Enrique R. Vivoni, Adam Schreiner-McGraw, Cody Anderson, Ryan Templeton, Dawn Browning and Albert Rango 1 Jornada LTER Site Review Meeting October 7, 2015 Arizona State University Jornada Experimental Range Watershed Ecohydrology and Land-Atmosphere Interactions at the Jornada LTER

2 Spatial Context The Jornada Experimental Range and NMSU College Range occupy a basin and range complex with a prominent bajada towards the east. Water Redistribution Plays a More Prominent Role in Bajada

3 The long-term record at the ‘Tromble Weir’ watershed has been extended during LTER V and VI and the mixed shrubland located upstream of the flume has become a focal point for ecohydrological and land-atmosphere studies at the watershed scale. Long-term Context Long-term hydrologic studies at Jornada LTER include a rain gauge and weather station network; studies on the role of dikes on hydrologically- enhanced productivity; and soil moisture studies along LTER Transect I. In addition, a major effort was commenced in 1977 with the establishment of a Santa Rita-type supercritical flume (designed by ARS at Walnut Gulch)

4 Long-term Context From Turnbull et al. (in 2013) This comparison of the two periods indicates that the rainfall distribution has shifted toward less total storm rainfall that are more frequent (thus the higher annual rainfall). These less-intense rainfall events generate less runoff in the study basin. Period 1: Period 2: Turnbull et al. (2013) revisited the rainfall and runoff records at the Tromble Weir watershed finding a change in the flow regime between two periods. Rainfall Distribution Runoff Distribution What is the role of changes in vegetation?

Relation to LTER VI Objective 3: Shrubland to shrubland transitions on the Bajada. The impact of phenology on watershed-scale soil moisture and runoff responses, and how these interact with rainfall and topographic position to induce hydrological connectivity, is not well understood in arid and semiarid systems. We hypothesize that: “Differences in phenology among mesquite, tarbush, and creosotebush relative to the seasonal timing of plant available water by depth govern historic shifts in shrub-dominated states, and determine current dominance patterns.” From Gibbens et al. (2005), Journal of Arid Environments

6 Relation to LTER VI Objective 5: Hydrologic lateral connectivity and transitions between units Hydrologic transports connecting geomorphic units across topographic gradients, and affecting current vegetation and soils depending on the degree of connectivity, are not well understood in arid and semiarid systems. We hypothesize that: “State changes in uplands with gravelly soils and high infiltration will have low runoff with little or no effect on vegetation on downslope sites. By contrast, state changes in uplands with deep, silty soils with low infiltration and high rates of runoff will influence vegetation dynamics downslope.” From Bestelmeyer et al. (2011), Journal of Applied Ecology

7 Experimental Watershed We established an environmental sensor network in a small, mixed shrubland watershed and are using it as a modeling testbed for land cover change analysis. Field Sensors: 4 runoff flumes 16 permanent soil moisture and temperature profiles 1 eddy covariance tower in watershed COSMOS sensor Continuous sensors that sample differences vegetation and terrain features. Direct estimates of total ET and a method for partitioning into transpiration and evaporation. Direct estimates of runoff generation at the outlet and within the basin. Each Design Draws upon Prior Modeling Experiments: Jornada Experimental Range (JER) May 2010-Present

Environmental Sensor Networks Internal Runoff Flumes Watershed Outlet Flume Soil Profiles Rain GaugeEddy Covariance Tower Q Q S, T P R n, I S, G, H, λET Closing the Water and Energy Balance at Jornada Experimental Range. From Templeton et al. (2014), J. Hydrology

9 A new generation of high-resolution terrain analyses in watersheds are made possible by 3D point clouds from UAV structure for motion processing. JER Watershed boundary and stream network determined more accurately with UAV imagery as compared to GPS ground survey. A decrease of 18% in basin area and a 250% increase in drainage density with UAV. T = Tower F = Outlet Flume C = COSMOS Sensor Vegetation-Runoff Interactions From Rango et al. (2014), Book Chapter

Vegetation-Runoff Interactions A modeling effort on vegetation-runoff interactions at Tromble Weir watershed demonstrated the use of UAV-based terrain/vegetation data. Progressive wetting toward the stream network with drier channel (steep) banks. Good representation of seasonal soil moisture dynamics averaged over basin. Simulated Soil Moisture Patterns Linked to UAV-based Maps: From Vivoni et al. (2014), Ecosphere

Land-Atmosphere Interactions High-resolution environmental sensor network consisting of 20 soil sensor profiles around eddy covariance towers placed using the UAV-imagery. Daily footprint analysis of the eddy covariance towers averaged for available summers showing the 50% extent of the contributing area to the turbulent flux measurements and soil sensor profiles. From Anderson and Vivoni (2015), Water Resources Research, in prep.

12 Land-Atmosphere Interactions Monsoon conditions exhibit significant control of the amount of bare soil cover on EF. Monsoon conditions indicate that T/ET increase with amount of mesquite and other shrub. Evaporative Fraction (EF) and Transpiration Ratio (T/ET) vary with plants: Analysis of the eddy covariance measurements, footprint sampling and UAV vegetation map showed importance of spatial patterns in plant cover. From Anderson and Vivoni (2015), Water Resources Research, in prep.

13 Closing the Water Balance Cosmic-ray soil moisture sensor provides spatially-averaged soil moisture that has been compared to traditional sensors and the water balance. An excellent comparison was found among the three different techniques for estimating soil moisture in the mixed shrubland, with some discrepancies identified related to the penetration of infiltration fronts.

Closing the Water Balance Water FluxJER CRS Estimates Precipitation (P, mm)533 Infiltration (I, mm)477 Outflow (O, mm)482 Leakage (L, mm)193 Outflow ratio (O/P)0.90 Runoff ratio (Q/P)0.11 Water FluxJER Sensor Estimates Precipitation (P, mm)533 Storage change (Δθ, mm)26 Outflow (O, mm)506 Leakage (L, mm)217 Evapotranspiration (ET, mm)289 Evaporation ratio (ET/P)0.54 Outflow ratio (O/P)0.95 Streamflow (Q, mm)5 Runoff ratio (Q/P)0.01 Using a limitation of the cosmic-ray sensor and its comparison to other methods, we identified that the study period had a substantial recharge loss. The low amounts of runoff at the watershed along with the high precipitation during this recent wet period has led to a significant amount of deep leakage (beyond 30 cm) that will likely be consumed by the mixed shrubland in subsequent dry periods.

15 Future Steps in LTER VI Objective 3: Shrubland to shrubland transitions on the Bajada. Refine the method of Moran et al. (2009) to partition ET into E and T. Comparison of ET and partitioning method to sapflow sensors installed in tarbush, mesquite, creosotebush (data from September 2014-present) Integrate our work with phenological study of Browning et al. Goal: To identify the relations between sapflow and phenology in order to tease out which species in the mixed shrubland consume water at which time and over which effective depths using environmental sensor network and COSMOS.

16 Future Steps in LTER VI Objective 5: Hydrologic lateral connectivity and transitions between units Improve simulations of Vivoni et al. (2014) based on new field knowledge acquired from COSMOS interpretations and sapflow/partitioning study. Test model performance versus observations from environmental sensor network over period of record (May 2010 – present) at the plant and watershed scale. Develop alternative scenarios (mesquite, creosotebush, tarbush, grass, bare soil) Goal: To quantify the impacts of vegetation change on the production of runoff to downstream areas and the amount and type of evapotranspiration produced based upon reliable simulations of the current spatial patterns.