Abstract

Rainfall data at fine spatial resolutions are often required for various studies in hydrology and water resources. However, such data are not widely available, as their collection is normally expensive and time-consuming. A common practice to obtain fine-spatial-resolution rainfall data is to employ interpolation schemes to derive them based on data available at nearby locations. Such interpolation schemes are generally based on rainfall correlation or distance between stations. The present study proposes a combined rainfall correlation-spatial scale-correlation threshold method for representing spatial rainfall variability. The method is applied to monthly rainfall data at a resolution of 0.25° × 0.25° latitude/longitude across Australia, available from the Tropical Rainfall Measuring Mission (TRMM 3B43 version). The results indicate that rainfall dynamics in northern and northeastern Australia have far greater spatial correlations when compared to the other regions, especially in southern and southeastern Australia, suggesting that tropical climates generally have greater spatial rainfall correlations when compared to temperate, oceanic, and continental climates, subject to other influencing factors. The implications of the outcomes for rainfall data interpolation and the rain gauge monitoring network are also discussed, especially based on results obtained for ten major cities in Australia.

Highlights

  • Rainfall data at fine spatial resolutions are crucial for a variety of studies in hydrology and water resources, including for flow/flood forecasting, soil erosion estimation, and water quality modeling, among others

  • We find that the Australia-wide 0.25◦ × 0.25◦ grid analysis provides some interesting results and interpretations on spatial rainfall variability against scale and correlation threshold

  • Spatial rainfall correlation decreases as the box size increases all across Australia

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Summary

Introduction

Rainfall data at fine spatial (and temporal) resolutions are crucial for a variety of studies in hydrology and water resources, including for flow/flood forecasting, soil erosion estimation, and water quality modeling, among others. (1) more accurate estimation of rainfall at certain scales is possible through merging satellite/radar products and rain gauge measurements [15,16,17,18]; and (2) renewed and fresh insights into theories of complexity, nonlinear dynamics, scaling, and pattern recognition offer new avenues for studying the dynamics of rainfall [10,19,20,21,22] Despite these advances in studying the spatial (and temporal) variability of rainfall, some important issues in the existing approaches still remain.

Methodology
Analysis
Results for Australia-Wide Analysis
Results for of
Spatial rainfall in respective
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