Modelling the hydrologic effects of land-use and climate changes

Transcription

1 7_Tong 15/3/06 10:29 pm Page Int. J. Risk Assessment and Management, Vol. 6, Nos. 4/5/6, 2006 Modelling the hydrologic effects of land-use and climate changes Susanna T.Y. Tong* Geography Department, University of Cincinnati, Cincinnati, OH , USA *Corresponding author Amy J. Liu Department of Geography and Planning, West Chester University of Pennsylvania, West Chester, Pennsylvania 19383, USA Abstract: Climate and land use affect water quantity and quality, however, the complex relations of climate and land use regarding flow and instream nutrient levels have yet to be elucidated. This study aims to assess the hydrologic effects of different land-use and climatic regimes in the Lower Great Miami River Basin. The modelling results from BASINS showed that, as expected, agricultural lands and the wettest scenario yielded the highest amount of streamflow, fecal coliform, and nutrient loadings. But, it was the dry scenario ( 2 C, 20% precipitation of the current average climatic conditions in SW Ohio), instead of the driest scenario ( 4 C, 20% precipitation), that produced the highest daily nutrient concentrations. When the future land-use and climate scenarios were coupled, the worst situation was found under the current land use and the wettest condition. Hence, a change in land-use pattern may help to alleviate the adverse hydrologic impacts of climate change. Keywords: BASINS; climate change; HSPF; hydrologic modelling; land-use change; water resources. Reference to this paper should be made as follows: Tong, S.T.Y. and Liu, A.J. (2006) Modelling the hydrologic effects of land-use and climate changes, Int. J. Risk Assessment and Management, Vol. 6, Nos. 4/5/6, pp Biographical notes: Susanna Tong is an associate professor in environmental geography. She has extensive research interests in water quality analyses, hydrologic modelling, ecological risk assessment and heavy metal contamination. She has worked with the USEPA on several research projects on global warming, land-use change and stressors and receptors response relationships. She has published in major journals. Amy Liu is an Assistant Professor at the Department of Geography and Planning, West Chester University of Pennsylvania. Her research focuses on water resources management, GIS and modelling. Copyright 2006 Inderscience Enterprises Ltd.

2 7_Tong 15/3/06 10:29 pm Page 345 Modelling the hydrologic effects of land-use and climate changes Introduction Watershed hydrology is intimately related to land use, soil type and climate (Chow et al., 1988). Changes in climate and land use alter the hydrologic cycle and affect the quantity of water available for runoff, streamflow and ground water flow (Changnon and Demissie, 1996). Any change in stream flow will affect the water quality and the availability for municipalities and industries (Ohio Environmental Protection Agency, 1996). During the last century, due to the accumulation of greenhouse gases, our climate was changing in an unprecedented way. In the late 1980s, the World Meteorological Organisation and the United Nations Environment Programme developed a multi-year scientific assessment of climate change under the auspices of the Intergovernmental Panel on Climate Change (IPCC). According to the IPCC (1995), the average global temperature could be as much as 6 C higher at the end of the 21st century. Among the climate research community, there is a general consensus that our climate is warming (Gleick, 1998). However, uncertainty does exist on the magnitude of these changes, especially in terms of rainfall. Besides, there are problems in translating the changes predicted for global and regional scales to a local scale (Miller and Kim, 2000). Unequivocally, global warming and changes in precipitation patterns would affect the hydrologic cycle, altering snow cover, sea level, frequency of severe storms and the quality of surface waters. It is, therefore, of paramount importance for resource managers to consider the plausible effects of future climate change on water resources. Viable strategies and mitigation schemes have to be devised to address the potential threats of climate change. While our climate had experienced changes in the past, these changes had never coincided with large-scale landscape modifications. Our landscape is rapidly being modified. Forested areas become agricultural farmlands and some may revert back after agricultural abandonment. Urbanisation and suburban sprawl convert agricultural and forested areas to impervious surfaces. In a watershed, any hydrologic and ecological effects of climate change can be exacerbated by land-use modifications. Although there have been some studies in the impacts of climate and land use on water flows and quality, most existing research efforts are focused on either the effects of climate change or land-use change (see for example, the work of Bhaduri et al. (2001), Hanratty and Stefan (1998) and Matikalli and Richards (1996)). Many of these studies examine the impacts on either the quantity or the quality aspect of runoff (for instance, the work by Bouraoui et al. (1998), Ferrier et al. (1995), Henderson-Sellers (1994), Hulme et al. (1993) and Wu and Haith (1993)). Some work is based only on field studies (for example, Edwards et al. (2000)), while others base their studies solely on hydrologic modelling of a specific river basin (Lettenmaier and Gan, 1990). To a large extent, the relationships between water pollution and land-use types and climate conditions are still unclear. Little research has been performed on modelling the relative and combined effects of different climate and land-use regimes on both the water quantity and quality in a local watershed. But the results of these earlier studies often suggest that neither land-use change nor climate change work in isolation; rather, it is the simultaneous action and the concerted efforts of these two forces which produce impacts on water quality (Murdoch et al., 2000). Besides, sound land-use management may offer an alternative approach to augment existing mitigation strategies to ensure long-term adaptation to future climate change (Krysanova et al., 1998). To protect our limited and valuable

