An intercomparison of multiresolution evapotranspiration (ET) datasets with reference to ground-based measurements for the development of regional reference (ET ref ) and actual (ET a ) evapotranspiration maps over Indiana is presented. A representative ET ref equation for the state is identified by evaluating 10 years of in situ measurements (2009–19). A statewide ET ref climatology is developed using the ET ref equation and high-resolution surface meteorological data from the gridded surface meteorological dataset (gridMET). For ET a analyses, MODIS, Simplified Surface Energy Balance Operational dataset (SSEBop), Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.3a and 3.3b), and NLDAS (Noah and VIC) datasets are evaluated using AmeriFlux data. Thirty years of rainfall data from Climate Hazards Group Infrared Precipitation with Station Data Rainfall (CHIRPS) are used with the ET datasets to develop effective precipitation fields. Results show that the standardized Penman–Monteith equation performs as the best ET ref equation with median symmetric accuracy (MSA) of 0.37, Taylor’s skill score (TSC) of 0.89, and r 2 = 0.83. The analysis shows that the gridMET dataset overestimates wind speed and requires adjustment before a series of statewide ET ref climatology maps are generated (1990–2020). For ET a , the MODIS and GLEAM (3.3b) datasets outperform the rest, with MSA = 0.5, TSC = 0.8, and r 2 = 0.8. The state ET a dataset is generated using all MODIS data from 2003 and blending the MODIS data with GLEAM (3.3b) to cover data unavailability. Using the top-performing datasets, annual ET ref for Indiana is computed as 1110 mm, ET a as 708 mm, and precipitation as 1091 mm. A marginal increasing climatological trend is found for Indiana’s ET ref (0.013 mm yr −1 ) while ET a is found to be relatively stable. The state’s water availability, defined as rainfall minus ET a , has remained positive and stable at 0.99 mm day −1 (annual magnitude of +3820 mm).

While the Midwestern USA ranks among the world’s most important corn-soybean production regions, the area also produces a variety of high-value specialty crops. These crops are an important component of the region’s rural economy with an estimated value of $1.8 billion in 2012. More profitable per-acre than many row crops, specialty crops also have higher production-related risks. They are generally more sensitive to climatic stressors and require more comprehensive management compared to traditional row crops. Temperature and precipitation fluctuations across the Midwest directly impact specialty crop production quantity and quality and indirectly influence the timing of crucial farm operations and the economic impacts of pests, weeds, and diseases. Increasingly variable weather and climate change pose a serious threat to specialty crop production in the Midwest. In this article, we assess how climate variability and observed climatic trends are impacting Midwestern specialty crop production using USDA Risk Management Agency data. In addition, we review current trends in grower perceptions of risks associated with a changing climate and assess sustainable adaptation strategies. Our results indicate that weather-induced losses vary by state with excessive moisture resulting in the highest total number of claims across all Midwestern states followed by freeze and drought events. Overall, specialty crop growers are aware of the increased production risk under a changing climate and have identified the need for crop-specific weather, production, and financial risk management tools and increased crop insurance coverage.

This study provides a climatology (1981–2012) of landfalling tropical systems in the eastern U.S. Corn Belt and investigates the total contribution of these storms to the monthly climatological rainfall in the Midwestern United States. The primary focus is on rainfall impacts from landfalling tropical systems on historic corn yields at the climate division and crop reporting district level. Climatologically dry to drought conditions for historic monthly observed rainfall are identified using the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index (SPI). It was found that without landfalling tropical system rainfall, the percentage increase in climatologically dry (or drier) conditions across the domain at state climate division resolution increased from 16% up to over 200%. The study also considers the effects of climatologically wet conditions on crop yields. Landfalling tropical system rainfall accounts for approximately 20% of the observed monthly rainfall during the tropical storm season (June–November) across the eastern U.S. Corn Belt (1981–2012). Correlation between the annual number of landfalling tropical systems and annual yield by state results in no relationship, but correlation of August monthly observed rainfall by climate division to crop reporting district annual yields has a weak to moderate, statistically significant correlation in Ohio districts 30–60 and Indiana CRD 90. ANOVA analysis suggests that landfalling tropical rainfall may actually reduce yields in some state's climate divisions/crop reporting districts while increasing yield in others. Results suggest that there is a balance between landfalling tropical storms providing sufficient rainfall or too much rainfall to be of benefit to crops. Findings aim to provide information to producers, crop advisers, risk managers and commodity groups so that seasonal hurricane forecasts can potentially be utilized in planning for above or below normal precipitation during phenologically important portions of the growing season.

.............................................................................................................. xvi CHAPTER

We investigate the ability of three different crop models of varying complexity for capturing El Niño–Southern Oscillation‐based climate variability impacts on the U.S. Corn Belt (1981–2010). Results indicate that crop models, irrespective of their complexity, are able to capture the impacts of climate variability on yield. Multiple‐model ensemble analysis provides best results. There was no significant difference between using on‐site and gridded meteorological data sets to drive the models. These results highlight the ability of using simpler crop models and gridded regional data sets for crop‐climate assessments.

El Nino-Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980-2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt.This study is unique in that it utilizes the oceanic Nino index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Nino events on yields compared to La Nina events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production.

