Renewable energy systems design using average year weather data is a standard approach that works well for grid-tied systems, but for stand-alone ones, it leads to dramatic mistakes. We considered the effect of meteorological data temporal resolution...
Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture and parameter value identification are critical for a physics-based distributed watershed modelling of flood and flood related disasters...
A methodology for deriving high-resolution (5-km) surface shortwave radiative (SWR) fluxes over the Arctic was applied to observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the spring and summer melt season (Mar...
A grid-based version of the Soil and Water Assessment Tool (SWAT) model, SWATgrid, was developed to perform simulations on a regularized grid with a modified routing algorithm to allow interaction between grid cells. However, SWATgrid remains largely...
Gridded CO2 emission maps at the urban scale can aid the design of low-carbon development strategies. However, the large uncertainties associated with such maps increase policy-related risks. Therefore, an investigation of the uncertainties in gridde...
Accurate prediction of subsurface temperature distributions is essential for geothermal reservoir assessment, thermal performance evaluation, and decision support in reservoir management. However, repeated high-resolution numerical simulations are co...
A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this te...
Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based exper...
High-resolution air quality simulations are often performed using different nested domains and resolutions. In this study, the variability of nitrogen dioxide (NO2) concentrations estimated from two nested domains focused on Portugal (D2 and D3), wit...
Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide at...
Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to...
A large number of 2D models were originally developed as 1D models for the calculation of water levels along the main course of a river. Due to their development as 2D distributed models, the majority have added precipitation as a source term. The mo...
Both research-grade and operational numerical weather prediction models perform simulations with horizontal grid spacings as fine as 1 km, and their multi-scale terrain data have become increasingly important for high-resolution model forecasting. Th...
To improve the fabrication efficiency of the two-photon polymerization (TPP) laser direct writing, the TPP exposure process was set to complete by a single-line scan, which was named 2D TPP. The voxel of the 2D TPP should be large enough to cross the...
High-resolution air quality models (AQMs) are critical for real-time air quality forecasting and exposure assessment, although their computational costs increase cubically with resolution. Quantifying model sensitivity to resolution is therefore cruc...
It is important to assess the reliability of high-resolution climate variables used as input to hydrologic models. High-resolution climate data is often obtained through the downscaling of Global Climate Models and/or historical reanalysis, depending...
The purpose of this study is to map shrub distributions and estimate shrub cover fractions based on the classification of high-spatial-resolution aerial orthoimagery and light detection and ranging (LiDAR) data for portions of the highly disturbed co...
This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT) over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-...
Curtailment losses for large-scale hybrid wind–solar photovoltaic (PV) plants with a single grid connection point are often calculated in 1 h time resolution, underestimating the actual curtailment losses due to the flattening of power peaks oc...
The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades,...
High-resolution gridded 2 m air temperature datasets are important input data for global and regional climate change studies, agrohydrologic model simulations and numerical weather predictions, etc. In this study, the digital elevation model (DEM) is...
One of the most common questions in hydrological modeling addresses the issue of input data resolution. Is the spatial analysis of the meteorological/climatological data adequate to ensure the description of simulated phenomena, e.g., the discharges...
Increasing population in urban areas drives urban cover expansion and spatial growth. Developing urban growth models enables better understanding and planning of sustainable urban areas. The SLEUTH model is an urban growth simulation model which uses...
Accurate assessment of wetland hydraulic performance and solute treatment depends on the spatial resolution of bed topography and vegetation density. To evaluate this influence, synthetic shallow-water wetlands with spatially correlated random fields...
Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluc...
Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a re...
Ecosites are required for stand-level forest management and can be determined within a two-dimensional edatopic grid with soil nutrient regimes (SNRs) and soil moisture regimes (SMRs) as coordinates. A new modeling method is introduced in this study...
The You Only Look Once (YOLO) series has consistently advanced the field of object detection, evolving from YOLOv1 to the latest YOLO26, achieving remarkable improvements in detection accuracy and computational efficiency. However, deploying such hig...
Modeling ecosystem services (ESs) intrinsically involves the use of spatial and temporal data. Correct estimates of ecosystem services are inherently dependent upon the scale (resolution and extent) of the input spatial data. Sensitivity of modeling...
In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysic...
Carbon Capture and Storage (CCS) is a critical strategy for reducing CO2 emissions from hard-to-abate sectors. Reliable and efficient reservoir simulation tools are essential for supporting the safe and effective deployment of CCS projects. This stud...
High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and ca...
Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) a...
Precipitation has an important impact on people’s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Fur...
Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to ove...
The scarcity and uneven distribution of precipitation stations in the inland river basins of the Northeastern Tibetan Plateau restrict the application of the distributed hydrological model and spatial analysis of water balance component characteristi...
The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to th...
As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids,...
With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional d...
Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing i...
In this work, high-resolution sparse channel estimation in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is addressed. Firstly, a block-structured compressed channel sensing (CCS) model with high spectr...
The frequency diverse multiple-input-multiple-output (FD-MIMO) radar synthesizes a wideband waveform by transmitting and receiving multiple frequency signals simultaneously. For FD-MIMO radar imaging, conventional imaging methods based on Matched Fil...
Crowd-related disasters in dense public spaces unfold into hazardous situations within seconds, repeatedly demonstrating that reactive response alone is insufficient to minimize damage. This reality has intensified the need for monitoring systems tha...
Using the method based on unmanned aerial vehicle (UAV) imagery, two kinds of data can be obtained: the digital elevation model (DEM) for the digital expression of terrain, and the digital surface model (DSM) for the digital expression of the surface...
Integrated watershed management relies on distributed hydrological models to simulate water transport processes and support decision-making. However, model reliability is often constrained by the resolution and quality of input data, particularly soi...
The hydrodynamics of low-lying riverine floodplains and wetlands play a critical role in hydrology and ecosystem processes. Because small topographic features affect floodplain storage and flow velocity, a hydrodynamic model setup of these regions im...
Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps...
The increasing interest in off-grid green hydrogen production has elevated the importance of reliable techno-economic assessment (TEA) tools to support investment and planning decisions. However, limited operational data and inconsistent modeling app...
Crowdsourced environmental data have the potential to augment traditional data sources during disasters. Traditional sensor networks, satellite remote sensing imagery, and models are all faced with limitations in observational inputs, forecasts, and...
As mobile mapping systems become a mature technology, there are many applications for the process of the measured data. One interesting application is the use of driving simulators that can be used to analyze the data of tire vibration or vehicle sim...
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