Assessment of climate change impact on floodplain and ... - BibBase

Journal of Hydrology 395 (2010) 264–278

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Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol

Assessment of climate change impact on floodplain and hydrologic ecotones Hamid Moradkhani ⇑, Ruben G. Baird, Susan A. Wherry Department of Civil and Environmental Engineering, Portland State University, OR, USA

a r t i c l e

i n f o

Article history: Received 11 March 2010 Received in revised form 26 September 2010 Accepted 26 October 2010 This manuscript was handled by K. Georgakakos, Editor-in-Chief, with the assistance of Ashish Sharma, Associate Editor Keywords: Climate change Floodplain mapping Vegetation mapping Ecotone LiDAR SWAT HEC-RAS

s u m m a r y Current modeling efforts continue to indicate that the effects of climate change will be both global and local in scale, and that ecohydrologic factors including vegetation pattern, altered precipitation events, reduced system yields due to streamflow changes, increased flooding and changes to current floodplain characteristics will be affected. Therefore, using technology such as light detection and ranging (LiDAR) data, using future general circulation model (GCM) data, and conducting floodplain analyses to predict the changes to ecohydrologic factors are critical for cataloging existing ecosystem resources and for understanding the effects that different climate change scenarios may have on these resources at the basin scale. This study considers the effects of three different GCM climate change emissions scenarios (high from the IPSL GCM’s A2 scenario, middle from the ECHAM5 GCM’s A2 scenario and low from the GISS GCM’s B1 scenario) using daily downscaled precipitation and temperature data over the Lower Tualatin basin in the Pacific Northwest US. The Tualatin River basin is a dynamic watershed that supports urban and agricultural uses and is also 50% forested. Its economic drivers include agricultural and forest products, as well as other consumer products including high-tech software and hardware industries. The Soil and Water Assessment Tool (SWAT) software was used as a distributed hydrologic model to predict the daily flows in the basin. It is predicted the 50-year recurrence interval (RI) flow will decrease significantly for the low and middle emissions scenarios (to between approximately 18,000–19,000 cfs compared to the observed 50-year RI of near 26,000 cfs) and will increase significantly under the high emissions scenario to nearly 33,000 cfs. Floodplain extents for the various climate scenarios and timeframes were delineated using the HEC-RAS model. A geo-processing procedure was employed to delineate hydrologic ecotones to evaluate the condition of riparian areas and streams. A current and future conditions analysis was performed using the combination of aerial imagery and LiDAR data for the vegetation within the ecotones in order to: (1) provide an existing inventory of vegetation within the basin and (2) to predict the effects that climate change may have on vegetation within the basin. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction Climate change is a very broad term that encompasses anthropogenic changes in the atmosphere and on land. Increases in the concentrations of greenhouse gases in the atmosphere have affected large scale atmospheric circulation patterns which in turn alter precipitation and temperature. Land development and channelization of waterways have changed climate on much more local scales by altering watershed hydrology, inducing greater erosion and accelerated sedimentation, all of which generally impact local ecosystems for the worse. Stormwater management is clearly impacted by climate change since atmospheric changes affect precipitation and land-based changes affect the routing of that precipitation. These complex relationships between changing climate and changing hydrology require that stormwater management practices focus on the key elements of the effects of ⇑ Corresponding author. Tel.: +1 503 725 2436. E-mail address: [email protected] (H. Moradkhani). 0022-1694/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2010.10.038

climate change namely, changes to precipitation, temperature, flooding, system yield, and vegetation. Climate change impact assessments are conducted presently for a variety of reasons, most notably to address regions’ abilities to cope with changes in flow regimes and flood events, and to predict the potential changes to the availability in municipal water supplies (Fowler et al., 2007; Wilby and Harris, 2006; Kay et al., 2009; Jung et al., submitted for publication; among others). General circulation models (GCMs) are important tools in the assessment of climate change (Fowler et al., 2007). GCMs are numerically coupled models representing various earth systems including atmosphere and land surface and are based on general principles of fluid dynamics and thermodynamics. Developments in GCMs have occurred recently, including improvements to dynamical cores and vertical and horizontal resolutions; the incorporation of more physical components such as land surface and sea ice processes, and parameterizations of physical processes have also been improved. However, many issues remain, such as model response to processes at the grid-level (Randall et al., 2007). The

