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  • View in gallery

    Map of the study area. The SARB and its six subbasins, with the USGS gauge locations indicated.

  • View in gallery

    The relationship between population growth and urban area in the city of San Antonio.

  • View in gallery

    SARB land-cover maps from 1929 to 2080. R100, R50, and R0 indicate that the map is created based on the 2000–10 migration rate, half of the 2000–10 migration rate, and a zero migration rate, respectively.

  • View in gallery

    Ensemble of monthly precipitation (mm day−1) from CMIP5 during the two future periods. The black solid lines represent the historical baseline observations. Diamonds show the range among GCMs, with max/median/min indicated.

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    (a)–(f) Calibration for each subbasin and (g) validation over the entire SARB. Monthly results are shown for better visualization, while error statistics (RB, R2, and NSE) are based on daily results.

  • View in gallery

    Urbanization effects on MAMS over the SARB based on DHSVM simulations, which were conducted with fixed forcing data (from 2000 to 2005) and different land-cover maps. Dots represent different points in time between 1929 and 2080 corresponding to an existing or constructed land-cover map (with the impervious percentage over the entire SARB shown). Shaded areas represent the future urbanization uncertainties, with the three lines (from top to bottom) corresponding to the 2000–10 rate, half of the 2000–10 rate, and zero migration, respectively.

  • View in gallery

    (a) Climate change and (b) combined urbanization and climate change effects on peak flows over the SARB. Black solid lines are historical observations (i.e., baseline) and diamonds represent the uncertainties from the CMIP5 ensemble, with max/median/min indicated.

  • View in gallery

    Uncertainties associated with the urbanization process and with changing climate (taking RCP 8.5 as an example). Dashed lines are the peak flows generated by max/median/min perturbed forcings using the CF method. Boxes represent the urbanization (migration) uncertainties.

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Effects of Urbanization and Climate Change on Peak Flows over the San Antonio River Basin, Texas

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  • 1 Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas
  • | 2 Center for Excellence in Tibetan Plateau Earth Sciences, Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
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Abstract

A thorough understanding of the peak flows under urbanization and climate change—with the associated uncertainties—is indispensable for mitigating the negative social, economic, and environmental impacts from flooding. In this paper, a case study was conducted by applying the Distributed Hydrology Soil Vegetation Model (DHSVM) to the San Antonio River basin (SARB), Texas. Historical and future land-cover maps were assembled to represent the urbanization process. Future climate and its uncertainties were represented by a series of designed scenarios using the Change Factor (CF) method. The factors were calculated by comparing the model ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) with baseline historical climatology during two future periods (2020–49, period 1; 2070–99, period 2). It was found that with urban impervious areas increasing alone, annual peak flows may increase from 601 (period 1) to 885 m3 s−1 (period 2). With regard to climate change, annual peak flows driven by forcings from maximum, median, and minimum CFs under four representative concentration pathways (RCPs) were analyzed. While the median values of future annual peak flows—forced by the median CF values—are very similar to the baseline under all RCPs, in each case the uncertainty range (calculated as the difference between annual peak flows driven by the maximum and minimum CFs) is very large. When urbanization and climate change coevolve, these averaged annual peak flows from the four RCPs will increase from 447 (period 1) to 707 m3 s−1 (period 2), with the uncertainties associated with climate change more than 3 times greater than those from urbanization.

Corresponding author address: Huilin Gao, Zachry Department of Civil Engineering, Texas A&M University, TAMU 3136, College Station, TX 77843. E-mail: hgao@civil.tamu.edu

Abstract

A thorough understanding of the peak flows under urbanization and climate change—with the associated uncertainties—is indispensable for mitigating the negative social, economic, and environmental impacts from flooding. In this paper, a case study was conducted by applying the Distributed Hydrology Soil Vegetation Model (DHSVM) to the San Antonio River basin (SARB), Texas. Historical and future land-cover maps were assembled to represent the urbanization process. Future climate and its uncertainties were represented by a series of designed scenarios using the Change Factor (CF) method. The factors were calculated by comparing the model ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) with baseline historical climatology during two future periods (2020–49, period 1; 2070–99, period 2). It was found that with urban impervious areas increasing alone, annual peak flows may increase from 601 (period 1) to 885 m3 s−1 (period 2). With regard to climate change, annual peak flows driven by forcings from maximum, median, and minimum CFs under four representative concentration pathways (RCPs) were analyzed. While the median values of future annual peak flows—forced by the median CF values—are very similar to the baseline under all RCPs, in each case the uncertainty range (calculated as the difference between annual peak flows driven by the maximum and minimum CFs) is very large. When urbanization and climate change coevolve, these averaged annual peak flows from the four RCPs will increase from 447 (period 1) to 707 m3 s−1 (period 2), with the uncertainties associated with climate change more than 3 times greater than those from urbanization.

Corresponding author address: Huilin Gao, Zachry Department of Civil Engineering, Texas A&M University, TAMU 3136, College Station, TX 77843. E-mail: hgao@civil.tamu.edu

1. Introduction

Flooding is one of the most destructive natural disasters. Compared with other types of disasters worldwide, flooding has the highest frequency of occurrence and has impacted the largest number of people (Jonkman 2005). In the United States, flooding has claimed the most lives among all weather-related hazards over the last 30 years (http://www.nws.noaa.gov/os/hazstats.shtml). For the second half of the twentieth century, the average annual flood cost in the United States was $2.0 billion (Pielke and Downton 2000; Pielke et al. 2002). Flooding is also accompanied by deteriorated water quality and increased sediment loading, which both adversely affect agricultural production, social–economic welfare, and environmental sustainability (Arthington et al. 2010; Changnon 2008; Howitt et al. 2007; Longfield and Macklin 1999).

