Flooding is the most damaging natural disaster worldwide, and the flood-vulnerable population is expected to grow in coming decades ( 1 ). Flood risks will likely increase because of both climate change ( 1 ) and shifting land uses, such as filling of wetlands and expansion of impervious surfaces, that lead to more rapid precipitation runoff into rivers. In the United States, annual river flood losses continue to rise ( 2 ), punctuated by major events in the Midwest (1993, $30 billion in total costs; 2008, $15 billion) and California's Central Valley (1995 and 1997; $4 billion each event) ( 3 ). Meanwhile, pressure to develop new housing in floodprone areas near rivers (floodplains) continues ( 4 ), even as levee-system maintenance is chronically underfunded ( 5 ).

Understanding the dynamics of food production is critical to improving food security. This is particularly important in regions that rely on subsistence agriculture with little adaptive capacity to climate change. Sorghum plays an important role in food security in some of the poorest parts of the world. This article reviews the literature to identify and examine the major factors affecting sorghum production in three major production regions. Factors were not categorized ex ante but rather determined from the review. Ten major factors were identified as having notable impacts on sorghum production: climate change, population growth/economic development, non-food demand, agricultural inputs, demand for other crops, agricultural resources scarcity, biodiversity, cultural influence, price and armed conflict. This synthesis revealed that (1) multiple factors simultaneously affect sorghum production; (2) the effect of each factor is greatly influenced by the magnitude and certainty of one or more other factors; and, (3) factors differ in relevance and degree with regard to geography. Generally, improved agricultural inputs, population growth/economic development and climate change have substantial influence on sorghum production. However, local dynamics likely go beyond these broad trends and more exhaustive, locally-focused studies are needed for actionable planning purposes.

Applications of the Soil and Water Assessment Tool (SWAT) model typically involve delineation of a watershed into subwatersheds/subbasins that are then further subdivided into hydrologic response units (HRUs) which are homogeneous areas of aggregated soil, landuse, and slope and are the smallest modeling units used within the model. In a given standard SWAT application, multiple potential HRUs (farm fields) in a subbasin are usually aggregated into a single HRU feature. In other words, the standard version of the model combines multiple potential HRUs (farm fields) with the same landuse/landcover, soil, and slope, but located at different places of a subbasin (spatially non-unique), and considers them as one HRU. In this study, ArcGIS pre-processing procedures were developed to spatially define a one-to-one match between farm fields and HRUs (spatially unique HRUs) within a subbasin prior to SWAT simulations to facilitate input processing, input/output mapping, and further analysis at the individual farm field level. Model input data such as landuse/landcover (LULC), soil, crop rotation, and other management data were processed through these HRUs. The SWAT model was then calibrated/validated for Raccoon River watershed in Iowa for 2002–2010 and Big Creek River watershed in Illinois for 2000–2003. SWAT was able to replicate annual, monthly, and daily streamflow, as well as sediment, nitrate and mineral phosphorous within recommended accuracy in most cases. The one-to-one match between farm fields and HRUs created and used in this study is a first step in performing LULC change, climate change impact, and other analyses in a more spatially explicit manner.

While biofuels may yield renewable fuel benefits, there could be downsides in terms of water quality and other environmental stressors, particularly if corn is relied upon exclusively as the feedstock. The consequences of increased corn production will depend importantly on where (and how) the additional corn is grown, which, in turn, depends on the characteristics of land and its associated profitability. Previous work has relied on rules of thumb for allocating land to increased acreage based on historical land use or other heuristics. Here, we advance our understanding of these phenomena by describing a modeling system that links an economics‐driven land use model with a watershed‐based water quality model for the Upper Mississippi River Basin (UMRB). This modeling system is used to assess the water quality changes due to increased corn acreage, which is associated with higher relative corn prices. We focus on six scenarios based on six realistic pairs of corn and soybean prices which correspond to a scale of decreasing soybean to corn price ratio. These price‐driven land use changes provide estimates of the water quality effects that current biofuel policies may have in the UMRB. Our analysis can help evaluate the costs and environmental consequences associated with implementation strategies for the biofuel mandates of the new energy bill. The amounts of total N and P delivered to the outlet of the UMRB (located at Grafton, Illinois, USA) rise as corn production becomes more intensive in the region. Our results indicate that a 14.4% in corn acreage in the watershed due to corn intensification in the most economically profitable locations would result in a 5.4% increase in total nitrogen loads and in a 4.1% increase in total phosphorus loads at Grafton. Our most aggressive scenario, driven by high but not out of reach crop prices, results in about a 57% increase in corn acreage with a corresponding 18.5% increase in N and 12% increase in P. These are somewhat conservative increases in nutrients, compared to those of previous studies, likely due to our focus on cultivated cropland which is already heavily fertilized.