3 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu water resources, we need a better understanding of the integrated effects of climate and land-use changes in local water resources. Without such knowledge, it would be difficult to predict the overall implications of climate and land-use changes and to make any meaningful anticipatory decision on adaptation and regulation. In a pilot study (Liu et al., 2000), the hydrologic consequences of climate and land-use changes in Cincinnati and Columbus in the Ohio River Basin were simulated using a Long-Term Hydrologic Impact Assessment method, L-THIA, (Bhaduri et al., 1997; Harbor, 1994). The results showed that forests generate the least amount of runoff and non-point source nitrogen pollution, followed by low-density residential, agriculture, high-density residential and commercial land use. This suggests that land-use change, such as conversion from agricultural to low-density residential land use, may decrease the amount of surface runoff and nitrogen. A change from agricultural to commercial land use may result in a greater amount of runoff and nitrogen pollution. Nevertheless, these results were preliminary. To obtain quantitatively more accurate and predictive results, we need detailed hydrologic assessments of the combined consequences of climate and land-use changes. Additional instream water quality parameters should also be included. The objective of this study was to analyse the plausible impacts of climate and land-use changes on the amount of streamflow and the quality of surface runoff (in terms of nutrient contamination) in a local river basin. Through the investigation of the complex interplay of climate and land-use changes on hydrology and water quality, the efficacy of utilising land-use management as a mitigation and adaptation option to ameliorate the impacts of global climate change on water resources was explored. As in our earlier study, we employed a number of land-use and climate change scenarios to accomplish our task. However, whereas we used a model that simulated mainly overland flow, in this study, we used a nonpoint source, watershed-based hydrologic model to portray streamflow and the quality of surface runoff. The model was calibrated and validated. It was used to characterise the current water quantity and quality conditions and to predict the hydrologic conditions under different scenarios of climate and land-use changes. 2 Methods and materials 2.1 Choice of the hydrologic and water quality model Modelling climate and land-use change requires the selection of an appropriate hydrologic model. There are different types of hydrologic models in use today. The basic types include the physical-based mathematical models, the conceptual models and the dynamic stochastic models. They range in complexity, scale and resolution, and there are often tradeoffs between model complexity and the resolution of the data inputs and outputs (Butcher, 1999; Singh, 1995). A number of hydrologic models were considered in this study, including the System Hydrologique Europeen model (Abbott et al., 1986), SWIM (Krysanova and Luik, 1989), and the Soil and Water Assessment Tool, SWAT (Arnold et al., 1994). The main criteria for choosing the model for this study were: data requirements, ease of use, as well as model accuracy, capabilities, versatility and flexibility. Based on extensive literature searches and communications with other model users, the Better Assessment Science

4 7_Tong 15/3/06 10:29 pm Page 347 Integrating Point and Nonpoint Sources, BASINS, was chosen to model the quantity and quality of runoff. 2.2 BASINS BASINS is a watershed-based multi-purpose water quality analysis system developed by the US Environmental Protection Agency (US Environmental Protection Agency, 2001) to more effectively integrate assessment of point and nonpoint pollution sources. It has been commonly used, especially in Total Maximum Daily Load (TMDL) determinations. In a previous study (Tong and Chen, 2002), BASINS was employed to simulate the current hydrologic conditions in the Little Miami River in Ohio. It was found that the system was powerful, accurate and flexible, capable of simulating the real world conditions, under both a subwatershed scale (comprising only a small reach in the US Geological Survey 14-digit Hydrologic Unit Code, HUC) and a watershed scale (similar to a US Geological Survey 8-digit HUC). The system could easily be calibrated and validated. Other scholars have also examined the system and reported that...basins was judged to be an excellent beginning tool to meet the complex environmental modelling needs in the 21st Century... (Whittemore and Beebe, 2000). Using the Windows environment and an ArcView-based GIS as an integrating framework, BASINS incorporates frequently used nationally derived environmental data (such as land use, point source loadings, etc.) and water quality conditions (STORET). Integrated into BASINS are a variety of analytical hydrologic and water quality stream models. One of these models is the Hydrologic Simulation Program Fortran (HSPF) model (Bicknell et al., 2000). 2.3 HSPF Modelling the hydrologic effects of land-use and climate changes 347 Adapted from the hydrologic land-based Stanford Watershed Model, HSPF is a lumped parameter model. Based on the meteorological and land-based processes, HSPF continuously simulates all streamflow components and models their nonpoint source pollutant contributions, point source discharges and water quality (Bicknell et al., 2000). It considers hydrolysis, oxidation, photolysis, biodegradation, volatilisation and sorption. As a robust, high resolution, reliable and comprehensive hydrologic model, it represents the state-of-the-art water quality modelling efforts of the US Environmental Protection Agency and has been used as the premier nonpoint source model not only for the agency, but also for the US Geological Society and the Army Corps of Engineers. The model has been widely applied in flood forecasting, river basin planning, water quality modelling and assessment of best management practices as well as climate and land-use changes (see for example, Bicknell et al. (1996); Chun et al. (2001); Cryer et al. (2001); Donigan and Crawford (1976); Donigan and Huber (1991); Tsihrintzis et al. (1996)). There are three basic modules (PERLND, IMPLND and RCHRES) in HSPF. They are linked in a hierarchical structure. PERLND is a module for pervious lands. In HSPF, each land segment is treated as a lumped catchment. When precipitation falls on pervious lands, it is assumed to be distributed between a series of storages, from which surface runoff, interflow and groundwater flow are generated. The primary functions of PERLND

