Water Programming Blog Guide (Part 2)

Water Programming Blog Guide (Part 1)

This second part of the blog guide will cover the following topics:

  1. Version control using git
  2. Generating maps and working with spatial data in python
  3. Reviews on synthetic streamflow and synthetic weather generation
  4. Conceptual posts

1. Version Control using git

If you are developing code it’s worth the time to gain familiarity with git to maintain reliable and stable development.  Git allows a group of people to work together developing large projects minimizing the chaos when multiple people are editing the same files.   It is also valuable for individual projects as it allows you to have multiple versions of a project, show the changes that you have made over time and undo those changes if necessary.  For a quick introduction to git terminology and functionality, check out  Getting Started: Git and GitHub. The Intro to git Part 1: Local version control and  Intro to git Part 2: Remote Repositories  posts will guide you through your first git project (local or remote) while providing a set of useful commands.  Other specialized tips can be found in: Git branch in bash prompt and GitHub Pages. And if you are wondering how to use git with pycharm, you’ll find these couple of posts useful: A Guide to Using Git in PyCharm – Part 1A Guide to Using Git in PyCharm – Part 2

2. Generating maps and working with spatial data in python

To learn more about python’s capabilities on this subject,  this  lecture  summarizes key python libraries relevant for spatial analysis.  Also,  Julie and the Jons have documented their efforts when working with spatial data and with python’s basemap, leaving us with some valuable examples:

Working with raster data

Python – Extract raster data value at a point

Python – Clip raster data with a shapefile

Using arcpy to calculate area-weighted averages of gridded spatial data over political units (Part 1) , (Part 2)

Generating maps

Making Watershed Maps in Python

Plotting geographic data from geojson files using Python

Generating map animations

Python makes the world go ’round

Making Movies of Time-Evolving Global Maps with Python

3. Reviews on synthetic streamflow and weather generation

We are lucky to have thorough reviews on synthetic weather and synthetic streamflow generation written by our experts Julie and Jon L.  The series on synthetic weather generation consists of five parts. Part I and Part II cover parametric and non-parametric methods, respectively. Part III covers multi-site generation.  Part IV discusses how to modify both parametric and non-parametric methods to simulate weather with climate change projections and finally Part V covers how to simulate weather with seasonal climate forecasts:

Synthetic Weather Generation: Part I , Part II , Part III , Part IV , Part V

The synthetic streamflow review provides a historical perspective while answering key questions on “Why do we care about synthetic streamflow generation?  “Why do we use it in water resources planning and management? and “What are the different methods available?

Synthetic streamflow generation

4.  Conceptual posts

Multi-objective evolutionary algorithms (MOEAs)

We frequently use multi-objective evolutionary algorithms due to their power and flexibility to solve multi-objective problems in water resources applications, so you’ll find sufficient documentation in the blog on basic concepts, applications and performance metrics:

MOEAs: Basic Concepts and Reading

You have a problem integrated into your MOEA, now what?

On constraints within MOEAs

MOEA Performance Metrics

Many Objective Robust Decision Making (MORDM) and Problem framing

The next post discusses the MORDM framework which combines many objective evolutionary optimization, robust decision making, and interactive visual analytics to frame and solve many objective problems under uncertainty.  This is a valuable reading along with the references within.  The second post listed provides a systematic way of thinking about problem formulation and defines the key components of a many-objective problem:

Many Objective Robust Decision Making (MORDM): Concepts and Methods

“The Problem” is the Problem Formulation! Definitions and Getting Started

Econometric analysis and handling multi-variate data

To close this second part of the blog guide, I leave you with a couple selected topics  from the Econometrics and Multivariate statistics courses at Cornell documented by Dave Gold:

A visual introduction to data compression through Principle Component Analysis

Dealing With Multicollinearity: A Brief Overview and Introduction to Tolerant Methods



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