Soil Moisture Dynamics in Agriculturally-Dominated Landscapes After the Introduction of Native Prairie Vegetation Jose Antonio Gutierrez Lopez Iowa State University

Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations

2012 Soil moisture dynamics in agriculturally-dominated landscapes after the introduction of native prairie vegetation Jose Antonio Gutierrez Lopez Iowa State University

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Recommended Citation Gutierrez Lopez, Jose Antonio, "Soil moisture dynamics in agriculturally-dominated landscapes after the introduction of native prairie vegetation" (2012). Graduate Theses and Dissertations. 12337. https://lib.dr.iastate.edu/etd/12337

This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Soil moisture dynamics in agriculturally-dominated landscapes after the 1

introduction of native prairie vegetation 2

By: 4

José Antonio Gutiérrez López 5

A thesis submitted to the graduate faculty 7

In partial fulfillment of the requirements for the degree of: 8

MASTER OF SCIENCE 10

Major: Sustainable Agriculture 12 13 Program of Study Committee: 14

Thomas Isenhart 16 Matthew Helmers 17 Heidi Asbjornsen 18 Alan D. Wanamaker 19 20

Iowa State University 21

Ames, Iowa 22

2012 23

Dedication 31

A ti, Agustina 36

A mis padres 37

Gloria y René 38

A mis hermanos 39

Patricia y René 40

A todos mis amigos 41

iii

Table of contents 49

List of Figures ...... viii! 50

List of Tables...... x! 51

CHAPTER I...... 1! 52

GENERAL INTRODUCTION...... 1! 53

1.1 Contributions of this thesis to the overall mission of the Neal Smith National Wildlife 54

Refuge ...... 1! 55

1.2 Introduction ...... 2! 56

1.3 Thesis organization ...... 5! 57

1.4 Literature review ...... 5! 58

1.4.1 History of native ecosystems in the Midwest ...... 5! 59

1.4.2 Soil moisture and its role in the ecosystem...... 6! 60

1.4.3 Factors influencing soil moisture...... 8! 61

1.4.3.1 Effects of land cover on soil moisture dynamics...... 8! 62

1.4.3.2 Effects of topographic position...... 9! 63

1.4.3.3 Effects of precipitation...... 10! 64

1.4.3.4 Water uptake by plants...... 10! 65

1.4.4 Study of plant water uptake using stable isotopes ...... 11! 66

1.4.5 Importance of the study of soil moisture dynamics...... 13! 67

1.5 Objectives...... 14! 68

1.6 Hypotheses ...... 15! 69

1.7 Study site and description of the experimental setup...... 18! 70

1.7.1 Study Site...... 18! 71

1.7.2 Experimental design...... 19! 72

1.8 Description of the overall methodology...... 21! 73

1.9 References ...... 24! 74

CHAPTER II...... 30! 75

ANALYSIS OF SOIL MOISTURE DYNAMICS UNDER MIXED ANNUAL-PERENNIAL 76

ECOSYSTEMS...... 30! 77

1 Introduction ...... 30! 78

2 Materials and Methods...... 33! 79

2.1 Study area...... 33! 80

2.2 Experimental design...... 34! 81

2.3 Data collection and processing ...... 35! 82

2.3.1 Conversion of dielectric constant to volumetric water content ...... 35! 83

2.3.2 Soil bulk density ...... 36! 84

2.3.3 Calibration of soil moisture readings...... 37! 85

2.4 Statistical analysis...... 38! 86

3 Results...... 39! 87

3.1 Precipitation ...... 39! 88

3.2 Soil bulk density ...... 39! 89

3.3 Volumetric water content by depth...... 40! 90

3.4 Average soil water storage in the upper 60 cm...... 40! 91

3.4.1 Soil water storage by depth increments ...... 41! 92

3.5 Effects of land cover on soil water storage...... 42! 93

3.6 Effects of topographic position on soil water storage...... 44! 94

3.7 Other factors influencing soil moisture...... 45! 95

4 Discussion ...... 47! 96

4.1 Variations under land cover...... 47! 97

4.2 Variations under topographic position...... 52! 98

4.3 Limitations to this study...... 55! 99

5 Conclusions...... 56! 100

6 References ...... 59! 101

7 Figures...... 66! 102

8 Tables ...... 76! 103

CHAPTER III ...... 87! 104

ASSESSING WATERFLOW AND UPTAKE DEPTH PATTERNS UNDER MIXED 105

ANNUAL-PERENNIAL ECOSYSTEMS USING STABLE OXYGEN (18O) AND 106

HYDROGEN (2H) ISOTOPES...... 87! 107

1 Introduction ...... 87! 108

2 Study design and methods...... 93! 109

2.1 Study area...... 93! 110

2.2 Experimental design...... 94! 111

2.3 Soil moisture monitoring ...... 95! 112

2.4 Isotopic tracer application...... 95! 113

2.5 Collection of soil and plant samples ...... 96! 114

2.6 Rainfall and groundwater collection...... 97! 115

2.7 Water extraction and isotopic analysis ...... 98! 116

2.8 Precision and reliability of the water extraction apparatus...... 100! 117

2.9 Estimation of depth of water uptake ...... 102! 118

2.10 Statistical analysis...... 103! 119

3. Results...... 103! 120

3.1 Precipitation ...... 103! 121

3.2 Groundwater ...... 104! 122

3.3 Soil moisture content ...... 105! 123

3.4 Isotopic signature of the soil water by depth ...... 105! 124

3.5 Plant water uptake...... 106! 125

3.6 Effects of topographic position and soil water content on DWU ...... 107! 126

3.7 Water uptake by functional group: C3 and C4 species...... 108! 127

4. Discussion ...... 108! 128

4.1 Using δ18O and δD as indicators of depth of water uptake...... 108! 129

4.2 Isotopic signature of rainfall water ...... 110! 130

4.3 Isotopic signature in soils and groundwater...... 111! 131

4.4 Variations in plant water uptake ...... 114! 132

5. Conclusions...... 118! 133

6 References ...... 120! 134

7 Figures...... 127! 135

8. Tables ...... 140! 136

CHAPTER IV...... 143! 137

GENERAL CONCLUSIONS ...... 143! 138

Major findings...... 144! 139

Implications for watershed management ...... 145! 140

Challenges faced in this research ...... 146! 141

Recommendations for future research ...... 147! 142

APPENDIX A. AVERAGE SWS IN THE UPPER 30 CM BY OBSERVATION DATE AND 143

LAND COVER [2007 AND 2008]...... 152! 144

vii

APPENDIX B. AVERAGE SWS IN THE UPPER 30 CM BY OBSERVATION DATE AND 145

LAND COVER [2009 AND 2010]...... 153! 146

APPENDIX C. AVERAGE SWS IN THE UPPER 60 CM BY OBSERVATION DATE AND 147

LAND COVER [2007 AND 2008]...... 154! 148

APPENDIX D. AVERAGE SWS IN THE UPPER 60 CM BY OBSERVATION DATE AND 149

LAND COVER [2009 AND 2010]...... 155! 150

APPENDIX E. DIFFERENCES BETWEEN δ18O OBSERVED AND THE STANDARD 151

USED TO DETERMINE WATER EXTRACTION TIMES ...... 156! 152

APPENDIX F. ISOTOPIC FRACTIONATION IN NON-FLAGGED SAMPLES ...... 157! 153

ACKNOWLEDGMENTS...... 158! 154

155

viii

List of Figures 155

Figure II-1.Graphical representation of the four treatments applied to the watersheds. ...66! 156

Figure II-2. Diagram of the Theta and PR2 probes, the area of influence used for every 157

sensor and the depths of gravimetric samples used in the calibration...... 67! 158

Figure II-3. Cumulative precipitation by year...... 68! 159

Figure II-4. Precipitation registered by day (2007-2010)...... 68! 160

Figure II-5. Bulk density by watershed and topographic position ...... 69! 161

Figure II-6. Average volumetric water by depth...... 70! 162

Figure II-7. Average soil water storage in the upper 60 cm for the entire site of study...71! 163

Figure II-8. Average soil water storage in the upper 60 cm by land cover ...... 72! 164

Figure II-9. Average soil water storage in the upper 30 cm by land cover ...... 73! 165

Figure II-10. Average soil water storage in the upper 60 cm in three topographic positions 166

...... 74! 167

Figure II-11. Average soil water storage in the upper 60 cm in three topographic positions 168

...... 75! 169

Figure III-1. Design of the three experimental watersheds used in this study, each black 170

rectangle represents a plot...... 127! 171

Figure III-2. Scheme of the soil and plant samples collected ...... 128! 172

Figure III-3. Scheme of the vacuum cryogenic distillation apparatus used in this study129! 173

Figure III-4. Precipitation registered from May to October 2010...... 130! 174

Figure III-5. δ18O values of the precipitation registered during this study ...... 130! 175

Figure III-6. δD values of the precipitation registered during this study ...... 131! 176

Figure III-7. Precipitation water plotted against the Global Meteoric Water Line (Craig, 177

1961), and the Local Meteoric Water Line (Simpkins, 1995)...... 131! 178

Figure III-8. δD values of groundwater plotted against the Global Meteoric Water Line 179

(Graig, 1961) and the Local Meteoric Water Line (Simpkins, 1995) ...... 132! 180

Figure III-9. δD values of groundwater by watershed and topographic position...... 133! 181

Figure III-10. Volumetric water content by watershed, depth and date...... 134! 182

Figure III-11. Volumetric water content by watershed, depth, observation date and 183

topographic position ...... 135! 184

Figure III-12. δ18O values of the soil samples averaged by plot. Horizontal gray lines 185

represent the SE of the mean...... 136! 186

Figure III-13. δD values of the soil samples averaged by plot. Only plots where the tracer 187

was applied are shown...... 137! 188

Figure III-14. δD tracer differences among observation periods. Averaged by watershed 189

...... 138! 190

Figure III-15. Depth of plant water uptake by species within observation periods. Black 191

circles represent the mean of the water uptake range and the gray vertical lines the 192

standard deviation...... 139! 193

194

List of Tables 195

Table II-1. Characteristics of the twelve watersheds ...... 76! 196

Table II-2. Summary of the data collected per watershed...... 77! 197

Table II-3. Glimmix Procedure by depth 2007-2010. Dependent variable: volumetric 198

water content (%) ...... 78! 199

Table II-4. Annual values of soil water storage in the upper 30 and 60 cm...... 79! 200

Table II-5. Four-year analysis of SWS by increment. Dependent variable: soil water 201

storage (cm)...... 80! 202

Table II-6. Analysis of SWS by increment. Year: 2007 ...... 81! 203

Table II-7. Analysis of SWS by increment. Year: 2008 ...... 82! 204

Table II-8. Analysis of SWS by increment. Year: 2009 ...... 83! 205

Table II-9. Analysis of SWS by increment. Year: 2010 ...... 84! 206

Table II-10. Annual average soil water storage in the upper 60 cm by land cover...... 85! 207

Table II-11. Average annual soil water storage in the upper 60 cm and standard 208

deviations in three topographic positions (Summit, Side, Foot) under two land 209

covers (Rowcrop, Native prairie) ...... 85! 210

Table II-12. Average annual soil water storage in the upper 60 cm and standard 211

deviations by season under two land covers ...... 86! 212

Table III-1 Description of the 3 watersheds and species sampled per watershed...... 140! 213

Table III-2 Depth of water uptake by topographic position in July and August...... 141! 214

Table III-3 GLM procedure by species and vegetative type (C3 and C4)...... 142! 215

216 1

216

CHAPTER I 217

GENERAL INTRODUCTION 218

1.1 Contributions of this thesis to the overall mission of the Neal Smith National 219

Wildlife Refuge 220

This research was developed to generate evidence of the benefits of the 221 reintegration of perennial vegetation into agricultural dominated landscapes and also to 222 provide key information about hydrological processes of native prairie ecosystems; 223 particularly to provide information about water use and soil water content differences 224 among prairie vegetation and cropland. This study also aims to help to understand how 225 the location of prairie vegetation within a watershed can contribute to the hydrological 226 balance of the entire watershed. This information is directly applicable to one of the 227

Refuge’s priority goals: to reconstruct the original tallgrass prairie. 228

Additionally, information related to changes in depth of water uptake between 229 prairie species and crops, and differences in soil water storage under prairie and crop 230 vegetation can be incorporated into the Refuge’s prairie science class program, 231 particularly the outdoor classroom activities that are currently being developed. 232

Finally, this research fits with the Refuge’s priorities to initiate a prairie/savanna 233 land management and research demonstration program, which strives to advance problem 234 solving via land-based research. Thus, the results from this research will contribute to the 235

Refuge’s goal, by providing information and tools for the Refuge’s priority of 236 strengthening its prairie land management program. 237

238

1.2 Introduction 239

Soil moisture dynamics are the outcome of complex and highly interconnected 240 processes. Some of the factors influencing these processes are vegetative cover, 241 topographic position, soil properties, and precipitation among others. Soil moisture varies 242 among ecosystems, across time, with depth and during the growing season (Isham et al., 243

2005; Zhang and Schilling, 2006; Tamea et al., 2009), and it is also highly affected by 244 land cover primarily through water uptake (Fohrer et al., 2001). 245

Vegetative cover, in particular, plays an important role on the regional and local 246 hydrologic balance(Cubera et al., 2004). Further, different changes in vegetative cover 247 due to land use can have varying effects on the magnitude and direction of change in the 248 water balance. Characteristics inherent to each species, such as rooting distribution and 249 depth, photosynthetic pathway (e.g., C3 vs. C4), aerial biomass, phenology, etc., all 250 dictate their influence on the hydrological balance. 251

Native prairie vegetation, because of its deeper root system, and longer annual life 252 cycle, has shown to have a higher influence to the hydrological balance of the ecosystem 253 when compared to short-rooted crop species. The phenology of native prairie species also 254 greatly influences soil moisture dynamics and, in turn, ecosystem water budget. For 255 example, certain C3 prairie species, particularly cool season forbs, start their vegetative 256

3 cycle early in the growing season and remain active until late fall, thus providing soil 257 cover when rowcrop species are not active. 258

In the Midwest United States, where native prairie vegetation has been 259 dramatically replaced by row-crop species, the benefits that the native prairie vegetation 260 provide to the hydrological balance of the ecosystem have been severely diminished, 261 resulting in more frequent drought and flooding, higher fluctuations in streamflow, loss 262 of nutrients and sediment and increased overland flow (Burkart and James, 1999; 263

Schilling and Libra, 2000; Fohrer et al., 2001; Rabalais et al., 2002). 264

Increasing evidence suggests that the reincorporation of native prairie vegetation 265 into agricultural dominated landscapes can mitigate the negative effects caused by the 266 intensive agricultural row-crop production, by retaining soil, and controlling the 267 hydrological balance through transpiration (Mersie and Seybold, 1997; Abu-Zreig et al., 268

2004; Blanco-Canqui et al., 2004; Gharabaghi et al., 2006). Currently, riparian buffers of 269 perennial vegetation are being used to protect riverbanks from erosion and to reduce the 270 discharge of agrochemicals into the stream (Schultz et al., 2004; Williard et al., 2005). 271

Perennial vegetation is also being used to preserve soil on site through the Conservation 272

Reserve (CRP) program, in which landowners of agricultural land can receive annual 273 rental payments to establish long-term, resource conserving covers on eligible farmland. 274

However, some of the fundamental questions that the reintroduction of native 275 vegetation raises, such as how much and where such vegetation should be planted on the 276 landscape for maximizing benefits, have not been fully answered. This lack of 277 information creates conflicts between production and conservation. Both productivity and 278

4 conservation are two fundamental elements of sustainable agriculture, which in the long 279 term, define the stability and health of the ecosystem. 280

The question “Where on the landscape should plant perennial vegetation be 281 planted?” refers to the relative position within a watershed. Prairie vegetation in the 282 summit and side landscape position help to preserve soil in the upper parts of the 283 watershed where large quantities of soil are eroded with rainfall, while placing it in the 284 footslope position appears to contribute to reduce runoff and to regulate water discharge. 285

Similarly, the question “How much should be planted?” is important to address to know 286 the amount of vegetation that needs to be restored in order to bring back the benefits of 287 native prairie vegetation without compromising production requirements. It is imperative 288 then, to find the right balance to promote both productivity and conservation to assure 289 farmland for future generations. 290

In this research, a total of 13 watersheds were used to provide evidence that can 291 be used to answer these specific questions (the amount of native prairie vegetation that 292 should be introduced and the location in which it should be planted). 5 treatments (100% 293 row-crop, 10% prairie vegetation in the footslope, 10% prairie vegetation distributed in 294 the watershed, 20% prairie vegetation distributed in the watershed, and 100% prairie 295 vegetation) were assigned to these watersheds, which we monitored from 2007 to 2010 to 296 assess the impacts of each treatment. 297

298

1.3 Thesis organization 299

This thesis is divided into four chapters; the first chapter is a general introduction 300 that includes a literature review and general information related to this study, the 301 overarching goal and specific objectives, and the hypotheses tested during the study. It 302 also includes a general description of the data that were collected and a brief description 303 of the methodology. The second chapter is a paper that explores soil moisture dynamics 304 in detail, particularly how soil moisture patterns are influenced by different factors such 305 as land cover, topographic position, soil depth, growing season, and precipitation. The 306 third chapter is a paper in which individual plant species were studied, specifically 307 dominant C3 and C4 prairie species and a C4 crop (corn), to enhance understanding of the 308 effects of their differences in water uptake depth on soil moisture dynamics. In this 309 chapter we also address the importance of plant diversity in prairie restoration. The fourth 310 and last chapter of this thesis is a general conclusion that will also discuss 311 recommendations and suggestions for future research. 312

313

1.4 Literature review 314

1.4.1 History of native ecosystems in the Midwest 315

Historically, the area occupied by Iowa was covered in prairie; 162 million 316 hectares covered an area known as the Great Plains that extended from Manitoba and 317

Saskatchewan south through the eastern Dakotas, Nebraska, Kansas, Central Oklahoma 318 and Texas to Mexico (Samson and Knopf, 1994; Brye and Moreno, 2006). The prairie 319

6 ecosystem was established thousands of years ago, driven by climatic, environmental 320 factors and the deposition of large amounts of till by glaciers. Adapted to the 321 environment, the prairie ecosystem was an integral part of more than 80% of the 322 landscape (Smith, 1998). 323

After the European settlement in the 1800’s, a conversion started to turn areas 324 occupied by native prairie vegetation into agricultural lands. This conversion, driven by 325 the high fertility of the soils, led to an almost total replacement of native prairie 326 vegetation by intensive rowcrop agriculture. From the estimated 162 million hectares of 327 tallgrass prairie, only a fraction remains in reserves or isolated patches in a fragmented 328 landscape. In Iowa alone, is it estimated that from the historic 12.5 million ha, only 329

12,140 remain. 99.9% of the original native vegetation was lost (Samson and Knopf, 330

1994) and with it, the ecosystem benefits that native prairie vegetation provides to 331 society. 332

333

1.4.2 Soil moisture and its role in the ecosystem 334

Water plays an important role in the dynamics of terrestrial ecosystems (Tamea et 335 al., 2009). It has a direct influence on ecosystem stability, and it is the major driver of 336 plant productivity (Gholz et al., 1990). Soil water also constitutes a physical connection 337 between soil, climate, and vegetation (Isham et al., 2005). This physically integrated 338 system, where several processes take place interdependently, is analogous to links in a 339 chain and is known as the soil-plant-atmosphere continuum (Philip, 1966). 340

The relationship between soil and plants is particularly important for the 341 hydrological balance of the ecosystem. Through water uptake, plants have a strong 342 influence on the local and regional hydrological balance through their role in cycling 343 water between the soil and the atmosphere (Chahine, 1992; Mahmood and Hubbard, 344

2003). The presence of water facilitates the control of temperature variations in 345 ecosystems. Wet, forested or vegetated areas show intermediate changes in temperature 346 between day and night compared to hot and dry areas (Chahine, 1992). Soil formation 347 and soil fertility are also greatly influenced by the presence of water. Several authors 348 have described the importance of soil moisture in soil formation and development. They 349 have attributed formation of soil structure and the production of tertiary minerals that 350 define soil fertility to the presence and movement of water in the soil (Jenny, 1994; 351

Hillel, 2004; Randall and Sharon, 2005). 352

In addition to the influence of soil water on these biological and chemical 353 processes, physical processes, such as rainfall infiltration, percolation, runoff generation, 354 capillarity, groundwater distribution, and pollutant transport into the soil matrix, are also 355 directly controlled by soil water content (Gardner, 1936; Helmke et al., 2005; Tamea et 356 al., 2009). Soil water represents a controlling factor in hydrological and geotechnical 357 processes that are responsible for slope stability (Isham et al., 2005). At a larger scale, 358 soil water is responsible for the formation of ore deposits, porosity occlusion, sediment 359 cementation, petroleum migration, landslides and gas hydrate formation among other 360 numerous features of geologic interest (Wood, 2002). 361

362

1.4.3 Factors influencing soil moisture 363

Despite being the major driver of plant productivity, soil moisture is subjected to 364 the influence of factors that underline its dynamics within an ecosystem. Soil moisture 365 dynamics are thus the outcome of complex and interconnected processes. Some of the 366 factors influencing these processes are: vegetative cover, soil characteristics, topographic 367 position and precipitation (Zhang and Schilling, 2006; Liu and Zhang, 2007; Wang et al., 368

2008; Kumagai et al., 2009; Qi and Helmers, 2010). 369

Understanding how these factors influence the hydrological balance of the 370 ecosystem and the degree to which each impact this balance is imperative for the health 371 of the ecosystems. More importantly, understanding the degree of influence of these 372 factors under a given configuration of mixed annual-perennial vegetation, may allow 373 researchers and scientist to make better decisions for conservation and productivity 374 purposes. 375

376

1.4.3.1 Effects of land cover on soil moisture dynamics 377

Land cover has been recognized as a key factor controlling patterns of soil 378 moisture by influencing infiltration rates, runoff, and evapotranspiration (Cubera et al., 379

2004). Different land covers have varying degrees of above and belowground biomass 380 production. Aboveground biomass determines the amount of precipitation water that it is 381 intercepted and thus the amount that reaches the ground (Brooks et al., 2003; Chang, 382

2006). Vegetation also deposits organic matter on the soil surface and belowground 383 through root growth, which enhances infiltration rate and soil moisture holding capacity, 384

9 making surface runoff smaller, runoff timing longer, and water yield lower in vegetated 385 areas than those in non-covered ones (Chang, 2006). 386

In the Midwest United States, two vegetative covers have historical significance; 387 native prairie vegetation and agricultural crops (e.g. corn and soybean). These two land 388 covers have contrasting effects on the hydrological balance due to their inherent 389 differences in water use and uptake patterns and in plant phenology. After the European 390 settlement (see 1.4.1), agricultural crops started to gain increasing influence on the 391 hydrological balance of the entire region. 392

393

1.4.3.2 Effects of topographic position 394

Water moves in a watershed obeying laws of gravity, capillarity and suction 395 primarily (Brooks et al., 2003; Hillel, 2004). Right after infiltration, as water penetrates 396 into the soil profile and the length of the wetted part of the profile increases, suction 397 gradient decreases, since the difference in pressure head divides itself over an ever- 398 increasing distance. As this trend continues, the suction gradient of the upper part of the 399 soil profile becomes negligible, leaving the gravitational head gradient as the only force 400 to move the water downward (Hillel, 2004). 401

This gravitational gradient tends to move water from the upper parts of the 402 watershed (i.e. recharge areas) towards lower parts (i.e. discharge areas). In these 403 recharge areas, due to the effects of the gravitational gradient, there is often a rather deep 404 unsaturated zone between the water table and the land surface. Conversely, the water 405 table is found either close to or at the land surface in discharge areas (Fetter, 2001). 406

Water then moves naturally from the upper parts of the watershed and from shallower 407 soil depths to deep horizons greatly influenced by gravity. 408

409

1.4.3.3 Effects of precipitation 410

Precipitation has a direct effect on soil moisture. Once net precipitation reaches 411 the ground, it moves into the soil, forms puddles on the soil surface or flows over the soil 412 surface, depending on preceding soil moisture content. The precipitation that enters the 413 soil and is not retained by it, moves either downwards to groundwater or laterally to a 414 stream channel (Brooks et al., 2003). As water moves into the soil profile, it influences 415 directly soil moisture content before leaving the system through evaporation, plant water 416 uptake or simply moves towards the lower parts of the watersheds due to the influence of 417 gravity. 418

The rate at which net precipitation enters the soil surface depends on several soil 419 properties as well as on soil surface conditions such as plant material or liter near the soil 420 surface (Brooks et al., 2003). However, the intensity of a precipitation and the 421 antecedent soil moisture are two important factors that control the effect of a precipitation 422 event on the soil. 423

