My wish list for the next 15 years

Last night I was asked by two colleagues what I would like to do in my next fifteen years of carrier. So this post goes quite on personal. I answered being happy. But this was obviously too generic.

Professionally-wise, I added

• Publishing about fifty papers
• Writing (at least) one book about hydrology
• Getting an ERC

Obviously writing fifty papers is not a very high objective, and all of it seems, maybe, mundane. However, the real wish would be that ten out of my fifty papers, would be better that the ones I already co-authored (reasonably five of them). Having one of two of them becoming benchmark papers.

Rainfall and Temperature Interpolation (for hydrologic purposes)

Spatial (hydrological) models require spatial hydrological inputs. Some measurements techniques, as radars and remote sensing, usually provide this spatial information. However, it is often not quantitatively reliable if not compared to ground measurements, because remoted sensed products are themselves the outcomes of some modelling. In any case, even if, remote measurements enter every day more and more in the practice of hydrologists, ground based, in station, measurements are today's standard. They provide localised information that has to be extrapolated to space. For accomplishing this task, several techniques were developed, moving from the Thiessen (1911) method to the use of inverse distance weighting (IDW), to splines (see for instance Hutchinson, 1995) to the use of Kringing (e.g. Goovaerts, 1997). 

When data are abundant, either splines, IDW, or kriging give acceptable results in interpolating temperatures and rainfall. The choice of one or another, more than on performances issues (either as computational resources needed or in reproducing known results), is actually related to the availability of tools to perform them. However, recently Kriging gained momentum, (because of the presence of good tools for doing it like gstat and) because it was generically found to perform better than the other methods, because it allows to include the effects of other explaining variables (as, for instance, elevation) in the method, and furnishes a built-in methodology to calculate estimations errors. 

In any case, please find below, a list of papers, certainly incomplete, where the general problem was analysed, and some more specific literature on rainfall and temperature interpolation.

The future will be certainly in mixed methods, where, for instance Kriging, will be mixed with machine learning techniques (see also here). However, in this direction I saw seeds,  not yet mature, mainstream work.

GENERAL

Attore, F., Alfo, M., De Sanctis, M., Francesconi, F., Bruno, F., 2007: Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. Int. J. Climatol. 27, 1825-1843.

Borga M. & Vizzaccaro A., 1997. On the interpolation of hydrologic variables: formal equivalence of multiquadratic surface fitting and kriging. J. Hydrol., 195(1-4), 160-171. 

Burrough PA, McDonnell RA. 1998. Principles of Geographical Information Systems. Oxford University Press: New York; 333. 

Carrea-Hernandez, J.J., Gaskin, S.J., 2007: Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. J. Hydrol. 336, 231-249.

Caruso C. & Quarta F., 1998. Interpolation methods comparison. Comput. Math. Appl., 35(12), 109-126.

Daley, R. 1991. Atmospheric Data Analysis. Cambridge University Press, Cambridge.

Daly, C. (2006). Guidelines for assessing the suitability of spatial climate data sets. International Journal of Climatology, 26(6), 707–721. http://doi.org/10.1002/joc.1322

Dubois, G., Malczewski, J. and De Cort, M. (2003). Mapping radioactivity in the environment. Spatial Interpolation Comparison 1997 (Eds.). EUR 20667 EN, EC. 268 p. 

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation (pp. 1–488). New York : Oxford University Press. 

Goovaerts P., 1998. Ordinary cokriging revisited. Mathematical Geology, 30(1), 21–42

Hengl, T., Heuvelink, G.B.M., Rossiter, D.G., 2007. About regression-kriging: from equations to case studies. Comput. Geosci. 33, 1301e1315.

Hofstra, N., M. R. Haylock, M. New, P. D. Jones, and C. Frei (2008), Comparison of six methods for the interpolation of daily European cli- mate data, J. Geophys. Res., doi:10.1029/2008JD010100, in press.

