spatiotemporal dynamics of landscape pattern and hydrologic process

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Spatiotemporal dynamics of landscape pattern and hydrologic process

in watershed systems

Timothy O. Randhir , Olga Tsvetkova

Department of Environmental Conservation, University of Massachusetts, Holdsworth Natural Resources Center, Rm. 320, Amherst, MA 01003, United States

a r t i c l e i n f o

Article history:

Received 29 November 2010Received in revised form 7 March 2011

Accepted 15 March 2011

Available online 21 March 2011

This manuscript was handled by G. Syme,

Editor-in-Chief

Keywords:

Nonpoint source pollution

Spatio-temporal modeling

Watershed management

Runoff

GIS

Dynamic modeling

s u m m a r y

Land use change is influenced by spatial and temporal factors that interact with watershed resources.

Modeling these changes is critical to evaluate emerging land use patterns and to predict variation in

water quantity and quality. The objective of this study is to model the nature and emergence of spatial

patterns in land use and water resource impacts using a spatially explicit and dynamic landscape simu-

lation. Temporal changes are predicted using a probabilistic Markovian process and spatial interaction

through cellular automation. The MCMC (Monte Carlo Markov Chain) analysis with cellular automation

is linked to hydrologic equations to simulate landscape patterns and processes. The spatiotemporal

watershed dynamics (SWD) model is applied to a subwatershed in the Blackstone River watershed of

Massachusetts to predict potential land use changes and expected runoff and sediment loading. Changes

in watershed land use and water resources are evaluated over 100 years at a yearly time step. Results

show high potential for rapid urbanization that could result in lowering of groundwater recharge and

increased storm water peaks. The watershed faces potential decreases in agricultural and forest area that

affect open space and pervious cover of the watershed system. Water quality deteriorated due to

increased runoff which can also impact stream morphology. While overland erosion decreased, instream

erosion increased from increased runoff from urban areas. Use of urban best management practices

(BMPs) in sensitive locations, preventive strategies, and long-term conservation planning will be useful

in sustaining the watershed system. 2011 Elsevier B.V. All rights reserved.

1. Introduction

Watershed systems provide multiple goods and services that

sustain human population and ecosystems (Randhir and Shriver,

2009). Rapid resource depletion and increasing demands from hu-

man populations impact the structure and function of watersheds,

thereby reducing the ability to sustain these services. Management

and policies to protect these resources require information on the

dynamics of the system, particularly an evaluation of the spatial

and temporal interactions among watershed components. An

assessment of the changes in the state of the system (pattern)and dynamics of flows (processes) is critical to evaluate and man-

age changes in landscape characteristics and environmental pro-

cesses. Such integrated assessment can be used to address long

term issues surrounding depletion of water resources and the

cumulative impairment of the watersheds capability to sustain

ecosystem services.

Land use change and environmental outcomes are a result of

the combined influence of biophysical and socioeconomic drivers.

Processes in watershed systems in particular, are essentially

dependent on the spatial distribution of components, interactions,

and temporal changes. For example, water purification and wildlife

habitat protection are watershed ecosystem services that are

dependent on land use pattern. Spatial patterns of land use, land-

cover change, and resource management change the nature and

spatial distribution of pollutant loading (Randhir et al., 2000). A dy-

namic assessment of the landscape is useful in the development of

spatial (targeting) and temporal (contracts) policies to protect

water resources. The dynamic information will also allow insights

into system trajectory, and adaptive management.

Land use and land-cover (LULC) changes are influenced by nat-ural processes and are both direct and indirect effects of human

activities (Turner and Meyer, 1991). Land use change influences

water-quality and habitat degradation (Randhir and Hawes,

2009; USGS, 2005), and loss of soil quality (Randhir, 2003). It also

influences water runoff, sedimentation rates (Marshall and

Randhir, 2008), earth-atmosphere interactions, biodiversity, water

budget, and biogeochemical cycling of carbon, nitrogen and other

elements at regional to global scales (Vitousek, 1994). Evaluating

the impacts of LULC change is important in land management deci-

sions and in protecting natural resources of watershed systems

(Marshall and Randhir, 2008). Documenting the rates of change,

driving forces, and impacts of LULC on watershed systems is a

0022-1694/$ - see front matter 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.jhydrol.2011.03.019

Corresponding author. Tel.: +1 413 545 3969.

E-mail addresses: [email protected], [email protected] (T.O. Randhir).