U.S. flood policy has evolved over the past century since the approval of the Flood Control Act in 1917. Major policy changes include the construction of massive infrastructure (dams and levees) led by the Bureau of Reclamation and the U.S. Army Corps of Engineers, as well as the creation of the National Flood Insurance Program (NFIP; American Institutes for Research 2005). Reliable quantification of peak flow statistics (e.g., 100-yr flood) is indispensable for effective flood control policies and for the associated decision-making. Despite governmental mitigation efforts (through insurance policies and building infrastructure), flood damage costs have been increasing continuously (Downton et al. 2005; Pielke and Downton 2000). As a global phenomenon, urbanization is a major driver for the exacerbation of flood damages (Ntelekos et al. 2010). As of 2014, 54% of the global population lives in urban areas, and this number is expected to increase to 66% by 2050 (United Nations 2014). This growth will not only increase the magnitude and frequency of floods (Olivera and DeFee 2007; Sheng and Wilson 2009; Weng 2001; Yang et al. 2014) but will also indirectly add to the per-event cost because of the increased infrastructure at risk associated with the growth. There has been an increasing number of devastating flash floods in recent years (Duan et al. 2014; Ntelekos et al. 2008; Smith et al. 2005). Climate change is another strong factor that could potentially increase flood severity via the intensified global energy and water cycles (Bates et al. 2008; Huntington 2006; Milly et al. 2002, 2008). Studies show that significant changes of precipitation extreme events have occurred as the result of anthropogenic climate change (Alexander et al. 2006; Easterling et al. 2000; Pall et al. 2011; Lehmann et al. 2015), and this trend will continue (Beniston et al. 2007; Marengo et al. 2009). In addition, Ivancic and Shaw (2015) found an increase in high discharge events in the northern United States, due to the large antecedent soil moisture induced by climate change.

From both the hydrological sciences and policy-making perspectives, our knowledge about the compound impacts of urbanization and climate change on peak flows is still critically limited, particularly in two areas.

First, the combined effects of long-term urbanization processes and changing climate conditions have typically been poorly investigated. In most urban areas, the impervious cover does not follow natural watershed/subwatershed boundaries, and its density varies according to land-use types. However, these facts are barely (or not at all) represented in most hydrologic models. For instance, the Soil and Water Assessment Tool (SWAT), which has been used in a number of studies on urbanization and climate change impacts on streamflows (Franczyk and Chang 2009; Zhang et al. 2012), assumes that each land-cover type (including urban) is uniformly distributed and bounded within a hydrologic research unit (HRU). Many climate change impact assessment studies have relied on lumped hydrologic models with no urban component (Ntegeka et al. 2014; Prudhomme and Davies 2009a; Wilby 2005). Therefore, adoption of a physically based fully distributed hydrologic model with impervious area realistically represented is essential for quantitatively evaluating future peak flows in urbanized watersheds (Wright et al. 2014; Smith et al. 2015).

Second, for efficient decision-making concerning the mitigation of flood risks, scientifically based advice to users should incorporate information about the uncertainty in the results (Bennett et al. 2012; Beven 2007; Gober and Kirkwood 2010; Prudhomme and Davies 2009b). The uncertainties of future peak flows are a mix from multiple sources: general circulation models (GCMs), emission scenarios, downscaling methods, hydrologic models, and urbanization/population projections. Although recognized as the most reliable tools for improving our understanding of climate change processes based on the Intergovernmental Panel on Climate Change (IPCC) report (IPCC 2013), GCMs cannot provide accurate and consistent information at a regional scale (Giorgi and Mearns 1991). This is because of their relatively coarse spatial resolutions and their different physical and computational algorithms (Barnett et al. 2006; Teng et al. 2012). Meanwhile, the different carbon emission scenarios, which directly affect global water and energy cycles, can result in very different outputs (IPCC 2013; Jones et al. 2013). For water management, a common practice for evaluating peak flow changes at the river basin scale is to drive hydrologic models using spatially (and temporally) downscaled outputs from GCMs (Dibike and Coulibaly 2005; Ntegeka et al. 2014; Tian et al. 2016). However, both the downscaling techniques (dynamical or statistical) and the hydrologic models (among which the complexity in the representation of physical processes varies significantly) contribute to additional uncertainties of the simulated peak flows. Previous studies (Prudhomme and Davies 2009b; Kay et al. 2009; Bennett et al. 2012) showed that uncertainties associated with downscaling methods and hydrological models are smaller than those of GCMs and emission scenarios. However, to our knowledge there have been no reports on the peak flow uncertainties of projected urbanization and climate change, let alone on how these two uncertainty sources weigh against each other.

Therefore, the objective of this paper is to advance our knowledge about urbanization and climate change impacts on peak flows by overcoming the above critical limitations. The strategy we employed is twofold. First, the deconvolved and combined effects of these two factors were simulated using a distributed hydrologic model and compared against the baseline conditions. Second, the uncertainties associated with both urbanization and climate change were fully investigated under various scenarios. Although this paper focuses on a case study over a densely populated river basin in Texas, the methodology and findings are transferable to other watersheds. Because annual peak flows have significant implications beyond flooding—such as water quality, sediment scour, and transport—a thorough understanding about the future alterations in peak flows and their uncertainties, in the context of various scenarios, is valuable for assisting decision-making.

2. Study area

The state of Texas is very vulnerable to flood damage. An analysis of flood fatalities from 1959 to 2005 (without including Hurricane Katrina) for the 48 contiguous states revealed that Texas ranked the highest (Ashley and Ashley 2008). Meanwhile, Texas has the largest urban area (22 651 km2) and the second-largest urban population (21.3 million) in the United States (U.S. Census Bureau 2011). From 2000 to 2010, the urban population in Texas increased by 2.16% per year. It is projected that the population in major urban areas will have nearly doubled from 2010 to 2040 (Texas State Data Center 2014). The future alteration of hydrological processes due to urban sprawl is expected to exacerbate flood risks in Texas (which is already the state with the most flood fatalities in the United States).

The San Antonio River basin (SARB; Fig. 1) was selected as our study area. It is located near the Balcones Escarpment in south-central Texas, which is a region with one of the highest rates of flooding-related fatalities in the United States (Ashley and Ashley 2008). The SARB has a drainage area of 10 826 km2, with an elevation ranging from 4 to 693 m MSL. The outlet of the basin is located at the most downstream point of the eight-digit hydrologic unit code (i.e., HUC8 12100303). The basin average annual rainfall from 1915 to 2011 was 768 mm. The dominant vegetation type is grass, with soil types that are mainly clay and clay loam—both of which have relatively low hydraulic conductivity, which subsequently leads to a higher surface runoff rate. The SARB intersects with the Edward Aquifer over an area of 1990 km2 (which covers 18% of the entire SARB), and a portion of the San Antonio city water supply system depends on the aquifer. However, the recharge water to the aquifer generally offsets the discharge from this aquifer (Edwards Aquifer Authority 2009), which results in an insignificant effect on San Antonio River flows (especially with regard to peak flows).

