Estimating Total Discharge in the Yangtze River Basin Using Satellite-Based Observations

Deriving River Discharge Using Remotely Sensed Water Surface Characteristics and Satellite Altimetry in the Mississippi River Basin

River discharges are critical for understanding hydrologic and ecological systems, yet in situ data are limited in many regions of the world. While approximating river discharge using satellite-derived water surface characteristics is possible, the key challenges are unknown channel bathymetry and roughness. Here, we present an application for merging mean river-reach characteristics and time-varying altimetry measurements to estimate river discharge for sites within the Mississippi River Basin (USA). This project leverages the Surface Water and Ocean Topography (SWOT) River Database (SWORD) for approximating mean river-reach widths and slopes and altimetry data from JASON-2/3 (2008-Present) and Sentinel-3A/B (2015-Present) obtained from the Hydroweb Theia virtual stations. River discharge is calculated using Manning’s Equation, with optimized parameters for surface roughness, bottom elevation, and channel shape determined using the Kling–Gupta Efficiency (KGE). The results of this study indicate the use of optimized characteristics return 87% of sites with KGE > −0.41, which indicates that the approach provides discharges that outperform using the mean discharge. The use of precipitation to approximate missing flows not observed by satellites results in 66% of sites with KGE > −0.41, while the use of TWSA results in 65% of sites with KGE > −0.41. Future research will focus on extending this application for all available sites in the United States, as well as trying to understand how climate and landscape factors (e.g., precipitation, temperature, soil moisture, landcover) relate to river and watershed characteristics.

Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.

Characterization of the hydro-geological regime of Yangtze River basin using remotely-sensed and modeled products

The hydrology of the Third Pole, Asia's freshwater tower, has shown considerable sensitivity to the impacts of climate change and human interventions, which affect the headwaters of many rivers that originate therein. For example, the Yangtze River has its basin (YRB) experiencing wetness of terrestrial water storage (TWS), whose rainfall seems to be the primary source as inferred from the previous studies. Consequently, it is crucial to understand the contributions of each TWS's sub-domain - i.e., groundwater (GWS); total water content (TWC) stored as soil moisture, ice/snow, and canopy; and the surface water (SWS) storages - on YRB's wetness. Hence, SWS, from altimetry and imagery satellites, and TWC, from Global Land Data Assimilation System, are inverted considering the same basis function as for TWS from the Gravity Recovery and Climate Experiment, which account for the differences in the resolutions inherent in each product. Furthermore, a "tie-in" signal approach is used to fit the temporal patterns of GWS, TWC, and SWS to TWS (i.e., the observations). Results show improvements in the reconstructed GWS series concerning standard deviation, correlation coefficient, and Nash-Sutcliffe efficiency of 22%, 27%, and 120%, respectively, regarding the use of the TWS-budget equation. The reconstructed time series of GWS, TWC, and SWS present an increase of 1.76, 2.69, and 0.14 mm per year (mm/yr) and that YRB loses water stored at its aquifers 55% of the time (regarding 2003-2016 period) based on the quantile function of storage (QFS). The QFS's slope shows that TWS has a fast and small storage potential w.r.t. GWS since inland waters and soil moisture reflect the dryness impacting TWS first. Despite the evidence of an increase of 19.05 mm/yr in annual precipitation, which seems to explain the bulk in TWS, further investigation to characterize controls on TWS memory within YRB is still necessary.

Improved Remotely Sensed Total Basin Discharge and Its Seasonal Error Characterization in the Yangtze River Basin

Total basin discharge is a critical component for the understanding of surface water exchange at the land-ocean interface. A continuous decline in the number of global hydrological stations over the past fifteen years has promoted the estimation of total basin discharge using remote sensing. Previous remotely sensed total basin discharge of the Yangtze River basin, expressed in terms of runoff, was estimated via the water balance equation, using a combination of remote sensing and modeled data products of various qualities. Nevertheless, the modeled data products are presented with large uncertainties and the seasonal error characteristics of the remotely sensed total basin discharge have rarely been investigated. In this study, we conducted total basin discharge estimation of the Yangtze River Basin, based purely on remotely sensed data. This estimation considered the period between January 2003 and December 2012 at a monthly temporal scale and was based on precipitation data collected from the Tropical Rainfall Measuring Mission (TRMM) satellite, evapotranspiration data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, and terrestrial water storage data collected from the Gravity Recovery and Climate Experiment (GRACE) satellite. A seasonal accuracy assessment was performed to detect poor performances and highlight any deficiencies in the modeled data products derived from the discharge estimation. Comparison of our estimated runoff results based purely on remotely sensed data, and the most accurate results of a previous study against the observed runoff revealed a Pearson correlation coefficient (PCC) of 0.89 and 0.74, and a root-mean-square error (RMSE) of 11.69 mm/month and 14.30 mm/month, respectively. We identified some deficiencies in capturing the maximum and the minimum of runoff rates during both summer and winter, due to an underestimation and overestimation of evapotranspiration, respectively.

