This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points' spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available.

The compiled dataset is the result of a field collection campaign to measure the depth of the ash and tephra layer in the aftermath of the 2021 volcanic eruption (Tajogaite) on the island of La Palma, Canary Islands, Spain.

The dataset consists of two files: a shapefile and a GeoTIFF raster. The shapefile is a point layer file that displays the location of all ash depth measurements (415 points) taken in the field. To improve our sampling near the crater, where safety and time constraints prevented field collection, we manually sampled additional drone-based measurements (66 points). We combined these data into a single dataset ("ash_depth"; 481 total points) that was then used as input for a spatial interpolation using Inverse Distance Weighting (IDW).

The IDW interpolation was performed using the Spatial Analyst toolbox in ArcMap 10.8.1 (Esri, 2021). As an exact deterministic interpolation, IDW estimates pixels values of unknown points by using average distance and a weight between sample points (Watson & Philip, 1985). This is ideal for a dataset that includes many field measurements since IDW interpolates between the minimum and maximum of the collected data. The model parameters were adjusted manually but the best results were yielded using the default settings, with the exception of the output cell size. The output cell size was calibrated to 2 m. The IDW parameters can be viewed in Table 1.

The sample point locations were resampled from the raster file to estimate the Root Square Mean Error (RMSE) and were saved to the shapefile as "ash_idw". The RMSE of the dataset is 0.34 m. For further inquiries please contact Christopher Shatto (email: christopher.shatto@uni-bayreuth.de).

Please cite the data paper link to this repository as:

C. Shatto, F. Weiser and A. Walentowitz et al., Volcanic tephra deposition dataset based on interpolated field measurements following the 2021 Tajogaite Eruption on La Palma, Canary Islands, Spain, Data in Brief, https://doi.org/10.1016/j.dib.2023.109949

Power 2

Output cell size 2

Search neighborhood type Standard (circular)

Major/minor semiaxis 12161.79/12161.79

Max/minimum neighbors 15/10

Sector type 1

Angle 0

Weight field None

Spatial interpolation techniques play an important role in hydrology as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, not all of them work consistently for hydrologic applications. One of the techniques, Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for interpolating hydrology data. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating hydrologic data and compare it with other widely used methods such as the inverse distance weighting (IDW), natural neighbor, and ordinary kriging. Comparison is performed quantitatively for using root mean square error (RMSE) and R2, visually for creating reasonable surfaces and computationally for ease of operation and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture are used for different areas in the United States. RMSE and R2 results show that LF is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to IDW and Kriging. Computationally, LF is faster than other methods in terms of speed when the datasets are not large. Overall, LF offers a robust alternative to existing methods for interpolating various hydrology data. Further work is required to improve its computational efficiency.

The water-budget-components geodatabase contains selected data from maps in the, "Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011). Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.

Inverse Distance Weighting (IDW) is a spatial interpolation technique used to estimate values at unsampled locations based on known values at nearby points. The method assumes that points closer to the location of interest have a greater influence on the predicted value than those farther away. IDW calculates the predicted value by taking a weighted average of the known values, where the weights are inversely proportional to the distances between the known points and the prediction location, raised to a power parameter. This power parameter controls the rate at which the influence of the known points decreases with distance, with higher values giving more weight to closer points. IDW is widely used in fields such as geostatistics, meteorology, and environmental science to interpolate spatial data like rainfall, temperature, and pollution levels.

Dataset in Excel version 2016 format

Comparison of the interpolation accuracies achieved using MSE, MAE and MRE based on a cross-validation analysis.

