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.
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.
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.
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.
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.
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.
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.