Flood inundations and risk mapping in a tidal river: a case study for the Kelani River basin, Sri Lanka

The downstream low-lying regions of the Kelani River, including some areas in the Districts of Colombo and Gampaha, Sri Lanka, frequently face severe inundations due to extreme rainfalls in the upper basin. In the present study, 1-D and 2-D hydrodynamic models in HEC-RAS have been used to examine the flood inundations in the tidal influenced Kelani River with ground observations and remote sensing. The HEC-RAS model has been used to produce a flood hazard map for hazard assessment in the lower Kelani River basin under different return periods. Furthermore, expected discharges for different return periods have been estimated using the hydrological model HEC–HMS with the updated intensity depth frequency curves for the Kelani River basin. Sentinel 1 imagery and field survey results are used to validate the simulated flood inundation extent; hydrodynamic model results validated against observed stage measurements; hydrological model validated against discharge measurements. Further, the validated hydrodynamic model showed the high capability to capture flow processes (Nash-Sutcliffe coefficient = 0.90 and Pearson coefficient of correlation = 0.95) along with inundation extent (Success Index = 0.90) of selected historical extreme events. Then the hydrological model is used to predict the flows of the Kelani River basin with a good agreement (Nash-Sutcliffe coefficient = 0.91 and the Pearson coefficient of correlation = 0.93). Finally, flood risk zoning for different return periods are developed based on the present model which would be a useful benchmark to design and implement flood control and mitigation measures for the river basin.

Artificial neural network based PERSIANN data sets in evaluation of hydrologic utility of precipitation estimations in a tropical watershed of Sri Lanka

<abstract> <p>The developments of satellite technologies and remote sensing (RS) have provided a way forward with potential for tremendous progress in estimating precipitation in many regions of the world. These products are especially useful in developing countries and regions, where ground-based rain gauge (RG) networks are either sparse or do not exist. In the present study the hydrologic utility of three satellite-based precipitation products (SbPPs) namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Cloud Classification System (PERSIANN-CCS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain Rate near real-time (PDIR-NOW) were examined by using them to drive the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrologic model for the Seethawaka watershed, a sub-basin of the Kelani River Basin of Sri Lanka. The hydrologic utility of SbPPs was examined by comparing the outputs of this modelling exercise against observed discharge records at the Deraniyagala streamflow gauging station during two extreme rainfall events from 2016 and 2017. The observed discharges were simulated considerably better by the model when RG data was used to drive it than when these SbPPs. The results demonstrated that PERSIANN family of precipitation products are not capable of producing peak discharges and timing of peaks essential for near-real time flood-forecasting applications in the Seethawaka watershed. The difference in performance is quantified using the Nash-Sutcliffe Efficiency, which was &gt; 0.80 for the model when driven by RGs, and &lt; 0.08 when driven by the SbPPs. Amongst the SbPPs, PERSIANN performed best. The outcomes of this study will provide useful insights and recommendations for future research expected to be carried out in the Seethawaka watershed using SbPPs. The results of this study calls for the refinement of retrieval algorithms in rainfall estimation techniques of PERSIANN family of rainfall products for the tropical region.</p> </abstract>