Hurricane Harvey made landfall near Rockport, Texas, on August 25, 2017, as a Category 4 hurricane with wind gusts exceeding 150 miles per hour. As Harvey moved inland, the forward motion of the storm slowed down and produced tremendous rainfall amounts over southeastern Texas, with 8-day rainfall amounts exceeding 60 inches in some locations, which is about 15 inches more than average annual amounts of rainfall for eastern Texas and the Texas coast. Historic flooding occurred in Texas as a result of the widespread, heavy rainfall; wind and flood damages were estimated to be $125 billion, and the storm resulted in at least 68 direct fatalities.In the immediate aftermath of the Harvey-related flood event, the U.S. Geological Survey (USGS) and the Federal Emergency Management Agency initiated a cooperative study to evaluate the magnitude of the flood, determine the probability of occurrence, and map the extent of the flood in Texas. Seventy-four USGS streamflow-gaging stations in Texas with at least 15 years of record and no large data gaps in the period of record had a 2017 annual peak streamflow related to Harvey ranking in the top five of all annual peaks for each given station. New peaks of record streamflow were recorded at 40 of the 74 USGS streamflow-gaging stations. The number of years of peak streamflow record for the 74 analyzed streamflow-gaging stations ranged from 18 to 105, with a mean number of 55 years. The annual exceedance probability estimates for the analyzed streamflow-gaging stations ranged from less than 0.2 to 14.0 percent. USGS field crews surveyed 2,123 high-water marks to obtain water-surface elevations, in feet above the North American Vertical Datum of 1988. In some locations, several water-surface elevations were averaged to obtain 1 water-surface elevation, resulting in 1,258 water-surface elevations. Some of these high-water marks were used, along with peak-stage data from USGS streamflow-gaging stations, to create 19 inundation maps to document the areal extent of the maximum depth of the flooding. Digital datasets of the inundation area, modeling boundary, water-depth rasters, and final map products are available from the USGS data release associated with this report (https://doi.org/10.5066/F7VH5N3N).

Freshwater algal blooms are a global concern,^{1 – 4} and there is evidence that they are becoming more common in response to climate change.^{1,5 – 7} Because algal blooms can adversely affect public health, economies, and ecosystem services by degrading water quality,^8,9 early identification of algal blooms can improve public safety and mitigate economic concerns.

Algae and cyanobacteria (commonly known as blue-green algae) are a speciose and functionally diverse group of photosynthetic microorganisms that are present in aquatic ecosystems worldwide.¹ The presence of certain types of algae and cyanobacteria potentially have nuisance or harmful effects on the water-quality in reservoirs. A reduction in dissolved-oxygen levels in aquatic ecosystems is associated with rapid increases in the population of algae and cyanobacteria that can lead to anoxic fish kills and cause nuisance effects, including taste and odor producing biochemical compounds, such as geosmin and 2-methylisoborneol (MIB).^{2 – 4} Toxins produced by certain cyanobacteria (i.e., cyanotoxins), such as microcystin, are known to cause skin irritation and acute and chronic toxicity.⁵ Exposure to cyanotoxins can lead to illness or death in humans, pets, wildlife, and livestock through direct contact, inhalation, and ingestion or indirectly from the bioaccumulation of cyanotoxins in fish that are consumed as food.⁶

Worldview-3 (WV3) 16-band multispectral data were used to map exposed bedrock and mine waste piles associated with legacy open-pit mining of sandstone-hosted roll-front uranium deposits along the South Texas Coastal Plain. We used the “spectral hourglass” approach to extract spectral endmembers representative of these features from the image. This approach first requires calibrating the imagery to reflectance, then masking for vegetation, followed by spatial and spectral data reduction using a principal component analysis-based procedure that reduces noise and identifies homogeneous targets which are “pure” enough to be considered spectral endmembers. In this case, we used a single WV3 image which covered an ~11.5 km by ~19.5 km area of Karnes, Atascosa and Live Oak Counties, underlain by mined rocks from the Jackson Group and Catahoula Formation. Up to 58 spectral endmembers were identified using a further multi-dimensional class segregation method and were used as inputs for spectral angle mapper (SAM) classification. SAM classification resulted in the identification of at least 117 mine- and mine waste-related features, most of which were previously unknown. Class similarity was further evaluated, and the dominant minerals in each class were identified by comparison to spectral libraries and measured samples of actual Jackson Group uranium host rocks. Redundant classes were eliminated, and SAM was run a second time using a reduced set of 23 endmembers, which were found to map these same features as effectively as using the full 58 set of endmembers, but with significantly reduced noise and spectral outliers. Our classification results were validated by evaluating detailed scale mapping of three known mine sites (Esse-Spoonamore, Wright-McCrady and Garbysch-Thane) with published ground truth information about the vegetation cover, extent of erosion and exposure of waste pile materials and/or geologic information about host lithology and mineralization. Despite successful demonstration of the utility of WV3 data for inventorying mine features, additional landscape features such as bare agricultural fields and oil and gas drill pads were also identified. The elimination of such features will require combining the spectral classification maps presented in this study with high-quality topographic data. Also, the spectral endmembers identified during the course of this study could be useful for larger-scale mapping efforts using additional well-calibrated WV3 images beyond the coverage of our initial study area.

This study presents a geochemical framework and geographic information system (GIS) method for assessing the intrinsic potential of surface water and groundwater to mobilize arsenic, molybdenum, selenium, uranium, and vanadium. The method was created using published groundwater and surface water geochemical data from the National Uranium Resource Evaluation database for 2302 groundwater and 915 surface water samples. The method was evaluated using published groundwater geochemical data from the Texas Water Development Board. Geochemical data were analyzed in GIS. Samples were categorized by environmental condition, which was determined by using reduction–oxidation—as indicated by pe—and pH ranges for each sample based on geochemical mobility frameworks developed by Smith (2007) and Perel’man (1986). Reduction–oxidation and pH influence the occurrence, persistence, and mobility of arsenic, molybdenum, selenium, uranium, and vanadium in groundwater and surface water. Reduction–oxidation categories were assigned to water samples using concentrations of redox-active constituents, including dissolved oxygen, iron, manganese, and sulfur. The presence of iron substrates and hydrogen sulfides were considered in relation to mobility mechanisms. Twelve-digit hydrologic unit code (HUC) boundaries were used in GIS as analysis areas to determine the most commonly occurring environmental condition in each HUC. The resulting maps identify the environmental conditions in different areas that can be used to identify where the elements are mobile. This methodology provides a systematic approach to identify areas where elements in groundwater and surface water may occur and persist and may be transferable to other locations.