H. Moradkhani et al. / Journal of Hydrology 395 (2010) 264–278

coarse resolution (generally greater than 2° 2°) of GCMs make them difficult to resolve to basin scale. Additionally, the use of GCMs for future simulations shows the consistent disagreement in the rainfall variable (Johnson and Sharma, 2009). The coarse resolution and GCM disagreement necessitate using downscaling and analytical studies to determine the most appropriate GCM for assessing climate change impacts at the watershed scale. There are two fundamental techniques for downscaling coarse GCM data to finer resolutions, namely, dynamical and statistical Downscaling. The dynamical downscaling approach uses higher-resolution regional climate models (RCMs) embedded within a GCM, while the statistical methods rely on empirical relationships between the coarse resolution GCM and high resolution local climate variables (Fowler et al., 2007; Najafi et al., accepted for publication). Either the dynamical or statistical regional climate models (RCMs) can provide meaningful results at the local or regional level (Block et al., 2008). Hydrologic models are used to provide flow estimations at the regional/watershed scale by accounting for various parameters such as elevation, soil type, and land use. Hydrologic models can generally fall in two broad categories: lumped and distributed. Grid-based or distributed models account for spatial variability in sub-catchment hydrologic response and can make use of gridded, spatially-distributed climate data obtained from climate models (Bell et al., 2007). The ultimate aim of this paper is to address the effect that climate change may have on riparian and floodplain-connected areas. Therefore, we utilize vegetation mapping as a method for riparian assessment at the watershed scale. Riparian vegetation serves several key ecohydrologic functions, namely: (1) providing organic material, food, and habitat for aquatic and terrestrial wildlife; (2) filtering runoff pollution and (3) slowing flood waters, providing areas for sediment deposition and providing bank stability (Clarke et al., 2004). Vegetation mapping is focused on the floodprone areas of the basin. Such areas are referred to as riparian ecotones, or floodplain-connected areas that incorporate aquatic and terrestrial elements (Verry et al., 2004). These areas are typically constrained to the floodprone area plus an arbitrary distance that tends to incorporate other upland functions. We employ two main techniques for assessment: normalized difference vegetative index (NDVI) and light detection and ranging (LiDAR) feature extraction. NDVI is a widely-known and -employed method for determining aerial distributions that will be discussed in later sections. LiDAR is a fairly new technology that provides fine-resolution elevation data for the earth’s surface. These data can be used for a multitude of purposes, including feature height extraction, which, in conjunction with NDVI analysis, is an effective means for providing threedimensional representation of vegetative features at a high resolution. This paper will evaluate the potential impacts of climate change on the Lower Tualatin River basin (TRB). We will use several established elements, such as downscaled GCM data, distributed hydrologic modeling, flood frequency analysis (FFA), and river analysis system software to predict the extent of future floodplains given various greenhouse gas emission scenarios. A study conducted by Kleinen and Petschel-Held (2007) suggests that river basins may be affected by increased flood events due to global warming. Similar to the findings of the Kleinen and Petschel-Held study, we suggest that the future floodplains derived in our analyses may be more exposed to flooding events, depending on the climate model and emission scenario. In addition to future floodplain delineation, we also predict the extents of riparian ecotones for various climate change scenarios. We use available LiDAR and aerial imagery coverages to extract vegetative features within each ecotone. The results follow a consistent pattern based on the emissions pathways and timeframe.

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Finally, this paper will introduce and propose several procedural elements that can be used as a sustainable stormwater management practice which will help to both qualify and quantify existing vegetative resources, as well as create a time series of such inventories which can be compared to predicted and actual changes in riparian ecotone areas to determine to what extent the climate change may impact at the regional watershed scale. 1.1. Study area The TRB is located in northwest Oregon (see Fig. 1 for overview map) and is a fourth-level hydrologic unit (US Geological Survey (USGS), http://www.usgs.gov/), draining an area of 712 square miles (see Table 1 for drainage details). There is an urban population of approximately 500,000 people residing in about 15% of the watershed’s area, while agricultural and forest land take up the remaining 35% and 50% of the watershed, respectively. Rainfall varies from 100 to 120 in. per year in the coastal ranges to 36–48 in. per year in the valley. Several streamflow gages are located throughout the watershed and are maintained by the USGS, United States Bureau of Reclamation (USBR), and the Oregon Water Resources Department (OWRD). The USGS has maintained a gage on the Tualatin River at West Linn (river mile 1.8, near the outlet of the watershed) since 1928. Several releases and withdrawals occur within the TRB—typically during the dry season—and include drinking water withdrawals, reservoir and wastewater treatment plant releases, and canal diversions (Source: Tualatin River Flow Management Technical Committee). Vegetation within the basin is classified according to the Environmental Protection Agency (EPA) Level IV ecoregions. Ecoregions represent areas of general similarity within ecosystems in terms of type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the assessment of an ecosystem’s components (Thorson et al., 2003). Table 2 lists the natural vegetation present in each ecoregion within the basin. 1.2. Projected climate change impacts on TRB Palmer et al. (2004) used downscaled data from six GCMs to predict the climate change impacts on the TRB. Precipitation was predicted to increase in the future, this increase will occur in winter months. Summer months will be drier. Therefore, greater strain will be placed on the overall system during these critical months. Using the average output of six global climate GCMs, the estimated average monthly temperatures will increase by as much as 2 °F by 2040 and 4 °F by 2080. Overall flooding will increase, especially in winter months as winter precipitation is predicted to increase. System yield is expected to decrease overall due to strains introduced in the predicted drier summer months—approximately 1.5% per decade during the next 40 years. Jung et al. (submitted for publication) used 16 (eight GCMs with two emission scenarios each) climate models to predict climate change impacts on the TRB. The results of their study indicate that seasonal changes in precipitation and temperature are higher in summer than in the winter season. For the higher emissions scenario, uncertainty ranges slightly increase with time. For the GISS B1 emissions scenario, the change in temperature increases with time, but the change in precipitation does not show a significant trend. They also found that change in winter runoff is more affected by GCM uncertainty and less affected by hydrologic parameter uncertainty. In another study conducted by Najafi et al. (submitted for publication) the impact of hydrologic model selection and structural model uncertainty on hydrologic climate change impact was investigated and it was concluded that

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Fig. 1. Tualatin River basin overview, GCM grid configuration and weather gage locations.

Table 1 Major drainages of lower Tualatin watershed (Hawksworth et al., 2001). Major stream

Area (mi2)

Mainstem length (mi)

Tualatin River (total at Willamette River) Tualatin River (lower Tualatin watershed) Chicken creek Fanno creek

712

80

–

97

28

–

17 32

7 14

15.5 9.3

Confluence w/Tualatin (RM)

Table 2 Vegetation types for ecoregions within the Tualatin River basin (Hawksworth et al., 2001, pp. 1–2). Level IV ecoregion

Elevation

Natural vegetation

Expected life span

3c. Prairie terraces

60–1250 ft.

Oregon asha Black poplarb

250 Years 150 Years, minimum 200 Years

Willow/cottonwoodb 3d. Valley foothills

680–1060 ft.

White oakb Western redcedar Douglas firc

a b c

c

500 Years, maximum 1000+ Years 100+ Years

McCain and Christy (2005). Niemiec et al. (1995). Van Pelt (2001).