Journal of Hydrology 404 (2011) 112

Contents lists available at ScienceDirect

Journal of Hydrology

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j h y d r o l
http://dx.doi.org/10.1016/j.jhydrol.2011.03.019mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.jhydrol.2011.03.019http://www.sciencedirect.com/science/journal/00221694http://www.elsevier.com/locate/jhydrolhttp://www.elsevier.com/locate/jhydrolhttp://www.sciencedirect.com/science/journal/00221694http://dx.doi.org/10.1016/j.jhydrol.2011.03.019mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.jhydrol.2011.03.019


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focus of government agencies such as the USEPA (Jones et al., 1999)

to bring integrated solutions to resource management. Changes in

LULC can be indicative of regional environmental problems that re-

sult from impairment of both abiotic (surface runoff dynamics,

lowering of groundwater tables, impacts on rates and types of land

degradation), and biotic (habitat loss, reduction in biodiversity, and

species extinction) processes (Shriver and Randhir, 2006). An

understanding of changes in land use and water use over the next3050 years is central to attaining environmental sustainability

(IIASA, 1999).

The watershed landscape is dynamic in spatial, structural and

functional patterns (Hobbs, 1997) that are essential characteristics

of landscape ecology (McGarigal and Marks, 1995). Spatially expli-

cit land use change models are needed. A system dynamics para-

digm (Forrester, 1971) that is based on conceptualizing a system

in terms of compartments (stocks) and flows provides an intuitive

way of modeling differential or difference equations associated

with system processes. The application of this framework to model

landscape pattern and processes has an excellent scope toward

integrated approaches to decision making (Randhir and Hawes,

2009).

Modeling the dynamic emergence of landscape patterns and

their dynamic implications are important to watershed research

(Marshall and Randhir, 2008) and are important for adaptive man-

agement of watershed resources. The dynamic assessment

(Randhir and Hawes, 2009) that links land cover to hydrologic

dynamics is useful in predicting long term impacts of land use

change on processes. A watershed manager can assess the system

dynamics and gain insights into long term dynamics in complexity,

structure, and functions of watershed systems. In addition, the

development of site specific policies and implementation of man-

agement practices essentially depends on this spatial information.

1.1. Background

Spatial decision support systems (Lovejoy et al., 1997; Djodjic

et al., 2002) are becoming important to local and regional environ-mental impact assessment, planning, and implementation of regu-

latory policies (Munier et al., 2002) and in decision-making

(Randhir and Shriver, 2009). Models of human decision-making

with spatial and temporal dimensions cover a range of methods

with different simulation techniques: statistical/econometric mod-

els, dynamic systems models, logistic function models, regression

models, spatial simulation models, linear planning models, nonlin-

ear mathematical planning models, mechanistic GIS models, and

cellular automata models (Agarwal et al., 2000). Interest in the

application of agent-based models to land-cover change (Marshall

and Randhir, 2008) has also been growing rapidly during recent

years. Agent-based models (Laine and Busemeyer, 2003) combine

a cellular model representing the landscape with an agent-based

model to represent decision-making entities (Marshall andRandhir, 2008). The agent-based model may include a variety of

spatial processes and influences relevant for land-cover change.

Cellular automation models are important tools for the prediction

of landscape changes such as the spread of urbanization and future

land cover patterns at different spatial scales (Marshall and

Randhir, 2008; Heiken et al., 2000). A Markovian cellular automa-

tion process can be used to predict land-cover changes (Marshall

and Randhir, 2008).

Differential equations in a visual environment are used to mod-

el system dynamics and implemented using Stella software

(Randhir and Hawes, 2009; Costanza and Voinov, 2001; Costanza

et al., 1998). Systems modeling can be linked to Geographic Infor-

mation Systems (GIS) to model landscape changes (Maxwell and

Costanza, 1994; Wilkie and Finn, 1988)). A dynamic landscapesimulation of socio-economic effects on landscape change can be

applied to the built environment (Wang and Zhang, 2001) and to

the peri-urban system (Anwar and Borne, 2005). Land use change

can be analyzed using multivariate logistic regression (Allen et

al., 1999) and remote sensing methods (Jane, 2003).

A multiple criteria, dynamic spatial optimization can also be

used to model water quality on a watershed scale ( Randhir et al.,

2000). A dynamic system approach can be used for managing

and understanding complex ecological-economic systems (Costan-za and Ruth, 1998). A multiattribute optimization can be used to

model restoration options in watersheds (Randhir and Shriver,

2009). While several approaches have been used to study land

use impacts on watersheds, there is a further need to develop a

spatiotemporal, systems framework for modeling land use change

and hydrological impacts for policy.

Studies in spatio-temporal modeling of land use dynamics cou-

pled with assessment of hydrologic process are limited in wa-

tershed literature (Marshall and Randhir, 2008). This study will

address a portion of this gap with development of a system analy-

sis of land use change and watershed response at a spatially and

temporally explicit scale. The ability to use land use and hydrolog-

ical assessment together to evaluate dynamic interactions and long

term trends can support management decisions toward sustain-

ability. This paper is unique in the development of such long term

spatial simulation that combines Markovian process of land use

change, cellular automation, nonlinear simulation, and spatially

explicit assessment to model watershed processes. Specifically,

the watershed is rasterized (grid structure to represent spatial

information) and subject to LULC change that is triggered by a Mar-

kov chain probabilistic transition matrix and spatial interactions.