5 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu are to simulate water budget, snow melt, sediment production, air, soil and water temperature and water quality. For agricultural lands, nutrient and pesticide transport will also be simulated. IMPLND is used to simulate the hydrodynamics and water quality processes on impervious land surfaces. IMPLND has only two storages and surface flow is the only contribution to stream flow. Evapotranspiration can occur from most of these moisture storages. RCHRES is the module for streams, reservoirs or well mixed lakes. Stream segments are divided into reaches. Each reach has inputs from upstream and external loadings. Within the reach, there are degradation and petitioning of conservative constituents. At the lowest pour point, the outputs are transported into the next reach. Routing is done based on the kinematic wave equation. For each reach, a fixed relationship is assumed between water level, surface area, storage and discharge. By assuming that the cross-section of the reach is constant throughout the reach, HSPF calculates the hydraulic variables (such as hydraulic radius, shear stress and velocity). Water quality constituents are modelled by simple relationships. Examples are the association of the constituents with flow components or sediment and the accumulation or depletion of the constituents in storage. 2.4 Selection of the study area HSPF requires continuous records of rainfall, evapotranspiration, temperature and solar intensity to drive the simulations. Thus, only those watersheds with reliable long-term climate data can be used in the study. Moreover, for calibration purposes, the watershed has to have gauge stations that have historical discharge and water quality information. Based on the availability of data as well as the diversity of land-use types in the area, the Lower Great Miami River, a tributary of the Ohio River in southwest Ohio, was chosen in this research as a case study (see Figure 1). The Lower Great Miami River flows from the northeast at Dayton, Ohio to the southwest, draining into the Ohio River west of Cincinnati. The basin is located within the Till Plains section of the Central Lowland physiographic province and was glaciated during the Pleistocene. Major tributaries of the Lower Great Miami River include Twin Creek and Four Mile Creek. The basin has a temperate continental climate characterised by well-defined winter and summer seasons that are accompanied by large annual temperature variations. The mean annual temperature ranges from 9.4 to 12.7 C. Precipitation ranges from 96.5 to cm per year (Debrewer et al., 2000). The watershed covers 3600 sq km, most of which is agricultural land for row-crop production of corn, soybeans and wheat. Urban land use is concentrated along the heavily industrialised Dayton-Cincinnati corridor. The Lower Great Miami River basin is undergoing rapid urbanisation and suburban sprawl processes. With natural forests and agricultural lands gradually replaced by developed areas, there is an urgent need to examine the plausible hydrologic changes in the river basin in anticipation of future climate and land-use changes.

6 7_Tong 15/3/06 10:29 pm Page 349 Modelling the hydrologic effects of land-use and climate changes 349 Figure 1 The map of the study area

7 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu 2.5 Data sources The watershed for the Lower Great Miami was first delineated based on the Reach Files Version 1 and Version 3 (RF1 and RF3, respectively) from the US Environmental Protection Agency and digital elevation data (DEM) from the US Geological Survey. Then GIS data were prepared. All these data were used to build the database as well as to characterise the land use (such as, the location of agricultural lands and the percentages of impervious and pervious urban areas), hydrology (for example, streamflow) and water quality (the levels of nitrogen, phosphorus and fecal coliform) conditions of the river basin. They were also utilised to develop, calibrate and validate the hydrologic and water quality models for the study area. Water quality observation data were obtained from the US Environmental Protection Agency s STORET archive. The permit point source pollution data were gathered from the Ohio Environmental Protection Agency. Information on river flow and hydrological units (HUC) were acquired from the US Geological Survey. In addition, the land use coverages were obtained from the Thematic Mapper data acquired by the Multi-Resolution Land Characterisation Consortium. Other information included the meteorological data from the National Climatic Data Center and soil data from the National State Soil Geographic database of the US Department of Agriculture. The data were compiled and managed in a database. Spatial coverages were merged and clipped using Arc/Info. 2.6 Modelling procedures Since water quality simulation is based on the general hydrologic model, the simulation was performed in two steps: the simulation of the hydrology and the simulation of water quality. Using the meteorological data for the period from January 1st, 1975 to December 31st, 1984, the 1980 land-use data and the default parameters provided by HSPF, the basic hydrologic model was generated to simulate the flow conditions in the watershed. This time period was chosen because within the ten-year span, there were dry and wet years as well as some infrequent events (droughts and floods) in the watershed. Land-use patterns had also been changed. The simulated streamflow results, in terms of the total rate of outflow of reaches, were compared with the actual observed daily discharge records during the simulation period from existing US Geological Survey monitoring data (US Geological Survey, 1997). This is to ensure that the simulated results can realistically reflect the real world observed conditions. Initially, using the default values, the simulated streamflow was lower than the observed streamflow. To increase the direct overland flow and interflow, the lower zone nominal soil moisture storage value (LZSN) and the index to mean soil infiltration rate (INFILT) were reduced. In addition, the fraction of groundwater inflow entering deep groundwater (DEEPFR) and the interflow parameter (INTFW) were adjusted and the interflow recession parameter (IRC) was reduced (see Table 1). With these changes, the difference between simulated and observed streamflow was approximately 18%. According to Bicknell et al. (2000), an error rate below 20% is generally regarded as acceptable. Consequently, the hydrologic model for the Lower Great Miami River was accepted.

