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Modern hydrology is embracing a data-intensive new era, information from diverse sources is currently providing support for hydrological inferences at broader scales. This results in a plethora of data reliability-related challenges that remain unsolved. The water budget non-closure is a widely reported phenomenon in hydrological and atmospheric systems. Many existing methods aim to enforce water budget closure constraints through data fusion and bias correction approaches, often neglecting the physical interconnections between water budget components. To solve this problem, this study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. The concept of decomposing the total water budget residuals into inconsistency and omission residuals is embedded in this framework to account for different residual sources. We examined the efficiency of PHPM-MDCF and the residuals distribution across 475 CONUS basins selected by hydrological simulation reliability. The results indicate that the inconsistency residuals dominate the total water budget residuals, exhibiting highly consistent spatiotemporal patterns. This portion of residuals can be significantly reduced through PHPM-MDCF correction and achieved satisfactory efficiency. The total water budget residuals have decreased by 49 % on average across all basins, with reductions exceeding 80 % in certain basins. The credibility of the correction framework was further verified through several noise experiments. In the end, we explored the potential factors influencing the distribution of residuals and found notable scale effects where residuals decrease with increasing basin area. This emphasizes the importance of carefully evaluating the water balance assumption when employing multisource datasets for hydrological inference in small basins. [ABSTRACT FROM AUTHOR]

Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Earlier, hydrological simulation calibration and validation relied on flow observations at hydrological stations, but multisource observations changed the basin hydrological simulation from single-flow validation to multivariate validation, including evaporation, soil water, and runoff. This study used the Soil and Water Assessment Tool (SWAT) distributed hydrological model to simulate and investigate hydrological processes in the Jinghe River Basin in China. After a single-station, single-variable calibration using flow observation data at the Zhangjiashan Hydrological Station, multisource data were used to validate actual evaporation, soil water, and runoff. Using the flow station data from Zhangjiashan station for parameter calibration and validation, the simulated values of R2, NSE, and KGE were all above 0.64, the PBIAS was within 20%, and the values of all the metrics in the calibration period were better than those in the validation period. The results show that the model performed satisfactorily, proving its regional applicability. Qingyang, Yangjiaping, and Zhangjiazhan stations had R2, NSE, and KGE values above 0.57 and PBIAS within 25% during regional calibration, considering spatial variability. Additionally, simulation accuracy downstream increased. R2, NSE, and KGE were above 0.50, and PBIAS was within 25% throughout validation, except for Qingyang, where the validation period was better than the calibration period. The Zhangjiashan station monthly runoff simulation improved after regional calibration. Runoff validation performed highest in the multivariate validation of evaporation–soil water–runoff, followed by actual evaporation and soil water content in China. The evaluation results for each hydrological variable improved after additional manual calibration. Multivariate verification based on multisource data improved the hydrological simulation at the basin scale. [ABSTRACT FROM AUTHOR]