Abstract
Using modelling approaches to predict stream flow from ungauged basins requires new model calibration strategies and evaluation methods that are different from the existing ones. Soil moisture information plays an important role in hydrological applications in basins. Increased availability of remote sensing data presents a significant opportunity to obtain the predictive performance of hydrological models (especially in ungauged basins), but there is still a limit to applying remote sensing soil moisture data directly to models. The Soil Moisture Active Passive (SMAP) satellite mission provides global soil moisture data estimated by assimilating remotely sensed brightness temperature to a land surface model. This study investigates the potential of a hydrological model calibrated using only global root zone soil moisture based on satellite observation when attempting to predict stream flow in ungauged basins. This approach’s advantage is that it is particularly useful for stream flow prediction in ungauged basins since it does not require observed stream flow data to calibrate a model. The modelling experiments were carried out on upstream watersheds of two dams in South Korea with high-quality stream flow data. The resulting model outputs when calibrated using soil moisture data without observed stream flow data are particularly impressive when simulating monthly stream flows upstream of the dams, and daily stream flows also showed a satisfactory level of predictive performance. In particular, the model calibrated using soil moisture data for dry years showed better predictive performance than for wet years. The performance of the model calibrated using soil moisture data was significantly improved under low flow conditions compared to the traditional regionalization approach. Additionally, the overall stream flow was also predicted better. In addition, the uncertainty of the model calibrated using soil moisture was not much different from that of the model calibrated using observed stream flow data, and showed more robust outputs when compared to the traditional regionalization approach. These results prove that the application of the global soil moisture product for predicting stream flows in ungauged basins is promising.
Highlights
Goodness of Fit and Uncertainty calibration schemes E and S for the validation period represented the mean NSE of 0.5407 and 0.5727, and the mean Kling–Guptaefficiency efficiency (KGE) of 0.7262 and 0.7647, respectively. These results show the applicability of Generalized Complementary Relationship (GCR)-based AET or global root zone soil moisture (GRZSM) data for ungauged watersheds
The goodness-of-fit and uncertainty of model outputs, calibrated by applying various schemes, were assessed to confirm the applicability of the global root zone soil moisture data based on satellite observation as calibrating a hydrologic model in ungauged basins
The goodness-of-fit and uncertainty performance of stream flow simulated by the calibration scheme using soil moisture data was much better than those by the calibration schemes using surface flow data or actual evapotranspiration data, which could be obtained indirectly from the ungauged basins
Summary
Prediction of stream flows is an essential part of surface hydrology and is often carried out, but is undoubtedly quite difficult. This task is of particular importance in upstream watersheds. Accurate stream flow prediction is essential to manage water and design related infrastructures for human life, agriculture, industry, and the environment as well as ecosystems. Hydrologists around the world have made considerable efforts into developing approaches to predict stream flows in watersheds accurately
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