New methods designed to estimate the daily discharges of rivers in the Tibetan Plateau

River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data

River discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are essential. In this study, 10-m Sentinel-2 multispectral instrument (MSI) imagery and digital elevation model (DEM) data, as well as in situ discharge and sediment data, are fused to extract bankfull river widths on the upper Yellow River. Using in situ cross-section morphology data and flood frequency estimations to calculate the bankfull discharge of 22 hydrological stations, the one-to-one correspondence relationship between the bankfull discharge data and the image cover data was determined. The machine learning (ML) method is used to extract water bodies from the Sentinel-2 images in the Google Earth Engine (GEE). The mean overall accuracy was above 0.87, and the mean kappa value was above 0.75. The research results show that (1) for rivers with high suspended sediment concentrations, the water quality index (SRMIR-Red) constitutes a higher contribution; the infrared band performs better in areas with greater amounts of vegetation coverage; and for rivers in general, the water indices perform best. (2) The effective river width of the extracted connected rivers is 30 m, which is 3 times the image resolution. The R2, root mean square error (RMSE), and mean bias error (MBE) of the estimated river width values are 0.991, 7.455 m, and −0.232 m, respectively. (3) The average river widths of the single-thread sections show linear increases along the main stream, and the R2 value is 0.801. The river width has a power function relationship with bankfull discharge and the contributing area, i.e., the downstream hydraulic geometry, with R2 values of 0.782 and 0.630, respectively. More importantly, the extracted river widths provide basic data to analyze the spatial distribution of bankfull widths along river networks and other applications in hydrology, fluvial geomorphology, and stream ecology.

A Modified ABCD Model with Temperature-Dependent Parameters for Cold Regions: Application to Reconstruct the Changing Runoff in the Headwater Catchment of the Golmud River, China

The runoff changes due to global warming in hydrological basins in the Qinghai–Tibetan Plateau (QTP) have received worldwide attention. The headwater catchment of the Golmud River, located in the northern QTP, is the main source of water resources for the Golmud city in an arid region but has been poorly known for the hydroclimatological behaviors. In this study, a widely-used hydrological model, the ABCD model (Thomas, H.A., Washington, DC, USA), is modified by incorporating temperature-dependent hydrological processes and groundwater evapotranspiration in cold regions with a few additional parameters. The new model is used to reconstruct the monthly runoff in the past decades for the headwater catchment of the Golmud River and performs better than other comparable models. As indicated, the annual snowmelt runoff increased with the increasing air temperature and became more concentrated in April than in May. The frozen soil degradation could increase the hydraulic conductivity of soils and lead to a rise in cold season runoff. The groundwater level in the Golmud city was positively correlated to the annual runoff in the headwater catchment of the Golmud River, indicating that an increase of the groundwater level could be triggered by the rising trend in the streamflow of the Golmud River. This study suggests a useful hydrological model for the groundwater management in the Golmud city.

Discharge Estimates for Ungauged Rivers Flowing over Complex High-Mountainous Regions based Solely on Remote Sensing-Derived Datasets

Reliable information about river discharge plays a key role in sustainably managing water resources and better understanding of hydrological systems. Therefore, river discharge estimation using remote sensing techniques is an ongoing research goal, especially in small, headwater catchments which are mostly ungauged due to environmental or financial limitations. Here, a novel method for river discharge estimation based entirely on remote sensing-derived parameters is presented. The model inputs include average river width, estimated from Landsat imagery by using the modified normalized difference water index (MNDWI) approach; average depth and velocity, based on empirical equations with inputs from remote sensing; channel slope from a high resolution shuttle radar topography mission digital elevation model (SRTM DEM); and channel roughness coefficient via further analysis and classification of Landsat images with support of previously published values. The discharge of the Lhasa River was then estimated based on these derived parameters and by using either the Manning equation (Model 1) or Bjerklie equation (Model 2). In general, both of the two models tend to overestimate discharge at moderate and high flows, and underestimate discharge at low flows. The overall performances of both models at the Lhasa gauge were satisfactory: comparisons with the observations yielded Nash–Sutcliffe efficiency coefficient (NSE) and R2 values ≥ 0.886. Both models also performed well at the upper gauge (Tanggya) of the Lhasa River (NSE ≥ 0.950) indicating the transferability of the methodology to river cross-sections with different morphologies, thus demonstrating the potential to quantify streamflow entirely from remote sensing data in poorly-gauged or ungauged rivers on the Tibetan Plateau.