High Rainfall Variability Research Articles

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Projected change in the rainfall behaviour over Odisha

ABSTRACT Climate change has impacted the rainfall characteristics and the extremes are on the rise across the world. These changes threaten agriculture, water resources, and disaster management. Odisha, an eastern Indian state with an agrarian economy, heavily relies on monsoon rainfall. The study analyses the projected change in the rainfall characteristics across Odisha at different specific warming targets of the Paris Climate Agreement using high-resolution regional climate models. The study adopts a bi-fold approach; first, it employs a robust method to select the best model experiments; afterwards, the model ensemble is used to examine the projected rainfall characteristics. The results indicate a 4–16% increase in projected rainfall over Odisha, with an extended rainy season. The projected number of consecutive wet days, moderate and extreme rainfall, is expected to rise under the global warming scenario. The prolonged rainy season with heavy rainfall can result in disasters like post-monsoon floods, while higher rainfall variability will increase the risk of floods and droughts across Odisha, threatening agriculture. The results will help pinpoint regions most vulnerable to climate change. The study also suggested measures to assist governments and planners in developing short-term and long-term strategies for adaptation and mitigation to lessen climate change impacts.

Beyond dams: Assessing integrated water storage in the Shashe catchment, Limpopo River Basin

Study regionThe Shashe catchment, Limpopo River Basin, Botswana, and Zimbabwe. Study focusThe Shashe catchment is the third largest flow contributor to the Limpopo River Basin. Water availability in the Shashe catchment is highly seasonal due to high seasonal rainfall variability. The seasonality and inter-annual variability cause shortfalls (demand exceeds the average water availability) in certain months and years. Storage is needed to bridge the seasonal water availability “gap” and mitigate the deficits in drought years, i.e., inter-annual variability. While the need for water storage through grey infrastructure such as dams has long been known, there is growing recognition of the need for approaches to water storage that capitalize on all storage types. However, the current capacity to plan in ways that utilize all storage types is limited. The analyses conducted for this paper assessed the volume and spatial and temporal variability of different storage options – large and small dams, sand dams, soil moisture, and aquifers – in the Shashe catchment of the Limpopo River Basin. An integrated SWAT-MODFLOW model and remote sensing approach were developed for 2015–2020. New hydrological insights for the regionThe total annual water storage in the Shashe catchment is approximately 44,000 Mm3, dominated by groundwater. The annual storage is about 42,000 Mm3 in aquifers, 1500 Mm3 in soil, 700 Mm3 in large dam reservoirs, 45 Mm3 in small dams/ponds, and 0.13 Mm3 in sand dams. There is high seasonality in water storage availability. Soil moisture storage is at its maximum from January to March and lowest from July to September. Dam storage is at its maximum from March to May, and the water storage is relatively stable throughout the year. Aquifer storage is relatively stable during the dry seasons compared to other storage options. Optimizing water use considering the seasonal variation in different storage types could improve water availability and climate resilience.

TRENDS AND SPATIAL VARIABILITY OF CLIMATE CHANGE IN NIGERIA’S COASTAL REGION

A major challenge facing the Coastal Region of Nigeria is climate change and climate variability. An assessment of the trends and spatial variability of climate, critical for knowledge-based strategies needed for planning adequate mitigation and adaptation measures for the region, requires urgent attention. This study analysed spatial variability and temporary trends of climate change in the Nigeria Coastal Region .. Gridded monthly climatic data from the Climate Research Unit (CRU 0.5 x 0.5) for two climatic periods ((1956- 1986 and 1987-2016) were obtained. Tests for trend detection and magnitude determination utilized: Mann-Kendal and Linear Regression—Thei-Sen Slope test. Climatic maps for rainfall and temperature from 1956 to 2016 for the coastal regions of Nigeria were developed by ordinary kriging. The results from statistical analyses suggest that there is very high rainfall variability with the coefficient of variability (CV) values ranging between 62.43% and 69.46 % and low-temperature variability with CV values ranging between 4.314% and 6.037%. The annual rainfall and temperature increased at the average rate of 3.047 mm yr-1. and 0.0126 oC yr-1 respectively. In 75% of the region, rainfall was decreasing, while in 25%, it was increasing. The implementation of adaptation and mitigation strategies should be mandatory due to the notable increase in temperature.

