Climate, river network, and vegetation cover relationships across a climate gradient and their potential for predicting effects of decadal-scale climate change

Journal of Hydrology 488 (2013) 101–109

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Climate, river network, and vegetation cover relationships across a climate gradient and their potential for predicting effects of decadal-scale climate change Virginia B. Smith ⇑, Cédric H. David, M. Bayani Cardenas, Zong-Liang Yang Department of Geological Sciences, The University of Texas at Austin, Austin, TX 78712, USA

a r t i c l e

i n f o

Article history: Received 24 April 2012 Received in revised form 26 November 2012 Accepted 23 February 2013 Available online 13 March 2013 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of David J. Gochis, Associate Editor Keywords: River network Drainage density Climate Runoff Vegetation

s u m m a r y We determined present-day (1981–2000) relationships between river network drainage density (Dd) and runoff (R), and between vegetation cover (V) and precipitation (P) across a contiguous 470,800 km2 area (the Texas Gulf Coast basin), where P varies from 438 to 1280 mm/yr. Dd(R) follows a saturation–growth model which is similar to process-based equilibrium landscape models. V(P) follows a linear relationship. The models for Dd(R) and V(P) were used to assess how Dd and V might respond to decadal-scale climate changes in R and P anomalies predicted by a regional climate model between 2041–2060 and 1981–2000. The regional climate model, CRCM, was forced following the SRES A2 emissions scenario. Our calculations indicate a tendency of 57,500 km of active river channels to drying up, representing 9.9% of the presentday total of 581,985 km, due to future decrease in R. This will be accompanied by a loss of 8150 km2 in V due to decrease in P. This study extends empirical studies of relationships between climate and landscape properties and explicitly links observations with process-based models. The results provide a simple framework for modeling potential trajectories of the landscape due to climate change. Published by Elsevier B.V.

1. Introduction Fluvial landscape morphology and vegetation cover are functions of multiple variables. In particular, they have been shown to be very sensitive to climate. Any change in climate leads to disequilibrium, forcing the landscape and vegetation to adjust. The influence of climatic factors on river networks and vegetation continues to pose many questions to scientists, engineers and environmental managers, particularly in the face of climate change. Expected shifts in climate make this a critical environmental, ecological, and even socio-economic issue, necessitating quantification or constraining potential responses. Here, we show an analysis of river networks and vegetation density across a climate gradient. Identifying these correlations allowed us establish parsimonious quantitative relationships between river network density and vegetation and hydro-climatic parameters through regression. Incorporating these relationships with results from a regional climate model enabled us to infer the geomorphic trajectory of river networks and vegetation response. River networks are the outcome of the interplay between myriad factors such as: climate (Abrahams and Ponczynski, 1985; Langbein and Schumm, 1958; Rinaldo et al., 1995), relief (Oguchi,

⇑ Corresponding author. Tel.: +1 512 537 5447. E-mail address: [email protected] (V.B. Smith). 0022-1694/$ - see front matter Published by Elsevier B.V. http://dx.doi.org/10.1016/j.jhydrol.2013.02.050

1997), vegetation (Dietrich and Perron, 2006), tectonics (Perez-Pena et al., 2010), and hillslope processes (Kirkby, 1971; Montgomery and Dietrich, 1992; Perron et al., 2009). Advances in process-based landscape modeling have allowed for scrutiny of the effects of these factors on drainage basin evolution (Collins and Bras, 2010; Tucker and Hancock, 2010; Wobus et al., 2010). However, parameters in these models are typically based on observations at the hillslope scale and the results of the models are seldom compared or calibrated to regional field observations. Due to computational and database limitations, most models are based on spatially uniform forcing conditions and typically ignore climate spatial variability. To our knowledge, no regional-scale processbased landscape evolution model has been successfully calibrated to observations, let alone used to predict how a real landscape’s evolution might be influenced by perturbation. At the base of our analysis were drainage density (Dd) which we related to runoff (R), and proportion of vegetation cover (V) which we related to precipitation (P). Dd, the total length of channels per watershed area, is a key metric for describing river networks and landscapes. The amount of water available for shaping landscapes has often been related to effective precipitation, calculated as the difference between P and evapotranspiration (E), which is roughly R (Melton, 1958). The effective precipitation or the R represents the water available for reshaping the landscape. Over the long term and at steady-state conditions where there are no watershed-scale changes in surface and subsurface storage,

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Texas

1205 1208

1206

1203 1201 1202

1207

1209

1204 1210 0

250

500 Km

1211 NHD High Resolution Flowline

Fig. 1. The river network for the study region, the Texas Gulf basins (USA), and the individual sub-watersheds. The data used to create the image of these drainage networks was obtained from the high resolution NHD.

the use of R (or P  E) should lead to comparable results. In this study, we focused on multi-decadal averages of yearly-accumulated P and R, and on multi-decadal averages of V to support our analysis. In our study region, a systematic variation in P from east to west corresponds with variation in mean annual evapotranspiration E, and correlates with Dd and V. A more robust test for climate-landscape coupling is to quantify their relationship across a climate gradient for a large but contiguous area with relatively homogeneous lithology, vegetation and uplift history. Here we examined the Dd(R) and V(P) relationship across most of Texas and some parts of New Mexico and Louisiana (USA) for eleven neighboring watersheds comprising the Texas Gulf Coast drainage basin (Fig. 1) from climate data averaged over 1981– 2000 (Figs. 1 and 2). This expansive area, spanning 470,800 km2, consists of relatively consistent lithology, vegetation and uplift (Sylvia and Galloway, 2006). The individual watersheds have areas ranging from 20,425 to 73,518 km2, and run from the dry lands in the west to swampier lands in the east, with all draining into the Gulf of Mexico. The study region is considered a climate change hotspot (Diffenbaugh et al., 2008) and is predicted to get drier in the future (Milly et al., 2005, 2008). 2. Background and previous work There have been many previous investigations on the empirical relationship between Dd and climate variables. The concept of drainage density was introduced by Horton (1945). Following its introduction, researchers have sought relationships between Dd and environmental variables. Pioneering studies showed that drainage density tends to show positive correlation with effective precipitation (Melton, 1958; Abrahams, 1984). Moreover, many studies have since shown that the relationship is not linear (Melton, 1958; Moglen et al., 1998). The dependence of Dd on effective precipitation increases steadily to a point and then begins to decrease (Bandara, 1974). However, climate is not the sole factor controlling Dd. The characteristics of the basin, such as geology, and variations in sediment transport mechanism are also important (Rodriguez-Iturbe and Escobar, 1982; Howard, 1994).

