We presented Project PRECISE to the last EUREGIO call. We know that competition is high but the project objctive are really important: of practical and theoretical use. Besides, they are based on existing experimental infrastructures and models, which would have the occasion to be maintained and evolved. Collaborations inside the project would be of very high quality.
The overall goal of the project PRECISE is to advance ecohydrological modeling in mountain grassland ecosystems (with an eye to towards generalisation for other types of vegetation), in order to have quantitative instruments that supports management and impact assessment studies. In particular, we want to improve our understanding and modeling capability of the effects of climate, soil, topography and plant functional types on the water balance (with a particular focus on evapotranspiration - ET) and vegetation productivity in alpine grassland ecosystems in a range of scales from plot to hillslope.
We address the following research questions:
R1. How does plant functional diversity and plant water-use strategy influence the watervarying abiotic conditions (i.e. soil physics, topography, climate)?
R2. Which is the relative role of biotic (plant functional diversity) versus abiotic (soils, topography, climate) processes in determining the spatial and-temporal variability of ET from the plot to the hillslope scale?
R3. Which is the right level of complexity necessary in models to produce R3 at any scale of interest?
R4. How to take advantage of a combination of advanced multi-sensor, multi scale observations to better constrain and improve spatial accuracy in coupled, process based ecohydrological models?
1.2 State of the art
1.2.1 Ecohydrological modeling of plant-water interactions
In recent years, plant-physiology studies provided an increasingly detailed knowledge of the small details of plants behavior, but only some of which started to be inserted in ecohydrological models (Fatichi et al., 2015b). These include stomata actions and photosynthesis. Two main categories of models can be roughly individuated to this respect: those who approach the problem very mechanistically (Fatichi et al., 2012a), by adding detailed processes parameterizations, and those who make reference to optimality principles (Prentice et al., 2015), claiming that feedback mechanisms were discovered during plants evolution to maintain good performances under sub-optimal conditions (Prentice et al., 2015).
Most advanced plot-to-catchment scale models include a three-dimensional treatment of the water fluxes in soil, explicit spatial variability of atmospheric forcing and turbulence, and a well-balanced complexity in the formulation of the water and energy budgets. These aspects cannot be simply reduced to factors external to the vegetation dynamics, when focusing on the hydrological cycle, and not on a single plant. Among these models are GEOtop-dv (Della Chiesa et al., 2014; Endrizzi et al., 2014) and Tethys-Chloris (Fatichi et al., 2012a, 2012b).
To further develop this models, a new infrastructure is deemed necessary in order to enable comparisons of the alternative models that are emerging very fast from research. In fact, the monolithic informatics of traditional design (Rizzoli et al., 2004) hinder any change of the code and slow-down progresses of research. Fortunately, recently “component-oriented” modeling approaches (e.g. David et al., 2013; Formetta et al., 2014) were deployed. Such approaches make it easier to change modules simulating specific processes, while maintaining unchanged the others.
Three modeling challenges are faced by modelers. The first is to model water and carbon processes of a single plant in its entirety from roots to leafs, upscaling cellular micro-physiology at a reasonable coarse-grained level. The second challenge is to differentiate vegetation types in a sound way. Today this is addressed by abstracting plants in functional types (PFT, e.g. Bonan, 2002), which definition is widely criticized. More recently, however, research has focused on the definition of plant traits which correspond more closely to models’ parameters (Fyllas et al., 2014). The third challenge is to link plant physiology with the biosphere as a whole, considering the interactions with pedo- and atmosphere (including spatial and temporal patterns). This task, has, in turn, many aspects. It involves: (1) an appropriate modeling of the environmental conditions, especially turbulence (Bertoldi et al., 2007; Siqueira et al., 2009);(2) the mathematical description of soil water interaction with roots and the reciprocal influence of plants for accessing energy and nutrient resources (Manoli et al., 2014); (3) a more accurate separation of soil evaporation from transpiration (Jung et al., 2010; Lawrence et al., 2007); (4) and of plant transpiration from groundwater and streamflow (Evaristo et al., 2015); (5) and, finally, the need to upscale the mathematics of plants behavior at the hillslope scale, with the appropriate degree of complexity. This last point is a key issue, especially in mountain terrain, given the nonlinear dynamics inherent to hydrological and vegetation processes. Although, the process’ importance and heterogeneity clearly changes with the spatial scale, the conceptualization remains the same, and - so far - similar approaches have been used on very different scales (Pappas et al., 2015). On the other hand, the pool of observational data vastly expanded in the past couple decades, bearing opportunities for modellers to pursue quantitative explanations of what is observed, and predict the spatial variation of parameters. The challenge is now to make use of the extensive data pool to test hypotheses generated from optimality principles, select the one that gives the right answer, and finally meet the requirement of models reliability (Prentice et al., 2015).
