Precipitation has been recognized as the most critical meteorological parameter in hydrological studies. Recent developments in space technology provide cost-effective alternative ground-based observations to simulate the hydrological process. Here, this paper aims to evaluate the performance of satellite-based datasets in the hydrological modeling of a sensitive area in terms of water quality and safety watershed. Three precipitation products, i.e., rain gauge observations (RO), the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), and Tropical Rainfall Measuring Mission Multi-satellite (TRMM) products, were used to develop the Soil and Water Assessment Tool (SWAT) model to simulate the streamflow in the Danjiang River Basin (DRB). The results show that: (1) these three precipitation products have a similar performance with regard to monthly time scale compared with the daily scale; (2) CMADS and TRMM performed better than RO in the runoff simulations. CMADS is a more accurate dataset when combined with satellite-based and ground-based data; (3) the results indicate that the CMADS dataset provides reliable results on both monthly and daily scales, and CMADS is a possible alternative climate product for developing a SWAT model for the DRB. This study is expected to serve as a reference for choosing the precipitation products for watersheds similar to DRB where the rain gauge data are limited.

Precise evaluation of the correlations among precipitation, groundwater and river water enhance our understanding on regional hydrological circulation and water resource management. The innovative and efficient use of wavelet analysis has been able to identify significant interactions in the spatial and temporal domains and to estimate the recharge travel time. In this paper, a wavelet analysis was utilized to analyse 43 years of monthly, and 2 years of daily, precipitation, river level and groundwater level data in the Yoshino River Basin, Japan. There were two main results: (1) There was a significant influence of precipitation and river on groundwater, with a periodicity of 4–128 days, 1 year and 2–7 years. The periodicity of 1 year was correlated with seasonal variability. The significant interaction at 4–128 days mainly occurred in the rainy season. The 2–7-year oscillation of aquifer water levels was determined by precipitation. (2) The recharge-water travel times in the study area estimated from the arrow patterns in the precipitation–groundwater wavelet coherence (WTC) were consistent for each observation well. The response times of the aquifer to precipitation were 1 day and 3–6 days in 2013 and 2014, respectively. The different time lags were likely determined by the timing of maximum daily precipitation.

Vegetation colonizing fluvial sandbars provides many noteworthy functions in river and floodplain systems, but it also influences hydrodynamic processes, mainly during flooding events. Numerical modelling is generally used to evaluate the impact of floods, but its reliability is very much connected with the accuracy of the bed and bank roughness, which is eventually altered by the presence of vegetation and its height. However, for the sake of simplicity, most models tend to ignore how the sandbar roughness varies over space and time, as a function of the local vegetation dynamics (spatial distribution and height). To determine the long-term dynamic vegetation condition using remote sensing multispectral indexes, this study leverages a deep-learning method to establish a relationship between vegetation height (h), a critical parameter for vegetation roughness estimation, and vegetation indexes (VIs) collected by an uncrewed aerial vehicle (UAV). A field campaign was performed in October 2024 covering the Baishazhou sandbar, located along a straight section of the Wuhan reach of the Changjiang River Basin, China. The results show that the R² and RMSE between the real and predicted vegetation height by the trained Fully Connected Neural Network (FCNN) are 0.85, 1.10 m, and the relative error reaches a maximum of 17.2%, meaning that the trained FCNN model performs rather well. Despite being tested on a single case study, the workflow presented here demonstrates the opportunity to use UAVs for depicting vegetation characteristics such as height over large areas, eventually using them to inform numerical models that consider sandbar roughness.

Climate change is impacting rivers worldwide, with a reduction in normal flow conditions in temperate regions like Poland. Such changes might have a significant influence on riparian vegetation and channel planform dynamics. To better understand how these changes impact the river morphology, this research focuses on a 250 km-long reach of the Polish Vistula River and investigates variations of monthly maximum discharges and vegetation conditions over the period 1984–2023 by means of Landsat satellite images. These satellite data were handled via Google Earth Engine, looking at a common index such as the Normalized Difference Vegetation Index, considered as a proxy of vegetation coverage variations. Results point out an increase in the median NDVI over the study area from 0.2 in 1984 to 0.3 in 2023, connected with a reduction of monthly discharge from around 920 m³/s to 880 m³/s. This suggests that changes in flow discharge are associated with a process of riparian vegetation growth, leading to a reduction of planform and bars dynamics and closure of secondary channels (i.e., oversimplification). This is particularly evident over the last couple of decades, during which water availability has decreased significantly, as more humid years in the middle of the study period are now no longer existing, with an observed decrease in the maximum monthly discharge during the last 20 years, likely connected with a more severe impact of climate change. This reduction in flooding events allows more time for vegetation to establish on river bars and banks, eventually creating new islands and causing the observed oversimplification of the active channel. In summary, using the Vistula River as an exemplary case study, this research suggests that prolonged dry periods, more common in recent decades due to climate change, might impact large rivers located in temperate climates, favouring the development of vegetation on exposed sandbars, eventually resulting in a less dynamic active channel.

Climate change is impacting rivers worldwide, with a reduction in normal flow conditions in temperate regions like Poland. Such changes have an influence on riparian vegetation, as depicted by this study, which focuses on a 250km-long reach of the Polish Vistula River and investigates variations of monthly maximum discharges and vegetation conditions over the period 1984-2023 by means of Landsat satellite images. These satellite data were handled via Google Earth Engine, looking at a common index such as the Normalized Difference Vegetation Index, considered as a proxy of vegetation coverage variations. Results suggest that a process of riparian vegetation growth and consequent oversimplification is affecting this river reach, particularly over the last couple of decades, during which water availability has decreased significantly. Using the Vistula River as an exemplary case study, this research suggests that prolonged dry periods, more common in recent decades due to climate change, might impact large rivers located in temperate climates, favouring the development of vegetation on exposed sandbars, eventually resulting in a less dynamic active channel.