Each raster is modeled as a cellular agent with dynamic processes

observed and modeled at a temporal scale of several decades to

identify long term watershed impacts.

The paper develops a unique approach to model spatial and

temporal changes in watershed land use and hydrologic changes

to evaluate opportunities to mitigate land use impacts on water re-

sources. While land use modeling has been developed indepen-

dently and later used in water resource and NPS qualityassessment, there are no coupled assessments that are linked with-

in a dynamic model framework. The spatiotemporal watershed

dynamics (SWD) model is applied to a typical watershed to evalu-

ate spatial distribution, temporal trends, and policy options. Such

integrated assessment is critical as we move toward improving

the sustainability of watershed systems. Land use change is influ-

enced by spatial and temporal factors that interact with watershed

resources. Modeling these changes is critical to evaluate emerging

land use patterns and to predict variation in water quantity and

quality. This study models the nature and emergence of spatial

patterns in land use and water resource impacts using a spatially

explicit and dynamic landscape simulation. Use of urban BMPs in

sensitive locations, preventive strategies, and long-term conserva-

tion planning will be useful in sustaining water and watershedsystems.

The general objective of this study is to assess the nature and

emergence of spatial patterns in land use and the response of

water resources in a small watershed system at long term temporal

scales. Specifically: (i) develop a spatially explicit, dynamic simula-

tion model that links land use change with response of a watershed

ecosystem; (ii) study the dynamic influence of land use on runoff

and sediment loading local and watershed scales; and (iii) to

identify runoff and sediment reduction policies through spatial

and temporal targeting. Hypotheses that will be tested include:

(i) Spatial and temporal changes in land use have significant effects

on runoff and sedimentation in a watershed ecosystem (ii) the

nature of spatial patterns in water quality in a watershed are

influenced by temporal transitions and spatial influences; and(iii) dynamic modeling can result in more accurate assessment of

2 T.O. Randhir, O. Tsvetkova/ Journal of Hydrology 404 (2011) 112


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policy effectiveness. The hypotheses will be tested using com-

parison of dynamic and static (baseline) in trends.

2. Methodology

We develop a decision model for watershed management and

policy. The watershed is specified as gridded spatial units at loca-

tion i and j within a matrix that includes the watershed. The stateof land cover in spatial unit at i row and j column in time tis spec-

ified as Xijt. The state variable Xijt, defined in Eq. (1), is dependent

on state of the spatial unit in time t 1 (Xij(t1)), dijt is the Markov-

ian driver operating on spatial unit i, j at time t for the temporal

processes, and X(i1)(j1)t is the states of spatial units neighboring

the current spatial unit at time t.

Xijt fXijt1; dijt;Xi1;j1;t 1

Let Yijt represent the outcome of hydrologic process g() in the spa-

tial unit at i, j at time t as represented in Eq. (2). The process out-

come of spatial unit is dependent on the state of the system Xijtand a policy variable Pijt representing conservation practices to

change process outcomes in spatial unit i, j at t.

Yijt gXijt;Pijt 2

The primary objective of the decision problems is in Eq. (3),

which minimizes watershed outcomes that create negative exter-

nalities (such as storm water runoff, and pollution).

MinYijt gXijt;Pijt 3

Other constraints to the decision problem include initial conditions

and budget constraints. We focus on identifying Pijt at each spatial

unit that minimizes negative externalities, especially soil loss and

runoff generated by the watershed.

The conceptual representation of the theoretical model is pre-

sented in Fig. 1. The spatiotemporal watershed dynamic model

(SWD) consists of temporal and spatial drivers that operate on

the baseline land use as the initial condition. Each spatial unit uses

the two drivers to predict land use change in the next time step.

The land use change is input into submodels to calculate runoff

and sediment loading. This process is repeated on all spatial units

in the modeling matrix of the watershed.

The SWD model is applied to a small watershed in the

Blackstone River Basin of central Massachusetts. This watershedis selected based on the diversity of land use types and the deteri-

oration in water resources as a result of urbanization (Randhir,

2003). The watershed is divided into spatial units (grid objects)

of 1 ha area, with four possible land use states: forest, agriculture,

urban, and other. The initial states for each spatial unit are as-

signed to each grid object using GIS mapping of land use. Each grid

object is modeled as automated agents that interact with neigh-

boring grid agent. Transition coefficients from one land use state

to another are based on historic land use probabilities. The SWD

model is used for predicting the land use change and impacts on

water resources at spatial and temporal dimensions. Predicted land

use change is assumed to be based on current land use, knowledge

of the past land use change, and the nature of spatial influence.

Land use transition (Clark and Mangel, 2000) is represented

through a probabilistic progression from one state of land use to

another through time. Land use change is dynamic with a cause-re-

sponse behavior involving multiple variables: a cell may shift from

one land use to another, driven by Markovian process and states of

neighboring cells through cellular automation.