Li, J., Heap, A., 2008. A Review of Spatial Interpolation Methods for Environmental Scientists. Geoscience Australia, p. 137. Record 2008/23.

Li, J., Heap, A., 2011. A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol. Inform. 6, 228e241.

Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review, Environmental Modelling & Software, Volume 53, March 2014, Pages 173–189

Mitasova, H., and Mitas, L., Interpolation by regularised spline with tension, I: theory and implementation, Mathematical Geology, 25:641-655

Moore, I.D., Terrain analysis programs for the environmental sciences, Agricultural System and information Technology, 2:37-39, 1992

Myers DE (1994) Spatial Interpolation: an overview. Geoderma 62(1): 17-28

Nalder I.A. & Wein R.W., 1998. Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric. For. Meteorol., 92(4), 211- 225. 

Shepard, D. 1968. A two dimensional interpolation function for irregularly spaced data, Proceedings of the 23rd National Conference, ACM, pp. 517-523.

Rivoirard, J.. On the structural link between variables in kriging with external drift [J]. Mathematical Geology, 2002, 34: 797–808

Thornton, P., Running, S. W., & White, M. A. (1997). Generating durfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology, 190, 214–251.

TobinC. et al., 2011. Improved interpolation of meteorological forcings for hydrologic applications in a Swiss alpine region. J. Hydrol., 401(1-2), 77-89.

Todini, E., 2001b. Influence of parameter estimation uncertainty  in Kriging. Part 1 – Theoretical Development, Hydrol. Earth. System Sci., 5, 215–223.

Todini, E., Pellegrini, F. and Mazzetti, C., 2001. Influence of  parameter estimation uncertainty in Kriging. Part 2 – Test and  case study applications, Hydrol. Earth. System Sci., 5, 225–232. 

Vizi, L., Hlasny, T., Farda, A., stepanek, P., Skalak, P., & Sitkova, S. (2011). Geostatistical modeling of high resolutionclimate change scenario data. Quartely Journal of the Hungarian Meteorological Service, 115(1-2), 1–16.

Weber, D. and Englund, E. 1992. ‘Evaluation and comparison of spatial interpolators’, Math. Geol., 24(4), 381-389.

Webster, R., Oliver, M., 2001. Geostatistics for Environmental Scientists. John Wiley

& Sons, Ltd, Chichester.

WMO, 1994. Guide to Hydrological Practices. WMO N. 168, Geneva.

RAINFALL

Basistha, A., Arya, D. S., and Goel, N. K.: Spatial Distribution of Rainfall in Indian Himalayas – A case study of Uttarakhand Region, Water Resour. Manag., 22, 1325–1346, 2008. 

Berne, A., Delrieu, G., Creutin, J.-D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, 2004.

Buytaert, W., Celleri, R., Willems, P., Bie`vre, D. B., and Wyseure, G.: Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes, J. Hydrol. 329, 413–421, 2006. 

Bussieres, N. and Hogg, W. 1989. The objective analysis of daily rainfall by distance weighting schemes on a mesoscale grid’, Atmos. Ocean, 27(3), 521-541.

Creutin, J.D., Obled, C., 1982. Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water Resources Research, 18(2), 413-431

Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158. 

Diodato N., 2005. The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. Int. J. Climatol., 25(3), 351- 363.

Dirks K.N., Hay J.E., Stow C.D. & Harris D., 1998. High- resolution studies of rainfall on Norfolk Island. Part 2: Interpolation of rainfall data. J. Hydrol., 208(3-4), 187- 193. 

Fiorucci, P., La Barbera, P., Lanza, L.G. and Minciardi, R., 2001. A geostatistical approach to multisensor rain field reconstruction and downscaling, Hydrol. Earth. System Sci., 5, 201–213. 

Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, J. Hydrol., 228, 113–129, 2000. 

