seyyed hossein sanaei nejad

Snow studies drastically influences on managing water resources and preventing the dangers caused by floods. Moreover, the modeling of snow melt runoff is faced with the lack of snow density data in the basin due to many changes in the distribution of snow masses, and creating snow measurement stations at high altitudes are difficult and expensive work. Considering the issues, this study attempted to invent a new equation and use remote sensing techniques in a simpler and more physical way to determine the degree-day factor (α) and then the simulation was performed by defining its calculated values using classical and new methods along with the annual and seasonal values ​​of the recession coefficient (k) for the snow-melt runoff model (SRM). The outcomes were evaluated and compared. In order to evaluate the performance of the model and its parameters, simulations were performed for the calibration and validation periods for the water years 2011-2012 and 2012-2013, respectively, and the MODIS snow cover product (MOD10A1) was used to estimate the snow-covered area and to estimate the radiation values. NEO net radiation product (CERES-NETFLUX-E) was used. The results showed that the best method for simulating runoff in the calibration period (2011-2012) was the new degree-day factor calculation method and the two values of the seasonal recession coefficient with the coefficient of determination of 0.72 and the volume difference of 4.17. In the validation period (2012-2013), the runoff simulation method with the new degree-day factor method and an annual recession coefficient with a coefficient of determination of 0.51 and the volume difference of 4.38 provided the best results in terms of assessment of the model accuracy criteria.

Precipitation is one of the main components of the hydrological cycle. Rainfall distribution plays a significant role in the Earth’s energy balance and human access to water resources, but rainfall is not always promising for humans. Floods cause a lot of human and financial losses to people and infrastructure all over the world annually. Planning for the damage reduction must be done with some measurements. It is necessary to record the amount of rainfall in high resolution, but measuring precipitation in high spatial and temporal resolution is a costly process. Scientists have developed many models to estimate precipitation values. It is necessary to evaluate the output data of these models and their performance before using them. In this study, the performance of two precipitation estimation products, PERSIANN-CCS and PDIR-Now, was evaluated against precipitation recorded in all synoptic stations of Iran for the devastating flood events from 2017 to 2019. For comparing precipitation estimations with the ground data, 9 indices (PC, BIAS, FAR, POD, HSS, , ME, MAE & RMSE) were used. The results showed high variability of indicators in different events. On average, PERSIANN-CCS and PDIR-Now products have a coefficient of determination of 0.16 and 0.19 and RMSE of 9.38 and 12.78, respectively. The values are not desirable, even though the average of PC is high. Moreover, the results showed that the PERSIANN-CCS and PDIR-Now products do not perform well due to the statistical indexes for all stations, and further researches for better heavy rainfall estimation are needed.

This research aims to assess the potential impacts of climate change on water resource in the headwater basin of Salman Farsi dam in Fars province using Soil and Water Assessment Tool (SWAT). Climate projections from three Global Circulation Models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) during future period (2020-2050) were incorporated into the calibrated SWAT model and changes in simulated runoff, blue water, green water flow, and green water storage were compared to the baseline period (1978-2008). SWAT model efficiency in simulating hydrological response of the basin was confirmed by performance evaluation indices in Tang-e-Karzin hydrometric station which is located at the outlet of basin in the calibration (NSE=0.77, R2=0.80) and validation (NSE=0.57, R2=0.67) periods. The GCMs projections indicate increasing the frequency of severe precipitation events and reducing precipitation events with low intensity in the future as compared to the baseline period, and also increasing the minimum and maximum temperature in the basin. These changes lead to an annual increase in runoff and blue water due to increase in extreme events and flood producing potential of the basin, as well as an annual increase in green water flow due to increase in temperature and precipitation over future.The findings of this research can be helpful for water resource managers and policymakers to provide management plans and strategies to encountering climate change.