The implementation of SWD as an object-based model (objects

and submodels in a dynamic system) is modeled in a declarative

modeling environment (Muetzelfeldt, 2004). Land use (state)

change in a cell is influenced by the interaction between temporal

and spatial factors in the watershed. Modeling these complex

changes is critical in evaluating emerging land use and potential

problems in water quality in the watershed. In predicting land

use change, two components that are integrated include: temporal

change from one state to other and spatial dimension involving

interaction between adjacent cells. The temporal change in landuse is evaluated using transition probabilities evaluated from his-

toric GIS land use datasets. The MCMC analysis and spatial analysis

are used to model land use dynamics in geographic space and over

time.

The transition probabilities from the transition matrix are used

in the MCMC analysis. The spatial dynamics are assessed using a

cellular interaction of contiguous neighborhood cells (agents)

through cellular automation. The modeling is performed using

the SWD model for 3025 cells (agents representing a geographic

unit) with geographic coordinates extracted from a land use map

using GIS. Each cell is modeled in a state space to study the dynam-

ics of shift in land use. The land use types are reclassified into four

major categories; the proportional areas are calculated for each

category, and the probabilities for each possible change are de-rived. The transition probabilities for the period 19711999 are

used in the model. Watershed and matrix (square matrix that in-

cludes watershed cells) boundaries are converted to a grid format.

Each grid represents a cell that is 100 m 100 m in size. The

elevation layer is used to derive the aspect (flow direction) layer.

The resulting grids are exported as ASCII text for use in the SWD

model.

The SWD model consists of cellular agents and processes as

submodels (MCMC, land transition, neighborhood, runoff, and soil

loss). The cell object is a fixed-membership, multiple-instance sub-

model, and represents each land use location. Each cell type repre-

sents a land use state, which is arbitrarily labeled 1. for agriculture,

2. for forest, 3. for urban, 4. for others. Land use type cells are rep-

resented in a state equation that switch state based on incomingand outgoing land type, conditional on the outcome of MCMCFig. 1. Spatiotemporal watershed dynamic model (SWD).

T.O. Randhir, O. Tsvetkova/ Journal of Hydrology 404 (2011) 112 3


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and spatial process. An additional constraint is that any cell can

only be in one state at any time.

The land use, soil loss, runoff, and neighborhood submodels are

specified as process equations and spatial relationships between

objects. The runoff is assessed using the curve number method

(USDA/SCS, 1972), while soil loss in each cell is estimated using

the RUSLE method (USDA/NRCS, 2004).

2.1. Monte Carlo Markov Chain process

The transition probability of each state defines the change in a

land use patch. The transition probabilities derived from a time

series of land use change in the study area (MassGIS) are incorpo-

rated into a transition matrix for MCMC analysis. The MCMC tech-nique involves Markovian transitions of the land use at time ( t) as

dependent on land use at a previous time (t1) and a Monte Carlo

process of simulation (Metropolis and Ulam, 1949). The likelihood

of a change to a particular state is a combined outcome of MCMC

and spatial influence.

Thus the specific state switches from 1 and 0 if the current land

use switches to another type. Spatial modeling is in the form of

disaggregating, with each spatial unit modeled as agents. Each spa-

tial agent is given spatial attributes (area, location), and interac-

tions between other spatial agents are represented (Muetzelfeldt

and Duckham, 2005) using neighborhood association between

the current cell and surrounding cells. A conditional evaluation is

used to identify the neighboring status of each cell. The spatial

dynamics are modeled through cellular interaction between con-tiguous neighboring cells.

2.1.1. Soil loss

To model soil loss in each cell, the revised universal soil loss

equation is used (USDA/NRCS, 2004), where soil loss is calculated

as At = Rt Kt LSt Ct PRt , where At is the soil loss in tons per acre

at time t, Rt is the rainfall factor at time t, Kt is the soil erodibility

factor at time t, LSt is the land flow length and slope factor at timet, Ct is the cropping factor at time t, and the PRt is the support

practice factor at time t. The Rt and Ct factors are derived from

RUSLE2 software (USDA/NRCS, 2004). LSt factor is calculated as

LSt = [0.065 + 0.0456(St) + 0.006541(St)2] (SLtK)

NN, where, St =

slope steepness (%), SLt = length of slope (ft.), and K= 72.5. The

NN value varies from 0.2 to 0.5 with changes in value of St. The

NN value equals 0.2 if St < 1. If 16 St < 3 then NN= 0.3. If

3 < St < 5, then NN= 0.4. NN= 0.5 for StP 5.The elevation layer is used to derive land slope and slope aspect

using the BASINS software (USEPA, 2001). The data are trans-

formed into ASCII files to calculate the LSt factor from the equation

listed above. The results are specified as a table of LSt factors for

each cell in the SWD model. The K-factor is derived from STATSGO

data through BASINS (USEPA, 2001) from the soils attribute table

and then clipped to the area of interest. The C values are assigned

using conditional equations for each land use in the SWD model,

which are estimated for each land use from the RUSLE2 software.