424

1.4.3.4 Water uptake by plants 425

Despite the laws of conservation, water uptake can in some ways be exceedingly 426 wasteful (Hillel, 2004). Plants are often required to withdraw large quantities of water 427 from the soil that is far beyond their essential metabolic needs. This water uptake has a 428

11 direct influence on the soil moisture content, which at a larger scale greatly influences the 429 hydrological balance of the watershed. 430

However, water uptake varies among species, and is influenced by soil water 431 availability. A study conducted in crops by (Araki and Iijima, 2005), found that water 432 uptake was influenced by the extent of dryness in the topsoil. Specifically, they observed 433 that the difference in water use among C3 forbs and C4 grasses varied according to water 434 availability in the surface soil and that this difference was driven by recent precipitation 435 history. When the water was available in the upper 30cm, the plants took water from 436 shallower sources, but during dry periods, the C3 species used proportionally more water 437 from deep profiles than the C4 species. 438

Studies have also found that there is a significant difference in plant water uptake 439 depth under different rooting patterns (Nippert and Knapp, 2007). Previous studies have 440 shown that on average corn (Zea mays) and big bluestem (Andropogon gerardii) (both C4 441 species) take water only from the first 20-30 cm of soil (Asbjornsen et al., 2007; 2008). 442

However, a greater seasonal variation has been observed in other studies with summer 443 corn, that was highly influence by the development stage of the plant, showing that corn 444 plants extract water from the upper 20 cm in the jointing and fully ripe stage, and from as 445 deep as 50 cm during the flowering state (Wang et al., 2010). 446

447

1.4.4 Study of plant water uptake using stable isotopes 448

The study of plant water uptake requires the use of techniques that allow 449 researchers to observe differences of water uptake not just by type of land cover (i.e. 450

crops, prairie), but also by species and vegetative plant types (e.g. C3, C4), and more 451 importantly, to identify patterns and differences by vegetative type and species at 452 different topographic positions, soil depths, and seasons. 453

In hydrological studies that aim to understand the effect of plant water uptake on 454 soil moisture dynamics, several tracers have been used to track water flow in soils, such 455 as chemical, fluorescent dye, radioactive, activable, biological, surface active, episodic, 456 and isotopes (Emilian, 1987; Gaspar, 1987; Kendall and McDonnell, 1998; Signh and 457

Kumar, 2005). Nevertheless, the stable isotopes of carbon (13C), hydrogen (2H) and 458 oxygen (18O) are by far the most widely used in hydrology processes, in part because they 459 do not pose any threat to the health of humans or the environment, and they are naturally 460 present in the hydrosphere. In nature, two stable isotopes of hydrogen can be found: 461 protium 1H and deuterium 2H (or D) with an abundance of 99.985% and 0.015% 462 respectively, as well as three stable oxygen isotopes, 16O, 17O, and 18O with average 463 abundances of 99.756%, 0.039%, and 0.205% respectively (Emilian, 1987; Brooks et al., 464

2003; Mook, 2006). 465

In soils were water movement is predominantly vertical, after a precipitation 466 event the water in the topsoil becomes enriched in 18O and 2H, primarily through 467 fractionation processes that take place during evaporation. Precipitation events drive this 468 enriched water into the soil profile thereby creating a gradient in isotopic composition 469 that can be measured. Because there is no isotopic fractionation during plant water 470 uptake, the isotopic concentration of plant water extracted from non-photosynthetic tissue 471 can be used to assess the approximate depth from which most water uptake occurs. 472

To assess depth of plant water uptake, soil samples at increasing depths are taken 473 and the water contained in them is analyzed for 18O and 2H. At the same time non- 474 photosynthetic plant tissue is collected and the water of this material is analyzed and 475 compared with the signature of the soil. Similarities between δ 18O and 2H in the water of 476 the vegetative tissue and the soil are used to infer depth of plant water uptake, while 477 mixing models are often used to derive more accurate estimates (Nippert and Knapp, 478

2007). Stable isotopes have been widely used to estimate water uptake depth of plants at 479 the plot scale (Plamboeck et al., 1999; Asbjornsen et al., 2008; Rowland et al., 2008). 480

At the watershed scale, naturally occurring stable isotopes have been used to 481 monitor water flowpaths according to well-defined methodologies (Emilian, 1987; 482

Kendall and McDonnell, 1998; Salem et al., 2004a; Salem et al., 2004b). Stable isotope 483 tracers offer unique virtues as water tracers in watersheds studies, as they are not 484 subjected to chemical reactions during contact with the soil, they undergo evaporation 485 and fractionation causing a gradient difference between meteoric and subsurficial water, 486 changes in isotopic concentrations increase as they move through the unsaturated zone, 487 and in theory isotopic concentrations in a given water will change only when it is mixed 488 with other water resources having different concentrations (Buttle and McDonnell, 2005). 489

490

1.4.5 Importance of the study of soil moisture dynamics 491

As already mentioned, soil moisture is the outcome of complex and highly 492 interconnected processes, these processes are at the same time highly influenced by 493 biological and physical factors. Understanding how these factors influence soil moisture 494

14 and ultimately the hydrological balance of the ecosystem and the degree to which every 495 of them impacts this balance it’s imperative for the hydrological balance an ecosystem. 496

More importantly, understanding the degree of influence of these factors, 497 particularly how the use of prairie vegetation influences the hydrological balance of the 498 ecosystem, may allow policy makers and land managers to make better decisions for soil 499 conservation without compromising crop productivity benefits. 500

However, the study of soil moisture dynamics goes beyond just the hydrological 501 balance of a watershed. At the ecosystem scale, the understanding of its role in the plant- 502 soil-atmosphere continuum provides the elements necessary in crop management and it 503 can also provide the basis for flood control management and sustainable agricultural 504 practices. 505

506

1.5 Objectives 507

This research addresses three main objectives related to soil water dynamics 508 under mixed annual-perennial (i.e. prairie and row-crop) vegetation. The literature review 509 revealed a good range of studies that examined specific factors that influence soil 510 moisture dynamics; however, very few of these studies were conducted under natural and 511 applicable field conditions. Specifically, my research will contribute to filling these 512 current knowledge gaps about the relationship of soil moisture to land cover type and 513 topographical position by achieving the following three objectives: 514

515

1. Quantify the differences in water content under perennial and annual vegetation in 516

a mixed agricultural watershed. 517

2. Estimate the depth of water uptake for dominant C3 and C4 species in a mixed 518

agricultural watershed using stable oxygen and hydrogen isotope ratios. 519

3. Assess the effect of topographic position on soil moisture dynamics and depth of 520

water uptake in perennial and annual row-crop vegetation. 521

522

The first and second objectives will be treated individually in chapter 2 and 3 523 respectively; the third objective will be part of both the 2nd and the 3rd chapters of this 524 thesis. An overall conclusion will be provided in chapter 4. 525

526

1.6 Hypotheses 527

In this thesis I tested three main hypotheses, one concerning the effects of land 528 cover on soil moisture dynamics, a second about the differences in water uptake among 529 species, with particular interest in C3 and C4, and a third about the effects of topographic 530 position on soil moisture dynamics and plant water uptake. Each hypothesis and the 531 supporting rationale is summarized below. 532

533

534

535

536

Hypothesis #1. Differences in soil water content 537

Soil water content will be lower (e.g., higher soil water storage capacity) under 538 prairie vegetation compared to under corn crops early in the growing season (June-early 539

July) when prairie plants are active but crops have not yet fully established, while the 540 reverse pattern will occur during the peak growing season (late July-August) when crops 541 have reached maximum growth rates. 542

Rationale. It is well known that different land covers have different effects on 543 hydrological processes such as evapotranspiration. In a study conducted at the NSNWR 544 comparing the effects of land cover on soil moisture, evapotranspiration and groundwater 545 recharge, Zhang and Schilling (Zhang and Schilling, 2006) observed that grassland cover 546 reduced soil moisture through evapotranspiration and that it was less susceptible to 547 changes in groundwater changes as compared to bare ground. In another recent study of 548 differences in water use between perennial plants big bluestem (Andropogon gerardii, a 549

C4 grass), coneflower (Ratibida pinnata, a C3 forb) and corn (Zea mays a C4 annual crop 550 species), it was shown that at the ecosystem level, total evapotranspiration was similar 551 under these two cover types, but on a per leaf area basis, transpiration for the two prairie 552 species was significantly greater than for the corn (Mateos Remigio et al., in review.). 553

Understanding how differences in depth of water uptake among crop and prairie, and 554 among C3 and C4 prairie species, contribute to different water use patterns is important 555 for selecting appropriate species for maximizing ecohydrological functions of 556 strategically located prairie strips in agricultural landscapes. 557

558

Hypothesis #2. Plant water uptake depth 559

During periods of adequate soil moisture availability (e.g., early in the growing 560 season), crops and prairie species will obtain their water from relatively shallow depths in 561 the soil profile. As soil moisture becomes more limiting (later in the growing season), 562 prairie species (especially C3 forbs) will shift their depth of water uptake to deeper depths 563 in the soil profile, whereas corn and C4 prairie species will have more limited capacity to 564 obtain water from deeper depths. This variation of depth of water uptake (especially by 565

C3 forbs) can contribute to a better control of the hydrological balance in those 566 watersheds with strips of perennial vegetation, due to the presence of roots at deeper 567 profiles that allow plants to access water form deeper profiles. 568

Rationale. Previous studies have shown that on average corn (Zea mays) and big 569 bluestem (Andropogon gerardii), both C4 species, take water only from the first 20-30 cm 570 of soil (Asbjornsen et al., 2007; 2008). However, a greater seasonal variation has been 571 observed in other studies with summer corn, showing that corn plants extract water from 572 as deep as 50 cm (Wang et al., 2010). Nippert an Knapp, (2007) in a study of C3 and C4 573 plants, observed that differences in water uptake depth between species were more 574 variable when water was most limiting: C4 plants shifted their depth of water uptake to 575 deep soil profiles in months when water availability decreased, while C3 forbs and shrubs 576 appeared to avoid competition by taking water from even deeper profiles than C4 plants. 577

More research is need to better understand patterns of depth of water uptake between 578 crop and prairie species and between C3 forbs and C4 grasses growing in reconstructed 579 prairie communities. 580

581

Hypothesis # 3. Effect of topographic position on soil moisture and plant water uptake 582

Prairie and crop species will use water from deeper depths in the soil profile in the 583 summit position compared to the footslope position due to lower water availability during 584 dry periods in the upper parts of the watershed. 585

Rationale. Topographic location of the prairie strips in the watershed will have 586 impacts on the patterns of both depth of water uptake and soil water content. Specifically 587 we expect to observe higher soil water content in the footslope position compared to the 588 upslope position, and thus relatively more shallow depths of plant water uptake for both 589 prairie and crop species in the toe position. In contrast, plants should take up water from 590 deeper depths at the summit position where soil moisture is likely to be more limiting, 591 while we expect C3 species to have greater capacity to take up water from deeper depths 592 than C4 species. As previous research has shown, when water is available in shallow 593 profiles, plants tend to use more of this shallower water (Nippert and Knapp, 2007). 594

595

1.7 Study site and description of the experimental setup 596

1.7.1 Study Site 597

The research was conducted in the Neal Smith National Wildlife Refuge, located 598 in Prairie City, Iowa (NSNWR, 41°33 ́N, 93°16 ́W). The refuge was created in 1991 to 599 convert over 3,400 ha of agriculturally dominated landscape to native perennial 600 vegetation. To this day, the Refuge consists of a mosaic of reconstructions and 601

19 agricultural land uses with approximately 1,200 ha planted to tallgrass prairie through 602 annually successive plantings. Most soils at the research sites are classified as Ladoga 603

(Mollic Hapludalf) or Otley (Oxyaquic Argiudolls) soil series, which are highly erodible 604 with slopes ranging from 5 to 14% (NRCS, 2010). The average precipitation is 910mm 605

(mean from 1981-2010). A more detailed description of the sites is given in Chapter 2 606 and 3 of this thesis. 607

608

1.7.2 Experimental design 609

A total of 13 watersheds were used to test the hypotheses. Strips and buffers of 610 prairie vegetation were planted at different topographic positions across agricultural 611 fields, yielding a total of 5 treatments as shown in Figure A; 100% rowcrop, 10% prairie 612 cover as buffer, 10% prairie vegetation distributed in strips and buffer, 20% prairie 613 vegetation distributed in strips and buffer, and 100% prairie vegetation. 614

Strips and buffers of prairie vegetation were planted in the summer of 2007 in 615 small watersheds under a corn-soybeans yearly rotation. The 100% prairie vegetation 616 watershed is an 18-year-old restored prairie. Three fiberglass access tubes were installed 617 per watershed at the summit, side and footslope positions to measure soil moisture and 618 two groundwater wells in the summit and footslope. 619

620

621

Figure A. Graphical representation of the watersheds 622 623

The watersheds were distributed in three sites, Basswood, Orbweaver and Interim, 624 containing 6, 3 and 4 watersheds respectively, as described in Table A. 625

Table A. Description of the 13 watersheds. 626

Topographic location of Watershed Site Prairie cover (%) Area (ha) prairie cover Basswood 1 Basswood 10% Footslope 0.53 Basswood 2 Basswood 10% Footslope & Summit 0.48

Basswood 3 Basswood 20% Footslope & Summit 0.47

Basswood 4 Basswood 20% Footslope & Summit 0.55

Basswood 5 Basswood 10% Footslope & Summit 1.24

Basswood 6 Basswood 100% rowcrop None 0.84 Orbweaver 1 Orbweaver 10% Footslope 1.18 Orbweaver 2 Orbweaver 20% Footslope, Side & Summit 2.40

Orbweaver 3 Orbweaver 100% rowcrop None 1.24

Interim 1 Interim 10% Footslope, Side & Summit 3.00

Interim 2 Interim 10% Footslope 3.19

Interim 3 Interim 100% rowcrop None 0.73

Interim 4 Interim 100% prairie None 0.60 627

To assess differences in depth of plant water uptake, three watersheds were 628 selected at the Interim site, based on their relative close location and because of their 629

21 configuration regarding land cover. The watersheds we used were: Interim 1, Interim 3, 630 and Interim 4. A total of 9 plots (three per watershed) were marked, two in the summit 631 and one in the footslope positions as shown in Figure B. 632

633

634

Figure B. Graphical representation of the watersheds used to assess depth 635 of water uptake. 636 637

638

1.8 Description of the overall methodology 639

This is a general description of the methodology used in this research, a more specific 640 description will be provided in Chapter 2, and Chapter 3 of this thesis. 641

Soil moisture readings were taken biweekly from previously installed access tubes 642 under the perennial strips, annual crop, and reconstructed prairie from April to November 643 during 2007, 2008, 2009 and 2010 using a Theta (ML2, Delta-T Devices, Cam-bridge, 644

UK) and a PR2 Probe (PR2, Delta-T Devices, Cambridge, UK). The Theta Probe was 645 used to measure soil moisture in the first 6 cm of soil and the PR2 probe to take readings 646

22 at 10, 20, 30, 40, 60 and 100 cm. Soil cores were collected from 0-15, 15-25, 25-35, 35- 647

50, 50-70, and 70-10cm close to the access tube to calibrate Theta and PR2 readings. 648

Soil cores were taken to assess soil bulk density in the study site [perennial 649 vegetation & annual crops]. The soil cores were collected in every watershed at three 650 topographic positions; Summit, Side and Footslope at five depths 10, 20, 30, 60 and 651

100cm.The samples were weighed and dried in a drying oven at 105ºC for 24 hours. Soil 652 bulk density was estimated dividing dry weight of every sample over its volume. 653

After the application of a Deuterium tracer in the study of depth of plant water 654 uptake, soil and plant samples were collected in July 22-28 and August 29-30, 2010 to 655 assess depth of water uptake. One set of six soil cores was collected per every plant 656 sample, and two replicates were collected per plant. Soil cores were collected at 657 increments from 0-10, 10-20, 20-30, 30-50, 50-70 and 70-100cm using a bucket auger at 658 the Interim 1 and Interim 4 sites, and from 0-10, 10-20, 20-30, 30-50 and 50-70 in 659

Interim 3 due to the shallower groundwater table. 660

Rainfall collectors were installed to sample water during rainfall events. These 661 data were used to estimate the percentage of meteoric water entering the watersheds. To 662 avoid evaporation a funnel was placed in the rain gauge, to allow the water to get into the 663 gauge, but reducing the area of evaporation, other investigators have used a ping-pong 664 balls, however, our collection flask was placed inside a wooden box to avoid evaporation 665 and the samples were collected right after the precipitation event reducing fractionation in 666

δ18O ‰ and δD ‰ values in rainfall water. 667

Groundwater was also collected to complement and compare concentrations of 668

δ18O ‰ and δD ‰. The groundwater wells consisted of ¾ inch PVC piping capped at the 669 bottom with a pointed tip. The wells were equipped with slits covering the bottom third 670 of the piping to allow movement of groundwater into the well. Water table depths were 671 determined biweekly over the course of the growing season with The Little Dipper 672

(Heron Instruments, Inc, Burlington, Ontario). 673

674

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

691

1.9 References 692

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Experimental investigation of runoff reduction and sediment removal by 694

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Araki, H., Iijima, M., 2005. Stable isotope analysis of water extraction from subsoil in 696

upland rice (Oryza sativa L.) as affected by drought and soil compaction. Plant 697

and Soil 270, 147-157. 698

Asbjornsen, H., Mora, G., Helmers, M.J., 2007. Variation in water uptake dynamics 699

among contrasting agricultural and native plant communities in the Midwestern 700

US. Agriculture Ecosystems & Environment 121, 343-356. 701

Asbjornsen, H., Shepherd, G., Helmers, M., Mora, G., 2008. Seasonal patterns in depth of 702

water uptake under contrasting annual and perennial systems in the Corn Belt 703

Region of the Midwestern US. Plant and Soil 308, 69-92. 704

Blanco-Canqui, H., Gantzer, C.J., Anderson, S.H., Alberts, E.E., Thompson, A.L., 2004. 705

Grass barrier and vegetative filter strip effectiveness in reducing runoff, sediment, 706

nitrogen, and phosphorus loss. Soil Science Society of America Journal 68, 1670- 707

1678. 708

Brooks, K.N., Ffolliot, P.F., Gregersen, H.M., DeBano, L.F., 2003. Hydrology and the 709

management of watersheds. Blackwell, Ames, Iowa. 710

Brye, K.R., Moreno, L., 2006. Vegetation removal effects on soil quality in a native 711

tallgrass prairie fragment in east-central Arkansas. Nat Area J 26, 94-100. 712

Burkart, M.R., James, D.E., 1999. Agricultural-nitrogen contributions to hypoxia in the 713

Gulf of Mexico. Journal of Environmental Quality 28, 850-859. 714

Buttle, J.M., McDonnell, J.J., 2005. Isotope tracers in catchment hydrology in the humid 715

tropics. Forests, Water and People in the Humid Tropics, 770-789. 716

Chahine, M.T., 1992. The hydrological cycle and its influence on climate. Nature 359, 717

373-380. 718

Chang, M., 2006. Forest hydrology : an introduction to water and forests. CRC/Taylor & 719

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Cubera, E., Montero, M.J., Moreno, G., 2004. Effect of land use on soil water dynamics 721

in dehesas of central-western Spain. Sustainability of Agrosilvopastoral Systems - 722

Dehesas, Montados 37, 109-123. 723

Emilian, G., Ph.D., 1987. Modern Trends In Tracer Hydrology. CRC Press. 724

Fetter, C.W., 2001. Applied hydrogeology. Prentice Hall, Upper Saddle River, N.J. 725

Fohrer, N., Haverkamp, S., Eckhardt, K., Frede, H.G., 2001. Hydrologic response to land 726

use changes on the catchment scale. Phys Chem Earth Pt B 26, 577-582. 727

Gardner, W., 1936. The role of the capillary potential in the dynamics of soil moisture. J 728

Agric Res 53, 0057-0060. 729

Gaspar, E., 1987. Modern trends in tracer hydrology. CRC Press, Boca Raton, Fla. 730

Gharabaghi, B., Rudra, R.P., Goel, P.K., 2006. Effectiveness of vegetative filter strips in 731

removal of sediments from overland flow. Water Qual Res J Can 41, 275-282. 732

Gholz, H.L., Ewel, K.C., Teskey, R.O., 1990. Water and Forest Productivity. Forest 733

Ecology and Management 30, 1-18. 734

Helmke, M.F., Simpkins, W.W., Horton, R., 2005. Fracture-controlled nitrate and 735

atrazine transport in four Iowa till units. Journal of Environmental Quality 34, 736

227-236. 737

Hillel, D., 2004. Introduction to environmental soil physics. Elsevier Academic Press, 738

Amsterdam ; Boston. 739

Isham, V., Cox, D.R., Rodriguez-Iturbe, I., Porporato, A., Manfreda, S., 2005. 740

Representation of space-time variability of soil moisture. P R Soc A 461, 4035- 741

4055. 742

Jenny, H., 1994. Factors of soil formation : a system of quantitative pedology. Dover, 743

New York. 744

Kendall, C., McDonnell, J.J., 1998. Isotope tracers in catchment hydrology. Elsiever, 745

Amsterdam, The Netherlands. 746

Kumagai, T., Yoshifuji, N., Tanaka, N., Suzuki, M., Kume, T., 2009. Comparison of soil 747

moisture dynamics between a tropical rain forest and a tropical seasonal forest in 748

Southeast Asia: Impact of seasonal and year-to-year variations in rainfall. Water 749

Resour Res 45, -. 750

Liu, G., Zhang, J.H., 2007. Spatial and temporal dynamics of soil moisture after rainfall 751

events along a slope in Regosols of southwest China. Hydrological Processes 21, 752

2778-2784. 753

Mahmood, R., Hubbard, K.G., 2003. Simulating sensitivity of soil moisture and 754

evapotranspiration under heterogeneous soils and land uses. Journal of Hydrology 755

280, 72-90. 756

Mateos Remigio, V.S., Asbjornsen, H., Tarara, J., T., S., in review. Evaluating 757

transpiration in an annual crop and perennial prarie species using the heat balance 758

method in Central Iowa, U.S.A. Manuscript in preparation. 759

Mersie, W., Seybold, C.A., 1997. Design, construction, and operation of tilted beds to 760

simulate agricultural runoff in vegetative filter strips. Weed Technol 11, 618-622. 761

Mook, W.G., 2006. Introduction to Isotope Hydrology. Taylor & Francis/Balkema, 762

Leiden, The Netherlands. 763

Nippert, J.B., Knapp, A.K., 2007. Linking water uptake with rooting patterns in grassland 764

species. Oecologia 153, 261-272. 765

NRCS, 2010. Keys to soil taxonomy. United States Dept. of Agriculture, Natural 766

Resources Conservation Service, Washington, D.C. 767

Philip, J.R., 1966. Plant Water Relations - Some Physical Aspects. Ann Rev Plant Physio 768

17, 245-&. 769

Plamboeck, A.H., Grip, H., Nygren, U., 1999. A hydrological tracer study of water 770

uptake depth in a Scots pine forest under two different water regimes. Oecologia 771

119, 452-460. 772

Qi, Z.M., Helmers, M.J., 2010. Soil Water Dynamics under Winter Rye Cover Crop in 773

Central Iowa. Vadose Zone Journal 9, 53-60. 774

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Mexico hypoxia and the Mississippi River. Bioscience 52, 129-142. 776

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Press Cambridge, UK. 778

Rowland, D.L., Leffler, A.J., Sorensen, R.B., Dorner, J.W., Lamb, M.C., 2008. Testing 779

the efficacy of deuterium application for tracing water uptake in peanuts. T Asabe 780

51, 455-461. 781

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and water quality to determine the pattern and spatial extent of groundwater flow: 783

Nagaoka area, Japan. Hydrogeology Journal 12, 563-575. 784

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isotopes to trace flow lines: Nagaoka area, Japan. Ground Water 42, 83-91. 786

Samson, F., Knopf, F., 1994. Prairie conservation in North-America. Bioscience 44, 418- 787

421. 788

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row crop land use in Iowa. Journal of Environmental Quality 29, 1846-1851. 790

Schultz, R.C., Isenhart, T.M., Simpkins, W.W., Colletti, J.P., 2004. Riparian forest 791

buffers in agroecosystems - lessons learned from the Bear Creek Watershed, 792

central Iowa, USA. Agroforestry Systems 61-2, 35-50. 793

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Resources. Narosa Publishing, Delhi, India. 795

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Province, China. Agricultural Water Management 97. 803

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Qiu, Y.Z., Xu, B., 2008. The dynamics variation of soil moisture of shelterbelts 805

along the Tarim Desert Highway. Chinese Sci Bull 53, 102-108. 806

Williard, K.W.J., Schoonover, J.E., Zaczek, J.J., Mangun, J.C., Carver, A.D., 2005. 807

