Stochastic simulation of rainfall in the semi‐arid Limpopo basin, Botswana

Abstract

ABSTRACT:The application of spatial-temporal stochastic rainfall models to semi-arid or arid areas is expected to be particularly challenging because of the high variability of rainfall, sparse rain gauge networks with significant periods of missing rainfall and potential data quality issues. In this article, a generalized linear model (GLM) has been fitted to daily rainfall data from the period 1975-1999 for 13 gauges in a 7660 km 2 sub-basin of the Limpopo basin in Botswana, with the objective of exploring applicability of the GLM for infilling historic records and for climate change analysis. Several relevant statistics of rainfall space-time variability were used to analyse model performance, including use of an independent validation period and sites that were not used in the fitting. The GLM was considered to simulate rainfall adequately for the purpose of sub-basin-scale water resource studies, although the model uncertainty is high. The main factors affecting rainfall space-time variability were found to be seasonality, autocorrelation of daily rainfall, altitude, latitude and longitude. Addition of large-scale drivers of rainfall (pressure, humidity and temperature) further improved representation of inter-annual variability, and this link to large-scale climate potentially facilitates downscaling of global climate model outputs. Although the model was locally sensitive to data quality issues, there was no evidence that these issues affected sub-basin scale analysis.

An analysis of mid‐summer rainfall occurrence in eastern China and its relationship with large‐scale warming using generalized linear models

“…Chandler and Wheater, 2002;Yan et al, 2002;Chandler, 2005;Fealy and Sweeney, 2007;Furrer and Katz, 2007;Kenabatho et al, 2009). The modelling here is simpler than in some of the other papers cited (and hence perhaps more accessible to the climatological community in general), because we fit separate models at each site rather than attempting to find a single model for all sites.…”

Analysis of rainfall and large-scale predictors using a stochastic model and artificial neural network for hydrological applications in southern Africa

“…Many investigators have made attempts to develop and understand the processes of rainfall occurrence and amounts using multi-sites stochastic A c c e p t e d M a n u s c r i p t rainfall models of different configurations and complexities in the past few decades (see Charles et al 1999, Chandler and Wheater 2002,Mehrotra and Sharma 2007,Burton et al 2010. The structural configurations of these models, their merits and limitations, as well as practical applications to semi-arid rainfall modelling are discussed in detail by Kigobe et al (2011) and Kenabatho et al (2012a). For most semi-arid areas with data limitations, particularly the presence of missing values in rainfall series, the generalized linear model (GLM) framework is more attractive than most of these methods since the missing values can be infilled by determining the distribution of the missing values conditional on the observed data .…”

“…Other advantages include the possibility of including rainfall predictors to represent the effects of large-scale climate variables such as El Niño Southern Oscillations (ENSO). The main limitations of the GLMs and how those could be overcome are documented in Kenabatho et al (2012a) and the references therein.…”

“…Recent studies include their applications for modelling drought indices under climate change scenarios (Chun et al 2013),application in semi-arid areas for identifying rainfall predictors associated with inter annual variability of rainfall series in Botswana catchment as well as for climate change assessment (Kenabatho et al 2012a(Kenabatho et al , 2012b,for agricultural impact assessment in South African catchments (Ambrosino et al 2013), in tropical African climate with high rainfall and bi-modal rainfall seasons in Ugandan catchments (Kigobe et al 2011), and more recently for downscaling General Circulation Model's rainfall projections in a South Australian catchment (Beecham et al 2014).In these applications, the GLMs were used for simulating daily rainfall at multiple sites based on site specific rainfall predictors (covariates) such as previous days' rainfall, or external predictors such as ENSO, temperature or sea-level pressure, among others.…”

A rainfall model for drought risk analysis in south-east UK

“…Downscaling tools provide a simulation capability at more applicable scales (e.g. Kenabatho et al, 2011;Kigobe et al, 2011). For example, the weather generator associated with UKCP09 (Jones et al, 2009) can be used to generate scenarios of extreme rainfall on a 5 km grid scale.…”