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A simple—yet powerful—indicator for monitoring agricultural drought is the water requirement satisfaction index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for "average conditions," little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions' growing seasons, from 1981 to 2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find that the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2%–23% in East, West, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. In comparison with the Extended WRSI, which creates a single forecast scenario using average inputs that are not covarying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events along with the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook in a way that is smoothed over in the Extended WRSI. We find that the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast and presents a less biased and more accurate scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid. [ABSTRACT FROM AUTHOR]

The limited amount of shared reservoir monitoring data around the world is insufficient to quantify the dynamic nature of reservoir operation with conventional ground-based methods. With the emergence of the Reservoir Assessment Tool (RAT) driven by a multitude of Earth-observing satellites and models, historical observation of reservoir operation spanning 35 years was made using open-source techniques. Trends in reservoir storage change were compared with trends of four critical hydrologic variables (precipitation, runoff, evaporation, and Palmer drought severity index) to understand the potential role of natural drivers in altering reservoir operating pattern. It was found that the reservoirs in Africa were losing active storage at a rate of more than 1% per year of total storage capacity. Smaller reservoirs (with a capacity of less than 0.5 km3) in Southeast Asia were found to experience a sharp gain in storage of 0.5%–1% per year of total storage capacity. Storage change trends of large reservoirs with multiple years of residence time that are designed for strategic water supply needs and drought control were found to be less affected by precipitation trends and influenced more by drought and evaporation trends. Over Africa, most reservoir storage change trends were dictated by evaporation trends, while South Asian reservoirs appear to have their storage change influenced by drought and evaporation trends. Finally, findings suggest that operation of newer reservoirs is more sensitive to long-term hydrological trends and the regulated surface water variability that is controlled by older dams in the upstream. [ABSTRACT FROM AUTHOR]

This article examines the diurnal cycle of lake-effect rains over Lake Victoria and of rainfall in the surrounding catchment. The analysis focuses on four months, which represent the two wet seasons (April and November) and the two dry seasons (February and July). Lake-effect rains are strongest in April and weakest in July. In all cases there is a nocturnal rainfall maximum over the lake and a daytime maximum over the catchment, with the transition between rainfall over the lake and over the catchment occurring between 1200 and 1500 local standard time (LST). During the night the surrounding catchment is mostly dry. Conversely, little to no rain falls over the lake during the afternoon and early evening. In most cases the maximum over the lake occurs at either 0600 or 0900 LST and the maximum over the catchment occurs around 1500–1800 LST. The diurnal cycle of mesoscale convective systems (MCSs) parallels that of overlake rainfall. MCS initiation generally begins over the catchment around 1500 LST and increases at 1800 LST. MCS initiation over the lake begins around 0300 LST and continues until 1200 LST. While some MCSs originate over the highlands to the east of the lake, most originate in situ over the lake. Maximum MCS activity over the lake occurs at 0600 LST and is associated with the systems that initiate in situ. Significance Statement: This article presents two new conclusions about rainfall over Lake Victoria. One is that rainfall is enhanced over the lake in all months, even during the dry seasons. The other is that most nocturnal mesoscale systems actually develop in situ over the lake. The results enhance our understanding of the Lake Victoria system, which helps to sustain some 30 million people in East Africa and some 300 million living in the Nile basin. With the threat of climate change looming large, increased knowledge of the lake system will allow for improved estimation of its impact. [ABSTRACT FROM AUTHOR]

The purpose of this article is to determine the meteorological factors controlling the lake-effect rains over Lake Victoria. Winds, divergence, vertical motion, specific humidity, convective available potential energy (CAPE), and convective inhibition (CIN) were examined. The local wind regime and associated divergence/convergence are the major factors determining the diurnal cycle of rainfall over the lake and catchment. The major contrast between overlake rainfall in the wet- and dry-season months is the vertical profile of omega. This appears to be a result of seasonal contrasts in CAPE, CIN, and specific humidity, parameters that play a critical role in vertical motion and convective development. Significance Statement: The results of this study suggest that local factors (the anabatic–katabatic flow and lake–land breezes) do not play a role in the seasonal contrast in the lake-effect rains. The major contrast between the dry and wet seasons is evident in the vertical profile of omega, which is stronger and extends higher in the atmosphere during the wet season. Atmospheric humidity is higher during the wet season, modifying the values of CAPE and CIN. By modulating omega, these determine the seasonal cycle of lake-effect rains. These results have implications for predicting changes in the lake that will occur under global warming. [ABSTRACT FROM AUTHOR]