8 7_Tong 15/3/06 10:29 pm Page 351 Table 1 Modelling the hydrologic effects of land-use and climate changes 351 Parameter values used in the calibration of the hydrologic model Parameters a Default values Adjusted values LZSN INFILT DEEPFR INTFW IRC Notes: a LZSN lower zone nominal soil moisture storage value (in cm). Notes: INFILT index to mean soil infiltration rate (in cm/hr). Notes: DEEPFR fraction of groundwater inflow entering deep groundwater. Notes: INTFW interflow inflow parameter. Notes: IRC interflow recession parameter per day. Once the hydrology was calibrated, the water quality parameters were modelled. In simulating the water quality conditions, modelling was confined to total nitrogen, total phosphorus and fecal coliform. This was mainly because the focus of this paper was to examine the likely impacts of climate and land-use changes on non-point source nutrient pollution. Besides, there is a lack of available historical monitoring data on other water quality parameters for the watershed, as well as a lack of parameter estimates available for calibration purposes. As in the hydrologic modelling, the simulated water quality results were calibrated. Overall, with the default parameter values, the simulated total nitrogen was similar to the observed ammonia plus organic nitrogen. Nevertheless, the simulated total phosphorus and fecal coliform counts were much lower than the observed total phosphorus and fecal coliform count. Thus, the initial storage of phosphorus and fecal coliform on the pervious land segment (SQO), the rates of accumulation for phosphorus and fecal coliform (ACQOP), and the maximum storage of phosphorus and fecal coliform (SQOLIM) in each land use were increased. Besides, the concentrations of phosphorus and fecal coliform in the interflow outflow (IOQC) and the active groundwater outflow (AOQC) were increased (Tables 2 and 3). After all these adjustments, the differences between simulated and observed total phosphorus and fecal coliform count decreased to approximately 14%. To confirm that the model could realistically simulate the real world conditions even under different land use and climate regimes, the model was further validated using another set of data from another time period. In this validation, the 1990s land use and weather data and the parameter values used in the calibration were utilised. The simulated results were compared with the actual daily flow data as well as the water quality data for the period from the gauging station (US Geological Survey, 1997). The differences between recorded and simulated streamflow, nitrogen, phosphorus and fecal coliform for the validation data set were all below 20% (Figure 2). Consequently, both the hydrologic and the water quality models were regarded as sufficient to simulate the real world situation.

9 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu Table 2 Parameter values used in the calibration of total phosphorus. Default values are in parentheses Parameters a SQO/ACQOP SQOLIM IOQC AOQC Land use Pasture (0.2125) (1.9027) ( ) ( ) Urban (0.4225) (3.8054) ( ) ( ) Agriculture (2.2042) ( ) ( ) ( ) Forest (0.0667) (0.5980) ( ) ( ) Barren (0.1804) (1.6309) ( ) ( ) Notes: a SQO initial storage of phosphorus on the pervious land segment (in quantity/day). Notes: ACQOP rate of accumulation for phosphorus (in quantity/day). Notes: SQOLIM maximum storage of phosphorus (in quantity). Notes: IOQC concentration of phosphorus in the interflow outflow (in quantity/l). Notes: AOQC concentration of phosphorus in the active groundwater outflow (in quantity/l). Table 3 Parameter values used in the calibration of fecal coliform counts. Default values are in parentheses Parameters a SQO ACQOP SQOLIM IOQC AOQC Land use Pasture ( ) ( ) ( ) (399) (399) Urban ( ) ( ) ( ) (250) (250) Agriculture ( ) ( ) ( ) (399) (399) Forest ( ) ( ) ( ) (200) (200) Barren ( ) ( ) ( ) (250) (250) Notes: a SQO initial storage of fecal coliform on the pervious land segment (in quantity/day). Notes: ACQOP rate of accumulation for fecal coliform (in quantity). Notes: SQOLIM maximum storage of fecal coliform (in quantity). Notes: IOQC concentration of fecal coliform in the interflow outflow (in quantity/l). Notes: AOQC concentration of fecal coliform in the active groundwater outflow (in quantity/l).