The PRt value is set to no conservation for the baseline run, and

the Rt value is calculated using the RUSLE2.

2.1.2. Runoff

The runoff curve number method (USDA/NRCS, 1986) is used toestimate runoff from each patch. The curve number (CNt) method

Fig. 2. Blackstone River watershed and subwatershed locus map.



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estimates direct surface runoff from a given amount of rainfall.

This method incorporates soils permeability, land use, and ante-

cedent soil moisture condition. The runoff is calculated as

Qt PPt0:2St

2

PPt0:8St, where St

1000CNt

10. Here, Qt is runoff in time t,

PPt is precipitation at time t, St is potential maximum retention

(storage) after runoff begins, and CNt is the runoff curve number

at time t.

The SWD model (Fig. 1) is implemented in dynamic simulation

software, SIMILE (Muetzelfeldt and Massheder, 2003; Muetzelfeldt

and Taylor, 2001). SIMILE is a declarative modeling language which

is appropriate to this study as it represents specifications of the

conceptual and mathematical structure of the model rather than

a set of instructions (Muetzelfeldt, 2004) in a dynamic and spatial

environment. It is also visual and declarative in approach(Muetzelfeldt and Taylor, 2001) that is useful in ecological and

environmental research and is used in a number of international

research projects (Muetzelfeldt and Massheder, 2003).

2.1.3. Data

Land use data from MassGIS has 37-categories for 1999, which

are reclassified into four major land use types. The Ct values for soil

loss estimation is estimated using RUSLE2 as 0.45, 1, 0.01, and 0 for

agriculture, forest, urban, and other land uses, respectively. The

Pt value is set at value 1 for the baseline run of the model, and

the Rt value is calculated as 135 for the region using RUSLE2. TheCNt values for the runoff estimation ranged from 30 to 100; lower

numbers indicate low runoff potential while larger numbers are for

increasing runoff potential. The model is run over 100 years at a

yearly time step. The slope steepness can change in model time,with current policies and strict enforcement of BMPs in the study

(A) Agriculture (B) Forest

(C) Urban (D) Other

Fig. 3. Spatial distribution of baseline land uses (watershed shown in greyscale).



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area we assumed no major slope changes over time. The system of

differential equations is solved using RungeKutta solution meth-

od (Cartwright and Piro, 1992). The results are displayed within

the Model Run Environment (MRE) of SIMILE platform as spatial

grids and time series graphs.

We use data from a regional study to calibrate and validate run-

off and soil erosion estimates. The soil loss and runoff estimates

from 1999 to 2005 of a regional study (Randhir and Tsvetkova,

2009) are used in calibrating and validating the SWD model. Re-

sults are calibrated through statistical evaluation of runoff and sed-

iment estimates. Land use predictions are validated using the

Kappa Index (Chust et al., 1999). The Kappa Index is calculatedusing MassGIS land use grids from 1985 and 1999. Simulated land

cover for the same period is compared to existing land cover de-

rived from GIS. The outputs from regional estimates (baseline

states for the regional estimate for the watershed) and predicted

(model run over 14 years) were analyzed in ArcMap using the

geostatistical analysis tool. The Cohens Kappa Index (K) (Cohen,

1960) is calculated as K= (Po Pe)/(1 Pe), where Po is the ob-

served match in prediction and Pe is hypothetical probability of

agreement. Soil loss is validated by comparison with a sub-sectiontime span (7 years from 1999 to 2005) prediction of subbasin out-

come of sediment from a regional estimate (Randhir and Tsvetk-

ova, 2009). Runoff is calibrated using 5 years data from 1999 to

2003 from Randhir and Tsvetkova (2009). Soil loss estimated by

SWD model agreed with outcome for the watershed from regional

estimates with an R2 of 0.59. Runoff estimates from SWD model

was similar to regional estimates at R2 of 0.58.

3. Study area

The study watershed is in the Blackstone River basin ( Randhir,

2003) that is 48 miles long, flowing from south-central Massachu-

setts into northeastern Rhode Island. The river basin (Fig. 2) has a

historical role in the industrialization of the northeast, and is

important to the environmental health of Narragansett Bay that re-

ceives the river waters. The Blackstone River Valley was formed by

glacial action about fifteen thousand years ago that slowly shaped

the course of the Blackstone River. The Blackstone River drainage

system is one of the seven major river systems of the northeast

and its tributaries, banks and floodplains provide a rich habitat

for flora, fish and wildlife (Wright et al., 2001).

