Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

Abstract

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

“…These large reservoirs were designed to operate in Ceará's climate, taking into account its interannual rainfall variability, i.e., the drastic oscillations between wet and dry years [14,15]. The second was based on the transparent water allocation process and hydrosystems management supported by an early warning operation system [16][17][18][19], vital to Ceará's resilience to drought. These initiatives were successful in preventing migration and the devastating loss of life but were insufficient to address other economic severe losses resulting from the 2012-2018 drought.Research [1,12] has shown that the adaptive capacity that had been built in Ceará State, e.g., hydraulic infrastructure and management actions, coupled with emergency measures taken to cope with the 2012-2018 drought, reduced its vulnerability.…”

Seasonal drought prediction for semiarid northeast Brazil: what is the added value of a process-based hydrological model?

“…However, what remains is the question of how to balance accuracy, operability, and usability from the perspective of water managers and stakeholders. As such, this study complements the work of Delgado et al (2018b), employing a process-based hydrological model instead of a statistical model. Thus, the aim is to present and evaluate a forecasting system, predicting seasonal reservoir levels and the occurrence of hydrological droughts for the Jaguaribe River basin, located within the NEB region.…”

“…In a recent study, Delgado et al (2018b) investigated the use of a statistical relationship to provide seasonal reservoir level predictions. They used the two GCMs ECHAM4.6 and ECMWF, with the meteorological output of each downscaled by three different statistical approaches, generating ensembles of wet-season (i.e.…”

“…Thus, the aim is to present and evaluate a forecasting system, predicting seasonal reservoir levels and the occurrence of hydrological droughts for the Jaguaribe River basin, located within the NEB region. Three different objectives are put into focus: first, the process-based hydrological model and the statistical model of Delgado et al (2018b) shall be evaluated and compared in terms of reservoir level simulation performance. Second, the process-based hydrological model as an operational forecasting tool is to be verified in a hindcast experiment.…”

Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale – Case Catalonia (Spain)

“…This classification was chosen arbitrarily, based on the information that BSS = 1 indicates perfect skill, and BSS ≤ 0 indicates no skill. Delgado et al (2018) also used the BSS with an arbitrary classification, but opted not to differentiate between values below zero. We did want to make a distinction between very negative and slightly negative values in our study, and therefore decided to decide on the aforementioned classification.…”