Guan, H., Wilson, J.L. and Makhnin, O.. Geostatistical mapping of mountain precipitation incorporating autosearched effects of terrain and climatic characteristics. Journal of Hydrometeorology, 2005, 6: 1018–1031

Hofierka, J., Parajka, J., Mitasova, H. and Mitas, L. (2002) Multivariate interpolation of precipitation using regularized spline with tension. Transactions in GIS, 6, 135-150. doi:10.1111/1467-9671.00101 

Hutchinson MF. 1995. Interpolating mean rainfall using thin plate smoothing splines. International Journal of Geographical Information Systems 9: 385–403.

Hutchinson, M. F., 1998: Interpolation of rainfall data with thin plate smoothing splines: II. Analysis of topographic dependence. J. Geogr. Inf. Decis. Anal., 2, 168–185. 

Kurtzman, D., Navon, S., & Morin, E. (2009). Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators. Hydrological Processes, 23(23), 3281–3291. http://doi.org/10.1002/hyp.7442

Kyriakidis P.C., Kim J. & Miller N.L., 2001. Geostatistical mapping of precipitation from rain gauge data using atmospheric and terrain characteristics. J. Appl. Meteorol., 40(11), 1855-1877

Lanza L.G., Ramirez J.A. & Todini E., 2001. Stochastic rainfall interpolation and downscaling. Hydrol. Earth  Syst. Sci., 5(2), 139-143.

Lloyd, C. D.: Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain, J. Hydrol., 308, 128–150, 2005.

Ly, S., Charles, C., & Degré, A. (2011). Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences, 15(7), 2259–2274. http://doi.org/10.5194/hess-15-2259-2011l., 2009) 

Ly, S, Charles, C., & Degree, A. (2013). Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale. A review.  Biotechnol. Agron. Soc. Environ., 17(2), 392–406.

Morin E, Gabella M. 2007 Radar-based quantitative precipitation estimation over Mediterranean and dry climate regimes. Journal of Geophysical Research 112: D20108. DOI:10.1029/2006JD008206.

Obled C., Wendling J. & Beven K., 1994. The sensitivity of hydrological models to spatial rainfall patterns: an evaluation using observed data. J. Hydrol., 159(1-4), 305-333.

Phillips, D.L., Dolph, J. and Marks, D., 1992. A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain. Agric. For. Meteorol., 58: 119-141.

Schiemann, R., Erdin, R., Willi, M., Frei, C., Berenguer, M., and Sempere-Torres, D.: Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland, Hydrol. Earth Syst. Sci., 15, 1515–1536, doi:10.5194/hess-15-1515-2011, 2011 

Schuurmans, J. M., Bierkens, M. F. P., Pebesma, E. J., and Uijlen- hoet, R.: Automatic prediction of high-resolution daily rainfall fields for multiple extents: The potential of operational radar, J. Hydrometeorol., 8, 1204–1224, 2007. 

Tabios,G.Q.and Salas, J.D.,1985. A comparative analysis of techniques for spatial interpolation of precipitation. Water Resour. Bull., 21: 365-380.

Tait A, Henderson R, Turner R, Zheng X. 2006. Thin plate smoothing spline interpolation of daily rainfall for New Zealand using a climatological rainfall surface. International Journal of Climatology 26: 2097–2115. 

Thiessen, A.H., 1911. Precipitation averages for large areas. Mon. Weather Rev., 39: 1082-1084.

Todini, E., 2001a. A Bayesian technique for conditioning radar precipitation estimates to rain gauge measurements, Hydrol. Earth. System Sci., 5, 187–199.

Velasco-Forero C. A., Sempere-Torres, D., Cassiraga E. F., and Gomez-Hernandez, J. J.: A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data, Adv. Water Resour., 32, 986–1002, 2009. 

Xie P, Yatagai A, Chen M, Hayasaka T, Fukushima Y, Liu C, Yang S. 2007. A gauge-based analysis of daily precipitation over east Asia. Journal of Hydrometeorology 8: 607–626. 

