Contrasting watershed-scale trends in runoff and sediment yield complicate rangeland water resources planning

Abstract

Rangelands cover a large portion of the earth’s land surface and are undergoing dramatic landscape changes. At the same time, these ecosystems face increasing expectations to meet growing water supply needs. To address major gaps in our understanding of rangeland hydrologic function, we investigated historical watershed-scale runoff and sediment yield in a dynamic landscape in central Texas, USA. We quantified the relationship between precipitation and runoff and analyzed reservoir sediment cores dated using Cesium-137 and Lead-210 radioisotopes. Local rainfall and streamflow showed no directional trend over a period of 85 years, resulting in a rainfall-runoff ratio that has been resilient to watershed changes. Reservoir sedimentation rates generally were higher before 1963, but have been much lower and very stable since that time. Our findings suggest that (1) rangeland water yields may be stable over long periods despite dramatic landscape changes while (2) these same landscape changes influence sediment yields that impact downstream reservoir storage. Relying on rangelands to meet water needs demands an understanding of how these dynamic landscapes function and a quantification of the physical processes at work.

Full text

1
Contrasting watershed-scale trends in runoff and sediment 1
yield complicate rangeland water resources planning 2
3
M. D. Berg1,*, F. Marcantonio2, M. A. Allison3,4, J. McAlister5, B. P. Wilcox1, and 4
W. E. Fox1,5 5
[1]{Texas A&M University Department of Ecosystem Science and Management, College 6
Station, Texas} 7
[2]{Texas A&M University Department of Geology and Geophysics, College Station, Texas} 8
[3]{The Water Institute of the Gulf, Baton Rouge, Louisiana} 9
[4]{Tulane University Department of Earth and Environmental Sciences, New Orleans, 10
Louisiana} 11
[5]{Texas A&M AgriLife Blackland Research & Extension Center, Temple, Texas} 12
[*]{now at: Save Water Co, Houston, Texas} 13
Correspondence to: M. D. Berg (mbergtamu@gmail.com) 14
15
Abstract 16
Rangelands cover a large portion of the earth’s land surface and are undergoing dramatic 17
landscape changes. At the same time, these ecosystems face increasing expectations to meet 18
growing water supply needs. To address major gaps in our understanding of rangeland 19
hydrologic function, we investigated historical watershed-scale runoff and sediment yield in a 20
dynamic landscape in central Texas, USA. We quantified the relationship between 21
precipitation and runoff and analyzed reservoir sediment cores dated using Cesium-137 and 22
Lead-210 radioisotopes. Local rainfall and streamflow showed no directional trend over a 23
period of 85 years, resulting in a rainfall-runoff ratio that has been resilient to watershed 24
changes. Reservoir sedimentation rates generally were higher before 1963, but have been 25
much lower and very stable since that time. Our findings suggest that (1) rangeland water 26
yields may be stable over long periods despite dramatic landscape changes while (2) these 27
same landscape changes influence sediment yields that impact downstream reservoir storage. 28
Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-540, 2016
Manuscript under review for journal Hydrol. Earth Syst. Sci.
Published: 18 January 2016
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Author(s) 2016. CC-BY 3.0 License.