Vulnerability of geoheritage sites in South Africa to climate change: Examples from the Eastern Cape Province

Climate change and variability have potential to impact on all types of heritage sites globally, through increasing weathering and erosion of geological and built heritage, changes in biome and ecosystem viability, sea-level rise and coastal flooding, and increased climate hazard events. This study examines the most likely impacts of climate change on different heritage sites in South Africa, with a particular regional focus on sites of potential geoheritage interest in the Eastern Cape Province of the country. The diverse examples described in this study are: (1) Klasies River cave, which is a Middle Stone Age archaeological site; (2) different localities along the coast where faunal footprints are preserved within aeolianite that dates mainly to the last interglacial period; (3) different localities inland where rock art panels are present within caves or rock shelters; and (4) Cape St Francis, which is a coastal foreland where different phases of late Quaternary coastal dunes with archaeological interest are preserved. Results show that many of these very varied sites of geoheritage interest are not protected as National or Provincial Heritage Sites under current legislation, which is weak and inconsistently applied across the country. This means that no specific management plans, under national policy guidelines, are in place for mitigating against the impacts of present and future climate change and variability, either regionally or nationally. These climate changes include: sea-level rise and increased coastal storminess and wave attack; and changes in weathering and erosion regimes as a result of increased temperatures and likely decreased rainfall or higher rainfall variability. A better appreciation of the interconnections that exist between geology and natural and cultural heritage may lead to valuing landscape and site geoheritage more highly at the local, national and international level.

Prospects and status of forecasting monthly mean subregional rainfall during the Indian summer monsoon using the coupled Unified Model

AbstractWhile there is huge demand for regional forecasts, information needed for selection of the most appropriate temporal and spatial scales is not available. The objective of this study is to demonstrate the basis of forecasting monthly mean rainfall over homogeneous regions by analyzing the forecasting skill and source of predictability. Reforecasts generated at the National Center for Medium Range Weather Forecasting (NCMRWF) for the period 1993–2015 using the coupled Unified Model are used in this study. Analysis of the forecasting skill over increasingly large lead times, averaging periods and spatial scales, is carried out to compare the skill at different time‐scales and to highlight the effect of spatial averaging over regions of coherent rainfall characteristics. Analysis of probabilistic forecasts is carried out to further demonstrate the usefulness of monthly mean forecasts. The influence of forcings on rainfall is studied both in model and in observations to understand the model's skill in representing interannual variability of monthly mean rainfall. Multiple regression analyses carried out for rainfall using climate indices as independent variables shows that the extent of forcings can largely explain the high variability of rainfall during the onset and withdrawal phase compared to the peak phase of monsoons. ENSO‐related subsidence is found to influence mainly the southern peninsular region, while tropical sea surface temperatures (SSTs) in the Indian Ocean are found to influence rainfall over northwest and central India by forcing circulation patterns typically associated with circumglobal teleconnections (CGTs) which are strongest during the month of June. Interestingly, the influence of CGTs on rainfall in the northeast is opposite to its influence on other homogeneous regions, which explains the contrast in influence of the North Indian Ocean SSTs on rainfall over the northeast and over All India. The model representation of influence of forcings and strength of teleconnections is better for specific region–month pairs, which is seen to influence the monthly variations in skill of forecasting rainfall over homogeneous regions.

Multi-Method Comparative Analysis of Hydroclimatic Trends and Variability in Dry Creek Catchment, South Australia

