Tree transpiration, a spatio-temporal approach in water limited ... - ITC

Tree transpiration, a spatio-temporal approach in water limited environments – Sardon study case

Ricardo Ontiveros Enríquez February, 2009

Tree transpiration, a spatio-temporal approach in water limited environments – Sardon study case by Ricardo Ontiveros Enríquez

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Water Resources and Environmental Management

Thesis Assessment Board Chairman: External examiner: Supervisor: 2nd Supervisor:

Prof. Dr. Z. Su Prof. Dr. O. Batelaan Dr. Ir. M.W. Lubczynski Drs. R. Becht

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer This document descr ibes wor k under taken as par t of a pr ogr amme of study at the Inter national Institute for Geo-infor mation Science and Ear th Obser vation. All views and opinions expr essed ther ein r emain the sole r esponsibility of the author , and do not necessar ily r epr esent those of the institute.

Abstract Tree transpiration (Tt) has a significant role in the water balance of a catchment whenever it is tree populated, especially in water-limited environments (WLE). In this balance, it is an important component of the ET flux and is usually underestimated when assessing for water resources due to measurement difficulties and high spatio-temporal variability. In some cases, Tt has a water uptake contribution in both, the unsaturated and the saturated zone, as some plant species have phreatophyte behaviour. This study aims to provide a spatiotemporal approach for tree transpiration flux evaluation in the Sardon catchment, a representative WLE. The methodology proposed focuses on the assessment of Tt of the two tree species that grows in the area, deciduous Quercus pyrenaica (Q.p.) and evergreen phreatophyte Quercus ilex (Q.i.) and their spatio-temporal upscaling. The species-specific flux patterns of transpiration were defined by assessing sap flux density (ν) of individual trees in different seasons using thermal dissipation probe (TDP) method. The transpiration of individual trees (Tt) was estimated by sap flow (Qs) defined as product of ν and xylem area (Ax). Ax was measured on field for tree size distributed species and then upscaled to stand transpiration (T) for spatial approach by applying species-specific biometric upscaling function (BUF). The ν was used for assessing temporal variability of Tt. Spatio-temporal variability of T was evaluated on the temporal base of representative days in climate contrasting seasons and in the spatial base of homogeneous and heterogeneous species stands. The model applied to simulate temporal variability uses two environmental factors: incoming solar radiation (Rin) and relative humidity (RH). Additionally, the use of PET was proposed as a modified version of that model for particular assessment of Tt in WLE. While analyzing sensitivity of these models, hysteresis response was observed and enhanced whenever adding the PET component. The calibrated model allowed the extrapolation of v over a five year period, from 2003 to 2008. An approach on seasonal spatio-temporal variability of tree stand transpiration is evaluated for both heterogeneous and homogeneous stands and a general attempt to upscale T for the Sardon catchment during the given period is presented.

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Acknowledgements

Gracias a mis padres por su aliento y orientación en la búsqueda y trazo de mi camino. A ellos, Jefe y Jefa y a mis hermanos Elisa y Paulo les dedico este trabajo.

Thanks to all people I met in ITC, for the ones who teached me on water resources and remote sensing; for the ones who teach me when sharing life for eighteen month. More important, for letting me continue discover what’s beyond science; for the wonderful humans are in their diversity. Special thanks to Dr. Ir. M.W. Lubczynski, for his guidance, encouraging and contagious way for enjoying research. Thanks also to PhD candidate Leonardo Reyes for the comments done on this study. To the fieldwork team Alain, Ruwan, Leonardo, Michael, Maciek and supporters Marc, Isabel, Ana and Guido, for the moments shared during this one month experience. Also my gratitude to the people at the CENTRO HISPANO-LUSO DE INVESTIGACIONES AGRARIAS (CIALE) in Salamanca; for their hospitality and disposition to work together in the aims of research. For my life at this side of the world, my appreciation for friends I’ll never forget and hope to see you all again. neta. ii

Table of contents 1.

Introduction ....................................................................................................................... 7 1.1. Research motivation .................................................................................................. 7 1.2. Current understanding ............................................................................................... 8 1.3. Research objective .................................................................................................... 9 1.3.1. Specific objectives .............................................................................................. 9 1.3.2. Research questions ......................................................................................... 10 1.4. Hypothesis ............................................................................................................... 10 2. Study area ...................................................................................................................... 11 2.1. Location ................................................................................................................... 11 2.2. Climate and hydrology ............................................................................................. 12 2.3. Hydrogeology .......................................................................................................... 13 2.4. Land cover ............................................................................................................... 13 2.4.1. Quercus ilex ..................................................................................................... 14 2.4.2. Quercus pyrenaica ........................................................................................... 15 2.4.3. Shrubs and grasses ......................................................................................... 15 2.5. Sardon studies, monitoring and database ............................................................... 15 3. Brief methodology ........................................................................................................... 18 4. Remote sensing and GIS for spatial approach ............................................................... 20 4.1. Xylem area Ax as upscaling parameter ................................................................... 20 4.2. Biometric upscaling function (BUF): Ax - Ac ............................................................. 21 4.3. Remote sensing and object-oriented tree classification .......................................... 22 4.3.1. Field identification of tree species .................................................................... 22 4.3.2. Remote sensing, catchment tree-species classification ................................... 23 4.4. Stand transpiration T ............................................................................................... 25 4.4.1. Selection of tree stands .................................................................................... 26 5. Sap flux density and a temporal approach ..................................................................... 29 5.1. Introduction .............................................................................................................. 29 5.2. Sap flux density ( v ) ................................................................................................. 29 5.2.1. Sources of uncertainty in Granier method ........................................................ 30 5.2.2. TDP measurements ......................................................................................... 31 5.3. Data processing ...................................................................................................... 32 5.3.1. TDP and climatic variables ............................................................................... 32 5.3.2. NTG correction ................................................................................................. 36