The first case study focuses on spatial interpolation for ground station-based environmental data. Spatial interpolation is a critical process in environmental science, where data from ground stations are used to estimate values in unmeasured locations. This case study showcases how our Common Workflow Language (CWL) based Workflow Management System (WfMS) can handle complex spatial data processes, ensuring accurate and efficient interpolation. In this case study, two spatial interpolation models were implemented. IDW (Inverse Distance Weighting) is a deterministic method for spatial interpolation, which estimates the values at unknown points using the values from known points weighted by the inverse of their distances. Kriging is a geostatistical interpolation technique that not only considers the distance between known and unknown points but also models the spatial autocorrelation among the measured points. It provides more accurate estimations by using a weighted average of known points, where the weights are determined based on the spatial structure of the data.

Spatial interpolation is critical in geographic information systems (GIS) and environmental science, particularly when dealing with high-dimensional and nonlinear data. Classical methods like Kriging and inverse distance weighting (IDW) often struggle with the complexities of irregular terrain and sparse datasets, and are inadequate for capturing the nonlinear characteristics of high-dimensional spatial data. In this paper, we introduce a novel interpolation method based on the Denoising Diffusion Probabilistic Model (DDPM), which incorporates ConvNeXt V2 blocks within a UNet architecture. To validate the performance of our model, we employ the Copernicus Digital Elevation Model (COP-DEM) dataset for simulation experiments. Experimental results demonstrate that the proposed DDPM method significantly outperforms classical interpolation techniques, particularly in scenarios with high-density control points, producing high-quality interpolation results with strong transferability. This approach shows considerable promise for spatial interpolation in high-dimensional, complex terrains, offering a more robust alternative to traditional methods. It not only addresses key challenges in interpolation accuracy but also opens up new possibilities for applying generative models in other spatial data processing domains, including environmental monitoring and geospatial modeling.

Data provided for Passiflora cincinnata reproductive phenology interpolation in Bahia, Brazil.

Results from a New Mexico county based gravity model measuring geographic accessibility using 2015 population and physician data. Both Euclidean and road distance measures were used. The relative difference between the Euclidean and road distance measures is presented. An IDW interpolation for road distance results is presented in addition choropleth maps. The 2015 census population estimates are from UNM-GPS and the 2015 primary care physician estimates were obtained from the New Mexico Health Care Workforce Committee, 2016 Annual Report: (http://hsc.unm.edu/assets/doc/economic-development/nmhcwc-presentation-2016.PDF).Additional results from a New Mexico Census Tract based gravity model measuring geographic accessibility using 2002 population and physician data. Both Euclidean and road distance measures were used. The relative difference between the Euclidean and road distance measures is presented. An IDW interpolation for road distance results is presented in addition choropleth maps. The 2015 census population estimates are from UNM-GPS and the 2002 primary care physicians estimates were from the Division of Government Research, UNM as part of work performed for the New Mexico Health Policy Commission from 1998 through 2002.Note: both choropleth and IDW interpolation examples are presented.More information at: (http://www.unm.edu/~lspear/health_stuff.html).

