leyla babaei

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.

Watershed degradation had negative effects on ecological and anthropologic functions at different scales. Therefore, strategic planning and conserving watershed resources is the main goal for managers and policy-makers. To achieve this goal, it is essential to provide a scientific roadmap concerning the health degree of the watershed in terms of its multi-functions. A healthy watershed improves the resilience of local ecology to climate change and provides essential services for human and ecological functions. Identifying healthy watersheds could be an effective managerial tool for monitoring natural and human phenomena and impacts. Although, in recent decades, there have been numerous types of research on watershed health and its assessment methods in different water and soil environments and in relation to environmental and social processes with economic models for decision-making in different fields. But regarding to the interpretation of different watershed health assessment models with fuzzy logic, limited studies have been carried out. This is the fact that fuzzy science has been well-considered in various sciences. In recent years, fuzzy logic has been mentioned as a powerful technique in hydrological component analysis and resource decision-making. Hydrological problems are associated with uncertainty, which is managed by fuzzy logic-based models. Fuzzy logic is based on the language of nature.  To this end, the present study was planned to accomplish our previous information on the KoozehTopraghi Watershed health and develop a new PSR-Fuzzy-based framework.

To do this research, firstly the pressure, state-response (PSR) model was conceptualized and customized for the study watershed. Secondly, the main criteria of road density, watershed slope, runoff coefficient, agriculture area with a slope of more than 25%, precipitation, and temperature were computed for building the pressure indicator. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) also were computed for building the state indicator. Then, the specific erosion (m3 y-1), erosion intensity coefficient, river density, and rangeland area were computed for building the response indicator. Thirdly, these criteria are converted to fuzzy bases using Fuzzy Linear membership functions in the ArcGIS 10.8 environment. Fuzzification is a method in which each pixel in the map is given a value between zero and one. This amount expresses its value according to the goal it pursues, and the higher it is in terms of value, the higher it is awarded to it as a result. Six operators including AND, OR, SUM, PRODUCT, Gamma 0.9, and Gamma 0.5 were used for incorporating three indicators of PSR and watershed health zoning. Fourthly, to evaluate and classify the output results of the operators used in the estimation of watershed health, the Quality Sum (QS) was used.

The results proved the better performance of two operators of Gamma 0.9 and PRODUCT. The Qs was 0.46 for PRODUCT as the first priority, followed by Gamma 0.9 operators with a Qs of 0.37 in the second priority as the most efficient operators in mapping watershed health. The pressure indicator results showed that 33.84, 0.16, 9.45, 50.51, and 6.04% of the total area of the KoozehTopraghi watershed were classified as very high, high, medium, low, and very low, respectively. The results of the state indicator, 7.55, 52.71, 39.67, and 0.07% of the total area of the study watershed were classified as very high, high, medium, and very low, respectively. The response indicator results indicated that 15.16, 13.30, 29.99, 34.80, and 6.76% of the total area of the KoozehTopraghi watershed were classified as very high, high, medium, low, and very low, respectively. According to the results of the PRODUCT operator, 67, 23, 9, and 1 % of the study watershed were classified as unhealthy, relatively unhealthy, medium, and relatively healthy, respectively. For Gamma 0.9 operator 0.9, 46, 1, 17, and 36% of watersheds were classified in unhealthy, medium, relatively healthy, and healthy classes. Based on this, it is a priority to provide suitable solutions for basic land management. Because it may be intensified the continuation of the irreparable process at the watershed level.

The results confirmed the spatial changes in health status throughout the KoozehTopraghi Watershed. Therefore, different scientific and rational programs need to be adapted to improve health to various degrees. It is highly suggested to prioritize nature-based solutions, integrated participatory management, and adaptive co-management for improving the KoozehTopraghi watershed health. Acquaintance with modern management patterns in the world, of course, with the different climatic and social conditions of our country, we can open up in the field of comprehensive watershed management compared to the past.  The watershed health index as a practical tool in watershed management can be used to determine priorities and monitor watershed status changes. In addition, since the factors affecting the management of ecosystems are considered in the health index, it can be considered as a tool for analyzing the vegetation, water, and soil resources for use with the needs of the living organism.