The current pattern of the landscape is a consequence of past

patterns and land use activity in the region. During the 18th and

19th centuries, most forests in New England were partly cleared

for agriculture, and partly harvested for wood products. By the late

19th century, agricultural lands were abandoned as a result of

industrialization and forests grew over again. The modern vegeta-

tion in New England is a result of complex human-impacted dis-turbance histories, with a composition that developed as a result

of prior land use (USEPA, 1999). Consequently, to forecast future

patterns of land use and land-cover change, this research uses his-

torical and contemporary drivers that operate at different spatial

and temporal scales.

The study watershed is located at the intersection of the four

towns (Uxbridge, Northbridge, Sutton, and Douglas) located in

Worcester County, Massachusetts. According to the 2000 Census,

Uxbridge had a population of 11,156 with a total area of

78.7 km2. Northbridge had a population of 13,182 with a total area

of 46.8 km2. The town of Sutton had a total population of 8250

with a total area of 87.9 km2. The town of Douglas had a population

of 7045 with a total area of 97.7 km2.

The total study area occupies 3172 ha in the modeling matrixand 1485 ha in the watershed itself. The study area is composed

of a mixture of forest, agricultural, suburban and urbanized areas

(Fig. 3). Most of the agricultural land is cropland and pasture and

is focused in the northeast part of the area. Urban land use in the

subbasin is generally concentrated in the southeast part of the area

and represents 34% of the total area. Forested land is evenly dis-

tributed throughout the basin and tends to be along stream chan-

nels, especially in the southern and northern parts of the basin.

Urbanization is a major problem in the watershed and impacts

water resources through alterations in hydrology (Randhir, 2003),

morphology, water quality (Randhir and Tsvetkova, 2009), and

habitat in the watershed. Changes to the stream attributes are

caused by an increase of impervious surface cover associated with

the process of urbanization (Randhir, 2003; Schueler, 1992). Rain-fall records from the National Weather Service (NWS) station in

(A) Land use distribution

(B) Soil loss

(C) Runoff

Fig. 4. Baseline spatial distribution of land use, soil loss, and runoff.



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25 years 50 years

75 years 100 years

Cropland

Forest

Urban

Fig. 5. Spatial land use distribution at varying time steps.

Fig. 6. Temporal land use change from baseline.



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Woonsocket, Rhode Island measure an average annual rainfall of

47.9 in/year or 121.67 cm/year.

4. Results and discussion

4.1. Baseline

The land within the study watershed is predominately forested

(46%) and distributed throughout the basin. The next largest land

use is urban (34%) which is located mostly in the north and north-

east parts, followed by agricultural land (12%) that is mostly in the

northwest portion, and the remaining 8%is under other land use

categories located mostly in the northern and western parts of

the study area.

The baseline land use distribution is presented in Fig 4a. The

agricultural land use is distributed mostly on the northwest side

of the watershed. It also has large contiguous and connected par-

cels going from the west side to the south end of the watershed.There is a small cluster of the cells in the north of the watershed

and several separated patches in the central part of the watershed.

Forested land is mostly in south, west and central portions of the

watershed with less density in the southern and northern end of

the watershed. The baseline distribution of forest land is character-ized by large connected and contiguous parcels. In the modeling

matrix, outside the boundary of the watershed, agricultural land

use is also defined by large connected cells in the north side and

a number of contiguous parcels in the south. Urban land use is

evenly distributed throughout the watershed with a concentration

on the east portion of the watershed. Urban land use distribution

consists of a large, contiguous region of connected cells with sev-

eral disconnected cells in the central part of the watershed. Within

the study matrix, urban land use is focused mostly in the south-

west of the watershed. Land use categorized as Other is mostly lo-

cated in the north and northeastern side of the watershed. Most of

the parcels are connected and contiguous. The land use distribu-

tion of all types as simulated is presented in Fig 4a.

There are three major soil classes in the study area with MUIDs MA007, MA014, and MA015. The soils texture in the study area is

After 25 years of simulation After 50 years of simulation


A

C

B

Fig. 7. Soil loss at varying time steps.



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composed of: fine sandy loam (FSL) at 28%; gravelly loamy sand

(GR-LS) at 33%; and very stony fine sandy loam (STV-FSL) at 39%

of the study watershed. The northern part of the watershed has a

soil texture of the GR-LS type, while the southern, the southeast,and the northeast parts of the watershed have mostly a texture

of STV-FSL type. The western, northwest, and southwest regions

of the watershed are generally composed of FSL textured soils.

All three types of soils texture are spatially distributed over the

central part of the watershed.

The baseline spatial distribution of soil loss in the watershed is

presented in Fig. 4b. High soil loss is observed in areas located in

the northwest and eastern parts of the study watershed, and there

isalsoa longband of cells withhigh soil loss traversingfromwest to

the southern part of the watershed. These areas have agricultural

and urban land uses. A lower soil loss is observed in the central area

of thewatershed.The rate of soil loss is also variable throughout the

watershed. This distribution can be explained with land use types

where highest soil loss is associated with agriculture and urbanareas, while forested areas had a lower rate of soil loss.

