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Gitau W.,University of Nairobi | Camberlin P.,University of Burgundy | Ogallo L.,Climate Prediction and Applications Center | Okoola R.,University of Nairobi
International Journal of Climatology

Despite earlier studies over various parts of the world including equatorial Eastern Africa (EEA) showing that intraseasonal statistics of wet and dry spells have spatially coherent signals and thus greater predictability potential, no attempts have been made to identify the predictors for these intraseasonal statistics. This study therefore attempts to identify the predictors (with a 1-month lead time) for some of the subregional intraseasonal statistics of wet and dry spells (SRISS) which showed the greatest predictability potential during the short rainfall season over EEA. Correlation analysis between the SRISS and seasonal rainfall totals on one hand and the predefined predictors on the other hand were initially computed and those that were significant at 95% confidence levels retained. To identify additional potential predictors, partial correlation analyses were undertaken between SRISS and large-scale oceanic and atmospheric fields while controlling the effects of the predefined predictors retained earlier. Cross-validated multivariate linear regression (MLR) models were finally developed and their residuals assessed for independence and for normal distribution. Four large-scale oceanic and atmospheric predictors with robust physical/dynamical linkages with SRISS were identified for the first time. The cross-validated MLR models for the SRISS of wet spells and seasonal rainfall totals mainly picked two of these predictors around the Bay of Bengal. The two predictors combined accounted for 39.5% of the magnitude of the SST changes between the July-August and October-November-December periods over the Western Pole of the Indian Ocean Dipole, subsequently impacting EEA rainfall. MLR models were defined yielding cross-validated correlations between observed and predicted values of seasonal totals and number of wet days ranging from 0.60 to 0.75, depending on the subregion. MLR models could not be developed over a few of the subregions suggesting that the local factors could have masked the global and regional signals encompassed in the additional potential predictors. © 2014 Royal Meteorological Society. Source

Camberlin P.,CNRS Biogeosciences Laboratory | Gitau W.,University of Nairobi | Oettli P.,University of Tokyo | Ogallo L.,Climate Prediction and Applications Center | Bois B.,CNRS Biogeosciences Laboratory
Climate Research

Downscaling seasonal rainfall predictions to daily time-scale, for crop yield simulation for instance, can be performed using stochastic generators (SGs). The spatial interpolation of the SG parameters is required to generate rainfall time-series at ungauged places. A methodology is defined which makes use of topography to interpolate these parameters, in a region with a rugged terrain covering Kenya and north-eastern Tanzania. A first-order Markov chain was used to model rainfall occurrence, and a gamma distribution was used to model amounts. The 2 para - meters of the Markov models, p01 and p11, and the 2 parameters of the gamma distribution are computed at 121 stations. The Kolmogorov-Smirnov test for goodness-of-fit shows that 88% (99%) of the stations and months have their dry (wet) spell frequencies successfully reproduced by first-order Markov chains, and two-third of the stations have their daily amounts satisfactorily fitted by the gamma distribution. Local regression, using elevation as the predictor and weighting stations according to distance from the target pixel and to environmental variables, is used to interpolate the 4 SG para meters. Cross-validation indicates that distance-weighted regression provides good estimates, but the inclusion of topographical variables (aspect in particular) improves the results further. The final maps show a strong orographic control of both the Markov and gamma parameters. However, while elevation has an effect on rainfall occurrence, rainfall intensity is more strongly related to local slope aspect, with eastward to southeastward oriented foothills and coastlines displaying the highest gamma scale values. These results suggest that a statistical disaggregation of daily rainfall is improved by taking explicitly into account topography through its effect on the spatial distribution of SG parameters. © Inter-Research 2014. Source

Githeko A.K.,Kenya Medical Research Institute | Ogallo L.,Climate Prediction and Applications Center | Lemnge M.,National Institute for Medical Research | Okia M.,Uganda Ministry of Health | Ototo E.N.,Kenya Medical Research Institute
Malaria Journal

Background: Malaria epidemics remain a serious threat to human populations living in the highlands of East Africa where transmission is unstable and climate sensitive. An existing early malaria epidemic prediction model required further development, validations and automation before its wide use and application in the region. The model has a lead-time of two to four months between the detection of the epidemic signal and the evolution of the epidemic. The validated models would be of great use in the early detection and prevention of malaria epidemics. Methods. Confirmed inpatient malaria data were collected from eight sites in Kenya, Tanzania and Uganda for the period 1995-2009. Temperature and rainfall data for the period 1960-2009 were collected from meteorological stations closest to the source of the malaria data. Process-based models were constructed for computing the risk of an epidemic in two general highland ecosystems using temperature and rainfall data. The sensitivity, specificity and positive predictive power were used to validate the models. Results: Depending on the availability and quality of the malaria and meteorological data, the models indicated good functionality at all sites. Only two sites in Kenya had data that met the criteria for the full validation of the models. The additive model was found most suited for the poorly drained U-shaped valley ecosystems while the multiplicative model was most suited for the well-drained V-shaped valley ecosystem. The +18°C model was adaptable to any of the ecosystems and was designed for conditions where climatology data were not available. The additive model scored 100% for sensitivity, specificity and positive predictive power. The multiplicative model had a sensitivity of 75% specificity of 99% and a positive predictive power of 86%. Conclusions: The additive and multiplicative models were validated and were shown to be robust and with high climate-based, early epidemic predictive power. They are designed for use in the common, well- and poorly drained valley ecosystems in the highlands of East Africa. © 2014 Githeko et al.; licensee BioMed Central Ltd. Source

