Wednesday, July 31, 2013

(Almost) a perfect Answer

I read this in a LinkedIn discussion, and I found it the perfect complement of my previous post "Essential for Hydrologists"

Question:

Hello,

I have a question specified in the following and hope some of you can guide me a
bit:

There is a lake which is the source of water for an industrial project. The project is known for being water intensive, approximately 20 tons fresh water is needed for production of 1 ton final product. According to satellite images, the lake area has been shrinking for approximately 62% since the project started operation about 5 years ago. Local farmers also complained that they had to dig water as deep as 100 meters in order to get water, indicating a severe drop of ground water level. We would like to know quantitatively how water extraction has caused local water scarcity problem. I wonder whether we need a hydrological model to do such assessment; if a hydrological model is not available for that specific lake, is that possible use time series data to run a regression model where lake area is seen as a function of water extraction, precipitation, temperature, etc.


I wonder how analysis like is done typically, if any of you is aware of any exisiting studies, please do let me know. Many thanks in advance.

Best wishes
Sissi

Answer:

Dear Sissi,

Five year data is not adequate for detailed analysis. The reason of decline is due to inadequate rainfall in these five years or increase of draft for different purposes during the five year.  Recently I have done a similar study in one of the lakes in India, though not for the same purpose. I would like to suggest the following points.

1. Study the rainfall / inflow pattern for at least for 50 years

2. Use the Soil and Water Assessment Tool (SWAT)and estimate the water balance taking the rainfall and temperature and other data for at least 15 year daily data.

3. If you have actual inflow data, compare the results with the actual inflow data.

4. If you do not have actual inflow data, asses the inflow by SCS-CN Method (Soil Conservation services (SCS), HEC_HMS  and compare the data with SWAT.

5. Use HEC-RAS and HEC-GEORAS for flood modelling if required to delineate the areas of flood in the lake catchment.

.........

With best wishes,

**

My comment:

The answer is certainly correct. HEC* and SWAT constitute very well engineered standard models used in operational (and operative) hydrology. SCS-CN method, was already commented. Certainly a term of reference. However, if you look at the physics under the hood, you can recognise the existence of many of those "old-style" parameterisations and simplifications that I tried to avoid in favour of more physically sound choices in GEOtop, more modernity, both in the way to implement the model solutions and the way to do simplifications, in JGrass-NewAGE and  my lectures in hydrology. 
Therefore the answer is perfect if you have to do (possibly is also perfect if you, at the end are an engineer) but I feel uncomfortable with it. Can't we do it better ?  

Thursday, July 25, 2013

Essential for Hydrologists

Some anonymous asked what is for me the  "Essential for a hydrologist".  Actually I tried to delineate this all along the blog. However, here below I will try a decalogue (with eleven statements actually ... it seems common):



GET KNOWLEDGE

0 - know which kind of hydrologist are you 
Are you studying all hydrological processes ? Or do you need to forecast (operationally) a particular part of the hydrological cycle ? Are you a modeller or an experimenter ? Do you study surface waters or groundwater ? Hydrology is very specialised and each sub-disciplines tends to use its own tools. One of my masters, Ignacio Rodriguez-Iturbe used to say that no hydrologist knows the whole hydrology. The water cycle, in fact, is so pervasive on the earth that it is difficult to know equally likely any hydrological subject.

 1-  In any case, know the fundamental processes (Hydrology in the XXI century is a physical science); remember that all in hydrology is a budget (mass, energy and momentum), and they are conserved.  Use sound theories and avoid unessential empiricism (this is what inspired my Slides about Hydrology)

2 - Be aware of scales. At which scale do you work ? Hydrology covers phenomena at closely molecular scale to continental scale. Methods for studying (models and measures) could be differentiated at different scales. Processes dominant at one scale can be negligible at other scales. Scaling characteristics are present everywhere … so actually some equations could be re-parametrized when used at a certain scale. Other equations simply emerge … (even if a bottom up statistical mechanics of hydrology is missing: lot of work for researchers!).

3 - Be aware of heterogeneities. Most of hydrological characteristics wildly vary in space, and this affects parameterisation of the phenomena at the scale of interest. Parameters could be sometimes thought as random variables (As in the case of hydraulic conductivity).



GET TOOLS (Select your ones, but if you can live with a community is better)

4 - know a GIS (e.g. gvSIG, open source is possible and better^1). 
Since hydrology is space varying and scale varying, you need something to visualise it properly. Spatial representation of phenomena and heterogeneities is, IMHO, one important key for understanding (so blessed is who discovers ways to identify spatial patterns and quantify them).

5 - Get a tool for quick calculations and visualisation (e.g. R^2) 
  Since Hydrology is making budgets you need some tool for making calculations. (Refrain: if it is open-source, it is better).

