An integrated modeling framework for exploring flow regime and water quality changes with increasing biofuel crop production in theU.S.CornBelt

Abstract

To better address the dynamic interactions between human and hydrologic systems, we develop an integrated modeling framework that employs a System of Systems optimization model to emulate human development decisions which are then incorporated into a watershed model to estimate the resulting hydrologic impacts. The two models are run interactively to simulate the coevolution of coupled human-nature systems, such that reciprocal feedbacks between hydrologic processes and human decisions (i.e., human impacts on critical low flows and hydrologic impacts on human decisions on land and water use) can be assessed. The framework is applied to a Midwestern U.S. agricultural watershed, in the context of proposed biofuels development. This operation is illustrated by projecting three possible future coevolution trajectories, two of which use dedicated biofuel crops to reduce annual watershed nitrate export while meeting ethanol production targets. Imposition of a primary external driver (biofuel mandate) combined with different secondary drivers (water quality targets) results in highly nonlinear and multiscale responses of both the human and hydrologic systems, including multiple tradeoffs, impacting the future coevolution of the system in complex, heterogeneous ways. The strength of the hydrologic response is sensitive to the magnitude of the secondary driver; 45% nitrate reduction target leads to noticeable impacts at the outlet, while a 30% reduction leads to noticeable impacts that are mainly local. The local responses are conditioned by previous human-hydrologic modifications and their spatial relationship to the new biofuel development, highlighting the importance of past coevolutionary history in predicting future trajectories of change.

System Dynamics Modeling of Water Level Variations of Lake Issyk-Kul, Kyrgyzstan

“…Climate change leads to an increase of the air temperature and more variable rainfall regimes, with severe consequences for the frequency and magnitude of droughts and flood events, and an accelerated meltdown of glaciers, which can increase the river runoff in the short term but ultimately alters the discharge regimes in the long term, for this ambitious task is the system dynamics (SD) model, which was originally developed by Forrester in 1961 [19], and is an approach for understanding the interactions among driving factors and interconnected sub-systems that drive the dynamic behavior of a system [48,49]. Over the years, a number of SD models have been developed for water balance simulation and have been used to evaluate various water-related solutions [50][51][52], such as water resource planning models [53][54][55][56], hydrologic extremes models [57], agriculture water management models [58,59], and water balance models, which have been developed to test water-related and environmental issues in developing countries where the data availability is lacking [60]. With this background, the SD model satisfies the requirements for a complex analysis of the Issyk-Kul water level fluctuations and its driving factors.…”

Debates—Perspectives on socio‐hydrology: Changing water systems and the “tyranny of small problems”—Socio‐hydrology

“…They range from complete earth system models that capture the socio-hydrological processes in quite a lot of detail in a spatially distributed manner, to stylized models that abstract the coupled socio-hydrologic system into a small number of interconnected subsystems, and everything in between. Examples of socio-hydrologic models in this hierarchy include the System of Systems model presented in Yaeger et al [2014], the stylized models presented in Srinivasan [2015] and van Emmerik et al [2014], and the more generic model presented in Di Baldassarre et al [2015].…”

“…The more realistic, detailed, and place-based models are better suited to analyzing and quantifying the sociohydrologic interactions and feedbacks in real time in specific places [Yaeger et al, 2014]. They would involve substantial data collection and experimentation (e.g., detailed process modeling) to parameterize the social and hydrologic processes and the socio-hydrologic feedbacks.…”

Mix of First- and Second-Generation Biofuels to Meet Multiple Environmental Objectives: Implications for Policy at a Watershed Scale

“…SWAT was calibrated using hydrological streamflow and nitrate load data from USGS for a 10-year period encompassing very dry and very wet years to simulate the hydrological impact of miscanthus on stream flows and nutrient loads (see details in Ng et al 2010). Following the calibration and validation, the SWAT model is used to estimate monthly water drainage and nitrate load from each grid under each of the different land covers (see the description in Yaeger et al 2014). The estimated nitrate yields in May (the month with the maximum contribution of nitrate) are shown in Figs.…”