REGIONAL CLIMATE MODEL PERFORMANCE AND PREDICTION oF SEASONAL RAINFALL AND SURFACE TEMPERATURE oF UGANDA

Knowledge about future climate provides valuable insights into how the challenges posed by climate change and variability can be addressed. This study assessed the skill of the United Kingdom (UK) Regional Climate Model (RCM) PRECIS (Providing REgional Climates for Impacts Studies) in simulating rainfall and temperature over Uganda and also assess future impacts of climate when forced by an ensemble of two Global Climate Models (GCMs) for the period 2070-2100. Results show that the models captured fairly well the large scale flow signals influencing rainfall and temperature patterns over Uganda. Rainfall and temperature patterns were better resolved by the RCM than the GCMs. The rainfall and temperature patterns differed among the three seasons. Rainy season March to May (MAM) is likely to experience increment in both surface temperature (0.9\ub0 C) and rainfall (0.2 mm day-1). For September to october (SON) rainy season, an opposite trend in the two climate parameters, temperature and rainfall, will be registered with the former increasing by 0.9 \ub0C and the latter dropping by 0.7 mm day-1. For the dry season, June to August (JJA), both temperature and rainfall are projected to decrease by 0.3 \ub0 C and 0.4 mm day-1, respectively.La connaissance du climat de demain fournit un aper\ue7u sur la mani\ue8re dont les d\ue9fis pos\ue9s peuvent \ueatre adress\ue9s. Cette \ue9tude a \ue9valu\ue9 la comp\ue9tence du Mod\ue8le Climatique R\ue9gional (RCM) PRECIS du Royaume Uni (fournissant des climats r\ue9gionaux pour des \ue9tudes d\u2019impacts) dans la simulation de la pluviom\ue9trie et la temp\ue9rature en ouganda et, d\u2019autre part, \ue9tudier les impacts des climats une fois forc\ue9e par un ensemble de deux Mod\ue8les Climatiques \ue0 l\u2019\ue9chelle de l\u2019Univers (GCMs) pour les p\ue9riodes 2070-2100. Les r\ue9sultats montrent que les mod\ue8les ont raisonnablement saisi une large \ue9chelle du flow des signaux qui influencent la tendance de la pluviom\ue9trie et la temp\ue9rature en ouganda. Les tendances de la pluviom\ue9trie et la temp\ue9rature \ue9taient mieux d\ue9termin\ue9es par RCM que GCMs. Les tendances de la pluviom\ue9trie et la temp\ue9rature diff\ue9raient au cours des trois saisons. La saison pluvieuse Mars \ue0 Mai (MAM) connaitra probablement une augmentation de la temp\ue9rature (0.9 \ub0C) et de la pluviom\ue9trie (0.2 mm jr-1). Pour la saison de pluie de Septembre \ue0 octobre, une tendance contraire dans les deux param\ue8tres climatiques sera enregistr\ue9e avec la m\ueame augmentation de 0.9 \ub0C et une diminution de 0.7 mm jr-1de pluie. Pour la saison s\ue8che de Juin \ue0 Ao\ufbt (JJA), les projections montrent une diminution de la temp\ue9rature et de la pluie de 0.3 \ub0C et 0.4 mm jr-1, respectivement

VEGETATION BIOMASS PREDICTION IN THE CATTLE CORRIDOR OF UGANDA

Pastoralists in Sub-Saharan Africa face complex problems notably frequent and severe droughts. This study was conducted in the cattle corridor of Uganda, a largely semiarid area to estimate the likely vegetative biomass production under the 2O71-2100 projected rainfall conditions. Spatio-temporal pattern of vegetative biomass production were determined by analysis of the seasonal variation of Normalised Difference Vegetation Index (NDVI) for 10 years from 2001-2010. A biomass relationship was established between the NDVI and the Standardised Precipitation Index (SPI); and used to project the period 2071-2100 NDVI using downscaled rainfall for the cattle corridor. A change trajectory performed on the annual means revealed the highest increase in vegetation in 2008 (0.031) and decrease in 2009 (-0.022). The SPI revealed two main droughts that were established to have occurred in the years of 2004 - 2005 and 2008-2009. The wettest year was 2003 and corresponded with the increase in NDVI. A strong positive correlation of rainfall and vegetation was established (r=0.99). Precipitation has influenced vegetative biomass in the cattle corridor as there is a positive correlation between precipitation and the vegetative biomass production. Secondly, vegetation is likely to be concentrated in areas that will have high precipitation in 2070-2100, such as Luwero and the districts south of it of the cattle corridor compared to those in the north of the cattle corridor of Uganda.Les \ue9leveurs en Afrique Sub-saharienne se confrontent aux probl\ue8mes complexes notamment les s\ue9cheresses fr\ue9quentes et plus graves. Cette \ue9tude a \ue9t\ue9 men\ue9e dans le corridor du b\ue9tail de l\u2019ouganda, une r\ue9gion largement semi-aride pour estimer la production susceptible de biomasse v\ue9g\ue9tale sous les conditions pluviom\ue9triques projet\ue9es en 2071-2100. Le mod\ue8le spatio-temporel de production de biomasse v\ue9g\ue9tale a \ue9t\ue9 d\ue9termin\ue9 par l\u2019analyse de la variation saisonni\ue8re de l\u2019Indice de V\ue9g\ue9tation par Diff\ue9rence Normalis\ue9e (NDVI) pendant 10 ans dans l\u2019intervalle de temps 2001-2010. Une relation de biomasse a \ue9t\ue9 \ue9tablie entre l\u2019indice de v\ue9g\ue9tation NDVI et l\u2019indice de pr\ue9cipitations normalis\ue9 (SPI), et elle est utilis\ue9e pour projeter le NDVI de la p\ue9riode 2071-2100 en utilisant les pr\ue9cipitations \ue0 \ue9chelle r\ue9duite pour le corridor du b\ue9tail. Une trajectoire de changement effectu\ue9e sur les moyennes annuelles a r\ue9v\ue9l\ue9 la plus forte augmentation de la v\ue9g\ue9tation en 2008 (0.031) et une diminution en 2009 (-0.022). Le SPI a r\ue9v\ue9l\ue9 deux principales s\ue9cheresses qui ont \ue9t\ue9 \ue9tablies pour avoir eu lieu dans les ann\ue9es 2004 - 2005 et 2008-2009. L\u2019ann\ue9e la plus humide \ue9tait 2OO3 et correspondait \ue0 une augmentation de l\u2019indice de v\ue9g\ue9tation NDVI. Une forte corr\ue9lation positive entre les pr\ue9cipitations et la v\ue9g\ue9tation a \ue9t\ue9 \ue9tablie (r = 0.99). Les pr\ue9cipitations ont influenc\ue9 la biomasse v\ue9g\ue9tale dans le corridor du b\ue9tail, car il existe une corr\ue9lation positive entre les pr\ue9cipitations et la production de la biomasse v\ue9g\ue9tale. Deuxi\ue8mement, la v\ue9g\ue9tation est susceptible d\u2019\ueatre concentr\ue9e dans les zones qui auront de fortes pr\ue9cipitations en 2070-2100, comme Luwero et les districts du Sud de celui-ci du corridor du b\ue9tail par rapport \ue0 ceux dans le nord du corridor du b\ue9tail de l\u2019ouganda