Several studies suggest that in hyper-arid to arid environments Dd increases with effective precipitation, but Dd decreases with effective precipitation in semi-arid climates, and levels off or increases slightly with effective precipitation in more humid climates (Abrahams, 1984; Kirkby, 1971; Melton, 1958; Moglen et al., 1998). However, most previous empirical studies were based on independent sub-watersheds from geographically separate areas integrated onto one graph (Abrahams, 1984; Abrahams and Ponczynski, 1985; Langbein and Schumm, 1958). In pursuit of providing a mechanistic basis for empirical observations, Moglen et al. (1998) developed a process-based equilibrium model for Dd:

Dd ¼ c

"

aRa c 1 þ bR

1mn1m

1 2 n

D

1mn1 m

1 2 n

1 1m1 m2 n

b1

m1

ðnRÞ1m1 m2 n

#g

ð1Þ

which is fundamentally based on integration of diffusive sediment transport in hillslopes (indicated by D), advective channelized sediment transport (indicated by b1), and the sediment yield-climate relationship (indicated by nR and the saturation–growth equation in the first parentheses). The transport processes represented in the equation above are parameterized with several power-law scaling relationships. If R is expressed in mm and Dd in km1, the coefficients for the equation above are typically a = 2.58  105, b = 3.14  105, a = 2.3, c = 3.33, n = 1–3, m1 = 0–3, m2 = 0.6–0.8 and g = 0.45–0.57 (Moglen et al., 1998). If all terms independent of R are compiled into a single coefficient A, the model simplifies to:

" Dd ¼ A

aRa c 1 þ bR

1mn1m

1 2 n

R

m1 1m1 m2 n

#g ð2Þ

The model above shows the strong dependence of Dd on and its non-linear relationship with climate; climate is represented by R, again noting that R = P  E. Most natural regional landscapes will respond in a rate slower than that of climatic change, and are influenced by more factors than are incorporated into the model. Previous studies have illustrated linkages between Dd and tectonic processes (Schumm, 1993), vegetation (Collins and Bras, 2010; Istanbulluoglu and Bras, 2005), and hillslope and landscape processes (Talling and Sowter, 1999; Tucker and Bras, 1998).

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Fig. 2. NARR data and predicted 21st century changes based on CRCM model output. All maps show the outline of Texas (USA) and the individual watersheds studied. (a) mean annual precipitation (P) averaged over 1981–2000 using NARR data; (b) % change in P between 2041–2060 and 1981–2000 calculated from CRCM climate model results; (c) mean annual evapotranspiration (E) from 1981 to 2000 NARR data; (d) % change in E between 2041–2060 and 1981–2000 from CRCM climate model results; (e) mean annual runoff (R) from 1981 to 2000 NARR data; (f) % change R between 2041–2060 and 1981–2000 calculated from CRCM results; (g) current drainage density (Dd in km1) for the watersheds based on NHD high resolution; (h) % change in Dd calculated using Eq. (2) with the input R accounting for % change in (f); (k) % annual vegetation cover (V) from 1981 to 2000 using NARR data; (i) % change in V calculated using Eq. (5) with the input P accounting for % change in (b). Large decreases are in red, large increases in blue, and smaller changes in black font.

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Due to constant landscape adjustments and disequilibrium, it can be difficult to establish relationships between drivers and observations for natural landscapes. In spite of this, several studies have found distinct geologic evidence linking climate change and stream network adjustments, providing support for these models (Blum et al., 1995; Hall, 1990; Hancock and Anderson, 2002; Molnar et al., 2006; Sylvia and Galloway, 2006). There is also evidence linking vegetation and climate. Studies have shown that a decrease in precipitation correlates to a decrease in aboveground biomass productivity, particularly in regions where the soil moisture is limiting to vegetation growth (Callaway and DeLucia, 1994). While V is influenced by other variables, such as temperature, this linear relationship has been shown in several other studies in various environments (Paruelo and Lauenroth, 1995,1998; Gunnula et al., 2011). In fact, the coupling between vegetation and climate is so strong that it has been utilized by scientists to assess climate change (Dunne et al., 2004). Further, by tracking changes in biomass, such as tree growth (Goldblum and Rigg, 2005) and leaf phenology (Kramer et al., 2000), and their connection to changes in the local precipitation trends, the vegetation response can be used to identify changes in climate patterns. While vegetation can be impacted by seasonal changes in precipitation or shifts in annual precipitation (Miranda et al., 2011), in this investigation we only considered the long-term effects of a change in annual precipitation on vegetation. The Texas Gulf basin climate is arid in the west and humid-subtropical in the east (Larkin and Bomar, 1983). The annual precipitation patterns in the west tend to be more sporadic, while eastern trends show a strong seasonal signal (Schwinning and Sala, 2004). Additionally, several studies have shown that regardless of the type of precipitation decrease, whether it is a general annual reduction, a seasonal reduction, or a combination of annual reduction and seasonal reduction, the biomass always decreases (Miranda et al., 2011). However, some changes in vegetation have a delayed response to climate, particularly in arid and semi-arid environments (Schwinning and Sala, 2004). Many woody plants take several years to show a change associated with a decrease in precipitation. Therefore, long-term datasets are required to measure ecosystem responses to climate that are not identifiable on short time scales (Nippert et al., 2006; Rastetter et al., 2003). One such data set is a compilation of the Normalized Difference Vegetation Index (NDVI) which are now used as the standard for assessing systemwide temporal changes in vegetation cover (Paruelo and Lauenroth, 1998; Yang et al., 2011).

