A Google Earth-GIS based approach to examine the potential of the current rainwater harvesting practices to meet water demands in Mityana district, Uganda

Rainwater harvesting (RWH) has become an integral part of global efforts to improve water access. Despite the increasing adoption of RWH in Uganda, there remains a significant knowledge gap in the assessment of RWH systems to meet water demands. In this study, a simplified methodology to estimate rainwater harvesting potential (RWHP) as a function of mean seasonal rainfall and rooftop area, generated using Google Earth and GIS tools is applied. Desired tank storage (DTS) capacities based on user population, demand and dry period lengths, were compared with RWHP to assess whether rooftop areas and tank storage can sustainably supply water for use during the March—May (MAM) and September-November (SON) 90-day dry periods, for three demand levels (i.e. for drinking and cooking (15 litres per capita per day (l/c/d)); for drinking, cooking and hand washing (20 l/c/d); and for drinking, cooking, hand washing, bathing and laundry (50 l/c/d)). Our findings document minimum catchment areas of 60m2 to have rainwater harvesting potential that can sustain households for 90-day dry periods for all three demand levels. However, considering their storage capacities, 25%, 48% and 97% of the existing RWHTs (with storage capacities below 8,000, 10,000 and 20,000 litres respectively) are unable to meet the demand of 15 l/c/d, 20 l/c/d and 50 l/c/d respectively for a 90-day dry period. The results document that the existing storage systems are under-sized for estimated water use under 50 l/c/d demand scenarios. Costs of between 2,000,000–4,500,000 Ugandan shillings (~ 600–1, 250 USD) would be needed to increase existing tank capacities to meet the 50 l/c/d demands for a 90-day dry period. These findings document onerous financial costs to achieve rainwater harvesting potential, meaning that households in Mityana district may have to resort to other sources of water during times of shortage

Evaluation of rainfall simulations over Uganda in CORDEX Regional Climate Models

The study evaluates the ability of ten regional climate models (RCMs) to simulate the present day rainfall over Uganda within the Coordinated Regional Downscaling Experiment (CORDEX) for the period 1990-2008. The models’ ability to reproduce the space-time variability of annual, seasonal and interannual rainfall has been diagnosed. A series of metrics have been employed to quantify the RCM-simulated rainfall pattern discrepancies and biases compared to three gridded observational datasets. It is found that most models underestimate the annual rainfall over the country; however, the seasonality of rainfall is properly reproduced by the RCMs with a bimodal component over the major part of the country and unimodal component over the North. Models reproduce the interannual variability of the dry season (December-February) but fail with the long and short rains seasons even if the ENSO signal is correctly simulated by most models. In many aspects, the UQAM-CRCM5 RCM is found to perform best over the region. Overall, the ensemble mean of the 10 RCMs reproduces the rainfall climatology over Uganda with reasonable skill.JRC.E.1-Disaster Risk Managemen

Evaluation of rainfall simulations over Uganda in CORDEX regional climate models.

18 pagesInternational audienceThe study evaluates the ability of ten regional climate models (RCMs) to simulate the present-day rainfall over Uganda within the Coordinated Regional Downscaling Experiment (CORDEX) for the period 1990–2008. The models’ ability to reproduce the space-time variability of annual, seasonal, and interannual rainfall has been diagnosed. A series of metrics have been employed to quantify the RCM-simulated rainfall pattern discrepancies and biases compared to three gridded observational datasets. It is found that most models underestimate the annual rainfall over the country; however, the seasonality of rainfall is properly reproduced by the RCMs with a bimodal component over the major part of the country and a unimodal component over the north. Models reproduce the interannual variability of the dry season (December–February) but fail with the long and short rains seasons even if the ENSO and IOD signal is correctly simulated by most models. In many aspects, the UQAM-CRCM5 RCM is found to perform best over the region. Overall, the ensemble mean of the ten RCMs reproduces the rainfall climatology over Uganda with reasonable skill

Changes in Convective Precipitation Reflectivity over the CONUS Revealed by High-Resolution Radar Observations from 2015 to 2021

The change in extreme precipitation events in the conterminous United States (CONUS) has been of interest to the research communities in recent years for its intensification under environmental and climate change. Previous studies have not yet used sub-hourly precipitation observations to examine convective precipitation change over the CONUS. This study aims to fill the gap by examining convective precipitation, identified by radar reflectivity, in the CONUS using the state-of-the-art Multi-radar Multi-sensor data, operated at the NOAA/National Severe Storms Laboratory, with an unprecedentedly high spatial (1 km) and temporal (2 min) resolutions. These high-resolution data are expected to better capture the precipitation peak and the precipitation pattern. The results showed that in CONUS, precipitation reflectivity increased both in magnitude and the number of convective days from 2015 to 2021. For example, in 2019, 60% of areas showed an increase in the magnitude of precipitation, and the average number of convective days over CONUS has increased by 19%. Changes in precipitation also vary by season and region. This study highlights the need for continued monitoring and understanding of the evolving pattern of extreme precipitation in the CONUS, especially at sub-hourly frequency, as it exposes significant impacts on various sectors, including agriculture, infrastructure, and human health