2
Relying on rangelands to meet water needs demands an understanding of how these dynamic 1
landscapes function and a quantification of the physical processes at work. 2
3
1 Introduction 4
Diverse rangeland ecosystems falling along a grassland–forest continuum cover roughly half 5
of the earth’s land surface (Breshears, 2006). Generally precipitation-limited, they are 6
typically used for livestock grazing and harvesting of woody products rather than crop 7
production. But rangelands worldwide face numerous challenges, including (1) conversion to 8
urban development or cultivation; (2) shifting plant cover, such as encroachment by woody 9
plants and invasion by non-native species; and (3) demands for increased production without 10
sacrificing sustainability (Tilman et al., 2002;Van Auken, 2000;Wilcox et al., 2012b). 11
As growing populations look to these dynamic landscapes to provide critical ecosystem 12
services—including water supply and water storage—their ability to keep pace with these 13
demands is uncertain (Havstad et al., 2007;Jackson et al., 2001). Some of this uncertainty is 14
due to the tremendous variability of runoff and erosion through time and space, which can 15
vary by orders of magnitude even between portions of a single small field (Gaspar et al., 16
2013;Ritchie et al., 2005). Landscape changes affect these processes further still; and water 17
and sediment yields depend on interactions between climate, vegetation, and local geology. 18
These complex interactions make predictions difficult; and the influence of human activity 19
adds yet another compounding layer of difficulty (Peel, 2009;Boardman, 2006;Vorosmarty 20
and Sahagian, 2000). As a result, major gaps remain in our understanding of rangeland 21
ecosystems. Further interdisciplinary study is imperative to develop a coherent picture of the 22
linkages between hydrological, ecological, and geological processes (Newman, 2006;Wilcox 23
and Thurow, 2006). 24
Some rangeland investigations have focused on the potential of these landscapes to provide 25
augmented water yields or storage in small reservoirs. Economic and modeling studies have 26
identified vegetation management as a possible means of increasing runoff and streamflow 27
(Griffin and McCarl, 1989;Afinowicz et al., 2005), and government agencies have 28
incorporated these goals into their programs (Texas State Soil and Water Conservation Board, 29
2005;USDA-NRCS, 2006). Other concerns center on sediment yield, which threatens 30
downstream surface water storage (Bennett et al., 2002;Dunbar et al., 2010). To determine 31
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how to respond to these issues and whether related investments are worthwhile, we must gain 1
a better understanding of how rangeland systems function with respect to water resources. 2
To date, most research has been based on extrapolation of findings from relatively small-scale 3
studies to larger scales or on modeled results. However, because runoff and sediment 4
production are scale-dependent processes, such extrapolation is often unreliable (de Vente and 5
Poesen, 2005;Wilcox et al., 2003). Since they more accurately reveal the true water and 6
sediment yields of watersheds, studies of these processes conducted at the catchment scale are 7
much more relevant to water planning efforts. But whereas catchment-scale data on 8
precipitation and streamflow are somewhat widely available, corresponding sediment data are 9
lacking. Since they serve as archives of historical watershed conditions, the use of reservoir 10
sediments provides one means of filling this data gap and of investigating the impact of 11
human activity (Edwards and Whittington, 2001;Winter et al., 2001). Linking the findings of 12
such investigations with observed changes at the watershed scale will greatly facilitate the 13
development of effective strategies for managing rangeland water resources. 14
In this study, we investigated the hydrological and sediment transport dynamics of rangeland 15
watersheds. Our main objectives were to (1) quantify long-term trends in precipitation and 16
streamflow using historical data; (2) estimate historical sedimentation rates through 17
radioisotope analysis of reservoir sediment cores; and (3) explore the potential effects of 18
drought conditions on sediment production with historical data. Addressing these objectives 19
not only improves our understanding of rangeland processes but also provides much-needed 20
information on the potential of these landscapes to provide for growing global water needs. 21
22
