Characterization of the Recharge-Storage-Runoff Process of the Yangtze River Source Region under Climate Change

Abstract

<jats:p>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.</jats:p>

Daily runoff and its potential error sources reconstructed using individual satellite hydrological variables at the basin upstream

“…Previous research studies have found that runoff lagged the upstream GPM-TRMM precipitation (Zhang et al, 2007;Du et al, 2020) and GRACE water storage (Riegger and Tourian, 2014) by a month or more. Thus, the time lag between the upstream GPM-TRMM precipitation (GRACE water storage) and the runoff must be determined before further calculation.…”

Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables

“…Owing to the hysteretic process between the remotely-sensed climatic variables (i.e., TRMM precipitation and GRACE land water storage) and the runoff within a river basin, there exists a time lag between the remotely-sensed climatic variables and the runoff time series, particularly for the Mekong Basin across different climate zones. Previous studies have shown that the runoff lagged behind the upstream TRMM precipitation [61,62] and GRACE land water storage [28] by a month or more, which is controlled by climate, geomorphology, and hydrogeology [29]. Thus, the time lag between the in situ runoff and the upstream spatially-averaged TRMM precipitation (or GRACE land water storage) has to be determined before the linear regression.…”

Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

“…However, hysteresis has been known for a long time in soil physics and soil hydrology and associated with water flowing through porous media [68,92]. Furthermore, hysteresis is found in surface hydrology, where sediment transport studies in streams show that sediment yields may depict such behaviour, as well as ionic species dissolved or adsorbed to the soiled phase [64,93,94].…”

“…Most studies referenced above used hydrological modelling based on water balance approaches applied at large spatial scales and a common conclusion was that the joint assimilation of soil moisture and total water storage remote data improves model performance [60][61][62]. Furthermore, the validation of radar products is commonly performed with meteorological data matrices that are obtained from satellite data, such as NOOA or Meteosat (e.g., [18,[60][61][62][63][64][65]). Table 1 is presented to condense the relevant literature on the topic.…”