Characteristics and changes in SON rainfall over Uganda (1901-2013)

This study investigated the characteristics and changes in September-November (SON) rainfall over Uganda. The dominant mode of variability of SON rainfall was identified by performing Empirical orthogonal functions (EOF) analysis, using rainfall data from Climate Research Unit (CRU) for the period 1901 to 2013. Results indicate that the dominant mode of variability of SON rainfall exhibits a unimodal pattern, explaining 50.2% of the total variance. Mann-Kendall analysis was deployed to examine sudden changes in SON rainfall over the country. The findings show that the abrupt change in SON rainfall occurred in 1994. Further analysis reveal that SON rainfall over Uganda has a correlation pattern with the sea surface temperature (SST) over Indian, which depicts the positive phase of the Indian Ocean Dipole (IOD). Positive correlation is exhibited in the western IOD subregion, while negative correlation is shown in the southeastern IOD sub-region. Further study of the both driest and wettest years during the investigated time span indicate that throughout the wettest year, there were positive anomalies in the western sub-region, contrary to the driest year, when same subregion observed distinct negative anomalies. This illustrates that the positive phase of IOD enhances SON rainfall over Uganda, as opposed to the negative phase which inhibits SON rainfall. The evolution of the IOD can therefore be monitored for the improvement of SON rainfall forecasts, especially over Uganda so as to avoid the losses associated with weather extremes

REGIONAL CLIMATE MODEL PERFORMANCE AND PREDICTION oF SEASONAL RAINFALL AND SURFACE TEMPERATURE oF UGANDA

Knowledge about future climate provides valuable insights into how the challenges posed by climate change and variability can be addressed. This study assessed the skill of the United Kingdom (UK) Regional Climate Model (RCM) PRECIS (Providing REgional Climates for Impacts Studies) in simulating rainfall and temperature over Uganda and also assess future impacts of climate when forced by an ensemble of two Global Climate Models (GCMs) for the period 2070-2100. Results show that the models captured fairly well the large scale flow signals influencing rainfall and temperature patterns over Uganda. Rainfall and temperature patterns were better resolved by the RCM than the GCMs. The rainfall and temperature patterns differed among the three seasons. Rainy season March to May (MAM) is likely to experience increment in both surface temperature (0.9° C) and rainfall (0.2 mm day-1). For September to october (SON) rainy season, an opposite trend in the two climate parameters, temperature and rainfall, will be registered with the former increasing by 0.9 °C and the latter dropping by 0.7 mm day-1. For the dry season, June to August (JJA), both temperature and rainfall are projected to decrease by 0.3 ° C and 0.4 mm day-1, respectively.La connaissance du climat de demain fournit un aperçu sur la manière dont les défis posés peuvent être adressés. Cette étude a évalué la compétence du Modèle Climatique Régional (RCM) PRECIS du Royaume Uni (fournissant des climats régionaux pour des études d’impacts) dans la simulation de la pluviométrie et la température en ouganda et, d’autre part, étudier les impacts des climats une fois forcée par un ensemble de deux Modèles Climatiques à l’échelle de l’Univers (GCMs) pour les périodes 2070-2100. Les résultats montrent que les modèles ont raisonnablement saisi une large échelle du flow des signaux qui influencent la tendance de la pluviométrie et la température en ouganda. Les tendances de la pluviométrie et la température étaient mieux déterminées par RCM que GCMs. Les tendances de la pluviométrie et la température différaient au cours des trois saisons. La saison pluvieuse Mars à Mai (MAM) connaitra probablement une augmentation de la température (0.9 °C) et de la pluviométrie (0.2 mm jr-1). Pour la saison de pluie de Septembre à octobre, une tendance contraire dans les deux paramètres climatiques sera enregistrée avec la même augmentation de 0.9 °C et une diminution de 0.7 mm jr-1de pluie. Pour la saison sèche de Juin à Août (JJA), les projections montrent une diminution de la température et de la pluie de 0.3 °C et 0.4 mm jr-1, respectivement