Awange J.L.,Curtin University Australia | Anyah R.,University of Connecticut | Agola N.,Doshisha University | Forootan E.,University of Bonn | Omondi P.,Climate Prediction and Applications Center
Water Resources Research

The changing climatic patterns and increasing human population within the Lake Victoria Basin (LVB), together with overexploitation of water for economic activities call for assessment of water management for the entire basin. This study focused on the analysis of a combination of available in situ climate data, Gravity Recovery And Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM) observations, and high resolution Regional Climate simulations during recent decade(s) to assess the water storage changes within LVB that may be linked to recent climatic variability/changes and anomalies. We employed trend analysis, principal component analysis (PCA), and temporal/spatial correlations to explore the associations and covariability among LVB stored water, rainfall variability, and large-scale forcings associated with El-Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Potential economic impacts of human and climate-induced changes in LVB stored water are also explored. Overall, observed in situ rainfall from lake-shore stations showed a modest increasing trend during the recent decades. The dominant patterns of rainfall data from the TRMM satellite estimates suggest that the spatial and temporal distribution of precipitation have not changed much during the period of 1998-2012 over the basin consistent with in situ observations. However, GRACEderived water storage changes over LVB indicate an average decline of 38.2 mm/yr for 2003-2006, likely due to the extension of the Owen Fall/Nalubale dam, and an increase of 4.5 mm/yr over 2007-2013, likely due to two massive rainfalls in 2006-2007 and 2010-2011. The temporal correlations between rainfall and ENSO/IOD indices during the study period, based on TRMM and model simulations, suggest significant influence of large-scale forcing on LVB rainfall, and thus stored water. The contributions of ENSO and IOD on the amplitude of TRMM-rainfall and GRACE-derived water storage changes, for the period of 2003-2013, are estimated to be ∼2.5 cm and ∼1.5 cm, respectively. © 2013. American Geophysical Union. All Rights Reserved. Source

Forootan E.,University of Bonn | Rietbroek R.,University of Bonn | Kusche J.,University of Bonn | Sharifi M.A.,University of Tehran | And 4 more authors.
Remote Sensing of Environment

Extracting large scale water storage (WS) patterns is essential for understanding the hydrological cycle and improving the water resource management of Iran, a country that is facing challenges of limited water resources. The Gravity Recovery and Climate Experiment (GRACE) mission offers a unique possibility of monitoring total water storage (TWS) changes. An accurate estimation of terrestrial and surface WS changes from GRACE-TWS products, however, requires a proper signal separation procedure. To perform this separation, this study proposes a statistical approach that uses a priori spatial patterns of terrestrial and surface WS changes from a hydrological model and altimetry data. The patterns are then adjusted to GRACE-TWS products using a least squares adjustment (LSA) procedure, thereby making the best use of the available data. For the period of October 2002 to March 2011, monthly GRACE-TWS changes were derived over a broad region encompassing Iran. A priori patterns were derived by decomposing the following auxiliary data into statistically independent components: (i) terrestrial WS change outputs of the Global Land Data Assimilation System (GLDAS); (ii) steric-corrected surface WS changes of the Caspian Sea; (iii) that of the Persian and Oman Gulfs; (iv) WS changes of the Aral Sea; and (v) that of small lakes of the selected region. Finally, the patterns of (i) to (v) were adjusted to GRACE-TWS maps so that their contributions were estimated and GRACE-TWS signals separated. After separation, our results indicated that the annual amplitude of WS changes over the Caspian Sea was 152. mm, 101. mm over both the Persian and Oman Gulfs, and 71. mm for the Aral Sea. Since January 2005, terrestrial WS in most parts of Iran, specifically over the center and northwestern parts, exhibited a mass decrease with an average linear rate of ~. 15. mm/yr. The estimated linear trends of groundwater storage for the drought period of 2005 to March 2011, corresponding to the six main basins of Iran: Khazar, Persian and Oman Gulfs, Urmia, Markazi, Hamoon, and Srakhs were -6.7, -6.1, -11.2, -9.1, -3.1, and -4.2. mm/yr, respectively. The estimated results after separation agree fairly well with 256 in-situ piezometric observations. © 2013 Elsevier Inc. Source

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