The availability of streamflow records in Africa has been declining since the 1980s due to malfunctioning gauging stations and data collection failures. Africa also has insufficient hydrological information owing to the allocation of few resources to research efforts. Unreliable runoff datasets and large uncertainties in runoff trends due to climate change patterns and human activities are major challenges to water resource management in Africa. Therefore, this study aimed to improve runoff estimates and to assess runoff trend responses to climate change and human activities in Africa during 1981–2016. Using statistical methods, monthly gridded runoff datasets were generated for the period of 1981–2016 from a modified runoff curve number method calibrated with river discharge data from 535 gauging stations. According to the cross-validation results, the constructed runoff datasets comprised the Nash and Sutcliffe coefficients ranging from 0.5 to 1, coefficients of determination ranging from 0.5 to 1, and percent biases between ±25% for a large number of stations up to 73%, 80%, and 91% of the 535 gauged catchments used as references. Analysis of runoff trend responses to climate change and human activities revealed that land cover change contributed more (72%) to the observed net runoff change (0.30% a−1) than continental climate changes (28%). These contributions were results of cropland expansion rate of 0.46% a−1 and a precipitation increase of 0.07% a−1. The performance and simplicity of the statistical methods used in this study could be useful for improving runoff estimations in other regions with limited streamflow data. The results of the current study could be important to natural resource managers and decision-makers in terms of raising awareness of climate change adaptation strategies and agricultural land-use policies in Africa. [ABSTRACT FROM AUTHOR]

Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days' lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed. [ABSTRACT FROM AUTHOR]

An ever-increasing number of rainfall estimates is available. They are used in many important applications such as flood/drought monitoring, water management, or climate monitoring. Such data are especially valuable in sub-Saharan Africa, where rainfall has considerable socioeconomic impacts and the gauge and radar networks are sparse. The choice of a rainfall product can significantly influence the performance of such applications. This study reviews previous works, evaluating or comparing rainfall products over different parts of sub-Saharan Africa. Three types of rainfall products are considered: the gauge-only, the satellite-based, and the reanalysis ones. In addition to the global rainfall products, we included three regional ones specifically developed for Africa: the African Rainfall Climatology version 2 (ARC2), the Rainfall Estimate version 2 (RFE2), and the Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) African Rainfall Climatology and Time Series (TARCAT). The gauge density, the orography, and the rainfall regime, which vary with the climate and the season, influence the performance of the rainfall products. This review does not focus on comparing results, as many other publications doing so are already available. Instead, we propose this review as a guide through the different rainfall products available over Africa, and the factors influencing their performances. With this review, the reader can make informed decisions about which products serve their specific purpose best. [ABSTRACT FROM AUTHOR]

The uncertainties in the version 5 Global Precipitation Measurement (GPM) Microwave Imager (GMI) precipitation retrievals are evaluated via comparison with the radar–radiometer (so-called "Combined") retrievals between 40°S and 40°N. Results show the precipitation estimates are close (~7% GMI overestimation) globally. However, some specific regions, such as central Africa, the Amazon, the Himalayan region, and the tropical eastern Pacific, show a large overestimation (up to 50%) in GMI retrievals when compared to Combined retrievals. The uncertainties are further evaluated based on precipitation system properties, such as size and intensity of the system. GMI tends to underestimate precipitation volume when the system is relatively warm (>250 K) and small (<200 km2) due to the lack of ice scattering signatures. However, for large systems (>2000 km2), GMI-derived precipitation is typically higher than Combined over all surfaces. Based on the system properties, a simple bias correction methodology is proposed to implement in the Goddard Profiling Algorithm (GPROF) to reduce GMI biases. GMI precipitation volume is adjusted in each precipitation system based on the size and minimum 89 GHz polarization-corrected temperature (PCT) over land and ocean separately. The overall GMI bias is reduced to 3%, with significant improvement over land. The GMI biases (up to 50%) over the previously mentioned regions are significantly or partially removed, becoming less than 20%. This method also shows effectiveness in removing zonal and seasonal biases from GMI estimates. These results suggest the importance of utilizing the information of whole precipitation systems instead of individual pixels in the precipitation retrieval. [ABSTRACT FROM AUTHOR]