Nutrient attenuation in agricultural surface runoff by riparian buffer zones in 808

southern Illinois, USA. Agroforestry Systems 64, 169-180. 809

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of the Southern High Plains, USA. Ground Water 40, 438-447. 811

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evapotranspiration, and groundwater recharge: a field observation and analysis. 813

Journal of Hydrology 319, 328-338. 814

815

CHAPTER II 816

ANALYSIS OF SOIL MOISTURE DYNAMICS UNDER MIXED ANNUAL- 817

PERENNIAL ECOSYSTEMS 818

A paper to be submitted to Geoderma 819 Jose Gutierrez Lopez, Heidi Asbjornsen, Thomas Isenhart, Matthew Helmers 820 821

1 Introduction 822

Soil moisture dynamics reflect highly interconnected processes such as vegetative 823 cover, soil characteristics, topographic position and precipitation. As a result, soil 824 moisture varies among ecosystems under different land covers, across time, and by depth 825 within the soil profile (Isham et al., 2005; Zhang and Schilling, 2006; Tamea et al., 826

2009). An understanding of soil moisture dynamics can provide land managers with 827 critical information to increase productivity and better implement conservation practices. 828

Land cover has been recognized as an important factor controlling patterns of soil 829 moisture by influencing infiltration rates, runoff, and evapotranspiration (Cubera et al., 830

2004). The variability of biomass under different land covers and its influence on rainfall 831 interception is of particular importance (Brooks et al., 2003; Chang, 2006). Above and 832 below-ground organic matter increases infiltration rate and soil moisture holding 833 capacity, reducing surface runoff and decreasing water yield in vegetated areas (Chang, 834

2006). 835

In the Midwest United States, where rowcrops have replaced the majority of the 836 native prairie vegetation, the impact of prairie on the hydrologic balance of the ecosystem 837 has been severely diminished. In Iowa alone, it is estimated that of the historic 12.5 838 million ha of prairie, only 12,140 ha remain, representing a 99.9% reduction of the native 839 vegetation (Samson and Knopf, 1994). Several researchers have indicated that the 840 increases in the frequency of drought and flooding events, higher fluctuations in 841 streamflow, and increased overland flow are the result of this landscape scale conversion 842 of native prairie vegetation to rowcrop agriculture (Burkart and James, 1999; Schilling 843 and Libra, 2000; Fohrer et al., 2001; Rabalais et al., 2002). 844

Over the past two decades, there has been growing emphasis on the potential 845 hydrologic benefits of the incorporation of perennial vegetation in areas dominated by 846 rowcrop agriculture (Tilman, 1999; Tilman et al., 2002; Boody et al., 2005). One of the 847 earliest accounts, by Weaver and Flory (1934), suggested that increased drought 848 resistance, the ability to take water from deeper soil depths, and greater plant diversity 849 were advantages of native prairie vegetation over annual crops (e.g. corn, wheat, oats, 850 rye, barley, and sorghums). Recent studies have shown that the incorporation of perennial 851 covers have greater hydrologic benefits for the ecosystem than annual crops (Brye et al., 852

2000; Zhang and Schilling, 2006). In a smaller scale, Weaver (1941) observed higher 853 water losses (ET) under prairie (Andropogon furcatus) cover than pasture (Bouteloua 854 curtipendula). Aditionally, he observed that the removal of aerial biomass lessened 855 transpiration and increased evaporation from the soil surface (Weaver, 1941). 856

Prairie restoration or reconstruction was first suggested by Ada Hayden (1919), 857 with the goal “to preserve a historic and banished ecosystem” and “to secure the present 858 and the coming generations a heritage” (Smith, 1998). Recently, the restoration or 859 reconstruction of native prairie vegetation has the goal not only to preserve a historic 860 feature, but also to restore such functions as hydrologic regulation (Hernandez-Santana et 861 al., In Press), nutrient regulation (Baer et al., 2002; Brye et al., 2003; Zhou et al., 2010), 862 and water purification (Schilling, 2002), particularly in landscapes dominated by row 863 crop agriculture. Much of the information about the use of perennial vegetation in 864 agricultural areas comes from riparian buffer research, where native shrubs, grasses and 865 trees are combined, primarily to mitigate sediment and nutrient loss (Schultz et al., 2004; 866

Lowrance and Sheridan, 2005; Williard et al., 2005). While numbers studies have 867 assessed the impact of buffers on sediment and nutrient loss and runoff (Schultz et al., 868

2004; Lowrance and Sheridan, 2005; Williard et al., 2005), very few have quantified the 869 effects on soil moisture dynamics. For example, some work has examined the effect of 870 perennial vegetative strips on soil hydrologic processes at the plot scale and under 871 controlled rainfall conditions (Abu-Zreig et al., 2004; Blanco-Canqui et al., 2004; 872

Gharabaghi et al., 2006), using tilted beds to simulate agricultural runoff in filter strips 873

(Mersie and Seybold, 1997), and through modeling (Fox et al., 2009). 874

Overall, there is a lack of studies of the impacts of perennial vegetation on soil 875 moisture dynamics, and few have studied the relevance of landscape position for 876 hydrologic regulation (Hoover and Hursh, 1943; Ziadat et al., 2010). In particular, a 877 robust understanding of soil-water dynamics within native prairie vegetation reintroduced 878

33 into rowcrop dominated landscapes requires a broader study under field-scale 879 configurations and natural field conditions. Understanding the effects of the topographic 880 location of the prairie strips within a watershed is also of great importance, as it will help 881 in the management of runoff dynamics of zero order watersheds. 882

This study examines the effects of the reintroduction of native prairie vegetation 883 into agricultural fields on the hydrological balance. The objectives of this study were [1] 884 to assess differences in soil water storage at four intervals (0-30, 30-60, 0-60 and 1-100 885 cm) and volumetric water content at six depths (6, 10, 20, 30, 60 and 100 cm) under two 886 land covers: native prairie and agricultural crops, and [2] to assess the effects of 887 topographic position, season, soil depth and precipitation on soil moisture dynamics. It 888 was hypothesized that soil water content will be lower (i.e. higher soil water storage 889 capacity) under prairie vegetation compared to row-crops (corn, soybeans) early in the 890 growing season (June, early-July) when prairie plants are active and crops are not fully 891 established, while the reversed pattern will occur during the peak growing season (late 892

July-August) when crops have reached maximum growth rates. 893

894

2 Materials and Methods 895

2.1 Study area 896

The research was conducted in the Neal Smith National Wildlife Refuge near 897

Prairie City, Iowa (NSNWR, 41°33 ́N, 93°16 ́W). The refuge was created in 1990 with 898 the central mission of converting over 3,400 ha of agriculturally dominated landscape to 899

34 native perennial vegetation. To date, the Refuge consists of a mosaic of reconstructions 900 and agricultural land uses with approximately 2500 ha planted to tallgrass prairie through 901 successive annual plantings. 902

Most soils at the research sites are classified as either Ladoga (Mollic Hapludalf) 903 or Otley (Oxyaquic Argiudolls) soil series, which are highly erodible with slopes ranging 904 from 5 to 14% (NRCS, 2010). Percentages of sand, silt and clay observed in each site are 905 presented in Table II.1. The mean average precipitation registered over the last 30 years 906

(1981-2010) was 910 mm, with the majority of the large storms occurring between May 907 and August (NCDC, 2011). Precipitation data for this study were recorded at the 908

MesoWest (ID = NSWI4) weather station located in the Neal Smith Wildlife Refuge in 909

Jasper County, Iowa. The weather station registered data from March to November in 910

2007, 2008, 2009 and 2010. 911

912

2.2 Experimental design 913

Twelve zero-order watersheds (ephemeral in hydrologic flow regime) were used 914 in this study under a balanced incomplete block design. The watersheds are distributed in 915 four blocks, located in three sites: Basswood (two blocks), Orbweaver (one block), and 916

Interim (one block), with six, three, and three watersheds per site, respectively (Table 917

II.1). Watershed area ranged from 0.5 to 3.2 ha. One of four treatments was assigned to 918 every watershed (three replicates per treatment). The treatments consisted of strips of 919 native prairie vegetation (hereafter “NPV”) and corn and soybean row-crop vegetation 920

(hereafter “ROWCROP”) planted in four configurations having different amounts and 921

35 topographical positions of NPV (100% rowcrop, 10% NPV in the footslope position, 922

10% NPV in the footslope, side and summit positions, 20% NPV distributed in the 923 footslope, side and summit positions) (Figure II.1). NPV was planted in different 924 topographic positions to assess optimal position to increase hydrological benefits. The 925

NPV was planted in July 2007. The seed mixture used in the planting consisted of 926 approximately 20 native prairie forbs and grasses with four primary species in the mix, 927 including indiangrass (Sorghastrum nutans Nash), little bluestem (Schizachyrium 928 scoparium Ness), big bluestem (Andropogon gerardii Vitman), and aster (Aster spp.). 929

This mixture is similar to the one commonly used by the NSNWR staff in prairie 930 reconstruction practices. The width of the NPV varied from 27 to 41 m at the footslope 931 and from 5 to 10 m at the side and summit positions. 932

Fiberglass access tubes (Delta-T Devices), used to monitor soil moisture (Figure 933

II.2) were installed in the summit, side and footslope positions of the watershed. These 934 access tubes were installed inside the NPV or in the row-crop area, in the approximate 935 center of the watersheds to evaluate the effects of land cover on soil moisture dynamics 936

(Table II.2). 937

938

2.3 Data collection and processing 939

2.3.1 Conversion of dielectric constant to volumetric water content 940

Soil moisture was assessed by recording voltage output (mV) approximately 941 every two weeks from April through November in 2007, 2008, 2009 and 2010 (Table 942

II.2), with a HH2 Meter, using a Theta (ML2, Delta-T Devices, Cambridge, UK) and a 943

PR2 Probe (PR2, Delta-T Devices, Cambridge, UK). The Theta Probe was used to 944

measure voltage outputs in the upper 5 cm of soil and the PR2 probe to take readings at 945

depths of 10, 20, 30, 40, 60 and 100 cm (Figure II.2). Three readings were taken around 946

each access tube using the Theta probe and three inside the access tubes at each depth 947

using the PR2 probe, twisting the probe 120º between readings to get a more 948

representative reading. Voltage outputs were averaged and then converted to the square 949

root of permittivity as follows: 950

951

ε =1.125 − 5.53V + 67.17V 2 − 234.42V 3 + 413.56V 4 − 356.68V 5 +121.53V 6 (1) 952

Where ε is the permittivity, and V the voltage output (mV) 953

€ 954

The square root of permittivity was then use to calibrate soil moisture readings via 955

linear regression with the observed volumetric water content (see below for details). 956

957

2.3.2 Soil bulk density 958

Soil bulk density (ρb) was estimated by taking soil cores at 10, 20, 30, 60 and 100 959

cm at three topographic locations in every watershed (summit, side and footslope). Soil 960

samples were taken using a soil corer fitted with aluminum rings of 7.5 cm in diameter by 961

7.5 cm in length. The samples were weighed and dried in a drying oven at 105ºC for 48 h 962

at the Porous Media Lab, Agricultural and Biosystems Engineering, Iowa State 963

University. Soil bulk density (ρb) was estimated by dividing dry weight of every sample 964

over the volume of the aluminum ring. 965

2.3.3 Calibration of soil moisture readings 966

Soil moisture readings were calibrated by assessing gravimetric water content (θg) 967 for one sensor of the Theta, and six sensors of the PR2 probe. Soil samples were collected 968 at a distance of one meter around the access tubes during periods of high and low soil 969 moisture contents (Figure II.2), at the same time the voltage output was recorded with the 970

HH2 Meter. The number of samples collected varied per sited, 2 to 4 sets of samples 971 were collected each year, except in 2009 where no soil samples for calibration were 972 collected. All the soil samples were dried for 48 h at 104ºC in a drying oven and θg was 973 estimated following the methodology indicated by Hillel (2004). 974

Soil parameters a0 and a1 (Equation 2) were estimated through a linear regression 975 between observed θv and the root square of the permittivity (ε), as estimated by 976

Equation 1. The θv used in the calibration was estimated by multiplying the θg of a 977 specific depth by the observed ρb of that same depth. A total of 273 sets of parameters 978 were estimated. 979

After the soil parameters (a0 and a1) were estimated, all voltage outputs were 980 converted to θv using the following equation: 981

ε − a0 θv = (2) 982 a1

3 3 Where θv is the volumetric water content (cm /cm ). Default a0 and a1 parameters 983 are provided€ by Delta-T Devices, for mineral (1.6, 8.4) and organic (1.3, 7.7) soils. 984

However, for optimum accuracy we obtained soil parameters for our specific soil types. 985

θv was converted to soil water storage (SWS) by multiplying the θv by the area of 986 influence of a given sensor in both the Theta and PR2 probes (Figure II.2). SWS was 987 estimated at four intervals (0-30, 30-60, 0-60 and 0-100 cm), by summing the SWS from 988 the corresponding depths of every interval. Depths 6, 10, 20 and 30 cm where used in the 989

0-30 interval, 40 and 60 cm for the 30-60 cm interval, depths 6, 10, 20, 30, 40 and 60 990 were used for the 0-60 cm interval, and all depths were used in the 0-100 cm interval. 991

992

2.4 Statistical analysis 993

Significant differences in θv (α= 0.05) by individual depths (6, 10, 20, 30, 40, 60, 994 and 100 cm) and by interval (0-30, 30-60, 0-60 and 0-100 cm) were determined using the 995

Glimmix Procedure in SAS (SAS, Institute, 2001), with site, watershed, year, 996 precipitation (the sum of the precipitation registered within the 7 days prior to the voltage 997 output reading), landcover (NPV and ROWCROP), topographic position and season as 998 fixed effects. Individual access tubes and observation dates were analyzed as random 999 effects because of the nature of our analysis (repeated measurements), and to account for 1000 variability within seasons in the case of observation dates. The analysis of SWS was first 1001 run using all four years (hereafter, “four-year” analysis), to increase the power of our 1002 analysis for variables like landcover and topographic position, and then for each 1003 individual year from 2007 to 2010 (hereafter “annual” analysis), to detect specific 1004 differences within years in variables like season or landcover. 1005

To assess the effects of the position of NPV within the watershed on soil water 1006 storage, the interaction between land cover and topographic position was included in the 1007

39 model. The interactions of season and landcover and season with topographic position 1008 were also included to assess the effectiveness of different land covers on soil water 1009 storage throughout the growing season and how it is affected by topographic position. In 1010 the four-year analysis the year variable is included to account for the variability of the 1011 annual crop rotation (corn, soybeans). 1012

1013

3 Results 1014

3.1 Precipitation 1015

Precipitation from March to November was 900, 951, 866, and 1326 mm in 2007, 1016

2008, 2009 and 2010 respectively (Figure II.3). The distribution of the precipitation 1017 varied in all four years with relatively wet and dry conditions observed in different 1018 months each year (Figure II.4). Relative to the 30-year average, annual precipitation was 1019

1% lower, 4% higher, 5% lower, and 45% higher in 2007, 2008, 2009, and 2010, 1020 respectively. 1021

1022

3.2 Soil bulk density 1023

-3 The average ρb of all samples (n=180) was 1.43 g⋅cm , and ranged from 1.06 to 1024

1.78 g⋅cm-3 with a standard deviation of 0.12 g⋅cm-3. For most of the sites sampled bulk 1025 density increased with depth (Figure II.5). Although visual differences were observed in 1026

Figure II.5, no statistical analysis was performed on bulk density samples. 1027

1028

3.3 Volumetric water content by depth 1029

Marked differences in volumetric water content (VWC) were observed by 1030 sampling depth. Shallower depths (5, 10, 20, and 30 cm) showed greater variation than 1031 deeper depths (60 and 100 cm) in all four years. The variation of θv at shallower depths 1032 was closely associated with rainfall (Figure II.6 and Table II.3). There was no effect of 1033 year on mean annual θv estimated by depth (Table II.3). 1034

The statistical four-year analysis indicates that VWC is highly influenced by site, 1035 watershed, precipitation, landcover, position and the interaction season*position 1036

(seasonal differences in VWC by topographic position) (Table II.3). Year, the interaction 1037 position*landcover (topographic differences in VWC by landcover), and 1038 season*landcover (seasonal differences in VWC by landcover) had less influence on the 1039

θv and no effect of year and season was observed for any depth (Table II.3). 1040

1041

3.4 Average soil water storage in the upper 60 cm 1042

Average SWS in the upper 60 cm across all 12 study watersheds and for the entire 1043 four-year study period ranged from 15.3 to 38.9 cm, with a standard deviation of 2.7 over 1044 the four years of our study. Comparisons of SWS by year showed a slight increase in the 1045

SWS variability within the upper 60 cm from 2007 to 2010, with 2007 having the lowest 1046

SWS (15.3 cm) and 2009 the highest (38.9 cm) (Table II.4). Annual averages within this 1047 soil depth range were 25.4, 26, 25.4 and 26.5 cm for 2007, 2008, 2009 and 2010 1048 respectively; these values closely mirrored the total precipitation observed by year. 1049

Analysis of the annual average trend of SWS across all watersheds in the upper 60 1050 showed that SWS increased early in the growing season around the months June-July, 1051 and decreased in July, August and September (Figure II.7). An increase of SWS was 1052 observed after September in the four years of our study. The low inputs of water from 1053 precipitation in the months of October and November in 2010 resulted in an overall 1054 decrease in the observed SWS in the upper 60 cm during this period. 1055

1056

3.4.1 Soil water storage by depth increments 1057

The four-year statistical analysis by soil depth increment showed that watershed, 1058 precipitation, landcover, position and the interaction season*position (seasonal 1059 differences in SWS by topographic position) had a strong influence on the SWS observed 1060 in our study (Table II.5). Site, the interaction position*landcover (topographic differences 1061 in SWS by landcover) and season*landcover (seasonal differences in SWS by landcover) 1062 showed less influence on the SWS of the entire soil profile, while year and season had no 1063 effect at any depth increment. 1064

The annual statistical analysis showed effects of land cover from 0-30 cm in 2007 1065

(Table II.6), and no effect of land cover in any of the other years for any of the four 1066 increments analyzed (Tables, II.6, II.7, II.8 and II.9). Our results showed a significant 1067 effect of the interaction season*landcover and season*position for nearly all soil depths 1068 in 2007 (Table II.6), and for the interaction season*landcover on two soil depth depths in 1069

2009 (Table II.8). In 2008, only the interaction season*position showed statistical 1070 differences in the 0-100 cm interval, the other factors appear to have no effect on SWS 1071

(Table II.7). In 2009, season had an influence only on two soil intervals 0-60 and 0-100 1072 cm (Table II.8). Precipitation showed no effect on any soil depth interval in 2007 (Table 1073

II.6) and 2008 (Table II.7), in 2009 for all the intervals except from 30-60 cm showed 1074 significant influence by precipitation (Table II.8). In contrast, in 2010 precipitation was 1075 the factor that had the greatest influence on all intervals. 1076

1077

3.5 Effects of land cover on soil water storage 1078

As shown on Table II.5, in the four-year analysis, landcover had a significant 1079 effect on SWS at all the four increments analyzed (0-30, 30-60, 0-60 and 0-100 cm). For 1080 the soil depth increment 0-60 cm, we observed differences in soil water storage among 1081 land covers that peaked around mid-growing season and decreased towards the end of the 1082 growing season, with the largest differences found in early August in 2007, mid October 1083 in 2008, mid August in 2009 and late June in 2010 (Figure II.8; Table II.12). The annual 1084 statistical analysis showed no significant effects of land cover at this increment 1085

(increment 0-60 in Table II.6, II.7, II.8, and II.9). 1086

When comparing mean annual SWS in the upper 60 cm, SWS was lowest under 1087

ROWCROP in 2007, and under NPV in 2008 and 2010, while there were no significant 1088 differences in 2009 (Table II.10; see Tables II.6, II.7, II.8, and II.9 for statistical 1089 analyses). Trends of average SWS by land cover shifted seasonally and by observation 1090 date (Figure II.8). In 2007, SWS was slightly lower under NPV than ROWCROP from 1091

May until the end of June, but the trend shifted from July to mid-October with a 1092 difference of as much as 12% in early August. The annual analysis of SWS for this soil 1093

43 increment (0-60 cm) showed significant (p=0.001) seasonal differences between land 1094 covers (interaction season*position; Table II.6). In 2008 SWS was lower under NPV for 1095 most of the observation dates, with as much as 5% lower SWS under NPV than 1096

ROWCROP. The only exceptions were early-September and mid-October, when SWS 1097 under ROWCROP was 5 to 6% lower than NPV. The annual statistical analysis showed 1098 no influence of any variable at this depth (Table II.7). In 2009 SWS was lower under 1099

NPV from May through late-July, with a maximum difference of 4% in late June. This 1100 trend shifted from August to mid-October in 2009 when SWS was lower under 1101

ROWCROP, with the greatest difference of 5% observed in late-August. The annual 1102 analysis showed significant (p=0.0029) seasonal differences among land covers 1103

(interaction season*landcover; Table II.8). In 2010, SWS was lower under NPV on 15 of 1104 the 16 observation dates. Maximum differences of 5 and 4% were observed in late-June 1105 and mid-September, respectively. Differences in SWS under NPV and ROWCROP were 1106 less pronounced in 2010, due to the influence of precipitation, which had a significant 1107

(p=<0.0001) influence on SWS for both land covers (Table II.9). 1108

When comparisons of SWS between land covers are limited to the top 0-30 cm, 1109 additional differences were observed by season and observation date (Figure II.9). On an 1110 annual basis, SWS within this increment was higher under NPV than ROWCROP in 1111

2007, and lower under NPV than ROWCROP in 2008, 2009, and 2010 as the prairie 1112 strips were becoming better established. Annual totals are shown in Table II.4. The 1113 annual statistical analysis of this soil increment (0-30 cm) indicates significant 1114

(p=0.0056) differences in SWS by land cover in 2007 (Table II.6), but no significant 1115

44 differences in consecutive years (Table II.5). The four-year analysis showed no effect of 1116 season on SWS in any of the soil depths analyzed for this soil increment (0-30 cm) (Table 1117

II.6, II.7, II.8 and II.9). Conversely, the analysis of seasonal SWS variations of land cover 1118 by season (interaction season*landcover), significant differences were observed in the 1119 upper three soil intervals in 2007 (Table II.6), 2008 showed no differences (Table II.7), 1120

30-60 and 0-60 cm were significantly different in 2009 (Table II.8) and no significant 1121 differences were observed in 2010 (Table II.9 and II.12). SWS was lower under NPV 1122 early in the growing season and at the end of the growing season in 2008, 2009, and 2010 1123 by 0.4 cm, 1 cm, and 0.9 cm, respectively. 1124

1125

3.6 Effects of topographic position on soil water storage 1126

Topographic position (summit, side and footslope) strongly effected SWS, as 1127 shown in the four-year statistical analysis for all increments (0-30, 30-60, 0-60 and 0-100 1128 cm) (Table II.5 and II.11). Mean annual SWS in the upper 60 cm was consistently higher 1129 in footslope than in the summit position in each of the four monitored years. This pattern 1130 was not consistent when comparing differences between footslope and sides positions. 1131

Annual average SWS in the side position was lower than summit in 2007 and 2009 1132

(Figure II.10). 1133

When comparing results by observation date (Figure II.11), SWS in the upper 60 1134 cm was higher in the footslope position than the summit position for the great majority of 1135 the observed dates (Figure II.11). This trend was consistent under conditions of both low 1136 and high total SWS. SWS in the summit positions exhibited a greater response to 1137

45 precipitation than footslope positions. Side positions exhibited the greatest variation in 1138

SWS among all the measured dates, with values as low as 22.7 cm in August 2007, and 1139 as high as 29.91 cm in June 2010. In the analysis by year, no statistical differences were 1140 found for any soil increment in any year (Tables II.6, II.7, II.8 and II.9). However, in the 1141 four-year analysis, a single depth interval (0-100) showed significant (p=0.0032) 1142 differences of SWS by topographic position among land covers (interaction 1143 position*landcover; Table II.5), The annual statistical analysis showed no statistical 1144 differences in SWS by topographic position in any depth analyzed in any year 1145

(interaction position*landcover in Tables II.6, II.7, II.8, and II.9). The four-year analysis 1146 found significant seasonal differences of SWS by topographic position for all intervals 1147

(Table II.5 and II.12). However, the annual analysis showed seasonal differences in SWS 1148 by topographic position for all the soil intervals analyzed in 2007 (interaction 1149 season*position in Table II.6), significant differences in one interval only in 2008 (Table 1150

II.7), no significant differences in 2009 (Table II.8), and no differences in 2010 (Table 1151