The spatial distribution of baseline runoff rates is presented in

Fig. 4c. High runoff areas are located in the north and northeast

of the watershed, and these areas are large, contiguous, and con-

nected. These areas are associated with agriculture and urban landuses. The less intense runoff areas are mostly in the central and

southern parts of the watershed with mostly forest cover. Runoff

is correlated with land use type, with highest runoff areas associ-

ated with urban and agriculture and the lowest runoff areas in for-

ests (Randhir and Tsvetkova, 2009).

4.2. Spatio-temporal simulation

Results of spatial and temporal changes in land cover over 25-

year intervals are presented in Fig. 5. The results show a steady de-

crease in agricultural parcels in the watershed during the initial

25 years of simulation. The agricultural land is subject to fragmen-

tation during the initial 25 years of the simulation.

A comparison of the forest land use distribution shows thatlarge, contiguous parcels that are observed in the central and



Fig. 8. Runoff simulation at varying time steps.



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southern parts of the watershed after 25 years of simulation are

disappearing and dispersing over the years. It is observed that

the forest land is fragmenting and declining in all areas of the wa-

tershed. The relatively low levels of parcels are accumulating in the

northwest and eastern areas, which may indicate that forests are

being replaced by urban areas. It is expected that high urbanization

will dominate in eastern portions of the watershed.

The pattern of rapid urbanization shows a trend toward subur-

banization and later development of more dense urban areas. The

urban pattern increased from the northeast portion to the central

and other parts of the watershed. Urbanization trends show thatafter 100 years urban land use will be the major land use and lead

to areas with the highest impervious cover in the watershed. The

simulated land use over 100 years of the Other category shows a

decrease in area over time. In general, land use changed over years

and is concentrated in the different portions of the watershed over

different simulation periods.

In Fig. 6, the overall simulated land use change over

100 years is presented in the form of graph. Urban land in-

creases rapidly during the first 25 years to 25% of baseline and

reaches a maximum increase of 35% of baseline. Forest land in-

creases slightly during the first 5 years and decreases gradually

thereafter to 15% of the baseline. Agricultural land use decreases

rapidly in the first 10 years and reaches a maximum reduction

of 12% of the baseline. The graph indicates that in comparisonwith the beginning period of time the results tend to increase

during the modeling period for urban land use and show a de-

crease in agricultural land use. There is a decline in the forest

land use type over time.

The spatial distribution of soil loss in each cell over 100 years is

presented in Fig. 7. It can be observed that the soil loss expands

from the middle section of the study area to the north and south

sections over the simulation period. This is because of increased

soil loss in cells changing from forest to urban land use (Randhir,

2003). These are also the most heavily urbanized areas of the wa-

tershed, especially in the central portion of the study area near the

southeast boundary.Spatial distribution of runoff over the simulation period is

shown in Fig. 8, which shows runoff during 25, 50 and 100 years

compared to the baseline scenario. Runoff is highly correlated with

land use types (Randhir and Tsvetkova, 2009) and is characterized

by the distribution of large contiguous parcels throughout the wa-

tershed area with high levels in cells at the central section as well

as in the east, northwest and southwest parts of the watershed. A

high runoff is associated with the urbanization process in the

watershed.

The trend in sediment loss is depicted in Fig. 9a. The overall rate

of overland sediment loss decreased in the first 20 years to 0.06 t/

ha. The decrease remained at 0.05 t/ha from baseline over the fol-

lowing eight decades. The reduction in overland soil loss can be

attributed to reduction in agricultural land and increased urbanland that could have an armoring effect on land surface.

(A) Soil Loss

(B) Runoff

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 20 40 60 80 100

Time

SoilLos

s(t/Ha)

97

98

99

100

101

102

103

104

105

106

107

0 20 40 60 80 100

Time

Runoff(cm/Ha)

Fig. 9. Temporal trend soil loss and runoff in the watershed.



11/12

The total runoff is presented in the Fig. 9b. The results show an

increase in runoff volume by 7 cm/ha over time and is associated

with increase in urban land.

It is observed that the increase in urban land use in the wa-

tershed is associated with decline in agricultural and forest areas.

4.3. Policy implications

Results show a potential for rapid urbanization in the wa-

tershed that can result in water quality and storm water issues.

Based on expected spatial land use change, site specific policies

can be implemented in areas that have the potential to impact

water resources.

Nonpoint source reductions in the watershed can be achieved

through the implementation of management measures that can re-

duce loads through land use policies and best management prac-

tices (BMPs) applied to sensitive sites. Three areas for targeting

(AC in Fig. 7) are identified from sensitivity to sediment loading

and runoff. It was observed that runoff and sediment impacts occur

in early years of the land-cover change and need policies to protect

from impacts. BMPs can reduce runoff and mitigate transfer of pol-

lutants, reduce degradation of soil and water resources, and main-tain infiltration levels in the watershed. BMPs could include an

array of practices to increase water infiltration and reduce runoff

that could be applied to urbanizing areas. Practices can also miti-

gate runoff, reduce the erosion, and attenuate transport of sedi-

ment from agricultural fields (USEPA, 2006).

