Reducing data and model errors in monthly water balance modeling based on remote sensing data

Abstract

The water balance model has been an indispensable tool for quantifying water supply and demand and regulating water resources for decades. By convention, hydrologists use in-situ measurements of river discharge for model calibration. Whereas hardly is it possible for areas suffering from data storage. Researchers have been working on methods dealing with this problem for many years. Satellite datasets are potential substitutes for the in-situ observation of hydrological variables. This research proposes a monthly strategy using satellite time series as model inputs. This research aims to apply this strategy in the absence of gauged data for discharge simulations and predictions. Instead of streamflow, the strategy uses variables like actual evapotranspiration (ETa) and/or water storage for calibration. The research challenges lie in the situation that there can be significant errors in data derived from satellite products. Also, hydrological models designed for discharge simulation cannot necessarily function well when calibrated on other terms. This boils down to the research questions as follow:
1. How large are water balance data errors and to what extent can they be reduced?
2. How large are water balance model errors and to what extent can they be reduced?
3. To what extent does quantifying and reducing data and model errors eliminate trade-offs in fitting multiple datasets?
The strategy is constructed based on an error estimation and water balance data fusion method and the original and the advanced version of the Water Partition and Balance model (Wapaba), then tested in the Smoky Hill River catchment. When using unprocessed data, the water balance is not closed for the basin. The discharge simulation has the fitting precision index the Box-Cox transformed root mean squared error (TRMSE) in the range of 0.70 - 1.43 for different datasets and in calibration and validation period. Indexes of discharge fitting σma$ exceed 1.66. After closing the water balance with the mean time series of all fluxes, TRMSE decreases to 0.55. Considering data uncertainties, TRMSE is further declined to 0.29 and σ drops to 0.46. After that, the model structure is also improved. When using the modified model to calibrate on only ETa and TWSA for calibration (TRMSE for discharge = 0.87), the performance is similar to that of using the original Wapaba on all fluxes (TRMSE for discharge = 0.86). The fitting precision index σ for TWSA also decreases.

The research demonstrates the effectiveness of the data fusion method in correcting satellite time series and sheds light on the potential of application of this strategy in the ungauged area through the comparison of different calibration cases. After modification, the strategy is able to reproduce the flow regime, without using in-situ data, to the same degree as all three hydrological components (discharge, actual evapotranspiration and water storage) are used for calibration.