∆T max determination ........................................................................................ 37 5.3.3. 5.3.4. Sap flux density ................................................................................................ 38 5.4. Sap flux density extrapolation from climatic data .................................................... 39 5.4.1. Model validation ............................................................................................... 41 6. Spatio-temporal approach .............................................................................................. 45 6.1. Tree transpiration in the Sardon catchment ............................................................ 47 6.1.1. Spatial approach .............................................................................................. 47 6.1.2. Temporal approach .......................................................................................... 48 7. Discussion and conclusions ........................................................................................... 49 iii

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References ..................................................................................................................... 53 Appendix A – Fieldwork calendar ....................................................................................... 57 Appendix B – Tree biometric ............................................................................................. 58 Appendix C – Biometric measurement procedure .............................................................. 59 Appendix D - Measured Quercus ilex trees ........................................................................ 60 Appendix E - Measured Quercus pyrenaica trees .............................................................. 61 Appendix F – Missclasification ........................................................................................... 62 Appendix G – Seasonal sap flux densities and environmental factors ............................... 63 Appendix H – Spatio-temporal approach for the evaluated period at stand level .............. 64 Appendix I – Spatio-temporal approach for the evaluated period at catchment level ........ 65

List of figures Page 11

Figure 2.1 Location of the study area

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Figure 3.1 Scheme of variables for ET partition

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Figure 3.2 Methodology flowchart

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Figure 4.1 Cross section of tree trunk and xylem core sample

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Figure 4.2 Statistical distribution of DBH

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Figure 4.3 BUF per tree species

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Figure 4.4 Tree field identification

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Figure 4.5 DN-signature for misclassified map

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Figure 4.6 Tree size distribution per species based on DBH

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Figure 4.7 Mixed stand areas

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Figure 4.8 Homogeneous stands

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Figure 5.1 TDP sensors installed in the tree

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Figure 5.2 Cumulative rainfalls

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Figure 5.3 Seasonal soil matric pressure profiles

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Figure 5.4 Seasonal environmental factors 2003

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Figure 5.5 Seasonal environmental factors 2004

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Figure 5.6 ∆T max determination

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Figure 5.7 Sap flux density measurements

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Figure 5.8 Average seasonal sap flux density of Q.i.

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Figure 5.9 Average seasonal sap flux density of Q.p.

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Figure 5.10 Seasonal sap flux densities per species

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Figure 5.11 Seasonal hysteresis

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Figure 5.12 Model validation

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Figure 5.13 v measured VS v estimated

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Figure 6.1 Seasonal mean sap flux density values measured and estimated

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Figure 6.2 Seasonal stand transpiration for sap flux density per season measured

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Figure 6.3 Seasonal stand transpiration for sap flux density per season estimated

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Figure 6.4 Tree density map

v

List of tables

Page 28

Table 4.1 Stand statistics and area calculations

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Table 5.1 % of availability for measured variables

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Table 5.2 Calibration curves for soil matric pressure

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Table 5.3 Seasonal correlation

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Table 5.4 Model parameters and corresponding error calculated

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Table 6.1 Spatio-temporal stand transpiration for measured seasons

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"One felt as if there was an enormous well behind them, filled up with ages of memory and long, slow, steady thinking; but their surface was sparkling with the present: like sun shimmering on the outer leaves of a vast tree, or on the ripples of a very deep lake. I don't know but it felt as if something that grew in the ground — asleep, you might say, or just feeling itself as something between root-tip and leaf-tip, between deep earth and sky had suddenly waked up, and was considering you with the same slow care that it had given to its own inside affairs for endless years."

J. R. R. Tolkien

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1. Introduction 1.1.

Research motivation

Most of the Earth’s liquid fresh-water reserves (~96%, excluding glaciers and permanent snowpack) are stored underground in aquifers (Dingman, 2002). Aquifers are an essential groundwater resource that requires a sustainable use for human race and various ecosystems subsistence depends on it. Groundwater management is vital for humankind; approximately two billion people depend directly upon drinking water from aquifers, and 40 per cent of the world’s food is produced by irrigated agriculture that relies largely on this source (Morris et al., 2003). According to the global assessment on groundwater published by the United Nations Environmental Programme (UNEP) and assembled by Morris et al. (2003), “despite its importance, groundwater is often misused, usually poorly understood and rarely well managed. The main threats to groundwater sustainability arise from the steady increase in demand for water (from rising population and per capita use, increasing need for irrigation, etc) and from the increasing use and disposal of chemicals to the land surface”. Moreover, groundwater systems will also suffer as climate change affects recharge of aquifers (Danielopol et al., 2003). Water-limited environments (WLE) represent half of the Earth’s land surface, containing some of the fastest growing population centers in the world where water scarcity is an important issue in these highly sensitive to change environments (Newman et al., 2006). WLE include arid, semiarid, and sub humid regions; having this classification when annual precipitation (P) is less than annual potential evapotranspiration (PET) in a ratio that ranges from 0.03 to 0.75 (P/PET) (Parsons and Abrahms, 1994). Besides, there is an extended lack of rain usually with long dry season, making water availability the dominant control on vegetation’s physiology (Guswa et al., 2004). WLE are characterized by a significant spatiotemporal variability of rainfall, quick infiltration events rarely entering the root zone, limited water availability on plants and hence vegetation adapted to water stress (Laio et al., 2001). Detail description on WLE can be found in Porporato et al. (2001), Rodriguez-Iturbe et al. (2001) and Newman et al. (2006). In addition, it has been proved quite recently that some of tree species called phreatophytes do uptake substantial amounts of groundwater in WLE (Cooper et al., 2006). Phreatophytes are usually long-rooted plants, adapted to arid and semi-arid environments by developing long deep root systems which uptake water from saturated zone (Cooper et al., 2006). Phreatophyte behaviour for different tree species has already been proven in numerous field experiments, showing their dependence on groundwater resources WLE (Lubczynski, 2008). Moreover, these trees reduce groundwater recharge by an efficient soil moisture extraction from the unsaturated zone (Lubczynski, 2008). For developing a realistic understanding of tree water use patterns, it is important to understand their water use response in both, spatial and temporal scales (Zeppel et al., 2006).