BMPs to reduce urban impacts on water resources include vege-

tative management practices such as shoreline revegetation, shore-

line stabilization, urban forestry and urban practices such as porous

pavement and water quality inlets. They may also consist of differ-

ent structural practices such as water and sediment control basins,

roof water collection devices, tree filter strips, terraces, diversions,

grassed waterways, woodland fencing and wetland development.

Another policy is to restrict land use and regulate land practices

through zoning laws. Current zoning laws promote residential usesin most of the watershed. Zoning should be updated to reduce loss

of open space under build-out conditions. Education in low impact

development can also be useful in sensitive areas of the watershed.

In summary, it is observed that spatial and temporal changes in

land use have significant effects on runoff in a watershed ecosys-

tem. While the overall sediment loading is decreased, the spatial

and temporal pattern of sediment loading is altered by land use

change in the watershed. A spatially targeted approach to protect

sensitive areas (both baseline and future potential) can be effective

in enhancing water quality in the watershed.

5. Conclusion

Urbanization and human population tend to grow and place de-mands on natural resources. Human activities can have a dramatic

impact on water resources in the Blackstone River watershed. Dy-

namic simulation modeling is used to simulate changes that may

occur to a subwatershed in Blackstone River watershed. Manage-

ment of these resources and land areas is important for protecting

water resources. With continued urban growth, policies to manage

impervious areas are necessary to improve watershed hydrologic

condition.

The study models dynamic and spatial patterns of hydrologic

processes of the watershed system. Spatio-temporal dynamic mod-

eling is used to study the emergence of the landscape pattern over

time. The model is run over a 100 year period and is used to assess

potential land use change as a result of MCMC and spatial pro-

cesses. The model is object-based and modular to represent changein land use states and hydrologic impacts on a yearly time-step.

The overall results show that land use modeling requires

knowledge of multiple factors to determine land use change. Spa-

tial and temporal changes in land use have significant effects on a

watershed ecosystem. The nature of spatial patterns is influenced

by temporal transitions and spatial influences. Understanding the

past and future impacts of changes in land cover is central to the

study of land use change impacts on watershed system compo-

nents. The modeling results show that the increase in urban landuse in the watershed is associated with the decline in agriculture

and forest land, indicating that urbanization could become a seri-

ous problem in the future. The results emphasize the need to pro-

tect agricultural area in rapidly changing watersheds. The highest

soil loss is associated with agriculture areas, while forested areas

had lower soil loss. A high runoff is associated with urban and agri-

culture types and a lower runoff in forested areas. There is the evi-

dence that highest runoff and soil loss areas are associated with

agriculture and early urban land uses.

In summary, it is observed that spatial and temporal changes in

land use are significant in the watershed. There is a high potential

for urbanization in most parts of the watershed. This urbanization

could have significant effects on runoff and infiltration in the wa-

tershed ecosystem. Loss of recharge to groundwater can result in

lowering of stream stages and groundwater levels. A higher vol-

ume of storm water can be expected from increased urban cover

in the watershed due to a lesser lag time. While the overall sedi-

ment loading decreases at the watershed scale, there is potential

for changes in spatial and temporal pattern of sediment loading.

This potential has implications for stream morphology and erosion

in certain sensitive areas of the watershed.

A potential exists to anticipate and implement policies that mit-

igate urban influences. Increasing infiltration in recharge areas (Se-

kar and Randhir, 2006) can be an important strategy in urbanizing

watersheds that can maintain groundwater and stream flows in

the watershed. Runoff mitigation is critical to reduce stream bank

erosion andloading of pollutants to the main stem of the river basin.

A spatially targeted approach to protect sensitive areas (both base-

line and potential) can be effective in enhancing water quality inthe watershed.

This study adds new knowledge to watershed literature through

a dynamic assessment of impacts of land use changes on wa-

tershed hydrology. The spatio-temporal assessment allows for pre-

diction of land use change and evaluation of potential water

resource problems. Further research is needed in dynamic interac-

tion involving multiple pollutants and under stochastic conditions.

Such comprehensive assessments that result from spatio-temporal

modeling are vital to achieving long-term sustainability of wa-

tershed systems.

Acknowledgements

We would like to thank the anonymous reviewers of the man-

uscript. This material is based upon work partially supported by

the Cooperative State Research Extension, Education Service, US

Department of Agriculture, Massachusetts Agricultural Experi-

ment-Station, under Projects MA500864, MAS000943, NE-1024,

NE-1044, MS 11, and MAS 00924.

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