Switchgrass (Panicum virgatum L.) is a C₄, warm season, perennial native grass that has been strongly recommended as an ideal biofuel feedstock. Accurate forecasting of switchgrass yield across a geographically diverse region and under future climate conditions is essential for determining realistic future ethanol production from switchgrass. This study compiled a switchgrass database through reviewing the existing literature from field trials across the U.S. Using observed switchgrass data, a process-based model (ALMANAC) was developed. The ALMANAC simulation results showed that crop management had more effect on yield than location. The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrients) giving realistic predictions in different climate conditions. This model was used to quantify the impacts of climate change on switchgrass yields. Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future (2021–2050) under both Representative Concentration Pathway (RCP) 4.5 and 8.5 pathways with low N fertilizer inputs than high N fertilizer inputs. There was no significant effect of climate variability on upland simulated yields, which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions.

The application of hydrologic and hydrodynamic models in flash flood hazard assessment is mainly limited by the availability of robust monitoring systems and long-term hydro-meteorological observations. Nevertheless, several studies have demonstrated that coupled modeling approaches based on event sampling (short-term observations) may cope with the lack of observed input data. This study evaluated the use of storm events and flood-survey reports to develop and validate a modeling framework for flash flood hazard assessment in data-scarce watersheds. Specifically, we coupled the hydrologic modeling system (HEC-HMS) and the Nays2Dflood hydrodynamic solver to simulate the system response to several storm events including one, equivalent in magnitude to a 500-year event, that flooded the City of Tena (Ecuador) on 2 September, 2017. Results from the coupled approach showed satisfactory model performance in simulating streamflow and water depths (0.40 ≤ Nash-Sutcliffe coefficient ≤ 0.95; −3.67% ≤ Percent Bias ≤ 23.4%) in six of the eight evaluated events, and a good agreement between simulated and surveyed flooded areas (Fit Index = 0.8) after the 500-year storm. The proposed methodology can be used by modelers and decision-makers for flood impact assessment in data-scarce watersheds and as a starting point for the establishment of flood forecasting systems to lessen the impacts of flood events at the local scale.

To support the hydrological assessment of Alpine ecosystems, we studied the suitability of the SWAT model to simulate neotropical alpine grasslands or so-called Andean Paramo. Given the paucity of observational data in paramo catchments, data-driven models are usually underutilized, and their outcomes are arguable. However, our research examined if SWAT can reasonably represent the hydrological response of grassland-dominated paramo catchments under data-abundance conditions. Therefore, we set up a soil-based SWAT model that emphasized the role of the soil in the hydrological response and the dominance of saturation excess surface runoff over infiltration excess. Specifically, we incorporated detailed characteristics of Andean soils by horizons, parameterized SWAT to replicate high infiltration rates and high lateral flow in the hillslopes, and restricted groundwater interactions to replicate local streamflow responses. Our soil-based modeling approach reasonably reproduced daily discharge during dry and wet periods throughout the year and the cumulative occurrence of high and low flows. The ratio of precipitation and simulated runoff and the partitioning of the total runoff into the lateral flow and surface runoff were physically meaningful. More significantly, SWAT was able to simulate saturation excess overland flow, which is dominant compared to infiltration excess, and it is a distinctive characteristic of paramo catchments. Based on the overall model performance, we conclude that SWAT can reasonably simulate the hydrological response of Andean paramo catchments, and therefore, its application can extend to similar tropical alpine catchments. Nevertheless, the model showed limitations for simulating low flows.