inevitable (USGS and EPA, 2007). At the time this study was completed, the NHD high resolution dataset is superior to any other database of the same scale that is in existence for the United States or could be reasonably created given the current available resources. Other studies calculate the flowlines directly based on a digital elevation model (DEM). The expanse of our study area made this infeasible. In fact, it might be more difficult to create flowlines that are more accurate than that provided by the NHD since there is some level of ground-truthing involved with NHD (Tarboton, 2003). This was critical for this study, as there are always issues on how a flowline is initiated during terrain processing, i.e., how the headwaters are defined (Montgomery and Dietrich, 1992; Tarboton, 2003). This is a particularly difficult issue for our study area since some channels in its arid portion may be ephemeral. Moreover, the best DEM available for the scale of the study area is the NED. Delineating the flowlines using this DEM yielded lower quality drainage networks. Again, we chose to use the NHD data instead of recalculating the flowlines from terrain processing, working under the assumption that mapped rivers are active rivers, and that mapped rivers better represent the current river networks and their drainage density than could be obtained through terrain processing. The total length of channels and watershed area were determined using NHD high resolution data (Fig. 1) with calculations for Dd conducted in ArcGIS (Fig. 2g). The flowline vector data and metadata and the watershed polygon features were used to calculate these values. These tasks were accomplished through the use of ArcMap tools. 3.2. Determination of vegetation The V used in this analysis was estimated using the high resolution data from the North American Regional Reanalysis (NARR, http://www.emc.ncep.noaa.gov/mmb/rreanl/) (Mesinger et al., 2006). These datasets, which are hosted by the National Centers for Environmental Prediction (NCEP), are provided on a 32-km grid. NARR V is calculated from satellite observations of the Normalized Difference Vegetation Index (NDVI). The NDVI is a measure of greenness within a grid. Due to the linear relationship between the NDVI and vegetation fraction we interpreted this to represent the vegetation cover (Gutman and Ignatov, 1998). At this resolution, several regularly distributed points are located within each watershed (each watershed contains 18–76 grids). Annual values were averaged to create a long term description of vegetation (Table 2 and Fig. 2i).

3. Datasets and methods

3.3. Determination of runoff and precipitation

3.1. Determination of drainage density

The analysis of modern patterns used high resolution climate data from the NARR (NARR, http://www.emc.ncep.noaa.gov/ mmb/rreanl/) (Mesinger et al., 2006) and was based on monthly data from 1981 to 2000 taken from the three-hourly NARR data. Annual values were averaged together to create a long term description of the climate (Table 2). Note that although averages were computed for E as well, E values were not used for our regression analysis. In lieu of calculating R as the difference P  E, we used the modeled R values. In NARR, the P values are gridded gauge observations; while the E and R values represent land surface model output. Although there is a degree of error in the NARR E and R data as compared to observations, NARR is one of the most accurate comprehensive dataset at this scale. Like the vegetation data, several regularly distributed points are located within each watershed (each watershed contains 18–76 grids) (Fig. 2a, c, e). Other analyses have used gage data to estimate these variables. R data can be calculated using USGS gage data or gage data from the National Climate Data Center (NCDC). However, NARR provides the two variables necessary for this analysis within the same

The analysis for the modern patterns in Dd used data from the National Hydrography Dataset (NHD, http://nhd.usgs.gov). The NHD is a database of the hydrologic regions and flowlines of the United States based on three resolutions: high at 1:24,000 scale, medium at 1:100,000 scale, and low at 1:500,000 scale. The NHD was created by the US Environmental Protection Agency, the United States Geological Survey (USGS), and various state agencies. In 2006, the NHDPlus was created using the NHD medium resolution database, the National Elevation Dataset (NED) and the Watershed Boundary Dataset (WBD). Both the NHD high resolution and the NHDPlus (Version 1) were initially considered for this project. However, the medium resolution flowlines of the NHDPlus were unable to adequately resolve the drainage network as well as the high resolution NHD (Table 1). The rivers of the NHD high resolution, we assume, are active rivers or perennially wet channels. Due to the dynamic and inconsistent nature of streams and rivers between datasets, some discrepancy in defining the flowlines is

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V.B. Smith et al. / Journal of Hydrology 488 (2013) 101–109 Table 1 Area (A), total river network length (L), and drainage density (Dd) for four-digit hydrologic unit code watersheds (HUC4). HUC4 watershed

NHD high resolution 2

Sabine Neches Trinity San Jacinto U. Brazos M. Brazos L. Brazos U. Colorado L. Colorado Guadalupe Nueces

1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211

NHDPlus medium resolution

A (km )

L (km)

Dd (km

25511.2 25780.2 46572.1 20424.7 37772.0 40296.8 40160.7 41426.4 72953.9 46384.2 73517.71

45922.7 44531.3 76217.9 29112.8 17542.6 61600.7 60063.7 16841.1 92614.6 60875.0 76661.7

1.80 1.73 1.64 1.43 0.46 1.53 1.50 0.41 1.27 1.31 1.04

1

)

A (km2)

L (km)

Dd (km1)

25659.7 26017.1 46386.8 18881.1 37545.5 40466.0 40248.6 41538.4 73044.1 44026.9 70321.6