AbstractThe trend and variability of hydroclimatic variables over time are apparent in seasonal creeks, especially those located in urbanized areas. Understanding hydro-climatic trends in urban areas is crucial for the sustainable management of water resources and the environment. This study aimed to explore the spatiotemporal variability and trends of hydroclimate variables as well as the potential connection between rainfall and streamflow in Dry Creek catchment, South Australia. The trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Innovative Trend Analysis (ITA) were utilized to examine the monotonic and nonmonotonic trends, respectively, and multiple statistical tests were employed to examine the change points in the hydroclimatic time series. Sen’s slope, Simple Linear Regression (SLR), and ITA were used as alternative approaches to assess the magnitudes of change and overcome the limitations in the underlying assumptions of the various methodologies. The variability in the hydroclimate time series was estimated using several indices, such as the coefficient of variation, seasonality indices, flashiness index, and mean zero flow index. The analyses revealed important findings, notably the high variability of rainfall and streamflow during dry periods. Streamflow displayed greater variability compared to rainfall, with high CV values recorded both seasonally and annually. Furthermore, there was a significant upward trend in seasonal rainfall during winter. Additionally, the maximum and mean temperatures demonstrated a statistically significant increase, which can be attributed to global warming and significant urbanization in the catchment area. Comparative analysis has confirmed that the ITA has superior detection capabilities for nonmonotonic trends, outperforming other methods. It excels at presenting graphical representations that accurately depict trends, effectively differentiating between low, medium, and high values. The strong relationship between rainfall and streamflow demonstrated by the Tanh curve suggests that rainfall is the most reliable predictor of streamflow. The outcomes of this investigation are expected to support local governmental organizations and decision-makers in comprehending the spatial and temporal features of rainfall, as well as its correlation with streamflow. This information will further assist in developing flood and drought mitigation strategies backed by empirical evidence. Graphical Abstract

APSIM-based modeling approach to understand sorghum production environments in Mali

Sorghum production system in the semi-arid region of Africa is characterized by low yields which are generally attributed to high rainfall variability, poor soil fertility, and biotic factors. Production constraints must be well understood and quantified to design effective sorghum-system improvements. This study uses the state-of-the-art in silico methods and focuses on characterizing the sorghum production regions in Mali for drought occurrence and its effects on sorghum productivity. For this purpose, we adapted the APSIM-sorghum module to reproduce two cultivated photoperiod-sensitive sorghum types across a latitude of major sorghum production regions in Western Africa. We used the simulation outputs to characterize drought stress scenarios. We identified three main drought scenarios: (i) no-stress; (ii) early pre-flowering drought stress; and (iii) drought stress onset around flowering. The frequency of drought stress scenarios experienced by the two sorghum types across rainfall zones and soil types differed. As expected, the early pre-flowering and flowering drought stress occurred more frequently in isohyets < 600 mm, for the photoperiod-sensitive, late-flowering sorghum type. In isohyets above 600 mm, the frequency of drought stress was very low for both cultivars. We quantified the consequences of these drought scenarios on grain and biomass productivity. The yields of the highly-photoperiod-sensitive sorghum type were quite stable across the higher rainfall zones > 600 mm, but was affected by the drought stress in the lower rainfall zones < 600 mm. Comparatively, the less photoperiod-sensitive cultivar had notable yield gain in the driest regions < 600 mm. The results suggest that, at least for the tested crop types, drought stress might not be the major constraint to sorghum production in isohyets > 600 mm. The findings from this study provide the entry point for further quantitative testing of the Genotype × Environment × Management options required to optimize sorghum production in Mali.

Spatiotemporal trends and variability of rainfall across agro-ecologies in East Guji Zone, Southeast Ethiopia

Distribution and trends of rainfall reveal spatial and temporal variability that have a paramount effect on the life and livelihood of small-holder farmers. This study aimed to analyze spatial variability and temporal trends of rainfall distribution across the three Agro-Ecological Zones (AEZs) of East Guji. Time series gridded daily rainfall data (1990–2020) were collected from the Ethiopian Meteorological Institution. Different descriptive statistics, trend tests: Man Kendal and Sen’s slope estimator, Inverse Distance Weighted Index and Precipitation Concentration Index (PCI) was used in the study. The finding demonstrated that altitude and rainfall decrease as one advances from the western (highland) to the eastern (lowland) direction in the study area where the highest rainfall was recorded in Solemo (highland) and the least in Negele (lowland).The study showed that as altitude increases annual rainfall also increases and rainfall variability decreases. Similarly the mean length of the growing season declines as one advance from the highlands to the lowlands. The PCI of the lowlands, midlands, and highlands AEZs was 19%, 17%, and 12% respectively. The PCI showed that those highlands had moderately concentrated rainfall but both lowlands, and midlands, had an irregular distribution of rainfall. The Coefficient of Variation (CV) indicated that highland areas had moderate variability in rainfall in all seasons except winter. In contrast, the low and midlands had shown high variability of rainfall (&gt;30%) in all seasons. From a seasonal perspective, both CV and PCI revealed that the winter season showed more variability than others. Moreover, a significant increasing trend of annual rainfall was observed in the highlands AEZs (Bore 15.3mm/year and Solemo14.6mm/year), lowland AEZs (Chembe 10.9mm/year, Dawa 8mm/year and Bitata 7.8mm/year) as well as midland AEZs (Kercha 14.5mm/year) at a significant level of 5%. Therefore, strategies should be designed to use additional water resources for irrigation; and provide short-cycle grown and drought-resistant crops in the rest of the midlands and lowlands AEZs.

