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The Spatial – Temporal dynamic model is applied to one of the subbasins within the Blackstone River Watershed to predict potential land use changes in the subbasin.

The selection of subbasin is based on existence of a diversity of land use types. The selected subbasin is considered as an area divided into grid squares, with each square having one of four possible states. Each cell has one of the four possible states. This corresponding to four land use types: forest, agriculture, urban, and other. Each grid behaves independent of each other. The equations (1-8) used in the STELLA model are presented in Figure 8, using the example of agriculture land use type.

AGRICULTURE[I,J](t) = AGRICULTURE[I,J](t - dt) + (AG_INFLOW[I,J] - AG_OUT[I,J]) * dt (1)

INFLOWS:

AG_INFLOW[I,J] = IF ((TOTAL_AREA[I,J] ) <= 100) THEN F_TO_A[1,1]+U_TO_A[I,J] ELSE 0 (2)

OUTFLOWS:

AG_OUT[I,J] = A_TO_F[I,J]+A_TO_U[I,J] (3)

A_TO_F[I,J] = IF(TRANSFER_FROM_A[I,J] = 1) THEN

(TRANSPORT_A_TO_F*AGRICULTURE[I,J]*(1-SPATIAL_INFLUENCE_FOREST[I,J])) ELSE 0 (4)

A_TO_U[I,J] = IF(TRANSFER_FROM_A[I,J] = 1) THEN

(TRANSPORT_A_TO_U*AGRICULTURE[I,J]*(1-SPATIAL_INFLUENCE_URBAN[I,J])) ELSE 0 (5)

SPATIAL_AGRICULTURE[1,1] =

(AGRICULTURE[1,2]+AGRICULTURE[2,1]+AGRICULTURE[2,2])/CELL_NUMBER[1,1] (6) TOTAL_AGRICULTURE = ARRAYSUM(AGRICULTURE[*,*])/16 (7)

TOTAL_AREA[I,J] = AGRICULTURE[I,J]+FOREST[I,J]+URBAN[I,J] (8)

Figure 8: Equations for agriculture land use type in STELLA model

Transport coefficients from one land use type to another are specified based on land use probabilities. The assessment is conducted to explore the change of three land use types over 100 years. It was observed that the increase in urban land use in the watershed was associated with a varying influence on the decline in agricultural and forest areas. The Spatial – Temporal dynamic model is applied to one of the subbasins within the Blackstone River Watershed to predict potential land use changes in the subbasin.

The spatial dynamic model builds up environmental change resulting from human land uses at modeling larger subwatershed scale. This will allow understanding of how land-use change emerges as a result of multiple factors in a watershed system.

We use a dynamic model, SIMILE simulation tool (Simulistic, 2003) to assess the land

changes in the study area. The dynamic simulation tool, SIMILE (Muetzelfeldt and Taylor, 2001), is a visual modeling environment providing integration of dynamic and spatial modeling with the ability to represent space.

2.4.2.3.1 Overall Model

The overall spatial-temporal dynamic model is presented in Figure 9.

Figure 9: Overall spatial-temporal dynamic model

Land use change is influenced by interaction between temporal and spatial factors in the watershed. Modeling these complex changes is critical to evaluate emerging land use and potential problems in water quality in the watershed. In predicting land use change, two components that are integrated include temporal (the transition probability from one state to other) and spatial (consideration of the spatial

adjacent parcels) dimensions. The temporal dynamics is evaluated using transition probabilities of land use time-series derived from GIS land use dataset. A MCMC (Monte Carlo Markov Chain) analysis is used to model spatially explicit changes in the watershed. The dynamic watershed (SWD) model is applied to one of the subbasins in the Blackstone River Watershed, of Massachusetts to predict potential land use changes and expected water quality changes. The subwatershed is chosen because of presence of all four kinds of land use types within the area and because of its location right in the middle of the Blackstone River watershed. The subwatershed scale is chosen due to limitation of SIMILE software to work with number of patches.

The land-use change model integrates two submodels: Spatial four-state Markov model and Land-use dynamics. This is created by considering the transition of land use types under the influence of Markovian and Spatial drivers. This model predicts how urbanization changes over time and demonstrates the effects of altering the extent of vegetation in the watershed. The transition probabilities are calculated based on historical land use change data information from MassGIS. The land use types are reclassified into four major categories; the proportional areas are calculated for each of the category and then the probabilities for each possible changes are derived (Table 7).

The transition probabilities from 1971 to 1999 are used in the model. The model uses 3,025 cells using different land use coefficients for four landuse types. Each cell has size of 100m x 100m. Subwatershed and matrix box boundaries as well as land use vector shape file are converted to a grid format with the cell size of 100 m. To indicate a subwatershed and modeling matrix boundaries, the ASCII file is created from grid where all cells that are within the subwatershed boundary have the value “1”, and the

rest - “0”. The DEM cell size is changed from 30 to 100 meters with spatial analyst tool and clipped to the subwatershed boundary. Aspect and aspect length are derived from the DEM (100 m). The resulting grids are exported to ASCII format and further used as comma delimited tables as the initial values in the model and for calculating of the required parameters for each of 3,025 cells. To explain the soil loss in different scenarios, the Revised Universal Soil Loss Equation (RUSLE, 1998) is used in the model. Modeling land use processes requires capturing the neighbor relationship between spatial patches: the model needs to know the land uses in neighboring patches in order to determine if a particular patch is likely to change from one land use type to another.

The model is illustrated as three types of objects (Patch, Land Transition, and Neighborhood).

1. PATCH

The Patch object is a fixed-membership, multiple-instance submodel, and represents each landuse patch with dimensions [55x55]. Each cell has row and column attributes, representing the spatial location of the patch.