1.2.2 Experimental estimation of plant-water interactions
In-depth understanding of plant-water interactions drives accurate quantification of the water budget, where biophysical parameters (e.g. biomass) play a key role. However, to correctly assess canopy stomatal conductance and biophysical parameters controlling the water balance equation, plant functional diversity (i.e. biomass abundance of grasses, herbs, legumes, dwarf shrubs) have to be considered. Regarding ET, which is the key part in the water budget driven by vegetation, plant water-use strategies of existing species within individual plant functional types significantly bias biomass-ET correlations (Della Chiesa et al., 2014; Leitinger et al., 2015). Mitchell et al., (2008) already defined ‘hydraulic functional types (HFT)’, which revealed promising results to characterize plant communities regarding their ecohydrological characteristics. However, although (1) methods to assess plant trait diversity in the field (Lavorel et al., 2008) and (2) a trait database with steadily increasing numbers of plant traits (Kattge et al., 2011) exist, this aspect is virtually inexistent in ecohydrological models. Moreover, once the implementation of plant functional diversity is satisfactorily achieved, the dynamics of ET under field conditions (i.e. soil moisture, and microclimate) have to be introduced to finally assess needed crop ET. When measuring ET, two types can be distinguished: (1) water budget- and (2) water vapour transfer measurements. Water budget methods measure incoming and outgoing fluxes of water, while water vapour transfer methods assess the flow of water vapour. Most known among the latter is Eddy Covariance, operating at field scale and not usable to fully address the water budget. Among the water budget methods, lysimeter measurements are of growing interest, as they operate at plot scale and with individual samples (also referred to as ‘sample’ scale). High precision lysimeters evaluate all the water budget components and are state-of-the-art to entangle biotic responses (Schrader et al., 2013). Accompanying phytosociological-, soil physical-, and soil hydrological data are needed to fully explore the relationship between biomass and crop ET. Moreover, lysimeters are suitable to separate evaporation from transpiration for varying micrometeorological conditions and soil characteristics ,providing valuable parameters for eco hydrological modeling. The overall aim of in-situ water budget analyses in PRECISE is to provide guidelines for ecohydrological model selection, considering sensitivity of model output to input parameters in order to subsequently detect structural deficits of the model itself (i.e. to reduce model complexity where possible and increase precision of system representation).
1.2.3 Use of proximal sensing of vegetation for ecohydrological modeling
Plant-water interactions can be addressed form the cellular up the global scale, and are studied by different scientific communities. There is an inconsistency – both in term of approaches and scales of interests - between the lysimeter community, focused on confined vegetation patches, the Eddy Covariance (EC) community (represented by the FLUXNET-ICOS networks), measuring carbon and water fluxes at the ecosystem level, the hydrological community working at watershed scale, and the remote sensing (RS) community working at regional scale (Fatichi et al., 2015b). If data from these communities can be interconnected, a step-change in the scientific understanding of ecohydrological cycling will be achievable. However, scale gaps first need to be bridged.
