ORCID Profile
0000-0002-9578-2257
Current Organisation
Asian Institute of Technology
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Publisher: Springer Science and Business Media LLC
Date: 2011
Publisher: Copernicus GmbH
Date: 13-03-2018
DOI: 10.5194/HESS-22-1811-2018
Abstract: Abstract. An accurate estimation of soil moisture and groundwater is essential for monitoring the availability of water supply in domestic and agricultural sectors. In order to improve the water storage estimates, previous studies assimilated terrestrial water storage variation (ΔTWS) derived from the Gravity Recovery and Climate Experiment (GRACE) into land surface models (LSMs). However, the GRACE-derived ΔTWS was generally computed from the high-level products (e.g. time-variable gravity fields, i.e. level 2, and land grid from the level 3 product). The gridded data products are subjected to several drawbacks such as signal attenuation and/or distortion caused by a posteriori filters and a lack of error covariance information. The post-processing of GRACE data might lead to the undesired alteration of the signal and its statistical property. This study uses the GRACE least-squares normal equation data to exploit the GRACE information rigorously and negate these limitations. Our approach combines GRACE's least-squares normal equation (obtained from ITSG-Grace2016 product) with the results from the Community Atmosphere Biosphere Land Exchange (CABLE) model to improve soil moisture and groundwater estimates. This study demonstrates, for the first time, an importance of using the GRACE raw data. The GRACE-combined (GC) approach is developed for optimal least-squares combination and the approach is applied to estimate the soil moisture and groundwater over 10 Australian river basins. The results are validated against the satellite soil moisture observation and the in situ groundwater data. Comparing to CABLE, we demonstrate the GC approach delivers evident improvement of water storage estimates, consistently from all basins, yielding better agreement on seasonal and inter-annual timescales. Significant improvement is found in groundwater storage while marginal improvement is observed in surface soil moisture estimates.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-4247
Abstract: Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [& #8722 ] & #8211 metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.
Publisher: Elsevier BV
Date: 11-2020
Publisher: MDPI AG
Date: 20-03-2018
DOI: 10.3390/RS10030483
Publisher: American Geophysical Union (AGU)
Date: 09-2019
DOI: 10.1029/2018WR024618
Publisher: Springer Science and Business Media LLC
Date: 30-03-2011
Publisher: MDPI AG
Date: 04-07-2023
DOI: 10.3390/W15132456
Abstract: Hydrology and land surface and models (HM and LSM) are essential tools for estimating global terrestrial water storage (TWS), an important component of the global water budget for assessing the accessibility and long-term variability of water supplies. With the expansion of open-source and open-data policies, the community can now perform model TWS simulation from source codes as well as directly exploit end-user hydrologic products for water resource applications. Regardless of the model effectiveness and usability, an accuracy assessment is necessary to quantify the model’s global and regional strengths, weaknesses, and reliability. This paper compares the most recent global TWS estimates from six models, namely the PCRaster Global Water Balance (PCR-GLOBWB), Noah, Noah-Multiparameterization (Noah-MP), Catchment LSM, and Variable Infiltration Capacity (VIC), and Community Atmosphere Biosphere Land Exchange (CABLE)—the latter of which is cross validated for the first time. TWS observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions between 2002 and 2021 are used to validate the model. The analyses show that Noah-MP outperforms other models in terms of global average correlations and root mean square errors. PCR-GLOBWB performance is superior in irrigated regions because of the inclusion of human intervention components in the model. CABLE, a core LSM of the Australian climate model, significantly outperforms all others in Australia. CLSM performs reasonably well, but the TWS long-term trend appears to be incorrect due to an overestimated groundwater component. Noah performs similarly (but inferiorly) to Noah-MP, most likely due to model physics sharing. VIC has the least agreement with GRACE and GRACE-FO. The evaluation also sheds some light on the role of forcing data in model performance, particularly for ready-to-use products such as GLDAS, where incorporating MERRA-2 or ERA5 data into GLDAS Noah simulations may potentially improve its TWS accuracy, which has previously been overlooked due to limited modeling capacity. Despite each model’s unique strength, the ensemble mean TWS, particularly when Noah-MP and PCR-GLOBWB are included, yields better TWS estimates than an in idual model result. The findings of this study could serve as a benchmark for future model development and the data published in this paper could aid in the scientific advancement and discoveries of the hydrology community.