10 7_Tong 15/3/06 10:29 pm Page 353 Modelling the hydrologic effects of land-use and climate changes 353 Figure 2 Validation of streamflow model results using 1990s data. The graphs showed the 30 days average streamflow values (in cu m/sec) After validation, the model was used for further prediction. Hypothetical scenarios of climate and land-use changes were postulated and the validated model was run under the new climate and land-use regimes. The simulated results (Tables 4 to 7) were used to elucidate the associated impacts of different climate and land-use regimes on flow and non-point source nutrient pollution. To compare the modelling results under different criteria (climate and/or land-use scenarios), a paired t-test was performed. This is to test whether the differences are statistically significant. 2.7 Scenarios for climate and land-use changes Future climate scenarios With the advent of computer technology, many General Circulation Models (GCMs) are generated to predict our future average climatic conditions. However, these regional models are less reliable in simulating features at a finer spatial and temporal resolution. Besides, outputs from different GCMs vary in terms of the magnitude of warming and the changes of precipitation (Leavesley, 1994). Given the uncertainty in deducing local climate patterns from regional trends, some scientists, such as Hotchkiss et al. (2000) and Stonefelt et al. (2000), adopted hypothetical climate scenarios in their studies. These scenarios are normally based on one or more GCMs estimates on the average annual changes in precipitation and temperature for a region (Nash and Gleick, 1991). In this paper, the hypothetical climate change scenarios were derived from the projections made by two prominent GCMs, the IPCC and United Kingdom Hadley Centre s climate model (HadCM2). Both models indicate that temperatures in Ohio could increase by 2 to 4 C and precipitation could change by 20% to 20% by the year 2010 (Karl et al., 1996; US Environmental Protection

11 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu Agency, 1998). Five scenarios were utilised in this study representing future conditions in the daily values of temperature and precipitation. They illustrated two warm and wet scenarios (WET1, 4 C, 20% precipitation; WET2, 2 C, 20% precipitation), two dry scenarios (DRY1, 4 C, 20% precipitation; DRY2, 2 C, 20% precipitation), and the base case scenario (BC, no change in temperature and precipitation); as such they covered the most likely range of temperature and precipitation changes induced by global warming (see Table 8). Table 4 The effects of future climate changes under current land-use distribution on streamflow, nutrient loadings and fecal coliform counts a Climate scenarios Wettest Wet Base case Dry Driest WET2 WET1 BC DRY2 DRY1 ( 2 C, 20%) ( 4 C, 20%) No change ( 2 C, 20%) ( 4 C, 20%) Streamflow b c Nitrogen simulation Agriculture 40,370 33,715 33,436 18,369 14,195 Impervious urban Forest Pervious urban Pasture Barren Total nitrogen loadings for the watershed b 43,185 36,333 36,067 20,272 15,942 Phosphorus simulation Agriculture 15,223 12,727 12, Impervious urban Forest Pervious urban Pasture Barren Total phosphorus loadings for the watershed b 15,707 13,169 13,079 7,277 5,668 Fecal coliform simulation Agriculture Impervious urban Forest Pervious urban Pasture Barren Notes: a Streamflow is in cu m/sec; nitrogen and phosphorus loadings are in kg/day, fecal coliform count results are in billions/100 ml. Notes: b The flow values and the total nitrogen and phosphorus values shown are the simulated results in the reach at the lowest pour point of the delineated watershed. Notes: c The italic figures show the flow and water quality conditions under current climate and current land-use conditions.

12 7_Tong 15/3/06 10:29 pm Page 355 Modelling the hydrologic effects of land-use and climate changes 355 Table 5 The simulated daily nutrient concentration values (in mg/l) under future climate scenarios and current land-use distribution Climate scenarios Wettest Wet Base case Dry Driest WET2 WET1 BC DRY2 DRY1 ( 2 C, 20%) ( 4 C, 20%) No change ( 2 C, 20%) ( 4 C, 20%) Nitrogen simulation Agriculture b Impervious urban Forest Pervious urban Pasture Barren Nitrogen concentration for the watershed a Phosphorus simulation Agriculture Impervious urban Forest Pervious urban Pasture Barren Phosphorus concentration for the watershed a Notes: a The total nitrogen and phosphorus values shown are the simulated results in the reach at the lowest pour point of the delineated watershed. Notes: b The italic figures show the water quality conditions under current climate and current land-use conditions.

13 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu Table 6 The combined effects of future climate and land-use changes on streamflow, nutrient loadings and fecal coliform counts a Climate scenarios Wettest Wet Base case Dry Driest WET2 WET1 BC DRY2 DRY1 ( 2 C, 20%) ( 4 C, 20%) No change ( 2 C, 20%) ( 4 C, 20%) Streamflow b c Nitrogen simulation Agriculture 39,934 33,350 33,073 18,170 14,042 Impervious urban Forest Pervious urban Pasture Barren Total nitrogen loadings for the watershed b 42,497 35,714 35,447 19,858 15,578 Phosphorus simulation Agriculture 15,058 12,589 12, Impervious urban Forest Pervious urban Pasture Barren Total phosphorus loadings for the watershed b 15,522 13,009 12,919 7,177 5,584 Fecal coliform simulation Agriculture Impervious urban Forest Pervious urban Pasture Barren Notes: a Streamflow is in cu m/sec; nitrogen and phosphorus loadings are in kg/day, fecal coliform count results are in billions/100 ml. Notes: b The flow values and the total nitrogen and phosphorus values shown are the simulated results in the reach at the lowest pour point of the delineated watershed. Notes: c The italic figures depict the flow and water quality conditions under current climate, but projected future land-use patterns.