Power Data Access Viewer-Based Meteorological Drought Analysis and Rainfall Variability in the Nile River Basin

Meteorological drought poses a frequent challenge in the Nile River basin, yet its comprehensive evaluation across the basin has been hindered by insufficient recorded rainfall data. Common indices like the standard precipitation index, coefficients of variation, and precipitation concentration index serve as pivotal tools in gauging drought severity. This research aimed to assess the meteorological drought status in the Nile River basin by using the Power Data Access Viewer product rainfall data. Bias correction procedures were implemented to refine the monthly rainfall data for Bahirdar, Markos, Nekemt, and Muger stations, resulting in notable improvements in the coefficient of determination (R2) that were increased from 0.74 to 0.93, 0.72 to 0.89, 0.71 to 0.96, and 0.69 to 0.84, respectively. The average spatial distribution of drought in the Nile basin was classified as extremely wet (3.81%), severely wet (9.01%), moderately wet (7.36%), near normal (9.97%), moderately drought (21.20%), severely drought (17.11%), and extremely drought (31.54%). Approximately 10.33% of the Nile River basin was situated in regions characterized by high rainfall variability, while around 21.17% was located in areas with a notably irregular precipitation concentration index. Overall, this study sheds light on the prevailing meteorological drought patterns in the Nile River basin, emphasizing the significance of understanding and managing these phenomena for the sustainable development of the region.

Investigation of the Variability of Dry / Wet Meteorological conditions using the SPEI / SPI Index in the Selo Watershed, Period 1981-2020.

The significant impact of climate change is high variability in rainfall and an increase in extreme rainfall events. The impacts resulted in severe dry/wet conditions. Severe dry conditions can lead to crop failure due to lack of water availability, while severe wet conditions can indicate flooding. This paper aims to investigate the variability of dry/wet meteorological conditions in the Selo watershed. The investigation was carried out using the SPEI and SPI. The results show that wet/dry conditions do not have a clear pattern variability in the long term. Seasonal wet/dry conditions show a changing trend towards wetter conditions in all seasons. Spatially, the changes occur throughout the study area, with the most significant changes occurring in the upstream and downstream parts of the watershed. In the last two decades, wet conditions are more dominant to replace dry conditions that were more prominent in the previous two decades. Other analysis results conclude that there is no influence of air temperature in creating wet/dry conditions in the Selo watershed area. So that the characteristics of wet/dry conditions are very useful for considering the selection of agricultural adaptation strategies in dealing with variability and climate change in the Selo watershed area.

ADOPTION OF SORGHUM NATIONAL INTEGRATED SOLUTIONS TECHNICAL PACKAGE BY RAINFED FARMERS, GEZIRA, SUDAN

The effect of climate change on African agriculture represents a major challenge to continental agricultural development including food security, nutrition and management. In Sudan, the yield of rainfed crops is characterized by high variability in the yield due to the high variability in seasonal rainfall and does not exceed two sacs/feddan because the rainfed farmers were adopted low input rainfed agriculture as a risk management option which led to reduce in the yield per unit of land and water. The Federal Ministry of Agriculture has designed and financed a special programme for the traditional the rainfed sector of the country called Integrated Solutions Programme as the main adaptation strategy for the 2014/ 2020 growing seasons to diversify and increase production and productivity of crops cultivated in traditional rainfed areas in each State of the country such as sorghum, sesame, millet and sunflower. This programme was implemented by the Administration of Agricultural Extension and Technology Transfer in each State in collaboration with Agricultural Research Corporation (ARC) and the Agricultural Bank of Sudan. Field survey was used to collect data from 50 rainfed farmers participated in the programme and an equal number from non- participant farmers from the study area were selected (for comparison) using the simple random sampling technique. A close ended questionnaire was constructed and the personal interview technique was used to administer the questionnaire. The collected data were coded, fed to computer and statistically analyzed using (SPSS), discussed interpreted using descriptive statistics and chi-square test. The results showed that the participant farmers adopted the programme components. The majority of chi-square test results revealed no significant association between farmer`s adoption of the programme components and participation in the programme and also no significant association between farmer`s adoption of the programme components and the selected socio-economic characteristics of the respondent farmers. From this study, it can be concluded that the NISP are very effective agricultural extension policy that can be applied in the rainfed sector of the country, which will help rainfed farmers to increase their income through their participation in various activities of the programme. The study recommends that NISP should become the main national policy specially designed for the agricultural rainfed sector of the country, the needed agricultural inputs should be available at a reasonable price, the curriculum of NISP should be updated and developed regularly to solve the constraints that might face the programme, in-service training should be organized for agricultural extension officers on various aspects related to the rainfed sector, various means for conduction the programme should be available, Further agricultural extension research should be conducted