Publisher: Copernicus GmbH
Date: 18-04-2017
DOI: 10.5194/HESS-21-2053-2017
Abstract: Abstract. An accurate estimation of water resources dynamics is crucial for proper management of both agriculture and the local ecology, particularly in semi-arid regions. Imperfections in model physics, uncertainties in model land parameters and meteorological data, as well as the human impact on land changes often limit the accuracy of hydrological models in estimating water storages. To mitigate this problem, this study investigated the assimilation of terrestrial water storage variation (TWSV) estimates derived from the Gravity Recovery And Climate Experiment (GRACE) data using an ensemble Kalman filter (EnKF) approach. The region considered was the Hexi Corridor in northern China. The hydrological model used for the analysis was PCR-GLOBWB, driven by satellite-based forcing data from April 2002 to December 2010. The impact of the GRACE data assimilation (DA) scheme was evaluated in terms of the TWSV, as well as the variation of in idual hydrological storage estimates. The capability of GRACE DA to adjust the storage level was apparent not only for the entire TWSV but also for the groundwater component. In this study, spatially correlated errors in GRACE data were taken into account, utilizing the full error variance–covariance matrices provided as a part of the GRACE data product. The benefits of this approach were demonstrated by comparing the EnKF results obtained with and without taking into account error correlations. The results were validated against in situ groundwater data from five well sites. On average, the experiments showed that GRACE DA improved the accuracy of groundwater storage estimates by as much as 25 %. The inclusion of error correlations provided an equal or greater improvement in the estimates. In contrast, a validation against in situ streamflow data from two river gauges showed no significant benefits of GRACE DA. This is likely due to the limited spatial and temporal resolution of GRACE observations. Finally, results of the GRACE DA study were used to assess the status of water resources over the Hexi Corridor over the considered 9-year time interval. Areally averaged values revealed that TWS, soil moisture, and groundwater storages over the region decreased with an average rate of approximately 0.2, 0.1, and 0.1 cm yr−1 in terms of equivalent water heights, respectively. A particularly rapid decline in TWS (approximately −0.4 cm yr−1) was seen over the Shiyang River basin located in the southeastern part of Hexi Corridor. The reduction mostly occurred in the groundwater layer. An investigation of the relationship between water resources and agricultural activities suggested that groundwater consumption required to maintain crop yield in the growing season for this specific basin was likely the cause of the groundwater depletion.
Publisher: MDPI AG
Date: 12-01-2021
DOI: 10.3390/RS13020235
Abstract: The vertical motion of the Earth’s surface is dominated by the hydrologic cycle on a seasonal scale. Accurate land deformation measurements can provide constructive insight into the regional geophysical process. Although the Global Positioning System (GPS) delivers relatively accurate measurements, GPS networks are not uniformly distributed across the globe, posing a challenge to obtaining accurate deformation information in data-sparse regions, e.g., Central South-East Asia (CSEA). Model simulations and gravity data (from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)) have been successfully used to improve the spatial coverage. While combining model estimates and GRACE/GRACE-FO data via the GRACE/GRACE-FO data assimilation (DA) framework can potentially improve the accuracy and resolution of deformation estimates, the approach has rarely been considered or investigated thus far. This study assesses the performance of vertical displacement estimates from GRACE/GRACE-FO, the PCRaster Global Water Balance (PCR-GLOBWB) hydrology model, and the GRACE/GRACE-FO DA approach (assimilating GRACE/GRACE-FO into PCR-GLOBWB) in CSEA, where measurements from six GPS sites are available for validation. The results show that GRACE/GRACE-FO, PCR-GLOBWB, and GRACE/GRACE-FO DA accurately capture regional-scale hydrologic- and flood-induced vertical displacements, with the correlation value and RMS reduction relative to GPS measurements up to 0.89 and 53%, respectively. The analyses also confirm the GRACE/GRACE-FO DA’s effectiveness in providing vertical displacement estimates consistent with GRACE/GRACE-FO data while maintaining high-spatial details of the PCR-GLOBWB model, highlighting the benefits of GRACE/GRACE-FO DA in data-sparse regions.