14 7_Tong 15/3/06 10:29 pm Page 357 Modelling the hydrologic effects of land-use and climate changes 357 Table 7 The simulated daily nutrient concentration values (in mg/l) under future climate scenarios and future land-use distribution Climate scenarios Wettest Wet Base case Dry Driest WET2 WET1 BC DRY2 DRY1 ( 2 C, 20%) ( 4 C, 20%) No change ( 2 C, 20%) ( 4 C, 20%) Nitrogen simulation Agriculture b Impervious urban Forest Pervious urban Pasture Barren Nitrogen concentration for the watershed a Phosphorus simulation Agriculture Impervious urban Forest Pervious urban Pasture Barren Phosphorus concentration for the watershed a Notes: a The total nitrogen and phosphorus values shown are the simulated results in the reach at the lowest pour point of the delineated watershed. Note b The italic figures depict the water quality conditions under current climate, but projected future land-use patterns. Non-point source modelling using HSPF requires the development of a Watershed Data Management (WDM) file. The WDM file contains time series data for all meteorological parameters required by HSPF. In order to implement the above scenarios, the average hourly and daily temperature and precipitation values from the default WDM file for the Covington Airport weather station for the last 80 years were first calculated. They were used to represent the base case scenarios. For the other wet or dry scenarios, corresponding changes in average hourly precipitation, average hourly temperature, daily maximum temperature and daily minimum temperature were made to the default WDM file using a WDM utility programme, WDMUtil. These newly derived daily maximum and minimum temperatures were used to compute the daily surface evaporation using the Penman Method and the daily potential evapotranspiration using the Hamon Method in WDMUtil. A new WDM file was then constructed for each climatic scenario.

15 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu Table 8 Future climate and land-use scenarios. The table shows the changes in temperature, precipitation and land-use distributions (in sq km) in the delineated Lower Great Miami River watershed Climate scenarios Change in temperature Change in precipitation Wettest (WET2) 2 C 20% Wet (WET1) 4 C 20% Base case (BC) Current climate, no change in temperature and precipitation Dry (DRY2) 2 C 20% Driest (DRY1) 4 C 20% Land-use scenarios Change in land use distributions Urban Urban Barren Forest Agriculture Pasture impervious pervious Current (Year 1990) Future (Projected) Future land-use scenario According to the 1990 land-use map (from the Ohio Land Cover Data Set; EROS Data Center, 1998), agricultural land uses comprised approximately 71% of the Lower Great Miami River Basin. Forested lands comprised approximately 16.7% of the watershed, whereas urban (including both pervious and impervious lands) was approximately 11.6%. Presently, there are three major urban centres in the Lower Great Miami River Basin: Dayton, Middletown and Hamilton. The hypothetical scenario for the future land-use pattern in the Lower Great Miami River Basin was based on the historical trends of urban and sub-urban development in the area. An examination of the population data, the extension of urban areas, the encroachment of surrounding areas by sub-urban sprawl, and the relative proportion of pervious and impervious urban lands in the past five decades revealed that these development trends remain rather consistent. Hence, in this research, it was assumed that these trends would continue into the future. That is, the existing major urban centres will continue to be expanded to the sub-urban areas; many urbanites will migrate to the sub-urban areas near the cities; the sub-urban sprawl process will lead to the conversion of surrounding areas to residential and commercial lands; and the relative proportion of impervious and pervious urban lands remain the same. Based on these assumptions, the future population was used as a surrogate for urban growth. The future land-use scenarios were derived from the demographic projections. In each city, the future population level was first projected by linear extrapolations of current population data. Then a density factor for each city was computed as the ratio between the 1990 population and the 1990 area provided by the 1990 US Census Bureau. To derive the hypothetical land use scenario, the future population level was multiplied by the density factor that was characteristic of

16 7_Tong 15/3/06 10:29 pm Page 359 Modelling the hydrologic effects of land-use and climate changes 359 that city. Finally, the future land-use scenario was created using GIS techniques and the Land-Use Editor in BASINS. For the hypothetical land-use scenario, based on the growth rates of each of these centres as experienced in the past, future urbanisation was modelled by changing the areas surrounding these urban growth centres in the 1990 land-use map to urban areas. In Dayton, using ArcView GIS, all of the urban lands in the metropolitan area were selected, and then a 2000-m buffer was created around the selected region. Next, the areas within the buffer in 1990 (mostly agricultural lands) were converted to urban lands. For Middletown, the same procedure was applied with a 500-m buffer; and for Hamilton, a 200-m buffer was used. The result was a watershed comprising of approximately 57.1% agricultural land use, 25.9% urban land use, 16.6% forest and less than 0.5% of pasture and barren lands (Table 8). It was realised that this approach in deriving the future land-use pattern would not provide an exact prediction of the future conditions. Nevertheless, since the estimates were based on historical trends, it is probable that the hypothetical scenarios derived in this study would occur in the future. Besides, this is a scenario analysis. The main theme of the study is to compare the plausible outcomes under various possible future scenarios. It is not our intention to predict the exact conditions in the future. 3 Results and discussion 3.1 The hydrologic effects of different types of land use under the current land-use and climate regimes To investigate the hydrologic effects of different types of land use on the receiving water bodies, a simulation was performed using the current climate and current land-use conditions. The modelling results under column BC (no change in climate) in Table 4 indicated that the amount of total nitrogen and total phosphorus loadings, as well as fecal coliform counts, differed greatly according to land-use type. In general, with the existing land-use patterns, agricultural lands yielded the highest total nitrogen and phosphorus loadings, followed by impervious urban, forest, pervious urban, pasture and barren lands. The amount of nitrogen produced from agricultural lands was more than 17.8 times as much as impervious urban. It was also observed that forested lands produced roughly 2.7 times more nitrogen as pervious urban lands (Table 4). These differences are all significant at a P Similar results were also found with the average daily concentrations for nitrogen and phosphorus (Table 5). The pattern for fecal coliform was slightly different among land-use types (Table 4, under the column BC). Notwithstanding the fact that agricultural land use still produced the highest number of fecal coliform bacteria (about 32.9 times more than pervious urban lands), pervious urban and forested lands produced more bacteria counts than impervious urban. Pasture produced less than the impervious urban, and barren land use produced the least number of bacteria (about 1120 times less than agricultural lands). 3.2 The hydrologic effects of climate change under the current land-use pattern To separate the effects of land-use change, the hydrologic responses to different future climate change scenarios were examined under the existing land-use pattern (see Table 4).