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WLE are also distinguished by a normally low net recharge (Rn) mainly due to low recharge and relatively large groundwater evapotranspiration (ETg). The Rn together with lateral groundwater outflow represents a physical reason of water table decline (Lubczynski and Gurwin, 2005). Large amount of trees in WLE typically results in water table decline and reduction of baseflow whereas deforestation in water table rise and an increase of the stream baseflow (Viramontes and Descroix, 2003). Therefore, quantifying water use of native vegetation, specially trees, is an imperative contribution for understanding landscape ecohydrology (Zeppel et al., 2006). Furthermore, there is little information on seasonal tree transpiration variability as a response to environmental factors (Ma et al., 2008). Therefore, a need for a better understanding on the hydrological role of trees in WLE coupled with environmental factors is evident for a sustainable management of water resources. An effective management of water resources in WLE requires consistent information of the water budget typically handled by hydrological models. Subsurface evapotranspiration (ETss) is an important component of the water balance in many arid and semi-arid environments (Loheide et al., 2005). Part of ETss called groundwater evapotranspiration (ETg) is of great interest in groundwater modelling in WLE, however, it is largely unknown and its importance is usually underestimated because of measurement difficulties and high spatio-temporal variability (Lubczynski, 2000). In environments with abundant phreatophytic vegetation, a fraction of ETg, the groundwater transpiration (Tg), has a key role in hydraulic dynamics, as it is a key flux that has intrinsically high uncertainty in simulations. Moreover, there is still an unsolved problem when partitioning unsaturated zone transpiration (Tu) and Tg. However, to solve this problem first, total tree transpiration Tt = Tu + Tg has to be defined in spatiotemporal manner. This MSc research aims to develop method to evaluate spatio-temporal variability of tree transpiration (Tt) in the Sardon catchment, which is considered as representative for WLE. The Sardon area is characterised by the occurrence of two tree species, phreatophytic, evergreen Quercus ilex (Q.i.) and deciduous Quercus pyrenaica (Q.p.). Considering: (i) large abundance of the investigated Q.i. and Q.p. species on the Iberian Peninsula; (ii) eventual importance of the analyzed Q.i. and Q.p. tree transpiration in water balances and importance of phreatophyte Q.i. in groundwater balances; and finally (iii) different phenology of the two analyzed tree species, this study focuses on evaluation of spatio-temporal tree transpiration (Tt) in the Sardon area. This study represents part of the research theme focusing on partitioning of Tt into Tu and Tg including their separate spatio-temporal representations. The results of this study are expected to contribute to a better understanding of the hydrological and ecological role of trees in WLE, in particular Q.i. and Q.p.

1.2.

Current understanding

Trees transpire water with different rates depending on: species type, size, climatic conditions, moisture status, depth to water table, shading, association with other plants, time of the day, time of the year etc. Plot tree transpiration, in hydrology referred also as tree transpiration flux, depends mainly on tree species present in the plot, their growing stage and density of trees in the analyzed plot. 8

The occurrence of certain species in the analyzed area depends on availability of water constrained by climatic and hydrogeological conditions. In WLE, plant adaptation mechanisms are controlled by water stress periods occurring in long dry seasons. These adaptation mechanisms reflected for example by large transpiration variability and efficient water use (Lubczynski, 2008), allow plants to survive long, dry season droughts. Lubczynski (2008) reviews the assessment of tree groundwater transpiration at multiple scales, which mainly involves the evaluation of the following steps (this study focuses on the first two steps): 1. Tree transpiration of individual trees (Tt). An overview of the different techniques applied to evaluate whole-tree water use and for understanding water transport and storage can be found in Wullschleger et al. (1998). Thermal methods of sap flow (Qs) measurements are currently the more relevant techniques for estimating Tt in hydrogeological studies. For the present study, Qs is evaluated considering the discussion presented by Lu et al. (2004). 2. Spatial representation of tree transpiration (T). Since the assessment of each individual tree is not efficient, for upscaling stand transpiration (T) is used (Cermak et al., 2004). 3. Tree root patterns and depths. Roots are assessed directly by root excavations in shallow rooted trees, however, in WLE this method is not sufficient to define the depth of phreatophytes (Lubczynski, 2008). An efficient and more reliable method is the depthselective root tracing method (Haase et al., 1996). 4. Groundwater transpiration of individual trees (Tg). This flux can be estimated using tracers to solve for the mixing model with unsaturated and saturated contributions (Brunel et al., 1995; Snyder and Williams, 2000). 5. Spatio-temporal assessment of groundwater transpiration (Tg) can be done either directly by GIS-based upscaling of individual Tg estimates or by distributed groundwater models such as MODLFLOW specially adapted for such purpose (Baird and Maddock, 2005; Lubczynski, 2008).