II.9). The annual averages observed by are presented in Table II.12. 1152

1153

3.7 Other factors influencing soil moisture 1154

In three sites, Basswood 4 and 5 in the sides position and Interim 3 in the summit 1155 position, excessive water at the soil surface created a wet soil environment that resulted 1156 in consistently higher readings of VWC at 5 and 10 cm at these sites with an average for 1157 the entire observation period (2007 to 2010) of 0.49 cm3⋅cm-3 at 5 cm and 0.50 cm3⋅cm-3 1158 at 10 cm in Basswood 4 and 0.50 cm3⋅cm-3 at 5 and 10 cm in Basswood 5. The average of 1159

46 the gravimetric samples collected throughout the study at Basswood 4 position side from 1160

0-15 cm was 0.33g/g (standard deviation = 0.05), and the average of the gravimetric 1161 samples in Basswood 5 side was 0.31g/g (standard deviation 0.05), indicating the little 1162 variation in soil water content in these sites. 1163

Conversely, in the watersheds Basswood 6 and Interim 3, low VWC values were 1164 recorded throughout our study in the side position, with little variation over time. In 1165

Basswood 6 side position, VWC values varied very little at 20 cm (mean = 0.34 cm3⋅cm- 1166

3, SD = 0.014), 30 cm (mean = 0.34 cm3⋅cm-3, SD = 0.000) and 100 cm (mean = 0.34 1167 cm3⋅cm-3, SD = 0.020) during the four years of our study. The mean of 6 gravimetric 1168 samples collected in Basswood 3 in the sides position, over the four years of this study 1169 showed an average of 0.23 g/g (SD = 0.02) at 20 cm, 0.22 g/g (SD = 0.02) at 30cm and 1170

0.18 g/g (SD = 0.01) at 100 cm, showing the natural low variation in gravimetric water 1171 content at these depths. 1172

Similarly, in Interim 3, position side, low VWC values were observed at all three 1173 depths (20 cm: mean = 0.29 cm3⋅cm-3; SD = 0.01; 40cm: mean = 0.25 cm3⋅cm-3; SD = 1174

0.02; 60 cm: mean = 0.27 cm3⋅cm-3; SD = 0.01; 100 cm: mean = 0.25 cm3⋅cm-3; SD = 1175

0.00). The mean of the gravimetric samples collected for these points were: 0.22 g/g at 1176

20cm (SD = 0.03), 0.18 g/g at 40 cm (SD = 0.02), 0.18g/g at 60cm (SD = 0.02) and 0.16 1177 g/g at 100cm (SD = 0.01), showing also natural low variation of gravimetric water 1178 content at these depths. 1179

1180

4 Discussion 1181

Previous research has shown that the reintroduction of NPV can reduce total SWS 1182 via evapotranspiration, or increase infiltration through the modification of soil properties 1183 such as porosity (Weaver, 1927; Weaver and Flory, 1934; Weaver, 1954; Ehrenreich and 1184

Aikman, 1963). By reducing the total amount of water in the soil via evapotranspiration, 1185 native prairie vegetation allows for the retention of higher amounts of precipitation or 1186 runoff (Hernandez-Santana et al., In Press), which would otherwise contribute to surface 1187 runoff and transport of nutrients (Schilling, 2002; Fox et al., 2009) and sediments 1188

(Gharabaghi et al., 2006) to receiving waters. 1189

In this study we analyzed the effects of the reintroduction of strips of NPV into 1190 agricultural lands on θv and SWS in twelve small watersheds. The influence of 1191 precipitation, season, soil depth and topographic position on SWS characteristics was 1192 analyzed. Our results indicate that by the second year after establishment, the 1193 reintroduction of NPV can contribute to the water balance of the watershed and to the 1194 ecosystem, by reducing soil moisture content via evapotranspiration, thereby increasing 1195 soil moisture storage capacity and controlling surficial water flow (Hernandez-Santana et 1196 al., In Press). 1197

1198

4.1 Variations under land cover 1199

In our study, the four-year analysis revealed that land cover significantly affected 1200 both VWC and SWS (Table II.3 and II.5). Zhang and Schilling (2006), conducted a study 1201 in the same research area to study the effects of land cover (grassland and bare ground) 1202

48 on soil moisture, evapotranspiration and groundwater recharge, and found similarly to 1203 our results that land cover directly influences soil moisture dynamics. In that study, in the 1204 absence of land cover (bare ground), soil moisture remained higher due to a lower ET, 1205 which in turn resulted in higher fluctuations and higher recharge of groundwater in the 1206 bare ground compared to the grass-covered areas. Qi et al. (2011) monitored SWS from 1207

2006 to 2008 and observed similar results in a comparison of six land covers including 1208 perennial forage and conventional corn and soybean crops. Their results support our 1209 findings that perennial vegetation helps to reduce SWS by increasing ET. In our analysis 1210 of VWC by depth, two shallow depths (5 and 10 cm) and a deep one (60 cm), showed no 1211 effect of land cover. Precipitation in the upper two depths significantly influenced VWC 1212

(p=>0.0001). Conversely, the analysis of SWS showed a significant influence of land 1213 cover at the four increments analyzed. This result could be due to the significant 1214 influence of factors such as precipitation that might overcome the influence of land cover 1215 in shallow depths (5 and 10 cm) when analyzed individually, as shown in Table II.3. 1216

Studies that have compared the influences of land cover on soil moisture 1217 dynamics, have found more significant differences among land covers when these are 1218 analyzed comparing soil intervals (Weaver, 1941; Brye et al., 2000; Cubera et al., 2004; 1219

Enloe et al., 2004; Qi and Helmers, 2010; Qi et al., 2011), rather than specific depths. 1220

Further, in locations having high moisture content soils during spring and fall (i.e. central 1221

Iowa), the effects of land cover on soil moisture dynamics can be better studied at shorter 1222 soil intervals (from 10 to 30 cm). In this study, we found different statistical differences 1223

49 when SWS was analyzed for the entire soil profile than when it was analyzed by smaller 1224 soil intervals. 1225

The observed SWS by land cover (Figure II.8) closely followed the development 1226 of the strips of native prairie vegetation over time. Researchers have found that the 1227 development stage of the vegetation greatly influences soil moisture dynamics (Weaver, 1228

1941; Brye et al., 2000; Cubera et al., 2004; Qi and Helmers, 2010; Qi et al., 2011). In a 1229 controlled experiment designed to compare water loss between prairie and pasture using 1230 phytometers, Weaver observed that the water loss profile was dictated by the amount of 1231 functioning vegetation demanding water (Weaver, 1941). In this study, the initially 1232 higher SWS observed in 2007 under prairie could have been a response of soil moisture 1233 accumulation in the soil due to the lack of actively transpiring vegetation together with 1234 the relatively shallow rooting depth of young plants, which resulted in a statistically 1235 significant difference among land covers, particularly in the first 30 cm of soil (Table 1236

II.6). 1237

While the NPV was planted in the strips in July 2007, little vegetation was 1238 observed throughout the growing season. The NPV developed more aerial biomass in 1239

2008, principally weeds and a few targeted prairie species, which were mowed for the 1240 first time from the 19 to the 21 of June 2008 and a second time in late August 2008. In 1241 this year, SWS in the upper 60 cm was lower under NPV, with exception of some 1242 sampling dates (May 16, September 2, and October 16, the later was included in the 1243 analysis but is not shown in the figures). The first observation seemed to be a 1244 continuation of the tendency observed at the end of 2007. Shi et al (2007), and Chen et al 1245

(2007), have identified antecedent or pre-existing soil moisture conditions as a factor 1246 influencing the soil moisture differences observed at a given time under a given land 1247 cover. Values observed on September 2, 2008 could be a result of the removal and thus 1248 the modification of the aerial biomass conducted in late August, an effect on soil 1249 hydrology previously documented for forested (Hamilton et al., 1983), open woodlands 1250 with scattered oak trees (Cubera et al., 2004), crop lands (Nejadhashemi et al., 2011; Qi 1251 et al., 2011; Bagley et al., 2012), and native grasslands (Ehrenreich and Aikman, 1963). 1252

The low SWS under ROWCROP observed on October 16, 2008 appear to be an effect of 1253 the number of values averaged for that date and their specific values. For this date only 1254 the observations for the Interim site were included, due to an equipment malfunction. As 1255 discussed earlier, low values of VWC and thus SWS were observed in the side position of 1256

Interim 3 (100% rowcrop treatment), which highly influenced mean SWS under rowcrop. 1257

Removal of the aerial biomass through diverse processes (i.e. grazing, mowing, 1258 burning, cutting) has been shown to influence soil moisture and run off dynamics (Brooks 1259 et al., 2003; Hillel, 2004). In forested and grassland areas, the removal of the land cover 1260 has shown to increase downstream water yields (Hamilton et al., 1983; Bruijnzeel, 1990; 1261

FAO, 2008). Light or selective removal of land cover appears to have little impact on the 1262 total water yield, however it has been shown that the effects in water yield increases as 1263 the removal of the land cover increases (Bruijnzeel, 1990). 1264

In 2009, the NPV was mowed on June 25, which could have led to the high SWS 1265 observed under NPV in the upper 60 cm. As the land cover is removed, soil water loss 1266 decreases due to a decrease in ET, and evaporation increases when insolation due to the 1267

51 presence of soil cover increases (Weaver, 1941; Hillel, 2004). SWS in the upper 60 cm 1268 under prairie was higher after August, with this difference decreasing by the end of the 1269 year. However, the observed SWS from 0-30 cm (Figure II.9) under NPV was similar to 1270

SWS under ROWCROP in August and then progressively decreased under NPV as the 1271 year progressed, showing lower SWS under NPV starting mid-September. Land cover 1272 thus appeared to have a higher effect in the upper 30 cm of soil. Contrary to these results, 1273 in their comparison of the water budget of prairie and maize land covers, Brye et al 1274

(2000), found consistently higher values of volumetric water content under prairie, 1275 compared to other two land covers consisting of corn, however, the comparisons in this 1276 study were done in large soil intervals (0-70, and 80-140 cm). We have indicated 1277 previously that among small plant species, it appears that differences in soil moisture are 1278 easier to identify when compared in shorter intervals (from 10 to 30 cm). 1279

In 2010, no management was imparted to the NPV until the end of the year. In 1280 this year, after the crops were harvested, the biomass in the NPV was cut and baled on 1281

October 30, 2010, with an average of 12.7 ton⋅ha-1 harvested, as part of the management 1282 of the NPV. Despite being the year that received the highest total precipitation (1326 mm 1283 from Match to November), SWS was lower under NPV during most of the year in the 0- 1284

60 cm (Figure II.8) increment and during the entire year from 0-30 cm (Figure II.9). The 1285 study conducted by Hernandez-Santa et al, (In Press) showed that runoff was lower in 1286 watershed covered with NPV. This result may be attributed to the higher soil water use 1287 under native prairie cover and its more advanced stage of establishment and maturity. 1288

Early studies of the prairie ecosystem by Weaver (1927; 1934; 1941) found that the water 1289

52 demands of the ecosystem are highly controlled by the vegetative stage and development 1290 of the plants. In a recent study, Mateos-Remigio et al. (in review.) used the heat balance 1291 method to measure plant water use at the NSNWR, and showed that water use was 1292 strongly govern by plant phenology and season, and that cumulative water use on a leaf 1293 area basis was greater for native prairie C3 and C4 plants compared to the annual C4 crop, 1294 corn. 1295

1296

4.2 Variations under topographic position 1297

It has been noted by different authors that slope or topographic position are key 1298 factors that contribute to soil moisture dynamics (Chang, 2006; Shi et al., 2007; Ziadat et 1299 al., 2010). In fact, Roessel (1950) advised caution when comparing watersheds based on 1300 their land cover only, since topographical factors may override the effects of land cover. 1301

In the four-year analysis of SWS and VWC by topographic position, we observed 1302 significant differences among topographic positions on the observed VWC in the upper 1303 six soil depth intervals and SWS in all four intervals analyzed (Table II.3 and II.5). In 1304 contrast, the analysis of SWS by year, showed no effect of topographic position in any of 1305 the years, which could be due to the variability within each year, or due to a loss in 1306 estimation power of the statistical analysis when it is split by year. 1307

The effect of topographic position has been previously studied, with varying 1308 results. Ziadat et al (2010), studied soil moisture content differences in four topographic 1309 positions (summit, shoulder, backslope and toeslope) in five different transects of 1310 different slope characteristics and found no marked differences among topographic 1311

53 positions in the five studied transects. Conversely, the results reported by Fu et al (2003), 1312 showed a strong influence of topographic position on soil moisture content in each of the 1313 five land cover studied, with a steady increase in soil moisture content from summit to 1314 footslope. Averaged on an annual basis (Figure II.10), our results indicate that the upper 1315 parts of the watershed (summit) tend to have lower SWS than the lower parts (foot). 1316

Particularly in the first 30 cm of soil profile. The middle part of the watersheds (side) 1317 showed varying results each year, however it was never higher than the foot. These 1318 results follow the water movement laws and principles dictated by homogeneous porous 1319 medium: gravity, capillarity and suction primarily (Brooks et al., 2003; Hillel, 2004). 1320

Hillel indicates that after infiltration, the gravitational head gradient is the only force that 1321 moves water into the soil (Hillel, 2004), and this gravitational gradient tends to move 1322 water from the upper parts of the watershed towards the lower parts (Fetter, 2001), which 1323 can explain the annual average values found in this study. 1324

We evaluated the effects of the strategic placement of strips of perennial 1325 vegetation into three different topographic positions within a watershed. Independent of 1326 the effects of topographic position on soil moisture dynamics, one of our objectives was 1327 to determine to what extent soil moisture is affected by the topographical position of the 1328

NPV placement within a watershed. Our four-year statistical analysis showed that SWS 1329 does not vary significantly by topographic position under each vegetative cover studied 1330

(interaction position*landcover; Table II.5), as the relative position of the vegetative 1331 cover only had a significant effect on VWC at a single interval (0-100 cm). Roessel 1332

(1950), mention that the effects of topographic position can in some cases override the 1333

54 effects of land cover on soil moisture dynamics, which we observed in our four-year 1334 analysis of VWC (Table II.3) and SWS (Table II.5), which showed strong influence of 1335 topographic position when analyzed as independent variable. However, the four-year 1336 analysis of the VWC differences by topographic positions under each land cover 1337

(interaction position*landcover) showed only one depth increment with significant 1338 differences (0-100 cm; Table II.4). The annual analysis showed no significant differences 1339 at any depth increment for year (Tables II.6, II.7, II.8 and II.9) which could be due to: (1) 1340 differences are not constant enough to be identified as significantly different by our 1341 analysis, (2) differences by watershed are greater than the differences among land covers 1342 in one watershed, as shown in Table II.5, or (3) that differences in SWS tend to be more 1343 distinguishable on a seasonal basis. 1344

VWC and SWS were no significantly different when analyzed by season as an 1345 independent factor in the four-year analysis (Table II.5). When analyzed as the 1346 interaction season*landcover (seasonal changes in soil moisture under a given land 1347 cover), we found little influence on VWC (Table II.3) and statistically different SWS in 1348 two soil depths (Table II.5). The analysis by year showed significant seasonal differences 1349 of SWS under each land cover in three soil depth intervals in 2007 (Table II.6), no 1350 differences in 2008 (Table II.7), two depths with significant seasonal differences in 2009 1351

(Table II.8) and no difference sin 2010 (Table II.9). Our data indicates strong seasonal 1352 variations in SWS in the upper soil depth increments (0-30, 30-60 and 0-60 cm) of soil, 1353 that are likely the result of a combination of precipitation patterns and seasonal changes 1354 in water use by the different vegetation. On average, June-July had the highest values of 1355

SWS in the upper 60 cm (26.5 cm), August September had the lowest (25.1 cm). Table 1356

II.12 shows the seasonal averages found by year under each land cover. 1357

Based on our results, it also appears that the benefits of the reintroduction of 1358 native prairie vegetation into agricultural watersheds, present a threshold effect at high 1359 and low soil moisture contents. In 2009, when we had the lowest values of SWS, no 1360 significant differences were detected among the two land covers studied, particularly in 1361 the upper 30 cm of soil (Figure II.9). In 2010, when we had the highest precipitation 1362 among the four years of our study, the differences in SWS between land covers was 1363 reduced, as compared to other years (Figure II.8). 1364

1365

4.3 Limitations to this study 1366

Several factors were thought to influence the observed VWC apart from the 1367 experimental treatments, and may have influenced the results obtained in this study. First, 1368 the four-year analysis showed significant differences in both VWC by depth and SWS by 1369 depth increments as shown in Table II.3 and II.5. High VWC values observed in the side 1370 positions in Basswood 4 and 5 and at the summit position of Interim 3 are possibly a 1371 result of: (a) a broken subsurface drainage tile that releases water at these positions, (b) 1372 the presence of a soil layer with low hydraulic conductivity that impedes water from 1373 moving into the soil matrix, or (c) topographic and geological conditions that cause 1374 groundwater to flow out of the soil surface to create return flow. 1375

Low VWC values observed in the side position of Basswood 6 and Interim 3 1376 could have resulted from: (a) little or no contact area between the soil and the fiber glass 1377

56 access tubes caused by a incorrect installation, soil contraction or physical deterioration 1378 of the contact between access tube and soil over time, yielding a low voltage output, (b) 1379 physical characteristics of the soil (e.g. high porosity, high organic matter content) or of a 1380 structure (e.g. roots) within the soil at that given point, or (c) presence of openings in the 1381 soil (burrows) created by animals or roots from pre-existing trees. 1382

Although we acknowledge the intrinsic differences among some watersheds, it is 1383 important to denote that our statistical analysis accounts for variations within access tubes 1384 or observation points (specific position at a specific watershed within a specific site), by 1385 including these as random effects in our analytical model. Similarly, the results of our 1386 analysis include all the watersheds, combining data for VWC or the SWS across of all the 1387 watersheds and topographical positions at a given depth or increment in the case of SWS. 1388

Natural variation in SWS and VWC was thus accounted for in our statistical analysis, as 1389 one of our original goals is to study soil moisture dynamics under natural field 1390 conditions. 1391

1392

5 Conclusions 1393

In this study, we monitored twelve small zero-order watersheds to assess how the 1394 reintroduction of native prairie vegetation into agriculturally dominated landscapes 1395 affected soil moisture dynamics, specifically volumetric water content (VWC), at seven 1396 soil depths (5, 10, 20, 30, 40, 60, 100), and soil water storage (SWS) within four 1397

57 increments (0-30, 30-60, 0-60 and 0-100). We analyzed the effects of precipitation, land 1398 cover, topographic position, season and year on each of these variables. 1399

Lower seasonal SWS was observed under ROWCROP than under NPV in 2007, 1400 when the prairie strips were planted. This trend shifted in 2008, 2009 and 2010, which 1401 had the greatest amount of total precipitation. Although SWS was not consistently lower 1402 under NPV in 2008, 2009 and 2010, significant differences were observed in out 1403 statistical analysis by year. The timing of the establishment of NPV played a critical role 1404 in explaining observed differences, with SWS under this vegetation type decreasing with 1405 time since prairie establishment, except in 2010 were our statistical analysis indicate that 1406 precipitation had the highest influence than the other variables in explaining differences 1407 in SWS. The SWS differences among land covers were manifested to a greater extent 1408 within the upper 0-30 cm relative to 0-60 cm of soil. Topographic position had a direct 1409 influence on SWS, with the upper parts of the watershed exhibiting less water storage on 1410 average, but higher responses to precipitation. In contrast, high SWS and low response to 1411 precipitation was observed in the lower landscape positions. 1412

The lack of significant differences in 2008 and 2010, an anomalously low and 1413 high rainfall year, respectively, led us to propose that SWS variations between NPV and 1414

ROWCROPS may represent a threshold effect, since SWS differences were not 1415 statistically different under these land covers when precipitation and thus soil moisture in 1416 the upper soil depths was either lower or higher than average. 1417

Our results have important implications for land managers and scientists, if 1418 similar studies are conducted in other agricultural watersheds, NPV can help regulate the 1419

58 hydrological balance of the watershed. However, special care must be taken since effects 1420 on the hydrological balance are not observed the year NPV is planted. During the first 1421 year, SWS is likely to be higher under NPV due to the lack of water-demanding 1422 vegetation, which eventually may lead to increased runoff and potentially to lower 1423 groundwater recharge. Further research is needed to understand the implications for 1424 runoff and water yield. 1425

1426

1427

1428

1429

1430

1431

1432

1433

1434

1435

1436

1437

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1569

1570

7 Figures 1570

1571 66

1572 1573 Figure II-1.Graphical representation of the four treatments applied to the watersheds. 1574 Proportions are not at real scale 1575

1576

1577 1578 Figure II-2. Diagram of the Theta and PR2 probes, the 1579 area of influence used for every sensor and the depths of 1580 gravimetric samples used in the calibration 1581 1582

1583 Figure II-3. Cumulative precipitation by year 1584 1585 1586 1587

1588 Figure II-4. Precipitation registered by day (2007-2010) 1589 1590 1591 1592

1593 Figure II-5. Bulk density by watershed and topographic position 1594

1595 1596 70

1597 Figure II-6. Average volumetric water by depth 1598 Lines in Figure 6 represent the average of all topographic positions and watersheds for that 1599 specific depth. The average includes both land covers (CROP and NPV) 1600 1601 1602 1603 1604 1605 1606 1607

1608 1609 1610 71

1611 Figure II-7. Average soil water storage in the upper 60 cm for the entire site of study 1612 Average soil water storage was estimated using all topographic positions and watersheds for 1613 this interval. The average includes both land covers (CROP and NPV). Gray vertical lines 1614 represent the standard error of the mean. 1615 1616 1617

1618 72

1619 Figure II-8. Average soil water storage in the upper 60 cm by land cover 1620 Soil water storage was averaged using all the topographic positions and watersheds 1621 corresponding to a given land cover. Gray vertical lines represent the standard error of 1622 the mean. P=Prairie vegetation planted July 7, 2007; M=Prairie vegetation mowed; 1623 B=Prairie vegetation baled. Significant statistical differences using Glimmix in SAS are 1624 indicated with “*”. See Appendix A and B for specific details. “n” = or < 4 are not 1625 shown in this graph 1626 1627 1628

1629 73

1630 Figure II-9. Average soil water storage in the upper 30 cm by land cover 1631 1632 Soil water storage was averaged using all the topographic positions and watersheds 1633 corresponding to a given land cover. Gray vertical lines represent the standard error of 1634 the mean. P=Prairie vegetation planted July 7, 2007; M=Prairie vegetation mowed; 1635 B=Prairie vegetation baled. Significant statistical differences using Glimmix in SAS are 1636 indicated with “*”. See Appendix C and D for specific details. “n” = or < 4 are not shown 1637 in this graph 1638 1639

1640 74

1641 Figure II-10. Average soil water storage in the upper 60 cm in three 1642 topographic positions 1643 Annual average soil water storage estimated using values from both land 1644 covers of a given topographic position (Summit, Side and Foot). The 1645 average is estimated using all readings observed in each of the monitored 1646 years (2007, 2008, 2009 and 2010) 1647 1648

1649 75

1650 Figure II-11. Average soil water storage in the upper 60 cm in three topographic positions 1651 Lines in Figure 11 represent the average soil water storage estimated using values from 1652 both land covers of a given topographic position (Summit, Side and Foot) 1653

1654

8 Tables 1655

1656 1657 Table II-1. Characteristics of the twelve watersheds 1658

Total Topographic Area Sand Silt Clay Watershed Site prairie position of prairie (ha) (%) (%) (%) cover (%) cover

Basswood 1 Basswood 10% Footslope 0.53 ⇑2.54 68.88 28.58 ⇓16.81 57.43 25.76 Basswood 2 Basswood 10% Footslope & 0.48 ⇑2.54 68.88 28.58 Summit ⇓16.81 57.43 25.76 Basswood 3 Basswood 20% Footslope & 0.47 ⇑2.54 68.88 28.58 Summit ⇓16.81 57.43 25.76 Basswood 4 Basswood 20% Footslope & 0.55 ⇑2.54 68.88 28.58 Summit ⇓16.81 57.43 25.76 Basswood 5 Basswood 10% Footslope & 1.24 ⇑2.54 68.88 28.58 Summit ⇓16.81 57.43 25.76 Basswood 6 Basswood 100% None 0.84 ⇑2.54 68.88 28.58 rowcrop ⇓16.81 57.43 25.76 Orbweaver 1 Orbweaver 10% Footslope 1.18 ⇑2.26 66.89 30.85 ⇓12.99 61.22 25.79 Orbweaver 2 Orbweaver 20% Footslope, Side 2.40 ⇑2.26 66.89 30.85 & Summit ⇓12.99 61.22 25.79 Orbweaver 3 Orbweaver 100% None 1.24 ⇑2.26 66.89 30.85 rowcrop ⇓12.99 61.22 25.79 Interim 1 Interim 10% Footslope, Side 3.00 ⇑3.75 69.89 36.38 & Summit ⇓10.52 66.01 23.47 Interim 2 Interim 10% Footslope 3.19 ⇑3.75 69.89 36.38 ⇓10.52 66.01 23.47

