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M. P. Maneta and N. Silverman
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Olivia Kellner and Dev Niyogi

Abstract

Land surface heterogeneity affects mesoscale interactions, including the evolution of severe convection. However, its contribution to tornadogenesis is not well known. Indiana is selected as an example to present an assessment of documented tornadoes and land surface heterogeneity to better understand the spatial distribution of tornadoes. This assessment is developed using a GIS framework taking data from 1950 to 2012 and investigates the following topics: temporal analysis, effect of ENSO, antecedent rainfall linkages, population density, land use/land cover, and topography, placing them in the context of land surface heterogeneity.

Spatial analysis of tornado touchdown locations reveals several spatial relationships with regard to cities, population density, land-use classification, and topography. A total of 61% of F0–F5 tornadoes and 43% of F0–F5 tornadoes in Indiana have touched down within 1 km of urban land use and land area classified as forest, respectively, suggesting the possible role of land-use surface roughness on tornado occurrences. The correlation of tornado touchdown points to population density suggests a moderate to strong relationship. A temporal analysis of tornado days shows favored time of day, months, seasons, and active tornado years. Tornado days for 1950–2012 are compared to antecedent rainfall and ENSO phases, which both show no discernible relationship with the average number of annual tornado days. Analysis of tornado touchdowns and topography does not indicate any strong relationship between tornado touchdowns and elevation. Results suggest a possible signature of land surface heterogeneity—particularly that around urban and forested land cover—in tornado climatology.

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Lauren E. Hay, Jacob LaFontaine, and Steven L. Markstrom

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The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks.

The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output.

Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.

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Eugene S. Takle, Christopher J. Anderson, Jeffrey Andresen, James Angel, Roger W. Elmore, Benjamin M. Gramig, Patrick Guinan, Steven Hilberg, Doug Kluck, Raymond Massey, Dev Niyogi, Jeanne M. Schneider, Martha D. Shulski, Dennis Todey, and Melissa Widhalm

Abstract

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

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Hal F. Needham and Barry D. Keim

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This paper investigates relationships between storm surge heights and tropical cyclone wind speeds at 3-h increments preceding landfall. A unique dataset containing hourly tropical cyclone position and wind speed is used in conjunction with a comprehensive storm surge dataset that provides maximum water levels for 189 surge events along the U.S. Gulf Coast from 1880 to 2011. A landfall/surge classification was developed for analyzing the relationship between surge magnitudes and prelandfall winds. Ten of the landfall/surge event types provided useable data, producing 117 wind–surge events that were incorporated into this study. Statistical analysis indicates that storm surge heights correlate better with prelandfall tropical cyclone winds than with wind speeds at landfall. Wind speeds 18 h before landfall correlated best with surge heights. Raising wind speeds to exponential powers produced the best wind–surge fit. Higher wind–surge correlations were found when testing a more recent sample of data that contained 63 wind–surge events since 1960. The highest correlation for these data was found when wind speeds 18 h before landfall were raised to a power of 2.2, which provided R 2 values that approached 0.70. The R 2 values at landfall for these same data were only 0.44. Such results will be useful to storm surge modelers, coastal scientists, and emergency management personnel, especially when tropical cyclones rapidly strengthen or weaken while approaching the coast.

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Hal F. Needham and Barry D. Keim

Abstract

In the past decade, several large tropical cyclones have generated catastrophic storm surges along the U.S. Gulf and Atlantic Coasts. These storms include Hurricanes Katrina, Ike, Isaac, and Sandy. This study uses empirical analysis of tropical cyclone data and maximum storm surge observations to investigate the role of tropical cyclone size in storm surge generation. Storm surge data are provided by the Storm Surge Database (SURGEDAT), a global storm surge database, while a unique tropical cyclone size dataset built from nine different data sources provides the size of the radius of maximum winds (Rmax) and the radii of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Statistical analysis reveals an inverse correlation between storm surge magnitudes and Rmax sizes, while positive correlations exist between storm surge heights and the radius of 63 (34 kt), 93 (50 kt), and 119 km h−1 (64 kt) winds. Storm surge heights correlate best with the prelandfall radius of 93 km h−1 (50 kt) winds, with a Spearman correlation coefficient value of 0.82, significant at the 99.9% confidence level. Many historical examples support these statistical results. For example, the 1900 Galveston hurricane, the 1935 Labor Day hurricane, and Hurricane Camille all had small Rmax sizes but generated catastrophic surges. Hurricane Katrina provides an example of the importance of large wind fields, as hurricane-force winds extending 167 km [90 nautical miles (n mi)] from the center of circulation enabled this large storm to generate a higher storm surge level than Hurricane Camille along the same stretch of coast, even though Camille’s prelandfall winds were slightly stronger than Katrina’s. These results may be useful to the storm surge modeling community, as well as disaster science and emergency management professionals, who will benefit from better understanding the role of tropical cyclone size for storm surge generation.