1.3.

Research objective

The general objective of the present research is to evaluate hydrological role of trees in a WLE by spatio-temporal assessment of tree transpiration (Tt) in the Sardon catchment. 1.3.1.

Specific objectives

(i)

To classify Quercus ilex (Q.i.) and Quercus pyrenaica (Q.p.) trees in the stands, measure their tree transpiration (Tt) and upscale Tt into stands representing catchment tree transpiration (T).

(ii)

To model temporal variability of Q.i. and Q.p Tt seasonally regarding environmental factors.

(iii)

To present insights about role of T in the water balance of Sardon catchment.

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1.3.2.

Research questions

How are Quercus ilex and Quercus pyrenaica distributed in the Sardon catchment? What is the temporal variability of Tt for Quercus ilex and Quercus pyrenaica? What are the main environmental factors that affect Tt and how Tt can be modelled to fill in the data gaps? What is T contribution to the water balance in the Sardon catchment scale?

1.4.

Hypothesis

Quercus ilex and Quercus pyrenaica can be accurately classified applying remote sensing techniques with 3 band high resolution imagery (pixel size < 1m). Temporal variability of tree sap flux density ( v ) can be modelled by using combination of environmental factors. The upscaling of seasonal tree transpiration (Tt) of individual trees can be done by: - Averaging seasonal sap flux densities ( v ) per species; - Defining species-specific biometric upscaling functions (BUF) correlating canopy projected area (Ac) as scalar and xylem area (Ax).

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2. Study area 2.1.

Location

The study area is located in the Iberian Peninsula, between latitudes 41°09’, 41°02’ N and longitudes 6°06’, 6°14’ W (Figure 2.1). This area was selected because it is a semi-arid water limited environment subject of previous studies, representing a reliable source of historical climatic and hydrological data due to a current monitoring based on Automated Data Acquisition System (ADAS) with two multisensor stations: Trabadillo and Muelledes. Two tree species; evergreen Quercus ilex and broadleaved deciduous Quercus pyrenaica grow in this ecosystem. The selected Sardon area belongs to a catchment of the Rio Tormes basin. The Sardon river flows as the main stream, with a surrounding topography that delineates an 80km2 catchment that has an altitude difference ranging 840m.a.s.l. at the highest southern boundary to 740m.a.s.l. at the river´s outlet in the north.

Figure 2.1 Location of the study area. Eight clipped orthorectified aerial photographs are shown as background of the Sardon catchment. See section 4.4.1 for stands description.

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Lubczynski and Gurwin (2005) argue the suitability of this study area for research purposes because it is a small well-defined boundaries catchment with low human impact; allowing a better approach for modelling natural conditions. Topographic boundaries are distinguished by outcrops and shallow subcrops of massive non-fractured rocks with a distribution of: impermeable schists and massive granites at the highest southern boundary, massive granites at the western and northern boundaries, and fractures filled with quartzite material at the eastern boundary (Lubczynski and Gurwin, 2005). Inside the catchment, there are valleys, formed as the result of alteration processes along stream and because of an intense joint system of the granitic rocks. These valleys are filled with alluvial and weathered arenaceous and argillaceous loose materials, while hills are distributed mainly in parts covered by thin veneer of weathered zones (Cornejo, 2000).

2.2.

Climate and hydrology

The Sardon catchment is a WLE, characterized by semi-arid climate with rain ~500 mm/year (23 –year mean rainfall). It has little subsurface water circulation due to a low rainfall (P) and high PET overall the year, with short periods of high rainfall and high recharge, thus pretty high water circulation. The warmest and driest months are July - August, with average values of temperature ~22˚C, PET ~5 mm /d and rainfall ~20 mm/month. Coldest months are January - February with an average temperature ~5 ˚C. Wettest months are November December with rainfall above 100 mm/month. An average ~0.5 mm/d as lowest PET can be observed in December and January (Lubczynski and Gurwin, 2005). The drainage network is mainly described as a numerous stream system, mostly influenced by the intermittent regime of Sardon River. Along this main river, there is a local brittle fault zone described by Lubczynski and Gurwin (2005) as a division of two geomorphological different parts. The western part is a gently undulated while the eastern is steeper. The existence of an open-fracture zone (few tens to more than thousand meters wide and few tens deep) is found along the downthrown, western side of the fault. Eroded in the rock basement, this fault is in-filled with alluvial deposits and weathered rocks; acting as groundwater drainage for all-year and having a crucial role in controlling fluid regimes in the groundwater system. This river has a function of drain system as direct runoff for most of the year, becoming dry from mid-June to mid-October (Shakya, 2001). The runoff processes of the Sardon catchment are characterized by a low retention capacity due to a thin-highly permeable upper unconsolidated layer, typical for semi-arid hard rock catchments. Direct runoff occurs because of a large-rapid overland and subsurface runoff, caused by the deep and dense drainage network. This is a respond to high-intensity rain showers. In rainy seasons, during and shortly after heavy rain showers, temporary flooding of the terrain depressions with temporal saturation of vadose zone may also take place (Lubczynski and Gurwin, 2005).