Interim 3 Interim 100% None 0.73 ⇑3.75 69.89 36.38 rowcrop ⇓10.52 66.01 23.47 1659 ⇑ = Summit 1660 ⇓ = Foot 1661 Soil percentages correspond to the upper 30 cm of soil 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672

Table II-2. Summary of the data collected per watershed 1673 Position of the access tubes and its Watershed Seasons monitored per year land cover Basswood 1 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Prairie buffer Basswood 2 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Prairie buffer Basswood 3 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Prairie strip Side: Rowcrop Footslope: Prairie buffer Basswood 4 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Prairie buffer Basswood 5 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Prairie buffer Basswood 6 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Rowcrop Orbweaver 1 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Prairie buffer Orbweaver 2 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Prairie strip Side: Rowcrop Footslope: Rowcrop Orbweaver 3 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Rowcrop Interim 1 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Prairie strip Side: Prairie strip Footslope: Prairie buffer Interim 2 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Prairie buffer Interim 3 4 [Apr-May, Jun-Jul, Aug-Sep, Oct-Nov] Summit: Rowcrop Side: Rowcrop Footslope: Rowcrop 1674 1675 1676 1677 1678

1679 1680 Table II-3. Glimmix Procedure by depth 2007-2010. Dependent variable: volumetric water content (%) 1681 1682 Position Season Season Depth Site Watershed Year Precipitation Land cover Position Season *Land *Land *Position cover cover

F= 3.69 F=3.37 F= 0.63 F= 19.17 F= 0.00 F= 8.82 F= 0.63 F= 2.75 F= 1.37 F= 0.95 5 p=0.0278 p=0.0010 p=0.5983 p=<0.0001 p=0.9858 p=<0.0003 p=0.5979 p=0.0681 p=0.2508 p=0.4603

F= 0.68 F= 3.32 F= 0.32 F= 24.99 F= 0.75 F= 8.27 F=1.15 F=1.35 F=0.83 F=3.30 10 p=0.5096 p=0.0012 p=0.8075 p=<0.0001 p=0.3881 p=<0.0004 p=0.3381 p=0.2633 p=0.4767 p=0.0031 F=0.95 F=4.09 F=8.80 F=0.76 F=23.88 F=20.88 F=13.50 F=0.92 F=3.22 F=1.25 20 p=0.4578 p=0.0190 p=<0.0001 p=0.5232 p=<0.0001 p=<0.0001 p=<0.0001 p=0.4366 p=0.0433 p=0.2902

F=2.58 F=1.08 F=6.61 F=2.87 F=13.77 F=5.56 F=4.64 F=0.60 F=1.12 F=0.34 30 p=0.0171

p=0.3421 p=<0.0001 p=0.0002 p=0.0002 p=0.0199 p=0.0114 p=0.6158 p=0.3293 p=0.7945 78

F=6.33 F=8.87 F=2.10 F=23.59 F=20.27 F=7.49 F=1.62 F=0.65 F=2.19 F=4.35 40 p=0.0024 p=<0.0001 p=0.1130 p=<0.001 p=<0.0001 p=0.0008 p=0.1985 p=0.5253 p=0.0873 p=<0.0002

F=6.09 F=11.98 F=0.74 F=2.29 F=3.43 F=16.70 F=1.53 F=2.59 F=6.49 F= 1.73 60 p=0.0030 p=<0.0001 p=0.5333 p=0.1307 p=0.0664 p=<0.0001 p=0.2191 p=0.0793 p=0.0002 p=0.1100

F=1.28 F=8.88 F=0.24 F=6.79 F=9.60 F=3.02 F=122 F=5.98 F=2.51 F=4.88 100 p=0.2812 p=<0.0001 p=0.8668 p=0.0092 p=0.0024 p=0.0526 p=0.3126 p=0.0033 p=0.0569 p=<0.0001 1683 Depth: in cm 1684 Site: Basswood, Orbweaver and Interim 1685 Watershed: 12 individual watersheds 1686 Year: 2007, 2008, 2009, 2010 1687 Precipitation: sum of the precipitation registered in the previous 7 days 1688 Land cover: native prairie vegetation vs. cropland (corn, soybean) 1689 Position: Summit, Side, and footslope 1690 Season: Four seasons, two months each (April-May, Aug-Sept, Jun-Jul, Oct-Nov) 1691

1692 1693 1694 1695 1696 1697 1698 Table II-4. Annual values of soil water storage in the upper 30 and 60 cm 1699

Depth Year (cm) Estimate 2007 2008 2009 2010 0-30 Mean 13.3 13.7 13.3 13.9 Std Dev 1.2 1.7 1.7 2.0 Max 17.7 23.5 23.8 23.5

Min 7.2 10.0 9.2 9.5 79 n 459 455 425 523 0-60 Mean 25.4 26.0 25.4 26.5 Std Dev 2.2 2.5 2.7 3.0 Max 33.8 38.0 38.9 38.1 Min 15.3 17.4 16.2 17.5 n 458 455 425 521 1700 1701 1702 1703 1704 1705 1706 1707

1708 1709 Table II-5. Four-year analysis of SWS by increment. Dependent variable: soil water storage (cm) 1710

Position Season Season Depth Site Watershed Year Precipitation Land cover Position Season *Land *Land cover *Position cover

F= 1.60 F=4.29 F= 0.69 F= 22.54 F= 5.64 F= 7.31 F= 0.93 F= 1.80 F= 0.83 F= 2.87 0-30 p=0.2059 p=0.0001 p=0.5638 p=<0.0001 p=0.0191 p=0.0010 p=0.4357 p=0.1696 p=0.4748 p=0.0088

F=8.10 F=13.21 F=1.81 F=13.27 F=14.08 F=14.28 F=1.96 F=0.68 F=5.70 F=3.23 30-60 p=0.0005 p=<0.0001 p=0.1581 p=0.0003 p=0.0003 p=<0.0001 p=0.1326 p=0.5107 p=0.0007 p=0.0037

F=4.61 F=7.24 F=1.10 F=23.58 F=10.79 F=10.59 F=1.49 F=1.59 F=2.92 F=4.23 0-60 p=0.0118 p=<0.0001 p=0.3587 p=<0.0001 p=0.0013 p=<0.0001 p=0.2292 p=0.2079 p=0.0085 p=0.0003 80 F=2.50 F=9.93 F=0.77 F=23.28 F=0.08 F=0.43 F=1.69 F=6.00 F=1.59 F=6.66

0-100 p=0.0863 p=<0.0001 p=0.5176 p=<0.0001 p=0.7806 p=0.0004 p=0.1822 p=0.0032 p=0.1905 p=<0.0001

F=2.50 F=9.93 F=0.77 F=23.28 F=0.08 F=0.43 F=1.69 F=6.00 F=1.59 F=6.66 0-100 p=0.0863 p=<0.0001 p=0.5176 p=<0.0001 p=0.7806 p=0.0004 p=0.1822 p=0.0032 p=0.1905 p=<0.0001 1711

Depth: increments expressed in cm 1712 Site: Basswood, Orbweaver and Interim 1713 Watershed: 12 individual watersheds 1714 Year: 2007, 2008, 2009, 2010 1715 Precipitation: sum of the precipitation registered in the previous 7 days 1716 Land cover: prairie vegetation vs. cropland (corn, soybean) 1717 Position: Summit, Side, and footslope 1718 Season: Four seasons, two months each (April-May, Aug-Sept, Jun-Jul, Oct-Nov) 1719 1720 1721

1722 1723 1724 Table II-6. Analysis of SWS by increment. Year: 2007 1725 1726

Position Season Season Depth Site Watershed Precipitation Land cover Position Season *Land cover *Land cover *Position

F= 0.29 F=0.74 F= 0.18 F= 0.00 F= 1.16 F= 0.88 F= 1.23 F= 3.73 F= 3.59 0-30 p=0.7500 p=0.6704 p=0.6740 p=0.0056 p=0.3362 p=0.4925 p=0.3139 p=0.0115 p=0.0018

F=0.39 F=2.36 F=0.76 F=1.48 F=1.87 F=1.43 F=0.24 F=9.79 F=4.21 30-60 p=0.6822 p=<0.0552 p=0.3836 p=0.2388 p=0.1816 p=0.3042 p=0.7869 p=<0.0001 p=0.0004

F=0.25 F=1.32 F=0.06 F=0.39 F=1.13 F=0.99 F=0.84 F=7.14 F=5.09 0-60 p=0.7819 p=0.2903 p=0.8092 p=0.5411 p=0.3440 p=0.4443 p=0.4445 p=0.0001 p=<0.0001 81

F=0.40 F=1.94 F=0.54 F=0.11 F=0.98 F=1.28 F=0.60 F=2.21 F=2.94 0-100 p=0.6747 p=0.1071 p=0.4642 p=0.7398 p=0.3944 p=0.3450 p=0.5568 p=0.0870 p=0.0082

1727 Depth: increments expressed in cm 1728 Site: Basswood, Orbweaver and Interim 1729 Watershed: 12 individual watersheds 1730 Precipitation: sum of the precipitation registered in the previous 7 days 1731 Land cover: prairie vegetation vs. cropland (corn, soybean) 1732 Position: Summit, Side, and footslope 1733 Season: Four seasons, two months each (April-May, Aug-Sept, Jun-Jul, Oct-Nov) 1734 1735

1736

1737

1738

Table II-7. Analysis of SWS by increment. Year: 2008 1739

Position Season Season Depth Site Watershed Precipitation Land cover Position Season *Land *Land *Position cover cover

F= 0.07 F=0.66 F= 1.27 F= 1.21 F= 1.50 F= 2.07 F= 0.22 F= 2.11 F= 1.60 0-30 p=0.9372 p=0.7333 p=0.2611 p=0.2847 p=0.2489 p=0.1822 p=0.8072 p=0.0989 p=0.1451

F=1.18 F=2.15 F=0.71 F=3.44 F=2.80 F=1.05 F=0.15 F=0.69 F=0.87 30-60 p=0.3290 p=0.0766 p=0.4002 p=0.0793 p=0.0858 p=0.4238 p=0.8582 p=0.5579 p=0.5200

F=0.40 F=1.11 F=1.07 F=2.50 F=2.28 F=1.52 F=0.23 F=2.21 F=1.62 0-60 p=0.6782 p=0.4033 p=0.3008 p=0.1305 p=0.1295 p=0.2820 p=0.7972 p=0.0872 p=0.1398 82

F=0.23 F=1.39 F=1.51 F=0.00 F=1.79 F=0.95 F=1.30 F=1.19 F=62.22 0-100 p=0.7983 p=0.2601 p=0.2203 p=0.9475 p=0.1941 p=0.4619 p=0.2960 p=0.3136 p=0.0109

1740 Depth: increments expressed in cm 1741 Site: Basswood, Orbweaver and Interim 1742 Watershed: 12 individual watersheds 1743 Precipitation: sum of the precipitation registered in the previous 7 days 1744 Land cover: prairie vegetation vs. cropland (corn, soybean) 1745 Position: Summit, Side, and footslope 1746 Season: Four seasons, two months each (April-May, Aug-Sept, Jun-Jul, Oct-Nov) 1747 1748 1749 1750 1751 1752 1753 1754 1755

1756 1757 1758 Table II-8. Analysis of SWS by increment. Year: 2009 1759

Position Season Season Depth Site Watershed Precipitation Land cover Position Season *Land *Land *Position cover cover

F= 1.00 F=1.13 F= 10.51 F= 2.62 F= 1.43 F= 3.84 F= 0.12 F= 2.03 F= 1.47 0-30 p=0.3877 p=0.3880 p=0.0013 p=0.1221 p=0.2638 p=0.0755 p=0.8901 p=0.1096 p=0.1869

F=2.40 F=3.21 F=1.65 F=2.79 F=3.10 F=1.42 F=0.07 F=4.81 F=0.80 30-60 p=0.1176 p=0.0154 p=0.2001 p=0.1113 p=0.0682 p=0.3161 p=0.9307 p=0.0027 p=0.5738

F=1.79 F=1.83 F=15.55 F=3.21 F=2.15 F=7.01 F=0.11 F=4.76 F=1.41 0-60 p=0.1946 p=0.1280 p=<0.0001 p=0.0890 p=0.1435 p=0.0219 p=0.8920 p=0.0029 p=0.2094 83

F=0.76 F=1.82 F=20.90 F=0.00 F=1.38 F=9.67 F=0.80 F=1.87 F=1.47 0-100 p=0.4820 p=0.1305 p=<0.0001 p=0.9944 p=0.2750 p=0.0103 p=0.4632 p=0.1342 p=0.1893 1760 Depth: increments expressed in cm 1761 Site: Basswood, Orbweaver and Interim 1762 Watershed: 12 individual watersheds 1763 Precipitation: sum of the precipitation registered in the previous 7 days 1764 Land cover: prairie vegetation vs. cropland (corn, soybean) 1765 Position: Summit, Side, and footslope 1766 Season: Four seasons, two months each (April-May, Aug-Sept, Jun-Jul, Oct-Nov) 1767 1768 1769 1770 1771 1772 1773 1774

1775 1776 1777 Table II-9. Analysis of SWS by increment. Year: 2010 1778

Position Season Season Depth Site Watershed Precipitation Land cover Position Season *Land *Land *Position cover cover

F= 0.71 F=1.09 F= 15.35 F= 1.47 F= 1.60 F= 1.07 F= 0.39 F= 0.59 F= 1.16 0-30 p=0.5029 p=0.4158 p=0.0001 p=0.2403 p=0.2288 p=0.4059 p=0.6809 p=0.6249 p=0.3281

F=2.20 F=1.89 F=13.42 F=2.98 F=2.73 F=0.82 F=0.15 F=2.10 F=1.25 30-60 p=0.1382 p=0.1154 p=0.0003 p=0.1005 p=0.0905 p=0.5116 p=0.8611 p=0.0997 p=0.2793

F=1.41 F=1.24 F=16.26 F=2.52 F=2.23 F=1.01 F=0.32 F=1.20 F=1.46 0-60 p=0.2686 p=0.3292 p=<0.0001 p=0.1289 p=0.1351 p=0.4299 p=0.7308 p=0.3078 p=0.1897

F=0.56 F=1.54 F=15.61 F=0.00 F=1.56 F=1.39 F=1.16 F=2.30 F=2.30 84 0-100 p=0.5790 p=0.2023 p=<0.0001 p=0.9463 p=0.2362 p=0.3020 p=0.3347 p=0.0765 p=0.0091

1779 Depth: increments expressed in cm 1780 Site: Basswood, Orbweaver and Interim 1781 Watershed: 12 individual watersheds 1782 Precipitation: sum of the precipitation registered in the previous 7 days 1783 Land cover: prairie vegetation vs. cropland (corn, soybean) 1784 Position: Summit, Side, and footslope 1785 Season: Four seasons, two months each (April-May, Aug-Sept, Jun-Jul, Oct-Nov) 1786

1787 1788 1789 Table II-10. Annual average soil water storage in the upper 60 1790 cm by land cover 1791

Land Year Estimate cover 2007 2008 2009 2010 Rowcrop Mean 25.1 26.2 25.4 26.7 Standard Deviation 2.4 2.8 3.1 3.4 Min 15.3 17.4 16.2 17.5 Max 33.8 38.0 38.9 38.1 No. of observations 306 303 281 341 NPV Mean 25.9 25.8 25.5 26.2 Standard Deviation 1.6 1.8 1.7 2.1 Min 22.8 19.1 19.7 19.2 Max 31.2 31.6 29.6 31.5 No. of observations 152 152 144 180 1792 1793 Table II-11. Average annual soil water storage in the upper 60 cm and 1794 standard deviations in three topographic positions (Summit, Side, Foot) 1795 under two land covers (Rowcrop, Native prairie) 1796

Position Estimate Year 2007 2008 2009 2010 Summit Mean 25.4 25.6 25.2 26.1 Standard Deviation 1.5 1.9 2.0 2.2 Max 29.2 29.2 29.4 30.8 Min 20.1 19.1 19.3 20.3 No. of observations 153 152 144 178 Side Mean 24.9 26.1 25.1 26.5 Standard Deviation 3.0 3.4 3.7 4.0 Max 32.7 38.0 38.9 38.1 Min 15.3 17.4 16.12 17.5 No. of observations 152 152 139 175 Foot Mean 25.9 26.6 26.0 27.0 Standard Deviation 1.8 2.0 2.1 2.5 Max 33.8 35.0 34.3 35.3 Min 21.3 21.5 19.7 19.2 No. of observations 153 151 142 168 1797

1798

Table II-12. Average annual soil water storage in the upper 60 cm and standard 1799 deviations by season under two land covers 1800

2007 2008 2009 2010 Season Estimate RC NP RC NP RC NP RC NP

Apr-May Mean 23.6 26.0 26.0 26.0 26.7 26.0 26.6 26.4 St. Dev. 2.8 2.3 2.3 1.4 3.1 1.4 3.3 1.9 Max 33.8 31.2 32.0 28.3 38.9 29.0 37.2 31.5 Min 19.3 22.9 19.0 23.4 18.2 23.1 18.1 22.6 n 48 24 96 48 69 36 87 46 Jun-Jul Mean 25.4 25.9 27.0 26.0 26.6 25.9 27.5 26.9 St. Dev. 2.3 1.6 2.9 2.0 2.7 1.5 3.4 2.0 Max 30.2 28.9 38.0 31.6 35.1 28.5 37.4 31.5 Min 16.1 22.8 19.0 20.9 18.9 23.6 19.5 19.2 n 96 48 91 44 48 24 111 58 Aug-Sept Mean 24.25 25.9 24.6 24.6 23.7 24.4 27.4 26.7 St. Dev. 2.1 1.5 2.7 2.1 2.5 1.8 3.3 1.8 Max 27.7 28.8 29.4 28.1 29.2 27.6 38.1 29.8 Min 17.4 23.5 17.4 19.1 16.2 19.7 18.3 22.4 N 90 44 48 24 93 48 74 40 Oct-Nov Mean 25.1 25.8 26.7 26.3 25.7 26.0 24.7 24.4 St. Dev. 2.4 1.4 3.0 1.4 3.1 1.4 2.4 2.1 Max 30.0 28.0 36.6 28.9 34.3 29.6 29.0 28.1 Min 15.3 23.3 18.7 23.8 16.9 23.5 17.5 21.0 n 72 36 68 36 71 36 69 36 1801 RC = rowcrop 1802 NP = native prairie). 1803

CHAPTER III 1804

ASSESSING WATERFLOW AND UPTAKE DEPTH PATTERNS UNDER 1805

MIXED ANNUAL-PERENNIAL ECOSYSTEMS USING STABLE OXYGEN (18O) 1806

AND HYDROGEN (2H) ISOTOPES 1807

A paper to be submitted to the Journal of Plant and Soil 1808 Jose Gutierrez Lopez, Heidi Asbjornsen, Thomas Isenhart, Matthew Helmers, Alan 1809 Wanamaker 1810 1811

1 Introduction 1812

Understanding the complexity of ecohydrological processes in agroecosystems, 1813 such as depth of plant water uptake and the effects of vegetation on soil hydrology 1814 requires research approaches that assess the interactions between multiple important and 1815 relevant factors that influence water fluxes at a specific site. Given that water is the major 1816 factor determining plant productivity (Gholz et al., 1990) and that vegetation directly 1817 affects water balance and streamflow (Fohrer et al., 2001; Schilling, 2002), 1818 understanding the mechanisms and processes that determine patterns of plant water 1819 uptake from soil is crucial for managing agroecosystems for sustained productivity and 1820 other ecosystems services. 1821

Variation in water use patterns among plant species has been studied using 1822 different methods that assess changes in soil moisture as a measure of ET: direct methods 1823 include the use of metal recipients (phytometers) containing transplanted sods of different 1824 plant species (Weaver, 1941) and weighing lysimeters (Young et al., 1996; Evett et al., 1825

2009; Bryla et al., 2010), while indirect methods include neutron scattering probes 1826

(Yoder et al., 1998) and time-domain reflectrometry (TDR) probes (Qi and Helmers, 1827

2010a; Qi and Helmers, 2010b). However, although these methods provide good 1828 information about soil moisture differences among plant communities or soil covers and 1829 water use differences at specific depths in the case of the neutron and TDR probes, they 1830 lack the capacity to provide information about from where in the soil profile individual 1831 plants or species are obtaining water. Such plant or species-specific information on plant 1832 water uptake patterns may be particularly important when selecting species for specific 1833 management practices (e.g. hydrological services, landscaping) in highly diverse mixed 1834 agroecosystems or native prairie communities or when establishing strips of native prairie 1835 vegetation (SNPV) for ecosystem restoration purposes. 1836

In agricultural landscapes in temperate-northern regions, most of the water use by 1837 crops takes place during the growing season and in the months with the greatest 1838 evaporative demand. In the Midwestern U.S., studies have shown that annual crops take 1839 most of their water from the upper 30 cm of soil which is where most of the root system 1840 is concentrated in crop species like corn and soybeans (Asbjornsen et al., 2007; Nippert 1841 and Knapp, 2007; Asbjornsen et al., 2008). Conversely, native prairie vegetation is 1842 characterized by a fine and extremely branched root system, that extends to depths 1843 greater than 1.5 m (Weaver, 1931; Weaver et al., 1934), allowing it to access water from 1844

deeper soil profiles than rowcrop species. These contrasting rooting patterns can result in 1845

differences in depth of plant water uptake (DWU) among different plant species and 1846 vegetative cover types (Zhang and Schilling, 2006). 1847

Recent studies are starting to investigate differences in DWU using stable 1848 isotopes, a method that allows researchers to infer from which depths within the soil 1849 profile co-existing plant species are acquiring water (Araki and Iijima, 2005; Asbjornsen 1850 et al., 2007; Nippert et al., 2010; Wang et al., 2010). These studies have provided good 1851 insights about DWU in both annual and perennial vegetation. For example, Nippert an 1852

Knapp, (2007) in their study of C3 and C4 plants growing in a native prairie in Kansas, 1853 observed interspecific differences in DWU in response to changes in water availability: 1854 when water was available in the upper 30 cm, all plant species took water from shallower 1855 sources, but during dry periods, C3 species used proportionally more water from deeper 1856

depths than C4 species. In a study comparing water uptake patterns by corn and native 1857 prairie species in central Iowa, Asbjornsen et al. (2007; 2008) found that early in the 1858 season when water was abundant, C3 and C4 plants extracted water from the upper 20 cm 1859 of soil, but as water became progressively more limiting, C3 shrubs and trees in the 1860

savanna and woodland ecosystem (Quercus alba, Symphoricarpos orbiculatus and Carex 1861

sp.), shifted their water uptake to deeper horizons, and C4 species (Andropogon gerardii 1862

and Zea mays) used water from shallower soil depths. Seasonal variation in water uptake 1863

has also been documented for annual crops. For example, Wang et al. (2010), found that 1864

summer corn extracted water from the upper 20 cm in the jointing and fully ripe stage, 1865

and from as deep as 50 cm in the flowering state. Under controlled conditions of water 1866

availability and soil compaction, Araki and Iijima (2005) found that variations in depth of 1867

water uptake by rice (Oryza sativia L.) was also influenced by the availability of water in 1868

the top soil, when water was restricted in the upper layers of soil, plants took water from 1869

deeper soil layers. 1870

Despite the substantial research conducted on DWU in agricultural crops and 1871 prairie vegetation (above), and the use of perennial vegetation in waterways or buffer 1872 strips for conservation purposes (Schultz et al., 2004; Williard et al., 2005; Gharabaghi et 1873 al., 2006; Fox et al., 2009; Zhou et al., 2010), we are unaware of previous studies that 1874 have examined DWU uptake patterns between native prairie vegetation and annual crops 1875 established as mixed agroecosystems. Under these conditions, competition over water 1876 resources may develop between SNPV and crops that may compromise the health of the 1877 entire ecosystem. More research is needed to enhance understanding of patterns of DWU 1878 between annual crops and reconstructed prairie vegetation to allow land managers and 1879 scientists to make more informed decisions regarding the incorporation of NPVS to 1880 enhance regulation of the hydrological balance in rowcrop dominated landscapes. In 1881 particular, because C3 forbs and C4 grasses may vary widely in water use patterns, 1882 knowledge about these differences can be critical for determining the most effective 1883 combination of plant species when designing NPVS for specific objectives. 1884