17 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu Furthermore, some of the comparisons were confined to the simulated results in the reaches at the lowest pour point of the watershed. Since the purpose of this section was to evaluate the hydrologic effects of climate, instead of land-use types, the examination of the changes in streamflow and water quality at the lowest pour point would allow us to compare the relative impacts of various climatic regimes for the whole watershed. The simulated streamflow values in Table 4 revealed that precipitation, instead of temperature, was a more pronounced factor determining the amount of runoff. The evidence included: Firstly, there is not much difference in streamflow between WET1 and the base case (BC). That means, even with a 4 C increase in temperature, the difference in the amount of runoff is negligible. The 20% increase in precipitation had compensated for the effects of a 4 C temperature increase in evapotranspiration. Secondly, the coolest (BC) temperature regime did not produce the highest volume of runoff. Rather, the highest volume of runoff was found under the wettest regime. These results were similar to those reported by Stonefelt et al. (2000). Another observation was that the impacts of rainfall were particularly prominent under the driest condition. Under DRY1, a 20% decrease in rainfall had translated to a 59.8% decrease in streamflow from the base case (from to cu m/sec). But, the absolute amount of increase or decrease in runoff was not compatible with the amount of changes in rainfall. For example, with the same 2 C rise in current temperature, an increase of precipitation by 20% (i.e. under WET2 scenario) would result in an increase of runoff by 19.4% from the base case, from to cu m/sec. In contrast, a decrease of precipitation by 20% (i.e. under DRY2) would bring a decrease of 49.6% in runoff (from to cu m/sec). Table 4 also shows the relative effects of various climate scenarios on the levels of nutrients and bacteria in the surface runoff. As expected, the changes in nutrient loads were dependent on the changes in runoff. The climate scenario that generated the most daily nitrogen and phosphorus loadings and fecal coliform counts under the current land-use pattern was found under the wettest scenario, WET2 ( 2 C, 20% precipitation). It produced more than 1.2 times as much nitrogen and phosphorus and about 11.5 times as much f. coliform than the base case (BC) from agricultural lands; these differences were significant at a P 0.05 level. The simulated nutrients and bacteria results for WET1 ( 4 C, 20% precipitation) were very close to the base case. The dry scenario, DRY2 ( 2 C, 20% precipitation), produced fewer nutrients and bacteria than the base case, and the driest scenario, DRY1 ( 4 C, 20% precipitation), produced the least amount of nutrients and bacteria. Under the existing land-use pattern, DRY1 produced less than half of the nutrient loadings and fecal coliform bacteria than those generated from the base case scenario. This occurred in all of the land-use types, except in impervious urban lands. To reveal the overall effects of current land-use in the whole watershed under various climate conditions, the analysis of the average daily nutrient concentrations was performed using the instream nutrient concentrations at the lowest pour point of the whole watershed for different climate scenarios (as shown in Table 5, under the rows of total nitrogen runoff from the watershed and total phosphorus runoff from the watershed ). Under these conditions, the climate scenario that produced the highest nitrogen and phosphorus concentrations was the dry scenario (DRY2). It produced about 11.6% more nitrogen and 10.5% more phosphorus than the base case. The differences were significant at P The second highest nutrient concentration was found under the driest scenario (DRY1). The wettest scenario (WET2) and the wet scenario (WET1) had very similar levels of

