Improving Spatio-Temporal Rainfall Interpolation Using Remote Sensing CCD Data in a Tropical Basin: A Geostatistical Modeling Approach

Introduction

One of the factors that determine the quantity of rainfall occurrence in a basin is the distribution and nature of rain producing clouds in the region. There is interest in the development of a model to reproduce observed rainfall variation in space and time and its relationship with remote-sensing based rain-producing cloud information at a basin level. This is because it would bring greater understanding of atmosphere–ecosystem interaction in a typical tropical climatic region of Africa.) More interest is developing to investigate the potential for improvement of rainfall interpolation at a basin scale using the time-series of the cold cloud duration (CCD) data (Grimes & Diop 2003). Several studies (Hill & Donald, 2003; Reynolds et al., 2000; Diallo, et al.,1991; Sannier et al., 1998; Kobayashi & Dye, 2005) show that the link between rainfall and vegetation dynamics in-turn advances our understanding of various crop growth dynamics and agricultural production for large areas.

For the hydroinformatics community adequate and accurate information at a catchment or basin level is required to draw on and integrate principles of hydraulics, hydrology, environmental engineering, GIS and remote sensing among others (e.g. Abrahart et al., 2008). Rainfall estimate as one of the main input variables of a hydrologic system, this chapter sees a strong application in solving rainfall-runoff processes as well as in understanding any human-induced and climate-related changes in a basin’s water resources. Accurately estimated areal precipitation through combination of ground based raingauge measurement and remote-sensing based CCD data is an integral part of hydroinfomatics that also provides support for decision making at all levels from governance and policy through management to operation, which is an effective means of water management – a challenges faced by governments to implement integrated water resources management at different levels.

Immediate interest of improving rainfall forecasts at a catchment or basin scales is in flood and flood forecasting applications. Mazzetti & Todini (2009) demonstrate a methodological framework and a tool to improve spatial rainfall estimates by combining weather radar and raingauge data for hydrologic applications especially in improving real-time flood forecasting and flood management. Their approach strongly developed in the MUSIC Project (MUSIC Project, 2009). MUSIC is an acronym for Multi-Sensor Precipitation Measurements Integration, Calibration and Flood Forecasting – a project name that was used to represent a project funded by the European Commission funded project under the 5^th Framework programme (MUSIC Project, 2009) a summary of which is presented in Mazzetti & Todini (2009).

A wider literature exists on alternative techniques that can be used to eliminate the radar bias through combining radar estimates and raingauges measurements (e.g. Seo and Smith, 1991a,b). The Co-Kriging model is widely used with the aim of eliminating the bias in radar-based rainfall estimates (e.g. Krajewski, 1987; Seo et al., 1990a, b). In more recent development, Todini (2001a,b) introduced a new Block Kriging-Bayesian Combination (BKBC) technique based upon the use of block-Kriging and of Kalman filtering to optimally combine, in a Bayesian sense, areal precipitation fields estimated from meteorological radar to point measurements of precipitation, such as the ones provided by a network of rain-gauges (Mazzetti & Todini, 2009). This approach was adopted to develop a full package known as RAIN-MUSIC, which allows combining together raingages, meteorological radar and satellite rainfall estimates at the different spatial scales (MUSIC Project, 2009; Mazzetti & Todini, 2009).