With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration.
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Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal transferability of the time-varying parameter was rarely investigated. This paper aims to investigate the predictive ability and robustness of a hydrological model with time-varying parameter under changing environments. The conceptual hydrological model GR4J (Génie Rural à 4 paramètres Journalier) with only four parameters was chosen and the sensitive parameters were treated as functions of several external covariates that represent the variation of climate and watershed conditions. The investigation was carried out in Weihe Basin and Tuojiang Basin of Western China in the period from 1981 to 2010. Several sub-periods with different climate and watershed conditions were set up to test the temporal parameter transferability of the original GR4J model and the GR4J model with time-varying parameters. The results showed that the performance of streamflow simulation was improved when applying the time-varying parameters. Furthermore, in a series of split-sample tests, the GR4J model with time-varying parameters outperformed the original GR4J model by improving the model robustness. Further studies focus on more diversified model structures and watersheds conditions are necessary to verify the superiority of applying time-varying parameters.
Accurate soil moisture estimation plays a crucial role in agricultural management and hydrological studies. Considering the scarcity of direct in-situ measurements, it is important to evaluate the consistency of soil moisture data acquired in indirect ways, including both satellite products and simulation values obtained via hydrological models. In this study, two types of high spatial-resolution remotely sensed values, namely the surface soil moisture (SSM) and the profile soil water index (SWI), are estimated from each of the ASCAT-A, ASCAT-B, SMAP and SMOS microwave satellites. They are compared with two groups of model-simulated daily soil moisture values, which are obtained by implementing the lumped Xinanjiang (XAJ) model and the DEM-based distributed hydrological model (DDRM) across the Qujiang catchment, located in southwest China. The results indicate that for each satellite product, SWI values always show closer agreement with model-simulated soil moisture values than SSM values, and SWI values estimated from ASCAT products perform best in terms of correlation coefficient with the model-simulated soil moisture, at around 0.8 on average, followed by the SMAP product, which shows a correlation coefficient of 0.48 on average, but the SMOS product shows poor performance. This evaluation of consistency provides useful information on their systematic differences and suggests subsequent studies to ensure their reconciliation in long-term records.
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