Abstract. Long-term precipitation forecasts can help to reduce drought risk through proper management of water resources. This study took the saline Maharloo Lake, which is located in the north of Persian Gulf, southern Iran, and is continuously suffering from drought disaster, as a case to investigate the relationships between climatic indices and precipitation. Cross-correlation in combination with stepwise regression technique was used to determine the best variables among 40 indices and identify the proper time lag between dependent and independent variables for each month. The monthly precipitation was predicted using an artificial neural network (ANN) and multi-regression stepwise methods, and results were compared with observed rainfall data. Initial findings indicated that climate indices such as NAO (North Atlantic Oscillation), PNA (Pacific North America) and El Niño are the main indices to forecast drought in the study area. According to R 2 , root mean square error (RMSE) and Nash-Sutcliffe efficiency, the ANN model performed better than the multi-regression model, which was also confirmed by classification results. Moreover, the model accuracy to forecast the rare rainfall events in dry months (June to October) was higher than the other months.From the findings it can be concluded that there is a relationship between monthly precipitation anomalies and climatic indices in the previous 10 months in Maharloo Basin. The highest and lowest accuracy of the ANN model were in September and March, respectively. However, these results are subject to some uncertainty due to a coarse data set and high system complexity. Therefore, more research is necessary to further elucidate the relationship between climatic indices and precipitation for drought relief. In this regard, consideration of other climatic and physiographic factors (e.g., wind and physiography) can be helpful.

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

Among natural disasters, flood is increasingly recognized as a serious worldwide concern that causes the most damages in parts of agriculture, fishery, housing, and infrastructure, and strongly affects economic and social activities. Universally, there is a requirement to increase our conception of flood vulnerability and to outstretch methods and tools to assess it. Spatial analysis of flood vulnerability is part of non-structural measures to prevent and reduce flood destructive effects. Hence, the current study proposes a methodology for assessing the flood vulnerability in the area of watershed in a severely flooded area of Iran (i.e., Kashkan Watershed). First interdependency analysis among criteria (including population density, PD; livestock density, LD; percentage of farmers and ranchers, PFR; distance to industrial and mining areas, DTIM; distance to tourist and cultural heritage areas, DTTCH; land use; distance to residential areas, DTRe; distance to road, DTR; and distance to stream, DTS) was conducted using the decision-making trial and evaluation laboratory (DEMATEL) method. Hence, the cause and effect factors and their interaction levels in the whole network were investigated. Then, using the interdependency relationships among criteria, a network structure from flood vulnerability factors to determine their importance of factors was constructed and the analytical network process (ANP) was applied.Finally, with aim of overcome ambiguity, reduce uncertainty, and keep the data availability, an appropriate Fuzzy membership function was applied to each layer by analyzing the relationship of each layer with flood vulnerability. Importance analysis indicated that the variables of land use (0.197), DTS (0.181), PD (0.180), DTRe (0.140), and DTR (0.138) were the most important variables. The flood vulnerability map produced by the integrated method of DEMATEL-ANP-FUZZY showed that about 19.2% of the region has a high to very high flood vulnerability.