Fig. 1.
Fig. 1.

Map of the study area. The SARB and its six subbasins, with the USGS gauge locations indicated.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

There are six subbasins (according to HUC8) in the SARB, including the Medina River, Leon Creek, Salado Creek, the upper San Antonio River, Cibolo Creek, and the lower San Antonio River subbasins. The city of San Antonio, which is the seventh-largest city in the United States (based on population), lies approximately in the middle of the SARB. About 76% of the basin’s population currently resides in the city of San Antonio and its suburbs. Based on the impervious fractions for each type of urban pixel as suggested by the National Land Cover Database (NLCD; Xian et al. 2011; Yang et al. 2010), the basin- and subbasin-scale impervious areas were calculated and compared. The total impervious area in the SARB expanded from 317.0 to 901.0 km2 between 1970 and 2011. However, the urbanization intensity within each subbasin varies considerably. According to the 1970 land-cover map, the impervious area in the Leon Creek, Salado Creek, and upper San Antonio River subbasins was 43.3, 77.1, and 134.9 km2, respectively. By 2011, these numbers had increased to 158.4, 206.3, and 232.6 km2. The expansion of the impervious urban area is closely related to the population growth. Figure 2 shows the relationship between the population and the urban area in the city of San Antonio based on historical data. This relationship was later used in this study to extrapolate future urban areas based on the projected population growth (section 3b).

Fig. 2.
Fig. 2.

The relationship between population growth and urban area in the city of San Antonio.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

Streamflow of the San Antonio River is highly variable, especially with regard to peak flows. According to U.S. Geological Survey (USGS) observed streamflow data obtained close to the basin outlet (gauge ID 08188500), the annual maximum flow from 1939 to 2013 ranges from 41 (1988) to 3426 m3 s−1 (1967), with a median value of 224 m3 s−1. The region is vulnerable to both river and inland flooding. In 1998, the SARB experienced a catastrophic flood (with 32 deaths and over $750 million in damages) caused by Hurricanes Madeline and Lester (NWS 1999). A number of USGS streamflow gauges (e.g., 08185000 and 08186000) monitored peak flows with values greater than the 100-yr return period. In 2002, there was another severe flood event in the SARB that resulted in 12 deaths and nearly $1.0 billion in damages (Knebl et al. 2005; Sharif et al. 2013).

3. Methods

a. DHSVM

The Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al. 1994) was employed in this study to simulate the streamflow. DHSVM explicitly simulates the water and energy balance across the domain at a high spatial resolution (e.g., 10–200 m). The model is physically based and can represent hydrological processes such as evapotranspiration, infiltration, snowmelt, and urban area detention. It calculates evapotranspiration using a full energy balance method, and it also accounts for the evaporation from the canopy-intercepted water. Overland, surface and subsurface (including unsaturated and saturated zones) water for each grid cell is routed to its downslope neighbor cells based on topography until the water reaches the stream channels. Within the stream, the linear reservoir routing method is used for each river segment at each time step. DHSVM can simulate streamflow at a subdaily time step over multiple years. Here, a 3-hourly time step was chosen to accurately simulate the high flows, which are sensitive to the urban impervious cover as well as the diurnal cycles of energy and water balance. In addition, when compared with an hourly time step, it significantly reduces the computational expense (especially given that our objective mainly focuses on the long-term trend of daily peak flows).

For this study, a key advantage of DHSVM is its urban module, which quantitatively mimics the two dominant controls in urban runoff: constructed detention ponds and engineered channel systems (Cuo et al. 2008). For the grid cells with urban coverage, the model will route the precipitation that falls on the impervious part directly to the river channels (through engineered channel systems), and it will calculate the natural hydrological processes (e.g., evapotranspiration and infiltration) on the pervious part. The grid cells containing urban impervious area are separated into two classes, dense urban and light urban. In this study, we set the impervious fraction thresholds of the dense urban and light urban cells to 78% and 27%, respectively. Furthermore, the high spatial resolution allows DHSVM to realistically represent urban coverage and expansion. The model has been used successfully to study the effects of land cover and climate change on the hydrology of the Puget Sound basin in Washington State, United States (Cao et al. 2016; Cuo et al. 2008, 2009, 2011; Vano et al. 2010).

The DHSVM input data include spatially static and temporally varied data. Temporally varied meteorological forcing data include precipitation, temperature, relative humidity, incoming longwave/shortwave radiation, and wind speed. Spatially static data include a digital elevation map (DEM), a basin boundary mask, soil depth, soil texture, land-cover types, channel distribution, and morphology information. In this study, the DEM was obtained from the Shuttle Radar Topography Mission (SRTM) 30-m resolution product (Jarvis et al. 2008), which was then resampled to 200 m (i.e., the DHSVM gridcell resolution). Based on the DEM information and the hydrometric station locations, geographical information system (GIS) tools were used to generate the soil depth map, the river network, and the basin boundary mask. The soil texture was acquired from the State Soil Geographic (STATSGO) database (Miller and White 1998). STATSGO was selected because it has been successfully adopted in other hydrologic simulations using DHSVM (Cuo et al. 2009; Du et al. 2014).

b. Land-cover data

Land-cover types for years 1992, 2001, 2006, and 2011 were obtained from the USGS NLCD (http://www.mrlc.gov/; Fry et al. 2011; Homer et al. 2007; Jin et al. 2013; Vogelmann et al. 2001). Classified from Landsat imagery, these 30-m NLCD products provide consistent land-cover classifications across the United States. In addition, based on historical land surface information from the 1970s and 1980s, the USGS published Enhanced Historical Land-Use and Land-Cover Datasets (LC1970 for short; Price et al. 2006).