The ecological security of the watershed depends on the land capacity and the level of demand expected to ensure the survival of the population living in it and and its regional assessment is necessary to advance the strategic planning and poliy-making. Towards this, the aim of this study was to analyze the heterogeneity of ecological security of the Samian Watershed located in the center of Ardabil Province. Accordingly, a comprehensive evaluation method based on the analysis of various dimensions of supply and demand of material and spiritual security for 27 sub-watersheds was conceptualized and implemented. It was found that the mean value of both supply and demand indicators of ecological security in the whole watershed are equal to 0.53. In general, this watershed has marginal security. However, in terms of spatial heterogeneity, the maximum value of the ecological security index (0.49) was allocated to sub-watershed 22 located in the central part of the watershed. One of the general features of this sub-watershed is the existence of the irrigated agriculture, which has caused the region to be at a high level in terms of ecological security and supplied the demands of region population. Also, the minimum value of this index (-0.43) is related to sub-watershed 27 located in the northwest. The Ardabil City, the capital of Ardabil Province, is located in this sub-watershed, and the urban nature and lack of agriculture, forest, and garden land use are among the characteristics of this sub-watershed, which has led to the sub-watershed being in an unsafe situation. The spatial distribution of the ecological security index showed that 12, 51, 33, and 4% of the region, respectively characterized by security, marginal security, marginal insecurity, and insecurity states cover.

The flow duration curve (FDC) represents the frequency distribution of water flow over a period of time, which is widely used in hydrology to evaluate different ranges of river water flow applications. Therefore, it is necessary to develop a suitable estimation model and method in un-gauged watersheds. To this end, in the present study, a modeling method based on nonlinear regression, for the preparation of FDC based on water flow (Q) and precipitation (P) data in the statistical period 2002-2013, area (A) and mean slope (S) of upland watersheds of 30 stations of Ardabil Province were used. For nonlinear regression analysis, about 70 and 30% of the stations were considered for calibration and validation, respectively. The development of the FDC estimation model involved several steps, including nondimensionalization of flow data, normalization, definition of the parameters of nonlinear regression equations, and calculation of required quartiles. According  to the results, after testing different equations, the equation Q=0.00002385(AP)0.571S0.845  considering all input variables with a coefficient of determination (R2) 0.78 and mean absolute error (MAE) 0.021, was introduced as the final equation for FDC modeling in watersheds without statistics.

The present research was conducted to evaluate the health of the KoozehTopraghi Watershed in Ardabil Province using 27 criteria analysis related to different hydrological, anthropogenic and climatic aspects. For this purpose, base regional data including hydrology, erosion and sediment, economic, social and climatic data were collected and analyzed from previous reports. Then, according to the nature of the data used, several appropriate databases were prepared in Fragstats 4.2.1, Excel 2016, IBM SPSS Statistics 25 and ArcGIS 10.6 softwares. Then, using the principles governing the conceptual model of pressure-state-response (PSR), the status and spatial variations of the watershed health were evaluated at 36 sub-watersheds level. Different degrees of watershed health were classified into five categories: healthy, relatively healthy, moderately healthy, relatively unhealthy and unhealthy. Based on the results analysis, it was found that this watershed is in average health condition. Also, the values of pressure, state and response indicators were 0.46, 0.55 and 0.31, respectively. The mean score of comprehensive watershed health assessment index is 0.41. The maximum value of comprehensive watershed health index was 0.66 for sub-watersheds 8 and 12 and the minimum value for this index was 0.14 for sub-watershed 26. The eastern and southeastern part of the watershed is relatively healthy and moderately healthy and the watershed health status was better in the western and northern parts. The results of the present study are applicable to identifying and restoration priority areas and guiding management strategies in terms of water and soil resources at national and regional levels.