River discharge is of great significance in the development of water resources and ecological protection. There are several large ungauged catchments around the word still lacking sufficient hydrological data. Obtaining accurate hydrological information from these areas is an important scientific issue. New data and methods must be used to address this issue. In this study, a new method that couples unmanned aerial vehicle (UAV) data with the classical slope–area method is developed to calculate river discharges in typical ungauged catchments. UAV data is used to obtain topographic information of the river channels. In situ experiments are carried out to validate the river data. Based on slope–area method, namely the Manning–Strickler formula (M–S), Saint-Venant system of equivalence (which has two definitions, S-V-1 and S-V-2), and the Darcy–Weisbach equivalence (D–W) are used to estimate river discharge in ten sections of the Tibet Plateau and Dzungaria Basin. Results show that the overall qualification rate of the calculated discharge is 70% and the average Nash–Sutcliffe efficiency coefficient is 0.97, indicating strong practical application in the study area. When the discharge is less than 10 m³⁄s, D–W is the most appropriate method; M–S and S-V-1 are better than other methods when the discharge is between 10 m³⁄s and 50 m³⁄s. However, if the discharge is greater than 50 m³⁄s, S-V-2 provides the most accurate results. Furthermore, we found that hydraulic radius is an important parameter in the slope–area method. This study offers a quick and convenient solution to extract hydrological information in ungauged catchments.

Research into global water resources is challenged by the lack of ground-based hydrometric stations and limited data sharing. It is difficult to collect good quality, long-term information about river discharges in ungauged regions. Herein, an approach was developed to determine the river discharges of 24 rivers in ungauged regions on the Tibetan Plateau on a long-term scale. This method involved coupling the Manning–Strickler formula, and data from an unmanned aerial vehicle (UAV) and the Gaofen-2, SPOT-5, and Sentinel-2 satellites. We also compared the discharges calculated by using the three satellites’ data. Fundamental information about the rivers was extracted from the UAV data. Comparison of the discharges calculated from the in-situ measurements and the UAV data gave an R² value of 0.84, an average NSE of 0.79, and an RMSE of 0.11 m³/s. The river discharges calculated with the GF-2 remote sensing data and the in-situ experiments for the same months were compared and the R², RMSE, and the NSE were 0.80, 1.8 m³/s, and 0.78, respectively. Comparing the discharges calculated over the long term from the measured in-situ data and the SPOT-5 and Sentinel-2 data gave R² values of 0.93 and 0.92, and RMSE values of 2.56 m³/s and 3.16 m³/s, respectively. The results showed that the GF-2 and UAV were useful for calculating the discharges for low-flow rivers, while the SPOT-5 or the Sentinel-2 satellite gave good results for high-flow river discharges in the long-term. Our results demonstrate that the discharges in ungauged tributaries can be reliably estimated in the long-term with this method. This method extended the previous research, which described river discharge only in one period and provided more support to the monitoring and management of the tributaries in ungauged regions.

River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.

The Tianshan Mountains, known as the “water tower” of Central Asia, are the major source of water for the most part of Xinjiang and oasis region of Central Asia. However, climate warming has amplified the discharges of glacial meltwater in the Tianshan Mountains. In this study, we calculated river discharge by integrating cross-sections mapped using unmanned aerial vehicles (UAV) and water velocity data collected in the field. Multiple remote sensing images, such as Landsat and Sentinel-2 imagery, were applied to estimate the long-term discharge of 19 river sections in ungauged regions of the Tianshan Mountains. River discharge variations under climate change were also examined. Using our in-situ measured discharges as reference, the UAV derived discharge results have an NSE (Nash–Sutcliffe efficiency) of 0.98, an RMSE (root mean square error) of 8.49 m³/s, and an average qualification rate of 80%. The monthly discharge of glacial meltwater-dominated river sections showed an average decrease of 2.46% during 1989–2019. The shrinking and even disappearance of mountain glaciers (approximately −4.98 km²/year) was the main reasons for the decrease trend. However, the precipitation-dominated river sections showed an average increase of 2.27% for the same period. The increase in precipitation (approximately 1.93 mm/year) was the key cause for the increase tendency. This study highlights remote sensing hydrological station technology and its application in the long-term prediction of river discharge, which is critical for decision-making regarding integrated water resource management in alpine regions.

Ephemeral rivers are vital to ecosystem balance and human activities as essential surface runoff, while convenient and effective ways of calculating the peak discharge of ephemeral rivers are scarce, especially in ungauged areas. In this study, a new method was proposed using an unmanned aerial vehicle (UAV) combined with the incipient motion of stones to calculate the peak discharge of ephemeral rivers in northwestern China, a typical arid ungauged region. Two field surveys were conducted in dry seasons of 2017 and 2018. Both the logarithmic and the exponential velocity distribution methods were examined when estimating critical initial velocities of moving stones. Results reveal that centimeter-level orthoimages derived from UAV data can demonstrate the movement of stones in the ephemeral river channel throughout one year. Validations with peak discharge through downstream culverts confirmed the effectiveness of the method. The exponential velocity distribution method performs better than the logarithmic method regardless of the amount of water through the two channels. The proposed method performs best in the combination of the exponential method and the river channel with evident flooding (>20 m³/s), with the relative accuracy within 10%. In contrast, in the river channel with a little flow (around 1 m³/s), the accuracies are weak because of the limited number of small moving stones found due to the current resolution of UAV data. The poor performance in the river channel with a little flow could further be improved by identifying smaller moving stones, especially using UAV data with better spatial resolution. The presented method is easy and flexible to apply with appropriate accuracy. It also has great potential for extensive applications in obtaining runoff information of ephemeral rivers in ungauged regions, especially with the quick advance of UAV technology.