1.3. Changes in vegetation Although no data currently exists for the Tualatin River basin that directly estimates changes in vegetation due to climate change, it can be reasoned that changes in vegetation could prove to be significant, especially when considering how increased flooding (and hence, increased floodplain size) and changes in precipitation are expected to occur (higher flows in the winter, lower flows and more system strain in the summer). Changes in temperature can also be expected to affect vegetation. Lenihan et al. (2003) found that the response to increasing temperatures under two different GCMs (the Hadley Climate Center HADCM2 and the National Center for Atmospheric Research Parallel Climate (PCM) models) could be characterized by shifts in vegetation types, increases in productivity, changes to species competition, and an overall increase in ecosystem carbon. This study will attempt to show how the important vegetative interface, the riparian ecotone, may be affected by climate change, and will identify important areas to consider when tracking climate changes on vegetation within a basin. Our contribution shows how, where, and by how much vegetation will be effected as a result of climate change by rigorously defining and applying the ecotone as the areas of interest within our study area, and using the end-to-end climate impact process from our study to zero in on precise locations in our river system that will be impacted through the use of continuous simulations, river system modeling, and vegetative feature extraction. 2. Defining the floodplain using climate data and modeling

Bayesian Model averaging (BMA) results in more reliable streamflow projection than those of individual models.

As mentioned in Section 1, riparian ecotones are defined in part by the floodprone area, or the floodplain area at the extent of the

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50-year return interval (RI) streamflow. Therefore, in order to evaluate potential climate change impacts at the riparian ecotone scale along the Lower Tualatin River, we must first determine the extent of these ecotones by hydrologic and hydraulic modeling using downscaled GCM data as input for various climate scenarios to delineate potential future floodplain areas. This section will discuss how these potential future floodplains were delineated for the 50year RI using climate data and modeling. 2.1. Hydroclimate data The GCM data for precipitation, maximum temperature and minimum temperature were obtained from the Climate Impacts Group (CIG) at University of Washington. As part of the Fourth Intergovernmental Panel on Climate Change (IPCC) Assessment, the CIG analyzed GCM data over the Pacific Northwest from ten different models including HADCM3, ECHAM5, CCSM3, PCM1, CNRM-CM3, CSIRO-MK3, MIROC-3.2, IPSL-CM4, CGCM-3.1 and GISS-ER. The CIG considered all ten GCMs run for the IPCC Special Report on Emissions Scenarios (SRES) A2 and B1 emissions scenarios (20 total GCM simulations considered for the 21st century), over the Pacific Northwest (PNW). The SRES A2 scenario generally represents a higher emissions path produced by rapid economic and technological development, while SRES B1 scenario represents a lower emissions path brought on by increased levels of social and environmental consciousness and sustainable energy developments. The GCM simulations for these scenarios were compared by mapping the change in temperature versus the percent change in precipitation between the future period 2030–2059 and the historical period 1970–1999. The map indicated three clusters of future scenarios over the PNW: (1) a low scenario cluster with models exhibiting slight increases in temperature and slight decreases in precipitation; (2) a mid-level scenario cluster with models exhibiting moderate increases in both temperature and precipitation and (3) a high-level scenario cluster with models exhibiting the greatest increases in both temperature and precipitation (Mote et al., 2005). From the ‘‘low PNW scenario’’ cluster, CIG selected the GISS model run with the B1 scenario as a representative GCM, from the ‘‘middle PNW scenario’’ cluster, CIG selected the ECHAM model run with the A2 scenario as a representative GCM and from the ‘‘high PNW scenario’’ cluster, CIG selected the IPSL model run with the A2 scenario as a representative GCM. As seen in Table 3, the SRES B1 scenario for the GISS_ER model matched up with the lowest warming over the PNW. Additionally, the CIG determined that the A2 scenario applied to ECHAM5 and IPSL_CM4 would result in the middle and highest warming scenarios, respectively, over the PNW. Using these representative GCMs allowed us to consider three unique future trends from three unique GCMs for hydrologic modeling without performing rigorous modeling efforts over 20–30 different simulations. This approach is consistent with the Mehrotra and Sharma (2010) study, which justifies the use of a single GCM by selection based on strong correlation between GCM variables and regional climate. The three representative GCM simulations were downscaled to daily, 1/8-degree gridded data over the period of 1900–2100 by the CIG using an improved version of the Bias-Corrected Spatial

Downscaling (BCSD) method. The BCSD method has been shown to successfully reproduce the main features of observed hydrometeorology when applied to climate model outputs, and for future climate scenarios this method was shown to be able to produce hydrologically plausible results (Wood et al., 2004). Previous studies investigating predictor combinations found large scale precipitation and sea-level pressure to be robust predictors of precipitation over the PNW while large scale temperature alone was a robust predictor of regional scale temperature over the PNW (Salathé et al., 2007; Widmann et al., 2003). The first step in the downscaling process was to bias correct the GCM grids using observed precipitation and temperature data. The bias correction was carried out by first determining the cumulative distribution function (CDF) for each GCM grid cell for each calendar month over 1950–1999 and the CDF for observed precipitation within the GCM grid cell boundaries for each calendar month over 1950–1999. GCM data was bias corrected by performing a quantile-mapping procedure that determined transfer functions between the two CDFs. Using the transfer functions, GCM variables were corrected so that the GCM CDF exhibited the same probabilistic trends as the observed data CDF. After bias correction was completed, GCM data were statistically downscaled to gridded, observed data using perturbation factors based on monthly means during the 1950– 1999 period (Salathé et al., 2007; Salathe 2005). Since statistical methods do not incorporate regional characteristics, the CIG then utilized the MM5 mesoscale model at 15 km resolution in order to capture orographic effects, land–water contrasts and mesoscale circulations (Salathé et al., 2007). Other GCM data parameters for this study are as follows: Bounding coordinates: Latitude 45.8125 N through 45.1875 N. Longitude 123.4375 E through 122.5625 E. Time periods: Historical: 1/1/1960–12/31/1999. Future: 1/1/2010–12/31/2049 and 1/1/2050–12/31/2089. Processing of the CIG generated precipitation and temperature data into forms appropriate for ArcSWAT was performed using Matlab software. Since the CIG data is gridded and ArcSWAT calls for gage data, we decided to treat each grid point as a ‘‘gage’’. Therefore, the study area contained within 15 grid cells was prepared as 15 gages (note: the centroid of one grid cell fell outside the watershed boundary and was omitted as a gage from the ArcSWAT model). Fig. 1 shows the grid configuration for how the downscaled data was used in defining the precipitation and temperature gage locations. Use of the gridded, downscaled climate data as the input to the hydrologic model is discussed in the next section. Once the downscaled CIG temperature and precipitation data were determined for the TRB study area, we performed an assessment of linear correlation between observed and GCM values over a historical period of 1973–1999. We found the correlation between observed and GCM modeled precipitation to be low (correlation coefficients ranged 0.13–0.17) and the correlation

