Framing the search for a theory of land use

ABSTRACT

Land system science and affiliated research linked to sustainability require improved understanding and theorization of land and its change as a social-ecological system (SES). The absence of a general land-use theory, anchored in the social subsystem but with explicit links to the environmental subsystem, hampers this effort. Drawing on land-use explanations, meta-analyses, and associated frameworks, we advance a broad framework structure of eight elements – aggregations of explanatory variables – with links to the biophysical subsystem, for systematic comparisons of extant explanations. Tests and models can be employed to identify which set of variables and their configurations provide robust explanations of across land uses, identifying the potential for theory development. The framework and its application are applicable to both top-down and bottom-up explanatory approaches employed in the social sciences. Links to the environmental subsystem invite future exploration of SES explanations that reach across the different dimensions of global change and sustainability science.     

Application of SWAT with and without Variable Source Area Hydrology to a Large Watershed

In this study, two different versions of the Soil and Water Assessment Tool (SWAT) model were used to simulate the hydrology and biogeochemical response of the Cannonsville Reservoir watershed, in New York. The first version distributes overland flow in ways that are consistent with variable source area (VSA) hydrology driven by saturation excess runoff, whereas the second version is the standard version of SWAT. These two models were each calibrated for streamflow (Flow), particulate phosphorus (PP), total dissolved phosphorus (TDP), and sediment (Sed) against measured data from the 1,200 km² Cannonsville watershed. The standard version of the model yielded an r² between the measured and simulated data of 0.85, 0.73, 0.70, and 0.72 for Flow, Sed, TDP, and PP, respectively. The VSA version yielded an r² of 0.84, 0.69, 0.72, and 0.53 for Flow, Sed, TDP, and PP, respectively. The two models were then used to determine the maximum upper bound on the reduction in phosphorus loading by removing all of the corn in the watershed. The average reductions between the two models were 65 and 37% for PP and TDP, respectively. The VSA version was also used to estimate the effect of moving corn land in the watershed from the wettest, most runoff prone areas to the driest, least runoff prone areas, which cannot be done directly with the standard SWAT model.     

A New Open‐Access HUC‐8 Based Downscaled CMIP‐5 Climate Model Forecast Dataset for the Conterminous United States

Watershed‐scale hydrologic simulation models generally require climate data inputs including precipitation and temperature. These climate inputs can be derived from downscaled global climate simulations which have the potential to drive runoff forecasts at the scale of local watersheds. While a simulation designed to drive a local watershed model would ideally be constructed at an appropriate scale, global climate simulations are, by definition, arbitrarily determined large rectangular spatial grids. This paper addresses the technical challenge of making climate simulation model results readily available in the form of downscaled datasets that can be used for watershed scale models. Specifically, we present the development and deployment of a new Coupled Model Intercomparison Project phase 5 (CMIP5) based database which has been prepared through a scaling and weighted averaging process for use at the level of U.S. Geological Survey (USGS) Hydrologic Unit Code (HUC)‐8 watersheds. The resulting dataset includes 2,106 virtual observation sites (watershed centroids) each with 698 associated time series datasets representing average monthly temperature and precipitation between 1950 and 2099 based on 234 unique climate model simulations. The new dataset is deployed on a HydroServer and distributed using WaterOneFlow web services in the WaterML format. These methods can be adapted for downscaled General Circulation Model (GCM) results for specific drainage areas smaller than HUC‐8. Two example use cases for the dataset also are presented.     

Land use change effects on forest carbon cycling throughout the southern United States

We modeled the effects of afforestation and deforestation on carbon cycling in forest floor and soil from 1900 to 2050 throughout 13 states in the southern United States. The model uses historical data on gross (two-way) transitions between forest, pasture, plowed agriculture, and urban lands along with equations describing changes in carbon over many decades for each type of land use change. Use of gross rather than net land use transition data is important because afforestation causes a gradual gain in carbon stocks for many decades, while deforestation causes a much more rapid loss in carbon stocks. In the South-Central region (Texas to Kentucky) land use changes caused a net emission of carbon before the 1980s, followed by a net sequestration of carbon subsequently. In the Southeast region (Florida to Virginia), there was net emission of carbon until the 1940s, again followed by net sequestration of carbon. These results could improve greenhouse gas inventories produced to meet reporting requirements under the United Nations Framework Convention on Climate Change. Specifically, from 1990 to 2004 for the entire 13-state study area, afforestation caused sequestration of 88 Tg C, and deforestation caused emission of 49 Tg C. However, the net effect of land use change on carbon stocks in soil and forest floor from 1990 to 2004 was about sixfold smaller than the net change in carbon stocks in trees on all forestland. Thus land use change effects and forest carbon cycling during this period are dominated by changes in tree carbon stocks.