Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

Abstract

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.

Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response

“…Therefore, rainfall fields at high spatial and temporal resolutions which are suitable to capture rainfall convective features (i.e. 1 km and 10 min, or finer) are desirable for hydrological and geomorphological climate change impact studies (Coulthard and Skinner, 2016;Gires et al, 2015;Li and Fang, 2016;Morin et al, 2006;Ochoa-Rodriguez et al, 2015;Peleg et al, 2015;Skinner et al, 2020;Zhu et al, 2018).…”

WegenerNet high-resolution weather and climate data from 2007 to 2020

“…Related scientific output has grown over time, as illustrated by the fact that 11 of 22 scientific journal papers collected as "WegenerNet-related publications" were published in 2018 and 2019. They cover a wide range of topics, including works with a focus on high-resolution rainfall variability and heavy precipitation (Hiebl and Frei, 2018;O et al, 2018;Frei and Isotta, 2019;O and Foelsche, 2019), works with a focus on temperature variability and change (Kabas et al, 2011a;Kann et al, 2011;Krähenmann et al, 2011), works with a focus on wind field data and dynamic modeling (Schlager et al, 2017(Schlager et al, , 2018(Schlager et al, , 2019, works evaluating precipitation data from radar or satellite measurements (Kann et al, 2015;O et al, 2017;Tan et al, 2018;Lasser et al, 2019;Hu et al, 2020), works with a focus on hydrological modeling of high-and low-flow extremes (Hohmann et al, 2018(Hohmann et al, , 2020, and works with a focus on ecosystem research (Denk and Berg, 2014). Some of them are directly linked to aspects of the WegenerNet data processing: Schlager et al (2017Schlager et al ( , 2018Schlager et al ( , 2019 focus on the development and evaluation of high-resolution wind fields for both WegenerNet regions, and O et al (2018) address the validation and correction of rainfall data from the WegenerNet FBR.…”

Small Catchment Runoff Sensitivity to Station Density and Spatial Interpolation: Hydrological Modeling of Heavy Rainfall Using a Dense Rain Gauge Network

“…Convective storm cells with large volumes of precipitation can easily trigger hazards, but the limited spatial and temporal extent of these cells is associated with huge levels of measurement uncertainty [10]. In addition to the measurement uncertainty of rain gauges, considerable uncertainty can arise when point-level measurements are spatially interpolated to obtain final gridded products [10][11][12][13]. Such gridded datasets are crucial in that they allow researchers to collect areal precipitation information within catchment and subcatchment areas, which can then especially be used in spatially distributed hydrological models.…”

“…When using station data as rainfall input for hydrological models, the spatial interpolation schemes must also be considered. Many different interpolation options and possibilities have been broadly studied [28][29][30] including arithmetic mean [12,25,31], Thiessen polygons (TP) [6,26,32], inverse distance weighting (IDW) [13,[33][34][35], and different types of kriging, such as ordinary kriging [8,33,35] or external drift kriging [5,27,33]. The differences between the interpolation schemes are especially pronounced when extreme values are included [30].…”