Performance of the WRF model to simulate the seasonal and interannual variability of hydrometeorological variables in East Africa: a case study for the Tana River basin in Kenya

Abstract

This study investigates the ability of the regional climate model Weather Research and Forecasting (WRF) in simulating the seasonal and interannual variability of hydrometeorological variables in the Tana River basin (TRB) in Kenya, East Africa. The impact of two different land use classifications, i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Geological Survey (USGS) at two horizontal resolutions (50 and 25 km) is investigated. Simulated precipitation and temperature for the period 2011-2014 are compared with Tropical Rainfall Measuring Mission (TRMM), Climate Research Unit (CRU), and station data. The ability of Tropical Rainfall Measuring Mission (TRMM) and Climate Research Unit (CRU) data in reproducing in situ observation in the TRB is analyzed. All considered WRF simulations capture well the annual as well as the interannual and spatial distribution of precipitation in the TRB according to station data and the TRMM estimates. Our results demonstrate that the increase of horizontal resolution from 50 to 25 km, together with the use of the MODIS land use classification, significantly improves the precipitation results. In the case of temperature, spatial patterns and seasonal cycle are well reproduced, although there is a systematic cold bias with respect to both station and CRU data. Our results contribute to the identification of suitable and regionally adapted regional climate models (RCMs) for East Africa.

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

“…This could be attributed to the coverage of the gauge rainfall, which comes from only three stations and which is not fully representative for the whole subcatchment unlike CHIRPS and TRMM data, which takes averaged values over the whole subcatchment. In general, however, the gauge rainfall and TRMM over MSS agree very closely in terms of their annual and interannual variability consistent with previous studies (e.g., Kerandi et al 2016). This is also the case with CHIRPS datasets compared with the gauge rainfall (r (44) = 0.92, p < 0.001).…”

“…1), like most parts of East Africa, receives its rainfall in two seasons during March, April, and May (MAM) and October, November, and December (OND) locally known as the Blong rains^and Bshort rains,^respectively, due to the south-north oscillation of the Intertropical Convergence Zone (ITCZ) (Kitheka et al 2005;Nakaegawa and Wachana 2012;Oludhe et al 2013). The mean annual rainfall ranges between 960 and 1200 mm, while climatologically, the region experiences low annual/monthly mean temperatures of about 17°C or less (Kerandi et al 2016). According to the Moderate Resolution Imaging Spectroradiometer (MODIS, 20 classes; Friedl et al 2002) based land use classification, the dominant land use classes are the evergreen broadleaf forest and the savannas and woody savannas (Fig.…”

“…Details of the WRF physics schemes and experimental details for this study are shown in Table 1 and explained in more details by Kerandi et al (2016).…”

Spatial variations and trends in AOD climatology over East Africa during 2002–2016: a comparative study using three satellite data sets

“…In the present study, the negative trends observed during pre‐2009 (though insignificant) interprets that EA experienced low AOD attributed to lower population (by then), characterized by reduced anthropogenic activities, shrunken economic growth and sparse industries (Makokha et al , ; Boiyo et al , ). In contrast, increasing AOD trends during the entire period and post‐2009 indicates an increase in the aerosol loading attributed to: (1) population growth leading to increased anthropogenic activities (Gatebe et al , ; Ngaina and Muthama, ), (2) industrialization resulting to increased anthropogenic emissions (Mabasi, ; Ngo et al , ) and (3) climate change occasioned by increased temperature and reduced precipitation (Yang et al , ; Kerandi et al , ). These results in increased photochemical processes with reduced deposition and increased emission of dust aerosols from the arid and semi‐arid regions of EA.…”

“…The monthly averaged temperature, wind speed and relative humidity at 850 hPa and spatial resolution of 0.75 × 0.75° were sourced from the European Centre for Medium‐Range Weather Forecasts (ECMWF), previously used by a number of authors over EA (Yang et al ., ; Ogwang et al ., ). However, the Climate Research Unit (CRU) temperature (Harris et al ., ) and Tropical Rainfall Measuring Mission (TRMM) (Huffman et al ., ) precipitation data sets derived at spatial resolutions of 0.5 × 0.5° and 0.25 × 0.25°, respectively, were preferred due to their better performance over the region (Kerandi et al ., ; Makokha et al ., ).…”