UAV platforms are a key instrument for solving many of the scale issues in measuring and modeling processes involving vegetation interactions with the earth and the atmosphere. First, UAV-borne observations can support ground measurements, allowing not only to upscale local observations to entire ecosystems, but also to interpret limited observations in a wider context. Second, they can be integrated with hydrological models both by providing high-resolution distributed input data, and for evaluating model performances. Third, they are a unique source of validation data for remote sensing observations.
UAV applications in geoscience, rely on the collection of multi-, hyper-spectral in the visible and near infrared portion of the spectrum and thermal imagery. The first allows retrieving information of vegetation structure, calculating vegetation indexes, like NDVI, and inverting radiative transfer models for retrieving spatially explicit information about biophysical parameters (Calderón et al., 2013; Duan et al., 2014; Zarco-Tejada et al., 2012). The second is useful for measuring land surface temperature (LST) at a very fine resolution, up to the single leaves (Gonzalez-Dugo et al., 2013).
The combination of an energy balance model with UAV thermal infrared data with a resolution of few centimetres offers a new perspective for ET and SM mapping. Involved processes can be addressed at a proper spatial scale. One promising approach is the two-source energy balance model (TSEB) (Kustas and Norman, 1999), and it extensions ALEXI/DisALEXI (Anderson et al., 2008), which computes the surface energy budget for the soil and canopy components directly from LST and LAI observations. From the point of view of the spatial and temporal resolution, the availability of UAVs allows a big improvement with respect to satellites (Hoffmann et al., 2015).
In this project, we want to exploit hi-resolution maps of vegetation properties, LST and surface energy fluxes for a spatially distributed validation of process-based, distributed ecohydrological models. The current research challenge is to directly implement in process-based models the possibility to use observations coming from remote and proximal sensing. In this sense, high resolution data integrated with the modular modeling system we will implement in this project will offer unforeseen chances for testing new hypotheses with different model formulations.
Anderson, K., Gaston, K.J., 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 11, 138–146. doi:10.1890/120150
Anderson, M.C., Norman, J.M., Kustas, W.P., Houborg, R., Starks, P.J., Agam, N., 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sens. Environ. 112, 4227–4241. doi:10.1016/j.rse.2008.07.009
Beniston, M., 2012. Impacts of climatic change on water and associated economic activities in the Swiss Alps. J. Hydrol. 412-413, 291–296. doi:10.1016/j.jhydrol.2010.06.046
Bertoldi, G., Albertson, J.D., Kustas, W.P., Li, F., Anderson, M.C., 2007. On the opposing roles of air temperature and wind speed variability in flux estimation from remotely sensed land surface states. Water Resour. Res. 43, 1–13. doi:10.1029/2007WR005911
Bertoldi, G., Della Chiesa, S., Niedrist, G., Rist, A., Tasser, E., Tappeiner, U., 2010. Space-time evolution of soil moisture, evapotranspiration and snow cover patterns in a dry alpine catchment: an interdisciplinary numerical and experimental approach. Geophys. Res. Abstr. 12, 12109. doi:http://adsabs.harvard.edu/abs/2010EGUGA..1212109B
Bertoldi, G., Rigon, R., Over, T.M., 2006. Impact of Watershed Geomorphic Characteristics on the Energy and Water Budgets. J. Hydrometeorol. 7, 389–403. doi:10.1175/JHM500.1
Bonan, G.B., 2002. Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models. Global Biogeochem. Cycles 16, 5.1–5.18. doi:10.1029/2000GB001360
