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Long-term effects of land-use change on water resources in urbanizing watersheds [1]

['Ammara Talib', 'Department Of Civil', 'Environmental Engineering', 'University Of Wisconsin', 'Madison', 'Wisconsin', 'United States Of America', 'Timothy O. Randhir', 'Department Of Environmental Conservation', 'University Of Massachusetts']

Date: 2023-07

The changes in energy balance resulting from land-use change may significantly affect the amount and timing of water loss to the atmosphere as evapotranspiration (ET). Also, these will impact water fluxes in the watershed system, influencing runoff rate, flow volume, intensity, and frequency of floods. During the past century, land-use change in the SuAsCo (Sudbury-Assabet and Concord) watershed has altered basin hydrology, sediment, and nutrient load that is detrimental to water resources in SuAsCo. This study uses an integrated physically-based model Hydrological Simulation Program-FORTRAN (HSPF), along with Land Transformation Model (LTM), to assess predicted temporal and spatial changes in water, nutrient, and sediment yields for future land-use scenarios of 2035, 2065, and 2100. Results showed that a 75% increase in effective impervious area and a 50% decrease in forest area in 2100 (from 2005 baseline levels) are projected to cause a 3% increase in annual streamflow and a 69% increase in total yearly mean surface runoff. The average annual total suspended solid (TSS) yield at the watershed outlet is estimated to increase by 54% in 2100. An increase of 12% and 13% concentrations of average annual total phosphorus (TP) and total nitrogen (TN) are predicted by 2100 due to urban expansion and increased runoff volume. This integrated modeling approach will inform watershed managers and landowners about critical areas of the SuAsCo watershed to apply best management practices (BMPs) to mitigate the effects of land-use land cover (LULC) change.

Funding: We would like to thank the U.S.A Fulbright Program for providing the scholarship to Ammara Talib. Partial support is provided to Dr. Randhir by the National Institute of Food and Agriculture, CSREES, U.S. Department of Agriculture, Massachusetts Agricultural Experiment Station (MAES), under Projects MAS00036, MAS00035, and MAS00045. Partial support to Dr. Randhir through the U.S. National Science Foundation’s Grant No. 2120948 under Growing Convergence Research is gratefully acknowledged. The data that support and underlie this study is from publicly available sources.

1 Introduction

The global expansion of agricultural and urban areas, along with significant increases in energy, water, and fertilizer consumption, led to changes in hydrological processes and tremendous losses to biodiversity [1–5]. Land-use change directly influences hydrological processes, such as ET [6, 7], infiltration [8], and runoff [9, 10]. For instance, land-use conversion from forest to agricultural or urban would typically be associated with increased runoff with constant precipitation. This is because transpiration rates and ET in farmlands are generally lower than in the forest [11, 12]. Moreover, infiltration through impervious urban land is lower [13–15]. Studies also suggested that land-use change is directly responsible for a 0.08 mm/year increase in global runoff [16, 17].

In addition to water balance, land use impacts water quality, especially sediment loading from uncontrolled urban runoff and soil erosion in the watersheds [18–22]. Stream sediment loads influence water quality, aquatic geochemistry [23], aquatic habitats [24], channel morphology, and downstream habitat [25–28]. Hence use of lakes and streams for drinking water supply and other designated applications is impacted by turbidity caused by high suspended sediment loads. Along with sediment, some LULC changes, such as from forest to agricultural land, also cause excessive nutrient loading [29–31], such as nitrogen and phosphorus. The eutrophication of water bodies through excess nutrient export from natural and anthropogenic sources can have detrimental effects in the form of decreased water clarity, harmful algal blooms, and hypoxia [32–35]. Hence, excess nutrients can increase aquatic plants and algae, affecting fishing, recreation, industry, agriculture, and drinking water, which could have substantial economic impacts. So, it is necessary to address the issue of sediment and nutrient loadings in streams/rivers through holistic landscape management [36] using a watershed system model.