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Assessing the value of deep reinforcement learning for irrigation scheduling

Due to increasing global water scarcity pressure, researchers, policy makers and industry are looking for innovative solutions to increasing agricultural water productivity. Motivated by recent success within complex decision-making environments, Deep Reinforcement Learning (DRL) is being proposed as a method for optimizing irrigation strategies. Early research has hinted towards increased profits with DRL compared to heuristic approaches such as soil-moisture thresholds or fixed schedules. However, an assessment of the value of DRL for irrigation scheduling that incorporates local climate variability and water-use restrictions has yet to be performed. To address this gap in the literature, we created aquacrop-gym, an open-source Python framework for researchers to train and evaluate customized irrigation strategies within the crop-water model AquaCrop-OSPy. In this analysis, aquacrop-gym was used to quantify the value of DRL in comparison to conventional irrigation scheduling techniques (e.g., optimized soil-moisture heuristic) for maize production in an intensively irrigated region of the central United States. The DRL and heuristic approaches were both trained on 70 years of weather data produced from the weather generator LARS-WG, and evaluated on 30 unseen validation years of generated weather data. Findings from this analysis show that in the presence of high rainfall variability, DRL does not outperform conventional optimized heuristics. However, in the scenario where rainfall is set to zero, DRL approaches achieve higher profits on the unseen validation years. Similarly, DRL approaches also outperform optimized heuristics when severe water-use restrictions are introduced. Our analysis demonstrates that DRL approaches are a promising method of irrigation scheduling, notably in regions where farmers are faced with significant physical or regulatory water scarcity.

Pilgrimage tourism in Uttarakhand Himalaya: Pilgrims’ inflows and trends

This article aims to describe pilgrimage tourism in the Uttarakhand Himalaya: pilgrims’ inflows and trends. Pilgrimages – highlands and valleys are the main centers of spiritual tourism in the Uttarakhand Himalaya. Data on pilgrims’ inflow in the major pilgrimage centers from 2000 to 2018 were gathered from the Uttarakhand Tourism Development Board, Dehradun. The pilgrims from Indian subcontinents and abroad have been visiting the pilgrimage centers for time immemorial. Inflows of pilgrims in the pilgrimage centers vary from the river valley pilgrimages to the highland pilgrimages. Haridwar, a valley pilgrimage, receives the highest inflow of pilgrims, which is more than 50% of the total pilgrims’ inflows. Meanwhile, Yamunotri, a highland pilgrimage has the lowest inflow of pilgrims from within and outside countries. The main reason for the change in the number of pilgrims in these highland and valley pilgrimages was the availability of infrastructural facilities in the form of transportation and accommodation. In terms of the trends of pilgrims’ inflows during the period, it is not uniform. The state of Uttarakhand is highly vulnerable to climate-induced calamities, which are highly catastrophic, and leads to land degradation and roadblocks. The pilgrimage season in the Himalaya falls during the monsoon season when happy rainfall occurs. High rainfall variability has led to changing the number of pilgrims. This study reveals that pilgrimage tourism has immense potential for the economic development of the Uttarakhand Himalaya if suitable tourism infrastructural facilities are provided.