Publisher: Elsevier BV
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 27-05-2021
Publisher: Elsevier BV
Date: 09-2018
Publisher: Springer Science and Business Media LLC
Date: 2016
Publisher: Copernicus GmbH
Date: 24-10-2014
DOI: 10.5194/HESSD-11-11837-2014
Abstract: Abstract. The ability to estimate Terrestrial Water Storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery And Climate Experiment (GRACE) data into the OpenStreams-wflow model using an Ensemble Kalman Filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater and stream gauge data. The analysis showed a noticeable improvement in groundwater estimates when GRACE data were assimilated, with an overall improvement of up to 71% in correlation coefficient (from 0.31 to 0.53) and 35% in RMS error (from 8.4 to 5.4 cm) compared to the reference (ensemble open-loop) case. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short terms, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.
Publisher: Elsevier BV
Date: 08-2016
Publisher: MDPI AG
Date: 17-02-2022
DOI: 10.3390/W14040621
Abstract: Multivariate data assimilation (DA) of satellite soil moisture (SM) and terrestrial water storage (TWS) observations has recently been used to improve SM and groundwater storage (GWS) simulations. Previous studies employed the ensemble Kalman approach in multivariate DA schemes, which assumes that model and observation errors have a Gaussian distribution. Despite the success of the Kalman approaches, SM and GWS estimates can be suboptimal when the Gaussian assumption is violated. Other DA approaches, such as particle smoother (PS), ensemble Gaussian particle smoother (EnGPS), and evolutionary smoother (EvS), do not rely on the Gaussian assumption and may be better suited to non-Gaussian error systems. The objective of this paper is to evaluate the performance of these four DA approaches (EnKS, PS, EnGPS, and EvS) in multivariate DA systems by assimilating satellite data from the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery And Climate Experiment (GRACE) missions into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The analyses are carried out in Australia’s Goulburn River catchment, where in situ SM and groundwater data are available to comprehensively validate the DA performance. Results show that all four DA approaches have outstanding performances and improve correlation coefficients of SM and GWS estimates by ~20% and 100%, respectively. The EvS outperforms the others, but its benefit is relatively marginal compared to Gaussian approaches (e.g., EnKS). This is due to the fact that SM and TWS error distributions in this study are close to Gaussian: a suitable condition for, e.g., EnKS, EnGPS. The robust performance of EvS appears to be the optimal approach for jointly assimilating multi-source hydrological observations to improve regional hydrological analyses.
Publisher: Elsevier BV
Date: 11-2019
Publisher: MDPI AG
Date: 26-02-2021
DOI: 10.3390/RS13050874
Abstract: The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans.
Publisher: Elsevier BV
Date: 2021
Publisher: Copernicus GmbH
Date: 29-04-2015
DOI: 10.5194/HESS-19-2079-2015
Abstract: Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.
