On the application of rainfall projections from a convection-permitting climate model to lumped catchment models

Climate change is predicted to increase rainfall intensity in tropical regions. Convection permitting (CP) climate models have been developed to address deficiencies in conventional climate models that use parameterised convection. However, to date, precipitation projections from CP climate models have not been used in conjunction with hydrological models to explore potential impacts of explicit modelling of convective rainfall on river flows in the tropics. Here we apply the outputs of a continental scale CP climate model as inputs to lumped rainfall-runoff models in Africa for the first time. Applied to five catchments in the Lake Victoria Basin, we show that the CP climate model produces greater river flows than an equivalent model using parameterised convection in both the current and future (c. 2100) climate. However, the location of the catchments near to Lake Victoria results in limited changes in extreme rainfall and river flows relative to changes in mean rainfall and river flows. Application of CP model rainfall data from an area where rainfall extremes change more than the change in mean rainfall to the rainfall-runoff model does not result in significant changes in river flows. Instead, this is shown to be a result of the rainfall-runoff model structure and parameterisation, which we posit is due to large-scale storage in the catchments associated with wetland cover, that buffers the impact of rainfall extremes. Based on an assessment of hydrological attributes (wetland coverage, water table depth, topography, precipitation, evapotranspiration and river flow) using global-scale datasets for the catchments in this research, this buffering may be extensive across humid regions. Application of CP climate model data to lumped catchment models in these areas are unlikely to result in significant increases in extreme river flows relative to increases in mean flows

Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa

Groundwater in sub-Saharan Africa supports livelihoods and poverty alleviation, maintains vital ecosystems, and strongly influences terrestrial water and energy budgets. Yet the hydrological processes that govern groundwater recharge and sustainability—and their sensitivity to climatic variability—are poorly constrained. Given the absence of firm observational constraints, it remains to be seen whether model-based projections of decreased water resources in dry parts of the region are justified. Here we show, through analysis of multidecadal groundwater hydrographs across sub-Saharan Africa, that levels of aridity dictate the predominant recharge processes, whereas local hydrogeology influences the type and sensitivity of precipitation–recharge relationships. Recharge in some humid locations varies by as little as five per cent (by coefficient of variation) across a wide range of annual precipitation values. Other regions, by contrast, show roughly linear precipitation–recharge relationships, with precipitation thresholds (of roughly ten millimetres or less per day) governing the initiation of recharge. These thresholds tend to rise as aridity increases, and recharge in drylands is more episodic and increasingly dominated by focused recharge through losses from ephemeral overland flows. Extreme annual recharge is commonly associated with intense rainfall and flooding events, themselves often driven by large-scale climate controls. Intense precipitation, even during years of lower overall precipitation, produces some of the largest years of recharge in some dry subtropical locations. Our results therefore challenge the ‘high certainty’ consensus regarding decreasing water resources in such regions of sub-Saharan Africa. The potential resilience of groundwater to climate variability in many areas that is revealed by these precipitation–recharge relationships is essential for informing reliable predictions of climate-change impacts and adaptation strategies

Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls within Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions

Supplementary information files for Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

Supplementary information files for article Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls within Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions. </p