Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling

Abstract

Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe and the United States) and a multi-model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias-corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events.The skill of the multi-model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0-5 day model accumulation than for the first 24 hours, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias-correction. Compared to individual models, the multi-model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 hour lead times.

Optimization Examples for Water Allocation, Energy, Carbon Emissions, and Costs

“…This logic has been a useful approach for several aspects of WRM research and practice, with various applications [8,9], including water resources allocation [10][11][12], water infrastructure, irrigation networks, dams and reservoirs, hydropower works, etc. [13][14][15], hydrology and hydraulics [16][17][18], disaster analysis and management [19][20][21], water quality management [22][23][24][25], transboundary water management [26][27][28][29], policy/governance/development [30][31][32][33][34][35], Water-Energy-Food Nexus [36][37][38], and other cross-disciplinary fields such as hydro-economics, socio-hydrology, ecohydrology, etc. [39][40][41][42][43][44][45][46].…”

The Accuracy of Precipitation Forecasts at Timescales of 1–15 Days in the Volta River Basin

“…In the event of GFS forecasts containing large errors, post-processing techniques (e.g., calibration and bias-correction (e.g., Stellingwerf et al [25]) using near-real-time satellite products could be considered to reduce the errors in the forecasts. Although satellite precipitation products that incorporate rain gauge information (usually referred to as "research-version" products), such as IMERG Final and CHIRPS, may seem appropriate for validation, they are not suitable for the dynamic calibration of GFS forecasts due to the long data latency period before they are produced and made available to users [18].…”