TEMPERATURE

Benavides, et al. 2007. Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agricultural and Forest Meteorology 146: 173−188. DOI:10.1016 /j.agrformet.2007.05.014.

Blandford TR, et al. 2008. Seasonal and synoptic variations in near-surface air temperature lapse rates in a mountainous basin. Journal of Applied Meteorology and Climatology 47: 249−261. DOI: 10.1175/2007JAMC1565.1. 

Courault, D., & Monestiez, P. (1999). Spatial interpolation of air temperature according to atmospheric circulation patterns in southeast France. International Journal of Climatology, 19(4), 365–378. http://doi.org/10.1002/(SICI)1097-0088(19990330)19:4<365::AID-JOC369>3.0.CO;2-E

Dobrowski SZ, et al. 2009. How much influence does landscape-scale physiography have on air temperature in a mountain environment? Agricultural and Forest Meteorology 149: 1751−1758. DOI:10.10

Dodson, R., & Marks, D. (1997). Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate Research, 8, 1–20.

Fury, R. and Joly, D. 1995. ‘Interpolation spatiale a` maille fine des temperatures journalieres’, Meteorol., 8(11), 36–43.

Hudson, G., Wackernagel, H., 1994: Mapping temperature using kriging with external drift: Theory

and example from Scotland. Int. J. Climatol. 14, 77-91.

Ishida, T. and Kawashima, S. (1993) Use of cokriging to estimate surface air temperature from elevation. Theoretical and Applied Climatology, 47, 147-157. doi:10.1007/BF00867447 

Jabot, E., Lebel, T., Gautheron, A., & Obled, C. (2011). Spatial interpolation of sub-daily air temperatures for improving snow and hydrological forecasts on Alpine catchments (pp. 1–19). Presented at the th Eastern Snow Conference, Montreal. (But see Jabot on HP, 2012)

Jabot, E., Zin, I., Lebel, T., Gautheron, A., & Obled, C. (2012). Spatial interpolation of sub-daily air temperatures for snow and hydrologic applications in mesoscale Alpine catchments. Hydrological Processes, 26(17), 2618–2630. http://doi.org/10.1002/hyp.942316/j.agrformet. 2009.06.006. 

Lookingbill TR, and Urban DL. 2003. Spatial estimation of air temperature differences for 

landscape scale studies in montane environments. Agricultural and Forest Meteorology 114: 

141−151.

Robeson, S. M. 1993. ‘Spatial interpolation, network bias, and terrestrial air temperature variability’, Publ. Climatol.,*I), 1-51. 

Robeson, S. M. and Willmott, C. J. 1993. ‘Spherical spatial interpolation and terrestrial air temperature variability’, Proceedings. Second International Conference on Integrating GIS and Environmental Modeling, Breckenridge, CO, in press.

Stahl K, et al. 2006. Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agricultural and Forest Meteorology 139: 224-236. DOI:10.1016/j.agrformet.2006.07.004.

Willmott, C. J., & Robeson, S. M. (1995). CLIMATOLOGICALLY AIDED INTERPOLATION (CAI) OF TERRESTRIAL AIR TEMPERATURE. International Journal of Climatology, 15, 221–229.

Snow vs rain separation

Usually hydrologists talk of "precipitation" and are quite reticent to talk about its phase. This is because it is not easy to separate the snowfall and rainfall. In simple approaches, temperature alone is chosen as separator since ACOE (1956). However, temperature alone is not enough. As they say well Harder and Pomeroy (GS), 2014, and Ye (RG) et al., 2013.  However, we  still stick with the only temperature for practical purposes, and  because other solutions are unfeasible (missing data, tools or time to go deeply) or not so important. The core of this methods is to identify a threshold temperature over which precipitation is rain and above which precipitation is snow. Most of the time the two temperature (below and above which) are not the same. So we better talk of temperature thresholds.
Besides, researchers agree that these thresholds can vary from location to location, due to several meteorological and terrain factors.
A promising method that can potentially be used for making temperature thresholds variable in space in connection with the use of satellite data is proposed in the paper by Abera et al., 2015.
Waiting for it to be available, please find below a collection of papers on the topic. They, obviously contain further references.

