Graphing a Water Balance — a Python Port of Tony Ladson's R Method
A Python port of Tony Ladson's waterfall-chart method for visualising an urban catchment water balance — plus a check the original R script doesn't run: does...
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Reproducible Python workflows for Australian flood hydrology, grounded in ARR 2019 and calibrated against established Australian methods. Each post links to a companion Jupyter notebook you can run on your own data.
Why Rainbow Colour Scales Mislead — a Python Follow-up to Tony Ladson's Post
'Don't use rainbow colourmaps' is good advice usually delivered as an assertion. This is the same advice with the receipts — a measurable reason a jet colour...
Read MorePython Packages for Hydrology — a Companion to Tony Ladson's R Roundup
Tony Ladson's 2017 roundup of R packages for hydrology is a genuinely useful map of that ecosystem. This is the Python-side equivalent — independently resear...
Read MoreStochastic Rainfall Generation with pyraingen: A Practitioner's Evaluation
pyraingen promises stochastic daily/subdaily rainfall generation with IFD constraining — a genuinely useful way to explore rainfall behaviour beyond a single...
Read MoreFitting a Probability Model to POT Data — a Python Port of Tony Ladson's R Method
Tony Ladson's R blog has quietly taught a lot of Australian hydrologists how to actually do this stuff in code. This is a validated Python port of his method...
Read MoreFitting Non-linear Models in Python: Confidence vs. Prediction Intervals
R's nls() doesn't give you confidence or prediction intervals the way lm() does for linear models — you have to do the propagation yourself, or reach for ano...
Read MoreWas That Flood an Outlier? A Python Diagnostic for Non-Stationarity in Flood Frequency Analysis
'Unprecedented' gets used loosely after a big flood. Fitting a stationary distribution to the historical record, running a trend test, and checking what AEP ...
Read MoreBetter Line Graphs for Hydrologic Data — a Python Port of Tony Ladson's R Method
A sequential colour palette is the wrong tool for distinguishing unrelated categories — it implies a ranking that isn't there and gives the least contrast ex...
Read MoreScreening ARR 2019 Temporal Patterns for Embedded Burst Errors
Running 10 ARR temporal patterns is standard practice. Some contain a specific, recognisable error pattern that produces physically unrealistic flood peaks. ...
Read MoreAreal Reduction Factors in Python — a Port of Tony Ladson's ARR 2019 Method
An earlier attempt at this post stalled on a missing long-duration equation, 10 regional coefficient sets, and an interpolation rule that isn't obvious from ...
Read MoreSampling ARR 2019 Loss Distributions in Python — A URBS Pre-Processor
ARR 2019 recommends Monte Carlo simulation treating initial loss as a random variable. Most practitioners still use fixed median values because the sampling ...
Read MoreIs Nash-Sutcliffe Efficiency Enough? A Python Comparison of Calibration Metrics for Australian Flood Models
Automated calibration routinely produces high NSE values on models that are physically unrealistic. This post implements NSE, KGE, PBIAS, and peak flow bias ...
Read MoreBaseflow Separation Using the Lyne-Hollick Filter — A Python Implementation
The standard Australian baseflow separation method exists only in R. This post provides a validated Python translation, applied to the Ladson reference examp...
Read MoreThe series
Five starter posts covering the Python tools that Australian flood hydrology practitioners need but do not yet have in one place:
- Baseflow Separation — Lyne-Hollick filter, Python translation of Ladson et al. (2013)
- ARR Temporal Pattern QA — screen for embedded burst errors before ensemble runs
- ARF Calculator — ARR 2019 Book 2 equations, all 10 regions, importable module
- Monte Carlo Loss Sampling — ARR 2019 IL/CL distributions, URBS pre-processor output
- Model Performance Metrics — NSE, KGE, PBIAS, and peak flow bias compared