Use of Remotely Sensed Actual Evapotranspiration to Improve Rainfall–Runoff Modeling in Southeast Australia

Abstract

This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined with the Penman-Monteith equation [remote sensing evapotranspiration (E RS )] in a lumped conceptual daily rainfall-runoff model. The model is a simplified version of the HYDROLOG (SIMHYD) model, which is used to estimate runoff in ungauged catchments. Two applications were explored: (i) the calibration of SIMHYD against both the observed streamflow and E RS , and (ii) the modification of SIMHYD to use MODIS LAI data directly. Data from 2001 to 2005 from 120 catchments in southeast Australia were used for the study. To assess the modeling results for ungauged catchments, optimized parameter values from the geographically nearest gauged catchment were used to model runoff in the ungauged catchment. The results indicate that the SIMHYD calibration against both the observed streamflow and E RS produced better simulations of daily and monthly runoff in ungauged catchments compared to the SIMHYD calibration against only the observed streamflow data, despite the modeling results being assessed solely against the observed streamflow data. The runoff simulations were even better for the modified SIMHYD model that used the MODIS LAI directly. It is likely that the use of other remotely sensed data (such as soil moisture) and smarter modification of rainfall-runoff models to use remotely sensed data directly can further improve the prediction of runoff in ungauged catchments.

Long‐term trends in evapotranspiration and runoff over the drainage basins of the Gulf of Mexico during 1901–2008

“…Zhang et al 2009 have indicated that calibrations both on ET and streamflow can significant improve the model 0 s capabilities in simulating regional water fluxes and streamflow simulations, particularly over ungauged basins. To enhance DLEM 0 s performance in seasonal streamflow, parameters related to water routing processes, such as the stream flow rate and residential times of lakes, surface and ground water reservoirs, and runoff processes 1901-1978 1979-2008 1901-2008 b 1901-1978 1979-2008 1901-2008 b 1901-1978 1979-2008 1901-2008 1901-1978 and 1979-2008; c The effects of P and T are calculated as the differences in long-term trends between base simulations (i.e., all environmental factors) and experiment of FixP and FixT, respectively.…”

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

“…(v) discharge estimation (Callow and Boggs, 2013;Frappart et al, 2005;Kouraev et al, 2004;Smith et al, 1996;Zhang et al, 2004); (vi) evapotranspiration estimation (Donohue et al, 2010;Glenn et al, 2011;Mohamed et al, 2004;Smettem et al, 2013;Zhang et al, 2009);…”

Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags

“…Remotely sensed soil moisture [Crow and Ryu, 2009;Parajka et al, 2006;Pauwels et al, 2001] and evapotranspiration (or latent heat flux) [Schuurmans et al, 2003;Zhang et al, 2009] can be assimilated into rainfall-runoff models, while remotely sensed snow data Nagler et al, 2008;Slater and Clark, 2006] and brightness temperature [DeChant and Moradkhani, 2011] are used where snowmelt-runoff process is dominant. However, as Pauwels and De Lannoy [2009] pointed out, assimilating remotely sensed data involves converting the remotely sensed signal into state variables either offline or via a built-in observation operator, which is relatively complicated.…”