Table 3 GCM projection abbreviations, group/country, and primary reference. Projection name

CIG scenario assessment

Modeling group/country

GISS_ER SRES B1 ECHAM5 SRES A2 IPSL_CM4 SRES A2

Lowest warming (low) Middle of the road (mid) Highest warming (high)

NASA/Goddard Institute for Space Studies, USA Max Planck Institute for Meteorology, Germany Institut Pierre Simon Laplace, France

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between observed and GCM modeled temperature to be good (correlation coefficients ranged 0.77–0.79). Since the correlation for precipitation was low, we performed a delta method bias correction on the GCM data for the TRB using observed data in order to reproduce the observed probability distribution (Salathé et al., 2007). This step was important for our hydrologic study of the TRB as we desired matched observed probability distribution for modeled streamflow and recurrence interval flows (Salathé et al., 2007). The bias correction method we used finds a correction factor for each month based on the relationship between historical observed and GCM data. For precipitation bias, the correction factor is determined for each month by finding the ratio between average observed precipitation and average GCM precipitation. This factor is then multiplied by future GCM precipitation values. For temperature bias, the correction factor is determined for each month by finding the difference between average observed temperature and average GCM temperature. The factor is then added to future GCM values to provide bias corrected temperature. We employed this method for all three of our GCM models to provide corrected inputs for ARCSWAT modeling. It is noted that a consensus may exist among hydroclimate modelers that in general precipitation downscaling represents more challenges comparing to other meteorological variables. Recent study by Johnson and Sharma (2009) showed that just 6% skill to GCM rainfall simulations for future climates could be achieved as compared to a skill of 80–90% for pressure and temperature. However, Huth (2004) showed that the downscaled temperature is highly sensitive to the predictor combinations. Similarly, the skills obtained by statistical downscaling of precipitation, are dependent on the choice of predictors that would provide accurate information of the regional precipitation (Mehrotra and Sharma, 2010). Najafi et al. (accepted for publication) used an objective procedure for selecting GCM predictors using Independent Component Analysis (Moradkhani and Meier, 2010) and compared downscaling methods with different structures and complexities and investigated the influence of predictor selection on the performance of each method.

model was performing. Overall the TRB was organized into 28 sub-basins (five representing gages, 23 representing sub-basin outflow). Within the sub-basins the hydrologic response units (HRUs) were also defined based on the combinations of land use, soil type and slope. Fig. 2a–c shows the individual GIS inputs of land use, soil type, and slope, respectively. Fig. 3 shows the HRU definitions based on these GIS inputs for the watershed.

2.2. Hydrologic modeling Hydrologic modeling was performed using the Soil and Water Assessment Tool (SWAT) extension for ArcGIS mapping analysis software, called ArcSWAT. SWAT is a physically-based, continuous simulation watershed-scale model, developed by the United States Department of Agriculture (USDA) and Texas A&M University. The model was created to help predict the water and other processes in watersheds with varying soils, land use, and other conditions over long periods of time (Neitsch et al., 2005). SWAT requires specific information about weather, soil types, topography and other hydrological factors. ArcSWAT enables readily available watershed-scale data (such as DEM, land use and soils data) to be input to the model directly. Simulation of the hydrology in the watershed is governed by two phases: the land phase and the water or routing phase. The land phase defines the amount of water, loading to the main channel in the sub-basin, while the water or routing phase governs the movement of water through the channel network of the watershed outlet. A digital elevation model (DEM) for the Tualatin basin region was obtained from the Oregon Geospatial Enterprise Office (GEO). We used this DEM in the ArcSWAT and delineated the major streams and sub-basins of the watershed. Sub-basins outlets were added to represent seven stream gages monitored by the USGS, OWRD and the United States Bureau of Reclamation (USBR). The stream gages were used as part of the model calibration and validation so that we could provide comparisons of observed and modeled streamflow at these locations and determine how well the

Fig. 2. Tualatin Basin features: (a) land use, (b) soils and (c) Tualatin slopes.


269

Fig. 3. Tualatin Basin HRUs (unique soil/land use/slope combinations).

Once the basin, sub-basins and HRUs were defined, the gage precipitation and temperature data were input. As mentioned above, the gages were defined as the centroids of the 1/8-degree grids of downscaled temperature and precipitation data within the basin. Elevations of the grid midpoints were determined using the DEM and assigned to each precipitation/temperature gage for use by the model. A data file was created for each of the precipitation and temperature gages defined by the gridded, downscaled GCM data (shown in Fig. 1); the precipitation gage files contained average daily rainfall (mm) and temperature files contained maximum and minimum daily values (°C). Based on the available observed rainfall, temperature, and streamflow data for the period of 1994–2008 (five weather gages within the TRB), we calibrated and evaluated the model, as discussed in the Section 2.2.2. Using observed flow data from the five stream gages near the basin outlet; we performed a sensitivity analysis on all 27 of the ArcSWAT parameters and subsequently conducted the calibration on the five most sensitive parameters. 2.2.1. Baseflow filter parameter extraction The baseflow factor (aBF) is used in the SWAT model to specify the ratio of surface runoff to baseflow (i.e., groundwater recharge). This factor was identified in our sensitivity analysis as being the most sensitive model parameter. Successful adjustment of this parameter required a detailed procedure for data acquisition and analysis of observed streamflow hydrographs from six stream gages in the watershed. Separation and interpretation of baseflow (long-term delayed streamflow from groundwater) from quickflow (short-term rainfall runoff event response) is a well-established and valuable strategy in understanding the principles of groundwater discharge to streams (Brodie and Hostetler, 2005). In order to adjust this parameter, we used baseflow filter software that separates the base-

