On the uncertainties associated with using gridded rainfall data as a proxy for observed

Abstract

Abstract. Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets -the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids -particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

“…This spatially interpolated gridded data may introduce some artificiality and make it difficult to verify the "validity" of the gridded data values in regions or periods with none or sparse rain gauges [22,23]. However, the CGDPA data were used only as a benchmark for the spatial comparison of daily mean precipitation values with satellite products.…”

Links between the Big Dry in Australia and hemispheric multi-decadal climate variability – implications for water resource management

“…Therefore, in order to provide a spatial/geographical context to the recent dry conditions in SEA, and to ensure limitations of gridded rainfall data sets are not skewing our results (see Tozer et al, 2012), station-based rainfall data from regions south of 30 degrees (shown in Fig. 1a) from the following data sets was also used: 1994-2009 compared to 1948-1993), (b) precipitable water (1994-2009 compared to 1948-1993), (c) surface precipitation rate (1976-1993 compared to 1948-1975) and (d) precipitable water (1976-1993 compared to 1948-1975).…”

“…The year 1976 was chosen as the start year of this analysis, as this corresponds to a statistically significant change in ocean/atmosphere processes and a reorganisation of the climate system (Mantua et al, 1997). Tozer et al (2012), and other references reviewed within, demonstrate that gridded rainfall data sets often do not capture extreme events particularly well (due to smoothing during the interpolation process, inadequate representation of topographical effects, uncertainties associated with remotely sensed data, density of available station data that varies in space and time, etc.). Further, as mentioned in Sect.…”

Links between East Coast Lows and the spatial and temporal variability of rainfall along the eastern seaboard of Australia

“…However, this paper focuses only on rainfall patterns and large-scale changes in ECL behaviour so care should be taken, for example, when using the insights here to develop inputs for hydrological modelling as the AWAP dataset has documented limitations and uncertainties, especially at higher elevations and/or in areas or time periods where gauged information is sparse (e.g. Jones et al 2009, Tozer et al 2012, King et al 2013). Figure 2 shows the frequency of occurrence for each of the five different ECL sub-types within the different months of the year.…”

“…To allow easier visualisation of spatial patterns, gridded daily rainfall data from the Australian Water Availability Project (AWAP; Jones et al 2009), a joint initiative of the BOM and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) was also used (refer to Tozer et al 2012 for discussion on why both observed rainfall data, i.e. obtained directly from rainfall stations, and gridded AWAP data should be used).…”