14427.0 15432.5 27,011 12838.1 6450.4 20239.1 22601.7 7813.3 36174.2 26052.2 30567.7

0.56 0.59 0.58 0.68 0.17 0.50 0.56 0.19 0.50 0.59 0.43

Table 2 Modern annual precipitation (P), evapotranspiration (E), runoff (R), and vegetation cover (V) from NARR (averaged over the period 1981–2000). HUC4 watershed Sabine Neches Trinity San Jacinto U. Brazos M. Brazos L. Brazos U. Colorado L. Colorado Guadalupe Nueces Region 12

1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211

P (mm)

E (mm)

R (mm)

V (%)

1280.1 1247.2 1011.0 1190.2 481.1 673.9 779.9 437.8 608.2 731.1 551.7 708.2

1118.7 1105.4 895.9 1044.3 481.1 673.9 779.9 437.8 608.2 731.1 551.6 708.2

113.4 86.0 68.5 116.8 13.5 32.9 57.4 10.7 41.8 62.3 33.1 50.1

61.9 64.7 48.3 57.9 36.5 37.5 43.6 31.1 37.9 42.3 31.9 44.1

a

dataset. Within the NARR dataset, the hydrologic budget (P  E = R) is generally consistent. Finally, the format and resolution of the NARR products are comparable to the climate model outputs which are used here for prediction. A similar framework between datasets enables quantitative comparisons between the datasets. To better understand the NARR R we compared them with R using the USGS National Water Information System (NWIS), although the NWIS data were not used for our regression analysis (Fig. 3a). The comparison of NWIS R and NARR R provided some insight regarding the NARR dataset. We found that the R differences between the USGS NWIS dataset and the NARR datasets are substantial (Fig. 4). The USGS R values are much higher than that of the NARR R values.

b

3.4. Determination of the Dd (R) and V(P) relationships There are theoretical Dd (R) relationships (Eqs. (1) and (2)). By reducing the runoff variables (Langbein and Schumm, 1958; Moglen et al., 1998), we hypothesize that the more parsimonious relationship will show a reasonable fit with the data from our study region:

Dd ¼

aR bþR

ð3Þ

where a represents erosivity and b represents erodibility. This saturation growth model is of similar form as the sediment-yield curve proposed previously and is also similar to the parenthetical term in (1) and (2). This model was chosen since it can be interpreted as purely a statistical or empirical correlation, despite the obvious physical basis. Eq. (3) was fitted to the NARR R data averaged for each watershed using the Matlab curve-fitting toolbox (Fig. 3a). We hypothesize the following relationship between vegetation and precipitation holds for the study region:

V ¼ cP

ð4Þ

Fig. 3. Observations of drainage density (Dd), annual runoff (R), vegetation cover (V), and annual precipitation (P), and results of regression. (a) Non-linear dependence of Dd on R and fitted curves for Eq. (3) (R2 = 0.82); (b) linear dependence of V on P and results of linear regression with Eq. (4) (R2 = 0.84). The fit in (b) is based on the averaged watershed values.

Based on this equation, we expect that vegetation will increase linearly as precipitation increases and vice versa. Eq. (4) is fitted to the NARR V and P data averaged for each watershed also using the Matlab curve-fitting toolbox3.5. Determination of decadal-scale changes in runoff and precipitation Predicted changes in P, E and R, were calculated using model outputs from the North American Regional Climate Change Assessment Program (NARCCAP, http://www.narccap.ucar.edu/). The output data were obtained from the Canadian Regional Climate Model (CRCM) ran with drivers from the Third Generation Coupled Global Climate Model (CGCM3) and forced with the Intergovernmental

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between the average annual CRCM simulation result for the period 2041–2060 and for the period 1981–2000, and not using the difference between modeled 21st-century and observed present-day climate (i.e., data from NARR, 1981–2000). Note that for present-day CRCM values we used output from a CRCM run driven by the CGCM3 and not by NCEP-2 reanalysis; we were comparing output from the same model driven by the same global climate model (CGCM3) but with representative emissions scenario. The 2041– 2060 values for P and R from the CRCM data for each watershed were then calculated by adding the predicted % changes to the present-day NARR values (Table 4). This approach is effective to minimize the systematic mean biases in both CGCM current and future runs and in CRCM downscaling simulations, a method that has been commonly employed in climate modeling, although a more accurate and computationally expensive approach was described by Xu and Yang (2012). Fig. 4. Comparison of NARR and NWIS annual runoff (R) observations for 1981– 2000.

4. Results and discussion 4.1. Modern patterns and relationships

Table 3 Percent difference in precipitation (P), evapotranspiration (E), and runoff (R) taken from NARR data and from CRCM simulation driven with NCEP-2 reanalysis data. The difference is taken as (NARR-CRCM)/NARR. HUC4 Watershed Sabine Neches Trinity San Jacinto U. Brazos M. Brazos L. Brazos U. Colorado L. Colorado Guadalupe Nueces Region 12

1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211

% Diff. P

% Diff. E

% Diff. R

18.3 17.9 12.5 3.3 18.3 5.1 2.4 30.8 11.5 16.4 55.6 7.5

15.7 15.4 6.5 1.8 20.5 1.2 8.1 32.1 18.6 21.7 62.6 12.6

12.8 1.5 36.8 40.2 99.8 76.6 11.8 86.3 28.6 28.0 25.4 11.0

Panel on Climate Change Special Report on Emissions Scenarios (SRESs) A2 emissions scenario for the 21st century. These data are provided on a three-hourly basis and a 50-km grid. At this resolution, several regularly distributed points are located within each watershed (each watershed contains 10–44 grids). Temporal averages were computed for each grid cell and all time steps between 01-01-1981 and 12-31-2000, and 01-01-2041 and 12-31-2060 using a FORTRAN program. Spatial averages were computed using ArcGIS (Table 3). We also evaluated how well CRCM replicates present-day conditions. The relative changes in P, E, and R between the future and present-day conditions were calculated using the difference