flow component from the streamflow time series using an automated recursive digital filtering technique (Arnold and Allen, 1999). Once the six aBF values for each gage were obtained from the baseflow filter software, the values were entered in ArcSWAT for the corresponding upstream sub-basin draining to each stream gage. Fig. 4 illustrates how the baseflow parameters were assigned within the watershed. Sub-basins (highlighted in Fig. 4) for which sufficient stream flow data were available and had the aBF values applied to them using the results from the baseflow filter software. This calibration process is discussed further in Section 2.2.2. 2.2.2. Hydrologic model parameter sensitivity analysis, calibration and evaluation After we input all observed and geographical data into the SWAT model, a parameter sensitivity analysis and model calibration needs to be performed. Since our focus in this study was on streamflow, we separated the watersheds into five regions for sensitivity and calibration analyses based on locations of the stream gages (Table 4), and sub-basins were assigned to a region where they were closest upstream of the gage. Flow data were first created based on unregulated or natural flow calculations to account for regulated releases and withdrawals during the calibration period (Source: OWRD, District 18 Watermaster). We performed an initial parameter sensitivity analysis using ArcSWAT’s built-in method (Van Griensven et al., 2006), which combines Latin Hypercube (LH) and One-factor-At-a-Time (OAT) sampling of observed streamflow data to rank the sensitivity of all considered for each region over the calibration period of 1994–2003. The effects caused by changing each parameter are then compared using sum of the squares of residuals (SSQ). The sensitivity analysis allowed us to rank the 27 flow parameters based on the highest SSQ values, afterwards which we performed auto-calibration for the region’s

270


Fig. 4. Application of baseflow parameter values to ArcSWAT sub-basins.

Table 4 Stream Flow Gages Used for SWAT Model Calibration (natural flow values calculated). Stream flow gage OWRD name

Location

River mile

Xcoordinate

Ycoordinate

Calibration period used

Validation period used

DLLO TRGC ROOD FRMO WSLO

Tualatin River near Dilley, OR Tualatin River near golf course Tualatin River near Rood Bridge Tualatin River near Farmington Road Tualatin River near West Linn

58.8 51.5 38.4 33.3 1.75

123°070 2300 123°030 2200 122°570 0600 122°570 0200 122°400 3000

45°280 3000 45°300 0800 45°290 2400 45°260 5800 45°210 0300

1994–2003 1994–2003 1994–2003 1994–2003 1994–2003

2004–2008 2004–2008 2004–2008 2004–2008 2004–2008

five most sensitive parameters. Results from the sensitivity analysis are shown in Table 5. Calibration was conducted on a data set of 10 years over the period of 1994–2003 for the five most sensitive flow parameters mentioned previously. This was performed using observed rainfall and temperature data obtained from the Western Regional Climate Center (WRCC). The calibration program in ArcSWAT uses the Parameter Solutions Method (PARASOL) to estimate the parameter values. The optimization scheme used by PARASOL is the shuffled complex evolution (SCE) (Duan et al., 1992). SWAT utilizes SCE by selecting an initial random population of parameters based on the pre-defined parameter bounds and places those points into a pre-defined number of complexes. Each complex is then allowed to evolve towards minima in the error objective function; the new set of parameters in the complexes are shuffled amongst the complexes and then allowed to evolve again. This continues until the objective function does not improve by more than 1% for five loops in a row. The results of the calibration analysis are given in Table 5. Three calibration scenarios were considered to determine whether the ArcSWAT-derived baseflow parameters provided a

more accurate simulation for the evaluation period than using those obtained from the baseflow separation analysis. Then, the calibrated model was validated for the period of 2004–2008: (1) Using the results from the model calibration process described earlier, five calibrated model parameters from each five streamflow gages with grouped sub-basins were estimated. For the evaluation period, we obtained the Nash–Sutcliffe Efficiency (NSE) value of 0.737. We also found that the correlation coefficient between the observed and modeled flows, rflow, to be 0.892. (2) We estimated the model parameters for all of the values from the model calibration step, with the exception of the baseflow parameter. For this parameter, we assigned the values obtained from the baseflow filter process (see Section 2.2.1) only for those sub-basins that drained to each of the six stream gages. Other sub-basins were given the parameter value obtained from the model calibration step. This method gave a reasonable NSE value of 0.643 and a good rflow of 0.829.

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H. Moradkhani et al. / Journal of Hydrology 395 (2010) 264–278 Table 5 SWAT calibration details. SWAT flow parameter

Downstream gage

Sub-basins

Values

Baseflow factor (days)

ROOD FRMO TRGC DLLO WSLO

1, 2, 3, 4, 6, 9, 13 5, 7, 11, 12, 20 8, 10, 14 15, 16, 17, 18, 19 21, 22, 23, 24, 25, 26, 27, 28

1 0.506 0.733 0.921 0.739

Effective hydraulic conductivity (mm/h)

ROOD

1, 2, 3, 4, 6, 9, 13

0

FRMO TRGC DLLO WSLO

5, 7, 11, 12, 20 8, 10, 14 15, 16, 17, 18, 19 21, 22, 23, 24, 25, 26, 27, 28

142.46 146.57 127.92 143.27

ROOD FRMO TRGC DLLO WSLO

1, 2, 3, 4, 6, 9, 13 5, 7, 11, 12, 20 8, 10, 14 15, 16, 17, 18, 19 21, 22, 23, 24, 25, 26, 27, 28

