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].

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9Lake Chad has recently been perceived to be completely desiccated and almost extinct due 10 to insufficient published ground observations. Given the high spatial variability of rainfall in 11 the region, and the fact that extreme climatic conditions (for example, droughts) could be 12 intensifying in the Lake Chad basin (LCB) due to human activities, a spatio-temporal ap-13 proach to drought analysis becomes essential. This study employed independent component 14 analysis (ICA), a fourth-order cumulant statistics, to decompose standardised precipitation 15 index (SPI), standardised soil moisture index (SSI), and terrestrial water storage (TWS) de-16 rived from Gravity Recovery and Climate Experiment (GRACE) into spatial and temporal 17 patterns over the LCB. In addition, this study uses satellite altimetry data to estimate vari-18 ations in the Lake Chad water levels, and further employs relevant climate teleconnection 19 indices (El-Niño Southern Oscillation-ENSO, Atlantic Multi-decadal Oscillation-AMO, and 20 Atlantic Meridional Mode-AMM) to examine their links to the observed drought temporal 21 patterns over the basin. From the spatio-temporal drought analysis, temporal evolutions of 22 SPI at 12 month aggregation show relatively wet conditions in the last two decades (although 23 with marked alterations) with the 2012 − 2014 period being the wettest. In addition to the 24 improved rainfall conditions during this period, there was a statistically significant increase of 25 0.04 m/yr in altimetry water levels observed over Lake Chad between 2008 and 2014, which 26 confirms a shift in the hydrological conditions of the basin. Observed trend in TWS changes 27 during the 2002 − 2014 period shows a statistically insignificant increase of 3.0 mm/yr at the 28 center of the basin, coinciding with soil moisture deficit indicated by the temporal evolutions 29 of SSI at all monthly accumulations during the 2002 − 2003 and 2009 − 2012 periods. Further, 30 SPI at 3 and 6 month scales indicated fluctuating drought conditions at the extreme south of 31 the basin, coinciding with a statistically insignificant decline in TWS of about 4.5 mm/yr at 32 the southern catchment of the basin. Finally, correlation analyses indicate that ENSO, AMO, 33 and AMM are associated with extreme rainfall conditions in the basin, with AMO showing 34 the strongest association (statistically significant correlation of 0.55) with SPI 12 month ag-35gregation. Therefore, this study provides a framework that will support drought monitoring 36 in the LCB. 37

Abstract. Currently, various satellite processing centers produce extensive data, with different solutions of the same field being available. For instance, the Gravity Recovery and Climate Experiment (GRACE) has been monitoring terrestrial water storage (TWS) since April 2002, while the Center for Space Research (CSR), the Jet Propulsion Laboratory (JPL), the GeoForschungsZentrum (GFZ), and the Groupe de Recherche de Géodésie Spatiale (GRGS) provide individual monthly solutions in the form of Stokes coefficients. The inverted TWS maps (or the regionally averaged values) from these coefficients are being used in many applications; however, as no ground truth data exist, the uncertainties are unknown. Consequently, the purpose of this work is to assess the quality of each processing center by estimating their uncertainties using a generalized formulation of the three-cornered hat (TCH) method. Overall, the TCH results for the study period of August 2002 to June 2014 indicate that at a global scale, the CSR, GFZ, GRGS, and JPL presented uncertainties of 9.4, 13.7, 14.8, and 13.2 mm, respectively. At a basin scale, the overall good performance of the CSR was observed at 91 river basins. The TCH-based results were confirmed by a comparison with an ensemble solution from the four GRACE processing centers.

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.