Rerferences

ACOE, US Army Corps of Engineers. 1956. Snow Hydrology: Summary Report of the Snow Investigations. North Pacific Division; Portland, OR, 437.

AuerAH. 1974. The rain versus snow threshold temperatures. Weatherwise 27: 67.

Dai, A., 2008: Temperature and pressure dependence of the rain- snow phase transition over land and ocean. Geophys. Res. Lett., 35, L12802, doi:10.1029/2008GL033295.

Feiccabrino, J., & Lundberg, A. (2009). Precipitation Phase Discrimination in Sweden (pp. 1–16). Presented at the the 65th Eastern Snow Conference, 2008

Harder P, Pomeroy JW. 2013. Estimating precipitation phase using a psychrometric energy balance method. Hydrological Processes. DOI: 10.1002/hyp.9799

Harder, P., & Pomeroy, J. W. (2014). Hydrological model uncertainty due to precipitation-phase partitioning methods. Hydrological Processes, 28(14), 4311–4327. http://doi.org/10.1002/hyp.10214

Harpold, A. A., Kaplan, M. L., Klos, P. Z., Link, T., McNamara, J. P., Rajagopal, S., Schumer, R., and Steele, C. M.: Rain or snow: hydrologic processes, observations, prediction, and research needs, Hydrol. Earth Syst. Sci., 21, 1-22, doi:10.5194/hess-21-1-2017, 2017

Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Calibration of conceptual hydrological models revisited: 1. Overcoming numerical artefacts. Journal of Hydrology, 320(1-2), 173–186. http://doi.org/10.1016/j.jhydrol.2005.07.012

Kienzle, S. W. (2008). A new temperature based method to separate rain and snow. Hydrological Processes, 22(26), 5067–5085. http://doi.org/10.1002/hyp.7131

Matsuo, T., Y. Sasyo, and Y. Sato, 1981: Relationship between types of precipitation on the ground and surface meteorological elements. J. Meteor. Soc. Japan, 59, 462–476.

Motoyama, H., 1990: Simulation of seasonal snow cover based on air temperature and precipitation. J. Appl. Meteor., 29, 1104– 1110.

Rohrer MD. 1989. Determination of the transition air temperature from snow to rain and intensity of precipitation. WMO TD No.328, International Workshop on Precipitation  Measurement (ed. by B. Sevruk), St. Moritz, Switzerland, (Instruments and Observing Methods Report No. 48), 475–482.

Steinacker R 1983. Diagnose und Prognose der Schneefallgrenze. Wetter
& Leben 35: 81–90.


Ye, H., Cohen, J., & Rawlins, M. (2013). Discrimination of Solid from Liquid Precipitation over, Northern Eurasia Using Surface Atmospheric Conditions, Journal of Hydrometeorology, 14, 1345–1356. http://doi.org/10.1175/JHM-D

Precompiled Executables of GEOtop 2.0 for Linux

How to create GEOtop 2.0 executables in Linux was actually documented long time ago. However now you actually have the executables.

Please refers to this post at the GrowWorkingHard blog by Francesco Serafin.

Therefore we have:

• Executables for Mac OS X Yosemite
• Executables for Windows 7
• Executables for Linux

Enjoy!

