Using seasonal rainfall clusters to explain the interannual variability of the rain belt over the Greater Horn of Africa

The seasonal cycle of rainfall over the Greater Horn of Africa (GHA) is dominated by the latitudinal migration and activity of the tropical rain belt (TRB). The TRB exhibits high interannual variability in the GHA and the reasons for the recent dry period in the Long Rains (March–May) are poorly understood. In addition, few studies have addressed the rainfall fluctuations during the Msimu Rains (Dec.–Mar.) in the southern GHA region. Interannual variations of the seasonal cycle of the TRB between 1981 and 2018 were analysed using two statistical indices. The Rainfall Cluster Index (RCI) describes the seasonal cycle as a succession of six characteristic rainfall patterns, while the Seasonal Location Index (SLI) captures the latitudinal location of the TRB. The SLI and RCI depict the full seasonal cycle of the TRB supporting interpretations of the interannual variations and trends. The Msimu Rains are dominated by two clusters with opposite rainfall characteristics between the Congo Basin and Tanzania. The associated anomalies in moisture flux and divergence indicate variations in the location of the TRB originating from an interplay between low‐level air flows from the Atlantic and Indian Oceans and tropical and subtropical teleconnections. The peak period of the Long Rains shows a complex composition of five clusters, which is tightly connected to intraseasonal and interannual variability of latitudinal locations of the TRB. A persistent location of the TRB near the equator, evidenced in a frequent occurrence of a cluster related to an anomalously weak Walker circulation, is associated with wet conditions over East Africa. Dry Long Rains are associated with strong and frequent latitudinal variations of the TRB position with a late onset and intermittent rainfall. These results offer new opportunities to understand recent variability and trends in the GHA region

Development of a wind gust model to estimate gust speeds and their return periods

Spatially dense observations of gust speeds are necessary for various applications, but their availability is limited in space and time. This work presents an approach to help to overcome this problem. The main objective is the generation of synthetic wind gust velocities. With this aim, theoretical wind and gust distributions are estimated from 10 yr of hourly observations collected at 123 synoptic weather stations provided by the German Weather Service. As pre-processing, an exposure correction is applied on measurements of the mean wind velocity to reduce the influence of local urban and topographic effects. The wind gust model is built as a transfer function between distribution parameters of wind and gust velocities. The aim of this procedure is to estimate the parameters of gusts at stations where only wind speed data is available. These parameters can be used to generate synthetic gusts, which can improve the accuracy of return periods at test sites with a lack of observations. The second objective is to determine return periods much longer than the nominal length of the original time series by considering extreme value statistics. Estimates for both local maximum return periods and average return periods for single historical events are provided. The comparison of maximum and average return periods shows that even storms with short average return periods may lead to local wind gusts with return periods of several decades. Despite uncertainties caused by the short length of the observational records, the method leads to consistent results, enabling a wide range of possible applications

Impact of Climate Change on Water Resources in the Kilombero Catchment in Tanzania

This article illustrates the impact of potential future climate scenarios on water quantity in time and space for an East African floodplain catchment surrounded by mountainous areas. In East Africa, agricultural intensification is shifting from upland cultivation into the wetlands due to year-round water availability and fertile soils. These advantageous agricultural conditions might be hampered through climate change impacts. Additionally, water-related risks, like droughts and flooding events, are likely to increase. Hence, this study investigates future climate patterns and their impact on water resources in one production cluster in Tanzania. To account for these changes, a regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) Africa project was analyzed to investigate changes in climatic patterns until 2060, according to the RCP4.5 (representative concentration pathways) and RCP8.5 scenarios. The semi-distributed Soil and Water Assessment Tool (SWAT) was utilized to analyze the impacts on water resources according to all scenarios. Modeling results indicate increasing temperatures, especially in the hot dry season, intensifying the distinctive features of the dry and rainy season. This consequently aggravates hydrological extremes, such as more-pronounced flooding and decreasing low flows. Overall, annual averages of water yield and surface runoff increase up to 61.6% and 67.8%, respectively, within the bias-corrected scenario simulations, compared to the historical simulations. However, changes in precipitation among the analyzed scenarios vary between −8.3% and +22.5% of the annual averages. Hydrological modeling results also show heterogeneous spatial patterns inside the catchment. These spatio-temporal patterns indicate the possibility of an aggravation for severe floods in wet seasons, as well as an increasing drought risk in dry seasons across the scenario simulations. Apart from that, the discharge peak, which is crucial for the flood recession agriculture in the floodplain, is likely to shift from April to May from the 2020s onwards

