A spatial regression analysis of the influence of topography on monthly rainfall in East Africa

Abstract

Precipitation in Kenya is highly variable and dominated by a variety of physical processes. Statistical studies of climate patterns have historically focused on application of ordinary least squares (OLS) regression to test hypotheses related to multiple predictive variables, perhaps in an attempt to better understand the physical mechanisms that drive precipitation, or on use of spatially explicit models, typically kriging-or spline-based analyses, for the purpose of improving predictions. Each of these approaches may be individually useful; however, they all possess limitations. OLS approaches have yielded biased results in the presence of spatially autocorrelated data. Kriging-and spline-based studies often focus on providing improved predictions rather than understanding. Here we use spatial regression, a method not commonly used in analysis of climate data, to assess the role of predictive variables while explicitly incorporating spatial autocorrelation in parameter estimation and hypothesis testing. This approach can yield a better understanding of relationships between precipitation and multiple predictive variables with improved statistical rigour. Using spatial regression, we show that topographic variables such as elevation and slope strongly influence rainfall during the 'long rains' and 'short rains', which are vital for Kenyan agriculture. Outside these seasons, we find that smaller (mesoscale) variations in elevation are statistically significant. Further, we demonstrate the shortcomings of automated selection procedures such as stepwise regression through the identification of spurious results due to confounding.

Analysis of Precipitation Extremes in the Qinghai-Tibetan Plateau, China: Spatio-Temporal Characteristics and Topography Effects

“…Koinrad, II [60] also found that elevation is strongly associated with light precipitation events. Hession and Moore [61] found that rainfall was strongly influenced by elevation and slope during the 'long rains' and 'short rains'. In this study, elevation was not found to be a significant effect on the change of extreme precipitation except for PRCPTOT, which was similar to the above studies.…”

Uso de modelos de regresión para interpolar espacialmente la precipitación media mensual en la cuenca del río Conchos

“…Asimismo y con el propósito de determinar si la σ 2 analytically expressed as ε~iidN(0, σ 2 ). To verify these assumptions were respectively applied the following tests Durbin-Watson statistics and Kolmogorov-Smirnov widely described by Marquínez et al (2003), while the homogeneity of variance was evaluated graphically, verifying the absence of any systematic pattern or trend between errors and model predictions as suggested Hession and Moore (2011).…”

“…The method allows to include all independent variables in a single step and subsequent steps discard variables that do not meet the expected level of statistical significance (p <.05). Every time a variable is removed from the function, the model is adjusted and the value of R 2 resulting overall variance explained, further details of the process are described by Marquínez et al (2003) and Hession and Moore (2011).…”

Analysis of Climate and Topography Impacts on the Spatial Distribution of Vegetation in the Virunga Volcanoes Massif of East-Central Africa

“…Topographic attributes such as elevation and aspect have been revealed to have a great influence on the patterns and distribution of vegetation in mountainous areas [10,11]. Therefore, it is of great importance to evaluate the role of climate and topography in shaping the spatial distribution of vegetation at high altitudes, with respect to different vegetation cover types [12].…”