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2.3.

Hydrogeology

The general hydrogeological setup of the study area is supported by both the regional and local groundwater flow systems, mainly controlled by the geological structures. Local flow system is topographically and morphologically dependent and strong influenced by the local fracture systems. The regional flow system is ruled by the interconnected fractures in the region (Shakya, 2001). According to Shakya (2001), the flow system of the Sardon catchment arrives to an equilibrium with respect to all fluxes on a long-term basis. These fluxes are dependent on the transmissivity of the aquifer and reflected by hydraulic gradient. Spatially, this flow system is influenced by regional tectonic structures, resulting in a NNE-SSW trending fault system. The modelling results showed a higher horizontal hydraulic conductivity (K) along eastern and western high areas for the first layer, contrary to the very low values in the north. In the rest of the modelled area, K values were moderate, between 1-3 mday-1. For the second layer, K was minimum in the upper catchment, with maximum values in the northwest part. Groundwater runoff is less seasonal influenced and more moderate in motion and quantity than direct runoff. Inside the catchment boundaries groundwater flows to the main S-N centrally located fault zone and afterwards conducted towards the northern outlet of the study area. The geology and hydrogeology is strongly influenced by prevailing granitic rock composition. Also, the hydrogeology is largely influenced by fracturing processes and weathering. Three main layers are identified (i) a top unconsolidated, composed of weathered and alluvial deposits (average 0-5m) (ii) a fracture granite with different intercalations (with depth constraint by granite fracturing, ranging from few meters in the uplands to 60 m.b.g.s. (iii) a massive granite with some gneiss inclusions (Lubczynski and Gurwin, 2005). The groundwater table is shallow in the river valleys (0-3 m.b.g.s.) and deeper at the watershed divides (2-6 m.b.g.s.); a typical characteristic in granitic areas. Groundwater use can be considered as negligible for it is only utilized by cattle farms. Farms use this resource by extracting from man-made ponds that dry in summer due to seasonal groundwater table lowering and surface evaporation (Lubczynski and Gurwin, 2005). ETg in the Sardon catchment was first approached by Lubczynski and Gurwin (2005) as a first attempt to evaluate for dry season tree transpiration; resulting in flux rates of 0.40 for Q.i. and 0.15 mm/d for Q.p. from homogeneous stands and deriving this result into Eg with fluxes ~0.5 mm/d.

2.4.

Land cover

Land cover is characterized by natural woody-shrub vegetation and Quercus trees, mainly used for pasture because of the large weathered granite soils along the catchment, which makes it unsuitable for agriculture. However, there are some cultivated lands, most of them near villages. Two major well-irrigated inner catchment areas can be seen from aerial photographs; one east of Muelledes village (~37 ha) and other east of Villosinio (~27 ha).

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Quercus tree genus represents about 200 different species, some of which are shrubby and mainly exists on the Northern Hemisphere. All of these generally named Oaks have scaly winter buds, commonly clustered at the ends of the twigs, but single at the end nodes. Nuts are the fruits of these trees, with an acorn surrounded by an enveloping acorn cup at the base. The pith is star-shaped, with simple leaves typically lobed (Von Willdenow and Von Linné, 2008). There are two tree species in the Sardon catchment, evergreen Quercus ilex and deciduous Quercus pyrenaica, locally named “encina” and “roble” respectively. General distribution of trees has more dispersed stands in the north of the catchment than in the clustered south tree stands. Also, within the few meters wide buffering along the drainage network, trees are denser. Besides trees, grasses and abundant common shrub (Cytisus scoparius) are distributed all around the area in clustered stands. This vegetation is considered to have a minor role in groundwater uptake for the present study. 2.4.1.

Quercus ilex

The sclerophyllous evergreen trees, such as Quercus ilex, are mainly constituent in forests under dry sub-humid climate in the Mediterranean, with an optimum distribution in the Western Mediterranean Basin at the altitudes ranging from 0 to 1400 m.a.s.l. Because of its high availability to withstand water variability, Q.i. grows well in all bioclimates, from semiarid to per-humid and preferably in sub humid. Moreover, its ability in resprouting from stumps and roots allows its survival in zones with periodic wildfire (REDMED, 2008). This oak is a medium-size tree with an average 15 m height and maximum between 20-27 m tall. Its trunk is ~1 m in diameter and is characterized by a finely squared-fissured blackish bark in the stem with a branched populated crown, at first columnar and becoming domed. Old leaves persist for 1-2 years before the new ones emerge; they are dark green above and pale whitish-grey with dense short hairs below. Leaf shape is variable, oval, from 4-8 cm long and 1-3 cm broad for adult leaves. The leaves are toothed in their perimeter. The flowers are yellow catkins 4-7 cm long with stamens longer than petals and are produced during spring. Fruits are pointed acorn (1.5-2 cm) maturing in about 6 months to a year. The most common habitat conditions are hardy in all types of soils, were it sprouts easily and forms root suckers. They can live up to 300 years and more (Von Willdenow and Von Linné, 2008). The edible acorns are an important food for free-range pigs raised for Serrano ham production, an important economic activity in the Iberian Peninsula. The Quercus ilex species is considered to have efficient water use. This efficiency is reflected by small leaves with efficient internal structure that minimizes evaporation losses, specific water searcher root patterns, seasonal water dependent flowering and the ability to capture atmospheric, shallow and deep subsurface moisture (Porporato et al., 2001). David et al. (2007) proved the groundwater uptake status of Quercus ilex in Mediterranean weather, thus it is classified as phreatophyte. They estimated also that approximately 70% of the total tree transpiration of that species has a groundwater origin during summer drought.

