Analysis of rain-shadows in the Ethiopian Mountains using climatological model data

Abstract

Orography is known to affect local meteorological conditions by inducing orographic rainfall and a rain shadow i.e. reduced rainfall on the mountain's leeside with respect to the windward side. Therefore it has a strong effect on the local population and agriculture. Recent work highlights the ambiguities in the definition and difficulties in quantification of the rain shadow effect using observational data. A statistical approach is presented that allows its investigation based on climatological model data in geographically complex regions. This approach requires gridded rainfall and wind along with the model topography. The statistical aspects that contribute to the rainfall enhancement at the windward side are disentangled. These include, for windward and leeward events separately: frequency of occurrence, rainfall-event frequency, rainfall depth per event. By spatial aggregation the regional dependence of these statistics are calculated and visualized. The approach is used to characterize summer rain over the Ethiopian Highlands based a 21year long simulation with the regional climate model ALARO-0 at 4 km resolution. There is an overall increased rainfall of 40% for windward events as compared to leeward events, but regionally this can exceed 150%. This increase can be attributed to the positive differences between windward and leeward events in their frequency of occurrence (on average 20%), and, in the rainfall per rainfall event (on average 16%). Mapped rain shadows correspond well to earlier qualitative observations and the small differences in probability of precipitation confirm that the mechanisms underlying the shadow effect are more complex than the textbook explanation.

Understanding the Present-Day Spatiotemporal Variability of Precipitable Water Vapor over Ethiopia: A Comparative Study between ERA5 and GPS

“…While ERA5 is much closer to observations than its predecessor, the horizontal resolution of 0.25°x0.25° can still be considered too coarse for assessing the PWV variability over Ethiopia. Additionally, in order to obtain reliable climate projections, especially for extreme precipitation, climate simulations at convection-permitting resolutions are necessary [21,67]. In this study, in which we wanted to assess the present-day representation of PWV by GPS and ERA5, we are spatially limited by the coverage of the GPS network in Ethiopia, which are mainly "point" observations.…”

Land use and climate change effects on water yield from East African forested water towers

“…Moreover, the lack of deviations in the two water towers may indicate the resilience of forested regions (i.e., adaptable nature of forests), as described in Van der Velde et al (2014), Creed et al (2014), and Helman et al (2017. Such resilience (measured as elasticity) could be a key factor in forested water towers, indicating their ability to resist change or bounce back to their initial natural conditions, hence plotting along the reference Budyko curve.…”

“…Du et al (2016) demonstrated that the basins in the upper slopes met steady-state conditions, while this was not the case for downstream basins due to human interference and water inflows from the upstream basins. In their study, Van der Velde et al (2014) demonstrated that the climate and land use changes do not outpace the ability of the forests to adapt their water use and energy use strategies to the prevailing conditions. Han et al (2020) studied 1057 global unimpaired catchments and showed that over 70 % of the catchments attain a steady-state within 10 years, with co-evolution between climate and vegetation coverage playing a key role in maintaining the steady-state conditions.…”

“…where P is precipitation, ET is actual evapotranspiration, and Q is water yield. The steady-state assumption of the Budyko curve has been challenged in several studies (Van der Velde et al, 2014;Donohue et al, 2007;Mianabadi et al, 2020;Du et al, 2016). Most of these studies have, however, challenged the steady-state assumption for studies focused on catchments in the lower slopes at finer temporal or spatial scales and the unreliability of the Budyko framework to predict future water cycles.…”

Evaluating Probability Distribution Functions for the Standardized Precipitation Evapotranspiration Index over Ethiopia

“…The precipitation has two main seasonal variations, where August to September are the wettest months, while December to April are the driest months. The spatiotemporal rainfall patterns are highly inhomogeneous due to the complex orography and convective nature of rainfall [21]. Moreover, the decadal variability of Ethiopian rainfall is known to be significant [22,23].…”