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Anthony E. Akpan, Mahesh Narayanan, and T. Harinarayana

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A constructive back-propagation code that was designed to run as a single-hidden-layer, feed-forward neural network (SLFFNN) has been adapted and used to estimate subsurface temperature from a small volume of magnetotelluric (MT)-derived electrical resistivity data and borehole thermograms. The code was adapted to use a looping procedure in searching for better initialization conditions that can optimally solve nonlinear problems using the random weight initialization approach. Available one-dimensional (1D) MT-derived resistivity data and borehole temperature records from the Tattapani geothermal field, central India, were collated and digitized at 10-m intervals. The two datasets were paired to form a set of input–output pairs. The paired data were randomized, standardized, and partitioned into three mutually exclusive subsets. The various subsets had 52% (later increased to 61%), 30%, and 18% (later reduced to 9%) for training, validation, and testing, respectively, in the first and second training phases. The second training phase was meant to assess the influence of the training data volume on network performance. Standard statistical techniques including adjusted coefficient of determination (R2a), relative error (ɛ), absolute average deviation (AAD), root-mean-square error (RMSE), and regression analysis were used to quantitatively rate network performance. A manually designed two-hidden-layer, feed-forward network with 20 and 15 neurons in the first and second layers was also adopted in solving the same problem. Performance ratings were observed to be 0.97, 3.75, 4.09, 1.41, 1.18, and 1.08 for R2a, AAD, ɛ, RMSE, slope, and intercept, respectively, compared to an ɛ of 20.33 observed with the manually designed network. The SLFFNN is thus a structurally flexible network that performs better in spite of the small volume of data used in testing the network. The network needs to be tested further.

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Elizabeth I. Okoyeh, Anthony E. Akpan, B. C. E. Egboka, and H. I. Okeke

Abstract

Gully erosion–induced problems have been challenging the people and government of Anambra State in southeastern Nigeria for a long time. In spite of the numerous geoscientific and engineering studies so far conducted in the area, the underlying causes of these problems still remain poorly understood. In an attempt to contribute to the understanding of the underlying processes responsible for the persistent gully erosion problems in Anambra State, an integrated study utilizing hydrological, geomorphological, and geophysical data was undertaken. Results of the analyses show that bulk density, pH, and organic matter content of the soil range from 1610 to 1740 kg m−3, 5.10 to 5.30, and 0.32% to 0.46%, respectively. Particle size analyses results show that the soils are dominated by coarse sand materials (50%–68%). Variations in the Atterberg limit parameters (liquid limit, plastic limit, and plasticity index) also point to the dominance of coarse materials in the shallow subsurface. Vertical electrical sounding results capture the shallow surface as being dominated by resistive sandy materials that are underlain by lowly resistive clayey materials. Thus, the area is dominated by porous, friable, and poorly cemented coarse materials that are located on a long and steeply sloping terrain of the tectonically elevated Awka–Orlu cuesta. Both overland and subsurface flow processes are responsible for the gully erosion problems confronting the area. Human activities (e.g., deforestation, uncontrolled urbanization, and absence of requisite legislation to protect the environment) and the high elevation of the Awka–Orlu cuesta have aggravated the severity of the problems. An aggressive reforestation program particularly with native trees, promulgation of necessary legislation to protect the environment, and setting up and empowering an enforcement agency should be vigorously pursued. Also, necessary enlightenment campaigns on best agricultural practices that can reduce surface runoff in soil and water conservation may also be helpful in changing the mindset of people.

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Kirk Zmijewski and Richard Becker

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The decrease in size the Aral Sea in central Asia, seen as both lower water levels and reduction in areal extent, has been one of the greatest examples of anthropogenic modification of a natural system in recent history. Many studies have monitored the extent and rate of this water loss and provided estimates on the expected life span of the remaining water. However, with little data for groundwater monitoring in the post-Soviet era, it is unclear what the water balance currently is in the remainder of the watershed. Redistribution of water upstream in the watershed including damming to create reservoirs and groundwater recharge from irrigation has not only deprived the sea of water but also increased evapotranspiration and altered local climate patterns. Using Tropical Rainfall Measurement Mission (TRMM) and Global Precipitation Climatology Centre (GPCC) data, rainfall trends for the Aral Sea watershed were analyzed over 10- and 30-yr periods and only minimal changes in rainfall were detected. Using Gravity Recovery and Climate Experiment (GRACE) gravity data from 2003 to 2012, trends in equivalent water mass were determined for the entire watershed. Estimates show up to 14 km3 of equivalent water mass has been lost from the watershed annually from 2002 to 2013. The mass loss throughout the basin is most likely attributable to increased evapotranspiration due to the inefficient irrigation systems and other human modification increasing the need for international cooperation and conservation programs to minimize negative impacts throughout the region.

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P. Grady Dixon, Andrew E. Mercer, Katarzyna Grala, and William H. Cooke

Abstract

The fundamental purpose of this research is to highlight the spatial seasonality of tornado risk. This requires the use of objective methods to determine the appropriate spatial extent of the bandwidth used to calculate tornado density values (i.e., smoothing the raw tornado data). With the understanding that a smoothing radius depends partially upon the period of study, the next step is to identify objectively ideal periods of tornado analysis. To avoid decisions about spatial or temporal boundaries, this project makes use of storm speed and tornado pathlength data, along with statistical cluster analysis, to establish tornado seasons that display significantly different temporal and spatial patterns. This method yields four seasons with unique characteristics of storm speed and tornado pathlength.

The results show that the ideal bandwidth depends partially upon the temporal analysis period and the lengths of the tornadoes studied. Hence, there is not a “one size fits all,” but the bandwidth can be quantitatively chosen for a given dataset. Results from this research, based upon tornado data for 1950–2011, yield ideal bandwidths ranging from 55 to 180 km. The ideal smoothing radii are then applied via a kernel density analysis of each new tornado season.

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