No Communication, No Science

This is a first trial to produce some standard templates for documenting our modeling work. It starts from the idea that we use a component-based environment (OMS) and there are at least three types of actors that can use (or help to produce) our software:

• Developers: these are those who develops the components. What they need to know is the overall scope of the component, its design, its classes, the algorithms it uses and some reference to check it all. 
• Linkers: these are those who uses the components to create modelling solutions (MS). Their work is made before run-time by means of a scripting language (in our case based on Groovy). In our case they produce a .sim file that allows the execution of the model inside the OMS3 console. They need also to have information about the IO data each component require and they could not be necessarily know the internals of each component, like the cook do not need to know how the cooker internals are made (they call it encapsulation of information, and information hiding, which has a positive connotation in object oriented modeling)
• Users : They just run MS, but they should also provide information about their run. So one of the template can be used for documenting single runs of MS, specifying the inputs and the setup  of the numerical experiment.

Thus, we actually delineated four types of documentation (for internals, for externals of each component, for their compositions, and for specific runs)  which we can solve with four templates. They are produced in LaTeX and are still experimental:

• The Developer's template
• The Linkers Template
• The Users  (MS) Template 
• The Users (Run) Template 

One could observe that actually these four types of documentation can should be produced at different stages of the component's production.
The Developer documentation should be produced while designing the component, and, potentially, before the component is produces. Its structure can greatly help the design, and avoid the production of software without design.  The Linkers' documentation should be produced just after the production of the component, while the component is tested. The Users documentation which presente assembly (composition) of components during experiments setup, or, if of the fourth type, before (in contemporary) a run is sent.  This implies a little discipline but it would help a lot to save personal and collective time. Besides, it would be easy then to obey to  the requirement of making replicable research.

All of this is work in progress and will be improved during the effective use of the templates. Suggestions are welcomed.

Saying this is to produce components documentation, we still are missing an appropriate type of documentation for Application, like, for instance the OMS console. For this type of documentation, we also have a template, copied from the uDig walktroughs. An example, also in LaTeX, can be found here.

Executables of GEOtop 2.0 for Windows 7

Please refers to this post at the GrowWorkingHard blog by Francesco Serafin.

Therefore we have:

• Executables for Mac OS X Yosemite
• Executables for Windows 7
• Executables for Linux

Please observe that these executables assume the use of the MeteoIO libraries, for input data interpolation and treatment, and not at all the use of the internal routines, called meteodistr. Therefore some of the examples, based on the latter routines, could not work.

Enjoy!

On failing and getting sick

There is an ongoing discussion in the GSoC mentors of which I am part this years, which regards a student felt depresses, and the way we judge his/her performance. The issue has a broader perspective brought up by Robert C. Helling (GS), which is interesting to annotate. 

"To put the issue in a broader perspective: In my paid life, I am the scientific coordinator of a graduate program at a major German university. After a few years, I realised there was quite a number of students that have to take breaks from university due to mental health problems (roughly 2 / 100 students / year) and I was worried that somehow this was the fault of our program (for example by putting to much pressure on the students). So I looked into this a but and sought advice from experts in this area. 

What I learned was that this is totally normal, it’s a phenomenon that is just not visible as there is a shyness to talk about this as mental health problems are still too much regarded as a personal weakness: Roughly one in three people at some point during their life suffer from a mental health problem that is so severe that it affects their ability to work and/or participate in social life for an extended period of time. And these do not show up at random times but usually a turning points in their life, for example a loved one dies, relationship break-up, you move to a new social environment (e.g. city / country / university) etc etc.

In the cases I met, they were relieved to hear that it is not just their personal problem (often perceived as a failure) but that this is quite common as I just explained. So in conclusion, I don’t think it helps to cover up the existence of these problems and pretend they don’t exist but talk about them openly (of course anonymously)."

On my personal experience, I know too that failing is not so uncommon and can happen, for various reasons, to everybody.  Not only geniuses get sick (not to forget John Nash) even if the debate if  creative and mental disorders are connected support certain evidence that "mads" re also creative. Anyway, I better support the idea, that looked very close, all of us have insane little behaviors, which, paraphrasing  Marcel Proust, " are necessary to make reality more bearable" but the equilibrium is unstable.