Saturday, May 21, 2016

GEOframe a system for doing hydrology by computer

This was the remodelling of a presentation I gave at the CUASHI biennial meeting in 2008. It is, more or less, the manifest that guided me, throughout the modelling I did in the subsequent years, and I am still pursuing. Some reference can be a little old, but not certainly obsolete. While revising it in preparing my last two presentation in Parma and Grado,  I found that the general idea still remains valid, and it seems not anachronistic.

There is actually some news which regards integration in the adoption of OMS and the possibility to use related web services. Click on the figure above to see the presentation. Any comment is welcomed.

Thursday, May 19, 2016

Tools and methods for operational hydrological forecasting

This is the talk I gave at the meeting entitled "Numeric simulations as a tool for prevention by hydro-geological hazards" in Grado. I covered many of the arguments I usually talk about: modelling, hydro-informatics, and hazards. Nothing especially new for my followers. Just presented in a different way. But, you know, something also perspectives count.

The meeting was nice and I could see what some colleagues and some Italian institutions are doing, which is always important. I did not always agreed with what I herd. However most of the participant, at least those I could see in the morning sessions, gave me the impression of dedicated people. Which is encouraging. Clicking on the figure, you can see the presentation in Italian. English presentation will follow soon.

Monday, May 16, 2016

The JGrass-NewAGE system essentials: concepts, deployment, case studies and use cases

This is the talk I gave in Parma at ARPAE. In a mood for collaboration, I presented our modelling ssytem JGrass-NewAGE and out process-based model GEOtop 2.0. The presentation about GEOtop does not contain anything essentially new. It is a synthesis of the talk I gave in San Francisco in December 2013 (I and II). The presentation about JGrass-NewAGE, at the beginning, revisited a presentation I gave in 2008 at CUASHI biennial meeting (and includes now OMS instead than OpenMI)
However, it continues by showing and discussing some of the main components of the system, now documented in the GEOframe blog. Eventually shows some applications of the model and some ways to combine the components in modelling solutions.
The fact that many thoughts that I made at that time are still valid is reassuring. Obviously now we are much more close to the objective, and the codes are more robust and reliable than eight years ago.  Clicking on the figure above, please find the presentation on one of my channel in SlideShare. A longer version of the concepts part will be in a companion posts.

Saturday, May 14, 2016

PRECISE: PRocess-based ECohydrology In grasSland Ecosystems

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:

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

Tuesday, May 10, 2016


Why putting on-line Project Proposals ? Well, I think this is, one step in "open sourceness" and in replicability of research. I usually put a lot of efforts in writing proposals, and most of the time they do not obtain the financial support they were written for. So they remain in one of mine (informatics) drawers and lay forgotten. To this destiny is certainly preferable a public exposition where other researchers can find, if possible, inspiration. I obviously hope that my projects get financed,  and so, I hope for this one. If approved it will give support to a young start-up (of my former Ph.D. students) and to some new postdocs, and/or doctoral students.

Adige-CARITRO, is a project presented for the CARITRO call 2016. It is, in a sense, the continuation of the CLIMAWARE project, and its aim is to produce an operational core modelling solution for River Adige. This work is based on OMS3 and JGrass-NewAGE but, obviously, it will contain the huge set of refinements necessary to have a working model, and will include the large database we created in other projects, and especially with funding from CLIMAWARE and GLOBAQUA.

The Adige-CARITRO model will be able to estimate all the hydrological flows (discharge, evapotranspiration, recharge, liquid precipitation and snowfall) in the basin,  divided into sub-basins of few square kilometers (for a total of several thousand sub-basins ).  Modelling will include reservoirs, intakes,  the main lakes.
This will allow  to have a capillary control over the hydrology of the basin, even in real-time, either for the management of water uses (irrigation, snow, production of energy) and extreme phenomena (floods and drought ) and for the evaluation of ecosystem services related to water. It will also allows to make realistic projections of the effects of climate change in the Trentino-Alto Adige.
This project will focus on deployment of modeling solutions that require great integration between databases and models, as well as the development of appropriate tools for processing, analysis and representation of the output data. The project will also pursue some theoretical developments  which will be promptly implemented.

The project is made in collaboration with MobyGIS which will produce the snow modelling trough its platform MySnowmaps. By clicking on the image above you can download the project.

Monday, May 9, 2016

Age-ranked hydrological budgets and a travel time description of catchment hydrology

This new paper submitted to HESS and available in HESSD deals with the theory of travel times. It summarises some of our work of  the last year, whose partial results I had  the occasion to discuss in some Conferences.

As the abstract says, the theory of travel time and residence time distributions is reworked from the point of view of the hydrological storages and fluxes involved. The forward and backward travel time distri- bution functions are defined in terms of conditional probabilities. We explain Niemi's formula and show how it can be interpreted as an expression of the Bayes theorem. Some connections between this theory and population theory are identified by introducing an expression which connects life expectancy with travel times. The theory can be applied to conservative solutes, including a method of estimating the storage selection functions. An example, based on the Nash hydrograph, illustrates some key aspects of the theory.

Any comment is welcomed.

Sunday, May 8, 2016

Dear incoming students who want to work with me

In these days, there are many students that write and candidate themselves to a Ph.D. position. I thank these students for the attention they give to me. However, they should read  what I write below carefully.
I want to tell them that in Italy, the procedure is not like in other States where the professor choses directly his/her Ph.D at any time of the year. We have a selection (meaning a competition) to which they should apply. In my Department, in any case, the professor has to say that, for a certain year, he/she wants to support a student, and has to co-finance the grant. So the fact that you show up and start a discussion is positive.

Regarding the matter, these students send me their CV which is sometime notable but rarely coincident with my research directions.  I do not want to be brutal, however, they have to refocus on the idea that I work hard  to pursue my own research, and, if they want to work with me, they need to like what I like. So in their presentation to me a statement like, "I would really like to work with you on the topic [put here one topic on which I work]",  is relieving me from some pain and shows that you are a smart man or woman.  Occasionally I also organise summer schools that are a good way to get in touch with me, and a place were I can evaluate you directly. [We can often give up your tuition fees (but we do not have money to support your travelling).]

Usually, I am not interested in river hydraulics, nor in sediment transport, not even in computational fluid mechanics (except maybe to integrate Navier-Stokes equations). Neither I am a structural engineer or a civil engneer, in strict sense. Other people at my Department are very good in the above topics, and they should be searched if the student want to pursue  those researches instead than mine. 

I am a hydrologist, and my interest are more or less depicted here, in these posts. What I am really working in these days are the Jgrass-NewAGE system (see also Wuletawu Abera defense post) and the informatics to build the new GEOtop. In perspective, I am also very interested in the thermodynamics (theory and implementation) of hydrological processes.

I pretend that a candidate has programming skills in Java or C++, or the willing to pursue them. All the code my group develops is intended to be free software, and must be produced with appropriate documentation. Do not bother me, if you do not agree with this or you do not want to write code.
The reflection about research reproducibility and replicability is part itself of  my research work and, in my view "open-sourceness" is part of the process to obtain them.

To have an idea of my research see the projects I recently presented (PRIN 2015, PRECISE, WATSUP, Adige-CARITRO) for a possible funding. 

To see what I mean for a Ph.D. you can read here. It can be exciting, but it deserves the right mental and general attitude.