Tuesday, September 29, 2015

Writing a paper (in hydrologiy or related field) rule by N* L*

... therefore a paper must have:
  • Five sections
  • Five Figures
  • Eight or nine  paragraphs for section
  • Each paragraph must contain a concept
If you use words with synonyms, use the shorter one. 

   These are simple instructions. They are obviously oversimplifications, and assume that you have material to work with. They probably also gives for granted that you know all the others golden rules that you can find, for instance, here. However, if I look at some papers that I am reviewing, I say:  how I like this holy simplicity and clarity.

Friday, September 25, 2015

The model of River Adige - Step 0

This is a mostly theoretical and illustrative of some aspects of the structure of the new model of the River Adige I am building with collaborators for  CLIMAWARE and GLOBAQUA projects and for myself. In the to-do-list there is a complete treatment of fluxes according to travel times theories.

Looking at the slides, it can be seen that I recycled some material in previous posts, and, obviously there are great connections with the post related to JGrass-NewAGE, and those on the physico-statistical modelling of the hydrological cycle. The presentation was made at the 2015 Padua Conference on coupled hydrological modelling, about which I will refer in another post.

Saturday, September 19, 2015

Soil in art

I used some soilscapes by Jay Stratton Noller as cover of some of my slides, meaning that soils can be the object of some artistic research. A papaer was recently brought to my attention from the GeoLog blog .

This is published in the EGU journal Soil, and was written by C. Feller, E. R. Landa, A. Toland, and G. Wessolek. The blog and the paper have also the merit also to give an author name to the very beatiful figure above, which I use in my slides, when I tal about soils, but I did not know to whom give attribution. Now I know that it was published first in Walter Kubiena’s textbook. Landa and Feller also edited a book on soil and culture (pretty expensive indeed).


Feller, C., Landa, E. R., Toland, A., and Wessolek, G.: Case studies of soil in art, SOIL, 1, 543-559, doi:10.5194/soil-1-543-2015, 2015.

Kubiena, W. L.: Bestimmungsbuch und Systematik der Boden Europas (The soils of Europe), Ferdinand Enke Verlag, Stuttgart, 392 pp., 1953.

Landa, E. and Feller, C. (Eds.),  Soil and Culture, Springer, 2010

Friday, September 18, 2015

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
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.

Tuesday, September 8, 2015

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.


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.

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. 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.

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.


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. 
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. 

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.

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. 


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: 

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.

Monday, September 7, 2015

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.


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

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