0.3 0.147 N/A N/A 0.3

ROOD

1, 2, 3, 4, 6, 9, 13

FRMO

5, 7, 11, 12, 20

TRGC

8, 10, 14

DLLO

15, 16, 17, 18, 19

WSLO

21, 22, 23, 24, 25, 26, 27, 28

+25% Initial value +22.18% Initial value +24.98% initial value +24.98% Initial value +25% Initial value

ROOD

1, 2, 3, 4, 6, 9, 13

N/A

FRMO TRGC DLLO WSLO

5, 7, 11, 12, 20 8, 10, 14 15, 16, 17, 18, 19 21, 22, 23, 24, 25, 26, 27, 28

N/A N/A 1 N/A

ROOD

1, 2, 3, 4, 6, 9, 13

N/A

FRMO TRGC DLLO WSLO

5, 7, 11, 12, 20 8, 10, 14 15, 16, 17, 18, 19 21, 22, 23, 24, 25, 26, 27, 28

N/A N/A 328.51 N/A

N/A

All

1

N/A

All

0.768

Manning’s value

Initial SCS Cn II value

Soil evaporation compensation factor

Threshold water depth for flow (mm)

Surface runoff lag time (days) Snow pack temperature lag time

(3) Same as scenario (2) above except that we assigned the baseflow parameter values to any sub-basin upstream or near to the gages for which we obtain values while using the baseflow filter procedure. This method gave the lowest correlation between observed and modeled flow with a NSE of 0.414 and an rflow value of 0.667. Given the above analysis we decided that the manual adjustments of sub-basins described in items (2) and (3) above was not sufficient for providing the most accurate simulation in comparison to observed flow. Therefore, we used the method described in item (1) for our modeling analysis using the GCM data.

Fig. 5. Projected flows from flood frequency analysis of ArcSWAT-modeled climate scenarios.

Table 6 50-Year RI flows from FFA for modeled scenariosa. Model projection

Timeframe

50-Year RI flow (cfs)

GISS_SRES B1 (low) GISS_SRES B1 (low) GISS_SRES B1 (low) GISS_SRES B1 (low) GISS_SRES B1 (low) ECHAM_SRES A2 (mid) ECHAM_SRES A2 (mid) ECHAM_SRES A2 (mid) ECHAM_SRES A2 (mid) ECHAM_SRES A2 (mid) IPSL_SRES A2 (high) IPSL_SRES A2 (high) IPSL_SRES A2 (high) IPSL_SRES A2 (high) IPSL_SRES A2 (high) Observed (USGS)

2030 2040 2050 2060 2070 2030 2040 2050 2060 2070 2030 2040 2050 2060 2070 1988–1999

23,315 18,557 18,417 17,349 16,323 18,860 19,017 18,774 18,219 17,032 32,180 29,383 31,937 35,516 34,903 27,744

a All scenarios were modeled from 2010 to 2090. Timeframes are reported as ‘‘centered’’ 50-year flow values (i.e., 2030 is the interval from 2010 to 2049, 2040 is the interval from 2020 to 2059, etc.).

ployed the Log-Pearson Type III distribution which uses statistical information about a particular point along a river to generate various recurrence interval stream discharges at that point. Using various outlet points along the streams of the Lower Tualatin basin, we were able to determine the discharge for different recurrence intervals at the watershed outlet (Fig. 5). Using these results, a HEC-RAS model of the region was built to develop the future 50year floodplain and riparian ecotones. Table 6 shows the 50-year RI flows obtained from the FFA for each of the modeled scenarios. As seen in Table 6 and following in Fig. 6, the models behaved as predicted by their respective climate scenario (i.e., the projected flows increased over time for the IPSL A2 emissions scenario, and decreased for the ECHAM A2 and GISS B1 scenarios). 2.4. Floodplain analysis

2.3. Flood frequency analysis A flood frequency analysis (FFA) was performed on the observed streamflow data and simulated streamflow generated by ArcSWAT for the various historical and future GCM time periods. We em-

Floodplain analysis was performed in two parts: First, predicted peak flows from the FFA described in Section 2.3 were input to separate HEC-RAS models (Section 2.4.1). Second, HEC-RAS model outputs were post-processed using GIS software to create floodplain coverages (Section 2.4.2).

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(channel), floodprone (flood dispersal of sediment, plants, and animals), and upland functions (slumps, slides, subsurface water and nutrient flow). Riparian ecotones, therefore, contain those elements (as described above) that interact with water during average, bankfull, and flood flow conditions. Floodprone area is the measured stream yield elevation at the 50- to 60-year recurrence interval (RI) (Verry et al., 2004). The floodprone area width is an important stream entrenchment measure. The measurement of twice the vertical distance from the stream thalweg to the bankfull elevation indicates the elevation of the floodprone area, or floodprone elevation. Entrenchment ratio is the width of the valley at the 50- to 60-year flood stage (or floodprone elevation) compared to the bankfull width of the stream. This indicates how well the stream/bank cross-section handles the energy of water/sediment/debris. It is also an indication of the vertical containment of a stream (i.e., how connected a stream is to its floodplain) (Rosgen, 2001). Fig. 6. 50-Year flood projected by three emission scenarios.