Geoscience Australia has been deriving raster sediment datasets for the continental Australian Exclusive Economic Zone (AEEZ) using existing marine samples collected by Geoscience Australia and external organisations. Since seabed sediment data are collected at sparsely and unevenly distributed locations, spatial interpolation methods become essential tools for generating spatially continuous information. Previous studies have examined a number of factors that affect the performance of spatial interpolation methods. These factors include sample density, data variation, sampling design, spatial distribution of samples, data quality, correlation of primary and secondary variables, and interaction among some of these factors. Apart from these factors, a spatial reference system used to define sample locations is potentially another factor and is worth investigating. In this study, we aim to examine the degree to which spatial reference systems can affect the predictive accuracy of spatial interpolation methods in predicting marine environmental variables in the continental AEEZ. Firstly, we reviewed spatial reference systems including geographic coordinate systems and projected coordinate systems/map projections, with particular attention paid to map projection classification, distortion and selection schemes; secondly, we selected eight systems that are suitable for the spatial prediction of marine environmental data in the continental AEEZ. These systems include two geographic coordinate systems (WGS84 and GDA94) and six map projections (Lambert Equal-area Azimuthal, Equidistant Azimuthal, Stereographic Conformal Azimuthal, Albers Equal-Area Conic, Equidistant Conic and Lambert Conformal Conic); thirdly, we applied two most commonly used spatial interpolation methods, i.e. inverse distance squared (IDS) and ordinary kriging (OK) to a marine dataset projected using the eight systems. The accuracy of the methods was assessed using leave-one-out cross validation in terms of their predictive errors and, visualization of prediction maps. The difference in the predictive errors between WGS84 and the map projections were compared using paired Mann-Whitney test for both IDW and OK. The data manipulation and modelling work were implemented in ArcGIS and R. The result from this study confirms that the little shift caused by the tectonic movement between WGS84 and GDA94 does not affect the accuracy of the spatial interpolation methods examined (IDS and OK). With respect to whether the unit difference in geographical coordinates or distortions introduced by map projections has more effect on the performance of the spatial interpolation methods, the result shows that the accuracies of the spatial interpolation methods in predicting seabed sediment data in the SW region of AEEZ are similar and the differences are considered negligible, both in terms of predictive errors and prediction map visualisations. Among the six map projections, the slightly better prediction performance from Lambert Equal-Area Azimuthal and Equidistant Azimuthal projections for both IDS and OK indicates that Equal-Area and Equidistant projections with Azimuthal surfaces are more suitable than other projections for spatial predictions of seabed sediment data in the SW region of AEEZ. The outcomes of this study have significant implications for spatial predictions in environmental science. Future spatial prediction work using a data density greater than that in this study may use data based on WGS84 directly and may not have to project the data using certain spatial reference systems. The findings are applicable to spatial predictions of both marine and terrestrial environmental variables.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

Urmia Lake, the second largest hyper-saline lake in the world, has experienced a significant drop in water level during the last decade. This study was designed to examine the water quality of Urmia Lake and to characterize the spatial heterogeneity and temporal changes of the physico-chemical parameters between October 2009 and July 2010. Two spatial interpolation methods, Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), were used and compared with each other to derive the spatial distribution of ionic constituents as well as TDS and density along the lake. Results showed that the main dominant cations and anions in Urmia Lake were Na+, Mg++, K+, Ca++, Cl- , SO4--, and HCO3-, respectively. Although water quality of the lake is homogeneous with depth, it differs between the northern and southern parts. Water quality also varies seasonally, determined by river inflows and the lake bathymetry. Moreover, with the present salinity level, salt precipitation is likely in Urmia Lake and is becoming one of the principal factors determining the distribution of solutes within the lake. This study shows that the combined use of temporal and spatial water quality data improves our understanding of complex, large aquatic systems like Urmia Lake.

This resource belongs to the manuscript "Statistical-Topographical Mapping of Rainfall Over Mountainous Terrain Using Beta Scaling". It collects the R code and the data needed to reproduce the analyses and generate the figures.

We present a robust approach for quantitative precipitation estimation (QPE) for water resources management in mountainous catchments, where rainfall sums and variability are correlated with orographic elevation, but density of rain gauges does not allow for advanced geostatistical interpolation of rainfall fields. Key of the method is modelling rainfall at unobserved locations by their elevation-dependent expected daily mean, and a daily fluctuation which is determined by spatial interpolation of the residuals of neighbouring rain gauges, which are scaled according to the elevation difference. The scaling factor is defined as the ratio of covariance and variance, in analogy to the "beta" used in economics. The approach is illustrated for the Chirilu catchments (Chillón, Rímac, Lurín) in the Andes near Lima, Peru. The results are compared to conventional IDW interpolation and a merged national rainfall product. The method results in QPE that are better matching with observed discharges. The β-IDW approach thus provides a robust and flexible means to estimate rainfall input to mesoscale mountainous catchments.

Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.

The data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: 10.1016/j.energy.2019.116212

Code for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr

The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.

"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).

"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).

"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).

The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.

