Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s core satellite sensors and CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product generated over the continental United States (CONUS). The considered algorithms include: Dual-Frequency Precipitation Radar (DPR) product and its single frequency counterparts (Ka- and Ku-only); the combined DPR and multifrequency microwave imager (CORRA) product; the CloudSat SnowProfile product (2C-SNOW-PROFILE); two passive microwave products i.e. the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The spaceborne and ground-based snowfall products are collocated spatially and temporally and compared at the spatial resolution of spaceborne instruments over the period spanning from January 2016 to March 2020 (4 winters). Detection capabilities of the sensors is assessed in terms of the most commonly used forecast metrices (Probability of Detection, False Alarm Ratio, etc.) whereas precision of the products is quantified by the mean error (ME) and root-mean-square-error (RMSE). 2C-SNOW product agrees with MRMS by far better than any other product. Passive microwave algorithms tend to detect more precipitation events than the DPR and CORRA retrievals, but they also trigger more false alarms. Due to limited sensitivity, DPR detects only approx. 30% of the snow events. All the retrievals underestimate snowfall rates, for the detected snowstorms they produce approximately only a half of the precipitation reported by MRMS. Large discrepancies (RMSE from 0.7 to 2.5 mm/h) between spaceborne and ground-based snowfall rate estimates is the result of limitations of both systems and complex ice scattering properties. The MRMS product is based on a power law relation and it has difficulties in detecting precipitation at far ranges; the DPR system is affected by low sensitivity while the GPM Microwave Imager (GMI) measurements are affected by the confounding effect of the background surface emissivity for snow-covered surfaces and of the emission of supercooled liquid droplet layers.

Information concerning the types, patterns, and intensity of hydroclimatic variability is needed in various sectors, including water resources planning and climate change adaptation, among numerous others. In transboundary river basins, the nature of hydroclimatic variability frequently is a critical input in the treaties governing water resources allocation to competing parties and sectors. Yet, the pattern of hydroclimatic variability across Africa’s Transboundary Rivers Basins (ATRB) remains poorly investigated owing primarily to lack of access to required data. To the extent that such studies exist, they often have been conducted at different times using different data sets and reference periods, making comparisons across the continent difficult. In this paper, we make use of NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite data to extend the Terrestrial Water Storage (TWS) +50 years prior GRACE era using the unique Generalized Additive Model for Location Scale and Shape (GAMLSS) approach on the Global Land Data Assimilation System Version 2 data (GLDAS V2). The results revealed a downward trend of the TWS over Africa with a decrease rate of 0.14 cm/yr. The spatial patterns of TWS in ATRB showed a significant decreasing trend for Nile, Niger, Chad, Volta, and Congo River Basins, compared to the insignificant trends for Zambezi, Okavango, Limpopo and Orange River Basins. The spatial trends in annual TWS is primarily related to the regional variation in the precipitation trends. The largest negative trend in precipitation were observed over West Africa and Sahel region. The dry trend over south Africa were intervened by wet records. The nature of these hydroclimatic patterns are explained by the significant reduction in the total precipitation and the increasing demands on water resources.