Improving WRF‐Hydro runoff simulations of heavy floods through the sea surface temperature fields with higher spatio‐temporal resolution

Abstract

This study investigates the impact of the spatio-temporal accuracy of four different sea surface temperature (SST) datasets on the accuracy of the Weather Research and Forecasting (WRF)-Hydro system to simulate hydrological response during two catastrophic flood events over the Eastern Black Sea (EBS) and the Mediterranean (MED) regions of Turkey. Three time-variant and high spatial resolution external SST products (GHRSST, Medspiration and NCEP-SST) and one coarse-resolution and timeinvariant SST product (ERA5-and GFS-SST for EBS and MED regions, respectively) already embedded in the initial and the boundary conditions datasets of WRF model are used in deriving near-surface atmospheric variables through WRF. After the proper event-based calibration is performed to the WRF-Hydro system using hourly and daily streamflow data in both regions, uncoupled model simulations for independent SST events are conducted to assess the impact of SST-triggered precipitation on simulated extreme runoff. Some localized and temporal differences in the occurrence of the flood events with respect to observations depending on the SST representation are noticeable. SST products represented with higher cross-correlations (GHRSST and Medspiration) revealed significant improvement in flood hydrographs for both regions. The GHRSST dataset shows a substantial improvement in NSE ($70%), RMSE reduction up to 20%, and an increase in correlation from 0.3 to 0.8 with respect to the invariable SST (ERA5) in simulated runoffs over the EBS region.The use of both GHRSST and Medspiration SST data characterized with high spatiotemporal correlation resulted in runoff simulations exactly matching the observed runoff peak of 300 m 3 /s by reducing the overestimation seen in invariable SST (GFS) in the MED region. Improved precipitation simulation skills of the WRF model with the detailed SST representation show that the hydrographs of GHRSST and Medspiration simulations show better performance compared to the simulated hydrographs by observed precipitation.

Preface – special issue on “coupled atmosphere‐hydrological processes: Novel system developments and cross‐compartment evaluations”

“…Studies addressing ocean and groundwater feedback on hydrological processes presented more evident improvements. Kilicarslan et al (2021) showed that higher space–time resolution of SST representation as a lower boundary condition to a hydrometeorological modelling chain significantly improved flow hydrographs for the analysed heavy floods, with an RMSE reduction of up to 20%. Papaioannou et al (2021) found that accounting for the sea surface roughness in their coupled modelling framework led to an increase in the peak discharge values and an improvement in inundation forecast by 4.5% for a flash flood event.…”

“…Kilicarslan et al (2021) applied an uncoupled high‐resolution hydrometeorological modelling chain to two catastrophic events in the Anatolian peninsula, varying the atmospheric model lower boundary conditions according to the four sea surface temperature (STT) datasets with different space–time accuracy. Even though the study aimed to evaluate the reproducibility of the hydrological response in some small, orographically complex coastal catchments, the impact of SST representation on precipitation was preliminary analysed.…”

“…The third is evaluated as a result of the different levels of accuracy of the preceding hydrometeorological modelling chain. Kilicarslan et al (2021) applied an uncoupled high-resolution hydrometeorological modelling chain to two catastrophic events in the Anatolian peninsula, varying the atmospheric model lower boundary conditions according to the four sea surface temperature (STT)…”

WRF-Hydro for Streamflow Simulation in the MATOPIBA Region within the Tocantins/Araguaia River Basin—Brazil: Implications for Water Resource Management

“…A critical aspect of our study involves delving into four pivotal parameters: the infiltration factor (REFKDT), surface retention depth (RETDEPRT), surface roughness (OVROUGHRT), and channel roughness (MannN). Prior research has underscored the significance of these parameters in the nuanced calibration and optimization of the model to capture hydrological responses accurately, especially in scenarios characterized by extreme rainfall and flooding events [16][17][18][19][20]. While previous studies have conducted numerous simulations in various global regions, the utilization of the WRF-Hydro model in the MATOPIBA region of Brazil remains largely unexplored.…”

“…treme rainfall and flooding events [16][17][18][19][20]. While previous studies have conducted numerous simulations in various global regions, the utilization of the WRF-Hydro model in the MATOPIBA region of Brazil remains largely unexplored.…”