Integration of HEC-HMS Model with the Satellite-based High Spatiotemporal Resolution Dataset for Applying in Flood Simulation

Flood simulation using the hydrological model requires an appropriate rainfall dataset and unfortunately, in most parts of the Iran country the spatiotemporal resolution and density of ground gauges aren’t suitable. Hence, using remotely sensed high spatiotemporal resolution datasets can be useful for filling this gap. The main objective of this research is the assessment of PERSIANN-CCs hourly rainfall dataset for simulation of flood hydrograph using HEC-HMS event-based model at the Asalem river basin (ARB), Iran. Furthermore, to better evaluation of this model, three different spatial scenarios (including lumped, Thiessen and link-lumped) at 1, 3 and 6 hour time steps are used. Findings showed that using of link-lumped scenario at 3 and 6-hour time steps for simulation of flood hydrograph leads to the best results. For example, in this case, the average values of Nash-Sutcliffe efficiency (NSE) and Correlation Coefficient (CC) values for all events are about 0.58 and 0.78 (for Δt= 3 hr) and 0.56 and 0.74 (for Δt= 6 hr), respectively. Moreover, if the main purpose of modeling is the accurate estimation of peak flow, using the second spatial scenario leads to minimum error (with the relative error ranging between 0.2 and 7.6 %) at 1 and 3 hr time steps, while the third spatial scenario(link-lumped) hasn’t the required potential for simulation of peak flow. Also, combing the first spatial scenario and PERSIANN- CCs dataset is the best case for estimation of time to peak (Tp) which is very important in flood warning systems. Findings of this study indicate that in the lack of ground observations, the high spatiotemporal resolution rainfall datasets such as PERSIANN-CCs can be used for flood simulation.