Use of bias correction techniques to improve seasonal forecasts for reservoirs - A case-study in northwestern Mediterranean.

In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter.

Seasonal predictability of water resources in a Mediterranean freshwater reservoir and assessment of its utility for end-users.

In this study we explore the seasonal predictability of water resources in a Mediterranean environment (the Boadella reservoir, in north-eastern Spain). Its utility for end-users is assessed through the analysis of economic value curve areas (EVA). Firstly, we have built monthly multiple linear regression (MLR) models for the in-flow, out-flow and volume anomalies by identifying the underlying relationships between these predictands and their potential predictors, both meteorological and human influenced: rainfall, maximum and minimum temperatures, reservoir volume and discharge. Subsequently, we have forecast the monthly anomalies with these models for the period 1981-2010 (up to seven months ahead). We have tested the aforementioned models with four strategies in a leave-one-out cross-validation procedure (LOOCV): a) Climatology (Clim.), b) persistence (Pers.), c) antecedent observations+climatology (A+Clim.), d) antecedent observations+European Centre for Medium-range Weather Forecasts (ECMWF) System 4 anomalies (A+S4). Climatology is the operational strategy against which the other approximations are compared. The second and third approaches only use observations as input data. Finally, the last one combines both observations and ECMWF System 4 forecasts. The LOOCV revealed that reservoir volume is the variable best described by the MLR models, followed by in-flow and out-flow anomalies. In the case of volume anomalies, the predictability displayed provides added value with respect to climatology with a minimum of four months in advance. For in-flow and out-flow this is true at one month ahead, and regarding the latter variable we encounter enhanced predictability also at longer horizons for the summer months, when water demands peak (a valuable result for end-users). Hence, there is a window of opportunity to develop future operational frameworks that could outperform the use of climatology for these variables and forecast horizons.