2 Methods 23
2.1 Study area 24
As part of a broader study of landscape change and ecosystem function, we examined 25
rangeland processes in the Lampasas Cut Plain of central Texas, USA. This savanna 26
landscape is characterized by low buttes and mesas separated by broad, flat valleys. Local 27
prevailing geology is Cretaceous limestone; soils are loamy and clayey, with occasional sandy 28
loams, and are susceptible to sheet and gully erosion (Allison, 1991;Clower, 1980). The area 29
is drained by the Lampasas River. Streamflow in the upper reaches of the river is runoff-30
dominated, with localized contributions from springflow (Prcin et al., 2013), and has been 31
Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-540, 2016
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4
recorded at two primary stations (Figure 1). Annual precipitation averages approximately 800 1
mm, decreasing to the north and west (Figure 2). 2
For the sediment study, we examined eight flood-control reservoirs and their watersheds 3
within the Lampasas River basin. Reservoirs L1, L2, L3, L4, L9, and LX are located in 4
Lampasas County and were constructed between 1958 and 1961. Before impoundment, the 5
parallel watersheds of L1, L2, and L3, contributed to the downstream watershed of LX. 6
Reservoirs M1 and M4, in Mills County, were completed in 1974. Basic attributes of the 7
reservoirs and their watersheds are compiled in Table 1. 8
Current local land use is predominantly rangeland, and livestock numbers have fluctuated 9
over the last several decades (Figure 3a) while remaining among the highest in the region 10
(Wilcox et al., 2012a). Cropland was widespread early in the 20th century (Figure 3b) but had 11
declined by nearly 80% by 2012 (Berg, M. D., manuscript in review, 2015). Amid this 12
shifting land use, the area has been characterized by large fluctuations in the extent of woody 13
plant cover, due to brush management and regrowth (Figure 3c), and a dramatic increase in 14
the density of farm ponds (Figure 3d) over the last several decades (Berg et al., 2015a). 15
2.2 Rainfall and runoff trends 16
To investigate local hydrological trends, we analyzed historical precipitation and streamflow 17
data for the Lampasas River basin. We created a composite record of annual precipitation 18
using a Thiessen polygon approach, centering polygons on available NWS stations (Figure 2). 19
Streamflow data were derived from the two USGS stream gage stations downstream from the 20
study watersheds. The lower Youngsport station, with a drainage area of 3,212 km2, operated 21
between 1924 and 1980; the Kempner station, with a drainage area of 2,119 km2 has remained 22
active from 1963 to the present. 23
We performed an automated baseflow separation of streamflow data from each station 24
(Arnold and Allen, 1999). This digital filter approach is objective and reproducible and 25
partitions annual baseflow and stormflow with high efficiency (Arnold et al., 1995)—26
enabling these components to be interpreted in light of changing landscape conditions. 27
Using the precipitation and two streamflow datasets (1924—1980; 1963—2010), we applied a 28
nonparametric Mann-Kendall trend test to detect directional changes (Lettenmaier et al., 29
1994). We performed two-tailed statistical tests for significance, with α = 0.10. 30
Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-540, 2016
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2.3 Reservoir sedimentation rates 1
To shed light on sediment transport processes, we extracted cores from each of the eight 2
reservoirs and analyzed sediments using Cesium-137 (137Cs) and Lead-210 (210Pb) tracers. 3
137Cs is present in the environment as a result of atomic weapons testing and accidental 4
emissions. 210Pb occurs naturally. Both can be used to estimate sedimentation rates and 5
interpret transport history in a variety of environments (Walling et al., 2003;Ritchie and 6
McHenry, 1990;Appleby and Oldfield, 1978). Coring sites were selected by locating the 7
thickest sediment deposits through exploratory hydroacoustic surveys (U.S. Army Corps of 8
Engineers, 2013, 1989;Dunbar et al., 2002). In each reservoir, we extracted sediment cores at 9
identified sites near the dam structure, from locations corresponding to the pre-impoundment 10
floodplain (Figure 4). Taking cores from these areas reduces the likelihood of capturing 11
mixed profiles, which skew analysis (Sanchez-Cabeza and Ruiz-Fernández, 2012). It also 12
ensures the collection of fine sediments, to which the radioisotopes preferentially adsorb 13
