The Role of Rainfall Temporal and Spatial Averaging in Seasonal Simulations of the Terrestrial Water Balance

Abstract

The partitioning of rainfall into surface runoff and infiltration influences many other aspects of the hydrologic cycle including evapotranspiration, deep drainage and soil moisture. This partitioning is an instantaneous non-linear process that is strongly dependent on rainfall rate, soil moisture and soil hydraulic properties. Though all rainfall datasets involve some degree of spatial or temporal averaging, it is not understood how this averaging affects simulated partitioning and the land surface water balance across a wide range of soil and climate types. We used a one-dimensional physics-based model of the near-surface unsaturated zone to compare the effects of different rainfall discretization (5-min point-scale; hourly point-scale; hourly 0.125 gridded) on the simulated partitioning of rainfall for many locations across the United States. Coarser temporal resolution rainfall data underpredicted seasonal surface runoff for all soil types except those with very high infiltration capacities (i.e., sand, loamy sand). Soils with intermediate infiltration capacities (i.e., loam, sandy loam)were the most affected, with less than half of the expected surface runoff produced in most soil types when the gridded rainfall dataset was used as input. The impact of averaging on the water balance was less extreme but non-negligible, with the hourly point-scale predictions exhibiting median evapotranspiration, drainage and soil moisture values within 10% of those predicted using the higher resolution 5-min rainfall.Water balance impacts were greater using the gridded hourly dataset, with average underpredictions of ET up to 27% in fine-grained soils. The results suggest that "hyperresolution" modelling at continental to global scales may produce inaccurate predictions if there is not parallel effort to produce higher resolution precipitation inputs or sub-grid precipitation parameterizations. K E Y W O R D S error, infiltration, modeling, rainfall, runoff, water balance

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

“…Another issue not covered in this paper but equally important is the temporal scale effect of precipitation, which has been discussed in many previous studies. In these studies, it is also called precipitation time averaging (temporal resolution), or time resampling (sampling error) [29,31,67]. An example is the density scatter plot (see Figure 4 in [64]).…”

“…In another study about urban flood simulation, researchers discussed the response of urban hydrological dynamics to the spatial and temporal resolution of precipitation using radar precipitation data and showed that urban flood processes are more sensitive to spatial resolution compared to temporal resolution [72]. Both spatial averaging and temporal averaging result in a flattening of the flood peak simulations [29,67].…”

EMDNA: an Ensemble Meteorological Dataset for North America

“…Station temperature measurements also contain errors due to microclimate and sensor design, which is generally small and not discussed here. The undercatch of precipitation is particularly severe in high latitudes and mountains due to the stronger wind and frequent snowfall (Sevruk, 1984;Goodison et al, 1998;Nešpor and Sevruk, 1999;Yang et al, 2005;Scaff et al, 2015;Kochendorfer et al, 2018). For example, underestimation of precipitation could be larger than 100 % in Alaska (Yang et al, 1998).…”

“…The uncertainty in spatial meteorological estimates depends on both the measurements available and the climate of the region of study. Whilst meteorological stations provide the most reliable observations at the point scale, spatial meteorological estimates based on station data can be uncertain because of both sparse station networks in remote regions and because of measurement errors caused by factors such as evaporation or wetting loss and undercatch of precipitation (Sevruk, 1984;Goodison et al, 1998;Nešpor and Sevruk, 1999;Yang et al, 2005;Scaff et al, 2015;Kochendorfer et al, 2018). Interpolating station data to a regular grid can introduce additional uncertainties, especially in regions where there are strong spatial gradients in meteorological fields.…”

A multistate first-order Markov model for modeling time distribution of extreme rainfall events

“…Independent rainfall events possess multiple properties such as total rainfall depth (R), duration (D), peak intensity (I), and time distribution during the occurrence. The results of rainfallrunoff models are usually affected by all these properties of extremes, the association between them (Azarnivand et al 2020), and the input timestep of rainfall pulses (Sampson et al 2020). Multivariate frequency analysis based on copulas (Nelsen 2006) is generally adequate to characterize the extreme rainfall properties R, D, and I and the associations among them (Kao and Govindaraju 2007;Zhang and Singh 2007).…”