When there were no existing land-cover products to represent the urbanization process (e.g., before 1970 or in the future), a GIS technique was used to create corresponding maps (see the appendix). To reconstruct the urban conditions prior to the 1970s, we generated three additional land-cover maps (i.e., LC1929, LC1958, and LC1964) by overlaying historical San Antonio city maps onto LC1970. For future projections, there is a large uncertainty about urbanization because of different possible migration policies, technologies, and economic development scenarios. In the state of Texas, migration is the major factor influencing population growth. To reflect these uncertainties, the Texas State Data Center designed three future population growth scenarios based on different migration rates: the 2000–10 rate (i.e., R100), half of the 2000–10 rate (i.e., R50), and zero migration (i.e., R0; Texas State Data Center 2014). It is worth noting that for the R0 scenario, there is still slight growth (about 0.5% per year) because migration is not the entire cause for population growth. Accordingly, we created six future land-cover maps (i.e., LC2030R100, LC2030R50, LC2030R0, LC2080R100, LC2080R50, and LC2080R0). To focus on investigating the impacts from increased impervious area due to urbanization, the land-cover types not affected by urban expansion were held constant after LC1970. For instance, it was assumed that there are no land-cover conversions from forest to grassland or from wetland to bare land. Figure 3 shows the 15 land-cover maps—nine historical maps (including a base map without urban coverage) and six future maps. Both LC2030 and LC2080 have three urbanization scenarios (i.e., R100, R50, and R0).

Fig. 3.
Fig. 3.

SARB land-cover maps from 1929 to 2080. R100, R50, and R0 indicate that the map is created based on the 2000–10 migration rate, half of the 2000–10 migration rate, and a zero migration rate, respectively.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

c. Forcing data

Two forcing data sources were employed to drive DHSVM during the historical and future periods. During the historical period, we utilized a long-term, observation-based dataset for calibrating and validating DHSVM (Livneh et al. 2013). This dataset covers the contiguous United States (CONUS) at ⅝1/16° spatial resolution from 1915 to 2011 at a daily time step. Following the same approach as in Livneh et al. (2013), the dataset was then disaggregated to 3-hourly with a full set of the forcing variables required by DHSVM. The ⅝1/16° forcings within the SARB were further interpolated to DHSVM resolution (i.e., 200 m for this study) using the variable Cressman technique (Cressman 1959), which is one of the model’s three internal interpolation schemes (i.e., nearest, inverse distance, and variable Cressman). This high-spatiotemporal-resolution forcing dataset also served as the baseline data in this study.

For the future period, climate change scenarios at high resolution were designed using the Change Factor (CF) method (Arnell and Reynard 1996). Basically, CFs are simple multiplicative values calculated as the percentage change from the reference climatology (which is also used for the baseline run) to the GCM projections at the basin scale. Using the CFs, the high-spatiotemporal-resolution reference climatology can be adjusted as needed for designing future scenarios. The CF method has been adopted in many applications because of its robustness and simplicity in capturing key features of the climate model ensemble (Arnell and Reynard 1996; Kay et al. 2014; Manh et al. 2015; Zahmatkesh et al. 2014). This approach has an advantage of acknowledging uncertainties in projected interdecadal and intra-annual climate changes, which are essential for evaluating flood risks (Prudhomme et al. 2010).

In this study, GCM outputs from the Downscaled Climate and Hydrology Projections (DCHP) archive for phase 5 of the Coupled Model Intercomparison Project (CMIP5) at ⅛1/8° spatial resolution were adopted (Brekke et al. 2014). The DCHP database contains precipitation and temperature data from 1950 to 2099 at monthly and daily time steps. The downscaling method applied to the monthly data is the bias-corrected and spatial disaggregation (BCSD) technique, which has been widely used and validated in a number of studies (Maurer et al. 2007; Wood et al. 2002, 2004). In DCHP, there are 17 downscaled GCMs containing all four of the representative concentration pathway (RCP) scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) at a monthly time step. These four scenarios represent different radiative forcing increase levels induced by various greenhouse gas emission projections. During the baseline period (1970–99), the relative biases from historical observation for the 17 GCM precipitation datasets range from −3.0% to 4.7%, with a median value of only 3.1%. Since each GCM has its own physical and numerical algorithms, the outputs from the different models vary enormously. Compared to the large uncertainties due to different GCMs, the impact on climate change assessment due to the above biases is relatively minor. This lends a strong level of confidence in using the DCHP dataset in this study. This dataset was mainly used to calculate CF values in the next step (instead of driving DHSVM directly).

To best represent the uncertainties associated with the GCMs, a typical practice is to use an ensemble to characterize the differences and similarities among these GCMs and then to analyze the mean/median and uncertainties (Murphy et al. 2004). However, because of the GCM structure uncertainties, relative changes of climate variability can be more reliably simulated than absolute values (Hay et al. 2000). Thus, in order to represent the relative changes from the CMIP5 ensemble both properly and at reasonable computational expense, we used the CF method to design future scenarios from the baseline climatological data.

The CF method can be applied to both precipitation and temperature. However, because most southwestern parts of Texas (including the SARB) are highly water limited [with an annual precipitation/potential evaporation ratio of 0.35, based on the data from Zomer et al. (2008)], precipitation plays a more dominant role in streamflow variation than does temperature, which is consistent with the findings from Kwadijk and Middelkoop (1994). In addition, since there is no accumulative snowpack in the SARB, temperature has limited effects on the timing and magnitude of peak flows (Miller et al. 2003). Therefore, we only calculated the CFs for precipitation. The CF method was employed for designing future climate forcings via three steps (after Prudhomme et al. 2010):

  1. Observation-based climatology (i.e., precipitation) from 1970 to 1999 was selected as our baseline climatology, and two targeted future scenarios from CMIP5 over two future periods (2020–49, period 1; 2070–99, period 2) were chosen to calculate the CF values.
  2. Monthly percentage changes of precipitation (i.e., CFs from January to December) from the baseline to each future period were calculated for each of the 17 GCMs from DCHP (listed in Table 1) under each RCP scenario over the SARB. This is best explained by means of an example, for which we will use NorESM1-M under the RCP 4.5 scenario (for period 1) to demonstrate the approach. First, mean precipitation for the baseline period was calculated for each month (e.g., 2.24 mm day−1 for April). Then, mean precipitation for NorESM1-M period 1 (under RCP 4.5) was calculated for each month (e.g., 2.02 mm day−1 for April). Thus, a CF of −9.92% was found for April (period 1, RCP 4.5). To represent the wide spread of GCM simulations, we selected the maximum, median, and minimum CF values among these 17 GCMs under each RCP scenario for each month.
  3. Subsequently, for each future period and each RCP, three sets of future forcing scenarios were designed by multiplying the three sets of CFs (i.e., maximum, median, and minimum from previous step) to the baseline climatology to examine the peak flow trend and the uncertainties associated with the GCMs and emission scenarios.
Table 1.