Brilli, F., Hörtnagl, L., Hammerle, A., Haslwanter, A., Hansel, A., Loreto, F., Wohlfahrt, G., 2011. Leaf and ecosystem response to soil water availability in mountain grasslands. Agric. For. Meteorol. 151, 1731– 1740. doi:10.1016/j.agrformet.2011.07.007
Calderón, R., Navas-Cortés, J.A., Lucena, C., Zarco-Tejada, P.J., 2013. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 139, 231–245. doi:10.1016/j.rse.2013.07.031
Capolupo, A., Kooistra, L., Berendonk, C., Boccia, L., Suomalainen, J., 2015. Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches. ISPRS Int. J. Geo-Information 4, 2792–2820. doi:10.3390/ijgi4042792
David, O., Ascough, J.C., Lloyd, W., Green, T.R., Rojas, K.W., Leavesley, G.H., Ahuja, L.R., 2013. A software engineering perspective on environmental modeling framework design: The Object Modeling System. Environ. Model. Softw. 39, 201–213. doi:10.1016/j.envsoft.2012.03.006
Della Chiesa, S., Bertoldi, G., Niedrist, G., Obojes, N., Endrizzi, S., Albertson, J.D., Wohlfahrt, G., Hörtnagl, L., Tappeiner, U., 2014. Modelling changes in grassland hydrological cycling along an elevational gradient in the Alps. Ecohydrology 7, 1453–1473. doi:10.1002/eco.1471
Duan, S.B., Li, Z.L., Wu, H., Tang, B.H., Ma, L., Zhao, E., Li, C., 2014. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 26, 12–20. doi:10.1016/j.jag.2013.05.007
Endrizzi, S., Gruber, S., Amico, M.D., Rigon, R., 2014. GEOtop 2.0 : simulating the combined energy and water balance at and below the land surface accounting for soil freezing , snow cover and terrain effects. Geosci. Model Dev. 7, 2831–2857. doi:10.5194/gmd-7-2831-2014
Estrela, T., Menéndez, M., Dimas, M., Marcuello, C., 2001. Sustainable water use in Europe. Part 3: Extreme hydrological events: floods and droughts. Environ. issue Rep. 21, 1–84.
Evaristo, J., Jasechko, S., McDonnell, J.J., 2015. Global separation of plant transpiration from groundwater and streamflow. Nature 525, 91–94. doi:10.1038/nature14983
Fatichi, S., Ivanov, V.Y., Caporali, E., 2012a. A mechanistic ecohydrological model to investigate complex interactions in cold and warm water-controlled environments: 1. Theoretical framework and plot-scale analysis. J. Adv. Model. Earth Syst. 4, M05002. doi:10.1029/2011MS000086
Fatichi, S., Ivanov, V.Y., Caporali, E., 2012b. A mechanistic ecohydrological model to investigate complex interactions in cold and warm water-controlled environments: 2. Spatiotemporal analyses. J. Adv. Model. Earth Syst. 4, 1–22. doi:10.1029/2011MS000087
Fatichi, S., Katul, G.G., Ivanov, V.Y., Pappas, C., Paschalis, A., Consolo, A., Kim, J., Burlando, P., 2015a. Abiotic and biotic controls of soil moisture spatiotemporal variability and the occurence of hysteresis. Water
Resour. Res. 51, 3505–3524. doi:10.1016/0022-1694(68)90080-2
Fatichi, S., Pappas, C., Ivanov, V.Y., 2015b. Modeling plant-water interactions: an ecohydrological overview from the cell to the global scale. Wiley Interdiscip. Rev. Water n/a–n/a. doi:10.1002/wat2.1125
Fatichi, S., Zeeman, M.J., Fuhrer, J., Burlando, P., 2014. Ecohydrological effects of management on subalpine grasslands: From local to catchment scale. Water Resour. Res. 50, 148–164. doi:10.1002/2013WR014535
Foley, J.A., 2005. Global Consequences of Land Use. Science (80-. ). 309, 570–574. doi:10.1126/science.1111772
Formetta, G., Antonello, A., Franceschi, S., David, O., Rigon, R., 2014. Hydrological modelling with components: A GIS-based open-source framework. Environ. Model. Softw. 55, 190–200. doi:10.1016/j.envsoft.2014.01.019
Fyllas, N.M., Gloor, E., Mercado, L.M., Sitch, S., Quesada, C.A., Domingues, T.F., Galbraith, D.R., Torre- Lezama, A., Vilanova, E., Ramírez-Angulo, H.,
Higuchi, N., Neill, D.A., Silveira, M., Ferreira, L., Aymard C., G.A., Malhi, Y., Phillips, O.L., Lloyd, J., 2014. Analysing Amazonian forest productivity using a new individual and trait-based model (TFS v.1). Geosci. Model Dev. 7, 1251–1269. doi:10.5194/gmd-7-1251- 2014
Gonzalez-Dugo, V., Zarco-Tejada, P., Nicolás, E., Nortes, P.A., Alarcón, J.J., Intrigliolo, D.S., Fereres, E., 2013.
Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree
species within a commercial orchard. Precis. Agric. 14, 660–678. doi:10.1007/s11119-013-9322-9
Hannes, M., Wollschläger, U., Schrader, F., Durner, W.,
Gebler, S., Pütz, T., Fank, J., Von Unold, G., Vogel,
H.J., 2015. A comprehensive filtering scheme for high-resolution estimation of the water balance components from high-precision lysimeters. Hydrol. Earth Syst. Sci. 19, 3405–3418. doi:10.5194/hess-19- 3405-2015
Hoffmann, H., Nieto, H., Jensen, R., Guzinski, R., Zarco-Tejada, P.J., Friborg, T., 2015. Estimating evapotranspiration with thermal UAV data and two source energy balance models. Hydrol. Earth Syst. Sci. Discuss. 12, 7469–7502. doi:10.5194/hessd-12-7469-2015
Hölttä, T., Cochard, H., Nikinmaa, E., Mencuccini, M., 2009. Capacitive effect of cavitation in xylem conduits: Results from a dynamic model. Plant, Cell Environ. 32, 10–21. doi:10.1111/j.1365-3040.2008.01894.x Inauen, N.,
Körner, C., Hiltbrunner, E., 2013. Hydrological consequences of declining land use and elevated CO2 in alpine grassland. J. Ecol. 101, 86–96. doi:10.1111/1365-2745.12029
Ivanov, V.Y., Bras, R.L., Vivoni, E.R., 2008. Vegetation-hydrology dynamics in complex terrain of semiarid areas: 2. Energy-water controls of vegetation spatiotemporal dynamics and topographic niches of
favorability. Water Resour. Res. 44, 1–20. doi:10.1029/2006WR005595
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., Francois, C., Ustin, S.L., 2009. PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sens. Environ.
113, S56–S66. doi:10.1016/j.rse.2008.01.026
Jasechko, S., Sharp, Z.D., Gibson, J.J., Birks, S.J., Yi, Y., Fawcett, P.J., 2013. Terrestrial water fluxes dominated
by transpiration. Nature 496, 347–350. doi:10.1038/nature11983
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S.I., Sheffield, J., Goulden, M.L., Bonan, G., Cescatti, A., Chen,
J., de Jeu, R., Dolman, a J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., ..., 2010. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954. doi:10.1038/nature09396
Kattge, J., Díaz, S., Lavorel, S., Prentice, I.C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P.B., Wright, I.J., Cornelissen, J.H.C.,
Violle, C., Harrison, S.P., Van Bodegom, P.M., Reichstein, ..., 2011. TRY - a global database of plant traits. Glob. Chang. Biol. 17, 2905–2935. doi:10.1111/j.1365- 2486.2011.02451.x
Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., Zacharias, S., 2015. Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169
Kustas, W.P., Norman, J.M., 1999. Evaluation of soil and vegetation heat flux predictions using a simple two- source model with radiometric temperatures for partial canopy cover. Agric. For. Meteorol. 94, 13–29. doi:10.1016/S0168-1923(99)00005-2
Lavorel, S., Grigulis, K., McIntyre, S., Williams, N.S.G., Garden, D., Dorrough, J., Berman, S., Quétier, F., Thébault, A., Bonis, A., 2008. Assessing functional diversity in the field - Methodology matters! Funct.