The use of stable isotopes to assess DWU by plants relies on: (a) the presence of a 1885 natural gradient in δ18O and δ2H in the soil profile (< 3 ‰ and < 30 ‰ in δ18O and δ2H 1886 respectively preferentially), and (b) the ability to obtain the right isotopic signature from 1887 a given soil depth. However, natural gradients are not always present and there are 1888 significant limitations to relying solely on naturally occurring stable isotopes, specially 1889 without the presence of clear isotopic gradients, especially in humid environments where 1890 frequent rainfall together with mixing of water having different source isotopic 1891

concentrations can create ‘noisy’ vertical concentrations in the soil profile rather than a 1892

clear and continuous gradient. In other words, when assessing DWU the isotopic 1893 concentration of the plant tissue may match the isotopic value of more than one layer of 1894 soil (Moreira et al., 2000). Numerous studies have faced this problem, which has limited 1895 the interpretation of their data and ability to infer DWU (Moreira et al., 2000; Asbjornsen 1896 et al., 2007; Asbjornsen et al., 2008). As an alternative approach, researchers have used 1897 manipulative irrigation experiments whereby water enriched in the stable isotope is 1898 applied to the study area as a means of artificially establishing the isotopic gradient in the 1899 soil profile (Yoder et al., 1998; Araki and Iijima, 2005; Rowland et al., 2008). 1900

In clay rich soils, water extraction for isotopic analysis in DWU studies presents a 1901 mayor challenge, since strong intramolecular forces (e.g. van der Waals) tend to retain 1902 hydrogen and oxygen molecules (Hillel, 2004), thus increasing the water extraction time 1903 needed to get a unfractionated sample. Araguás-Araguás et al (1995), extracted water 1904 from clay rich soils (50 to 80 % clay content) for up to 7 hours to get unfractionated 1905 samples, and suggested that calibration is required for specific soil types. In a recent 1906 study, West et al (2006) proposed a minimum extraction time of 40 minutes for clay 1907 soils, but no clay content is provided to make direct comparisons. 1908

This research comprises part of a long-term study involving the integration of 1909 native perennial vegetation strips (NPVS) into rowcrop agricultural fields to assess their 1910 effectiveness in restoring the natural hydrological balance of agroecosystems (Zhou et 1911 al., 2010; Hernandez-Santana et al., In Press). The goals of the present study were to 1912 compare DWU by dominant plant species within an annual rowcrop system and prairie 1913

vegetation in central Iowa, at two different topographical positions (upslope and 1914 footslope) and in three watersheds having different configurations of rowcrop and prairie 1915 vegetation, to propose a calibration method for water extraction techniques for clay rich 1916 soils, and to test the applicability of artificially created isotopic gradients in mixed 1917 agricultural ecosystems in DWU studies. Our specific objectives were to: (a) assess the 1918 average DWU of dominant annual crop and native prairie species within each watershed 1919 during the growing season using two methods: natural variability in stable isotope 1920 concentrations and a stable isotope tracer δD, and (b) assess the effects of landscape 1921 position and soil water content on depth of plant water uptake under each of these cover 1922 types. 1923

We hypothesized that (1) during periods of adequate soil moisture availability 1924

(e.g., early in the growing season), corn and prairie species will obtain their water from 1925 relatively shallow depths in the soil profile. As soil moisture becomes more limiting (later 1926 in the growing season), prairie species (especially C3 forbs) will shift their depth of water 1927

uptake to deeper depths in the soil profile, whereas corn and C4 prairie species will have 1928

more limited capacity to obtain water from deeper depths, and (2) prairie and crop 1929

species will use water from deeper depths in the soil profile in the summit position 1930

compared to the footslope position due to lower water availability during dry periods in 1931

the upper parts of the watershed. 1932

1933

2 Study design and methods 1934

2.1 Study area 1935

This study was conducted at the Neal Smith National Wildlife Refuge (NSNWR; 1936

41°33 ́N, 93°16 ́W) located in Prairie City, Jasper County, Iowa. The NSNWR, which 1937 comprises 3500 Ha administrated by the National Fish and Wildlife Service, and was 1938 created by an act of Congress in 1990 with the mission to reconstruct presettlement 1939 vegetation on the landscape, particularly native tallgrass prairie. To date, the NSNWR 1940 has converted approximate 2250 Ha of previous agricultural land into reconstructed 1941 native prairie vegetation, while areas that are still awaiting reconstruction are currently 1942 maintained under pasture or corn-soybean rotation. 1943

The NSNWR includes part of the southern Iowa drift plain, characterized by the 1944 presence of steep rolling hills of Wisconsinan-age loess on pre-Illinoian till (Prior, 1991). 1945

Walnut Creek is a third order stream that drains into the Des Moines River at the upper 1946 end of the Red Rock Reservoir. Most soils at the research sites are classified as Ladoga 1947

(Mollic Hapludalf) or Otley (Oxyaquic Argiudolls) soil series, which are highly erodible 1948 with slopes ranging from 5 to 14%. Texture of Ladoga soils is silt loam and silty clay 1949 loam for Otley soils, with clay contents from 15 to 42% and 20 to 42% respectively 1950

(NRCS, 2010). The mean average precipitation registered over the last 30 years (1981- 1951

2010) is 910 mm, with the majority of the large storms occurring between May and 1952

August (NCDC, 2011). Precipitation data for this study were recorded at the MesoWest 1953

(ID = NSWI4) weather station located in the Neal Smith Wildlife Refuge in Jasper 1954

County, Iowa, from March to November 2010. 1955

1956

2.2 Experimental design 1957

Three experimental zero-order (intermittent in hydrological outflow) watersheds 1958 were used in this study, each subjected to a different treatment: Interim 1, with 10% 1959 prairie vegetation distributed as contour strips of native prairie vegetation (hereafter 1960 referred to as “SNPV”) within a crop matrix, Interim 3 with 100% row-crop (hereafter 1961

“CROP”) and Interim 4 with 100% reconstructed prairie vegetation (hereafter 1962

“PRAIRIE”; Figure III.1). SNPV were planted in July 2007 and PRAIRIE in 1994. The 1963 seed mixture used in the plantings consisted of 20 native prairie forbs and grasses with 1964 four primary species in the mix, including indiangrass (Sorghastrum nutans Nash), little 1965 bluestem (Schizachyrium scoparium Ness), big bluestem (Andropogon gerardii Vitman), 1966 and aster (Aster spp. L.). This mixture is similar to the one commonly used by the 1967

NSNWR staff in prairie reconstruction practices. Fire is also used as a management tool 1968 in the prairies under reconstruction at the NSNWR; the PRAIRIE watershed was burned 1969 in the spring of 2010. 1970

In each study watershed, two 2 m2 plots were marked in the summit and one in 1971

the footslope position, yielding a total of 9 plots (3 per watershed). A deuterated tracer 1972

(i.e. highly enriched δD values) was applied in two plots per watersheds, one in the 1973

summit and one in the footslope; the second summit plot was left as a control (i.e. no 1974

tracer was applied and naturally occurring isotopic concentrations were assessed; see 1975

details below). 1976

In the CROP watershed, corn (Zea mays), an annual C4 crop, was analyzed in all 1977

the plots. In the SNPV watershed, we selected 3 dominant species for assessment of 1978

depth of water uptake: coneflower (Ratibida pinnata), a C3 forb, brome grass (Bromus 1979

ciliatus), a C3 grass, and wild rye (Elymus canadensis), a C3 grass. In the PRAIRIE 1980

watershed, two species were selected: big bluestem (Andropogon gerardii), a C4 grass, 1981 and coneflower (Table III.1). Coneflower was thus sampled in two different watersheds, 1982

SNPV and PRAIRIE. These species were selected based on dominancy at the watershed 1983 level and their presence in all three plots within each watershed. Other species (e.g. Aster 1984

spp. L.) were also dominant but not present in all plots in a given watershed. 1985

1986

2.3 Soil moisture monitoring 1987

Fiberglass access tubes (Delta-T Devices) were used to monitor soil moisture 1988 using a Theta Probe (ML2, Delta-T Devices, Cambridge, UK) and a PR2 Probe (PR2, 1989

Delta-T Devices, Cambridge, UK). The Theta Probe was used to measure voltage outputs 1990 in the upper 5 cm of soil and the PR2 probe to take readings at soil depths of 10, 20, 30, 1991

40, 60 and 100 cm. Data conversion and calibration details can be found in Chapter II of 1992 this thesis. Soil moisture readings were taken to coincide with the timing of the 1993 deuterated water tracer application and the collection the soil and plant sample collection. 1994

1995

2.4 Isotopic tracer application 1996

A solution of 500 mL of D2O [deuterium oxide 99.9%] diluted in 12 L of regular tap 1997 water of known δ18O ‰ (VSMOW) and δD ‰ (VSMOW) was applied on DOY 184, 1998

2010 in 6 plots (two per watershed) using a backpack water pump. The deuterated tracer 1999 was applied prior to a forecasted rainfall event the next day (DOY 185) of 24 mm to 2000

ensure rapid vertical movement of the tracer into the soil profile. Two L of labeled water 2001

were applied per plot, equivalent to 1 mm of precipitation. The application was 2002

conducted in the late afternoon and early evening in order to minimize fractionation of 2003

the stable isotope due to evaporation. The tracer was applied covering as evenly as 2004

possible the soil surface. Special care was taken to apply the δD tracer slowly and 2005 precisely within each plot to avoid immediate runoff as well as minimize contact with the 2006 vegetation, as this would reduce the amount of tracer applied to the soil. 2007

Previous to the application of labeled water, one set of soil samples was collected at 2008 six intervals (0-10, 10-20, 20-30, 30-50, 50-70 and 70-100 cm) from each plot using a 2009 bucket auger from the soil surface at a depth of 100 cm. Each sample was placed in vials 2010 and stored in a freezer at -4ºC for future 18O and δD analysis to provide baseline ratios 2011 prior to the irrigation application of the deuterated water. 2012

2013

2.5 Collection of soil and plant samples 2014

Soil and plant samples were collected in two periods during the 2010 growing 2015 season: July (DOY 203 and 206) and August (DOY 240, 241 and 242; hereafter July and 2016

August sample). Coneflower plants collected in the PRAIRIE watershed were not fully 2017 developed due to a prescribed burning treatment applied to this watershed in the spring. 2018

Big bluestem, bromegrass, coneflower and corn were collected on DOY 203 and wildrye 2019 was collected on DOY 206 in the July sample. Bromegrass and corn were sampled on 2020

DOY 240, coneflower and big bluestem on DOY 241 and wildrye on DOY 242 in the 2021

August sample. One set of six soil cores was collected adjacent to each plant sample 2022 using a bucket auger. Soil cores were collected at increments from 0-10, 10-20, 20-30, 2023

30-50, 50-70 and 70-100 cm in SNPV and PRAIRIE, and from 0-10, 10-20, 20-30, 30-50 2024

and 50-70 in CROP due to the shallower groundwater table (Figure III.3). A total of 282 2025

soil and plant samples were collected during the July and August sampling periods. For 2026

plants, stems and leave were sampled and analyzed, however only stems were used to 2027

assess DWU (see below). 2028

For sampling of plant tissue, we collected non-photosynthetic stem tissue from the 2029 base of each of the study species, based on the principle of no fractionation upon water 2030 uptake (White et al., 1985; Roden and Ehleringer, 1999). For plants with small stems, 2031 tissue from several stems was pooled into one sample. In corn plants vertical segments of 2032 two stems were combined into one sample. Soil and plant samples were collected in vials 2033 and immediately placed in a cooler with ice to avoid evaporation and promote stomatal 2034 closure. Samples were transported to the Stable Isotope Lab at Iowa State University and 2035 kept frozen (-4ºC) until analysis. 2036

2037

2.6 Rainfall and groundwater collection 2038

Rainwater samples were collected from June to September, 2010 after a 2039 precipitation event in two watersheds, SNPV and CROP, using custom-design rainfall 2040 collectors consisting of a funnel connected through a house to a collector bottle, which 2041 was inside a wooden box to avoid fractionation due to evaporation. All the samples were 2042 collected within two hours after the precipitation events that occur during the daytime, 2043 and early in the morning for nighttime precipitation events. These data were used to 2044 estimate the percentage of meteoric water entering the watersheds. Groundwater was 2045 collected to complement and compare concentrations of δ18O ‰ and δD ‰ in the soil. 2046

One well was installed at the summit and footslope positions of the SNPV and CROP 2047

watersheds to a depth of approximately 6 m. The groundwater wells consisted of ¾ inch 2048

PVC piping capped at the bottom with a pointed tip. The wells were equipped with slits 2049 covering the bottom third of the piping to allow movement of groundwater into the well. 2050

2051

2.7 Water extraction and isotopic analysis 2052

Water from plant and soil samples was extracted using a custom-design vacuum 2053 cryogenic distillation apparatus (Figure III.3). The water extraction apparatus consisted of 2054 five extraction arms attached via an 18/9 ball joint to a 2.54 cm o.d. vacuum line powered 2055 by a vacuum pump C Plus Maxima model M4C. A Millitorr Vacuum Gauge was attached 2056 to the vacuum line to measure pressure. A Chem-Vac high vacuum valve (CG-962-01) 2057 was used to isolate each extraction arm from the main vacuum line. Each extraction arm 2058 was attached to an extraction tube on one side and to a collection tube on the other. 2059

Extraction and collection tubes were 2.54 cm o.d. each, attached to their respective 2060 extraction arm with a stainless steel Ultra-Torr vacuum fitting (SS-16-UT-6). A 25 Watt 2061 incandescent light bulb was used as a heat source and a thin cardboard circle covered in 2062 aluminum foil was used to keep the light bulb in place and the heat insulated inside the 2063

“heat lamp dewar”. 2064

Prior to extraction, plant and soil samples were removed from the freezer and 2065 allowed to thaw at room temperature. In the case of soils, the sample was removed from 2066 the vial, homogenized with a spatula and roots were removed to obtain the isotopic 2067 signature of the soil alone and discard the value of the water being transported in these 2068 roots from an unknown depth. The sample was then placed in the extraction tube and a 2069

custom-made filter consisting of a plastic ring of about 1 cm in length covered with filter 2070

paper was placed inside the extraction tube to avoid soil particles from moving into the 2071 vacuum line, extraction arm or collection tube. Another filter was placed in the collection 2072 tube for better protection. Both the extraction and collection tubes were attached to the 2073 extraction arm via Ultra-Torr fittings. A Dewar with liquid nitrogen was placed under the 2074 extraction tube and the sample submerged in the liquid nitrogen until the sample was 2075 completely frozen (about 5 min). Once the sample was frozen, the isolation valve was 2076 opened and the extraction arm and the tubes pumped down to at least 50 mTorr. Once the 2077 desired vacuum was reached, the isolation valve was closed and the Dewar with liquid 2078 nitrogen was replaced with the heat lamp Dewar. The Dewar containing liquid nitrogen 2079 was refilled and placed under the collection tube. The extraction time was 60 min for soil 2080 samples and 30 to 60 min for plant tissue (see 2.8 for further details). Once the extraction 2081 time was completed, collection and extraction tubes were removed from the extraction 2082 arm and the collection tube was sealed with Parafilm and allowed to thaw at room 2083 temperature. The extracted water was then transferred to a 10 mL vial and stored in a 2084 cooler at 4ºC for isotopic analysis. 2085

All plant and soil water extraction samples were measured for δD and δ18O on a 2086

Picarro L1102-i Isotopic Liquid Water Analyzer attached to an autosampler and using 2087

ChemCorrect software, at the Department of Geological and Atmospheric Sciences at 2088

Iowa State University. Each sample was measured a total of six times, and to account for 2089

memory effects (Barbour, 2007), only the last four injections were used to calculate mean 2090

isotopic values. Reference standards (OH-1, OH-2, OH-3, OH-4) were used for isotopic 2091

100

corrections, and to assign the data to the appropriate isotopic scale. At least one reference 2092

standard was used for every five samples. The combined uncertainty (analytical 2093 uncertainty and average correction factor) for δ18O was ± 0.09‰ (VSMOW) and δD was 2094

± 0.45‰ (VSMOW). 2095

As indicated by West et al, infrared spectroscopy is highly influenced by the 2096 presence of organic compounds in water samples (West et al., 2010), to overcome this 2097 problem, all the δ18O samples flagged as contaminated (with presence of organic 2098

compounds) by the ChemCorrect software, and 22 (8%) of the non-flagged samples (for 2099

precision comparison purposes) were measured on a Finnigan MAT Delta Plus XL mass 2100

spectrometer in continuous flow mode connected to a Gas Bench with a CombiPAL 2101

autosampler at Iowa State University (Department of Geological and Atmospheric 2102

Sciences) using reference standards [OH1, OH2, OH3, ISU Tap (lab internal std)] for 2103

isotopic corrections, and to assign the data to the appropriate isotopic scale. At least one 2104

reference standard was used for every eight samples. The combined uncertainty 2105

(analytical uncertainty and average correction factor) for δ18O was ± 0.16‰ (VSMOW). 2106

Further, only corrected and calibrated δ18O (aided by δD when necessary) values were 2107

used in the results section. 2108

2109

2.8 Precision and reliability of the water extraction apparatus 2110

To assess the precision of the water extraction apparatus, a series of soil samples 2111

were collected in all study watersheds at different topographic positions and depths. All 2112

soil samples were mixed together and homogenized. Plant roots and other plant materials 2113

101

and rocks were removed and the remaining soil was dried in the oven drier for 24 h at 2114

104ºC. The dried soil was then dampened with tap water of known isotopic composition 2115

(internal lab standard) to a ratio of 400 mL of water per every Kg of soil. The soil was 2116 homogenized one more time to assure even moisture and subsamples were run through 2117 the extraction process. 2118

To estimate the optimum time for extraction, samples were run from 10 to 109 2119 minutes (See Appendix E). The δ18O ‰ and δD ‰ values of the water extracted from 2120 these soils were compared with the known values of the tap water that was applied to the 2121 soils. The differences in isotopic concentrations were estimated and the mean of the 2122 differences was regarded as the average extraction systematic error, or extraction error. 2123

We observe differences of less than 0.5 ‰ (observed – standard) starting at 39 min, 2124 similar to the results observed by West et al, (2006), however consistent differences were 2125 observed after 50 min. Average error estimated for extraction times greater than 30 min 2126 was ±0.51 ‰ and ±7.69 ‰ for δ18O and δD, with a standard error of 0.06 ‰ and 0.22 ‰, 2127 respectively. The extraction error for extraction times greater than 59 min was ±0.44 ‰ 2128 and ±7.42 ‰ for δ18O and δD, with a standard error of 0.07 ‰ and 0.29 ‰, respectively. 2129

Since the extraction time for our samples varied from 30 to 60 minutes, we used the mean 2130 extraction systematic error for extraction times greater than 30 min to adjust our isotopic 2131 data prior to assessment of DWU. The average extraction error for extraction times 2132

greater than 30 min was chosen, to include all our extraction times, as previously 2133

mentioned the extraction times ranged from 30 to 60 min including plant and soil 2134

samples. Adjusted isotopic values of δ18O and δD of soil water from non-flagged samples 2135

102

(plot 1, 4, and 7) were plotted to verify the linearity of the fractionation form our 2136 extraction apparatus (See Appendix F for further details). 2137

2138

2.9 Estimation of depth of water uptake 2139

Once the δ18O values were adjusted with the extraction error, depth of water 2140

uptake was estimated using the direct inference method (White et al., 1985; Brunel et al., 2141

1995), in which the isotopic values of the water extracted from the soils at different 2142

depths are compared with the isotopic values of the plant. Similarly, in this study we 2143

compared the δ18O values of water extracted from soils at different depth intervals 2144

(Figure III.2) with the δ18O values of the water extracted from a plant (only stems were 2145

used). Each of the 36 plant samples was compared to its own set of soil samples. The 2146

isotopic value of one of the 6 corresponding soil samples with the highest similarity to 2147

the value of the plant samples was regarded as the probable DWU. 2148

δ18O values were used as the main tracer of DWU, since no δD values were 2149 determined in the mass spectrometer for samples flagged by the ChemCorrect Software. 2150

However, in cases where the δ18O value of the plant matched the δ18O value of more than 2151

one depth (e.g. 0-10 and 50-70), the artificial gradient created with the deuterated tracer, 2152

which had a higher positive signature, was used to eliminate ambiguous DWU suggested 2153

by δ18O. The analytical error of the measuring instruments was accounted for at the time 2154

of assessing probable DWU. From the 36 soil sets used to determine DWU, 18 of them 2155

had clear gradients (i.e. clear gradient with no overlapping values), 9 had similar or 2156

103

overlapping isotopic values in only two intervals, and 9 had overlapping values in 3 or 2157

more depths. 2158

2159

2.10 Statistical analysis 2160

Significant differences between mean DWU per species observed for the two 2161 sampling periods (July, August; Table III.2) were determined using the GLM procedure 2162 in the statistical program SAS. Vegetative type (C3 and C4) and the five different species 2163

(big bluestem, bromegrass, coneflower, corn and wild rye), were the independent 2164 variables, with DWU as the dependent variable. We analyzed the effects of plot, 2165 watershed, sampling time, volumetric water content in the topsoil and topographic 2166 position on the variation observed in DWU. 2167

2168

3. Results 2169

3.1 Precipitation 2170

The total precipitation registered during the study period was 1326 mm (Figure 2171

III.4), 45% greater than the average precipitation of 910 mm recorded from 1981 to 2010 2172

(NCDC, 2011), with precipitation events greater than 10 mm observed from early-April 2173 to mid-November. 2174

Analysis of the isotopic signature of precipitation samples revealed variations in 2175

δ18O from -12.6 to -1.1 ‰, and in δD from -85.3 to -3.3 ‰ (Figures III.5 and III.6). The 2176 variations in the isotopic concentrations appeared to be a response of the frequency and 2177

104

intensity of the precipitation. δ18O and δD values became more positive as the frequency 2178 and the intensity of the precipitation increased, and more negative as they decreased. 2179

Additionally, the graph showing δ18O and δD precipitation values versus the global 2180 meteoric water line (GMWL) (Craig, 1961), and the local meteoric water line (MWL) 2181

(Simpkins, 1995), demonstrates that the collection protocol did not influence the isotopic 2182 composition of precipitation (i.e. evaporation effects) (Figure III.7). 2183

2184

3.2 Groundwater 2185

Unlike the isotopic values of rainfall water, groundwater showed little variation 2186 during the monitored period (Figure III.7). δ18O showed an average of -7.30 ‰ and δD - 2187

45.60 ‰ with standard deviations of 0.31 ‰ and 2.32 ‰, respectively. Plotting δD 2188

versus δ18O values, we observed linear fractionation of both isotopes (R2=0.94) with a 2189 light enrichment of δD. The δ18O and δD values were plotted against the global (Craig, 2190

1961) and the local meteoric water line (Simpkins, 1995) to denote the δD enrichment, 2191

which remained constant in three of the four sites where samples were collected (Figure 2192

III.8, III.9). Differences in isotopic composition were detected by topographic position. 2193

Plotted by watershed and observation date, δD values of groundwater samples showed 2194

enriched values in summit position of the SNPV (Interim 1) watershed, and lower δD 2195

values in the Foot position of the SNPV (Interim I) and CROP (Interim 3). Only the 2196

Summit position of the CROP (Interim 3) watershed showed significant changes in 2197

isotopic concentration with time (Figure III.9). 2198

105

3.3 Soil moisture content 2199

θv declined in the upper layers of the soil profile during the study period (Figure 2200

III.10). Deeper depths remained fairly constant and were not as dynamic as shallower 2201

depths. Mean θv in the upper 5 to 10 cm of soil at the time the tracer was applied was 2202

0.36 and 0.38 respectively. Lower parts of the watershed showed higher θv, compared 2203 with the upper parts, particularly in the SNPV and PRAIRIE watersheds (Figure III.11). 2204

In contrast, the CROP watershed maintained relatively higher θv compared to the other 2205

two watersheds, particularly in the upper layers of the summit position (Figure III.11). 2206

2207

3.4 Isotopic signature of the soil water by depth 2208

Fifty percent of the samples analyzed showed clear natural isotopic gradients, and 2209

25% of them had similar of overlapping isotopic values in only two depths, which 2210

facilitated the estimation of depth of water uptake. δ18O values ranged from -1.48 to -9.06 2211

‰ with a mean average difference (gradient) of the 36 soil sets of 3.24 ‰ between the 2212

uppermost and deepest soil depths. The gradients observed per watershed were: 3.25 ‰, 2213

2.39 ‰ and 3.66 ‰ in the SNPV, CROP and PRAIRIE, respectively. Despite the 2214

expectation of noisy gradients due to the precipitation registered in 2010 (45% above the 2215