List of the 17 CMIP5 GCMs used in this study.

Table 1.

After applying the CF method, 24 climatological forcing datasets were generated (representing two future periods, four RCP scenarios, and three statistical selections—median, maximum, and minimum—corresponding to the uncertainties among the GCMs).

d. Model setups for various purposes

Three steps were taken to facilitate the modeling-based analyses in this paper. First, DHSVM was calibrated and validated during the historical period. By reconstructing the observed streamflow under the historical conditions of land cover and climate, the model and its parameters were proven robust for testing out future scenarios. Second, the urbanization and climate change effects on the peak flows were evaluated separately. Third, the combined effects of urbanization and climate change were investigated. Particularly, the uncertainties from climate change (characterized by the maximum/minimum CFs) and urbanization (represented by different migration scenarios) were explored both individually and jointly. The DHSVM inputs and model settings used for these purposes are summarized in Table 2. A detailed explanation of these choices is provided in the following subsections.

Table 2.

Summary of the DHSVM inputs used for model calibration, validation, and urban/climate change impact analyses.

Table 2.

1) Calibration and validation

DHSVM was calibrated during the period from 1 January 1996 to 31 December 2000. Soil parameters (porosity, wilting point, vertical conductivity, and maximum infiltration) and vegetation parameters (detention fraction, detention decay, monthly leaf area index, canopy resistance, and vapor pressure deficit) were calibrated based on comparisons between simulated daily streamflow and USGS observations. The statistical variables used for evaluating the results include relative bias (RB), coefficient of determination R2, and Nash–Sutcliffe efficiency (NSE; Nash and Sutcliffe 1970). Considering the different land cover and soil types among the six subbasins, each subbasin was calibrated separately. To avoid errors associated with upper-basin calibrations affecting lower-basin calibrations, an inlet flow module was added to the latest version of DHSVM (i.e., version 3.1.1). Inspired by a similar approach by Gao et al. (2011), the inlet flow module uses the observed outflow from the immediate upstream subbasin as an independent forcing. In this module, DHSVM reads the observed streamflow from upstream at each time step (along with the meteorological forcing variables) to simulate the flows in the studied downstream subbasin. Using this module, calibration accuracy can be maximized at each subbasin, and model performance across the entire SARB can be enhanced. In addition, calibration over multiple locations can significantly reduce the risk of overfitting the model parameters.

The validation period was from 1 January 1950 to 31 December 2010. This period is long enough to cover a range of decadal and interdecadal climate variability and hence provides good evidence for model suitability. First, the subbasins without inlet flows from upper subbasins (i.e., the Medina River, Leon Creek, and Salado Creek) were simulated. Then, the upper San Antonio River subbasin and the Cibolo Creek subbasin were modeled, while accounting for the simulated streamflow outputs from the three upper subbasins. Finally, the lower San Antonio River subbasin was simulated to estimate the streamflow at the basin outlet. During the validation period, several land-cover maps were adopted (Table 2). All other parameters, regardless of whether calibrated or prescribed, remained constant over time. From 1 January 1996 to 31 December 2000 (a period that was previously used for calibration), the model was rerun using simulated inflow data from upstream subbasins. This allowed for validation of DHSVM performance consistently over the entire historical period.

2) Modeling design

After calibration and validation, a suite of DHSVM simulations (as detailed in Table 2) were conducted to evaluate the urbanization and climate change effects on SARB peak flows. To evaluate the basin-scale, long-term trend of peak flows, the analysis performed for this study mainly focused on the outflow of the SARB (USGS 08188500).

To identify the urbanization effect, DHSVM simulations were conducted by changing land-cover maps while using the same meteorological forcings. Specifically, we executed DHSVM 14 times, each time using a different land-cover map, but driven by the same forcing data (i.e., 2000–05). There was no particular limitation in selecting the period or the forcing data source, except the entire period could neither be overly dry nor overly wet.

To isolate the climate change effects, the land-cover map was fixed to LC2001. The forcing inputs were designed using the full range of maximum/median/minimum CF values (as described in section 3c) under different RCP scenarios.

The combined effects, which characterize the past and future more realistically, were explored by incorporating urbanization and climate change scenarios simultaneously. For each of the two 30-yr future periods (2020–49 and 2070–99), land-cover maps for 2030 and 2080 were employed. Although within these future periods the urban areas do change with time, we chose land-cover maps from these two particular years to focus on representing the long-term changes (2020–99). Two phases of analyses were conducted to thoroughly quantify the combined effects and their uncertainties. In the first phase, only LC2030R100 and LC2080R100 (urbanization scenario with the 2000–10 migration rate) were combined with the designed future climate scenarios, with the uncertainties solely from the GCMs. In the second phase, the uncertainties associated with the urbanization process were characterized by different land covers driven by varying population growth rates (e.g., LC2030R100, LC2030R50, and LC2030R0). Since the spread of outputs from the GCM ensemble is much larger than that from the different RCPs [see section 4c(2)], we used the RCP 8.5 scenario as an example to demonstrate the difference in uncertainties between climate change and urbanization. RCP 8.5 was selected as it encapsulates the largest range of precipitation uncertainties (including both high and low extremes) among the four climate scenarios (Fig. 4). In this study, peak flow differences driven by the maximum and minimum CFs were employed to represent the uncertainty range. Uncertainties associated with hydrologic model structure, input, and parameterization exist but are not specified here, since they are usually much smaller compared to those from GCMs and emission scenarios (Kay et al. 2009; Prudhomme and Davies 2009b; Bennett et al. 2012).

Fig. 4.
Fig. 4.

Ensemble of monthly precipitation (mm day−1) from CMIP5 during the two future periods. The black solid lines represent the historical baseline observations. Diamonds show the range among GCMs, with max/median/min indicated.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

3) Peak flow variables

In hydrology, annual maximum series (AMS) is defined as a series of data consisting of the largest streamflow for each year (Bowling et al. 2000). Accordingly, the peak flows for each 30-yr simulation period were characterized by the median of the AMS (MAMS) of simulated ensemble members. We focused on the 30-yr MAMS because they represent the long-term peak flow trend. In addition, since peak flows are usually not normally distributed, median values are more appropriate to use (than mean values) in this study. Similarly, to evaluate the monthly and seasonal variations, we defined monthly maximum series (MMS) as a series of data containing the largest streamflow for each given month over a period of multiple years. The median of the MMS (MMMS) was used to depict the monthly peak flows over a period.

