the EQI was calculated based on the sum weight of all variables for 11 sub-watersheds of Nir.

The wide range of variability of the variables used in the entire Nir Watershed area was confirmed. In general, sub-watershed 1 had the maximum mean of the first to fourth variables (FVC, NDVI, NPP, and LAI) and the minimum mean LST. While sub-watershed 11 had the opposite situation in terms of these five variables. All five variables reflect the structure and function of vegetation and therefore the ecological quality. The status of other indicators except ecological continuity (EC) was also evaluated in sub-watershed 1 in a medium status. Based on the PPM weighting method, the FVC (0.39) has the highest importance, and moisture and runoff retention indices with an equal weight of 0.01 have been assigned the least priority. In addition, the ecological quality results showed that watersheds 11 (0.10) and 1 (0.90) respectively have the lowest and highest levels of EQI. In general, according to the zoning resultd, it was found that the northeastern and southeastern parts of the watershed have the lowest EQI which covered 29% of the area.

The results can be used as one of the main goals of managing watersheds regarding preserving the ecosystem's health and integrity. Moreover, the priority of rehabilitation budget allocation can be assigned according to the resilience degrees of studied sub-watersheds.