6 - Get a programming language, possibly Object Oriented  (e.g. Java. Other choices are possible indeed ^3) and put your hands in models^3b.
Learning is a process in which repetition and  calculation are important. Tools for quick calculations are not always suitable for more complex tasks, in which many people interact, exchange data, and explore complex non-linear physical environments like the hydrosphere. OO programming highly helps in maintaining and evolve complex code. (Java has a multitude of tools for doing that. In fact they were able to build a GIS and entire modelling systems in Java)

7 -Learn statistics (for data analysis and models outcomes  inspection)
Someone sees statistics as a tool for a forecasting. I tend to see in it more a tool for understanding (and building null hypotheses).  Data handling and understanding is at a core of any physical science. Maybe you do not perform experiments or field works, nevertheless a physical science has to do with data (huge amount of noisy data indeed), and you have anyway to cope with it. To do statistics you need tools (e.g. R). 



BEHAVE

8- Read the best papers of the best  researchers (learn from  smart people, and copy them). Remind that there are "poets" (original thinkers) and "translators" (followers).  Read the poets, even if the translators can be good. (e.g. see Benchmark papers - Scientists -WRR best papers)

9 - Avoid models and theories that "just work"^4  (but use them for surviving).
Programs and models that just work are useful to engineers who have to give answers and numbers. But at a certain point they fail miserably to give the right answer. Work for the right answer for the right reasons (anyway getting puzzled by the reasons something works despite the known evidence is important).

10 - Do not give up to improve your knowledge, and search for switching paradigms before paradigms reveal obsolete.


P.S. - One thing I forgot to say is that all model are obviously imperfect, and quantifying the forecasting error should be an exercise that any hydrologist should do any time he/she uses a model. Uncertainty in models, is certainly an issue.

___________________________________________________________________________________

^1  - I have to say that for professional printing people, even many open source users, eventually use Arc* to create maps. Printing options are certainly a weak point in open source GIS

^2 - Many use Matlab. I use to be a proficient Mathematica user. Others use IDL. All very good commercial product. But I decided to go open source.

^3 - Now is the moment of Python, which is, however, used conjointly with FORTRAN and/or C for the real task. So I believe that using Java for (almost) everything, and paying a little bit in performances,  is an economical choice.

^3b - If you work for an administration or a company, at the end, you will probably not program by yourself. You will simply use programs made by others (HEC-RAS, SWAT, SHETRAN to name a few). So you will turn your expertise in using them. But, obviously you have also to know the core hypotheses on which these models are based, and you certainly experiment that they will fail in some of your critical task. So somewhere there must be someone that, for you and for others, eliminate these drawbacks and push modelling on. That is the reason I strongly support modelling by components.

^4 Here a link to the Klemes Paper: "Dilettantism in Hydrology, Transition or Destiny", Water Resources Research, Water Resources Research, 1986, that further expands this topic.

Wednesday, July 24, 2013

GB ET Potential Dataset for United Kingdom

In this post I am presenting another R exercise. Not that I believe very much on the physical content of the what Scottischsnow does here. (Actually this bold way of treating data and empirical formulas at large scale time and spatial is something that  I usually blame).
However the information gathered in this way could be useful as a reference step for doing physically based things, at a more detailed scale (for instance by using our JGrass-NewAGE).

Moreover I like it because it uses R all the way for extracting the data and visualising them, and this can be equally likely instructive.

Monday, July 22, 2013

Rainfall statistics for the Netherlands

One of my most successful postings is the one of R resources for hydrologists.   Sometimes contributions arrive from non -hydrologist. This is the case that  of two posts from Wiekvoet whose authors download and analyse rainfall data from the Netherlands  Royal Institute. The first post mainly performs an exploratory analysis of the data. The second one looks for rainfall changes in the past 100 years. Enjoy the reading.

As a byproduct one learn also how to use ggplot2 through some examples. 

Tuesday, July 16, 2013

Java for Hydrologists 101

There are a few postings on Java in this blog. Since I want to teach it to my students, I am quietly starting to populate this page with presentations which, eventually, will constitute the core of an informal class (;-)) the Java for Hydrologsts 101.
The focus of the JfH-101 is not simply to gain Java knowledge from the scratch (or so), but to address those topics and issues that have to do with my experience of hydrologist. So, I will try to cover Java as well as OMS3, and at the right time Geotools, and jgrasstools, not forgetting the tools of the tool (Ant, Maven, Git)^*, but in the meanwhile I will try to address the topics related to object oriented programming.  My first goal will be to rewrite the Optimal Channel Networks (OCNs) programs I already did in C several year ago in Java.  In doing so, I will touch many topics really useful for any hydrologist programmer. The schedule is ambitious, and as many other tasks I started, I am not pretty sure that (or when) I will finish it. But, possibly with contribution of others, I will arrive to something useful. Programming is actually very much not talking about  that but doing it, so many of the slides will actually recall to actions.

