Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

Abstract

Improved information on the distribution of seasonal rainfall is important for crop production in Ghana. The predictability of key agro-meteorological indices, namely, seasonal rainfall, maximum dry spell length (MDSL) and dry spell frequency (DSF) was investigated across Ghana (with an interest on the coastal savannah agro-ecological zone). These three variables are relevant for local agricultural water management. A dynamical model (i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 seasonal forecasts) and a statistical model (i.e. response to sea surface temperatures (SSTs)) were used and analysed using correlation and other discrimination skill metrics. ECMWF-System 4 was bias-corrected and verified with 14 local stations' observations. Results show that differences in variability and skills of the agro-meteorological indices are small between agro-ecological zones as compared to the differences between stations. The dynamic model System 4 explains up to 31% of the variability of the MDSL and seasonal rainfall indices. Coastal savannah exhibits the highest level of discrimination skills. However, these skills are generally higher for the below and above normal MDSL and seasonal rainfall categories at lead time 0. Similarity in skills for the agro-meteorological indices over the same zones and stations is found both for the dynamical and statistical models. Although System 4 performs slightly better than the statistical model, especially, for dry spell length and seasonal rainfall. For dry spell frequency and longer lead time dry spell length, the statistical model tends to perform better. These results suggest that the agro-meteorological indices derived from System 4′ updated versions, corrected with local observations, together with the response to SST information, can potentially support decision-making of local smallholder farmers in Ghana.

Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles

“…The map in Figure 1 shows the location of communities with field study participants. In this region, crop growth is affected by changing climatic conditions, including greater variability in the onset date of the rainy season, more erratic total seasonal rainfall, and dry spells [10,15,43]. Unpredictable early and late onset dates and dry spell occurrence affect AED farmers' decision-making strategies [26].…”

Verification of Weather and Seasonal Forecast Information Concerning the Peri-Urban Farmers’ Needs in the Lower Ganges Delta in Bangladesh

“…Thus, a first step before using them was to bias-correct them against reference observations from the ground network of the Bangladesh Meteorological Department (BMD) using the quantile mapping method [68] through the 'qmap' R-package [69]. For each given station, the method applies cumulative density functions (CDF) to match daily observed and forecasted (SEAS5) rainfall [70]. The quantile mapping method has been successfully used in multiple hydro-climatological and climate impact studies as well as medium-range or seasonal hindcasts verification [35,[70][71][72][73][74][75][76][77].…”

“…PCC measures how well the forecast anomalies correspond to the observed anomalies over the hindcast period 1981-2016 at each ground station. The H-K discriminant score is also used to verify (or better classify) events and non-events and it is universally acceptable to provide the best estimate when evaluating binary (yes/no) forecasts for decision-making purposes [70,83]. The H-K metric has been employed by many scholars to verify precipitations forecasts against ground observations [84][85][86][87][88][89].…”

“…The H-K metric has been employed by many scholars to verify precipitations forecasts against ground observations [84][85][86][87][88][89]. In general, all the skill metrics have been widely used to evaluate the skill of climate predictions [35,70,[90][91][92]. The Pearson correlation coefficient and H-K skill score were calculated with base R. At the same time, the ROC and corresponding AUC were computed using the 'ROCR' R-package [93].…”

Designing agrometeorological information services in Ghana by combining local and scientific forecasting knowledge

“…The definitions were chosen to reflect the local farmers cropping calendar 3 and the northward evolution in rainfall distribution across Ghana. The forecasts start dates were selected based the cropping calendar at Ada district, taken as reference for the coastal savannah and considering the northward rainfall distribution in Ghana as described in previous (Manzanas et al, 2014a, Owusu andWaylen, 2009).…”