From provocative narrative scenarios to quantitative biophysical model results: Simulating plausible futures to 2070 in an urbanizing agricultural watershed in Wisconsin, USA

Abstract:

Scenario analysis is a powerful tool for envisioning future social-ecological change and its consequences on human well-being. Scenarios that integrate qualitative storylines and quantitative biophysical models can create a vivid picture of these potential futures but the integration process is not straightforward. We present – using the Yahara Watershed in southern Wisconsin (USA) as a case study – a method for developing quantitative inputs (climate, land use/cover, and land management) to drive a biophysical modeling suite based on four provocative and contrasting narrative scenarios that describe plausible futures of the watershed to 2070. The modeling suite consists of an agroecosystem model (AgroIBIS-VSF), hydrologic routing model (THMB), and empirical lake water quality model and estimates several biophysical indicators to evaluate the watershed system under each scenario. These indicators include water supply, lake flooding, agricultural production, and lake water quality. Climate (daily precipitation and air temperature) for each scenario was determined using statistics from 210 different downscaled future climate projections for two 20-year time periods (2046-2065 and 2081-2100) and modified using a stochastic weather generator to allow flexibility for matching specific climate events within the scenario narratives. Land use/cover for each scenario was determined first by quantifying changes in areal extent every decade for 15 categories at the watershed scale to be consistent with the storyline events and theme. Next, these changes were spatially distributed using a rule-based framework based on land suitability metrics that determine transition probabilities. Finally, agricultural inputs including manure and fertilizer application rates were determined for each scenario based on the prevalence of livestock, water quality regulations, and technological innovations. Each scenario is compared using model inputs (maps and time-series of land use/cover and nutrient inputs, climate metrics) and outputs (maps and time-series of biophysical indicators).