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2.4.2.

Quercus pyrenaica

These oak tree species are native from the Iberian Peninsula and grow at altitudes between 400 and 1600 m.a.s.l. They tend to grow in clusters of small trees; however, they also grow isolated up to 25 m tall with a twisted trunk average 0.4 m in diameter and a characteristic opened irregular crown. Bark is grey with deeply furrowed into small square scales. The grey-green leaves of Quercus pyrenaica are 8-20 cm long and 4-7 cm wide, margin with irregular deep sinuses lobes, covered on both surfaces by short hairs. Long and numerous yellow catkin flowers appear in April-May and their acorns mature on November-December of the same year. They require acid soils, decalcified, with organic matter content and well structure horizons. They also have a mercescent behaviour, which in botany means withering without falling off. This means that deciduous Q.p. retains leaves through the winter and it is more common to happen in younger trees. Moreover, mercescent leaves are considered to protect species from water and temperature stress periods. This oak has a potent rooting system, with a deep tap root which develops several horizontal roots, mainly in the shallow subsurface; allowing the development of peripheral vegetation around the trunk. Common vegetation surrounding this oak is the Cytisus scoparius. Sometimes adult trees are surrounded by plentiful bush and even small trees (Blanca et al., 2000). It is resistant to extreme cold due to its late flowering and its short vegetative period, becoming the deciduous species that better survives to droughts (Siso et al., 2001). However, its phreatophyte behaviour has not yet been proved. 2.4.3.

Shrubs and grasses

As a constituent of the natural woody-shrub vegetation, Sardon catchment has different kind of grasses and shrubs were Cytisus scoparius (C.s.) the commonly named Scotch broom, grows in most of the area. C.s. is a perennial, leguminous shrub native to western and central Europe, extending from the Iberian Peninsula north to the British Isles and southern Scandinavia. It is usually found in clear sites with dry climates and sandy soils at low altitudes. Typically it grows 1-3 m tall with main stems up to 5 cm thick. It is configured by green shoots with small deciduous trifoliate leaves 5-15 mm long. In spring and summer it is almost totally covered with yellow flowers, which extends 20-30 mm and 15-20 mm wide. It is considered to be the hardiest species of broom, tolerating temperatures below -25°C (McGraw-Hill's, 2008). In the Sardon study area, C.s. does not exceed the height of 1m. Transpiration of C.s. shrub is not considered in this study that focuses on transpiration of trees only.

2.5.

Sardon studies, monitoring and database

There have been a number of studies in the field of groundwater, carried out in the Sardon catchment since 1996. Rwarinda (2000) completed a hydrogeological investigation in the Sardon catchment, as part of the lower Rio Tormes basin. Tesfai (2000) described the subsurface characteristics of the granitic basement from resistivity data and Cornejo (2000) focused on groundwater recharge modeling of the study area. Shakya (2001) integrated remote sensing and GIS techniques for a spatio-temporal groundwater modeling of the catchment and Lubczynski and Gurwin (2005) concluded the ongoing studies with journal article. 15

These studies had been data-supported by Automatic Data Acquisition Systems (ADAS). The remotely controlled systems ensemble monitoring sensors connected to a logger which manages their performance and provides a digital output. Variables such as climate, stream discharges and groundwater table are being monitored in the two station sites of the Sardon study area, Trabadillo (since May 1997) and Muelledes (one year after). Multichannel loggers operated this stations; a DataHog2 in Muelledes and a Delta-2e in Trabadillo, with an hourly time intervals for data acquisition. The climate monitoring is mainly focused on rainfall and evapotranspiration fluxes. Two tipping bucket sensors remains measuring rainfall with sensitivity 0.2 mmtip-1, one per station. A micro-climatic station was designed to determine PET for each station, measuring wind speed, solar incoming radiation, relative humidity and temperature; according to the Penman-Monteith reference specifications. The stream discharge is focused on baseflow for a hydrogeological assessment. The monitoring consists of three trapezoidal calibrated steel flumes installed, one in the northern outlet and two at the upper Sardon catchment. The last two were abandoned after one year due to evidence of mostly direct runoff discharge origin. The groundwater table monitoring was performed by barometrically compensated pressure transducer piezometric sensors, operated by the two loggers at each ADAS station. The seasonal fluctuations of the groundwater table are characterized by an abruptly rising and quickly recessing phreatic level (Shakya, 2001). Sap flux density had been monitored in the study area using Granier thermal dissipation probe (TDP) method (reference) on 2 trees (1 Q.i. and 1 Q.p.) connected to the Trabadillo ADAS station since September 1998. This monitoring was carried from distance, so provided an irregular database because of different events that had altered the recording. The TDP monitoring had been subject to numerous changes and sensor’s malfunctions due to natural, animal and anthropogenic causes as well as lack of local regular maintenance of the sensors. Next to long term TDP monitoring, also short sap flow campaigns had been carried out in the Sardon study area, also using thermal dissipation probe (TDP) method, the first one in late August 2001 (end of dry season), the second in the middle of the growing season by the beginning of June 2002 and finally in early September 2003, the end dry season. Q.i. was measured in the first two campaigns and Q.p. during the second and third. For the tree field campaigns, homogeneous tree stands of Q.i. and Q.p. were selected for measuring sap flow in dry season. Trees with different biometric characteristics where chosen. The average dry season sap flux density ( v ) of Q.i. was 4.7 cm h-1 with a maximum of 10.2 cm h-1; while Q.p. presented lower values of 2.3 cm h-1 as average and 4.3 cm h-1 as daily maximum. According to sap flow Eq. (5), xylem area (Ax) is the second parameter, next to ν necessary to determine sap flow (Q). In all the studies in Sardon catchment the Ax was measured using increment borer sampling method, with the aim of establishing relationships called biometric upscalling functions (BUF) characterizing tree species specific relations of Ax with stem area (As) and with canopy area (Ac). The BUF Ax-Ac was of particular interest in this study because it allows to upscale Ax by using above-ground canopy observation such as remote sensing technique. Lubczynski and Gurwin (2005), found that Ax - Ac BUF was for both Q.i and Q.p. was linear and expressed by the following relationships: for Quercus ilex, 16

