The storm water management model (SWMM) is a widely used tool for urban drainage design and planning. Hundreds of peer-reviewed articles and conference proceedings have been written describing applications of SWMM. This review focuses on collecting information on model performance with respect to calibration and validation in the peer-reviewed literature. The major developmental history and applications of the model are also presented. The results provide utility to others looking for a quick reference to gauge the integrity of their own unique SWMM application. A gap analysis assesses the model’s ability to perform water-quality simulations considering green infrastructure (GI)/low impact development (LID) designs and effectiveness. It is concluded that the level of detail underlying the conceptual model of SWMM versus its overall computational parsimony is well balanced—making it an adequate model for large and medium-scale hydrologic applications. However, embedding a new mechanistic algorithm or providing user guidance for coupling with other models will be necessary to realistically simulate diffuse pollutant sources, their fate and transport, and the effectiveness of GI/LID implementation scenarios.

Global assessments of climate extremes typically do not account for the unique characteristics of individual crops. A consistent definition of the exposure of specific crops to extreme weather would enable agriculturally-relevant hazard quantification. To this end, we develop a database of both the temperature and moisture extremes facing individual crops by explicitly accounting for crop characteristics. To do this, we collate crop-specific temperature and moisture parameters from the agronomy literature, which are then combined with time-varying crop locations and high-resolution climate information to quantify crop-specific exposure to extreme weather. Specifically, we estimate crop-specific temperature and moisture shocks during the growing season for a 0.25^∘ spatial grid and daily time scale from 1961 to 2014 globally. We call this the Agriculturally-Relevant Exposure to Shocks (ARES) model and make all ARES output available with this paper. Our crop-specific approach leads to a smaller average value of the exposure rate and spatial extent than does a crop-agnostic approach. Of the 17 crops included in this study, 13 had an increase in exposure to extreme heat, while 9 were more exposed to extreme cold over the past half century. All crops in this study show a statistically significant increase in exposure to both extreme wetness and dryness. Cassava, sunflowers, soybeans, and oats had the greatest increase in hot, cold, dry, and wet exposure, respectively. We compare ARES model results with the EM-DAT disaster database. Our results highlight the importance of crop-specific characteristics in defining weather shocks in agriculture.

Food aid is a critical component of the global food system, particularly when emergency situations arise. For the first time, we evaluate the water footprint of food aid. To do this, we draw on food aid data from theWorld Food Programme and virtual water content estimates from WaterStat. We find that the total water footprint of food aid was 10 km3 in 2005, which represents approximately 0.5% of the water footprint of food trade and 2.0% of the water footprint of land grabbing (i.e., water appropriation associated with large agricultural land deals). The United States is by far the largest food aid donor and contributes 82% of the water footprint of food aid. The countries that receive the most water embodied in aid are Ethiopia, Sudan, North Korea, Bangladesh and Afghanistan. Notably, we find that there is significant overlap between countries that receive food aid and those that have their land grabbed. Multivariate regression results indicate that donor water footprints are driven by political and environmental variables, whereas recipient water footprints are driven by land grabbing and food indicators.

Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961 to 2014. To do this, pixels are assigned into probability clusters based upon crop-specific pixel suitability (based on mean climate and soil characteristics) and gridded historical agricultural areas. PCAM maps compare relatively well with an existing gridded dataset of crop-specific areas circa 2000 (simple matching coefficient value >0.8 for all crops). PCAM estimates compare less well with time series county-level agricultural census data for the United States. Importantly, deviations between census data and PCAM benchmark estimates (driven by soil and climate suitability) can be used to infer the importance of other factors of agricultural production (e.g. labor, agricultural policy, extreme climate) in future work. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century.