Ecol. 22, 134–147. doi:10.1111/j.1365-2435.2007.01339.x
Lawrence, D.M., Thornton, P.E., Oleson, K.W., Bonan, G.B., 2007. The Partitioning of Evapotranspiration into
Transpiration, Soil Evaporation, and Canopy Evaporation in a GCM: Impacts on Land–Atmosphere Interaction. J. Hydrometeorol. 8, 862–880. doi:10.1175/JHM596.1
Leitinger, G., Ruggenthaler, R., Hammerle, A., Lavorel, S., Schirpke, U., Clement, J.-C., Lamarque, P., Obojes,
N., Tappeiner, U., 2015. Impact of droughts on water provision in managed alpine grasslands in two
climatically different regions of the Alps. Ecohydrology n/a–n/a. doi:10.1002/eco.1607
Lin, H., 2010. Earth’s Critical Zone and hydropedology: concepts, characteristics, and advances. Hydrol. Earth
Syst. Sci. 14, 25–45. doi:10.5194/hess-14-25-2010
Mackay, D.S., Roberts, D.E., Ewers, B.E., Sperry, J.S., McDowell, N.G., Pockman, W.T., 2015.
Interdependence of chronic hydraulic dysfunction and canopy processes can improve integratedmodels of
tree response to drought. Water Resour. Res. 51, 9127–9140. doi:10.1002/2014WR016259
Manoli, G., Bonetti, S., Domec, J.C., Putti, M., Katul, G., Marani, M., 2014. Tree root systems competing for
soil moisture in a 3D soil-plant model. Adv. Water Resour. 66, 32–42.
Marcolla, B., Cescatti, A., Manca, G., Zorer, R., Cavagna, M., Fiora, A., Gianelle, D., Rodeghiero, M., Sottocornola, M., Zampedri, R., 2011. Climatic controls and ecosystem responses drive the inter-annual variability of the net ecosystem exchange of an alpine meadow. Agric. For. Meteorol. 151, 1233–1243. doi:10.1016/j.agrformet.2011.04.015
Mitchell, P.J., Veneklaas, E.J., Lambers, H., Burgess, S.S.O., 2008. Using multiple trait associations to define hydraulic functional types in plant communities of south-western Australia. Oecologia 158, 385–397. doi:10.1007/s00442-008-1152-5
Montanarella, L., Panagos, P., 2015. Policy relevance of Critical Zone Science. Land use policy 49, 86–91. doi:10.1016/j.landusepol.2015.07.019
Moss, R.H., Edmonds, J. a, Hibbard, K. a, Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G. a, Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756. doi:10.1038/nature08823
Mountain Research Initiative EDW Working Group, 2015. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang. 5, 424–430. doi:10.1038/nclimate2563
Nedkov, S., Burkhard, B., 2012. Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria. Ecol. Indic. 21, 67–79. doi:10.1016/j.ecolind.2011.06.022
Nikinmaa, E., Sievänen, R., Hölttä, T., 2014. Dynamics of leaf gas exchange, xylem and phloem transport, water potential and carbohydrate concentration in a realistic 3-D model tree crown. Ann. Bot. 114, 653–666. doi:10.1093/aob/mcu068
Pappas, C., Fatichi, S., Burlando, P., 2016. Modeling terrestrial carbon and water dynamics across climatic gradients: Does plant trait diversity matter? New Phytol. 209, 137–151. doi:10.1111/nph.13590
Pappas, C., Fatichi, S., Rimkus, S., Burlando, P., Huber, M.O., 2015. The role of local-scale heterogeneities in terrestrial ecosystem modeling. J. Geophys. Res. Biogeosciences 120, 341–360. doi:10.1002/2014JG002735
Pflimlin, A., Faverdin, P., Béranger, C., 2009. Half a century of changes in cattle farming: results and prospects. Fourrages.