Topics

0 - Getting Started (mostly things to read -or start to read- before the start)
1 - Your first program (here the video on-line [download - 30 Mb] well the video is really out of focus but you can here me to comment the slides. I said a wrong thing when talking ... you have to discover the error!)
2 - Solving a linear equation (here the video on-line ...  [download - 30 Mb] a little better than the first one)
3 - Reading input data from the console (and solving linear equations) (Here again the video on-line [download - 41.1 Mb]. It includes also: a - Javadoc; b - naming conventions for Java programmers)
4 - Working with Git from Eclipse (EGit)


The source code is available for download to from GitHub.

UML - stands fro Universal Modelling Language. It is a set of graphic tools that can be used to sketch OO programs. The classical reference is Martin Fowler's, UML 2.0 distilled, 2003

^* - From the links you can quite understand the I rely very much on Lars Vogel site for the basic stuff. It is not obviously the only good resource available (stackoverflow is another one, for instance, and many others will be addressed).

Tuesday, July 9, 2013

Hillslope hydrology from the point of view of Richards equation

This is the lecture I gave at the second Summer school on Water Resources that I co-organised. The lecture was recorded in video and should be made available soon, together with the other lectures.
The lecture can be considered an expansion of the talk I gave at Ezio Todini's symposium with more details and, hopefully, more clear explanations. The presentation is, as usual, available on slideshare, and you can get it clicking on the image below.

The presentation mainly use a paper by Cordano and Rigon, 2008 to obtain various degree of simplification of the Richards equation. The scope (especially for what regards the Todini's symposium version) was to show that many approaches currently used derive, in fact, from a simplification of Richards, and there are not very much reasons to shoot to it as unphysical, at least if this is subsequently followed by the use of one of its simplifications.
However, the slides cover also a discussion over the time scales of flows in hillslopes, and the relative timing of vertical and lateral infiltration.

Here it is a short abstract:


The presentation covers Richards equation as applied to Hillslope Hydrology from its foundations. It is said that it assumes mass conservation and  the existence of the Darcy scale at which the soil medium can be treated as a continuum. Then it is specialised using some well known parameterisations (van-Genuchten Mualem), and subsequently is simplified to obtain other equations. In order: the 1-D Richards equation, the Boussinesq equation, the hillslope-storage Boussinesq equation, and finally its stationary approximations.  All of these are used in literature for various purposes, including soil moisture distribution and hillslope stability. The simplifications are based on the assumption that lateral (slope-parallel) flow is slower than slope-normal flow, which is subsequently shown not being necessarily true, true some simulation with a 3-D Richards equation solver. This eventuality is caused by hydraulic conductivity being (in some soils) high variable with water content. Eventually, a conceptual model is built on the knowledge acquired, in order to reduce the computational burden. Lastly some cases are discussed where Richards equation could fail using data from the Panola hillslope. It is shown that the fill-and-spill phenomena can be described properly, and that, on the contrary, the presence of macropores cannot.

The various schemes of simplification have a great effect on the identification of landslides' locations, and, in fact, many of the papers cited (and provided in the blog) are dealing with landslides hazard assessment. 

References cited in the presentation

Beven., K. J., M.J.. Kirkby, A physically based, variable contributing area model of basin hydrology, Hydrological Sciences bulletin-des Sciences Hydrologiques, 24, 1-3, 1979

Buckingham, E. 1907. Studies on the movement of soil moisture. Bulletin 38. USDA Bureau of Soils, Washington, DC.

Casulli, V,  Stelling GS, Semi‐implicit subgrid modelling of three‐dimensional free‐surface flows
International Journal for Numerical Methods in Fluids 67 (4), 441-449

Cordano, E., & Rigon, R. (2007). A perturbative view on the subsurface water pressure response at hillslope scale. Water Resources Research, 1–36.

Cordano, E., & Rigon, R. (2010). A mass-conservative method for the integration of two-dimensional groundwater (Boussinesq) equation. Water Resources Research, 1–24.
Dietrich, W. E., 1989, Slope morphology and erosion processes, in C. Wahrhaftig and D. Sloan (Eds.), Geology of San Francisco and Vicinity, Field Trip Guidebook T105, American Geophysical Union, p. 38-40.