(Bennett et al., 2002). We extracted cores using a portable vibracoring system suspended from 14
a floating platform. This method captures unconsolidated, saturated sediments with minimal 15
disturbance and compaction (Lanesky et al., 1979). The cores were collected with an 16
aluminum pipe lowered to the point of refusal, penetrating the pre-impoundment surface. 17
Retrieved cores were sealed and transported upright to cold storage (~5°C). 18
We sectioned each core vertically in 3-cm intervals, drying each section for analysis 19
according to IAEA (2003) protocols. A subsample of each core section was ground to 20
homogenize its contents, sealed in a 50 mm x 9 mm Petri dish, and allowed to ingrow for at 21
least 21 days so that 210Pb supported levels reached equilibrium. Counts for 210Pb and 137Cs 22
were performed according to Hanna et al. (2014) using a Canberra low-energy germanium 23
gamma spectrometer. Radioisotope activity was indicated by photopeaks at 46 keV (total 24
210Pb) and 661.6 keV (137Cs). Excess 210Pb was calculated by subtracting the supported 25
activity of the 226Ra parent—obtained by averaging the 295, 351.9, and 609.3 keV peaks of 26
the 214Pb and 214Bi daughter products—from total measured 210Pb activity at the 46 keV peak. 27
Activity measurements were validated with IAEA-300 standard reference material. 28
To determine historical linear sedimentation rates, we used as a chronological marker the 29
depth of peak 137Cs activity (corresponding to the 1963 peak in global atmospheric fallout) 30
(Ritchie et al., 1973). We calculated average linear sedimentation rates for the post-1963 31
period by dividing this depth by the time elapsed between 1963 and the coring date for each 32
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reservoir; we calculated the pre-1963 rates by dividing the depth of sediment below the 1
activity peak by the time elapsed between reservoir impoundment and 1963. 2
To complement 137Cs analysis, we used excess 210Pb activities to calculate the linear 3
sedimentation rate for each core (Krishnaswamy et al., 1971;Bierman et al., 1998). We also 4
searched for changing deposition rates within each core, as plots of the natural log of excess 5
210Pb versus depth indicate stable sedimentation rates over time when R2 approaches 1.0. 6
Finally, we obtained historical annual Palmer Modified Drought Index (PMDI) data for the 7
region to identify potential climatic drivers of sedimentation during different periods. We 8
plotted PMDI and annual peak flows (from USGS data) between 1924 and 2010, identifying 9
episodes conducive to increased sediment production (in particular, a wet year or years 10
following a period of intense drought). 11
12
3 Results 13
3.1 Rainfall and runoff trends 14
Despite a great deal of interannual variability, there was no directional change in local 15
precipitation 1924—1980 (p = 0.90) or 1963—2010 (p = 0.22), which has remained near a 16
long-term average of 800 mm (Figure 5a). The same is true of total streamflow (1924—1980: 17
p = 0.98, 1963—2010: p = 0.34), which has averaged between 60 and 70 mm (Figure 5b). As 18
a result, the rainfall–runoff ratio also remained unchanged, at approximately 8% (1924—19
1980: p = 0.90, 1963—2010: p = 0.45). Moreover, neither baseflow nor stormflow exhibited a 20
directional change over either period of record. However, baseflow as a proportion of total 21
streamflow did increase 1924—1980 (p = 0.02) despite minimal change in overall flow—22
almost doubling its contribution (Figure 5c). 23
3.2 Reservoir sedimentation rates 24
Sediment core profiles varied widely in depth between reservoirs—from less than 3 cm in LX 25
to 162 cm in L1 (Figure 6). Activity peaks of 137Cs supported the analysis of pre-1963 26
sedimentation rates for reservoirs L1, L2, L3, and L9. Overall, linear sedimentation rates were 27
higher before 1963 (Table 2; Figure 7). Except in the case of L3, sediment deposition has 28
slowed since 1963—by 54% in L1, 76% in L2, and 84% in L9. In reservoir L3, it increased 29
Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-540, 2016
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by 49% after 1963. Reservoir L1 exhibited the highest sedimentation rate both before and 1
after 1963. However, when normalized by catchment area, sedimentation rates varied much 2
more widely. That in L9 was by far the highest—surpassing the next highest reservoir by 3
nearly 1400% for the pre-1963 period and by 423% for the post-1963 period. 4
Cores from L4, LX, M1, and M4 did not display a 137Cs peak. For these cores, sedimentation 5