Data consists of conversion factors that can be used to convert between numerous vertical tidal datums and the North American Vertical Datum of 1988 (NAVD88). The data cover the Eastern Shore of Virginia and parts of southeastern Maryland along with the surrounding coastal waters and are represented as approximately 100m (100.584m) resolution grids. The six included tidal datums are local mean sea level (LMSL), mean tidal level (MTL), mean low water (MLW), mean lower low water (MLLW), mean high water (MHW), and mean higher high water (MHHW). All vertical units are in meters. By combining multiple conversions to and from NAVD88, conversion between the various tidal datums is possible. Two versions of the conversion factor grids are provided for each NAVD88-to-tidal-datum pairing: one that only contains data for areas not masked as nodata by the NOAA VDatum program (original source data) and one that contains both the original and interpolated data (see below for details). Naming conventions used were "cfactor_DDD" for the original VDatum-detrived dataset where "DDD" is the local tidal datum and "cf_nd_DDD" for the dataset that includes interpolated values within the nodata masks (IDW interpolation across masked areas, typically upland regions but also shallow seaside bays and creeks for which no adequate tidal benchmarks were available). By definition, the baseline elevation (sea level or 0.0m elevation) for NAVD88 is referenced to the fixed International Great Lakes Datum of 1985 local mean sea level height value, at Rimouski, Quebec, Canada. Additional tidal bench mark elevations were not used to calculate NAVD88 due to the demonstrated variations in sea surface topography, i.e., the fact that mean sea level is not the same equipotential surface at all tidal benchmarks. The magnitude of the difference between local mean sea level (LMSL) at the tidal benchmarks of the Eastern Shore of Virginia and the NAVD88 defined sea-level varies from 0.039 to 0.149 meters BELOW zero NAVD88. Tidal prisms also vary at each tidal benchmark (in part due to differences in basin configuration and tidal interactions) causing the conversion factors for the other tidal datums to also vary spatially in similar but not identical patterns. The VDatum 3.2 software program from NOAA (http://vdatum.noaa.gov/) was used to convert the x,y,z center points of the 100m gridded data wherein all Z elevations were set equal to zero (0) from NAVD88 to each of the six local tidal datums (the X,Y horizontal WGS84 UTM 18N coordinates remained unchanged). The resulting conversion factors represent the new elevation at which the NAVD88 zero level would lie in reference to the new datum; thus, to convert from NAVD88 and the new tidal datum, one would add this conversion factor to the NAVD88 elevations to get elevations relative to the chosen tidal datum. To convert to NAVD88 from a given tidal datum, one would subtract the conversion factor from the tidal elevation. Data were turned back into gridded data with the same resolution and horizontal extent as the original data grid. The internal data grids used by the VDatum program mask as nodata most land areas (including marshes) plus many of the seaside shallow bays, either in part or in full, for which reliable tidal benchmark data is/was not available. As a result, the program cannot be used in these nodata areas, even if immediately adjacent to data areas. So as to make conversion factors available for these coastal bays and marshes and seaside watersheds of interest to the VCRLTER, conversion factors for gridded regions within the NOAA nodata masks were interpolated from neighboring data values using the inverse distance weighting (IDW) techniques employed by ESRI's ArcGIS 10.1 software. IDW interpolation resulted in conversion factors that varied gradually spatially when adjacent to the NOAA VDatum data grids but that often showed relatively sharp transitions when equidistant between different far-apart basins (such as mid-peninsula between the Chesapeake Bay and Atlantic Ocean, or within South Bay bounded by data constructed from tidal datums for the Atlantic Ocean (east), Ship Shoal Inlet (south), Sand Shoal Inlet (north), and Magothy Channel (west)). It is suggested that the appropriate use of this data is to convert elevation datasets referenced to a tidal datum to NAVD88 if integrating multiple datasets together over large areas, such as across the full Eastern Shore or across multiple watersheds or coastal bays, so as to not introduce artificial IDW-related transitions into otherwise vertically-consistent upland elevations or basin-scale bathymetric surveys. When converting elevations of fringing upland marshes, the conversion factors (including interpolated values) can likely be used directly on a cell-by-cell level to a... Visit https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-vcr%2F219%2F4 for complete metadata about this dataset.

