The study expects to solve the bottleneck problem in the field of intelligent navigation visual sensing and build a network system of information physics, to achieve a visual sensor which can correspond the depth and color of a scene. It aims to improve the robustness and accuracy of color recognition of color structured light in a complex environment. Based on the digital twins (DTs) technology, the effective transformation of logistics process and physical entity to quasi‐real‐time digital image is realized. The omnidirectional vision sensing technology of a single viewpoint and the panoramic color volume structured light generation technology of a single emission point are integrated. The new active 3D panoramic vision sensor is achieved for which makes the current visual sensing technology developed into the visual perception of body structure. This technology is adopted in the preliminary design of environmental art in the scenic spot, and it can predict the design feasibility of environmental art in the scenic spot, avoid mistakes in decision‐making to a great extent and save human and material resources. And also it can analyze and predict some possible dangerous situations, which can greatly improve the environmental safety factor of the scenic spot. In the improvement stage of environmental art design in the scenic spot, using active 3D vision sensing technology can obtain more comprehensive information and is more conducive to the selection of design schemes.
Abstract Land surface water is a key component of the global water cycle. Compared to remote sensing by satellites, both temporal extension and spatial continuity are superior in modeling of water surface area. However, overall evaluation of models representing different kinds of surface waters at the global scale is lacking. We estimated land surface water area (LSWA) using the Catchment‐based Macro‐scale Floodplain model (CaMa‐Flood), a global hydrodynamic model, and compared the estimates with Landsat at 3″ resolution (∼90 m at the equator) globally. Results show that the two methodologies show agreement in the general spatial patterns of LSWA (e.g., major rivers and lakes, open‐to‐sky floodplains), but globally consistent mismatches are found under several land surface conditions. CaMa‐Flood underestimates LSWA in high northern latitudes and coastal areas, as the presence of isolated lakes in local depressions or small coastal rivers is not considered by the model's physical assumptions. In contrast, model‐estimated LSWA is larger than Landsat estimates in forest‐covered areas (e.g., Amazon basin) due to the opacity of vegetation for optical satellite sensing, and in cropland areas due to the lack of dynamic water processes (e.g., re‐infiltration, evaporation, and water consumption) and constraints of water infrastructure (e.g., canals, levees). These globally consistent differences can be reasonably explained by the model's physical assumptions or optical satellite sensing characteristics. Applying filters (e.g., floodplain topography mask, forest and cropland mask) to the two datasets improves the reliability of comparison and allows the remaining local‐scale discrepancies to be attributed to locally varying factors (e.g., channel parameters, atmospheric forcing).
Abstract. Land surface models (LSMs) use the atmospheric grid as their basic spatial decomposition because their main objective is to provide the lower boundary conditions to the atmosphere. Lateral water flows at the surface on the other hand require a much higher spatial discretization as they are closely linked to topographic details. We propose here a methodology to automatically tile the atmospheric grid into hydrological coherent units which are connected through a graph. As water is transported on sub-grids of the LSM, land variables can easily be transferred to the routing network and advected if needed. This is demonstrated here for temperature. The quality of the river networks generated, as represented by the connected hydrological transfer units, are compared to the original data in order to quantify the degradation introduced by the discretization method. The conditions the sub-grid elements impose on the time step of the water transport scheme are evaluated, and a methodology is proposed to find an optimal value. Finally the scheme is applied in an off-line version of the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) LSM over Europe to show that realistic river discharge and temperatures are predicted over the major catchments of the region. The simulated solutions are largely independent of the atmospheric grid used thanks to the proposed sub-grid approach.
Abstract. Water quality data represent a critical resource for evaluation of the well-being of aquatic ecosystems and assurance of clean water sources for human populations. While the availability of water quality datasets is growing, the absence of a publicly accessible national water quality dataset for both inland and the ocean in China has been notable. To address this issue, we utilized R and Python programming languages to collect, tidy, reorganize, curate, and compile three publicly available datasets, thereby creating an extensive spatiotemporal repository of surface water quality data for China. Distinguished as the most expansive, clean, and easily accessible water quality dataset in China to date, this repository comprised over 330 000 observations encompassing daily (3588), weekly (217 751), and monthly (114 954) records of surface water quality covering the period from 1980 to 2022. It spanned 18 distinct indicators, meticulously gathered at 2384 monitoring sites, which were further categorized as daily (244 sites), weekly (149 sites), and monthly (1991 sites), ranging from inland locations to coastal and oceanic areas. This dataset will support studies relevant to the assessment, modeling, and projection of water quality, ocean biomass, and biodiversity in China, and therefore make substantial contributions to both national and global water resources management. This water quality dataset and supplementary metadata are available for download from the figshare repository at https://doi.org/10.6084/m9.figshare.22584742 (Lin et al., 2023b).
Abstract. The streamflow of the Yellow River (YR) is strongly affected by human activities like irrigation and dam operation. Many attribution studies have focused on the long-term trends of streamflows, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified with fully mechanistic models. This study aims to (1) demonstrate whether the mechanistic global land surface model ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) is able to simulate the streamflows of this complex rivers with human activities using a generic parameterization for human activities and (2) preliminarily quantify the roles of irrigation and dam operation in monthly streamflow fluctuations of the YR from 1982 to 2014 with a newly developed irrigation module and an offline dam operation model. Validations with observed streamflows near the outlet of the YR demonstrated that model performances improved notably with incrementally considering irrigation (mean square error (MSE) decreased by 56.9 %) and dam operation (MSE decreased by another 30.5 %). Irrigation withdrawals were found to substantially reduce the river streamflows by approximately 242.8±27.8×108 m3 yr−1 in line with independent census data (231.4±31.6×108 m3 yr−1). Dam operation does not change the mean streamflows in our model, but it impacts streamflow seasonality, more than the seasonal change of precipitation. By only considering generic operation schemes, our dam model is able to reproduce the water storage changes of the two large reservoirs, LongYangXia and LiuJiaXia (correlation coefficient of ∼ 0.9). Moreover, other commonly neglected factors, such as the large operation contribution from multiple medium/small reservoirs, the dominance of large irrigation districts for streamflows (e.g., the Hetao Plateau), and special management policies during extreme years, are highlighted in this study. Related processes should be integrated into models to better project future YR water resources under climate change and optimize adaption strategies.
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