NARR precipitation values vary from 1280 mm/yr (Sabine River watershed, the easternmost) to 437.8 mm/yr (Upper Colorado River watershed, the westernmost) with an average of 708.2 mm/yr for the entire region (Fig. 2a and Table 2). Evapotranspiration and runoff generally follow similar patterns (Fig. 2c and e and Table 2). Vegetation cover varies from 64.7% (Neches River watershed, in the eastern part) to 31.1% (Upper Colorado River watershed), as shown in Fig. 2i and Table 2. From west to east, Dd more than quadruples from 0.41 km1 to 1.80 km1 and varies systematically with P (Fig. 2g). If R is expressed in mm and Dd in km1, the regressed coefficients for Eq. (3) are a = 2.188 and b = 30.7 (Fig. 3a). With 95% confidence intervals, the ranges for the coefficients are 1.513–2.862 and 3.51–57.8, respectively. The R2 for regression is 0.823. Eq. (2) was also fitted to the observations while constraining parameters to their suggested range, i.e., those in Moglen et al. (1998) which are also presented in Section 2. The fit for (2) is just as good, but the fit for (3) is slightly worse simply because of less degrees-of-freedom. The results for fitting (3) are not shown here and analyzed further since we are interested in a simpler model with minimal assumptions and there are too many parameters in (3) relative to the number of data points. Nevertheless, our results are consistent with theoretical expectations. As mentioned above, although Dd is driven by R, there are a host of variables impacting Dd aside from R. It should be noted that in this study, Dd is calculated based on the density of blue lines (active streams), not necessarily channel formations. Additionally, landscapes adjust at various rates, often at much slower rates than

Table 4 Percent change in precipitation (P), evapotranspiration (E), and runoff (R) between future conditions and present conditions from CRCM runs; and future conditions calculated values by adding the change to values from NARR for four-digit hydrologic unit code watersheds (HUC4). HUC4 watershed Sabine Neches Trinity San Jacinto U. Brazos M. Brazos L. Brazos U. Colorado L. Colorado Guadalupe Nueces Region 12

1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211

% P change

P (mm)

% E change

E (mm)

% R change

R (mm)

2.3 2.0 0.8 0.8 5.6 0.2 1.3 2.1 2.1 1.7 7.7 1.5

1298.2 1281.9 985.5 1198.2 525.7 735.8 875.6 471.1 673.2 833.1 573.1 782.2

2.1 3.0 3.2 0.8 4.7 0.9 3.5 1.7 2.2 1.2 3.5 1.9

1094.7 1072.0 867.5 1035.5 503.9 668.0 752.9 445.2 594.7 722.1 532.0 694.8

31.5 45.1 33.9 2.3 98.6 11.5 39.1 87.9 5.1 6.5 56.3 2.2

149.2 124.8 91.7 114.1 0.2 36.7 79.9 1.3 39.6 58.2 14.5 49.0

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V.B. Smith et al. / Journal of Hydrology 488 (2013) 101–109

Table 5 Modern and potential future drainage density (Dd in km1) and associated changes, relative to present-day, in active river length (DL); present-day (from NARR) and predicted % vegetation cover (V) and corresponding change in vegetated area (DA). HUC4 watershed Sabine Neches Trinity San Jacinto U. Brazos M. Brazos L. Brazos U. Colorado L. Colorado Guadalupe Nueces Region 12

1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211

Area (km2)

Present Dd

Predicted Dd

DL (km)

Present V

Predicted V

DA (km2)

25,511 25,780 46,572 20,425 37,772 40,297 40,161 41,426 72,954 46,384 73,518 470800

1.80 1.73 1.64 1.43 0.46 1.53 1.50 0.41 1.27 1.31 1.04 1.24

1.77 1.71 1.60 1.68 0.00 1.27 1.56 0.04 1.30 1.45 0.66 1.11

848 566 1589 5103 17,412 10,439 2478 15,345 2562 6454 27,892 57,493

61.9 64.7 48.3 57.9 36.5 37.5 43.6 31.1 37.9 42.3 31.9 44.1

66.1 65.2 50.2 61.0 26.8 37.5 44.6 24.0 34.3 42.4 29.2 39.8

1078 141 867 624 3672 40 394 2941 2632 39 2012 8156

climate change. As a result, the physical landscape may not reflect the current climate (e.g., dry channel beds), but on the other hand active streams do reflect the current climate. Fitting (4) to the watershed-aggregated V(P) observations lead to c = 0.051 (R2 = 0.85); with a range for c of 0.047–0.054 at the 95% confidence interval. Almost exactly the same equation results if V and P for all grids (500) are used (Fig. 3b). Most of the points are a close fit to the linear relationship. The outliers represent drier regions; the V(P) relationships are skewed for these regions due to an unnaturally high vegetation density due to crop irrigation. Therefore, there is a strong linear relationship between V and P as can be visually observed in Fig. 2a and i.