was assumed to be post-1963 and was estimated by dividing sediment depth by time since 6
impoundment. For cores L4 and M4, which did not capture the entire sediment profile, actual 7
rates likely are higher than those calculated. 8
Cores from reservoirs LX and M1 showed vertical mixing that prohibited 210Pb analysis. 9
However, remaining cores displayed high correlation between 210Pb activities and depth, 10
indicating linear sedimentation rates have remained quite stable over time (Table 2). 210Pb-11
based estimates generally resembled those based on 137Cs activities. In addition, rates 12
calculated from 210Pb activities were similar to the post-1963 rates based on 137Cs activities (p 13
= 0.84), suggesting good agreement between the two methods for the period since 1963. 14
Chronological data revealed periods of drought of varying intensity and occasional years of 15
very high streamflow (Figure 8). The historic 1950s drought was longer and more severe than 16
any other over the last century; it was followed by periods of very high flow in 1957 and 17
1960. Comparable high flows in 1965 occurred in the middle of a multi-year drought, and the 18
severe drought beginning in 2006 featured occasional elevated peak flows. In 1992, very high 19
flows occurred during a prolonged wet period. 20
21
4 Discussion 22
4.1 Rainfall and runoff trends 23
Given the varying trends in precipitation and streamflow observed in many regions (Lins and 24
Slack, 1999;Andreadis and Lettenmaier, 2006), the dynamic hydrological stability in our 25
study area is surprising. At the same time, such consistency sheds light on the effects of 26
watershed changes on local water budgets. Studies at small spatial scales frequently indicate 27
that landscape changes have important water resource impacts, with the specific response 28
depending on the relative importance of evapotranspiration, recharge, and runoff (Foley et al., 29
2005;Kim and Jackson, 2012). Such changes affect local water budgets and influence water 30
Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-540, 2016
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yields (Petersen and Stringham, 2008;Huxman et al., 2005;Farley et al., 2005). However, 1
complicated feedbacks make effects at larger scales highly uncertain and often overwhelmed 2
by climatic and physical characteristics (Peel, 2009;Wilcox et al., 2006;Kuhn et al., 2007). 3
Our rainfall–runoff ratio of 8% is essentially identical to early estimates of 7% for the area 4
(Tanner, 1937). The lack of a directional trend in streamflows suggests that this region, like 5
many semiarid landscapes dominated by surface runoff, is largely hydrologically insensitive 6
to shifting watershed characteristics (Wilcox, 2002). Changes in land use and land cover—7
and even the impoundment of small reservoirs—have had negligible impacts on streamflow. 8
It is still not understood why baseflow showed a proportional increase 1924—1980. In some 9
landscapes, improving range conditions have led to increased infiltration (Wilcox and Huang, 10
2010). However, local livestock numbers have remained high, and karst features are limited—11
unlike other regions where baseflow increases have been attributed to rangeland recovery. It 12
is possible that infiltration from local impoundments has added to baseflows. Despite minimal 13
effects on total streamflow, even small dams can create localized groundwater recharge (Graf, 14
1999;Smith et al., 2002), and Lampasas River tributaries are characterized by a high degree of 15
connectivity between surface water and local aquifers (Mills and Rawson, 1965). 16
Perennial flow in this part of the Lampasas River is maintained by isolated springs fed by an 17
aquifer extending beyond the basin (Mills and Rawson, 1965). As a result, the effective 18
catchment of the river is larger than it appears, and springflow contributions complicate the 19
interpretation of streamflows. At the same time, it is clear that the fundamental relationship 20
between rainfall and streamflow has not changed over more than 85 years—suggesting that 21
the Lampasas River is hydrologically resilient in the face of changing land use and land cover. 22
4.2 Reservoir sedimentation rates 23
Because sediment deposition affects reservoir storage and flood detention, understanding 24
sedimentation rates over time is critical to managing rangeland water resources. Though 25
questions do remain regarding the opposing trend in reservoir L3, changes in rates make it 26
clear that sedimentation was more rapid before 1963. The period since that time has been 27
