Abstract:The prediction of dominant hydrological processes is imperative with the available information in data scarce regions by means of the lumped hydrological models for the purpose of water resource management. This study is aims at an intercomparison of the performances of the conceptual hydrological models in predicting streamflow. The Veralgemeend Conceptueel Hydrologisch (VHM) and NedborAfstromnings Model (NAM) lumped rainfall-runoff models were manually calibrated and validated for periods of 1 January 1990-31 December 2000 and 1 January 2001-31 December 2005, respectively. Some of the parameters of the models (i.e., recession constants of subflow components) were estimated from the preprocessing of the streamflow data using the Water Engineering Time Series PROcessing tool (WETSPRO). These parameters were used for the initial model setup and subjected to slight adjustments during calibration. The performances of the models were evaluated by graphical and statistical means. The results depicted that the models reproduced the streamflow in a good way and that the overall shape of the hydrograph was properly captured. A Nash Sutcliffe efficiency (NSE) of 0.71 and 0.67 were obtained during calibration, whereas, for the validation period, NSE of 0.6 and 0.58 were obtained for VHM and NAM, respectively. The water balance discrepancy (WBD) of −0.1% and −13.7% were achieved for calibration, while −17% and −9% were acquired during validation for VHM and NAM, respectively. Though the models underestimated the high flows, the low flows were relatively well simulated. From the overall evaluation of the models, it is noted that the NAM model performed better than the VHM model in predicting the flow. In conclusion, the models can be used for water resource management and planning with precautions for extreme flow.
Soil moisture (SM) with a high spatial resolution plays a paramount role in many local and regional hydrological and agricultural applications. The advent of L-band passive microwave satellites allowed for it to be possible to measure near-surface SM at a global scale compared to in situ measurements. However, their use is often limited because of their coarse spatial resolution. Aiming to address this limitation, random forest (RF) models are adopted to downscale the SMAP level-3 (L3SMP, 36 km) and SMAP enhanced (L3SMP_E, 9 km) SM to 1 km. A suite of predictors derived from the Sentinel-1 C-band SAR and MODIS is used in the downscaling process. The RF models are separately trained and verified at both spatial scales (i.e., 36 and 9 km) considering two experiments: 1) using predictors derived from the MODIS and Sentinel-1 along with other predictors such as elevation and brightness temperature and 2) using all predictors of the first experiment except for the Sentinel-1 predictors. Only dates when the Sentinel-1 images were available are considered for the comparison of the two experiments. The comparison of the results of the two experiments indicates that the removal of Sentinel-1 predictors from the second experiment only reduces the R value from 0.84 to 0.83 and from 0.91 to 0.86 for 36 and 9 km spatial scales, respectively. Among the predictors used in the downscaling, the brightness temperature in VV polarization is identified as the most important predictor, followed by NDVI, surface albedo and API. On the contrary, the Sentinel-1 predictors play a less important role with no marked contribution in enhancing the predictive accuracy of RF models. In general, the two experiments have limitation, such as a small sample size for the training of the RF model because of the scarcity of Sentinel-1 images (i.e., revisit time of 12 days). Therefore, based on this limitation, a third experiment is proposed, in which the Sentinel-1 predictors are not considered at all in the training of the RF models. The results of the third experiment show a good agreement between the downscaled L3SMP and L3SMP_E SM, and in situ SM measurements at both spatial scales. In addition, the temporal availability of the downscaled SM increased. Moreover, the downscaled SM from both SMAP products presented greater spatial detail while preserving the spatial patterns found in their original products. The use of the two SMAP SM products as background fields for the downscaling process does not show marked differences. Overall, this study demonstrates encouraging results in the downscaling of SMAP SM products over humid climate with warm summers dominated by vegetation.
Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own strengths and limitations. For example, remote sensing has the strength of maintaining the spatial variability of near-surface soil moisture, while in-situ measurements are accurate and preserve the dynamics range of soil moisture at both surface and larger depths. Hence, this study is aimed at (1) merging the strength of SMAP with in-situ measurements and (2) exploring the effectiveness of merged SMAP/in-situ soil moisture in improving ensemble streamflow forecasts. The conditional merging technique was adopted to merge the SMAP-enhanced soil moisture (9 km) and its downscaled version (1 km) separately with the in-situ soil moisture collected over the au Saumon watershed, a 1025 km2 watershed located in Eastern Canada. The random forest machine learning technique was used for downscaling of the near-surface SMAP-enhanced soil moisture to 1 km resolution, whereas the exponential filter was used for vertical extrapolation of the SMAP near-surface soil moisture. A simple data assimilation technique known as direct insertion was used to update the topsoil layer of a physically-based distributed hydrological model with four soil moisture products: (1) the merged SMAP/in-situ soil moisture at 9 and 1 km resolutions; (2) the original SMAP-enhanced (9 km), (3) downscaled SMAP-enhanced (1 km), and (4) interpolated in-situ surface soil moisture. In addition, the vertically extrapolated merged SMAP/in-situ soil moisture and subsurface (rootzone) in-situ soil moisture were used to update the intermediate layer of the model. Results indicate that downscaling of the SMAP-enhanced soil moisture to 1 km resolution improved the spatial variability of soil moisture while maintaining the spatial pattern of its original counterpart. Similarly, merging of the SMAP with in- situ soil moisture preserved the dynamic range of in-situ soil moisture and maintained the spatial heterogeneity of SMAP soil moisture. Updating of the top layer of the model with the 1 km merged SMAP/in-situ soil moisture improved the ensemble streamflow forecast compared to the model updated with either the SMAP-enhanced or in-situ soil moisture alone. On the other hand, updating the top and intermediate layers of the model with surface and vertically extrapolated SMAP/in-situ soil moisture, respectively, did not further improve the accuracy of the ensemble streamflow forecast. Overall, this study demonstrated the potential of merging the SMAP and in-situ soil moisture for streamflow forecast.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.