Ax=0.0022Ac based on 22 sampled trees with r2=0.62; while for Quercus pyrenaica, Ax=0.0026Ac based on 25 sampled trees with r2=0.62. For spatial tree distribution, aerial photographs were scanned and stands for each tree species were selected (0.5X0.5 km) for pixel-count of canopy. Given the average canopy closure per stand, tree transpiration distribution for the entire study area was estimated on the base of a vegetation map. The resulting map was converted as a MODFLOW input, with 100X100 m cell format, used as dry season ETg flux. In the transient model, for dry season clear sky days, sap flow transpiration indicated 0.40 mm d-1 in the Quercus ilex stand and 0.15 mm d-1 in the Quercus pyrenaica stand, with an estimated 0.16 mm d-1 for the entire catchment. From August 29th to September 28th 2008, the fieldwork campaign was done for the present research; following proposal presented on August 26th. The first week of fieldwork was concentrated in tree species identification and TDP installation. The second week was mainly for drilling the piezometer network for the 24 hrs tracer test that was planned and executed during the third week (data not shown). Finally, the forth week was for measuring biometric variables of trees in the two mixed-species stands identified, one surrounding Trabadillo and the other near to Muelledes ADAS stations. A calendar of these activities is presented in Appendix A and the description for the fieldwork tasks is described within the following chapters.

17

3. Brief methodology Considering a catchment without human interference such as Sardon, for any given period of time Δt , the general water-balance equation can be written as:

P + Gin − (Q + ET + Gout ) = ΔS

(1)

where P is precipitation, Gin groundwater inflow, Q is stream outflow, Gout groundwater outflow, ET evapotranspiration and ΔS the change in all forms of storage over the given period of time (Dingman, 2002). Different fluxes accounts for ET in the water balance. Figure 3.1 shows the scheme of variables that compound ET and the position of Tt. According to Lubczynski (2008), Tt can be assumed to equal sap flow (Qs), stem water storage can be neglected (when not relevant) and Qs can be defined with separate measurements of sap flux density ( v ) and xylem area Ax. This research focuses on evaluating the role of Tt in water balance of the Sardon study area.

Figure 3.1 Scheme of variables for ET partition and Tt derivation from Tu and Tg which can be estimated as Qs by measuring v and Ax. TS&G stands for non-tree transpiration on the unsaturated zone.

An algebraic description of variables implied in ET for the groundwater balance is presented as follows according to Lubczynski and Gurwin (2005): Total evapotranspiration (ET) is the sum of surface evapotranspiration (ETs) and subsurface evapotranspiration (ETss). ETss consists of evapotranspiration from unsaturated zone (ETu) and of groundwater evapotranspiration (ETg). ETg is the evapotranspiration from saturated zone and capillary fringe while ETu consist only in the unsaturated zone.

ETss = ETu + ETg

(2)

Lubczynski and Gurwin (2005) states that for improvement in simulations of the water balance, ET fluxes may be split as:

ETu = Eu + Tu 18

(3)

ETg = E g + Tg

(4)

where, Eu is evaporation from the unsaturated zone, the liquid and/or vapour water loss from the unsaturated zone; Tu is transpiration from the unsaturated zone, the root water uptake from the unsaturated zone; Eg is groundwater evaporation, the liquid and/or vapour water loss from groundwater or the capillary fringe; and Tg is groundwater transpiration, the root water uptake from groundwater or the capillary fringe. Tg is often a major component of ETg and in WLE it is a substantial fraction of the whole plant root water use, referred as tree transpiration (Tt): (5) T = T +T t

u

g

Tt can be considered equal to Qs, which is the product of xylem sap flux density ( v ) and xylem area (Ax): (6) Q = vA s

x

The methodology of this study is structured following the specific objectives of research; (i) Temporal variability spatial tree transpiration distribution using remote sensing and GIS is described in chapter 4; (ii) Environmental factors temporal variability of Tt modelled with environmental factors is presented in chapter 5; Sap flux density(ν) and (iii) Tt role in the Sardon catchment is ET approached in chapter 6 Xylem area (A ) ET s and discussed in chapter 7. These can be seen ETss graphically in Figure 3.2; for each objective some in ETu Eu situ measurements combined with data processing needed to be done, aiming to answer the earlier posed research Eg ETg questions.