characterized by stable and lower yields. But what explains the higher rates seen during the 28
earlier period? Additional historical landscape data may offer a key interpretive lens. 29
Livestock can be powerful instruments of landscape change, both directly (trampling soils) 30
and indirectly (disturbing protective vegetation). When grazing is prolonged or intense, 31
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sediment yield can be great (Trimble and Mendel, 1995). The high animal densities in this 1
area around the time of reservoir impoundment doubtless contributed to erosion (Figure 3a). 2
Crop production also can result in accelerated erosion by damaging soil structure and 3
depleting organic matter (Quine et al., 1999). Cropland is a major source of sediment in many 4
landscapes (Foster and Lees, 1999;Blake et al., 2012). In our study area, cropland acreage has 5
declined dramatically since the 1930s (Figure 3b). Further, nationwide improvements in soil 6
conservation have reduced sediment yield from many agricultural lands (Knox, 2001). 7
While woody plant encroachment influences soil loss, removing undesirable shrubs and trees 8
also elevates short-term sediment yields (Porto et al., 2009). Since the time of initial 9
settlement, woody plant management has resulted in major land cover changes (Figure 3c). 10
Most early removal was done manually, and the first mechanical control methods were very 11
destructive, leading to high erosion rates (Hamilton and Hanselka, 2004). In recent decades, 12
however, brush removal has declined with shifting landowner priorities (Sorice et al., 2014). 13
Changes in precipitation frequency, duration, or intensity also affect sediment transport (Xie 14
et al., 2002;Allen et al., 2011). Similarly, drought is an important driver of sediment dynamics 15
in many rangelands. Extended dry periods can cause long-term shifts in plant cover, leading 16
to sediment pulses when rains return (Allen and Breshears, 1998;Nearing et al., 2007). The 17
Lampasas River experienced very high flows in 1957, 1960, 1965, and 1992, and some of 18
these were associated in time with severe droughts (Figure 8). Just before the impoundment of 19
most of the reservoirs we examined, the region was in the grip of drought conditions 20
unmatched since European settlement (Bradley and Malstaff, 2004). Our sediment records 21
cover only the end of this drought but show pre-1963 deposition 220–630% faster than 22
subsequent rates. However, any direct effects of the 1957 drought-breaking floods would not 23
be found in the sediments of the reservoirs, which were impounded beginning in 1958. 24
Interestingly, we also did not find spikes in sedimentation associated with high flows or 25
droughts later in the study period. The apparent low importance of drought and floods in 26
sediment delivery in these watersheds is surprising. 27
Together, these factors have acted over multiple temporal and spatial scales to influence 28
sediment yields in the study area. Yet because there is no clear link between contemporary 29
land use, land cover, and sedimentation rates, it is possible that another process has reduced 30
sediment yields. 31
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4.3 Sediment storage 1
To truly understand the local sediment processes at work, it is important to understand what 2
our findings actually show. Sedimentation rates are poor indicators of in-field soil erosion and 3
redistribution (Nearing et al., 2000;Ritchie et al., 2009); what they do reflect is more closely 4
related to net watershed sediment yield. Sediment yield is buffered by internal storage. 5
Especially at larger scales, watersheds can have a great deal of internal storage, so that very 6
little eroded soil actually leaves the watershed, even in the presence of extreme erosion 7
(Bennett et al., 2005;Porto et al., 2011). 8
In this study area, the increasing density of farm ponds (Figure 3d) represents a key potential 9
sink for watershed sediments. These ponds retain material that otherwise would be 10
transported downstream, reducing sediment yields. Because of their smaller contributing 11
watersheds, ponds have high trap efficiencies, magnifying their effects (Brainard and 12
Fairchild, 2012). Indeed, impoundments may be the single greatest anthropogenic modifier of 13
sediment transport; globally, most sedimentation now takes place in aquatic settings and will 14
be retained therein for long periods (Renwick et al., 2005;Verstraeten et al., 2006). 15
In addition to this storage of eroded sediments in local ponds, a vast amount of sediment from 16