Publisher: Copernicus GmbH
Date: 06-06-2017
Abstract: Abstract. An accurate estimation of soil moisture and groundwater is essential for monitoring the availability of water supply in domestic and agricultural sectors. In order to improve the water storage estimates, previous studies assimilated terrestrial water storage variation (∆TWS) derived from Gravity Recovery and Climate Experiment (GRACE) into land surface models. However, the GRACE-derived δTWS was generally computed from the high level products (e.g., land grid). The gridded data products are subjected to several drawbacks such as signal attenuation and/or distortion caused by ad hoc posteriori filters, and a lack of error covariance information. The consequence is undesired alteration of δTWS data and its statistical property. To exploit the GRACE information rigorously and negate these limitations, this study uses the fundamental GRACE satellite tracking Level 1B (L1B) data, not the post-processed ∆TWS grid data. The approach combines the GRACE's least-squares normal equation (full error variance-covariance information) of L1B data with the results from the Community Atmosphere Land Exchange (CABLE) model to improve soil moisture and groundwater estimates. This study demonstrates, for the first time, an importance of using the raw GRACE data. The GRACE-combine (GC) approach is developed for optimal least-squares combination maximizing the strength of the model and observations while suppressing the weaknesses. The approach is applied to estimate the soil moisture and groundwater over 10 Australian river basins and the results are validated against the satellite soil moisture observation and the in-situ groundwater data. We demonstrate the GC approach delivers evident improvement of water storage estimates, consistently from all basins, yielding better agreement at seasonal and inter-annual time scales. Significant improvement is found in groundwater storage while marginal improvement is observed in surface soil moisture estimates likely due to limitation of GRACE's temporal and spatial resolution.
Publisher: Copernicus GmbH
Date: 25-07-2016
Abstract: Abstract. An accurate estimation of water resources dynamics is crucial for proper management of both agriculture and the local ecology, particularly in semi-arid regions. Imperfections in model physics, uncertainties in model land parameters and meteorological data, as well as the human impact on land changes often limit the accuracy of hydrological models in estimating water storages. To mitigate this problem, this study investigated the assimilation of Terrestrial Water Storage (TWS) estimates derived from the Gravity Recovery And Climate Experiment (GRACE) data using an Ensemble Kalman Filter (EnKF) approach. The region considered was the Hexi Corridor of Northern China. The hydrological model used for the analysis was PCR-GLOBWB, driven by satellite-based forcing data from April 2002 to December 2010. In this study, EnKF 3D scheme, which accounts for the GRACE spatially-correlated errors, was used. The correlated errors were propagated from the full error variance-covariance matrices provided as a part of the GRACE data product. The impact of the GRACE Data Assimilation (DA) scheme was evaluated in terms of the TWS, as well as in idual hydrological storage estimates. The capability of GRACE DA to adjust the storage level was apparent not only for the entire TWS but also for the groundwater component, which had annual litude, phase, and long-term trend estimates closer to the GRACE observations. This study also assessed the benefits of taking into account correlations of errors in GRACE-based estimates. The assessment was carried out by comparing the EnKF results, with and without taking into account error correlations, with the in situ groundwater data from 5 well sites and the in situ streamflow data from two river gauges. On average, the experiments showed that GRACE DA improved the accuracy of groundwater storage estimates by as much as 25 %. The inclusion of error correlations provided an equal or greater improvement in the estimates. No significant benefits of GRACE DA were observed in terms of streamflow estimates, which reflect a limited spatial and temporal resolution of GRACE observations. Results from the 9-year long GRACE DA study were used to assess the status of water resources over the Hexi Corridor. Areally-averaged values revealed that TWS, soil moisture, and groundwater storages over the region decreased with an average rate of approximately 0.2, 0.1, and 0.1 cm/yr in terms of equivalent water heights, respectively. A substantial decline in TWS (approximately −0.4 cm/yr) was seen over the Shiyang River Basin in particular, and the reduction mostly occurred in the groundwater layer. An investigation of the relationship between water resources and agriculture suggested that groundwater consumption required to maintain the growing period in this specific basin was likely the cause of the groundwater depletion.
Publisher: American Geophysical Union (AGU)
Date: 11-2012
DOI: 10.1029/2012JB009310
Publisher: Elsevier BV
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 07-05-2020
Publisher: MDPI AG
Date: 20-09-2016
DOI: 10.3390/RS8090777
Publisher: MDPI AG
Date: 27-10-2017
DOI: 10.3390/RS9111100
Publisher: American Geophysical Union (AGU)
Date: 03-2017
DOI: 10.1002/2017JB014129
No related grants have been discovered for Natthachet Tangdamrongsub.