D'Odorico, P., Fagherazzi, S., & Rigon, R. (2005). Potential for landsliding: Dependence on hyetograph characteristics. Journal of Geophysical Research, 110(F1), 1–10. doi:10.1029/2004JF000127

Iverson, R. M., Landslide triggering by rain infiltration, Water Resour. Res., Vol. 36, N0. 7,  1897-1910, 2000

 Lanni, C.; McDonnell, J. J.; Rigon, R., On the relative role of upslope anddownslope topography for describing water flow path and storage dynamics:a theoretical analysis, Hydrological Processes Volume: 25 Issue: 25 Pages: 3909-3923, DEC 15 2011, DOI: 10.1002/hyp.8263

 Lanni C., J. McDonnell JJ, Hopp L., Rigon R., "Simulated effect of soil depthand bedrock topography on near-surface hydrologic response and slope stability" in EARTH SURFACE PROCESSES AND LANDFORMS, v. 2012, (In press). - URL: http://onlinelibrary.wiley.com/doi/10.1002/esp.3267/abstract . - DOI: 10.1002/esp.3267

Lanni C., Borga M., Rigon R., and Tarolli P., Modelling catchment-scale shallowlandslide occurrence by means of a subsurface flow path connectivity index, Hydrol. Earth Syst. Sci. Discuss., 9, 4101-4134, 2013

Montgomery, D. R., and W. E. Dietrich, Where do channels begin?, 1988, Nature, v. 336, p. 232-234.

Mualem, Y., A New Model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., vol 12, No 3, 1976

Narsilio, G. A., Buzzi, O., Fityus, S., Yun, T. S., & Smith, D. W. (2009). Upscaling Navier-Stokes Equations in porous media: Theoretical, numerical and experimental approach, Computers and Geotechnics, 36(7), 1200–1206. doi:10.1016/j.compgeo.2009.05.006

O'Loughlin, E.M., Prediction of Surface Saturation Zones in Natural Catchments by Topographic analysis, Water resour. Res., vol 22, no 5, 794-804, 1986

Orlandini, S., G. Moretti, M. Franchini, B. Aldighieri, and B. Testa (2003), Path-based methods for the determination of nondispersive drainage directions in grid-based digital elevation models, Water Resour. Res., 39(6), 1144, doi: 10.1029/2002WR001639.

Orlandini, S., P. Tarolli, G. Moretti, and G. Dalla Fontana (2011), On the prediction of channel heads in a complex alpine terrain using gridded elevation data, Water Resour. Res., 47(2), W02538, doi: 10.1029/2010WR009648.

Richards, L.A., Capillary conduction of liquids through porous mediums, Physics 1: 318-333, 1931

Troch P.A., Paniconi, C., van Loon E.E, Hillslope-storage Boussinesq model for subsurface flow and variable source areas along complex hillslopes: 1. Formulation and characteristics response, Water Resour. Res., Vol 39, No 11, 1316, doi:10.1029/2002WR001728, 2003


Tromp-Van Meerveld, H. J., & Mcdonnell, J. J. (2006). Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis. Water Resources Research, 42(2), W02411. doi:10.1029/2004WR003800

M. Th. van Genuchten, A Closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sc. Soc. of America, vol 44, no. 5, 1980

Whitaker, S., The Forcheimer equation: A theoretical development, Transport in Porous Media, October 1996, Volume 25, Issue 1, pp 27-61

Monday, July 1, 2013

2012 Water Resources Research Editors Award

Directly from the main page of Water Resources Research:

"With the approval of the AGU Hydrology Section Executive Committee, WRR has instituted an Editors’ Choice Award to be given to (about) the top one percent of published articles in any calendar year. Our goal is to provide professional recognition to scientists and students for their outstanding work. The selection is made by the WRR Editors based on technical significance, novelty, originality, presentation, and broader implications of the publication. Awards made in a given year are for articles published in the previous calendar year. We are pleased to announce that the following contributions are being recognized for 2012 award year:"



Grafton, R. Q., M. B. Ward, H. To, and T. Kompas (2011), Determinants of residential water consumption: Evidence and analysis from a 10-country household survey, Water Resour. Res.47, W08537, doi:10.1029/2010WR009685.

Sun, F., M. L. Roderick, W. H. Lim, and G. D. Farquhar (2011), Hydroclimatic projections for the Murray-Darling Basin based on an ensemble derived from Intergovernmental Panel on Climate Change AR4 climate models, Water Resour. Res.47, W00G02, doi:10.1029/2010WR009829.

Wigmosta, M. S., A. M. Coleman, R. J. Skaggs, M. H. Huesemann, and L. J. Lane (2011), National microalgae biofuel production potential and resource demand, Water Resour. Res.47, W00H04, doi:10.1029/2010WR009966, [printed 48(3), 2012].

Amiaz, Y., S. Sorek, Y. Enzel, and O. Dahan (2011), Solute transport in the vadose zone and groundwater during flash floods, Water Resour. Res.47, W10513, doi:10.1029/2011WR010747.

Gallant, J. C. and M. F. Hutchinson (2011), A differential equation for specific catchment area, Water Resour. Res.47, W05535, doi:10.1029/2009WR008540.


Huss, M. (2011), Present and future contribution of glacier storage change to runoff from macroscale drainage basins in Europe,Water Resour. Res.47, W07511, doi:10.1029/2010WR010299