4.2. Projections for the future The CRCM simulates modern climate well and has been vetted as a good model for North America especially for downscaled precipitation (de Elia et al., 2008; Mearns et al., 2012). Our comparison between present-day P, E, and R from NARR (1981–2000) and the output from the CRCM for present-day conditions (i.e., driven by NCEP-2 reanalysis data for 1981–2000) yielded average mean bias difference of 7.5%, 12.6%, and 11.0%, respectively (Table 3). However, the average mean bias difference from a per watershed basis may be higher. While an ensemble analysis is preferred for climate projections, the CRCM model is currently the one amongst several NARCCAP simulations that is closest to NARR when driven with NCEP-2 reanalysis (not shown), and the CRCM projected decrease of future precipitation is consistent with that from statistically downscaled ensembles of global climate models (Jiang and Yang, 2012). A comparative analysis of P, E, and R was a critical step for validating a model, and our analysis shows that the CRCM does reasonably well for our study region at the relevant scale. Furthermore, a recent NARCCAP analysis compared the CRCM model to five other RCMs and observations, which showed that the CRCM is the best fitting model, particularly for our study area (Mearns et al., 2012). The comparison between future and present-day runs for CRCM led to projections of 1.5%, 1.9%, and 2.2% decreases in P, E, and R, respectively, between the future (averaged over the period 2041–2060) and the present (averaged over the period 1981–2000) averaged over the region (Fig. 2b, d, and f and Table 3). Note that percent change was calculated as (future-present)/present. Most of the region was projected to be receiving less P with generally larger reductions towards the south and west, a result consistent with that reported in Jiang and Yang (2012). Reductions in R tend be much larger since reductions in P are larger than reductions in E (their difference is generally R, although in this case we took model output and simply did subtraction of P  E). The westernmost sub-watersheds in the region (Upper Brazos and

Upper Colorado) were projected to lose most of their runoff (Fig. 2f and Table 3). The regional decrease in runoff is consistent with previous ensemble modeling studies using the more conservative SRES A1B scenario which predict a 6.2% reduction in R in the region, which includes the Texas Gulf Region, between 2041– 2060 and 1900–1970 (Milly et al., 2005; Milly et al., 2008). The predicted 2041–2060 R and P values were then used to calculate 2041–2060 Dd and V using Eqs. (3) and (4). The projected R reduction would cause a decrease in Dd and loss of active river channels (Fig. 2h and Table 5). However, some watersheds might experience an increase in R while other watersheds experience a drastic reduction. The channels in watersheds which become drier would simply dry up, a potentially rapid response. For example, our models suggest that the Upper Brazos River would tend towards losing all its active channels. At present, this area is very flat and only has relatively few ephemeral rivers. Many of these ephemerally wet rivers would become perennially dry. It is important to note that we are not suggesting that the Table 3 channels will physically go away in this time period. Channel filling and other landscape adjustments would require a much longer time scale. We suggest that the total length of active streams will decrease. It is difficult to hypothesize the outcome of a wetter climate in watersheds experiencing wetter conditions, mainly in the northeastern section of the study area. For this region, wet climates have previously resulted in an increase in channel width (Baker and Penteado-Orellana, 1977) and an increase in sediment supply (Weight et al., 2011). It is possible that the additional runoff could lead to an increase in landscape disequilibrium, forcing erosion and speeding up the hillslope erosional processes. There would be a period of incision at the headwaters and river network expansion. Calculations for Dd indicate a total active stream and river channel loss of 57,500 km over the whole region (Table 5). It is apparent that the sourcing of water that is transported downstream through the network from different parts of the watersheds will change. These potential changes have yet to be considered in modeling of future hydrologic scenarios. It is important to note that there is inherent noise in natural landscapes; some watersheds have higher or lower Dd than the fitted curve (Fig. 3a). Therefore, certain watersheds that are expected to get less R in the future may still show a net increase in Dd and vice versa. Furthermore, the landscape response time may be much longer than the time scale of the study. However, the relationship described between the variables in the model is representative of the propensity for landscape evolution in the region. Nonetheless, the changes predicted for the region are dominantly driven by climate change and not noise or a certain level of disequilibrium in the landscape since the calculated change in total channel length if the watersheds are projected or forced to go towards the fitted curve, while keeping R fixed, leads to a net increase of 4200 km compared to a predicted loss of 57,500 km under climate change.

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Green or transpiring vegetation cover may respond to prevailing P quickly over one season or a few seasons. The reduced 2041–2060 P would lead to an over-all 1.1% reduction of vegetated area (difference in V) which corresponds to losing cover for 8150 km2 within the 470,800 km2 region (Table 5). The westernmost watersheds, labeled 1205 and 1208 in Fig. 1, are inferred to lose up to 26–30% of what little vegetation cover exists (Fig. 2j and Table 5). Note, however, that the V data does not discriminate between natural and crop vegetation or plant type. This suggests that there may be a certain level of disequilibrium that is driven and supported by irrigation. This introduces uncertainty. That is, a watershed may have more vegetation cover than it should under natural conditions. Therefore, the use of irrigated water to maintain crop production leads to a skewed relationship between precipitation and vegetation density. In fact, areas with the largest projected decrease in V are where the High Plains Aquifer is undergoing tremendous depletion due to irrigation (Scanlon, 2010; Texas Water Development Board, 2009). Additionally, plant type may play a role in vegetation response to changes in climate. However, unlike the river network, V response is more rapid if not instantaneous, and closer to or within the time scale of the study, making its prediction more viable. 5. Conclusions Using high-resolution digital data across a broad region with a pronounced climate gradient, we developed empirical relationships between landscape morphology and vegetation cover with rainfall and runoff. To our knowledge, this is the first time this has been done for a relatively large contiguous area with no very significant changes in topography (i.e., no major mountain ranges) and geology. This therefore allows partial isolation of the effect of climate on landscape properties. This also allowed for favorable comparison of empirical relationships to theoretical landscape models. That is, the statistically derived equations are of similar form to theoretical equations. The equations in turn opened the path for prediction. Based on the results, the Texas Gulf Basin region is facing the potential loss of both rivers and vegetation. The study region, which encompasses most of the state of Texas, represents a $1.2 trillion economy which would rank it near the top ten if it were a country. Presently, the state is facing difficulties meeting the growing water demand within the state. The regional economy, heavily dependent on agriculture, experiences billions of dollars of loss due to drought (Banner et al., 2010). In fact, the recent devastating drought of 2011 is proof of this when there was widespread loss in vegetation cover and record low streamflows accompanied by broad economic damages. A readjustment in the delivery of water in rivers, drying up of certain rivers (i.e., a decrease in drainage density), and inferred loss in natural and perhaps irrigated vegetation will have measurable economic consequences. However, it is important to note that while the regression-based models are robust based on the correlation coefficients, especially for the V(P) model, they are hostage to the uncertainty and error of predictions of the inputs, i.e., P and R. Nonetheless, our simple models provide a framework for rapid and oftentimes elusive quantitative assessment of decadal-scale physical and ecological trajectories of regional landscapes. Acknowledgements We thank Chris Milly (USGS), Seth Stein and Melissa Bukovsky (NCAR) for discussions. This work was partially supported by the US National Aeronautics and Space Administration under the Interdisciplinary Science Projects NNX07AL79G and NNX11AE42G, by

