Accessible flood modeling for low-resource, data-scarce communities currently does not exist. This paper proposes using drone imagery to compensate for the lack of other flood modeling data (i.e. streamflow measurements). Three flood models were run for Dzaleka Refugee Camp, located in Dowa, Malawi. Two of the models (the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Center River Analysis System (HEC-RAS)) are commonly used hydrological-hydraulic based models. The third model, the Water Caused Erosion Patterns (WCEP) model, was proposed by the author to capitalize on the high-resolution drone imagery using geological-geomorphological information. The drone imagery used in this study has a resolution of 3.5cm and shows erosion patterns throughout the refugee camp. By comparing the erosion patterns to flow direction of the surface, the erosion patterns were determined to be water caused or not water caused, the erosion patterns considered water caused were defined as high-risk flood areas, creating the WCEP model. The three models were compared using locations of collapsed houses throughout the camp. It was found that the WCEP model represents the location of collapsed houses significantly better (misclassification rate below 17%) than the SWAT or HEC-RAS models (misclassification rate below 54%, and 67% respectively). The WCEP model was combined with the best hydrological-hydraulic model (SWAT) to create a hydrogeomorphological model which capitalizes on both the drone imagery and the hydrological process. / Master of Science / The negative impact flooding has on communities can be reduced through flood modeling. But commonly used flood models are not accessible to data-scarce communities because of the historical data the models require. This paper explores using aerial imagery taken by a drone to make-up for the lack of historical data at Dzaleka Refugee Camp located in Dowa, Malawi. Drone imagery has a very high spatial resolution (3.5cm), so it is able to provide a lot of details, including marks that show an increase of flooding in certain areas and elevation information. The flood model presented in this paper is created using the found flood marks in drone imagery. The presented model is then compared to two commonly used flood models, and all three flood models are compared to locations of houses that collapsed from flooding throughout the refugee camp. The model created using drone imagery did the best job predicting high-risk locations with misclassification rates below 17%. The drone imagery model was then combined with a commonly used model to create a more comprehensive flood model, capitalizing on all available data.

<p>Cereal rye (<i>Secale cereale</i>, L., CR) is the most commonly utilized cover crop species within the United States. Yet, the total land area planted to CR on an annual basis remains relatively low despite its numerous proven environmental benefits. The relatively low rates of CR adoption could be due to a dearth of knowledge surrounding certain agronomic and economic components of CR adoption. Currently, there exists knowledge gaps within the scientific literature regarding CR performance, N cycling, and associated economic risk. <a>Thus, to address the above-mentioned knowledge gaps, three individual studies were developed to: i) investigate the fate of scavenged CR nitrogen (N) amongst soil N pools, ii) assess the suitability of visible-spectrum vegetation indices (VIs) to predict CR biomass and nutrient accumulation (BiNA), and iii) characterize the economic risk of CR adoption at a regional scale over time.</a></p> <p>In the first study, ¹⁵N, a stable isotope of N, was used in an aerobic incubation to track the fate of CR root and shoot N among the soil microbial biomass, inorganic, and organic N pools, as well as explore CR N bioavailability over a simulated corn growing season. In this study, the C:N ratio of the shoot residues was 16:1 and the roots was 31:1 and differences in residue quality affected the dynamics of CR N release from each residue type. On average, 14% of whole plant CR N was recovered in the soil inorganic N pool at the final sample date. Correspondingly, at the final sampling date 53%, 33%, and less than 1% of whole plant CR N was recovered as soil organic N, undecomposed residue, and as microbial biomass N, respectively. Most CR N remained unavailable to plants during the first cash crop growing season subsequent to termination. This knowledge could support the advancement of N fertilizer management strategies for cropping systems containing cereal rye.</p> <p>In the second study, a commercially available unmanned aerial vehicle (UAV) outfitted with a standard RGB sensor was used to collect aerial imagery of growing CR from which visible-spectrum VIs were computed. Computed VIs were then coupled with weather and geographic data using linear multiple regression to produce prediction models for CR biomass, carbon (C), N, phosphorus (P), potassium (K), and sulfur (S). Five visible-spectrum VIs (Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Green (ExG)) were evaluated and the results determined that MGRVI was the best predictor for CR biomass, C, K, and S and that RGBVI was the best predictor for CR N and P. Furthermore, the final prediction models for the VIs selected as the best predictors developed in this study performed satisfactorily in the prediction of CR biomass, C, N, P, K, and S producing adjusted R² values of 0.79, 0.79, 0.75, 0.81, 0.81, and 0.78, respectively. The results of this study have the potential to aid producers in making informed decisions regarding CR and fertility management. </p> <p>In the final study, agronomic data for corn and soybean cropping systems with and without CR was collected from six states (Illinois, Indiana, Iowa, Minnesota, Missouri, and Wisconsin) and used within a Monte-Carlo stochastic simulation to characterize the economic risk of adopting CR at a regional scale over time. The results of this study indicate that average net returns to CR are always negative regardless of CR tenure primarily due to added costs and increased variability in cash crop grain yields associated with CR adoption. Further, the results demonstrate that the additional risk assumed by adopting CR is not adequately compensated for with current CR adoption incentive programs and that the risk premium necessary can be 1.7 to 15 times greater than existing incentive payments. Knowledge gained from this study could be used to reimagine current incentive programs to further promote adoption of CR.</p>