Weather and climate events and agronomic enterprise are coupled via crop phenology and yield, which is temperature and precipitation dependent. Additional coupling between weather and climate and agronomic enterprise occurs through agricultural practices such as tillage, irrigation, erosion, livestock management, and forage. Thus, the relationship between precipitation, temperature, and yield is coupled to the relationship between temperature, precipitation, and drought. Unraveling the different meteorological and climatological patterns by comparing different growing seasons provides insight into how drought conditions develop and what agricultural producers can do to mitigate and adapt to drought conditions. The 2012 drought in the United States greatly impacted the agricultural sector of the economy. With comparable severity and spatial extent of the droughts of the 1930s, 1950s, and 1980s, the 2012 drought impacted much of the U.S. crop and livestock producers via decreased forage and feed. This brief summary of drought impacts to agricultural production systems includes 1) the basics of drought; 2) the meteorology and climatology involved in forecasting, predicting, and monitoring drought with attribution of the 2012 drought explored in detail; and 3) comparative analysis completed between the 2011 and 2012 growing season. This synthesis highlights the complex nature of drought in agriculture production systems as producers prepare for future climate variability.

Weather and climate events and agronomic enterprise are coupled via crop phenology and yield, which is temperature and precipitation dependent. Additional coupling between weather and climate and agronomic enterprise occurs through agricultural practices such as tillage, irrigation, erosion, livestock management, and forage. Thus, the relationship between precipitation, temperature, and yield is coupled to the relationship between temperature, precipitation, and drought. Unraveling the different meteorological and climatological patterns by comparing different growing seasons provides insight into how drought conditions develop and what agricultural producers can do to mitigate and adapt to drought conditions. The 2012 drought in the United States greatly impacted the agricultural sector of the economy. With comparable severity and spatial extent of the droughts of the 1930s, 1950s, and 1980s, the 2012 drought impacted much of the U.S. crop and livestock producers via decreased forage and feed. This brief summary of drought impacts to agricultural production systems includes 1) the basics of drought; 2) the meteorology and climatology involved in forecasting, predicting, and monitoring drought with attribution of the 2012 drought explored in detail; and 3) comparative analysis completed between the 2011 and 2012 growing season. This synthesis highlights the complex nature of drought in agriculture production systems as producers prepare for future climate variability.

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.

Using the HURDAT best track analysis of track and intensity of tropical cyclones that made landfall over the continental United States during the satellite era (1980–2005), we analyze the role of land surface variables on the cyclone decay process. The land surface variables considered in the present study included soil parameters (soil heat capacity and its surrogate soil bulk density), roughness, topography and local gradients of topography. The sensitivity analysis was carried out using a data-adaptive genetic algorithm approach that automatically selects the most suitable variables by fitting optimum empirical functions that estimates cyclone intensity decay in terms of given observed variables. Analysis indicates that soil bulk density (soil heat capacity) has a dominant influence on cyclone decay process. The decayed inland cyclone intensities were found to be positively correlated with the cube of the soil bulk density (heat capacity). The impact of the changes in soil bulk density (heat capacity) on the decayed cyclone intensity is higher for higher intensity cyclones. Since soil bulk density is closely related to the soil heat capacity and inversely proportional to the thermal diffusivity, the observed relationship can also be viewed as the influence of cooling rate of the land surface, as well as the transfer of heat and moisture underneath a land-falling storm. The optimized prediction function obtained by statistical model processes in the present study that predicts inland intensity changes during 6-h interval showed high fitness index and small errors. The performance of the prediction function was tested on inland tracks of eighteen hurricanes and tropical storms that made landfall over the United States between 2001 and 2010. The mean error of intensity prediction for these cyclones varied from 1.3 to 15.8 knots (0.67–8.12 m s−1). Results from the data-driven analysis thus indicate that soil heat flux feedback should be an important consideration for the inland decay of tropical cyclones. Experiments were also undertaken using Weather Research Forecasting (WRF) Advanced Research Version (ARW ver 3.3) to assess the sensitivity of the soil parameters (roughness, heat capacity and bulk density) on the post-landfall structure of select storms. The model was run with 1-km grid spacing, limited area single domain with boundary conditions from the North American Regional Reanalysis. Of different experiments, only the surface roughness change and soil bulk density (heat capacity) change experiments showed some sensitivity to the intensity change. The WRF results thus have a low sensitivity to the land parameters (with only the roughness length showing some impact). This calls for reassessing the land surface response on post-landfall characteristics with more detailed land surface representation within the mesoscale and hurricane modeling systems.

Prior research on tropical storm systems that have made landfall and undergone a period of sustainability or reintensification has been linked to the synoptic environment at the time the storm restrengthened. Tropical Storm (TS) Erin is an interesting case study in that it did not take on hurricane-like structure nor reach hurricane intensity until it moved through west-central Oklahoma on August 19, 2007. This study seeks to examine the possible impact of anomalously wet soils across much of Oklahoma on the reintensification of TS Erin during the early morning hours of August 19, 2007. To determine the degree to which the antecedent soil state impacted TS Erin’s inland evolution and reintensification, analyses of the synoptic environment and the mesoscale environment/boundary layer environment are undertaken using operational and research datasets such as upper air soundings, surface soil moisture and temperature data, and multiple products from the Storm Prediction Center (SPC) mesoanalysis archive. This observational assessment is complemented with numerical experiments using the Weather Research and Forecast Model, Advanced Research Version 3.2 (WRF-ARW) to further study the role of soil moisture availability and surface fluxes that may have led to the boundary layer feedback and inland reintensification. Observational analysis and model results indicate that anomalously wet conditions over the central Oklahoma region may have helped develop a regional boundary layer feedback that appears to have contributed to the inland reintensification of TS Erin. Thus, the anomalously wet land surface had a positive role in TS Erin reintensifying over Oklahoma during the early morning hours of August 19, 2007.