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.

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).

Water level (WL) and terrestrial water storage (TWS) are two important indicators for early alerts of hydrological extremes. Their variation is governed by precipitation under monsoon variability, in particular in the Mekong river basin, where it is affected by the interaction between the Indian summer monsoon (ISM) and western North Pacific monsoon (WNPM). This study aimed to quantify the contributions of two monsoons to the water levels of four hydrological stations (i.e., My Thuan, Can Tho, Chau Doc and Tan Chau) on the Mekong Delta and the terrestrial water storage of the entire Mekong River basin through relative importance analysis. Three methods—multivariate linear regression; Lindeman, Merenda and Gold (LMG); and the proportional marginal variance decomposition (PMVD) methods—were selected to quantitatively obtain the relative influence of two monsoons on water level and TWS. The results showed that, from 2010 to 2014, the proportions of the ISM impacts on the water level obtained with the three methods ranged from 55.48 to 81.35%, 50.69 to 57.55% and 55.41 to 93.64% via multivariate linear regression, LMG and PMVD, respectively. Further analysis showed that different choices of time spans could lead to different results, indicated that the corresponding proportion would be influenced by other factors, such as El Niño–Southern Oscillation (ENSO). The removal of ENSO further enlarged the relative importance of the ISM, and the mean values of the four stations were increased by 8.78%, 2.04% and 14.92%, respectively, via multivariate linear regression, LMG and PMVD. Meanwhile, based on the analysis of terrestrial water storage, it was found that the impact of the ISM on the whole Mekong River basin was dominant: the proportions of the impact of the ISM on terrestrial water storage increased to 68.79%, 54.60% and 79.43%, which rose by 11.24%, 2.96% and 19.77%, respectively, via linear regression, LMG and PMVD. The increases almost equaled the quantified proportion for the ENSO component. Overall, the novel technique of quantifying the contributions of monsoons to WL and TWS can be applied to the influence of other atmospheric factors or events on hydrological variables in different regions.

Storage and runoff are the two fundamental surface hydrological variables of a catchment. Research studies have been focused on the storage-runoff (S-R) hysteretic relationship of a catchment and its explanation very recently, thanks to satellite gravimetry. However, a complete analysis of a hydrological process starting from recharge to runoff has not been investigated. The S-R hysteretic relationship of Yangtze River Source Region (YRSR) situated in the northeast Tibetan Plateau is also unexplored. This study aims to investigate the Recharge-Storage-Runoff relationship of this catchment using gravimetrically-derived terrestrial water storage (TWS), satellite-derived and gauged precipitation, land surface modeled and gauged evapotranspiration, and runoff data measured during 2003–2012. We found that the Pearson correlation coefficient (PCC) of S-R relationship is 0.7070, in addition to the fact that the peak of runoff every year comes earlier than that of the storage. This finding enables us to further calculate equivalent runoff based on water balance equation using the above data, while comparing to measured runoff time series. The comparison of Global Land Data Assimilation System (GLDAS)-derived (gauge-derived) equivalent runoff against measured runoff reveals a PCC of 0.8992 (0.9402), respectively, indicating both storage and runoff are largely controlled by the recharge derived from precipitation and evapotranspiration. This implies the storage is not coupled with runoff prominently due to steep topography in YRSR unable to hold the water in the form of storage. Exceptional anomalous water storage time series in 2006 has also been investigated. We speculate that the low rainfall might partly be related to an El Niño Southern Oscillation event. The low rainfall and abrupt groundwater transfer are likely to be the causes of the anomaly in 2006.