Precipitation raster maps created using WPC TC rainfall page and PRISM rainfall data. Average rainfall maps for 1900-2020 (all storms) and 1950-2021 (LSX TC cases) as well as top 5 storm rainfall maps were created using IDW interpolation. Additional higher resolution PRISM rainfall raster data were summed to create top 5 storm rainfall maps for storms that occurred after 1980.

At the onset of the full reopening in Spring 2023 of the Difficult-to-Return Zone of Northeastern Japan following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident that took place in March 2011, several spatial layers were regrouped and compiled to facilitate environmental studies dealing with the redistribution of radiocesium fallout across landscapes.

The current dataset is composed of 23 shapefiles including those of the delineations of different spatial zones (Intensive Contamination Survey Areas – ICAs, Special Decontamination Zones – SDZ, Difficult-to-Return Zone – DTRZ, and FNDPP location) (Evrard et al. 2019), municipalities where mushroom consumption restrictions were enforced (restricted and partially lifted restrictions), river hydrographic networks and their respective drainage areas (Mano, Niida, Ota, Takase, and Ukedo), dam reservoirs and drainage areas (Mano, Ogaki, Takanokura, and Yokokawa), multiple administrative delineations in Japan (whole Japan administrative boundaries, Prefectures, and municipalities) (GIS, 2016), and one raster file of the reconstruction of initial 137Cs fallout across eastern Japan (from Kato et al., 2019).

The current dataset provides a support to a publication submitted to the SOIL journal:

Evrard, O., Chalaux-Clergue, T., Chaboche, P.-A., Wakiyama, Y., and Thiry Y. (2023). Research and Management Challenges Following Soil and Landscape Decontamination at the Onset of the Reopening of the Difficult-To-Return Zone, Fukushima (Japan)’. SOIL 9: 479–97. https://doi.org/10.5194/soil-9-479-2023.

All map processing was carried out using QGIS 3.26.0 (QGIS, 2022) and under the EPSG:WGS 84 projection system.

The 137Cs fallout raster (in Bq m-2, decay-corrected to July 2011) was generated from the point grid of Kato et al. (2019). A total of 126 tiles (0.25 x 0.25 degree) were generated by Inverse Distance Weighted (IDW) interpolation using the 'IDW interpolation' tool with the following settings: distance coefficient P = 1.0 and pixel size (x and y) = 0.0015 degree. Tiles were then merged into a single tile using the raster 'Merge' tool. The initial point grid footprint was manually delineated to define the spatial applicability zone of the airborne survey. A buffer zone corresponding to half plus 10% of the longest distance between two airborne points (x = 0.002, y = 0.003), i.e. 0.0017 degree, was generated using the 'buffer' tool. The single tile was then cut according to the footprint of the buffer zone using the 'clip a raster by a mask layer' tool. A single-band pseudo-colour scale is provided and displays pixels with a value above 1000 Bq m-2 (eq. global background).