4. Results and discussion

a. Projected precipitation change

Different GCMs associated with multiple RCP scenarios lead to various precipitation projections (in terms of the mean magnitude, variance, and seasonality). In this study, we used the downscaled monthly outputs from 17 GCMs under all four RCPs to evaluate the future precipitation, along with the associated range of uncertainties. The average monthly precipitation results (Fig. 4) show the dual-peak seasonality, which is typical in Texas. Although this general pattern will be maintained in the future, the magnitude of the two peaks will be different. For the May–June peak, the median precipitation from the 17 GCMs has a reduced magnitude. Yet for the September peak, the median increases under all RCPs. For example, the median September precipitation in period 2 under RCP 8.5 rises to 3.50 mm day−1 (i.e., it increases 27% compared to the historical period). This result indicates larger flood risks for September in the future. Because of the various physical and numerical algorithms adopted by these GCMs, the precipitation uncertainties in each month are quite large (the average uncertainty range is 80% of the median value), especially for the warmer months (May–October). This can be attributed to a more active climate (thermodynamically) during these months. Moreover, Fig. 4 indicates that the uncertainties in period 2 are generally larger than those in period 1 (and that the discrepancy becomes greater with higher levels of greenhouse gas emission, i.e., RCP 8.5).

b. Model implementation

The calibrated streamflow from DHSVM was compared with USGS observations at each of the subbasins (Figs. 5a–f). The daily RB ranged from −33% to 17%, the R2 values ranged from 0.66 to 0.92, and the NSE values were from 0.53 to 0.90. Overall, the streamflow events were simulated well. With 80% of the streamflow from upstream subbasins, the lower San Antonio subbasin performs the best. The largest errors were associated with the Medina River subbasin, which is characterized by conifer forests, clay/silty clay soil, and a small urban area. For the validation period (i.e., 1950–2010; Fig. 5g), the overall values for RB, R2, and NSE are −14%, 0.72, and 0.54, respectively. When specifically focusing on high flows, the RB, R2, and NSE (within the top 10 quantile, based on all of the streamflow data) are −4.7%, 0.79, and 0.74, respectively. Based on the monthly NSE criteria recommended by Moriasi et al. (2007), our daily statistics are still between “satisfactory” and “very good.” Because daily statistics usually perform worse than monthly statistics, these validation results lend a strong level of confidence to the accountability of the following peak flow analysis.

Fig. 5.
Fig. 5.

(a)–(f) Calibration for each subbasin and (g) validation over the entire SARB. Monthly results are shown for better visualization, while error statistics (RB, R2, and NSE) are based on daily results.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

c. Urbanization and climate change effects on peak flows

1) Urbanization effect on peak flows

To evaluate the influence of urbanization on peak flows, DHSVM simulations under different historical and future land-cover scenarios were examined. Figure 6 shows the modeled peak flows (in terms of MAMS) from 14 land-cover maps driven by the same set of meteorological forcings. When the urban area changed from the 1929 scenario to the 2011 scenario, MAMS increased from 285 to 494 m3 s−1. At the same time, the impervious area percentage of the SARB increased from 0.6% (LC1929) to 7.8% (LC2011). If the urban area keeps expanding at the 2000–10 migration rate (i.e., 21% per decade), the basin’s impervious area will increase to 25.0%—and its MAMS will surge to 885 m3 s−1—by 2080. The direct consequence of a greater extent of impervious surface area is less infiltration and more infiltration excess runoff. Therefore, during storm events, streamflow becomes flashier and peak discharge rates are magnified. The shaded areas in Fig. 6 depict the range of peak flow uncertainties under different migration rates (at 0%, 50%, and 100% of the 2000–10 rate). For example, if the migration rate is cut to half of the 2000–10 rate, the corresponding impervious area percentage for 2080 will be 19.2%—and its associated MAMS will be 743 m3 s−1. Nonetheless, the long-term trends are all positive for the three migration scenarios. Even at a zero migration rate, the MAMS will still increase from 495 m3 s−1 in 2011 to 531 m3 s−1 in 2030, and then to 572 m3 s−1 in 2080.

Fig. 6.
Fig. 6.

Urbanization effects on MAMS over the SARB based on DHSVM simulations, which were conducted with fixed forcing data (from 2000 to 2005) and different land-cover maps. Dots represent different points in time between 1929 and 2080 corresponding to an existing or constructed land-cover map (with the impervious percentage over the entire SARB shown). Shaded areas represent the future urbanization uncertainties, with the three lines (from top to bottom) corresponding to the 2000–10 rate, half of the 2000–10 rate, and zero migration, respectively.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

Urbanization has various impacts on the peak flows (in terms of MMMS) for different months (Table 3). In particular, November has the largest MMMS because of the largest maximum precipitation occurring in this month. The large mean precipitation starting in September (that results in high antecedent soil moisture) also contributes to this high value of MMMS in November. On the other hand, MMMS has larger increases in months that are already vulnerable to peak flows. For instance, from LC1929 to LC2011, the increase of MMMS is 74 m3 s−1 (from 18 to 92 m3 s−1) for April while it is 45 m3 s−1 (from 9 to 54 m3 s−1) for May. It is worth noting here that these results were driven by meteorological forcings from 2000 to 2005. Thus, Table 3 cannot be overinterpreted into long-term seasonality.

Table 3.

Summary of the monthly peak flows in terms of MMMS (m3 s−1) for urbanization effects only. Monthly mean and max precipitation (mm day−1) from 2000 to 2005 are attached for comparison.

Table 3.

The key message conveyed by these results is that urbanization alone has an irreversible effect of increasing high flows in this region (although the magnitude may vary according to the immigration rate). From a decision-making perspective, actions must be taken to mitigate the unavoidable, increased future potential losses. Some recommendations include—but are not limited to—incorporating low impact development (LID) into city areas to reduce high flows and improve the water quality and adopting new criteria for increasing water infrastructure resilience.

