عنوان مقاله [English]
Flood is one of the natural disasters which leaves behind many humans, financial and ecosystem losses. Consideration of situations and conditions in the basins has a high priority in reducing the effects of these losses. Study and research on Terrestrial Water Storage (TWS) point out the capacity of water storage in a basin and consider its potential for occurring possible floods. TWS is defined as the summation of all water stored above and below on the earth’s surface (e.g., lakes, rivers, soil moisture, snow, ice and waters inside the vegetation). In this study, using Terrestrial Water Storage Anomaly (TWSA) data attained from twin GRACE satellites and using monthly precipitation data acquired from Global Precipitation Climatology Project (GPCP), the capacity of water storage in basin and the Flood Potential Index (FPI) are calculated. FPI is defined as a storage capacity quantity of basin and has been utilizing in assessing the potential of flood occurrence. The more FPI is near to 1.0, the more possibility of flood event will be. Regarding previous flood events happened during March 2019 in Iran, FPI values related to Karun basin within interval of October 2018 and August 2019 are considered specially. FPI values of 0.21 in March and 0.42 in April show flood events in March and April (2019), respectively. Based on the proposed method, rising value of FPI causes more potential for occurring a flood event. On the contrary, falling value of FPI makes flood events less likely. Concerning results point out higher positive values of FPI connected to more precipitations and in reverse, lower values of FPI related to lower precipitations. Therefore, using study of FPI during desired time interval and regarding the increasing of its index value, forecasting the flood event is possible even several months before its happening and prevents the possible losses. However, temporary and small-scaled floods are not recognized by GRACE data and FPI is less effective. By the way, in some cases it is possible that FPI increases and causes a wrong flood forecasting. For example, in our case study within specific time intervals in May (FPI = 0.68) and June (FPI = 0.27) the FPI values are positive, while there has not occurred any flood. Therefore, definitely, this index is not merely capable of flood forecasting and it is necessary to use supplementary information utilizing other sources and methods to forecast it more precisely.