Tree transpiration spatio-temporal approach

Stand transpiration (T)

Individual tree transpiration (Tt)

Sap flow (Qs)

Remote sensing & GIS

Canopy area (Ac)

Biometric upscaling function

x

Selection of tree stands

Tree species identification

Tt

Tu

(If Phreatophyte)

Tg

Spatial approach Temporal approach

Figure 3.2 Methodology flowchart with the scheme of variables for ET included and located as conceptual model. 19

4. Remote sensing and GIS for spatial approach In order to describe tree transpiration (Tt) distribution spatially in a catchment level, the use of remote sensing data for upscaling sap flow (Qs) measurements is acceptable (Cermak et al., 2004). As stated in chapter 3, for an individual tree Tt can be considered equal to Qs. Sap flow Qs is defined as the water passage through tree tracheids and vessels (the xylem). Xylem area is the portion of sapwood between cambium and heartwood that contains living parenchyma ray cells and tracheary elements (Lu et al., 2004). Methods applied for measuring the two variables of Eq. (6) are described in this chapter for xylem area Ax and in the next chapter for sap flux density v . For upscaling Qs, Ax can be used as upscaling parameter because it can be considered as temporally invariant on a daily basis; so in combination with separate species-specific v measurements it better handles spatiotemporal variability of tree stand transpiration (T) (Lubczynski, 2008). For this study, Ax is considered as temporally invariant over the evaluated period.

4.1.

Xylem area Ax as upscaling parameter

The measurement of Ax is critical because it has a direct implication on the calculation of (Lu et al., 2004). Xylem area was Qs estimated by taking increment core samples of selected tree’s trunk using a Pressler borer (Grissino-Mayer, 2003) and immediately dyeing it with methyl orange for measuring the stained xylem length (without considering bark pieces, see Figure 4.1.). The core sample was taken approximately at diameter at breast high (DBH~1.3 m); looking for a cylindrical trunk shape. Then, drilling alignment with Pressler borer was done considering a perpendicular line to the trunk’s inclination plane. Once having the xylem length for 20 ‘circular-trunk’ trees per species, Ax was defined considering a two circles area equation; assuming one outside circle as trunk minus one inside circle

20

X m X m lle Xyyyllleeem leennngggttthhh Bark B Baarrkk

Figure 4.1 Above, cross section of tree trunk, conductive sapwood corresponds to Ax. Below, picture showing a core sample of the xylem length rx.

as non xylem, which can be written as: 2   Dt  2    Dt      − rx  Ax = π ∗   − π ∗    2     2     

(7)

where, Dt is tree DBH and rx is xylem length. Trees were selected based on tree size distribution of DBH which is a procedure for stand tree sample selection (see section 4.4.1) (Cermak et al., 2004). Figure 4.2 graphically shows the statistical distribution of DBH in boxplot for Q.i. and Q.p. Notice an extreme outlier measurement for Quercus ilex above 1.2 m in DBH distribution, the biggest tree measured.

4.2.

1.4

Diameter at breast height [m]

The method chosen for measuring rx is invasive and some uncertainties were expected. As stated by Lubczynski (2008) some sources of error are: subjective boundary interpretation when differentiating between sapwood and heartwood (minimized by immediately dyeing the sample) and temporal variability of conductive sapwood area (not considered for the present study). For some of the trees, three or two xylem core samples were measured and averaged for a representative rx. Information on sampled trees and their biometrics like xylem area, canopy area, trunk perimeter and height are presented in Appendix B. Also, the general biometric procedure on the 44 selected trees is described in Appendix C.

1.2 1

0.8 0.6

0.4 0.2

0 DBH Quercus pyrenaica

DBH Quercus ilex

Figure 4.2 Statistical distribution of DBH for sampled trees per species. Q.i. has a mean 0.48 m, σ =0.26 and Q.p. 0.38 m, σ =0.13.

Biometric upscaling function (BUF): Ax - Ac

The measured tree parameter relates the magnitude of tree transpiration with an upscaling scalar. Canopy projected area (Ac) is used for this purpose since it is efficient when using remote sensing techniques for assessing large areas (Lubczynski, 2008). Relations between upscaling scalar and the upscaled parameter are defined in field as species-specific biometric upscaling function (BUF). According to (Lubczynski, 2008), the accuracy of the upscaling procedure is largely dependent on the accuracy of BUF. An appropriate number of trees per species with a representative size distribution define the reliability of the BUF. Kumagai et al. (2005) showed that in upscaling Ax using DBH, potential errors of Tt were minimized and nearly stable when sampling for more than 20 trees per species after analyzing for ~1000 trees. Figure 4.3 shows species-specific BUF for 20 Q.i. and 20 Q.p. For obtaining Ac, tree canopies per species were measured in the field by their major and minor axis and the Ax was estimated using the ellipse area formula. 21

0.6

0.1

a)

Quercus pyrenaica

0.08 Xylem area [m2]

Quercus ilex

Xylem area [m2]

b)

0.4

0.2

0.06 0.04 0.02

0

0 0

40

80 120 Canopy area [m2]

160

200

30

40

50 Canopy area [m2]

60

70

Figure 4.3 BUF per tree species with a least-square linear fit. a) Quercus ilex with a BUF of Ax=0.0030Ac-0.03, r2=0.93 with average canopy area of 58m2 and xylem area 0.14m2 for 20 Q.i. sampled. b) Quercus pyrenaica BUF is Ax=0.0019Ac-0.05, r2=0.75 with average canopy area of 48m2 and xylem area 0.05m2 for the 20 Q.p. sampled.

4.3.

Remote sensing and object-oriented tree classification

Once a relation between canopy area and xylem area is established per species, Ac can be estimated using remote sensing technique. Tree canopy projected areas per species can be obtained from high spatial resolution images (