Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index(SPI) was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation(AMO),Atlantic Meridional Mode(AMM),the Bivariate ENSO Time series(BEST),the East Central Tropical Pacific Surface Temperature(NINO 3.4),the Central Tropical Pacific Surface Temperature(NINO 4),the North Tropical Atlantic Index(NTA),the Southern Oscillation Index(SOI),and the Tropical Northern Atlantic Index(TNA).These indices accounted for 81% of the variance in the Principal Components Analysis(PCA) method.The Atlantic surface temperature(SST:Atlantic) had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.

Watershed classification by remote sensing indices： A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.

Mapping sediment mobilization risks:Prioritizing results obtained at watershed and sub-watershed scales

Soils are key natural resources for the Earth's system;however,human impacts,especially,soil erosion are considered serious threats.Therefore,identifying and assessing effective factors to understand erosion hot spots at different scales is critical to developing effective land management plans and ensuring the sustainability of the territory.This study was conducted to determine and prepare an erosion risk map,but to prioritize the survey at different scales,such as sub-basin and watershed ones.To achieve this goal,geographic information system(GIS)and remote sensing data(RS)were used combining the analysis network process method(ANP)and ICONA model(Institute for the Conservation of Nature).As study case,we selected the degraded areas of the Gorganrood watershed located in the north of Iran.The study area was obtained for very low,low,medium,high,and very high-risk classi-fications of 14.0,21.4,17.9,31.3,and 15.4%,respectively.Results from the ICONA model also indicated that 12.8,28.8,22.1,27.9,8.5,and 0.03%belong to very low,low,medium,high,very high,and without risk of erosion,respectively.According to the validation results,it was found that the accuracy of ANP and ICONA models are 0.83 and 0.80,respectively,which indicates the suitability of the models for preparing the erosion map of the region is appropriate and useful for designing land management plans.We conclude that both models can be used to develop the erosion map potential and to prioritize sub-basins if a complete database of geomorphological characteriscs and human activities are accurate previously defined.