scholarly journals Upstream GPS Vertical Displacement and its Standardization for Mekong River Basin Surface Runoff Reconstruction and Estimation

2019 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Hok Sum Fok ◽  
Linghao Zhou ◽  
Yongxin Liu ◽  
Zhongtian Ma ◽  
Yutong Chen
Keyword(s):  
Time Series ◽  
River Water ◽  
River Basin ◽  
Relative Error ◽  
Surface Runoff ◽  
Water Discharge ◽  
Vertical Displacement ◽  
Mekong River ◽  
Mekong River Basin

Surface runoff (R), which is another expression for river water discharge of a river basin, is a critical measurement for regional water cycles. Over the past two decades, river water discharge has been widely investigated, which is based on remotely sensed hydraulic and hydrological variables as well as indices. This study aims to demonstrate the potential of upstream global positioning system (GPS) vertical displacement (VD) and its standardization to statistically derive R time series, which has not been reported in recent literature. The correlation between the in situ R at estuaries and averaged GPS-VD and its standardization in the river basin upstream on a monthly temporal scale of the Mekong River Basin (MRB) is examined. It was found that the reconstructed R time series from the latter agrees with and yields a similar performance to that from the terrestrial water storage based on gravimetric satellite (i.e., Gravity Recovery and Climate Experiment (GRACE)) and traditional remote sensing data. The reconstructed R time series from the standardized GPS-VD was found to have a 2–7% accuracy increase against those without standardization. On the other hand, it is comparable to data that are obtained by the Palmer drought severity index (PDSI). Similar accuracies are exhibited by the estimated R when externally validated through another station location with in situ time series. The comparison of the estimated R at the entrance of river delta against that at the estuaries indicates a 1–3% relative error induced by the residual ocean tidal effect at the estuary. The reconstructed R from the standardized GPS-VD yields the lowest total relative error of less than 9% when accounting for the main upstream area of the MRB. The remaining errors may be the result of the combined effect of the proposed methodology, remaining environmental signals in the data time series, and potential time lag (less than a month) between the upstream MRB and estuary.

scholarly journals Upstream Remotely-Sensed Hydrological Variables and Their Standardization for Surface Runoff Reconstruction and Estimation of the Entire Mekong River Basin

2019 ◽  
Vol 11 (9) ◽  
pp. 1064 ◽  
Author(s):  
Linghao Zhou ◽  
Hok Fok ◽  
Zhongtian Ma ◽  
Qiang Chen
Keyword(s):  
Water Balance ◽  
River Basin ◽  
Surface Runoff ◽  
Hydrological Modeling ◽  
Water Discharge ◽  
Remotely Sensed ◽  
Mekong River ◽  
Efficiency Coefficient ◽  
Mekong River Basin ◽  
Hydrological Variables

River water discharge (WD) is an essential component when monitoring a regional hydrological cycle. It is expressed in terms of surface runoff (R) when a unit of river basin surface area is considered. To compensate for the decreasing number of hydrological stations, remotely-sensed WD estimation has been widely promoted over the past two decades, due to its global coverage. Previously, remotely-sensed WD was reconstructed either by correlating nearby remotely-sensed surface responses (e.g., indices and hydraulic variables) with ground-based WD observations or by applying water balance formulations, in terms of R, over an entire river basin, assisted by hydrological modeling data. In contrast, the feasibility of using remotely-sensed hydrological variables (RSHVs) and their standardized forms together with water balance representations (WBR) obtained from the river upstream to reconstruct estuarine R for an entire basin, has been rarely investigated. Therefore, our study aimed to construct a correlative relationship between the estuarine observed R and the upstream, spatially averaged RSHVs, together with their standardized forms and WBR, for the Mekong River basin, using estuarine R reconstructions, at a monthly temporal scale. We found that the reconstructed R derived from the upstream, spatially averaged RSHVs agreed well with the observed R, which was also comparable to that calculated using traditional remote sensing data (RSD). Better performance was achieved using spatially averaged, standardized RSHVs, which should be potentially attributable to spatially integrated information and the ability to partly bypass systematic biases by both human (e.g., dam operation) and environmental effects in a standardized form. Comparison of the R reconstructed using the upstream, spatially averaged, standardized RSHVs with that reconstructed from the traditional RSD, against the observed R, revealed a Pearson correlation coefficient (PCC) above 0.91 and below 0.81, a root-mean-squares error (RMSE) below 6.1 mm and above 8.5 mm, and a Nash–Sutcliffe model efficiency coefficient (NSE) above 0.823 and below 0.657, respectively. In terms of the standardized water balance representation (SWBR), the reconstructed R yielded the best performance, with a PCC above 0.92, an RMSE below 5.9 mm, and an NSE above 0.838. External assessment demonstrated similar results. This finding indicated that the standardized RSHVs, in particular its water balance representations, could lead to further improvement in estuarine R reconstructions for river basins affected by various systematic influences. Comparison between hydrological stations at the Mekong River Delta entrance and near the estuary mouth revealed tidally-induced backwater effects on the estimated R, with an RMSE difference of 4–5 mm (equivalent to 9–11% relative error).

scholarly journals Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

2021 ◽  
Vol 13 (23) ◽  
pp. 4831
Author(s):  
Senlin Tang ◽  
Hong Wang ◽  
Yao Feng ◽  
Qinghua Liu ◽  
Tingting Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Correlation Coefficient ◽  
River Basin ◽  
Water Storage ◽  
Mekong River ◽  
Climatic Research Unit ◽  
Terrestrial Water Storage ◽  
Mekong River Basin ◽  
Terrestrial Water

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

scholarly journals Daily River Discharge Estimation Using Multi-Mission Radar Altimetry Data and Ensemble Learning Regression in the Lower Mekong River Basin