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

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).

Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method

Evapotranspiration (ET), a critical process in global climate change, is very difficult to estimate at regional and basin scales. In this study, we evaluated five ET products: the Global Land Surface Evaporation with the Amsterdam Methodology (GLEAM, the EartH2Observe ensemble (E2O)), the Global Land Data Assimilation System with Noah Land Surface Model-2 (GLDAS), a global ET product at 8 km resolution from Zhang (ZHANG) and a supplemental land surface product of the Modern-ERA Retrospective analysis for Research and Applications (MERRA_land), using the water balance method in the Yellow River Basin, China, including twelve catchments, during the period of 1982–2000. The results showed that these ET products have obvious different performances, in terms of either their magnitude or temporal variations. From the viewpoint of multiple-year averages, the MERRA_land product shows a fairly similar magnitude to the ETw derived from the water balance method, while the E2O product shows significant underestimations. The GLEAM product shows the highest correlation coefficient. From the viewpoint of interannual variations, the ZHANG product performs best in terms of magnitude, while the E2O still shows significant underestimations. However, the E2O product best describes the interannual variations among the five ET products. Further study has indicated that the discrepancies between the ET products in the Yellow River Basin are mainly due to the quality of precipitation forcing data. In addition, most ET products seem to not be sensitive to the downward shortwave radiation.

Water Level Reconstruction Based on Satellite Gravimetry in the Yangtze River Basin

The monitoring of hydrological extremes requires water level measurement. Owing to the decreasing number of continuous operating hydrological stations globally, remote sensing indices have been advocated for water level reconstruction recently. Nevertheless, the feasibility of gravimetrically derived terrestrial water storage (TWS) and its corresponding index for water level reconstruction have not been investigated. This paper aims to construct a correlative relationship between observed water level and basin-averaged Gravity Recovery and Climate Experiment (GRACE) TWS and its Drought Severity Index (GRACE-DSI), for the Yangtze river basin on a monthly temporal scale. The results are subsequently compared against traditional remote sensing, Palmer’s Drought Severity Index (PDSI), and El Niño Southern Oscillation (ENSO) indices. Comparison of the water level reconstructed from GRACE TWS and its index, and that of remote sensing against observed water level reveals a Pearson Correlation Coefficient (PCC) above 0.90 and below 0.84, with a Root-Mean-Squares Error (RMSE) of 0.88–1.46 m, and 1.41–1.88 m and a Nash-Sutcliffe model efficiency coefficient (NSE) above 0.81 and below 0.70, respectively. The ENSO-reconstructed water levels are comparable to those based on remote sensing, whereas the PDSI-reconstructed water level shows a similar performance to that of GRACE TWS. The water level predicted at the location of another station also exhibits a similar performance. It is anticipated that the basin-averaged, remotely-sensed hydrological variables and their standardized forms (e.g., GRACE TWS and GRACE-DSI) are viable alternatives for reconstructing water levels for large river basins affected by the hydrological extremes under ENSO influence.

Total Discharge Estimation in the Korean Peninsula Using Multi-Satellite Products

Estimation of total discharge is necessary to understand the hydrological cycle and to manage water resources efficiently. However, the task is problematic in an area where ground observations are limited. The North Korea region is one example. Here, the total discharge was estimated based on the water balance using multiple satellite products. They are the terrestrial water storage changes (TWSC) derived from the Gravity Recovery and Climate Experiment (GRACE), precipitation from the Tropical Rainfall Measuring Mission (TRMM), and evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS). The satellite-based discharge was compared with land surface model products of the Global Land Data Assimilation System (GLDAS), and a positive relationship between the results was obtained (r = 0.70–0.86; bias = −9.08–16.99 mm/month; RMSE = 36.90–62.56 mm/month; NSE = 0.01–0.62). Among the four land surface models of GLDAS (CLM, Mosaic, Noah, and VIC), CLM corresponded best with the satellite-based discharge, satellite-based discharge has a tendency to slightly overestimate compared to model-based discharge (CLM, Mosaic, Noah, and VIC) in the dry season. Also, the total discharge data based on the Precipitation-Runoff Modeling System (PRMS) and the in situ discharge for major five river basins in South Korea show comparable seasonality and high correlation with the satellite-based discharge. In spite of the relatively low spatial resolution of GRACE, and loss of information incurred during the process of integrating three different satellite products, the proposed methodology can be a practical tool to estimate the total discharge with reasonable accuracy, especially in a region with scarce hydrologic data.