18 7_Tong 15/3/06 10:29 pm Page 361 Modelling the hydrologic effects of land-use and climate changes 361 concentration, and they were slightly higher (less than 0.6%) than the base case. This implies that a drier future climate may have more impacts on the concentration levels of nutrients downstream. 3.3 The hydrologic effects of future climate and land-use regimes The predicted hydrologic responses to future land-use and climate changes are shown in Tables 6 and 7. An examination of these results would help us to elucidate the combined effects of climate and land-use changes, thereby enabling us to have a better appreciation of how climate may act in concert with land use in modifying the water quantity and quality in receiving waters The effects of different types of land use under the future climate and land-use regimes As in the case of current land-use simulation, the land-use type that produced the highest nutrient loadings and bacteria under the future land-use and future climate change conditions was agricultural land. It produced by far the highest amount of nutrient loadings and fecal coliform under all climate scenarios. Impervious urban land was found to be the second most contaminated land-use type for nutrients, but not for fecal coliform (Table 6). The rankings for the land-use types that produced the highest nutrient loadings for the current and future land-use patterns were very similar. In both cases, the order was: agriculture, impervious urban, forest and pervious urban. Nonetheless, the ranking for the land-use types that produced the least amount of nutrients under the future land-use scenario was slightly different from that of current land use. Under the future land-use scenario, pasture produced less nitrogen and phosphorus than barren land. There was indeed hardly any phosphorus produced by pasture (see Table 6). The ranking of the land-use types for the daily nitrogen and phosphorus concentration levels echoed that of the daily loads: agriculture, impervious urban, forest, pervious urban, barren and pasture (Table 7). Regarding fecal coliform counts, the respective contributions from different types of land use under the future land-use patterns were somewhat different from that of the nutrient loadings. The ranking for the f. coliform was: agriculture, forest, pervious urban, barren, impervious urban and pasture, with agricultural lands producing the highest number of bacteria in the runoff, and pasture the least amount. This pattern also differed slightly from the current land-use results, where the ranking was: agriculture, pervious urban, forest, impervious urban, pasture and barren The effects of climate under the future land-use conditions The effects of climate under the future climate and land-use conditions are very apparent in the case of streamflows. WET2 produced the highest volume (61.76 cu m/sec), about 1.2 times higher than the base case scenario (Table 6). The streamflow values under DRY2 and DRY1 were significantly lower (at P 0.05), decreasing almost 48.9 and 59.6% from the base case scenario, from to and cu m/sec, respectively. In terms of the quality of surface runoff under the conditions of future climate and land-use distribution pattern, the impacts of WET2 were the greatest, producing the highest nutrient loadings. The impact of WET1 climate scenario was not that noticeable, since the simulated nutrient loadings under this future climate and land-use scenarios were

19 7_Tong 15/3/06 10:29 pm Page S.T.Y. Tong and A.J. Liu very similar to the base case climate scenario. DRY2, and especially DRY1, climate scenarios produced a much lesser amount of nutrient loadings. Like the total nitrogen and phosphorus loadings, the highest fecal coliform counts occurred under the WET2 scenario, whereas the lowest number of fecal coliform count was found under the DRY1 scenario (see Table 6). The rankings of streamflow, nutrient loadings and bacteria counts for different climate scenarios under the future land-use distribution pattern therefore concurred with those exhibited under the current land-use pattern, indicating that WET2 is the climate scenario which will induce the most significant hydrologic impacts. When the average nutrient concentrations at the lowest pour point of the study area (Table 7, under the rows of total nitrogen runoff from the watershed and total phosphorus runoff from the watershed ) among different climate regimes were compared, the highest concentrations of nitrogen and phosphorus occurred under the dry scenario, DRY2 (an increase of 9.6% of nitrogen and 8.7% of phosphorus from the base case). The next highest nutrient concentration was found under the driest scenario (DRY1). Under the future land-use scenario, DRY2 yielded mg/l of nitrogen and mg/l of phosphorus, which were higher than those found under other cases. These results were similar to those found under the current land-use distribution condition. However, under the future land-use distribution conditions, the wet scenarios (WET1 and WET2) produced a slightly lower level of nutrient concentration than the base case scenario instead of a higher level as in the current land-use simulation The combined hydrologic effects of future climate and future land use When the future climate and land-use changes were coupled, the most prominent impacts were found under the WET2 ( 2 C, 20% precipitation) scenario from agricultural lands. Under this scenario, the amounts of nitrogen and phosphorus loadings produced from agricultural lands were about 25.0 times and 70.4 times as much as that from impervious urban lands (significant at P 0.05 level). This infers that in the future as the Lower Great Miami River Basin continues to develop, if we experience a wet (WET2) climate condition, we may have to be more cautious about nutrient enrichment problems from agricultural lands. The least amount of nutrient loadings was found associated with DRY1 ( 4 C, 20% precipitation) and pasture (Table 6). In terms of nutrient concentrations, DRY2 and agricultural land produced the highest levels. The lowest concentration levels were found in pasture, where there were hardly any nitrogen (0.002 mg/l) and practically no phosphorus (0.000 mg/l) in the stream (Table 7). This occurred under all climate conditions. In general, a change from the current to the future land-use and climate patterns had caused an extremely small change in nutrient concentrations at the lowest pour point of the watershed (ranging from almost 0% in the case of nitrogen simulation under DRY2 to an increase of less than 1.3% in the case of phosphorus simulation under the DRY1 condition). Even though under the base case climate scenario, a change from the current to the future land-use pattern had induced a relatively higher nutrient concentration, the increase was very small (1.8% increase in the case of nitrogen simulation and 2.3% increase for phosphorus simulation; see Tables 5 and 7). It seems that when a change of our current land-use distribution pattern is accompanied with climate change, the hydrologic effects on nutrient concentrations may not be that substantial. The percentage change was much lower than those induced by climate change alone.