Monday, March 18, 2019

We are going to  change GEOtop snow, we are struggling with the change since three years but beginning is always difficult. Today we are presenting some of the road we did and are goig to take.  At the fifth intercomparison meeting on SWE (modelling, measurements, remote sensing).
Get the presentation by clicking on the Figure above. The title is in Italian (Elements for the development of a new snow model for GEOtop 4.0), but the contents in English.

Tuesday, March 12, 2019

How beatiful is this stuff on matrixes and graphs ?

Yes I know that graphs are represented by incidence and adiacency matrixes. However I never realize how a matrix can be represented by bipartite (or multipartite graphs). My attention was brought to it by the Math3ma blog (by Tai Danae Bradley) which I follow with delight (I am not saying that I am understanding all I read there). In particular this blog post entitles "Viewing matrices and probability as graphs".

The figure above comes from that blog and explain the concept that a matrix can be represented by a bipartite graph. If you understand it, then you can click on the Figure and continue your reading at the original blog.
After that, I noticed that the matrix representation is actually quite concise. Is it the minimal (using less numbers, excluding indexes) representation of the graph ? And, viceversa, given a graph, can we partitions its nodes in  sets such that any node in a group is connected with nodes in the other groups, but not with those in the same group ? If we are able to do so, we can subsequently build the matrix representation of the graph, reverting the process used in figure. If our set of nodes in the graph is tripartite, then the resulting matrix will be three-dimensional and so on.

In 1D (on the line) the partition is obvious and  is a bi-partition.  In 2D, the problem seems to be necessary a qudri-partition, at least for those graphs that are grids (cw-complexes): in fact the problem is the same of that brought to the four color problem. What happens in 3D ?

Sunday, March 10, 2019

If you want to study the Critical Zone of hillslopes, start from here

Recently a paper by Fan et al,  Hillslope Hydrology in Global Change Research and Earth System Modeling,  was published on  Water Resources Research, 85(3), 319–36. At the beginning I was thinking: "Hey, here it is another of those review papers which do not add anyhing to the existing literature".  This is not actually the case. The paper  is a very good introduction to many issues related to the Critical zone and its modelling and a source of relevant literature, of which I give an excerpt below.  The paper is open access and therefore you do not need any subscription to get it.


Tuesday, February 26, 2019

Recent advances in big data machine learning in Hydrology

Chaopeng Shen (GS) of Penn State is organizing a series of Cyberseminars for CUASHI about Machine Learning in Hydrology.

Recently big data machine learning has led to substantial changes across many areas of study. In Hydrology, the introduction of big data and machine learning methods have substantially improved our ability to address existing challenges and encouraged novel perspectives and new applications. These advances present new opportunities methods that aid scientific discovery, data discovery, and predictive modeling. This series cover new techniques and findings that have emerged in Hydrology during the previous year, with a focus on catchment and land surface hydrology.

The announcement on the CUASHI site is here.

All talks take place on Fridays at 1:00 p.m. ET: Registration is free! You must register for the series in order to attend. To register, click here.

This is the foreseen schedule:
  • March 29, 2019: Machine Learning & Information Theory for Land Model Benchmarking & Process Diagnostics | Grey Nearing, University of Alabama
  • April 5, 2019: Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling | Frederik Kratzert, Johannes Kepler University
  • April 12, 2019: Use deep convolutional neural nets to learn patterns of mismatch between a land surface model and GRACE satellite | Alex Sun, University of Texas at Austin
  • April 19, 2019: Long-term projections of soil moisture using deep learning and SMAP data with aleatoric and epistemic uncertainty estimates | Chaopeng Shen, Pennsylvania State University
  • April 26, 2019: Exploring deep neural networks to retrieve rain and snow in high latitudes using multi-sensor and reanalysis data | Guoqiang Tang, Tsinghua University
  • May 3, 2019: Multioutput neural networks for estimating flow-duration curves in ungaged catchments | Scott Worland, Cornell University and USGS
  • May 10, 2019: Remote sensing precipitation using artificial neural networks and machine learning methods | Kuolin Hsu, University of California, Irvine

Thursday, February 21, 2019

Warredoc Winter School - Hydrology on data rich Hydrology

Last month, Fernando Nardi of the Università per stranieri (University for Foreigners) in Perugia and Warredoc organised a nice and successful Winter School on Data Rich Hydrology.
Many colleagues participated and gave very nice presentations. Here below I am reproducing verbatim the content of the School's pages with links to the pdf of the lectures.