2) Climate change effect on peak flows

Driven by the designed forcings (which were based on the CFs from the GCM ensemble), DHSVM-simulated results were employed to evaluate the climate change effects on peak flows. Figure 7a shows the MAMS driven by three designed forcing datasets based on the maximum, median, and minimum CFs under different RCP scenarios for the two future 30-yr periods. Among the three sets of MAMSs, the simulations driven by the median CFs represent the GCM ensemble median annual peak flows, and the other two denote the uncertainty range of the peak flows (given the spread of the CMIP5 ensemble precipitation). Results from the ensemble median suggest that future annual peak flows will be similar to those during the historical baseline period. However, there is a large uncertainty in the results, and this should not be overlooked. For instance, the uncertainty range for MAMS under RCP 8.5 in period 2 is 864 m3 s−1, which is about 4 times that of the median value (207 m3 s−1). For most RCP scenarios (except for RCP 2.6), the uncertainty range in period 2 is larger than that of period 1. As radiative forcing increases from 2.6 to 8.5 W m−2 in period 2, uncertainty ranges increase from 512 m3 s−1 in RCP 2.6 to 864 m3 s−1 in RCP 8.5. In period 1, the uncertainty ranges are relatively stable across the emission scenarios. For both periods, the difference between maximum and median scenarios is always a multiple of the difference between the median and minimum. For instance, if the precipitation values are multiplied by the maximum CFs, the averaged MAMS from the four RCPs will increase from 685 (period 1) to 781 m3 s−1 (period 2), indicating amplification of flooding by extreme precipitation. In contrast, it will decrease from 96 to 77 m3 s−1 if the precipitation values are multiplied by the minimum CFs. This phenomenon is likely due to the nonlinearity of precipitation variability and the flooding process (which involves precipitation, the land surface condition, and soil properties).

Fig. 7.
Fig. 7.

(a) Climate change and (b) combined urbanization and climate change effects on peak flows over the SARB. Black solid lines are historical observations (i.e., baseline) and diamonds represent the uncertainties from the CMIP5 ensemble, with max/median/min indicated.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

Using results based on RCP 8.5 as an example, the MMMS show large variability for all 12 months (Table 4). However, MMMS values simulated using median CFs indicate that the peak flows will increase in September but will decrease in most of the other months. These results are consistent with future precipitation projections from most GCMs (Fig. 4). In addition, the monthly results agree with the annual results (Fig. 7a) in terms of the uncertainties during the two future periods. It is worth noting that the ranges of uncertainty pertaining to the fall season (September–November) are notably larger than those pertaining to the rest of the year. Such changes of MMMS are accompanied by altered MMS skewness. For instance, the skewness in September is projected to increase from 1.77 (baseline) to 1.81 in period 1, and to 1.97 in period 2. They are most likely attributed to the large precipitation uncertainty during this season (Fig. 4d). Therefore, optimal planning should consider both the small changes of the ensemble median and the large uncertainties indicated by the ensemble range.

Table 4.

Summary of the monthly peak flows in terms of MMMS for climate change and urbanization effects (RCP 8.5 scenario). The cell for each month and period shows both the median value (outside of the parentheses; m3 s−1) and the min/max values (inside of the parentheses; m3 s−1).

Table 4.

3) Urbanization and climate change combined effects on peak flows

Since urbanization and climate change occur simultaneously, evaluation of their combined effects is essential for practical water management and for the associated decision-making. For this purpose, both the designed future climate forcings and their corresponding urbanization scenarios (see Table 2) were used to drive DHSVM, and the results were quantified to investigate their joint impacts.

During the first phase, urbanization and climate change coevolved—but only the uncertainties from the GCMs were considered (by fixing the land cover to LC2030R100 and LC2080R100 for periods 1 and 2, respectively)—while climate forcings were varied from maximum to median to minimum CFs. According to Fig. 7b, when driven by the forcings generated using the maximum/median/minimum CFs, the averaged MAMS value from the four RCPs increases from 447 m3 s−1 in period 1 to 707 m3 s−1 in period 2. By comparing this with Fig. 7a, it is evident that the expansion of urban impervious area is the key driver for escalated future peak flows. In addition, the uncertainties introduced by the GCMs are amplified by the fast expansion of urban areas, particularly in period 2. For instance, the uncertainty range for MAMS under RCP 8.5 in period 2 increased from 864 to 1045 m3 s−1 when LC2001 was replaced by LC2080R100. When the extra surface runoff from the increased urban cover is coupled with minimum CFs, the MAMS will only decrease slightly in all four scenarios for period 1, but it will increase considerably if coupled with maximum CFs. Indeed, peak flows are elevated significantly more by the combined effects (when compared with the climate change effect alone) at both the annual (Fig. 7b) and monthly scales, especially in September (Table 4). For example, when coupled with median precipitation, MAMS will be 207 m3 s−1 in period 2 under RCP 8.5 with only the climate change effect represented, but it will be 576 m3 s−1 with the combined effects considered.

In the second phase, the uncertainties from different urbanization scenarios were also incorporated. Designed climate forcings using maximum/median/minimum CFs under RCP 8.5 were chosen to represent the largest uncertainties across all four RCPs. Results shown in Fig. 8 convey several messages. First, although both climate change and urbanization can alter the peak flows notably, the ranges of uncertainty induced by the GCMs are significantly larger than those from the various urbanization scenarios. For example, in period 2, the uncertainty range for climate change (when coupled with half of the 2000–10 migration rate) is 971 m3 s−1, while the uncertainty range for urbanization (when coupled with forcings from maximum CF values) is only 283 m3 s−1. The uncertainties associated with climate change are more than 3 times greater than those from urbanization. Second, increased flood risk is unavoidable in the future within the SARB. The only exception is when future precipitation is equal to the minimum of the entire CMIP5 ensemble. Third, because urbanization and climate change effects can be exacerbated when they coevolve, the uncertainty range of the MAMS values during period 2 is 3.0, 2.7, and 3.1 times larger than that in period 1 under the maximum, median, and minimum CFs, respectively. The largest MAMS (i.e., 1296 m3 s−1) occurs in period 2 when the maximum CF is coupled with the 2000–10 migration rate. However, when compared with the largest historical AMS (3426 m3 s−1 in 1967), the value is still within a reasonable range. Considering all of the above, future policy-making should be especially concerned with the second half of twenty-first century. Infrastructure such as dam and sewage systems, built in the past to defend from a 100-yr return period flood, might not function well in the second half of twenty-first century. Meanwhile, the decisions should take into full consideration of the “certainty” associated with the urbanization effect, while also being prepared to cope with the “uncertainty” related to the climate change effect.