past erosion likely remains on the landscape (Beach, 1994;Meade, 1982). The initial decades 17
after European settlement in this area saw intensive cultivation and very high livestock 18
densities (Jordan-Bychkov et al., 1984;Wilcox et al., 2012a). This destructive combination 19
remained in place for nearly a century in the Lampasas Cut Plain. By the 1930s, many 20
rangelands were already seriously degraded (Mitchell, 2000;Bentley, 1898;Box, 1967). While 21
the methods we used do not allow us to determine whether reservoir sediments result from 22
contemporary erosion or are a legacy of earlier land use, stabilizing sediment yields and 23
observations of local gully erosion suggest that deposits from prior erosion continue to be a 24
source of sediment (Bartley et al., 2007;Mukundan et al., 2011;Phillips, 2003). 25
The lack of sediments in LX appears to lend support to the importance of internal deposits. 26
This reservoir’s watershed is comparable in size to those of L2, L3, and M4, yet 27
sedimentation rates were only 3%–14% of those in the other reservoirs. When L1, L2, and L3 28
were impounded, the effective catchment area of LX decreased by 86%. Without the 29
historical streamflows and sediment loads from those tributaries, deposits are no longer 30
mobilized and transported downstream. 31
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Given this complexity, we suggest that radioisotope tracers have great potential to elucidate 1
the dynamics of rangeland systems, particularly as their use evolves from primarily research 2
applications to use as a management and decision-support tool (Mukundan et al., 2012). 3
Further strides can be made in understanding rangeland processes by (1) incorporating 4
historical climate, land use, and land cover information to interpret sediment data (Venteris et 5
al., 2004;Boardman, 2006) and (2) including sediment surveys of the farm ponds that are 6
much smaller yet far more abundant than the reservoirs we examined (Downing et al., 2006). 7
8
5 Conclusion 9
We examined long-term trends in rainfall, runoff, and sediment yield in rangeland watersheds 10
with a dynamic land use history. Over more than 85 years, neither precipitation nor 11
streamflow showed any directional trend, suggesting a lack of hydrological sensitivity to 12
landscape change. This raises doubts over efforts to increase runoff by directing land cover 13
changes. Reservoir sedimentation rates generally were higher before 1963, and then stabilized 14
at a lower level over the 50 years since 1963. We believe that this decline in sediment yield is 15
related to long-term landscape changes and an increase in internal storage. As a result, future 16
changes in land use or sediment storage may impact downstream reservoir capacity. These 17
findings challenge simplistic assumptions about streamflow and sediment yield in dynamic 18
rangelands. Determining the role of these landscapes in meeting growing water resource 19
demands requires a creative approach. Integrating multiple techniques with historical 20
information enables a more complete understanding of rangeland processes and holds the key 21
to informed water planning. 22
23
Data availability 24
Streamflow data are available at the USGS National Water Information System. Stream 25
gages: 08103800 (Kempner) and 08104000 (Youngsport). Drought data are available at the 26
NOAA National Climate Data Center. Texas Climate Division: CD 3 (North Central) and CD 27
6 (Edwards Plateau). 28
29
Acknowledgements 30
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Dan Duncan, Andrea Hanna, and Diana di Leonardo performed activity counts of sediment 1
samples. This work was supported by USDA-NIFA Managed Ecosystems grant 2011-68002-2
30015, USDA-NIFA National Needs Program grant 2009-38420-05631, NSF-CNH grant 3
413900, and a Tom Slick Graduate Research Fellowship from the Texas A&M University 4
College of Agriculture and Life Sciences. 5
6
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Table 1. Sediment study reservoirs and watershed characteristics. 1
Reservoir
Primary Inflow
Surface
Area (km2)
Watershed
Area (km2)
Year
Impounded
Year
Cored
Min.
Elev. (m)
Max.
Elev. (m)
L1
Donalson Creek
0.20
50.9
1959
2010
367
500
L2
Pitt Creek
0.18
23.2
1959
2010
362
458
L3
Espy Branch
0.11
27.5
1958
2010
355
459
L4
Pillar Bluff Creek
0.07
41.2
1960
2012
345
467
L9
Cemetery Creek
0.02
1.2
1960
2012
322
363
LX
Bean Creek
0.20
23.1
1961
2012
338
420
M1
Middle Bennett Creek
0.14
34.6
1974
2012
422
536
M4
Mustang Creek
0.15
28.0
1974
2012
432