the National Basic Research Program of China (Grant No. 2012CB956203), by the State Key Program of National Natural Science of China (Grant No. 40830956), and by the Geology Foundation at the University of Texas at Austin. References Abrahams, A.D., 1984. Channel networks – a geomorphological perspective. Water Resour. Res. 20 (2), 161–188. Abrahams, A.D., Ponczynski, J.J., 1985. Drainage density in relation to precipitation intensity in the USA. J. Hydrol. 75 (1–4), 383–388. Baker, V.R., Penteado-Orellana, M.M., 1977. Adjustments to Quaternary climatic change by the Colorado River in Central Texas. J. Geol. 85, 395–422. Bandara, C.M.M., 1974. Drainage density and effective precipitation. J. Hydrol. 21, 187–190. Banner, J.L., Jackson, C.S., Yang, Z.L., Hayhoe, K., Woodhouse, C., Gulden, L., Jacobs, K., North, G., Leung, R., Washington, W., Jiang, X., Casteel, R., 2010. Climate change impacts on Texas water: a white paper assessment of the past, present and future and recommendations for action. Texas Water J. 1 (1), 1–19. Blum, M.D., Morton, R.A., Durbin, J.M., 1995. Deweyville terraces and deposits of the Texas Gulf coastal plain. Gulf Coast Assoc Geol. Soc. Trans. 65, 53–60. Callaway, R.M., DeLucia, E.H., 1994. Biomass allocation of montane and desert ponderosa pine: an analog for response to climate change. Ecology 75 (5), 1474–1481. Collins, D.B.G., Bras, R.L., 2010. Climatic and ecological controls of equilibrium drainage density, relief, and channel concavity in dry lands, Water Resour. Res. 46. de Elia, R., Caya, D., Cote, H., Frigon, A., Biner, S., Giguere, M., Paquin, D., Harvey, R., Plummer, D., 2008. Evaluation of uncertainties in the CRCM-simulated North American climate. Clim. Dyn. 30, 113–132. Dietrich, W.E., Perron, J.T., 2006. The search for a topographic signature of life. Nature 439 (7075), 411–418. Diffenbaugh, N.S., Giorgi, F., Pal, J.S., 2008. Climate change hotspots in the United States, Geophys. Res. Lett. 35. Dunne, J.A., Saleska, S.R., Fischer, M.L., Harte, J., 2004. Integrating experimental and gradient methods in ecological climate change research. Ecology 85 (4), 904– 916. Goldblum, D., Rigg, L.S., 2005. Tree growth response to climate change at the deciduous– boreal forest ecotone, Ontario, Canada. Can. J. Forest Res. 35, 2709–2718. Gunnula, W., Kosittrakun, M., Righetti, T.L., Weerathaworn, P., Prabpan, M., 2011. Normalized difference vegetation index relationships with rainfall patterns and yild in small plantings of rain-fed sugarcane. Aus. J. Crop Science 5 (13), 1845– 1851. Gutman, G., Ignatov, A., 1998. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens. 19 (8), 1533–1543. Hall, S.A., 1990. Channel trenching and climatic change in the southern US, Great Plains. Geology 18, 245–342. Hancock, G.S., Anderson, R.S., 2002. Numerical modeling of fluvial strath-terrace formation in response to oscillating climate. GSA Bull. 114 (9), 1131–1142. Horton, R.E., 1945. Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geol. Soc. Am. Bull. 56, 275–370. Howard, A.D., 1994. A detachment-limited model of drainage basin evolution. Water Resour. Res. 30 (7), 2261–2285. Istanbulluoglu, E., Bras, R.L., 2005. Vegetation-modulated landscape evolution: effects of vegetation on landscape processes, drainage density, and topography. J. Geophys. Res. 10, 1–19. Jiang, X., Yang, Z.-L., 2012. Projected changes of temperature and precipitation in Texas from downscaled global climate models. Clim. Res. 53, 229–244. Kirkby, M.J., 1971. Hillslope process-response models based on the continuity equation. In: Slopes: Form and Process. Institute of British Geography, London, pp. 15–30. Kramer, K., Leinonen, I., Loustau, D., 2000. The importance of phenology for the evaluation of impact of climate change on growth of boreal temperate and Mediterranean forests ecosystems: an overview. Int. J. Biometeorol. 44, 67–75. Langbein, W.B., Schumm, S.A., 1958. Yield of sediment in relation to mean annual precipitation. EOS Trans. Am. Geophys. Union 39 (6), 1076–1084. Larkin, T.J., Bomar, G.W., 1983. Climate Atlas of Texas, Texas Department of Water Resources. Mearns, L.O., Arritt, R., Biner, S., Bukovsky, M.S., McGinnis, S., Sain, S., Caya, D., Correia Jr., J., Flory, D., Gutowski, W., Talke, E.S., Jones, R., Leung, R., MoufoumaOkia, W., McDaniel, L., Nunes, A.M.B., Qian, Y., Roads, J., Sloan, L., Snyder, Mark, 2012. The North American regional climate change Assessment program overview and results. Bull. Am. Geol. Soc. 93 (9), 1337–1362. Melton, M.A., 1958. Correlation structure of morphometric properties of drainage systems and their controlling agents. J. Geol. 66 (4), 442–460. Mesinger, F., DiMego, G., Kalnay, E., et al., 2006. North American regional reanalysis. Bull. Am. Meteorol. Soc. 87 (3), 343–360. Milly, P.C.D., Dunne, K.A., Vecchia, A.V., 2005. Global pattern of trends in streamflow and water availability in a changing climate. Nature 438 (7066), 347–350. Milly, P.C.D., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W., Lettenmaier, D.P., Stouffer, R.J., 2008. Climate change – stationarity is dead: whither water management? Science 319 (5863), 573–574.