Evaluating climate Change's impact on hydroelectricity in the Zambezi river basin

This study investigates the effects of climate change on energy security in Africa, specifically focusing on the Upper and Lower Zambezi Basin. Data from the Kariba River basin sub-catchments, annual reports, the Climate Data Store, and Teal Tool Earth's country-by-country climate data were analysed through quantitative and qualitative data analysis techniques. The Mann-Kendal Trend Analysis was used to analyse time series and test the significance of changes to the climate. The historical climate and hydrological data analysis showed evidence of a slight increase in average rainfall amounts in the Zambezi River Basin but with high rainfall variability in some areas. Despite droughts increasing in frequency, there is a general increase in hydrological annual average water flow in the Zambezi River at two of the three sample sites. The increased water flow through the region could be attributed to population growth-induced land clearance and the degradation of wetlands in Angola's highlands. Although there is an increase in hydrological water flow into Kariba, there are water shortages for hydroelectricity generation due to increased generation capacity, resulting in increased demand for more water than in previous years. The unsustainable water abstraction to meet growing energy demands contributes to low water levels in the lake. The study recommends energy diversification and new hydroelectricity where the potential exists in the basin to reduce over-reliance on Lake Kariba with care not to disrupt the basin's hydrology and other economic activities. The study results provide insight into the potential effects of climate change on energy security in Africa.

Surface-groundwater interactions in hard rock, water-limited environments, simulated at very fine scale using long time-series observations and hydrotope-modeling concept

Hard rock systems are typically characterized by undulated landscapes, short flow path, dense drainage network, and low aquifer storage. If located in water-limited environments (WLE), then typically have variable rainfall characteristics, frequent floods and droughts, and savannah type of vegetation capable to uptake groundwater. In such environments, surface–groundwater interactions are of primary importance. In this study, surface–groundwater interactions are analyzed focusing on spatiotemporal net recharge dependence on hydrotopes, where a hydrotope is conceptualized as spatial unit (or object), which at a given scale of assessment can be assumed as internally homogeneous (e.g. tree canopy or rock outcrop), but hydro(geo)logically different than the adjacent area. The study was carried out in the small (7.6 ha) Trabadillo study area (TSA) in Spain, representative for hard rocks of WLE, using 3D-GSFLOW integrated hydrological model (IHM) at very fine grid (5x5m), extracted from coarser grid (100x100m) of Sardon 3D-GSFLOW model (Hassan et al., 2014, Journal of Hydrology 517, 390–410). The IHM was calibrated using: (i) 20-year head observation; (ii) 8-year soil moisture observations at 4 profiles, each at different hydrotope and each at 4 different depths; and (iii) MODIS satellite earth observation of evapotranspiration. Despite applying spatially uniform rainfall as forcing, surface–groundwater interactions were highly spatially variable, primarily, because of hydrotope-differences in rainfall interception loss and secondarily, because of differences in transpiration, in subsurface evaporation and in hydrological properties of subsurface. The high temporal variability of rainfall, ranging yearly from 310 to 925 mm, resulted in high temporal variability of all water fluxes, including groundwater fluxes; e.g. gross recharge (Rg) varied annually from 2 to 445 mm, net recharge (Rn), from −50 mm to + 89 mm, while the 20-year mean Rn was close to zero, because Rg was counterbalanced by groundwater exfiltration and less by groundwater evapotranspiration. The high temporal variability of groundwater fluxes and many dry years with largely negative Rn, in combination with low aquifer storage, emphasized TSA dependence on rainfall and its vulnerability to droughts. The hydrotope modeling concept applied at the very fine scale, emphasized also large Rn dependence on land cover type, especially on density and species composition of trees, invisible at coarser scale modeling. As such, the proposed hydrotope modelling approach can be applied as a tool to test whether in a given environment planting (or cutting) trees will result in increase or decrease of water resources.