Lunedì 28 Gennaio

Rafael L. Bras, The Era of Data Rich Hydrology
Stefan Uhlenbrook, The WWDR and SDG 6 Synthesis Report

Sessione pomeridiana
Aldo Fiori, Groundwater hydrology and hydrological process mechanics
Marco Marani, Beyond traditional extreme value theory: lessons learned from rainfall and hurricane intensity
Maria Cristina Rulli, The water-food-energy nexus

Martedì 29 Gennaio
Sessione mattutina
Rafael L. Bras, The Era of Data Rich Hydrology
Stefan Uhlenbrook, The WWDR and SDG 6 Synthesis Report
Fabio Castelli, Remote sensing and data assimilation in hydrology

Sessione pomeridiana
Roberto Deidda, Modelling scaling properties of precipitation fields
Salvatore Grimaldi, Hydrologic measurements and novel observation technologies
— Dinner & Social event —

Mercoledì 30 Gennaio
Sessione mattutina
Salvatore Manfreda, Drones in Hydrology (lecture & hands on)
Elena Volpi, Hydrological risk assessment: Return period and probability of failure

Sessione pomeridiana
Andrea Libertino, Advances in the space-time analysis of rainfall extremes
Riccardo Rigon, Hydrologic modelling in a data rich world

Giovedì 31 Gennaio
Sessione mattutina
Daniele Ganora, Data poor vs. data rich cases for flood hazard (lecture & hands on)
Gabriele Freni, Distributed Data quality and urban flood modelling uncertainty

Sessione pomeridiana
Fernando Nardi, Citizen science and big data in hydrology

Venerdì 1 Febbraio
Sessione mattutina
Tommaso Moramarco, Stream flow measurements: ground and satellite observations
Alessio Domeneghetti, Remote sensing data and tools to foster inland water monitoring and flood modeling

Tuesday, February 19, 2019

Ph.D. Miscellanea - Jupyter Notebook with R or Python on Statistics and Hydrology

This blog post is to share some of the notebooks provided by my Ph.D. students on the topics they follow in their Ph.D. classes. Please observe that some of the material are lecture notes by some of my colleagues. You can use them but you should cite the source when you do it.

Sunday, February 10, 2019

My Hydraulic Construction Class 2019

The new class  is obviously based on the one of previous classes:
There are some changes however, partially due to the different calendar. The course is essentially divided in three parts:
  • Rainfall analysis and statistics
  • How to design a storm water management system (SWMS)
  • How to design an aqueduct


Rainfall analysis and statistics is essential to the design of the storm water system and requires some use of Python in Jupyterlab. Design of the SWMS requires the use of some Python, QGIS 2.18, GISWATER and SWMM softwares. The aqueducts require the use of QGIS and its plugin QEPAnet that implements the tool for estimating pressure water called EPANET.  All these tools are open source.

More specifically:
  • Python - Python is a modern programming languages. It will be used for data treatment, estimation of the idf curves of precipitation, some hydraulic calculation and data visualisation. I will use Python mostly as a scripting language to bind and using existing tools. 
  • QGIS is a Geographic Information System. GIS are an essential tool for who works on landscape or planning infrastructures extended on the territory. 
  • SWMM - Is an acronym for Storm Water Management System. Essentially it is a model for the estimation of runoff adjusted to Urban environment. I do not endorse very much its hydrology. However, it is the most used tools by colleagues who cares about storm water management, and I adopt it. It is not a tool for designing storm water networks, and therefore, some more work should be done with Python to fill the gaps.
  • EPANET Is the tool developed by EPA to estimate water distribution networks. 
Installation Instructions (for Windows) by Daniele Della Torre:




As you can infer from the previous lines, the class needs to learn some hydrology, some hydraulics and the use of various softwares. As I try to explain in the Syllabus lesson, the first day, there is no space for exploiting all the possibilities implied by the software, nor even to go very deep in the theory of hydrological processes and even in the design of the systems. The student has to become comfortable with the idea that they (singular they) is going to get an introduction to all these topics and they will need further studies to use professionally the received information.  

Foreseen Schedule

T -  stands for a mostly theoretical class
L -  stands for a class in the lab

Precipitation analysis and statistics (and an intro to Python scripting)

2019-02-25 -


Storm Water Management System Design

2019-03-18 -  T - Introduction to Storm Water Management System Design
2019-03-21 T - Estimation of the flood wave 
2019-04-04 - First report due date

  • Intro and installation of SWMM
2019-04-11 L- 
  • Play around with SWMM
2019-04-15 L - 
  • Working on the  project

  • Intermediate Exam


  • Work in progress