This study assessed the uncertainty in estimating long-term (1971-2010) mean precipitation, its inter-annual variability, and linear trend of three network observation datasets over West Africa. A reference data, defined as a multi-dataset ensemble of precipitation observations of the Climate Research Unit (CRU) of the University of East Anglia, the Global Precipitation Climatology Centre (GPCC) and the University of Delaware (UDEL), all at horizontal resolutions of 0.5 ° by 0.5 ° were obtained and used in this study. Uncertainties in these climatological parameters of precipitation at both annual and seasonal time scales were examined in terms of inter-dataset variability using signal-to-noise ratio (SNR), correlation, root-mean-square errors and the normalised standard deviation. Results showed that the mean, inter-annual variability and trends climatology varied for different datasets. The three datasets had good agreement (SNR>5) in terms of the annual mean precipitation and its inter-annual variability in most parts of West Africa. However, the agreement between the datasets was poor in the very dry Sahel parts of northern Niger, Mali, and Mauritania (SNR ≤ 1) due to very little precipitation and possibility of relatively low station density in these regions of complex terrain. In terms of correlation (0.89 ≤ r ≤ 0.98), and normalised standard deviation, NSD (0.8 ≤ NSD ≤ 1.7), the uncertainties in the spatial variations in linear trend were larger than mean precipitation and their inter-annual variability for both annual and seasonal scales. The long-term annual precipitation trend in the region is highly uncertain except in a few small areas. observational errors are much more problematic, because their effects become relatively more pronounced as greater numbers of observations are aggregated. In this case, the author believed that averaging observations together from many different instruments/sources would tend to reduce the contribution of systematic observational errors to the uncertainty of the average. A number of researchers and institutions have developed observation-based gridded analysis datasets of global or regional coverage with fine spatial resolutions [8-14]. These network of observation datasets provide precipitation and/or surface air temperatures over extended periods of multiple decades at spatial resolutions of 0.5 ° or finer. This is, of course, a substantial improvement over previous generation data sets that are typically at much coarser (e.g. 2.5 °) horizontal resolutions [15]. These recent fine-scale datasets allow us to better examine the regional precipitation and temperature climatology and to perform more reliable evaluations of today's high-resolution climate simulations, especially over the regions of complex terrain, that are important for climate-change impact assessments and climate model evaluations [16].

Brazil has recently experienced one of its worst droughts in the last 80 years, with wide-ranging consequences for water supply restrictions, energy rationing, and agricultural losses. Northeast and Southeast Brazil, which share the São Francisco River basin (SFRB), have experienced serious precipitation reduction since 2011. We used terrestrial water-storage (TWS) fields, inverted from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, to assess and quantify the ongoing drought over the SFRB. We found a water loss rate of 3.30 km 3 /year over the time-span of April 2002 to March 2015. In addition, the TWS drought index (TWSDI) showed the extension of the recent drought that has jeopardized the SFRB since January 2012, and which reached its maximum in July 2015 (the end of TWS time series). In this sense there seems to be a linkage between the TWSDI (wetness/dryness) and the El Niño Southern Oscillation (ENSO), in terms of the wavelet coherence, at the semi-annual and biennial bands, suggesting a relationship between the two. While acknowledging that further investigation is needed, we believe that our findings should contribute to the water management policies by quantifying the impact of this drought event over the SFRB.

ABSTRACT:The estimation of large-scale evapotranspiration (ET) is complex, and typically relies on the outputs of land surface models (LSMs) or remote sensing observations. However, over some regions of Africa, inconsistencies exist between different estimations of ET fluxes, which should be investigated. In this study, we evaluate and combine different ET estimates from moderate-resolution imaging spectroradiometer (MODIS), Global Land Data Assimilation System (GLDAS) and terrestrial water budget (TWB) approaches over the Volta Basin, West Africa. ET estimates from water balance equation are obtained as residuals from monthly terrestrial water-storage (TWS) changes derived from Gravity Recovery and Climate Experiment (GRACE), Tropical Rainfall Measurement Mission (TRMM)'s rainfall data, and in situ discharge from Akosombo Dam (Ghana). An averaged estimation of ET time series is derived from all the ET estimations under study, while taking into account their uncertainties. The resulting ensemble-averaged ET was then used to assess each of the individual ET estimates. Overall, out of the seven investigated ET estimates (two from the water balance approach of which one considers water storage using GRACE-derived TWS and the other ignoring it, four from GLDAS and one from MODIS), only MODIS (28.12 mm month -1 ), GLDAS-NOAH (32.74 mm month -1 ) and TWB (32.84 mm month -1 ) were found to represent the range of variability close to the computed averaged reference ET (30.25 mm month -1 ). ET estimations inferred from MODIS were also found to represent relatively lower magnitude of uncertainties, that is, 3.99 mm month -1 over the Volta Basin (cf. 7.06 and 18.85 mm month -1 for GLDAS-NOAH and TWB-based ET estimations, respectively).