30 year average), only 9% of the soil sets showed undefined gradients. Analysis of δ18O 2216 showed well-defined gradients in most of the plots in the SNPV and PRAIRIE 2217 watersheds (Figure III.12). The majority of the observations with noisy gradients were 2218 collected from the plots 4, 5 and 6, which were located in the CROP watershed. 2219

106

The δD analysis showed that the application of the deuterated tracer significantly 2220 altered the isotopic gradient in the soil. Values of δD ranged from -63.02 to 1552.89‰, 2221 with the highest δD values observed during the August sample 20 days following 2222 application of the tracer. The highest δD value observed in the July sample was 282.18‰. 2223

The artificial gradient created with the deuterated tracer varied by plot and watershed 2224

(Fig. 13), with the SNPV and PRAIRIE watersheds having the most pronounced isotopic 2225 gradients. The strength of the gradients also decreased significantly by the second 2226

observation period in all watersheds (Figure III.14). 2227

2228

3.5 Plant water uptake 2229

The analysis of depth of DWU indicates that on average the upper 70 cm of soil were the 2230

main source of water for all species. Despite the overall high water content observed 2231

during our study in the three watersheds (See 3.1), all species shifted their depth of water 2232

uptake between the two sampling periods. In most cases plants shifted to deeper soil 2233

water sources in August, as compared to their DWU in July (Table III.2). The statistical 2234

analysis of DWU indicated that collection date (July, August) and variations in θv among 2235 observation dates had a significant (p=0.0401 and p=0.0092, respectively) influence on 2236

DWU for all the species (Table III.3). Further, our results suggest that big bluestem 2237 obtained water from 10-50 cm in July and shifted to a depth of 20-50 cm in August (Fig. 2238

12). Bromegrass showed a DWU of 0-20 cm in July and 10-50 cm in August. 2239

Coneflower, which was sampled in two different watersheds (SNPV and PRAIRIE), 2240 showed different patterns of DWU in each watershed. In SNPV, coneflower shifted from 2241

107

10-70 cm in July to a shallower depth, 0-30 cm, in August. In PRAIRIE, the inverse 2242

pattern was observed, as coneflower shifted from a shallower depth (0-10 cm) in July to a 2243 deeper depth, 20-70 cm, in August (see Discussion section for further details). Corn 2244 acquired water from 10-50 cm in July and from 20-70 cm in August. Wild rye shifted 2245 from 0-30 cm in July to a shallower depth, 0-20 cm, in August. 2246

2247

3.6 Effects of topographic position and soil water content on DWU 2248

Our statistical analysis showed no significant relationship between topographic 2249 position on DWU for all species (Table III.3). Changes in θv had a greater influence than 2250 topographic position for all species. In the toe position during the July sampling period, 2251 big bluestem, bromegrass, coneflower and wild rye showed shallower DWU for at least 2252

during one observation date, while corn exhibited the deepest DWU of 30 cm (Table 2253

III.2). In the summit position, the deepest DWU observed across all species in July 2254

corresponded to coneflower from the PRAIRIE watershed (50-70 cm). In August, most 2255

species shifted to deeper depths, independent of topographic position (Table III.2). 2256

The watersheds SNPV and PRAIRIE showed lower θv in the upper parts of the 2257 watershed (0.32 and 0.33), compared to their θv in the lower parts (0.36 and 0.38) 2258

particularly in the shallower depths (5-10 cm). However, only PRAIRIE showed a 2259

consistently lower θv in the summit position at most depths, compared with the Toe 2260

position (Figure III.8). The CROP watershed showed a higher θv (0.49) in the summit 2261 compared to the toe position (0.36). The differences in θv appeared to influence patterns 2262 of DWU, affecting primarily C3 species according to our statistical analysis. 2263

108

2264

3.7 Water uptake by functional group: C3 and C4 species 2265

Our data showed no difference between functional groups in the DWU patterns 2266 observed (Table III.3). The statistical analysis by functional groups however, showed that 2267

C3 plants were more influenced by changes in θv than C4 plants (Table III.3). The C3 forb, 2268

coneflower, from watershed PRAIRIE exhibited the deepest DWU across all study 2269

species (30-70 cm in August). The same species from watershed SNPV showed a range 2270

of DWU from 0-30 cm in August (Figure III.15). The C3 grass, wild rye, showed the 2271

shallowest range in DWU, 0-30 cm in July to 0-20 cm in August. Both C4 species (big 2272

bluestem and corn) showed similar patterns of DWU in July and shifted to a similar depth 2273

in August but their ranges were not different from the ones observed for C3 species. 2274

An inverse pattern in DWU was observed for both the C3 forbs coneflower and 2275

wild rye collected from the SNPV watershed (deeper DWU in July than August). When 2276

these two species were collected in July, their DWU was deeper compared to in August, 2277

10-70 cm in July to 0-30 cm in August for coneflower and 0-30 cm in July to 0-20 cm in 2278

August for wild rye. 2279

2280

4. Discussion 2281

4.1 Using δ18O and δD as indicators of depth of water uptake 2282

18 We used δ O and δD stable isotopes to determine the effects of variations in θv, 2283 topographic position and season on patterns of depth of plant water uptake in four 2284

109

dominant prairie species (big bluestem, bromegrass, coneflower, wildrye) and one crop 2285

(corn) in three small watersheds. Several studies have used this approach to assess DWU 2286

in a variety of land covers such as rice (Araki and Iijima, 2005), grassland (Nippert and 2287

Knapp, 2007), crops (Wang et al., 2010), shrubs (Nippert et al., 2010), woody plants 2288

(Midwood et al., 1998) and mixed agricultural-perennial ecosystems (Asbjornsen et al., 2289

2007; Asbjornsen et al., 2008). In restoration efforts, water dynamics and particularly 2290 water use patterns are of great importance for the entire ecosystem of interest. There is a 2291 need of research techniques to study these dynamics, such as the use of stable isotopes as 2292 an important tool for researchers and land managers. Most of these techniques rely on 2293 natural occurring isotopic gradients in the soil to assess. In saturated soils or where the 2294 preferential infiltration is not vertical, the isotopic concentration does not always present 2295 the necessary gradient for the assessment of DWU. 2296

In order to overcome the difficulties posed by the usually persistent wet soils 2297 present at our study site and hence the expected “noisy” isotopic gradients due to 2298 excessive rainfall, we applied a deuterated tracer to help determine DWU. Several studies 2299 of DWU have been conducted using δD tracers in different ecosystems and land covers 2300

(Yoder et al., 1998; Plamboeck et al., 1999; Moreira et al., 2000; Rowland et al., 2008), 2301 which facilitates the assessment of DWU. In this study the use of the deuterated tracer 2302 helped significantly the assessment of DWU in the plots where neither δ18O nor δD 2303

natural gradients were clear, by eliminating ambiguous DWU suggested by these 2304

isotopes, and introducing a more positive δD value. Using soils from our research sites, 2305

the water extraction apparatus was calibrated and minimum times of extraction to get 2306

110

consistent differences was estimated. The average extraction systematic error was 2307

accounted four when DWU was estimated. 2308

4.2 Isotopic signature of rainfall water 2309

Fractionation of rainfall water is a well-known process that is highly influenced 2310 by different parameters such as altitude, latitude and distance from the coast, the fraction 2311 precipitated from a vapor mass (Clark and Fritz, 1997; Mook, 2006), and also due to the 2312 effects of a Rayleigh-type distillation, in which condensation distills the heavy isotopes 2313

(δ18O and δD) from an air mass, depleting the air mass as rainout occurs (Clark and Fritz, 2314

1997). This variation and changes in isotopic concentration in precipitation water was 2315 observed in our data (Figure III.5 and III.6), where consecutive rainfall events showed 2316 slightly similar isotopic values, compared to isolated events. Previous studies have found 2317 similar results in Central Iowa (Simpkins, 1995), Kansas (Nippert and Knapp, 2007), 2318

Hebei, China (Li et al., 2007), Shanxi, China (Wang et al., 2010). The global meteoric 2319 water line (GMWL) developed in 1961, by Harmon Craig, which describes the linear 2320 fractionaton of δ18O and δD in meteoric waters, and the regional meteoric water line 2321

developed by Simpkins in 1995 (Craig, 1961; Simpkins, 1995), closely follow the linear 2322

regression of our precipitation data (R2=0.9635), showing no statistical significant 2323

differences (P=<0.001) for both Craig and Simpkins lines. 2324

2325

111

4.3 Isotopic signature in soils and groundwater 2326

Approximately 50% of the soil profiles examined in our plots had clear gradients, 2327

25% had similar values (or within the estimation error) at two depths, and 25% had 2328 similar values at more than two depths. The majority of the soil profiles that exhibited 2329 noisy gradients were located in the CROP watershed (Figure III.12), which has naturally 2330 occurring lateral seepage in the upper parts (summit)1. This seepage is present beginning 2331

early spring and depending on the rainfall conditions can remain active until late fall, 2332

creating conditions of increased θv content in the upper parts of the watershed. Of the 2333

three study watersheds, it is the only one that presents higher volumetric water content in 2334

the upper parts and also the one with the least variation in θv observed during our study 2335

(Figure III.10). 2336

Several authors have studied the influence of soil moisture dynamics on the 2337 development of isotopic gradients in the soil (Barnes and Allison, 1988; Gat, 1998; 2338

Leibundgut et al., 2009). This isotopic gradient, represents a balance between the upward 2339 convective flux and the downward diffusion of the evaporative signature (Barnes and 2340

Allison, 1988), and it is caused by hydrodynamic dispersion within the soil (Leibundgut 2341 et al., 2009). Then, the amount of water that moves into the soil directly affects the 2342 development of the isotopic gradient in the soil (Dalton, 1989; Gat, 1998; Leibundgut et 2343 al., 2009). This gradient was observed for most of our samples, particularly in the SNPV 2344 and PRAIRIE watersheds; however, it is possible that the gradient observed in plots 4, 5 2345

2345

1 See Chapter II of this thesis for further details.

112

and 6 in the CROP watershed in July, was a result of an uncontrolled movement of 2346

surface water into the soil profile in this watershed. Another possible explanation is that 2347

water originated from the lateral seepage modified or alter the attenuation of the isotopic 2348

signature of water into the soil, as discussed by Leibundgut et al, (2009). The attenuation 2349 of the isotopic gradient in the soil profile they mention was observed in these plots (4, 5, 2350

6). The depleted values in the light isotopes observed in July at the intervals 10-20, 20-30 2351 and 30-50 cm that created these irregular gradients, appeared to get enriched in the heavy 2352 isotopes (δ18O and δD) in August and thus creating a clearer gradient (Figure III.12). 2353

Despite the care that was taken to apply equal amounts of tracer in each plot, the 2354

concentration observed varied by plot and by watershed. Judging by the amount of tracer 2355

retained in every plot, we observed clear differences by watershed, with PRAIRIE 2356

retaining the highest and CROP retaining the least amount of tracer (Figure III.13). The 2357

movement of the tracer into the soil profile depends largely on the infiltration properties 2358

of the soil (Leibundgut et al., 2009). The uppermost layer of the soil plays a critical role 2359

in the infiltration process, vegetative cover, organic matter deposited on the ground and 2360

biomass aboveground control the amount of precipitation that it is intercepted and the 2361

amount that reaches the ground (Brooks et al., 2003; Chang, 2006). Plant cover also 2362

plays a critical role in the reduction of surface runoff, affecting with this the isotopic 2363

composition of recharge flux (Gat, 1998). In this study, values of the deuterated tracer 2364

observed in the first sampling period (Figure III.13, July period) may be the result of the 2365

different land cover properties among these sites. The thick soil cover in the PRAIRIE 2366

watershed, second only by the soil cover in the SNPV watershed, had greater potential to 2367

113

retain higher amounts of tracer closer to the mineral soil surface than the bare ground in 2368

the CROP watershed, and thus promoting a higher infiltration rate of the tracer into the 2369

soil in the SNPV and PRAIRIE watersheds, than the amount of tracer that entered the soil 2370

surface in the CROP watershed, as observed in Figure III.13. 2371

Without taking into account the summit position in the CROP watershed, the 2372 values for groundwater observed in this study remained constant during the observation 2373 period in each of the remaining sampling sites (Figure III.9). These results match the 2374 underlying principle of the combination of water having different isotopic values, in 2375 which if a small amount of water (i.e. infiltration) having a different isotopic value is 2376 combined with a large body of water (i.e. groundwater), its effects on the isotopic value 2377 of the receiving water body will be minimal. Similarly, as indicated by Leibundgut et al, 2378 the temporal variability of stable isotopes in groundwater is influenced by the variations 2379 in the isotopic values of meteoric waters. However, due to the attenuation of the isotopic 2380 values with depth, the effects observed on groundwater are minimal, and other 2381 approaches based on mass balance can be more appropriate to study these variations 2382

(Leibundgut et al., 2009). In the position summit in the CROP watershed, it is possible 2383 the a higher infiltration, or a high interaction of groundwater with surficial water (due to 2384 shallow groundwater) caused water enriched in the light isotopes to change the isotopic 2385 composition of groundwater. As shown in Figure III.9, the isotopic composition of 2386 groundwater in this particular position gradually changed from δD -47.4 ‰ June to δD - 2387

41.6 ‰ August. 2388

2389

114

4.4 Variations in plant water uptake 2390

Characteristics specific to annual crops and native prairie vegetation, such as root 2391

depth, water use patterns, phenology and adaptation to variations in soil moisture, gives 2392

each of these land covers the ability to access water from different soil profiles when they 2393

are subjected to conditions of limited water availability. In this study we observed 2394

differences in DWU in bluestem, bromegrass, coneflower (collected in SNPV) and corn. 2395

In the July sampling period when the average θv for all the sites in the upper 20 cm was 2396

0.39, these plants used proportionally more water from shallower sources (15 cm on 2397

average) and shifted to a deeper depth (33 cm on average) in August when the mean θv 2398 was 0.35. Not including the CROP watershed, which had a great influence on the θv 2399 average of all the sites due to the presence of lateral seepage, the average θv was 0.38 in 2400

July and decreased to 0.33 in August. The statistical analysis indicates that θv had a 2401

significant (p=0.0092) influence on the DWU observed in both July and August when the 2402

analysis was run for all species (Table III.3). 2403

Changes in DWU have been observed in other studies conducted on C3 shrubs 2404

and C4 crops and grasses (Asbjornsen et al., 2007; Nippert and Knapp, 2007; Asbjornsen 2405 et al., 2008). However our results differ in the range of PWU observed by previous 2406 research. For example, Asbjornsen et al (2007; 2008), observed that corn and big 2407 bluestem obtained water from 0 to 30 cm of soil during the growing season of 2008, 2408 results that were similar to a previous study where in 2007 similar results were observed 2409 for the range of water uptake for corn and bigbluestem (from 5 to 20 cm) In this study, 2410 the range of DWU observed for corn (including July and August) was 10-70 cm, and for 2411

115

big bluestem it was 10-50 cm. Given the lower (515 mm) precipitation registered during 2412

2007, a larger range of water uptake could be expected, however it is possible that the 2413

relative slope of our sites, which ranged from 6.6 to 10.3%, caused corn plant to take 2414

water from deeper soil profiles in the upper parts of the watershed where water was more 2415 limiting in the upper layers, and thus creating a larger range in DWU (See 4.3). Although 2416

Asbjornsen et al. (2007; 2008) provided information about the precipitation registered 2417 during each of the studies, it is not possible to assess to what extent differences in the 2418 range of DWU were due to dryness of the topsoil. The θv in the CORN watershed, 2419 primarily in the summit position, remained fairly stable (approx. 0.40) due previously 2420 described high soil moisture content, nonetheless our findings indicate that there was a 2421 change in DWU between sampling dates among species. In the watershed PRAIRIE, 2422 where big bluestem was sampled, θv in the upper 20 cm changed from 0.38 in July to 2423

0.32 in August. The range of DWU we found for corn is similar to a study conducted in 2424

summer corn by Wang et al., (2010), which reported that summer corn extracted water 2425

from 20-50 cm in the flowering state. According to their data, soil water potential 2426

decreased to almost -50 KPa during the flowering state, indicating a θv of approximately 2427

to 15%, which could have explained the wider range of DWU observed. 2428

Plant phenology among the studied species also seems to influence the patterns of 2429

DWU. Weaver found that water use requirements are highly influenced by the 2430

functioning vegetation demanding water (Weaver, 1941). As mentioned earlier, the 2431

NSNWR conducts prescribed burning on the restored areas like the interim 4 site, which 2432

was burned in the spring of 2010. Our field records indicate that at the time of collection 2433

116

in July sampling period, coneflower and wildrye were in the flowering state, by the 2434 second sampling period in August, coneflower had initiated leaf senescence. Growing at 2435 natural field conditions, C3 species tend to grow earlier in the season when temperatures 2436

are more favorable for photosynthesis (Pearcy and Ehleringer, 1984) and due to a 2437

temporal displacement of C3 and C4 as a function of the differential temperature 2438 responses to photosynthesis (Kemp and Williams, 1980). Coneflower in the PRAIRIE 2439

watershed was in an early vegetative stage (due to prescribed burning) and by the time 2440

the second sample was taken in late August, coneflower was already in flowering state. 2441

The study of summer corn and cotton conducted by Wang et al., (2010) found that DWU 2442

was highly linked to the vegetative state of the plant, rather than the time of the year. A 2443

study conducted by Mateos-Remigio et al., (In Review) of sap flow in a C4 grass 2444

(Andropogon gerardii), a C3 shrub (Ratibida pinnata) and a C4 crop (Zea mays), found 2445 that water use by plants was influenced by water requirements caused by phenological 2446 and physiological differences among functional groups. Given that the phenology of the 2447 plant is related to its development, this is indicative that plants shift their depth of plant 2448 water uptake in response to a variety of factors such water availability in the topsoil, 2449 precipitation, vegetative type and also metabolic needs during different development 2450 stages. 2451

The relative topographic position of a plant within a watershed also influences its 2452 biomass production. Lower parts of the watershed tend to have higher soil moisture 2453 contents, relative to the upslope (Brooks et al., 2003; Hillel, 2004), and since water and 2454 soil moisture regulate plant productivity (Gholz et al., 1990), higher amounts of biomass 2455

117

may expected in the lower parts of the watershed (provided that soil moisture does not 2456

exceeds plant limits), compared to the upslope, and thus higher water use due to the 2457 higher amounts of vegetation demanding water (Weaver, 1941). It would also be 2458 expected to see higher fluctuations in the DWU in plants located in upslope positions as 2459 compared to plants in the downslope, however our statistical analysis showed no effect of 2460 topographic position in changes of DWU in the analysis for all the plants (p=0.5549) and 2461 in the analysis by vegetative type [C3 (p=0.8501) and C4 (p=0.5549)]. These results could 2462

have been a response of the high precipitation and overall high θv observed during this 2463 study, but this explanation contradicts our findings about the influence on θv (See Table 2464

III.3). Another possible explanation can be the influence of the inverse patterns of DWU 2465

observed for coneflower and wildrye (both from the SNPV watershed) on the statistical 2466

analysis. 2467

Analyzed by photosynthetic pathway functional groups, soil moisture in the top 2468

20 cm of soil had a higher influence on C3 (p=0.0136) than C4 (p=0.4051) plants. C4 2469 plants are known for a lower leaf conductance and therefore transpiration than C3 plants 2470

due to a lower operational intercellular CO2 (Pearcy and Ehleringer, 1984). This provides 2471

C4 plants with higher water-use efficiencies (Pearcy and Ehleringer, 1984) an thus lower 2472

water requirements, compared with C3 plants. Our results may then reflect the adaptation 2473 of C4 plants to hot and dry environments, and the lower water-use efficiency of C3 plants. 2474

Since C4 plants are more adapted to lower water contents, the need to access water from a 2475

deeper soil profiles may not be as crucial as for C3 plants with lower water-efficiency. 2476

118

Other studies have found that on average C3 plant species take water from deeper 2477 soil profiles than C4 species when water becomes more limiting than C4 species (Nippert 2478

and Knapp, 2007; Asbjornsen et al., 2008), supporting the water-use efficiency factor in 2479

DWU. However, taking into consideration the change in DWU observed for the C3-forb 2480

coneflower collected in the PRAIRIE watershed, which was in a much younger state 2481

when first sampled, it is also possible that inverse DWU observed in coneflower samples 2482

collected in SNPV were a response of the phenology of the specie, which were at the 2483

flowering stage at the time of the first collection (July), compared with the “sprout” state 2484

of the samples of coneflower collected in PRAIRIE in July. This suggests that although 2485

photosynthetic pathways are essential in water-use patterns (and thus DWU), other 2486

factors such life form, phenology, changes in soil moisture in the top soil or the relative 2487

position of a plant within a topographic gradient, are also important factors to consider in 2488

the analysis of DWU. 2489

2490

5. Conclusions 2491

The study of plant water uptake and its role within the hydrologic cycle requires 2492 the use of research approaches that assess the interactions between multiple important 2493 and relevant factors that influence water fluxes at a specific site. Understanding of the 2494 effects of species composition and diversity on soil moisture dynamics in restoration 2495 efforts is of major importance to assess hydrological impacts at the long term. We used 2496 natural gradients of δ18O and δD and artificially created gradients of δD to determine 2497

depth of plant water uptake. The δD tracer significantly helped to in the assessment of 2498

119

DWU when natural δ18O gradient was not observed. A calibration process was developed 2499 for our specific soil type and our water extraction apparatus was modified to reduce 2500 extraction time. The estimated extraction error for extraction times greater than 30 min 2501 was ±0.51 ‰ and ±7.69 ‰ for δ18O and δD, with a standard error of 0.06 ‰ and 0.22 2502

‰, respectively, which were taken into account when assessing DWU. 2503

Our results support that C3 and C4 plant communities shift their depth of plant 2504 water uptake when soil water becomes more limiting. We found deeper ranges of water 2505 uptake in coneflower and corn than previously observed, that could have been influenced 2506 by the topographic positions and the relative volumetric water content differences 2507 documented among them. The development stage of the plants appeared to influence 2508 shifts in DWU, plants that were sampled in their early development states in July and 2509 were fully developed in August shifted to deeper soil depths in the second sampling 2510 period. Plants sampled in their full development state in July and had started to senesce in 2511

August shifted to shallower depths. This provides supporting evidence for the water use 2512 dynamics of native prairie ecosystems and their role in the control of the water balance of 2513 the ecosystem, as well as the importance of C3 and C4 plant diversity in prairie restoration 2514 efforts. 2515

2516

2517

2518

2519

2520

120

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2658

2659 2660

2661

2662

2663

2664

2665

2666

2667

2668

2669

127

7 Figures 2670

2671

2672 Figure III-1. Design of the three experimental watersheds used in this study, each 2673 black rectangle represents a plot. 2674 2675 2676

128

2677

Figure III-2. Scheme of the soil and plant 2678 samples collected 2679 Figure not at real scale 2680 2681

2682

129

2683

2684 Figure III-3. Scheme of the vacuum cryogenic distillation apparatus used in 2685 this study 2686 2687 2688 2689

2690

2691

130

2692 Figure III-4. Precipitation registered from May to October 2010 2693 2694

2695 Figure III-5. δ18O values of the precipitation registered during this 2696 study 2697

131

2698 Figure III-6. δD values of the precipitation registered during this 2699 study 2700

2701

Figure III-7. Precipitation water plotted against the Global Meteoric 2702 Water Line (Craig, 1961), and the Local Meteoric Water Line 2703 (Simpkins, 1995) 2704

132

2705

2706

Figure III-8. δD values of groundwater plotted against the Global 2707 Meteoric Water Line (Graig, 1961) and the Local Meteoric Water Line 2708 (Simpkins, 1995) 2709 2710

133

2711

Figure III-9. δD values of groundwater by watershed and topographic 2712 position 2713 2714

2715

134

2716

Figure III-10. Volumetric water content by watershed, depth and 2717 date 2718 2719

135

2720

Figure III-11. Volumetric water content by watershed, depth, 2721 observation date and topographic position 2722

136

2723

2724 2725 Figure III-12. δ18O values of the soil samples averaged by plot. Horizontal 2726 gray lines represent the SE of the mean 2727

137

2728

Figure III-13. δD values of the soil samples averaged by plot. Only plots 2729 where the tracer was applied are shown 2730 2731

138

2732

Figure III-14. δD tracer differences among observation periods. 2733 Averaged by watershed 2734

139

2735

2736

Figure III-15. Depth of plant water uptake by species within observation 2737 periods. Black circles represent the mean of the water uptake range and the 2738 gray vertical lines the standard deviation 2739

