Investigating the spatial scaling effect of the non-linear hydrological response to precipitation forcing in a physically based land surface model

Abstract

Precipitation is the most important component and critical to the study of water and energy cycle. In this study we investigated the propagation of precipitation retrieval uncertainty in the simulation of hydrological variables, such as soil moisture, temperature, runoff, and fluxes, for varying spatial resolution on different vegetation cover. Two remotely sensed rain retrievals were explored (one based on satellite IR-only data and the other one based on ground radar data) and three spatial grid resolutions: 0.25°, 0.5° and 1.0°. This investigation was facilitated by an offline Community Land Model (CLM) which is forced by in situ meteorological data from Oklahoma Mesonet and high-resolution (0.1 o /hourly) rain gauge-calibrated WSR-88D radar (Nexrad) based precipitation fields. In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution (0.25°, 0.5° and 1°) to determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some information on the uncertainty of hydrological prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach and hopefully it will contribute to water resource redistribution due to climate change in the Korean Peninsula.

Statistical analysis of the impact of radar rainfall uncertainties on water resources modeling

“…Identifying the spatial and temporal correlation in the error structure model is one of the important steps in the proposed methodology. The temporal correlation in ground‐measured point precipitation is typically within the range of several minutes to a few hours [ Gupta and Waymire , 1993; Nelson et al , 2005; Lee and Oh , 2006]. In the current implementation, ARNE provides rainfall estimation at a daily basis.…”

Comparing available rainfall gridded datasets for West Africa and the impact on rainfall-runoff modelling results, the case of Burkina-Faso

“…Such an approach allows the prediction of the depth of runoff from latitude/longitude grids of rainfall, potential evapotranspiration and soil-water capacity. Many publications on this topic have appeared about Southern Africa, among which Hughes (1995) or Lee and Oh (2006), which use different kinds of rainfall-runoff models for water resource assessment purposes. In West Africa rainfall-runoff models are most sensitive to rainfall, and it is therefore important to understand biases of rainfall data before their use in a water resource model (Paturel et al, 2003b), and the effect of these biases on predicted runoff and river flow (Lee and Oh, 2006;Rubarenzya et al, 2007).…”

“…This paper is not about the relationships between rainfall distribution and runoff modelling (others like St-Hilaire et al, 2003;Dong et al, 2005;and Lee and Oh, 2006 have done this) or about how well or not the existing data sets allow reproduction of the 'real'-or most probable -rainfall fields. The purpose of this paper is to evaluate data from three gridded rainfall data sets of monthly rainfall available for Burkina-Faso over the period 1922 to 1998, and to analyse the consequences of each choosing one of them on the simulated river flows in 5 basins across Burkina-Faso.…”