Hydrological modeling is essential for watershed management for simulating hydrological processes under different land-use change scenarios [37–39]. Many watershed models have been developed to simulate processes related to runoff, sediments, and nutrients through drainage networks. For example, the Soil Water Assessment Tool (SWAT), a physically-based model [40], has been used to study the effects of BMPs related to municipal wastewater treatment plant load and dairy manure management in urbanizing watersheds [41, 42]. In addition to physically-based hydrologic models, Agricultural Pollution Potential Index (APPI) and Pollution Load (PLOAD) models were used to assess the source and pollution load in the urban watersheds [43, 44].

HSPF (Hydrological Simulation Program) is a semi-distributed, physically-based model for simulating streamflow, sediments, and nutrients [45, 46]. Stern et al. [47] applied the HSPF model in the Sacramento River Basin, California, to characterize streamflow and sediment supply changes. In addition, HSPF has also been used as a prediction tool for in-stream fecal coliform bacterial concentration from both point and nonpoint sources in watersheds [48]. Another case study was done in the Big Sunflower River watershed, Mississippi, to study the impact of BMPs, such as vegetative filter strips and tailwater recovery ponds, on total nitrogen and phosphorus [49]. While hydrological models can simulate the watershed process based on current land use, artificial neural network (ANN) models can be used to forecast future land-use changes. For example, the Land transformation model (LTM) is an ANN-based model developed by Pijanowski et al. [50] to predict future land use by considering social, environmental, and political factors driving population growth and change in the climate. LTM model uses spatial interaction rules and a machine learning model to estimate projected urban growth by determining the interactions of drivers, such as the transportation, and presence of lakes and rivers [51–53].

Although studies have investigated possible watershed impacts of land-use change on streamflow and nutrients [26, 54, 55], there is a further need for studies on the possible effects of long-term future land-use change scenarios on water quality and quantity in semi-urban watersheds undergoing rapid changes. Therefore, in this study, the hydrological response of a semi-urban watershed SuAsCo (Sudbury-Assabet and Concord), located in eastern Massachusetts, on the east coast of the USA, is investigated in response to future land-use change. Before 1986, water in the SuAsCo basin was classified as "fishable and swimmable" [56]. In addition, the rivers in the SuAsCo watershed were designated for five "outstandingly remarkable values": history, scenery, recreation, ecology, and place in American literature. However, because of impairment caused by sediments and nutrients, the Assabet, Sudbury, and Concord rivers no longer met the criteria for Class B (fishable and swimmable) in 2001 [57]. Now, these rivers are on the State’s List of Impaired Waters (303d) under the Clean Water Act [58]. Nutrients enter these rivers from nonpoint sources carried by stormwater runoff and point sources such as discharge from wastewater treatment plants. In addition, during the past century, land use changes in the SuAsCo watershed have altered basin hydrology, sediment, and nutrient load that is detrimental to the ecology and societal significance of water resources in SuAsCo [26, 59].

This study is unique in applying a dynamic and long-term approach to understanding how water quantity and quality change spatially and temporally under future land use change, which is dominated by urbanizing processes in the SuAsCo watershed. An integrated system of a macroscale, physically based model HSPF, and machine learning ANN-based LTM model is used. HSPF is calibrated and validated to simulate SuAsCo watershed processes, and the LTM is used to generate future land-use scenarios. The statistical functional relationship built by ANN captured the pattern of the LULC change. This study also demonstrates how GIS and state-of-the-art ANN tools can be applied to predict LULC change. The purpose of this study is to assess the impacts of land-use change on water resources in an urbanizing watershed system, and the objectives include 1) to quantify the effects of land use on runoff, 2) to evaluate the impacts of future land use on total suspended sediments (TSS), and nutrients such as total nitrogen (TN), and (TP). In addition, this dynamic hydrologic modeling approach at a regional scale will provide an essential methodology for further research on urban expansion and its impacts on hydrology.

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[1] Url: https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000083

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