2019 ◽  
Vol 11 (22) ◽  
pp. 2684 ◽  
Author(s):  
Kim ◽  
Lee ◽  
Chang ◽  
Bui ◽  
Jayasinghe ◽  
...  
Keyword(s):  
River Basin ◽  
Ensemble Learning ◽  
River Discharge ◽  
Water Levels ◽  
Mekong River ◽  
Altimetry Data ◽  
Rating Curve ◽  
Mekong River Basin ◽  
Radar Altimetry

Estimating river discharge (Q) is critical for ecosystems and water resource management. Traditionally, estimating Q has depended on a single rating curve or the Manning equation. In contrast to the single rating curve, several rating curves at different locations have been linearly combined in an ensemble learning regression method to estimate Q (ELQ) at the Brazzaville gauge station in the central Congo River in a previous study. In this study, we further tested the proposed ELQ and apply it to the Lower Mekong River Basin (LMRB) with three locations: Stung Treng, Kratie, and Tan Chau. Two major advancements for estimating Q with ELQ are presented. First, ELQ successfully estimated Q at Tan Chau, downstream of Kratie, where hydrodynamic complexities exist. Since the hydrologic characteristics downstream of Kratie are extremely diverse and complex in time and space, most previous studies have estimated Q only upstream from Kratie with hydrologic models and statistical methods. Second, we estimated Q over the LMRB using ELQ with water levels (H) obtained from two radar altimetry missions, Envisat and Jason-2, which made it possible to estimate Q seamlessly from 2003 to 2016. Owing to ELQ with multi-mission radar altimetry data, we have overcome the problems of a single rating curve: Locations for estimating Q have to be close to virtual stations, e.g., a few tens of kilometers, because the performance of the single rating curve degrades as the distance between the location of Q estimation and a virtual station increases. Therefore, most previous studies had not used Jason-2 data whose cross-track interval is about 315 km at the equator. On the contrary, several H obtained from Jason-2 altimetry were used in this study regardless of distances from in-situ Q stations since the ELQ method compensates for degradation in the performance for Q estimation due to the poor rating curve with virtual stations away from in-situ Q stations. In general, the ELQ-estimated Q (QELQ) showed more accurate results compared to those obtained from a single rating curve. In the case of Tan Chau, the root mean square error (RMSE) of QELQ decreased by 1504/1338 m3/s using Envisat-derived H for the training/validation datasets. We successfully applied ELQ to the LMRB, which is one of the most complex basins to estimate Q with multi-mission radar altimetry data. Furthermore, our method can be used to obtain finer temporal resolution and enhance the performance of Q estimation with the current altimetry missions, such as Sentinel-3A/B and Jason-3.

scholarly journals Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 56 ◽  
Author(s):  
Chelsea Dandridge ◽  
Bin Fang ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
River Basin ◽  
River Basins ◽  
Large River ◽  
Mekong River ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Large River Basins

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

scholarly journals Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

2019 ◽  
Vol 11 (22) ◽  
pp. 2709 ◽  
Author(s):  
Chelsea Dandridge ◽  
Venkat Lakshmi ◽  
John Bolten ◽  
Raghavan Srinivasan
Keyword(s):  
Southeast Asia ◽  
River Basin ◽  
Watershed Modeling ◽  
Rain Gauge ◽  
Mekong River ◽  
Coefficient Of Determination ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Rainfall Estimates

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA.

scholarly journals Satellite observations reveal thirteen years of reservoir filling strategies, operating rules, and hydrological alterations in the Upper Mekong River Basin

2021 ◽  
Author(s):  
Dung Trung Vu ◽  
Thanh Duc Dang ◽  
Stefano Galelli ◽  
Faisal Hossain
Keyword(s):  
Time Series ◽  
River Basin ◽  
Satellite Observations ◽  
Mekong River ◽  
Limited Information ◽  
Energy Policies ◽  
Landsat Images ◽  
Mekong River Basin ◽  
Operating Rules ◽  
Satellite Radar

Abstract. The current situation in the Lancang–Mekong River Basin is emblematic of the issues faced by many transboundary basins around the world: riparian countries prioritize national water-energy policies and provide limited information on how major infrastructures are operated. In turn, such infrastructures and their management become a source of controversy. Here, we turn our attention to the Upper Mekong River, or Lancang, where a system of eleven mainstream dams controls about 55 % of the annual flow to Northern Thailand and Laos. Yet, assessing their actual impact is a challenging task because of the chronic lack of data on reservoir storage and dam release decisions. To overcome this challenge, we focus on the ten largest reservoirs and leverage satellite observations to infer 13-year time series of monthly storage variations. Specifically, we use area-storage curves (derived from a Digital Elevation Model) and time series of water surface area, which we estimate from Landsat images through a novel algorithm that removes the effects of clouds and other disturbances. We also use satellite radar altimetry data (Jason) to validate the results obtained from satellite imagery. Our results describe the evolution of the hydropower system and highlight the pivotal role played by Xiaowan and Nuozhadu reservoirs, which make up to ~85 % of the total system's storage in the Lancang River Basin. We show that these two reservoirs were filled in only two years, and that their operations did not change in response to the drought that occurred in the region in 2019–2020. Deciphering these operating strategies could help enrich existing monitoring tools and hydrological models, thereby supporting riparian countries in the design of more cooperative water-energy policies.

Intercomparison of ten ISI-MIP models in simulating discharges along the Lancang-Mekong River basin

2021 ◽  
Vol 765 ◽  
pp. 144494
Author(s):  
He Chen ◽  
Junguo Liu ◽  
Ganquan Mao ◽  
Zifeng Wang ◽  
Zhenzhong Zeng ◽  
...  
Keyword(s):  
River Basin ◽  
Mekong River ◽  
Mekong River Basin ◽  
Mip Models
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