Atmospheric drivers of rainfall events in the Republic of Djibouti

AbstractThe Republic of Djibouti is a small country in an arid context coupled with a high variability of rainfall that generates flash floods causing severe damage to the population and infrastructure. The mechanisms controlling extreme rainfall events in this part of the Horn of Africa remain poorly understood. In this study, we document the atmospheric circulation patterns associated with such events. To that end, we use rain‐gauge data (a network of 36 stations on the period 2013–2020), satellite‐based rainfall estimates (CHIRPS, IMERG, MSWEP and RFE) and atmospheric reanalyses (ERA5), all at the daily timescale, over their common period 2001–2020. A multivariate Agglomerative Hierarchical Clustering of rainy days in Djibouti (≥10% of grid‐points exceeding 1 mm·day−1, according to all four satellite products) reveal four clusters, which differentiate from each other by the intensity and spatial extent of rainfall. These clusters show a nonhomogeneous seasonal distribution, occurring mainly in the March–April–May (MAM) and July–August–September (JAS) seasons, and more rarely in October–November–December (OND). The atmospheric circulation anomalies associated with the clusters are quite similar and highly season‐dependent. In MAM most clusters display an anomalous trough over the Red Sea, from 700 to 200 hPa. In JAS, an anomalous low over the southern Red Sea drives a thicker than normal monsoon flow at 700 hPa, while upper northerlies prevail at 200 hPa. In OND, most rainy events result from moisture advection from the Western Indian Ocean, favoured by positive phases of the Indian Ocean Dipole. Some highly unusual atmospheric circulation patterns (e.g., associated with tropical cyclones) can also result in intense rainfall events in Djibouti. These findings provide new insights on the physical causes of extreme events in the Horn of Africa, with applications to improved early warning and skill evaluation of climate models for climate change projections.

Spatiotemporal climate and vegetation trends, and their relationship: A case of Genale Dawa basin, Ethiopia

Understanding the variability of climate and vegetation, and their relationship in space and time are crucial for agricultural planning, flood risk assessment, and drought monitoring. However, most studies depend on scarce and unevenly distributed climate station datasets in most basins of Ethiopia like the Genale Dawa basin. Detailed climate and vegetation variabilities and their relationship have not well studied in the Genale Dawa basin, which is often affected by recurrent drought. Satellite remote sensing-derived high spatial resolution climate and vegetation data can provide detailed information on climate variability and vegetation changes. This study assessed spatiotemporal variability of temperature, rainfall, and vegetation greenness and their relationship in the Genale Dawa basin. For this study, temperature (minimum and maximum), rainfall data (4 × 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (250 × 250 m) were used. Statistical indices like the Mann Kendall (MK) trend test, Coefficient of Variation (CV), Standard Anomaly Index (SAI), Precipitations Concentration Index (PCI), and Pearson correlation techniques were used to examine the trends and variabilities. The result generally indicated increasing trends in rainfall during the dry (“Bega”) season, and maximum and minimum temperatures during Kiremt (Wet season) and annual seasons. Moreover, high rainfall variability in the Dry and Kiremt seasons and low variability of temperature at all timescales were observed in the basin. In addition, anomalies in temperatures (minimum and maximum) and rainfall were observed in the basin during all the timescales. The result further showed a strong correlation of NDVI with rainfall at annual (r = 0.58), dry season (r = 0.9), and the short rainy season (r = 0.7) while weakly correlated at Kiremt (r = 0.31) seasons in the Genale Dawa basin. The result generally showed spatial and temporal variability of climate and vegetation greenness in the Genale Dawa basin, which negatively affects agricultural production, water resource availability, and vegetation growth. Therefore, appropriate management strategies should be implemented to minimize the negative effects.

Assessing Spatio-temporal Variations and Trends of Rainfall over Somalia from 1991 to 2020

This study aims to examine the spatiotemporal variability and rainfall trends over Somalia using the Mann-Kendall (MK) test and Sen’s slope estimator to investigate the trend and magnitude of the rainfall changes and the arithmetic mean and coefficient of variation (CV) to examine the rainfall variability. To achieve this aim, the gridded monthly satellite rainfall datasets from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) for the period between 1991 and 2020 were used. Results show that annual rainfall generally exhibits bimodal rainfall distribution, March to May (Gu) and September to November (Deyr), though southern coastal regions receive extended Gu rains into July and northwestern regions experience early onset of Deyr rains. The study further showed that a low and moderate degree of inter-annual variability (CV&lt;30%) is observed in most parts of the country, while on a seasonal basis, the short rainy season (Deyr) has a higher rainfall variability (CV&gt;30%) compared to the primary rainy season (Gu) and most regions in the south receiving highest amounts of rainfall experience highest variability (CV&gt;50%) during Deyr season. Furthermore, the trend analysis of the annual rainfall showed a non-significant increasing trend. On a seasonal basis, the trends of Gu rainfall decreased in some regions while trends for the Deyr season increased in most regions. However, rainfall trends observed were not statistically significant throughout the country at a significance level of 0.05.