Prentice, I.C., Liang, X., Medlyn, B.E., Wang, Y.P., 2015. Reliable, robust and realistic: The three R’s of next- generation land-surface modelling. Atmos. Chem. Phys. 15, 5987–6005. doi:10.5194/acp-15-5987-2015
Rigon, R., Bertoldi, G., Over, T.M., 2006. GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets. J. Hydrometeorol. 7, 371–388. doi:10.1175/JHM497.1
Rizzoli, A.E., Donatelli, M., Muetzelfeldt, R., Otjens, T., Svennson, M.G.E., Evert, F. van, Villa, F., Bolte, J., 2004. SEAMFRAME, a proposal for an integrated modelling framework for agricultural systems, in: Jacobsen, S.E.,
Jensen, C.R., Porter, J.R. (Eds.), Proc. of the 8th European Society for Agronomy Congress. 11-15 July, Copenhagen, Denmark, pp. 331–332.
Rollinson, C., Kaye, M., 2015. Modeling monthly temperature in mountainous ecoregions: importance of spatial scale for ecological research. Clim. Res. 64, 99–110. doi:10.3354/cr01306
Schirpke, U., Leitinger, G., Tasser, E., Schermer, M., Steinbacher, M., Tappeiner, U., 2013. Multiple ecosystem services of a changing Alpine landscape: past, present and future. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 9, 123–135. doi:10.1080/21513732.2012.751936
Schrader, F., Durner, W., Fank, J., Gebler, S., Pütz, T., Hannes, M., Wollschläger, U., 2013. Estimating Precipitation and Actual Evapotranspiration from Precision Lysimeter Measurements. Procedia Environ. Sci. 19, 543–552. doi:10.1016/j.proenv.2013.06.061
Shen, C., Niu, J., Phanikumar, M.S., 2013. Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface-land surface processes model. Water Resour. Res. 49, 2552–2572. doi:10.1002/wrcr.20189
Siqueira, M., Katul, G., Porporato, A., 2009. Soil Moisture Feedbacks on Convection Triggers: The Role of Soil–Plant Hydrodynamics. J. Hydrometeorol. 10, 96–112. doi:10.1175/2008JHM1027.1
Tague, C.L., McDowell, N.G., Allen, C.D., 2013. An integrated model of environmental effects on growth, carbohydrate balance, and mortality of Pinus ponderosa forests in the southern Rocky Mountains. PLoS One 8. doi:10.1371/journal.pone.0080286
Tappeiner, U., Borsdorf, A., Bahn, M., 2013. Long-Term Socio-ecological Research in Mountain Regions: Perspectives from the Tyrolean Alps, in: Singh, S.J., Haberl, H., Chertow, M., Mirtl, M., Schmid, M. (Eds.), Long Term Socio-
Ecological Research. Springer Netherlands, Dordrecht, pp. 505–525.
Von Bueren, S.K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M.P., Yule, I.J., 2015. Deploying four optical UAV-based sensors over grassland: Challenges and limitations. Biogeosciences 12, 163–175. doi:10.5194/bg-12-163-2015
Weiler, M., McDonnell, J., 2004. Virtual experiments: A new approach for improving process conceptualization in hillslope hydrology. J. Hydrol. 285, 3–18. doi:10.1016/S0022-1694(03)00271-3
Wohlfahrt, G., Tasser, E., 2014. A mobile system for quantifying the spatial variability of the surface energy balance: design and application. Int. J. Biometeorol. 617–627. doi:10.1007/s00484-014-0875-8 Zarco-Tejada, P.J.,
González-Dugo, V., Berni, J.A.J., 2012. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 117, 322–337. doi:10.1016/j.rse.2011.10.007
Zhou, X., Istanbulluoglu, E., Vivoni, E.R., 2013. Modeling the ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate. Water Resour. Res. 49, 2872–2895. doi:10.1002/wrcr.20259