Fig. 8.
Fig. 8.

Uncertainties associated with the urbanization process and with changing climate (taking RCP 8.5 as an example). Dashed lines are the peak flows generated by max/median/min perturbed forcings using the CF method. Boxes represent the urbanization (migration) uncertainties.

Citation: Journal of Hydrometeorology 17, 9; 10.1175/JHM-D-15-0216.1

5. Conclusions

In this study, we investigated the future climate change and urbanization effects on peak flows over the urbanized semiarid SARB. Both the stand-alone and joint impacts from urbanization and climate change were explored. In addition, the uncertainties associated with the ensemble CMIP5 projections and with the future migration rates were both examined. The conclusions from this study are summarized as follows:

  1. The spread of impervious cover in the SARB due to urbanization has a substantial impact on the peak flows, particularly over the months with large precipitation and/or high antecedent soil moisture. The MAMS will increase to 885 m3 s−1 by 2080 if migration follows the 2000–10 rate. Overall, uncertainty ranges increase as urbanization continues.
  2. The alterations of peak flows due to climate change have three notable characteristics. First, MAMS simulated using the median CFs (that best represent the CMIP5 ensemble) will barely change, while MMMS will increase in September and decrease during the rest of the year. This is because there is no clear trend with the projected median annual precipitation in this region, but the total amount will be redistributed intra-annually. Second, the large GCM ensemble uncertainties lead to much larger peak flow uncertainties (relative to the ensemble median uncertainties). Meanwhile, uncertainties in the fall are the largest among the four seasons. Third, the peak flow uncertainty range during period 2 is larger than that during period 1, in agreement with other studies showing that the changes in the second half of the twenty-first century are more significant than those of the near future (IPCC 2013; Neumann et al. 2015).
  3. The combined effect from urbanization and climate change is not a simple linear addition. Urbanization will amplify the climate change impacts by increasing both peak flow and its uncertainty range. Considering the uncertainties from both combined and isolated climate change and urbanization, it is evident that the urbanization component is much more predictable while the climate change part contains larger uncertainty.
  4. The research approach demonstrated in this study can be applied to any semiurbanized river basin where input data are available. Given that 38% of the global population is located in arid or semiarid regions (Huang et al. 2016), this study in the SARB is also geographically representative. More importantly, the results are represented in the context of uncertainties, both from urbanization and climate change. Such analyses—which have not been reported in previous studies—are especially meaningful for policy-making. For example, because urbanization will increase flood risks (with a high level of certainty), sustainable storm water principles such as LID (Dietz 2007) are needed to minimize the imperviousness effect (and future city development plans should always consider the hydrologic impacts). In contrast, peak flows forced by the CMIP5 ensemble median will remain the same as the baseline. Accompanying this are the large uncertainties associated with future climate prediction, which is the most challenging factor for planning. Considering these findings, new infrastructure standards—to meet the challenges from future floods—are worth pursuing to sustain the built environment.
  5. Urbanization and climate change effects on water resources management and environmental sustainability are multifaceted. Our future research will further explore water quality and flow regulations within this context.

Acknowledgments

This research was supported by U.S. National Science Foundation Grant CBET-1454297, and TAMU-CONACYT Collaborative Research Grant Program 2014-028. Gang Zhao is also partially supported by the W. G. Mills Scholarship (02-650509) and the USGS Graduate Student Research Program (G16AP00085) provided by the Texas Water Research Institute. This study has benefitted from the usage of the Texas A&M Supercomputing Facility (http://sc.tamu.edu). We also thank Ben Livneh for providing the historical forcing dataset. We are very grateful to the three anonymous reviewers who helped improve the manuscript with constructive suggestions.

APPENDIX

Generation of Historical and Future Land-Cover Maps

To approximately represent different levels of urbanization within the San Antonio River basin, several historical and future land-cover maps were created. These are based on existing datasets, which include land-cover types of the 1970s, 1992, 2001, 2006, and 2011. In the SARB, the urbanized land surface is mostly attributed to the city of San Antonio. Thus, in order to study the effect of urbanization, the urban area of San Antonio was recomposed to create land-cover maps with an assumption that no land-cover type changes occur in nonurban areas (e.g., from forest to grass, from bare ground to wetland).

For historical land-cover maps, an existing land-cover map from the 1970s was selected as the base map. First, a land-cover map without any impervious area was created. This was achieved by removing the urban area from the base map and filling it with other land-cover types (based on spatial characteristics and overall percentages of nonurban land-cover types). Next, the available historical city maps (1929, 1958, and 1964) were georeferenced and employed to delineate the profiles of historical urban areas of San Antonio. These profiles were then merged with the nonurban land-cover map to obtain the integrated land-cover products.

For future land-cover maps, the existing land-cover map in 2011 (LC2011) was selected as the base map. Future urban areas (in 2030 and 2080) were then estimated based on a population–urban area relationship and projected census data. The population–urban area relationship (Fig. 2) was developed based on the population and city land area data from 1960 to 2010 [provided by U.S. Census Bureau and Rappaport (2003)]. Then coefficients were obtained with the least squares fitting method:
ea1
where UrA is the urban area (km2) and is the population of the city of San Antonio. Total population for the years 2030 and 2080 was estimated using the same method as that used in the Texas Population Projections Program report (Texas State Data Center 2014). Using Eq. (A1), the urban areas of 2030 and 2080 were then estimated. Basically, expansion was conducted using the concentric method (Liu et al. 2005; Jiao 2015) based around the center of the city (San Antonio). In this step, we assumed there will be no significant expansion pattern changes (i.e., no change in the placement of urban areas) in the future, which is the key characteristic of the concentric method. After the completion of this expansion process, the new maps were resampled to 200-m resolution and then merged with the nonurban land-cover map (to keep the consistency of nonurban areas).

Through the methods mentioned above, new land-cover maps—including nonurban and 1929, 1958, 1964, 2030, and 2080 urban areas—were produced to support further analysis of urbanization effects on streamflow over the SARB. For 2030 and 2080, two additional land-cover maps were created for each year, to represent the urbanization effect at half the 2000–10 migration rate and at a zero migration rate, respectively.

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