2.4.1. River flow analysis for model assessment Hydrologic Engineering Centers River Analysis System (HECRAS) version 4.0 software was used to determine the extent to which each modeled climate scenario would affect the floodprone area of the Lower Tualatin River. The HEC-RAS software is developed by the US Army Corps of Engineers (USACE) and allows for water surface profile computations using measured or interpolated geometric and hydraulic data (USACE, 2008). Zhu et al. (2007) used HEC-RAS hydraulic models to gain insight to the effect of land use and climate change on levee-protected floodplains in California. The Lower Tualatin River geometry and other related data for HEC-RAS analysis were obtained from Clean Water Services (CWS), a water resource management utility for the Tualatin watershed. The Lower Tualatin main stem and cross-section cut lines were geo-referenced to the Oregon State Plane coordinate system. Results from the FFA were input into the HEC-RAS model at the watershed outfall location and floodplains were derived. 2.4.2. Floodplain delineation To derive the floodplain and ecotone coverages we employed the GeoRAS model, a HEC-RAS model extension. Fig. 7 displays a schematic of the HEC-RAS stream geometry followed by an example of floodplain delineation. Fig. 8 compares the predicted floodplain areas from each GCM and emission scenario. 3. Riparian ecotone assessment This section will discuss the concept of riparian ecotones, ecotone assessment theory, and ecotone assessment for the Lower Tualatin watershed. 3.1. Defining riparian ecotones A riparian ecotone is a derivation of valley and stream geomorphology. A formal definition is one as follows: ‘‘Riparian ecotones are a three-dimensional space of interaction that include terrestrial and aquatic ecosystems that extend down into the groundwater, up above the canopy, outward across the floodplain, up the nearslopes that drain to the water, laterally into the terrestrial ecosystem, and along the water course at a variable width (Verry et al., 2004)’’. A functional definition for a riparian ecotone would be the width of the valley floodprone area plus 30 m (or other arbitrary distance based on region) on each side, which contains all aquatic

3.2. Riparian ecotone assessment As mentioned in Section 3.1, the riparian ecotone approach attempts to combine the terrestrial and aquatic ecosystems. A typical riparian ecotone subplot sampling system is illustrated in Fig. 9. In the study by Verry et al. (2004), resources were totaled for all subplots within a hexagonal layout defined by the Environmental Protection Agency’s (EPA’s) Environmental Monitoring and Assessment Program (EMAP). EMAP provides goals and guidance for the advancement of ecological assessment and monitoring. However, EMAP sampling systems are often arbitrary (Diaz-Ramos et al., 1996). For our study, we neglected the subplot layout system. The system was designed for various-ordered streams, however since our study was limited to the fourth-order main stem of the lower Tualatin River, it was not possible to classify vegetative features according to stream order. Therefore, we decided to classify vegetation as the amount of overlapping of vegetation with the riparian ecotone coverages for each model scenario. Fig. 10 shows a schematic of the modified ecotone sampling system used for this study. The figure illustrates several of the components used in the study: (a) The delineated stream feature is shown in dark blue. This gives a realistic representation of the flow of the stream through the stream channel. (b) The 50-year floodplain (light blue) and ecotone (light green) for the IPSL A2 emissions scenario and with RI flows centered on the 2060 timeframe are displayed. The ecotone is shown as having a larger area than the floodplain, which is representative of the ecotone area being defined as the floodplain area plus a uniform 30-m buffer distance. (c) Overlaid on the floodplain and ecotone areas are the NDVI grid cells. These cells represent where vegetation occurs according to calculations made using aerial photography. The NDVI extraction process is discussed further in Section 3.3. (d) Finally, within the NDVI cells, the extracted LiDAR vegetation features are overlaid. The LiDAR features are classified according to various height ranges. These features, along with the corresponding NDVI cells, give a general indication of where vegetation within the ecotone exists, and approximate height of the vegetative features. 3.3. Normalized difference vegetation index assessment The NDVI (Sala et al., 2000) is a numeric indicator that is used to assess whether the spectral signature of multi-spectral imagery for


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Fig. 7. (a) Lower Tualatin River HEC-RAS geometry schematic and (b) an example of projected floodplain delineation using HEC-RAS output and GeoRAS processing for the IPSL A2 and 2060-centered timeframe.

observed area contains vegetation. The NDVI is calculated as follows in Eq. (1), where NIR is the spectral reflectance measurement acquired in the near-infrared region and RED is the spectral reflectance measurement acquired in the red region. The spectral reflectances are ratios of the reflected and incoming radiation in each

spectral band, with values between 0.0 and 1.0. The NDVI varies between ±1.0.

NDVI ¼

ðNIR REDÞ ðNIR þ REDÞ

ð1Þ

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Observed Low Emissions Middle Emissions High Emissions

400

350

331

Predicted Floodplain Area (acres)

328

338

331

326

337

307

300

289

288 290

287 289

284 286

278 282

2040

2050

2060

2070

250

200

150

100

50

0 2030

Centered Time Period Fig. 8. Comparison of predicted floodplain areas.

Fig. 9. Schematic rectangular plot layout for humid environments.

The NDVI of an area containing a dense vegetation canopy will tend to positive value (e.g., 0.3–0.8). For this study, aerial photography was obtained from the USGS Earth Resources Observation and Science Center (EROS) Landsat 7 photo archives (http://eros.usgs.gov/). The imagery was provided at a 30 30 m resolution. Landsat 7 imagery contains the separate NIR and RED spectral band coverages that were used to calculate the NDVI coverage for the study area. Once the NDVI calculation was made on the imagery for the study area, a 30 m2 grid cell coverage was created for the Lower Tualatin study area.

The United States Forest Service (USFS) has recently used LiDAR to develop canopy height models and is considered a useful sampling tool in the USFS forest inventory analysis (FIA) program (Andersen, 2009). LiDAR data obtained from the Puget Sound LiDAR Consortium (http://pugetsoundlidar.ess.washington.edu/) was used in this study. The LiDAR data was provided at a 6 6 foot resolution. LiDAR is currently being used in many hydrologic applications, including studying linkages between channel morphology and riparian ecology (Snyder, 2009). The purpose of our study was to determine not only how much vegetative area was inside our modeled floodplains, but also to begin to look at other vegetative features, such as tree sizes (height, width, etc.) and tree types. This enables us to display the features within each of the modeled floodplains as a way of keeping inventory of the vegetation over time. With the acquired data, we can establish a base scenario and as time passes and more LiDAR data is collected, existing inventories will be updated and one can see what changes in vegetation occurred and how these changes line up with the floodplains that were predicted for future timeframes over different climate scenarios. A ‘‘first-returns’’ LiDAR coverage was used with a ‘‘bare earth’’ LiDAR coverage – first return data will show features such as tree canopy, buildings, and the bare earth model shows the ground surface – to create a LiDAR ‘‘feature height’’ model. Fig. 11 shows the LiDAR feature height coverage of the study area.