Esri ArcGIS Online (AGOL) Map Image Layer for accessing the Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones data product.Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones consists of polygon geometric features which represent the geographic areas throughout the State of Maryland that are impacted by CRAB inundation (0 to 1ft, 1 to 2ft, and 2ft or more).The Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones data product was created using a GIS spatial analysis model, unique for each county in the State of Maryland. Coastal counties follow an analysis methodology that incorporates FEMA Stillwater wave action as it is understood from the FEMA identified VE zones. A Water Surface Elevation (WSE) and Still Water Elevation (SWEL) rasters are used as the baseline to identify existing water depths within each county. For all flood zones that are not classified as VE the WSE three feet was added to reflect a three-foot rise in the base flood elevations. For those WSEs falling within a FEMA floodplain identified V Zone, six feet was added (three feet for the increase in flood elevations for the CS-CRAB, and 3 feet to compensate for the minimum of 3 foot wave action typically mapped by FEMA) / wave heights greater than 3 feet were reduced to the 3 foot minimum for consistency across the shoreline. The newly calculated WSE plus three datasets were then converted to points and merged. Next, an Inverse Distance Weighted (IDW) Interpolation was used to compute the proportional weighted values between the WSE point locations based on proximity. The DEM for each county is then subtracted from the new IDW raster in order to show precise water locations as they relate to the land elevation, producing a freeboard depth grid representing the depth of flood waters above the existing ground elevation given a 3 foot increase in water level. A course resolution QAQC was applied to remove “islands” of data associated with DEM inaccuracies and other elevation anomalies. The analysis was run at a1 ft x 1 ft raster resolution. The DEM accuracy for each county varies based what is currently available. Here the breakdown of DEM accuracy for each county used in this project: Anne Arundel County DEM year is 2017 and horizontal resolution is 1ft. Baltimore County DEM year is 2015 and horizontal resolution is 2.5ft. Baltimore City DEM year is 2015 and horizontal resolution is 0.7m. Calvert County DEM year is 2017 and horizontal resolution is 1ft. Caroline County DEM year is 2013 and horizontal resolution is 3.125ft. Cecil county DEM year is 2013 and horizontal resolution is 0.6m. Charles County DEM year is 2014 and horizontal resolution is 0.9m. Dorchester County DEM year is 2013 and horizontal resolution is 0.9m. Harford County DEM is 2013 and horizontal accuracy is 1.5m. Kent County DEM year is 2015 and horizontal resolution is 0.7m. Prince George’s County DEM year is 2014 and horizontal resolution is 0.7m. Queen Anne’s County DEM year is 2013 and horizontal resolution is 0.6m. Somerset County DEM year is 2012 and horizontal accuracy is 1m. St Mary’s County DEM year is 2014 and Horizontal accuracy is 0.9m. Talbot County DEM year is 2015 and Horizontal accuracy is 0.7m. Wicomico County DEM year is 2012 and horizontal accuracy is 1m. Worchester County DEM year is 2011 and horizontal accuracy is 1m.The Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones data product was created by the Maryland Environmental Service (MES) in partnership with the Maryland Department of Environment (MDE) and the Coast Smart Council, under the guidance of the Maryland Department of Natural Resource (DNR).For additional information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

Limited borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods, such as ordinary kriging (OK) and inverse distance weighting (IDW), may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. To overcome this challenge, we propose an adaptive spatial interpolation graph convolutional network (ASI-GCN) model. This model synergistically integrated the principles of spatial auto-correlation with the capabilities of graph convolutional networks (GCN). By merging these elements, the ASI-GCN model effectively constrained the transfer of pollutant concentrations. It adeptly captures nuanced variations in spatial structure, thereby enhancing the precision of soil pollution characterization. We applied our model to a coking plant in Beijing based on 215 soil samples collected from 15 boreholes. To evaluate the robustness of the model, three pollutants with distinct volatilization characteristics were employed, including arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). The leave-one-out cross-validation procedure was used to estimate the performance of ASI-GCN and its derivatives, ASI-GCN_RC_G and ASI-GCN_RC_K, when they are used to make predictions at unsampled sites.The results showed that the ASI-GCN model demonstrated improved performance with R² values of 0.52, 0.67, and 0.57, and RMSE values of 5.041 mg/kg, 1.699 mg/kg , and 167.710 mg/kg for As, BaP, and Ben, respectively, outperforming traditional models like OK and IDW. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, semi-volatile BaP concentrated in the silty sand layer, while volatile Ben was predominantly found in the clay and fine sand layers. The ASI-GCN model provides key insights for risk assessment and remediation strategies by capturing complex contaminant distributions with limited borehole data, demonstrating its potential for application in various contaminated sites.