Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

María Carolina Rogelis,Micha Werner

doi:10.5194/hess-22-853-2018

Abstract

Abstract. Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zero-precipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area.

Highlights

Numerical weather prediction (NWP) models are fundamental to extend flood forecast lead times beyond the concentration time of a watershed
The significant advances in NWP and computer power during the last decades have led to the generation of high-resolution precipitation forecasts at the catchment scale, and quantitative precipitation forecasts (QPFs) from high-resolution NWPs are increasingly used in flood forecasting systems as a result (Xuan et al, 2009)
This paper presents the assessment of Weather Research and Forecasting (WRF) forecasts produced in a tropical high-mountain watershed in Bogotá, Colombia

Summary

Introduction

Numerical weather prediction (NWP) models are fundamental to extend flood forecast lead times beyond the concentration time of a watershed. The significant advances in NWP and computer power during the last decades have led to the generation of high-resolution precipitation forecasts at the catchment scale, and quantitative precipitation forecasts (QPFs) from high-resolution NWPs are increasingly used in flood forecasting systems as a result (Xuan et al, 2009). NWP results contain noise, are contaminated by model biases, are too coarse to adequately resolve all features such as convection, and are influenced by uncertainty inherent in the initial conditions (Colman et al, 2013). Weather forecasting in tropical mountains is highly challenging. The formation and movement of deep convection and mesoscale convective systems is affected by orography (Colman et al, 2013)

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Conclusion