3.5. Ecotone assessment for lower tualatin watershed 3.4. LiDAR assessment Light detection and ranging (LiDAR) is an integrated system that uses lasers, global positioning systems (GPS) and inertial navigation systems (INS) to acquire accurate digital elevation models of the earth’s surface (Ambercore, 2008). LiDAR was originally developed for military applications because of its advantages over less stable technologies that had less ability to detect weaker signals such as smaller objects or gradient changes (Harney, 1982). It is currently being used in myriad applications, including hydrology.

As mentioned in Section 3.2, the ecotone assessment was performed by overlapping vegetative features with delineated ecotone areas for each climate scenario model. Vegetation area was defined as the intersecting NDVI and LiDAR coverages (see Sections 3.3 and 3.4, respectively) within each ecotone delineation. Fig. 12a and b shows the projected vegetated ecotone areas and volumes, respectively, that would be impacted by each modeled climate scenario. As expected, the amount of resources within each ecotone boundary followed the pattern of the GCM emissions pathway


Fig. 10. Example ecotone sampling system (IPSL A2 2060-centered timeframe modeled ecotone shown).

Fig. 11. Lower Tualatin LiDAR feature height coverage.

275

276


(a)

Low Emissions Middle Emissions High Emissions

Predicted Effected Ecotone Vegetated Area (acres)

180

157

160

160

157

154

159

145 136

140

135

137

135

136

133

136

133

130

120

100

80

60

40

20

0 2030

2040

2050

2060

2070

Centered Time Period

Predicted Effected Ecotone Vegetated Volume (acre-feet)

(b)

Low Emissions Middle Emissions High Emissions

4500 4060

4135

4054

3994

4120

4000 3729 3491

3475 3503

2030

2040

3500

3468 3487

3427 3450

3358 3406

2050

2060

2070

3000

2500

2000

1500

1000

500

0

Centered Time Period Fig. 12. Comparison of affected vegetated ecotone areas (a) and volumes (b) under climate change scenario.

(i.e., more resources fall within the floodplains/ecotones for models with higher predicted emissions). This is a clear indication that, depending on the GCM used, the amount of resources that may be impacted by global climate change may be significant—especially for the IPSL A2 scenario. The potential future impacts are relevant to the current vegetation that exists in the study area. As seen previously in Table 2, all of the expected natural vegetation defined by each ecoregion has a life expectancy greater than the time span of the study. This indicates that most or all of the trees that are currently within the

study area will likely be present and would be impacted should the predicted changes for the modeled ecotone boundaries occur.

4. Discussion and recommendations Uncertainty in our study resulted from GCM data, ArcSWAT modeling, the HEC-RAS hydraulic modeling, and the difference of vegetative resolutions of NDVI and LiDAR coverages. Kay et al. (2009) concluded that the uncertainty from GCM structure was


by far their largest source of uncertainty when assessing the impact of climate change on flood frequency. Our own observation was that climate data provided by the GCMs had poor correlation compared to the observed precipitation for gages within the watershed. Correlation of modeled and observed streamflows, however, was acceptable after we calibrated the hydrologic model. Acquisition of additional streamflow from tributaries to the Tualatin River and other hydrologic data may be useful for future calibrations. The inclusion of other watershed features in the hydrologic model, such as lakes, ponds, and wetlands, would better represent streamflow and would improve the overall rainfall response of the watershed in the model. Additional stream survey data or LiDAR-derived cross-sections and stream profiles, and elevations would provide for a more accurate hydraulic representation for the HEC-RAS hydraulic model and hence, more accurately delineate floodplains. We recommend that next steps be taken to improve the accuracy and ability to predict climate change impacts on riparian ecotones: Consistently scheduled LiDAR acquisition – a more consistent LiDAR acquisition schedule will allow for a more thorough assessment of how features within the riparian ecotone are changing. The same feature extraction process can be used and compared to the base condition, along with measured changes in climatic variables over time. Continuous extraction of vegetative indices (e.g., NDVI) – similar to LiDAR extraction, the vegetative indices can be calculated and compared to the base conditions and changes in observed climatic variables. Evaluate changes due to land use vs. climate change – changes in land use must be recorded, since many hydrologic models (such as the ArcSWAT model used in the project) consider land use and a parameter for surface runoff. Land use changes could potentially impact modeled stream flows to a greater degree than altered climatic variables. Extend study beyond Lower Tualatin River – this will allow for more comprehensive ecotone assessment using established standards (our study had to focus on the main stem of Lower Tualatin only); this would potentially require survey data to be obtained. Another possibility may be to use available LiDAR coverages for use in developing stream cross-sections for zerothird ordered streams. Many organizations could benefit from the knowledge of how climate change may affect riparian ecotones. These include land management entities and US Forest Service, private land owners, insurance companies, and ecological restoration organizations. Understanding where and how floodplains may change, and the potential consequences that may be a result of those changes, could provide such entities the ability to plan ahead for future conditions, such as preparing adaptive management strategies that would implement certain actions triggered by observed changes to the riparian ecotone. A coordinated effort by decision makers would ensure that valuable properties and resources are protected and that the integrity of the ecological system is maintained.

5. Summary and conclusions This study found that the use of GCM data with ArcSWAT and floodplain analysis enabled us to predict future floodplain and riparian ecotone areas based on various climate scenarios. Bias correction of GCM weather data was necessary, especially for correcting the correlation between observed and predicted rainfall. A thorough sensitivity analysis was performed using available stream gage data from five stream gages in the TRB. Flows were

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