A new and flexible rainy season definition: Validation for the Greater Horn of Africa and application to rainfall trends

Previous studies on observed or projected rainfall trends for the Greater Horn of Africa (GHA) generally focus on calendric 3-month periods, and thus partly neglect the complexity of rainfall seasonality in this topographically heterogeneous region. This study introduces a novel and flexible methodology to identify the rainfall seasonality, the onset, cessation and duration of the rainy seasons and the associated uncertainties from rainfall time series. The definition is applied to the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) satellite product and an extensive rain gauge data set. A strong agreement with known seasonal dynamics in the region and the commonly used calendric rainy seasons is demonstrated. Compared to the latter definition, a clear added value is found for the new approach as it captures the local rainfall features (associated with, for example, the sea breeze), thus facilitating evaluations across rainfall seasonality borders. While previously known trends are qualitatively confirmed, trends are amplified in some regions using the flexible definition method. Notably, a drying trend in Tanzania and Democratic Republic of Congo and a wetting trend in central Sudan and parts of eastern Ethiopia and Kenya can be detected. The trends are regionally associated with changes in rainy season cessation. CHIRPS and station trend patterns are consistent over larger regions of the GHA, but differ in regions with known rainfall contributions from warmer cloud tops. Discrepancies are found in coastal and topographically complex areas, and regions with an unstable seasonality of rainfall. As expected, CHIRPS shows spatially more homogeneous trends compared to station data. The more precise definition of the rainy season facilitates the assessment of rainfall characteristics like intensity, rainfall amounts or temporal shifts of rainy seasons. This novel methodology could also provide a more adequate calibration of climate model simulations thus potentially enabling more realistic climate change projections for the GHA

Hydrological Modeling in Data-Scarce Catchments: The Kilombero Floodplain in Tanzania

Deterioration of upland soils, demographic growth, and climate change all lead to an increased utilization of wetlands in East Africa. This considerable pressure on wetland resources results in trade-offs between those resources and their related ecosystem services. Furthermore, relationships between catchment attributes and available wetland water resources are one of the key drivers that might lead to wetland degradation. To investigate the impacts of these developments on catchment-wetland water resources, the Soil and Water Assessment Tool (SWAT) was applied to the Kilombero Catchment in Tanzania, which is like many other East African catchments, as it is characterized by overall data scarcity. Due to the lack of recent discharge data, the model was calibrated for the period from 1958-1965 (R-2 = 0.86, NSE = 0.85, KGE = 0.93) and validated from 1966-1970 (R-2 = 0.80, NSE = 0.80, KGE = 0.89) with the sequential uncertainty fitting algorithm (SUFI-2) on a daily resolution. Results show the dependency of the wetland on baseflow contribution from the enclosing catchment, especially in dry season. Main contributions with regard to overall water yield arise from the northern mountains and the southeastern highlands, which are characterized by steep slopes and a high share of forest and savanna vegetation, respectively. Simulations of land use change effects, generated with Landsat images from the 1970s up to 2014, show severe shifts in the water balance components on the subcatchment scale due to anthropogenic activities. Sustainable management of the investigated catchment should therefore account for the catchment-wetland interaction concerning water resources, with a special emphasis on groundwater fluxes to ensure future food production as well as the preservation of the wetland ecosystem