534
2
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22
Table 2. Linear sedimentation rates derived from radioisotope activities. 1
137Cs
210Pb
Pre-1963
Post-1963
Core mean
R2
Core
cm y-1
cm y-1 km-2
cm y-1
cm y-1 km-2
cm y-1
cm y-1 km-2
ln dpm g-1
vs. depth
L1
6.4
0.13
2.9
0.06
3.1
0.06
0.90
L2
3.4
0.15
0.8
0.03
0.9
0.04
0.97
L3
1.4
0.05
2.1
0.08
1.3
0.04
0.96
L4
a
a
0.5b
0.01b
1.2
0.03
0.93
L9
2.5
2.02
0.4
0.32
0.4
0.19
0.94
LX
a
a
0.1
< 0.01
c
c
c
M1
a
a
1.5
0.04
c
c
c
M4
a
a
0.4b
0.01b
0.8
0.01
1.00
aCore did not display a 137Cs peak, and rates were calculated using the time elapsed since 2
impoundment. 3
bCore did not capture the pre-impoundment surface and likely underestimates true values. 4
cCore showed significant vertical mixing, preventing calculation of sedimentation rate. 5
6
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P
P
fl
Legend
Counties
StudyWatershed s
Streams
PUSGSGage
FloodControlReservoir
010205km
N
0 20 km 5 10
Mills
County
Lampasas
County
Burnet
County
95°W 100°W 105°W
35°N
30°N
Counties
Lampasas River basin streams
Study watersheds
Flood control reservoirs
USGS stream gages
Legend
Kempner
Lampasas River
Youngsport
L1
L4
L3
LX
L9 L2
M1
M4
1
Figure 1. Study area in Texas, USA. Each study watershed encloses a flood control reservoir 2
from which sediment cores were collected. All watersheds contribute flow to the Lampasas 3
River. 4
5
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660-711 mm
711-762 mm
762-813 mm
813-864 mm
NWS stations
1
Figure 2. Average annual precipitation gradient and location of National Weather Service 2
(NWS) stations used to construct historical precipitation record.3
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25
0
5
10
15
20
25
30
1890 1915 1940 1965 1990 2015
Animal Units km-2
Total
Cows
Sheep and Goats
a
0%
10%
20%
30%
40%
50%
1935 1955 1975 1995 2015
Woody cover
0
1
2
3
1935 1955 1975 1995 2015
Ponds km-2
1940 1937
Lampasas
County
Mills
County
2012
Cropland extent
b
c
d
1
Figure 3. Historical landscape changes in the study area. (a) Livestock numbers in the 2
Lampasas Cut Plain. Recreated from Wilcox et al. (2012a). (b) Extent of active cropland in 3
1937-40 and 2012 (Berg, M. D., manuscript in review, 2015). (c) Historical extent of woody 4
plant cover in the study watersheds (Berg et al., 2015b). (d) Pond density over time in the 5
study watersheds (Berg et al., 2015a). 6
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1
Figure 4. Reservoir sediment coring apparatus (top) and representative sediment profile 2
(bottom). 3
4
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0
50
100
150
200
250
300
350
400
450
1925 1940 1955 1970 1985 2000 2015
Annual Q (mm)
Streamflow (Y)
Streamflow (K)
Baseflow (Y)
Baseflow (K)
0
200
400
600
800
1000
1200
1400
1925 1940 1955 1970 1985 2000 2015
Annual P (mm)
a
b
c
0
0.2
0.4
0.6
0.8
1925 1940 1955 1970 1985 2000 2015
Qb : Q
Youngsport
Kempner
1
Figure 5. Precipitation and streamflow trends of the Lampasas River basin. (a) Precipitation 2
showed no directional trend. (b) Streamflow showed no directional trend at either the 3
Youngsport (Y) or Kempner (K) station, despite being highly variable. (c) Baseflow as a 4
proportion of total streamflow displayed an upward trend over the first portion of the study 5
period. 6
7
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1
Figure 6. Sediment core profiles of bulk density and radioisotope activities from the eight 2
reservoirs. Solid horizontal lines indicate the pre-impoundment surface (no line indicates the 3
core did not capture the pre-impoundment surface). Dashed lines in 137Cs graphs represent the 4
depth of peak activity. The 210Pb profile for L3 is from a second core collected at the same 5
location.6
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1
Figure 6 (continued). Sediment core profiles of bulk density and radioisotope activities from 2
the eight reservoirs. Solid horizontal lines indicate the pre-impoundment surface (no line 3
indicates the core did not capture the pre-impoundment surface). Dashed lines in 137Cs graphs 4
represent the depth of peak activity. 5
6
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1
Figure 7. Linear sedimentation rates derived from 137Cs activities. Summary comparison of 2
pre-1963 and post-1963 rates. 3
4
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1
Figure 8. Chronology of regional drought (annual Palmer Modified Drought Index) and peak 2
flows on the Lampasas River. 3
Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2015-540, 2016
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