8. Tables 2740

Table III-1 Description of the 3 watersheds and species sampled per watershed 2741 2742 Topographic Total prairie Vegetative Watershed Sand Silt Clay Watershed position of Species sampled cover (%) type area (ha) (%) (%) (%) prairie cover Cone flower C forb Ratibida pinnata 3 Summit Interim 1 Brome grass ⇑3 70 27 10% Side C grass 3.00 [SNPV] Bromus ciliatus 3 ⇓12 65 23 Footslope Wild rye C grass Elymus canadensis 3 Interim 3 100% Corn ⇑4 70 26 None C4 crop 0.73

[CROP] rowcrop Zea mays ⇓12 63 25 140 Big bluestem Summit C grass Interim 4 100% Rec. Andropogon gerardii 4 ⇑4 70 26 Side 0.60 [PRAIRIE] prairie Cone flower ⇓12 63 25 Footslope C forb Ratibida pinnata 3 2743

⇑ = Summit 2744 ⇓ = Foot 2745 Percentages of sand, silt and clay correspond to the upper 30 cm of soil 2746 2747 2748 2749 2750 2751 2752 2753

2754

Table III-2 Depth of water uptake by topographic position in July and August 2755

Big bluestem (C ) Bromegrass (C ) Coneflower (C ) Corn (C ) Wildrye (C ) Depth 4 3 3 4 3 Jul Aug Jul Aug Jul Aug Jul Aug Jul Aug Foot* Foot Foot 0-10 Sum* Sum Foot Sum Sum Sum* Foot 10-20 Sum Sum Foot Sum Sum Sum Sum Sum* Sum 20-30 Foot Sum Foot Foot Sum

Sum Sum Foot 30-50 Sum Sum Foot Sum Sum

Foot* 141 50-70 Sum Sum Sum*

70-100

2756

*Summit=Sum 2757 *Corresponds to coneflower samples collected in the Interim 4 site 2758 The topographic position of a given specie is placed on the estimated depth of water uptake. For any given date and plant 2759 two plant samples were collected in the summit and one in the foot, hence three topographic positions (2 summit and 1 2760 foot) are used in each sampling period (July and August) 2761 2762

2763

2764

2765

2766

2767

Table III-3 GLM procedure by species and vegetative type (C3 and C4) 2768 2769 Average DPWU* of all C C 3 4 species F=0.04 F=0.91 F=0.36 Topographic position p=0.8501 p=0.3633 p=0.5549 F=3.23 F=3.37 F=4.55 Month p=0.1390 P=0.0963 P=0.0401 Volumetric F=5.31 F=1.40 F=4.09

water 142 p=0.0136 p=0.4051 p=0.0092 content

F=1.29 F=0.37 F=1.58 Specie p=0.2959 p=0.5580 p=0.2052 F=3.08 Functional group NA NA p=0.882 *Corresponds to the mid-point of the observed range of depth of plant water uptake 2770

143

2771

CHAPTER IV 2772

2773

GENERAL CONCLUSIONS 2774

2775

We studied the effects of the establishment of native prairie vegetation on soil 2776 water dynamics within landscapes dominated by row-crop agriculture. This research is a 2777 component of the larger Science-based Trials of Rowcrops Integrated with Prairies 2778

(STRIPS) project that has the objective to quantify the influence of different proportions 2779 and landscape configurations of annual (e.g., corn and soybean) and perennial (e.g., 2780 prairie, savanna, agroforestry) plant communities on the storage, cycling, and output of 2781 nutrients, water, and carbon at the field and catchment scale. The research described 2782 herein specifically compared the depth of plant water uptake by annual and perennial 2783 vegetation and the resulting effect on soil water storage. Results were discussed in the 2784 context of regulation of larger-scale hydrologic balance, impacts on watershed 2785 management, and implications for sustainable agricultural practices. A calibration 2786 process was developed for vacuum cryogenic distillation extractions, which can be 2787 applicable to DWU studies in clay rich soils. 2788

2789

144

Major findings 2790

Lower soil water storage was observed under native prairie vegetation than annual 2791 rowcrops within one year after establishment of prairie vegetation. This pattern 2792 contrasted with results in 2007 prior to prairie establishment when soil water storage was 2793 lower under rowcrop, and illustrates the potential critical role of perennial vegetation in 2794 regulating soil water balance. The pattern of lower soil water storage under prairie 2795 vegetation was less significant in 2010, when annual precipitation was 45% above the 2796 long-term annual average, which seems to indicate the presence of a threshold effect in 2797 the benefits the prairie ecosystem, as far as its benefits on soil water storage. Observed 2798 differences in soil water storage were more pronounced within the upper 30 cm of soil 2799 than upper first 60 cm. 2800

Topographic position had a significant influence on soil water storage, with lower 2801 average storage in the summit slope positions compared to footslopes. Greater 2802 fluctuations in soil water storage were also observed in the upper slope positions. 2803

Precipitation, which was analyzed as the total precipitation during the seven days 2804 previous to the monitoring date, significantly influenced the variability in soil water 2805 storage, particularly in 2010 when it seemed to override other factors. 2806

In our study of depth of water uptake we found that the first 70 cm were the main 2807 source of water for all species for both observation periods, with variations within this 2808 range among species and observation dates. The deuterated tracer artificially applied 2809 significantly helped to determine depth of water uptake in plots where the isotopic 2810 gradient in the soil was not clear, by eliminating ambiguous depths suggested by the 2811

145 naturally occurring oxygen isotopes. It was also observed that available water in the 2812 topsoil, and observation date influenced all species. When analyzed by functional group, 2813

C3 plants appeared to be more responsible to changes in soil water in the topsoil than C4 2814 plants. 2815

Phenological and physiological differences among the plants also influenced the 2816 depth of water uptake observed. Plants sampled in their full development state in July 2817 that had started to senesce in August shifted to shallower depths. While plants that were 2818 sampled in their early development stages in July, shifted to deeper depths in August. 2819

This provides supporting evidence for the water use dynamics of native prairie 2820 ecosystems and their role in the control of the water balance of the ecosystem 2821

2822

Implications for watershed management 2823

This study highlights the importance of understanding the effects of vegetation on 2824 soil water dynamics. Such information is useful in predicting the impact at the catchment- 2825 scale of the re-incorporation of native prairie vegetation into agricultural landscapes. Our 2826

4-year study indicates that the re-incorporation of native prairie vegetation can lower 2827 average annual soil water contents within one-year after the establishment of prairie 2828 vegetation. Soils with low water storage (i.e. larger storage capacity) are more likely 2829 allow precipitation to infiltrate, thereby reducing runoff and sediment and nutrient flux to 2830 receiving waters. In areas with tile drainage, the presence of strips of native prairie 2831 vegetation can help to regulate water yield. 2832

146

Increasing evidence indicates that watersheds with strategically established native 2833 prairie or other perennial vegetation have lower surface runoff than those dominated with 2834 annual rowcrops [e.g. corn, soybean]. In places like Iowa, where the native prairie 2835 vegetation has been replaced with annual crops, watershed hydrology has been 2836 dramatically altered, resulting in increased loss of nutrients and sediments and an 2837 increased risk of flooding. Understanding the need for agricultural goods, and the 2838 importance of sustainable agricultural practices, the use of small amounts of prairie 2839 vegetation to regulate the water balance of an ecosystem appears to be a viable and 2840 sustainable alternative, that requires only to assign a small percentage of cropland surface 2841 to native prairie vegetation, which can in turn return multiple benefits to land owners and 2842 managers. 2843

2844

Challenges faced in this research 2845

In this study, volumetric water content was measured using a standing wave 2846 measurement to determine the impedance of a sensing rod array using a combination of a 2847

Theta and PR2 Probes (Delta-T Devices, Cambridge, UK). The acquisition of accurate 2848 data from such sensors requires precise calibration. Since we decided to calibrate every 2849 sensor of the PR2 and one of the Theta probes used in this study, the number of 2850 gravimetric samples required for the calibration increased exponentially. In order to 2851 achieve a reliable calibration, it is important to collect gravimetric samples over a range 2852 of water contents, especially at lower water contents. During our study there were few 2853

147 periods of time when the soil water content was low enough to collect calibration samples 2854 for this range. The few opportunities to collect gravimetric samples at lower water 2855 contents, combined with the large number of access tubes and depths to calibrate, made 2856 the calibration process a challenging task. 2857

For our study of depth of plant water uptake we decided to collect a set of soil 2858 samples for every plant sampled. Including stems, leaves and the soil samples, we 2859 extracted water from 282 samples (including stems, leaves and soils), and although we 2860 did not use the leaf samples in our interpretation, the time required to process and to 2861 extract water from all the samples made this process a time consuming task. We strongly 2862 recommend future studies of depth of plant water uptake, to determine isotopic variability 2863 in the soil, prior to decide the number of samples needed per plot, and take a decision 2864 about the number of samples needed to get a representative and accurate sample. 2865

2866

Recommendations for future research 2867

Study of the effects of management practices of the prairie strips 2868

It is important to get a better understanding of the effects of the removal of the 2869 aboveground biomass in the prairie strips on soil water dynamics. The effects of mowing 2870 and burning are not well understood under these configurations of mixed annual- 2871 perennial vegetation. This study would provide important information relevant for the 2872 future management of the prairie strips. If it were decided to harvest the prairie strips for 2873

148 biomass, biofuels, etc., it would be important to have a prior understanding of the impacts 2874 on soil water dynamics. 2875

The watersheds Interim 1, Interim 4, Orbweaver 2 and Basswood 3 would be four 2876 potential sites for such a study. It would be important to cover as much of the area of the 2877 prairie strip as possible. In Interim 1 and 4, there are already three access tubes per 2878 topographic positions, which appear to be enough to detect changes in soil water content. 2879

More access tubes would be needed in Orbweaver 2 and Basswood 3. If possible, this 2880 should be tested during several growing seasons, and the removal times of the 2881 aboveground biomass should be arranged at specific times to compare soil water storage 2882 differences with the rowcrop area. 2883

2884

Water infiltration using stable isotope tracers 2885

Understanding the effectiveness of strips of native prairie to increase infiltration 2886 requires the use of techniques that are not limited by the capability of the equipment used 2887 to estimate infiltration. Traditional techniques are limited by the area covered by the 2888 infiltration equipment, the number of replicates that can be conducted per day, and are 2889 limited in their ability to assess seasonal changes in infiltration. Advantages of using 2890 stable isotopes as tracers for estimating infiltration include a larger area of measurement, 2891 a small sample volume that can be later processed for isotopic analysis. Samples can be 2892 collected periodically at several soil depths and the movement of the tracer into the soil 2893 profile can be followed during an entire growing season. Such a study would require 2894

149 significant laboratory work processing soil samples, but with the extraction apparatus and 2895 the methodology already developed, this should be a straightforward task. 2896

2897

Intensive study of depth of plant water uptake 2898

Our study of depth of plant water uptake provides supporting evidence of the 2899 ability of prairie species to shift their depth of water uptake in response to environmental 2900 conditions. Without the use of tracers, this would be difficult to assess. We included 2901 several dominant prairie species in our study; however there are others equally important 2902 that were not included. An intensive study over more than one growing season that 2903 included more species would provide additional insight into the depth of plant water 2904 uptake patterns in mixes annual-perennial watersheds. Differences in depth of plant water 2905 uptake can be used as a tool for selecting species in prairie re-establishment based on 2906 characteristics, which may regulate the hydrologic balance these ecosystems. 2907

Site-specific characteristics often present trade-offs for experimental design. For 2908 example, in our study the watershed Interim 1 has a higher slope allowing the assessment 2909 of the effects of topographic position in depth of plant water uptake, while the prairie 2910 strip in the watershed Orbweaver 2 is wider which allows the establishment of larger 2911 plots. Also, Orbweaver 2 is a lot less disturbed than Interim 1. Thus, the watersheds 2912

Interim 1, Orbweaver 2, Interim 3, Orbweaver 3 and the 100% reconstructed prairie 2913

Interim 4 would be good sites for the suggested study. In a future study, we strongly 2914 recommend even numbers of plots per topographic positions. 2915

2916

150

Design of a water sampler for isotopic analysis 2917

In isotopic analysis, the time required to process samples, particularly water 2918 extraction, limits the number of samples that can be analyzed. Thus, the design of a soil 2919 water sampler for use in isotopic studies would open up new possibilities for studies 2920 requiring large sample numbers. In addition to its use for studies of depth of plant water 2921 uptake, such equipment could potentially be used for studies assessing water residence 2922 times within soils under different vegetation. While this principle has been used before, it 2923 could be improved through the use of nests of micro-lysimeters installed at different 2924 depths. These micro-lysimeters should be specially designed to reduce evaporation within 2925 the lysimeter. They should also be small in diameter to reduce the opening in the soil, and 2926 to better seal the walls of the opening and avoid surficial water from contaminating the 2927 sample. These nests of micro-lysimeters could be placed at different topographic 2928 positions under several land covers to address questions about subsurface water 2929 movement. 2930

2931

Improved PR2 Probes calibration 2932

During this study we were able to collect enough gravimetric samples at low 2933 water contents, which dramatically improved the precision of our instruments. However, 2934 due to the high clay content and a shallow depth to groundwater in some areas of our 2935 study sites, the gravimetric water content in a few depths in some access tubes was not 2936 low enough for precise calibration, resulting in a low R2 of the regression. This was a 2937

151 greater challenge in the deeper depths, where the groundwater level prevented the 2938 collection of gravimetric samples at different water contents in several access tubes. 2939

Laboratory calibration could help to improve the calibration in areas with 2940 saturated soils. A calibration apparatus can be developed using soil from the saturated 2941 areas, filling PVC cylinders at the same density as indicated in the records by fitting a 2942 given mass of homogenized soil into a given volume of the PVC cylinders. The PVC 2943 cylinders do not need o be more than 30 cm in height for the 100 cm sensor of the PR2. 2944

Larger [110 cm] calibration apparatus can be built if an entire profile needs to be 2945 calibrated. However, this might be difficult because the cylinders need to be weighted 2946 constantly. 2947

152

APPENDIX A. AVERAGE SWS IN THE UPPER 30 CM BY OBSERVATION 2948

DATE AND LAND COVER [2007 AND 2008] 2949

Landcover Cropland Native prairie vegetation SWS 0-30 cm SWS 0-30 cm Date Mean StdErr n Mean StdErr n 10-May-07 14.1 0.3 24 13.7 0.5 12 25-May-07 13.7 0.3 24 13.3 0.4 12 5-Jun-07 12.9 0.2 24 13.3 0.3 12 8-Jun-07 13.7 0.2 24 13.5 0.3 12 20-Jun-07 13.6 0.2 24 13.4 0.2 12 20-Jul-07 12.3 0.2 24 13.3 0.3 12 5-Aug-07 12.6 0.6 5 13.8 0.3 4 20-Aug-07 12.7 0.2 19 13.5 0.4 8 31-Aug-07 13.3 0.2 19 13.6 0.3 8 14-Sep-07 13.1 0.2 24 13.5 0.2 12 29-Sep-07 13.3 0.2 24 13.3 0.2 12 10-Oct-07 12.9 0.4 24 13.4 0.3 12 24-Oct-07 13.7 0.2 24 13.9 0.2 12 19-Nov-07 13.1 0.2 24 13.1 0.2 12 16-Apr-08 13.7 0.2 24 13.5 0.3 12 30-Apr-08 14 0.3 24 13.5 0.2 12 14-May-08 13.5 0.3 24 13.1 0.3 12 28-May-08 13.9 0.4 24 13.8 0.3 12 10-Jun-08 15.6 0.7 19 14.5 0.4 8 18-Jun-08 14.1 0.3 24 13.2 0.3 12 1-Jul-08 14.2 0.3 24 13.5 0.3 12 17-Jul-08 13.5 0.3 24 12.5 0.4 12 7-Aug-08 13.3 0.4 24 12.9 0.4 12 2-Sep-08 11.6 0.4 12 11.9 0.6 6 9-Sep-08 12.6 0.4 12 11.9 0.4 6 9-Oct-08 14.6 0.5 19 13.7 0.4 8 16-Oct-08 13.1 0.6 5 13.6 0.2 4 28-Oct-08 14 0.3 20 13.5 0.2 12 12-Nov-08 14.8 0.5 24 14.1 0.3 12 2950

153

APPENDIX B. AVERAGE SWS IN THE UPPER 30 CM BY OBSERVATION 2951

DATE AND LAND COVER [2009 AND 2010] 2952

Landcover Cropland Native prairie vegetation SWS 0-30 cm SWS 0-30 cm Date Mean StdErr n Mean StdErr n 21-May-09 13.5 0.3 22 12.8 0.2 12 12-Jun-09 14.1 0.3 23 13.3 0.2 12 24-Jun-09 15.2 0.5 24 14.3 0.3 12 9-Jul-09 14.6 0.4 24 13.9 0.2 12 22-Jul-09 13.7 0.3 24 12.9 0.2 12 6-Aug-09 11.5 0.3 22 11.5 0.3 12 21-Aug-09 13 0.3 24 13 0.2 12 15-Sep-09 11.7 0.3 23 11.5 0.2 12 29-Sep-09 13.1 0.2 24 12.9 0.2 12 13-Oct-09 13.5 0.3 24 13.3 0.1 12 26-Oct-09 14.2 0.4 23 13.8 0.3 12 12-Nov-09 13.6 0.3 24 13.1 0.2 12 16-Apr-10 13.4 0.3 24 12.8 0.3 12 29-Apr-10 14.3 0.3 24 13.7 0.2 12 13-May-10 15.3 0.6 23 14.9 0.4 12 27-May-10 13.3 0.5 17 12.9 0.3 10 9-Jun-10 14.3 0.5 19 13.2 0.4 11 14-Jun-10 15.3 0.5 22 14.9 0.4 12 25-Jun-10 14.6 0.4 23 14.1 0.4 12 8-Jul-10 14.5 0.4 23 14.1 0.2 12 21-Jul-10 14.3 0.4 24 14.1 0.2 12 3-Aug-10 15.2 0.5 23 14.9 0.3 12 18-Aug-10 15.1 0.5 23 14.6 0.3 12 16-Sep-10 12.6 0.7 5 11.3 0.6 4 30-Sep-10 14 0.4 23 13.6 0.3 12 13-Oct-10 12.9 0.3 23 12.5 0.4 12 28-Oct-10 12.8 0.3 23 12.3 0.4 12 9-Nov-10 12.5 0.3 23 11.9 0.5 12 2953 2954

154

APPENDIX C. AVERAGE SWS IN THE UPPER 60 CM BY OBSERVATION 2955

DATE AND LAND COVER [2007 AND 2008] 2956

Landcover Cropland Native prairie vegetation SWS 0-60 cm SWS 0-60 cm Mean StdErr n Mean StdErr n 10-May-07 26.7 0.6 24 26.5 0.8 12 25-May-07 26 0.5 24 25.5 0.5 12 5-Jun-07 25.3 0.4 24 25.9 0.5 12 8-Jun-07 26.3 0.5 24 26 0.4 12 20-Jun-07 26.1 0.4 24 26 0.5 12 20-Jul-07 23.9 0.5 24 25.9 0.5 12 5-Aug-07 23.8 1.6 5 26.7 0.4 4 20-Aug-07 23.9 0.5 19 26 0.7 8 31-Aug-07 25.1 0.3 19 26.2 0.6 8 14-Sep-07 23.6 0.5 23 25.5 0.4 12 29-Sep-07 24.4 0.4 24 25.6 0.4 12 10-Oct-07 24.5 0.6 24 25.6 0.5 12 24-Oct-07 25.9 0.5 24 26.3 0.4 12 19-Nov-07 24.9 0.4 24 25.5 0.4 12 16-Apr-08 25.8 0.4 24 26 0.4 12 30-Apr-08 26.4 0.5 24 26 0.4 12 14-May-08 25.5 0.4 24 25.5 0.4 12 28-May-08 26.2 0.5 24 26.3 0.4 12 10-Jun-08 29.1 0.9 19 27.7 0.8 8 18-Jun-08 26.7 0.5 24 25.8 0.4 12 1-Jul-08 26.9 0.5 24 26.2 0.4 12 17-Jul-08 25.9 0.5 24 24.8 0.6 12 7-Aug-08 25.7 0.5 24 25.4 0.5 12 2-Sep-08 23 0.5 12 24.3 1 6 9-Sep-08 23.9 0.9 12 23.4 0.9 6 9-Oct-08 27.1 0.7 19 26.1 0.6 8 16-Oct-08 24.4 1.4 5 26.4 0.2 4 28-Oct-08 26.2 0.5 20 26 0.4 12 12-Nov-08 27.4 0.7 24 26.7 0.4 12 2957

155

APPENDIX D. AVERAGE SWS IN THE UPPER 60 CM BY OBSERVATION 2958

DATE AND LAND COVER [2009 AND 2010] 2959

Landcover Cropland Native prairie vegetation SWS 0-60 cm SWS 0-60 cm Mean StdErr n Mean StdErr n 21-May-09 25.6 0.5 22 25.4 0.4 12 12-Jun-09 26.3 0.5 23 25.7 0.4 12 24-Jun-09 28.2 0.8 24 27 0.3 12 9-Jul-09 27.3 0.6 24 26.6 0.4 12 22-Jul-09 26 0.5 24 25.3 0.4 12 6-Aug-09 23 0.5 22 23.5 0.6 12 21-Aug-09 23.8 0.6 24 25.1 0.5 12 15-Sep-09 23.2 0.6 23 24 0.5 12 29-Sep-09 24.5 0.4 24 25.2 0.4 12 13-Oct-09 24.9 0.6 24 25.7 0.4 12 26-Oct-09 26.8 0.8 23 26.8 0.4 12 12-Nov-09 25.4 0.5 24 25.5 0.4 12 16-Apr-10 25.3 0.5 24 25.2 0.4 12 29-Apr-10 26.9 0.6 24 26.6 0.4 12 13-May-10 28.5 0.8 22 28.2 0.5 12 27-May-10 25.4 0.7 17 25.5 0.5 10 9-Jun-10 26 0.9 19 25.8 0.9 10 14-Jun-10 28.6 0.8 22 28.2 0.4 12 25-Jun-10 27.8 0.7 23 26.6 0.7 12 8-Jul-10 27.3 0.6 23 26.8 0.3 12 21-Jul-10 27.4 0.6 24 26.7 0.3 12 3-Aug-10 28.1 0.7 23 27.8 0.3 12 18-Aug-10 28.2 0.7 23 27.5 0.3 12 16-Sep-10 24.2 1.6 5 23.2 0.6 4 30-Sep-10 26.5 0.6 23 26.1 0.4 12 13-Oct-10 25.1 0.5 23 24.8 0.6 12 28-Oct-10 24.7 0.5 23 24.6 0.6 12 9-Nov-10 24.2 0.5 23 23.9 0.7 12 2960 2961 2962

156

APPENDIX E. DIFFERENCES BETWEEN δ18O OBSERVED AND THE 2963

STANDARD USED TO DETERMINE WATER EXTRACTION TIMES 2964

Soil samples were run at different extraction times to determine the 2965 optimum point between extraction time and precision. A total of 55 2966 samples were run and two of them were lost. In this graph we show 53, 2967 including the samples where we observed loss of pressure during the 2968 extraction process. 2969 2970

2971 2972 2973 2974 2975 2976 2977 2978

157

2979

APPENDIX F. ISOTOPIC FRACTIONATION IN NON-FLAGGED SAMPLES 2980

This graph shows the linear fractionation observed between δ18O and δD 2981 in each of the watersheds studied where no tracer was applied. n=15 in 2982 Interim 1, 8 in Interim 3, and 24 in Interim 4. Where 1 observation = [δ18O 2983 and δD pair]. 2984 2985

2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998

158

2999 3000

ACKNOWLEDGMENTS 3001

3002

The author expresses sincere gratitude to those who helped make this research possible: 3003 3004 3005 To the Neal Smith National Wildlife Refuge staff specifically to Pauline Drobney 3006 To my advisors 3007 3008 3009 To our funding agencies and institutions: 3010 3011 The Leopold Center for Sustainable Agriculture 3012 The Iowa Department of Agriculture and Land Stewardship, Division of Soil 3013 Conservation 3014 The U.S. Forest Service Northern Research Station 3015 Iowa State University College of Agriculture and Life Sciences 3016 USDA-NCR-SARE 3017 USDA-NIFA-AFRI-Managed Ecosystems 3018 3019 3020 To the Porous Media laboratory research group, department of Agricultural and 3021 Biosystems Engineering 3022 To the Stable Isotope Paleo Environments Research Group, department of Geological and 3023 Atmospheric Sciences 3024 To the Ecohydrology laboratory staff, department of Natural Resource Ecology and 3025 Management 3026 To Jonathan Hobbs and Dennis Lock from the Statistics Department 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037

159

3038 Soil moisture dynamics in agriculturally-dominated landscapes 3039 after the introduction of native prairie vegetation 3040 3041 3042

3043 José Antonio Gutiérrez López. 2012 3044