East Africa experienced in the 2001-11 time period some of the worst drought events to date, culminating in the high-impact drought of 2010/11. Long-term monitoring of precipitation is thus essential, and satellite-based precipitation products can help in coping with the relatively sparse rain gauge ground networks of this area of the world. However, the complex topography and the marked geographic variability of precipitation in the region make precipitation retrieval from satellites problematic and product validation and intercomparison necessary. Six state-of-the-art monthly satellite precipitation products over East Africa during the 2001-09 time frame are evaluated. Eight areas (clusters) are identified by investigating the precipitation seasonality through the Global Precipitation Climatology Centre (GPCC) climatological gauge data. Seasonality was fully reproduced by satellite data in each of the GPCC-identified clusters. Not surprisingly, complex terrain (mountain regions in particular) represents a challenge for satellite precipitation estimates, as demonstrated by the standard deviations of the six-product ensemble. A further confirmation comes from the comparison between satellite estimates and rain gauge measurements as a function of terrain elevation. The 3B42 product performs best, although the satellite-gauge comparative analysis was not completely independent since a few of the products include a rain gauge bias correction. [ABSTRACT FROM AUTHOR]

Within the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Ouémé River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum-Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region, a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005-08 AMMA field campaign period during which rainfall and streamflow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily streamflow at five gauges within the basin. Results demonstrate that the RRS simulates streamflow at all gauges with relative errors varying from −20% to 3% and Nash-Sutcliffe coefficients varying from 0.62 to 0.90. DWI varies from 24% to 67% of the base flow as a function of the subbasin. The relatively simple reservoir DWI approach is quite robust, and further improvements would likely necessitate more complex solutions (e.g., considering seasonality and soil type in ISBA); thus, such modifications are recommended for future studies. Although the evaluation shows that the simulated streamflows are generally satisfactory, further field investigations are necessary to confirm some of the model assumptions. [ABSTRACT FROM AUTHOR]

Daily precipitation retrievals from three algorithms [the Tropical Rainfall Measuring Mission 3B42 rain product (TRMM-3B42), the Climate Prediction Center morphing technique (CMORPH), and the second version (RFEv2) of the Famine Early Warning System (FEWS)] and CloudSat retrievals of cloud liquid water, ice amount, and cloud fraction are used to document the cloud structures associated with rainfall location and intensity in the West African monsoon. The different rainfall retrieval approaches lead to contrasting cloud sensitivities between all three algorithms most apparent in the onset period of June and July. During the monsoon preonset phase, CMORPH produces a precipitation peak at around 12°N associated with upper-level cirrus clouds, while FEWS and TRMM both produce rainfall maxima collocated with the tropospheric-deep convective cloud structures at 4°-6°N. In July similar relative displacements of the rainfall maxima are observed. Conditional sampling of several hundred convection systems proves that, while upper-level cirrus is advected northward relative to the motion of the convective system cores, the reduced cover and water content of lower-tropospheric clouds in the northern zone could be due to signal attenuation as the systems there appear to be more intense, producing higher ice water contents. Thus, while CMORPH may overestimate rainfall in the northern zone due to its reliance on cloud ice, TRMM and FEWS are likely underestimating precipitation in this zone, potentially due to the use of infrared based products in TRMM and FEWS when microwave is not available. Mapping the CloudSat retrievals as a function of rain rate confirms the greater sensitivity of CMORPH to ice cloud and indicates that high-intensity rainfall events are associated with systems that are deeper and of a greater spatial scale. [ABSTRACT FROM AUTHOR]

Six satellite-based rainfall estimates (SRFE)-namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]-were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba-Shabelle, and Baro-Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003-06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs. [ABSTRACT FROM AUTHOR]

Multispectral Spinning Enhanced Visible and IR Interferometer (SEVIRI) data, calibrated with daily rain gauge estimates, were used to produce daily high-resolution rainfall estimates over Africa. An artificial neural network (ANN) approach was used, producing an output of satellite pixel-scale daily rainfall totals. This product, known as the Rainfall Intensity Artificial Neural Network African Algorithm (RIANNAA), was calibrated and validated using gauge data from the highland Oromiya region of Ethiopia. Validation was performed at a variety of spatial and temporal scales, and results were also compared against Tropical Applications of Meteorology Using Satellite Data (TAMSAT) single-channel IR-based rainfall estimates. Several versions of RIANNAA, with different combinations of SEVIRI channels as inputs, were developed. RIANNAA was an improvement over TAMSAT at all validation scales, for all versions of RIANNAA. However, the addition of multispectral data to RIANNAA only provided a statistically significant improvement over the single-channel RIANNAA at the highest spatial and temporal-resolution validation scale. It appears that multispectral data add more value to rainfall estimates at high-resolution scales than at averaged time scales, where the cloud microphysical information that they provide may be less important for determining rainfall totals than larger-scale processes such as total moisture advection aloft. [ABSTRACT FROM AUTHOR]