V.B. Smith et al. / Journal of Hydrology 488 (2013) 101–109 Miranda, J.D., Armas, C., Padilla, F.M., Pugnaire, F.I., 2011. Climate change and rainfall patterns: Effects on semi-arid plant communities of Iberian Southeast. J. Arid Environments 75, 1302–1309. Moglen, G.E., Eltahir, E.A.B., Bras, R.L., 1998. On the sensitivity of drainage density to climate change. Water Resour. Res. 34 (4), 855–862. Molnar, P., Anderson, R.S., Kier, G., Rose, J., 2006. Relationships among probability of stream discharges in floods, climate, bed load transport, and river incision, J. Geophys. Res. 111. Montgomery, D.R., Dietrich, W.E., 1992. Channel initiation and the problem of landscape scale. Science 255 (5046), 826–830. Nippert, J.B., Knapp, A.K., Briggs, J.M., 2006. Intra-annual rainfall variability and grassland productivity: can the past predict the future? Plant Ecol. 110, 65–74. Oguchi, T., 1997. Drainage density and relative relief in humid steep mountains with frequent slope failure. Earth Surf. Proc. Land. 22 (2), 107–120. Paruelo, J.M., Lauenroth, W.K., 1995. Regional patterns of normalized difference vegetation index in North American shrublands and grasslands. Ecology 76 (6), 1888–1898. Paruelo, J.M., Lauenroth, W.K., 1998. Interannual variability of NDVI and its relationship to climate for North America shrublands and grasslands. J. Biogeogr. 25, 721–733. Perez-Pena, J.V., Azor, A., Azanon, J.M., Keller, E.A., 2010. Active tectonics in the Sierra Nevada (Betic Cordillera, SE Spain): insights from geomorphic indexes and drainage pattern analysis. Geomorphology 119, 74–87. Perron, J.T., Kirchner, J.W., Dietrich, W.E., 2009. Formation of evenly spaced ridges and valleys. Nature 460 (7254), 502–505. Rastetter, E.B., Aber, J.D., Peters, D.P.C., Ojima, D.S., Burke, I.C., 2003. Using mechanistic models to scale ecological processes across space and time. Bioscience 53 (1), 68–76. Rinaldo, A., Dietrich, W.E., Rigon, R., Vogel, G.K., Rodriguez-Iturbe, I., 1995. Geomorphological signatures of varying climate. Nature 374 (6523), 632– 635. Rodriguez-Iturbe, I., Escobar, L.A., 1982. The dependence of drainage density on climate and geomorphology. J. Hydrol. Sci. 27 (2), 129–137.

109

Scanlon, B.R., Reedy, R.C., Gates, J.B., 2010. Effects of irrigated agro ecosystems: 1. Quantity of soil water and groundwater in the southern High Plains, Texas, Water Resour. Res. 46 (14). Schumm, S.A., 1993. River response to base level change: implications for sequence stratigraphy. J. Geol. 101, 279–294. Schwinning, S., Sala, O.E., 2004. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220. Sylvia, D.A., Galloway, W.E., 2006. Morphology and stratigraphy of the late Quaternary lower Brazos valley: implications for paleo-climate, discharge and sediment delivery. Sed. Geol. 190 (1–4), 159–175. Talling, P.J., Sowter, M.J., 1999. Drainage density on progressively tilted surfaces with different gradients, Wheeler Ridge, California. Earth Surf. Process. Land. 24, 809–824. Tarboton, D., 2003. Terrain Analysis Using Digital Elevation Models in Hydrology, In: 23rd ESRI International User Conference. Texas Water Development Board, 2009. Far West Texas Water Plan (2009). Rep., Austin, TX. Tucker, G.E., Bras, R.L., 1998. Hillslope processes, drainage density, and landscape morphology. Water Resour. Res. 34 (10), 2751–2764. Tucker, G.E., Hancock, G.R., 2010. Modeling landscape evolution. Earth Surf. Proc. Land. 35 (1), 28–50. USGS and EPA, 2007. NHDPlus User’s Guide. Rep., Washington, DC. Weight, R.W.R., Anderson, J.B., Fernandez, R., 2011. Rapid mud accumulation on the Central Texas Shelf linked to climate change and sea-level rise. J. Sediment. Res. 81, 743–764. Wobus, C.W., Tucker, G.E., Anderson, R.S., 2010. Does climate change create distinctive patterns of landscape incision? J. Geophys. Resour. – Earth Surf. 115. Xu, Z.-F., Yang, Z.-L., 2012. An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. J. Clim. 25, 6271–6286. Yang, Z.P., Gao, J.X., Zhou, C.P., Shi, P.L., Zhao, L., Shen, W.S., Ouyang, H., 2011. Spatiotemporal changes of NDVI and its relation with climatic variables in the source regions of the Yangtze and Yellow rivers. J. Geog. Sci. 21 (6), 979–993.