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Abstract The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events. It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CN NDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CN NDVI model calibration and validation. Twelve years of rainfall and runoff data (2001–2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CN NDVI performed significantly better in predicting runoff with calibrated CN NDVI runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CN NDVI runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (−0.87 and −94.86 respectively). In the validated model, CN NDVI runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CN NDVI can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions. Highlights • Phenology-adjusted curve number development for improved rainfall-runoff relationship prediction. • Normalized Difference Vegetation Index to address time-varying watershed's hydrologic condition in runoff estimation. • Capturing spatiotemporal variability of runoff estimation for better water and land management decisions. • Enhancement of curve number method to account seasonal land use/cover changes. [ABSTRACT FROM AUTHOR]

Highlights • The SPoRT-LIS surface soil moisture estimate was validated across the U.S. Great Plains region. • Validation performance was evaluated across varying soil types, land cover, depth, slope, aspect, and pixel heterogeneity. • Results demonstrate that SPoRT-LIS surface soil moisture is suitable for water resources applications. Abstract Evaluation of surface soil moisture is necessary to understand spatiotemporal soil moisture trends and their implications on water resources management. This research evaluated a real-time instantiation of NASA's Land Information System (LIS) for water resources management applications at a higher spatial and temporal resolution than is currently available with remotely-sensed satellite estimates or in situ measurements of the same product. Managed by NASA's Short-term Prediction Research and Transition (SPoRT) Center, the "SPoRT-LIS" is an observation-driven, real-time simulation of the Noah land surface model at a 3-km resolution over the full continental United States. Surface soil moisture estimates from SPoRT-LIS (0–10 cm layer) were validated against in situ soil moisture from the International Soil Moisture Network in the Missouri and Arkansas-Red-White River Basins. Validation was conducted at in situ measurement depths of 5-cm and 10-cm, and performance was evaluated across varying soil types, land cover, depth, slope, aspect, and pixel heterogeneity to determine conditions under which SPoRT-LIS surface soil moisture had excellent estimation capability. Results demonstrate that 53% of data at a depth of 5-cm and 51% of the data at a depth of 10-cm were significantly correlated with a Spearman's ρ greater than 0.5 on a daily basis. Based upon validation results, it is evident that the SPoRT-LIS surface soil moisture estimate is satisfactory for research and operational water resources management applications. [ABSTRACT FROM AUTHOR]

Abstract: Grain storage and processing facilities consist of a landscape of indoor and outdoor habitats that can potentially support stored-product insect pests, and understanding patterns of species diversity and spatial distribution in the landscape surrounding structures can provide insight into how the outdoor environment can be more effectively monitored and managed. The spatial and temporal distribution of stored-product pests was assessed at three food processing facilities using two types of traps and the influence of landscape features on their outside distribution was evaluated. For corrugated traps, targeting walking individuals, placed both inside and outside facilities, the predominant groups, accounting for 59% of captures, were Cryptolestes spp. (Coleoptera: Laemophloeidae), Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae) and Sitophilus spp. (Coleoptera: Curculionidae). Numbers captured in outside corrugated traps tended to be less than captures inside structures, and while level of species diversity was similar fungal feeding species were more common in outside traps. In outside corrugated traps, Cryptolestes spp., Typhaea stercorea (L.) (Coleoptera: Mycetophagidae) and O. surinamensis were most abundant and in outside Lindgren traps that targeted flying individuals, T. stercorea, Cryptolestes spp. and Ahasverus advena (Waltl) (Coleoptera: Silvanidae) were most abundant. No correlation was observed between total captures and species diversity between inside and outside traps. Distribution of stored-product insects in corrugated traps tended not to be spatially clustered (Global Moran''s I values ranged from −0.25 to 0.22). However, Anselin local Moran''s I indicated that at local level some traps with greatest captures had traps in the vicinity with similar values, but these specific locations were temporally variable. Landscape around each outside corrugated trap was characterized, and increased captures were associated with proximity to grain storage or processing structures, but not with presence of spillage as originally hypothesized. Overall, results support hypothesis that there is considerable movement of insects in landscape surrounding facilities, resulting in limited spatial pattern other than temporally variable hot spots inside or near structures. [Copyright &y& Elsevier]

Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%. [ABSTRACT FROM AUTHOR]

Abstract: Hydrologic and soil-hydrologic variables were evaluated retrospectively as potential risk factors for canine leptospirosis in Kansas and Nebraska using Geographic Information Systems (GIS). The sample included 94 positive and 185 negative dogs for leptospirosis predominantly based on PCR test for leptospires in urine. Hydrologic variables for the region were derived from National Hydrographic Dataset, National Flood Hazard Layer, National Wetlands Inventory; and soil-hydrologic variables from Soil Survey Geographic Database around geocoded addresses of case/control locations. Multivariable logistic models were used to determine association between hydrologic and soil-hydrologic variables and test status. Distance from water features (OR=0.82; 95% CI=0.79, 0.86), hydrologic density (OR=2.80; 95% CI=1.58, 4.96) and frequently flooded areas (OR=4.05; 95% CI=2.17, 7.55) within 2500m surrounding case/control locations were significant risk factors for canine leptospirosis. Vaccination for dogs that live closer to water features, landscapes dominated by water features and frequent floods should be considered for leptospirosis prevention. [Copyright &y& Elsevier]

For millennia grasslands have provided a myriad of ecosystem services and have been coupled with human resource use. The loss of 46% of grasslands worldwide necessitates the need for conservation that is spatially, temporally, and socioeconomically strategic. In the Southern Great Plains of the United States, conversion of native grasslands to cropland, woody encroachment, and establishment of vertical anthropogenic features have made large intact grasslands rare for lesser prairie-chickens (Tympanuchus pallidicinctus). However, it remains unclear how the spatial distribution of grasslands and anthropogenic features constrain populations and influence conservation. We estimated the distribution of lesser prairie-chickens using data from individuals marked with GPS transmitters in Kansas and Colorado, USA, and empirically derived relationships with anthropogenic structure densities and grassland composition. Our model suggested decreased probability of use in 2-km radius (12.6 km2) landscapes that had greater than two vertical features, two oil wells, 8 km of county roads, and 0.15 km of major roads or transmission lines. Predicted probability of use was greatest in 5-km radius landscapes that were 77% grassland. Based on our model predictions, ~10% of the current expected lesser prairie-chicken distribution was available as habitat. We used our estimated species distribution to provide spatially explicit prescriptions for CRP enrollment and tree removal in locations most likely to benefit lesser prairie-chickens. Spatially incentivized CRP sign up has the potential to provide 4189 km2 of additional habitat and strategic application of tree removal has the potential to restore 1154 km2. Tree removal and CRP enrollment are conservation tools that can align with landowner goals and are much more likely to be effective on privately owned working lands. [ABSTRACT FROM AUTHOR]