Information related to land surface is immensely important to global change science. For example, land surface changes can alter regional climate through its effects on fluxes of water, energy, and carbon. In the past decades, data sources and methodologies for characterizing land surface heterogeneity (e.g., land cover, leaf area index, fractional vegetation cover, bare soil, and vegetation albedos) from remote sensing have evolved rapidly. The double ECOCLIMAP database-constituted of a land cover map and land surface variables and derived from Advanced Very High Resolution Radiometer (AVHRR) observations acquired between April 1992 and March 1993-was developed to support investigations that require information related to spatiotemporal dynamics of land surface. Here is the description of ECOCLIMAP-II: a new characterization of the land surface heterogeneity based on the latest generation of sensors, which represents an update of the ECOCLIMAP-I database over Africa. Owing to the many features of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (more accurate in spatial resolution and spectral information compared to the AVHRR sensor), a variety of methods have been developed for an extended period of 8 yr (2000-07) to strengthen consistency between land surface variables as required by the meteorological and ecological communities. The relative accuracy (or performance) quality of ECOCLIMAP-II was assessed (i.e., by comparison with other global datasets). Results illustrate a substantial refinement; for instance, the fractional vegetation cover resulting in a root-mean-square error of 34%% instead of 64%% in comparison with the original version of ECOCLIMAP. [ABSTRACT FROM AUTHOR]

The Noah land surface model (LSM) has recently been implemented into the Experimental Climate Prediction Center’s (ECPC’s) global Seasonal Forecast Model (SFM). Its performance is compared to the older ECPC SFM with the Oregon State University (OSU) LSM using two sets of 10-member 50-yr Atmospheric Model Intercomparison Project (AMIP) runs. The climatological biases of several fields tend to increase with the Noah LSM. The differences in near-surface temperature bias are traced to changes in the energy budget. In addition to climatology, the variability and skill (anomaly correlation with observations) of the two ensembles are considered. Unlike the climatology, the near-surface temperature skill of the ECPC SFM generally improves with the Noah LSM. Other climatological fields, such as precipitation, show little change in skill. While the global results are mixed, there are however significant regional improvements over Africa both in terms of climatological bias and skill. In the central African Congo River basin, the Noah LSM removed a warm-dry bias and improved upon the near-surface temperature skill of the OSU LSM. In the African Sahel, the Noah LSM greatly enhanced the climatology, variability, and skill of the ECPC SFM as well as improving the location of the African easterly jet. [ABSTRACT FROM AUTHOR]

Past studies have indicated that deforestation of the Amazon basin would result in an important rainfall decrease in that region but that this process had no significant impact on the global temperature or precipitation and had only local implications. Here it is shown that deforestation of tropical regions significantly affects precipitation at mid- and high latitudes through hydrometeorological teleconnections. In particular, it is found that the deforestation of Amazonia and Central Africa severely reduces rainfall in the lower U.S. Midwest during the spring and summer seasons and in the upper U.S. Midwest during the winter and spring, respectively, when water is crucial for agricultural productivity in these regions. Deforestation of Southeast Asia affects China and the Balkan Peninsula most significantly. On the other hand, the elimination of any of these tropical forests considerably enhances summer rainfall in the southern tip of the Arabian Peninsula. The combined effect of deforestation of these three tropical regions causes a significant decrease in winter precipitation in California and seems to generate a cumulative enhancement of precipitation during the summer in the southern tip of the Arabian Peninsula. [ABSTRACT FROM AUTHOR]

Operational, real-time rainfall estimation on a daily timescale is potentially of great benefit for hydrological forecasting in African river basins. Sparseness of ground-based observations often means that only methodologies based predominantly on satellite data are feasible. An approach is presented here in which Cold Cloud Duration (CCD) imagery derived from Meteosat thermal infrared imagery is used in conjunction with numerical weather model analysis data as the input to an artificial neural network. Novel features of this approach are the use of principal component analysis to reduce the data requirements for the weather model analyses and the use of a pruning technique to identify redundant input data. The methodology has been tested using 4 yr of daily rain gauge data from Zambia in central Africa. Calibration and validation were carried out using pixel area rainfall estimates derived from daily rain gauge data. When compared with a standard CCD approach using the same dataset, the neural network shows a small but consistent improvement over the standard method. The improvement is greatest for higher rainfalls, which is important for hydological applications. [ABSTRACT FROM AUTHOR]