Impact of Rain Gauge Quality Control and Interpolation on Streamflow Simulation: An Application to the Warwick Catchment, Australia

Abstract

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash-Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In terms of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2008-31/12/2011) and four gauges during the validation period (01/01/2012-30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness, and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored under different catchment hydrological systems in future studies.

Identification of Rainfall Events and Heavy Rainfall Events From Radar Measurements in Southeastern Australia

“…This poses a major issue when analyzing past heavy rainfall events. Gridded data sets derived from gauge data are also subject to spatial errors originating from the interpolation techniques used (Liu et al., 2018; Tozer et al., 2012).…”

“…While notable work has been done with radar rainfall estimation for flood events (Cusworth, 1997; Hoy, 1975; Liu et al., 2018; Sun et al., 2000) and investigating drop size distributions (Bringi et al., 2009; Thurai et al., 2010) in Australia, there have not yet been any long‐term radar studies that focus on rainfall event characteristics on a national scale, despite such studies existing in other parts of the world. Furthermore, initial identification of extreme rainfall events in many studies both in Australia and overseas is performed using a rain gauge network, before radar data is later used to investigate event attributes in more detail (Hitchcock et al., 2021; Lee & Kim, 2007; Moral et al., 2020; Rigo & Llasat, 2004; Schumacher & Johnson, 2006; L. Zhang et al., 2019).…”

“…A common way to measure rainfall is to use rain gauges (Gourley et al., 2013; Liu et al., 2018). This form of in situ observation is generally based on accumulated measurements over a fixed time period from minutes to days.…”

The Development of a Combined Satellite-Based Precipitation Dataset across Bolivia from 2000 to 2015

“…Prior to the usage of rain gauge data, data pre-processing (i.e., gridding) is required to change its spatial distribution from isolated points to a distributed grid. Many previous studies have attempted to determine the optimal interpolation method to use with point rain gauges [5][6][7]; unfortunately, the rain gauges can be affected by atmospheric effects [8] and the possibility of instrument failure, among other issues. When applying interpolation methods, the number and location of rain gauges in some basins are often insufficient and, in some instances, may be extremely poor.…”

Insights from a new methodology to optimize rain gauge weighting for rainfall-runoff models

“…Ly et al (2013) found no optimal spatial interpolation method for operational hydrology, and that interpolation performance often depended on the density of the gauge network, the spatial and temporal resolution of the data, and parameters such as those used by the semi-variogram in kriging. Mair and Fares (2011); Chen et al (2017), and Liu et al (2018) compared interpolation methods based on their ability to simulate streamflow, with each study using a different hydrological model and catchment.…”