Search

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]

Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model's superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation. [ABSTRACT FROM AUTHOR]

State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network. [ABSTRACT FROM AUTHOR]

Water, energy, food, and ecology are essential for achieving sustainable development in a region, and in order to achieve the Sustainable Development Goals, their security is also essential at a river basin scale. This study investigated the interrelationships among the water system, food system, energy system, and ecosystem in China's Upper Han River, in alignment with Goals 2, 6, 7, and 15 of the United Nations' Sustainable Development Goals (SDGs). To evaluate the achievement of the SDGs in the Upper Han River, this water–energy–food–ecology system was evaluated by a thorough evaluation index system according to Goals 2, 6, 7, and 15, and the weights of the indices were given using a combination of the CRITIC weighting method and entropy approach. The level of coupling coordination of the system from 2000 to 2021 was quantitatively evaluated by using a coupling coordination degree model. The autoregressive integrated moving average model was built to forecast the process of the indices from 2022 to 2041, and the predicted processes of the system were evaluated by the coupling coordination degree model. The degree of coupling coordination improved from 0.396 to 0.845, and the comprehensive assessment development index increased by 113% from 2000 to 2021, demonstrating that it was a stable development period in general. The fragile support capacity of the water system for the energy system, food system, and ecosystem had a great impact on the overall comprehensive evaluation index. SDG2 (food system), SDG6 (water system), SDG7 (energy system), and SDG15 (ecosystem) all have higher levels of internal conflict. These bi-directional dynamics tended to converge in the sufficiency development mode in the future period as well as the historical period. The analysis of the relationship showed that there were inherent connections and interactions between the four goals, as presented by the high level of coupling that persisted between SDG2, SDG6, SDG7, and SDG15. In the process of promoting the achievement of these goals, the coupling degree also tends to be coordinated from 2022 to 2041. The results offer a view for the river basin's sustainable development and management. [ABSTRACT FROM AUTHOR]

Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will be used to predict the deep soil temperature. The models were forced by combinations of environmental factors, including daily air temperature (Tair), water vapor pressure (Pw), net radiation (Rn), and soil moisture (VWC), which were observed in the Hejiashan watershed on the Loess Plateau in China. The results showed that the accuracy of the model for predicting deep soil temperature proposed in this paper is higher than that of directly using environmental factors to predict deep soil temperature. In testing data, the range of MAE was 1.158–1.610 °C, the range of RMSE was 1.449–2.088 °C, the range of R2 was 0.665–0.928, and the range of KGE was 0.708–0.885 at different depths. The study not only provides a critical reference for predicting soil temperature but also helps people to better carry out agricultural production activities. [ABSTRACT FROM AUTHOR]

A field experiment was conducted in the Zhangjiachong watershed to analyze the characteristics of rainfall–runoff process and sediment yield under different types of terraces. Various runoff plots were utilized, and a two serial tank model was employed to simulate runoff generation and concentration processes, taking into account vegetation interception. A soil erosion model incorporating raindrop erosion and sheet erosion was established to replicate sediment yield and concentration process. The parameters of the rainfall–runoff model and soil erosion model were calibrated and validated using observed runoff and sediment yield data. The controlling factors on runoff and sediment yield were explored and compared. The results indicate that both calibration and validation outcomes were acceptable. The earth‐banked terraces–citrus demonstrated the largest coefficients of upper and lower surface runoff, while its upper runoff threshold of the upper tank and the runoff threshold of the lower tank were the lowest, suggesting that it was more likely to produce surface and subsurface runoff. On the other hand, the stone dike terraces–crops exhibited the smallest coefficients of upper and lower surface runoff, while its upper runoff threshold of the upper tank was the highest. The lower runoff threshold of the upper tank was the same across different types of terraces. Compared to the earth‐banked terraces, the stone dike terraces have a larger infiltration coefficient in the upper tank and a smaller infiltration coefficient in the lower tank. Sensitivity analysis indicates that the most sensitive factors affecting runoff simulation results are the runoff threshold and the infiltration coefficient of the lower tank. Terraces, whether constructed with earth banks or stone dikes, are effective in reducing soil erosion. However, during storm rainfall events, these terraces may be partially destroyed, resulting in a higher sediment yield than that of the same vegetation cover on slope land. Terraces with hedgerows have a more significant effect on reducing runoff and sediment than those without hedgerows. [ABSTRACT FROM AUTHOR]

The circadian clock is generated by a molecular timekeeping mechanism coordinating daily oscillations of physiology and behaviors in mammals. In the mammalian circadian clockwork, basic helix–loop–helix ARNT‐like protein 1 (BMAL1) is a core circadian component whose defects lead to circadian disruption and elicit behavioral arrhythmicity. To identify previously unknown regulators for circadian clocks, we searched for genes influencing BMAL1 protein level by using a CRISPR/Cas9‐based genome‐wide knockout library. As a result, we found that the deubiquitinase ubiquitin carboxyl‐terminal hydrolase 1 (USP1) positively affects BMAL1 protein abundance. Overexpression of wild‐type USP1, but not a deubiquitinase‐inactive mutant USP1, upregulated BMAL1 protein level, whereas genetic ablation of USP1 downregulated BMAL1 protein level in U2OS cells. Furthermore, treatment with USP1 inhibitors led to significant downregulation of BMAL1 protein in U2OS cells as well as mouse tissues. Subsequently, genetic ablation or pharmacological inhibition of USP1 resulted in reduced mRNA levels of a panel of clock genes and disrupted circadian rhythms in U2OS cells. Mechanistically, USP1 was able to de‐ubiquitinate BMAL1 and inhibit the proteasomal degradation of BMAL1. Interestingly, the expression of Usp1 was much higher than the other two deubiquitinases of BMAL1 (Usp2 and Usp9X) in the mouse heart, implying a tissue‐specific function of USP1 in the regulation of BMAL1 stability. Our work thus identifies deubiquitinase USP1 as a previously unknown regulator of the mammalian circadian clock and highlights the potential of genome‐wide CRISPR screens in the identification of regulators for the circadian clock. [ABSTRACT FROM AUTHOR]