Suggested usage of ChatGPT | Dr. Mauricio Zambrano-Bigiarini

How to use ChatGPT to learn Hydrology - Part I: configuration, context and useful prompts

Sun, 16 Aug 2026 00:00:00 +0000

Estimated reading and practice time: 15-25 minutes.

Why this series of tutorials?

Preparing university students today for their future professional practice is unusually challenging because we cannot know precisely what Artificial Intelligence systems will be able to do in a few years. The capabilities of generative models are evolving rapidly, barriers to access are falling, and the range of tasks that can be automated is changing. Teaching only how to operate the current version of ChatGPT therefore has limited long-term value: the tools will change, but engineers will still need to understand a problem, judge the quality of an answer, verify evidence, detect errors and make responsible decisions.

Recent discussion in higher education points in the same direction. AI can broaden access to explanations, feedback and personalised learning, but it can also foster dependence, superficial learning and reduced exercise of critical thinking when it replaces the student’s own cognitive effort ( ). The challenge is therefore not simply to adopt AI more quickly, but to decide what it should be used for and what kind of learning we want it to support ( ). Likewise, recent analyses argue that higher education should strengthen capacities such as critical thinking, adaptability, curiosity and lifelong learning ( ), and that many of the decisions posed by AI are fundamentally pedagogical before they are technological ( ).

For that reason, the purpose of this three-part series is not to teach you how to obtain answers more quickly. It is to help you use generative AI to understand Hydrology more deeply, practise more effectively, identify what you still do not understand, and develop the autonomy required to solve unfamiliar problems. In short:

Use AI to increase learning, not to avoid the process of learning.

ChatGPT can help you learn Hydrology, or it can help you avoid learning it. The difference depends mainly on how you use it.

In IIO422-Hydrology, obtaining the correct numerical answer is not enough. The course expects you to analyse hydrological processes, quantify variables, work probabilistically, interpret results, detect errors and justify engineering decisions. Generative AI is explicitly permitted as a learning and computational support tool outside assessments, but its use must be critical, verifiable, transparent and responsible.

1. The fundamental rule: tutor, not autopilot

A weak query would be:

Explain the water balance.

A much better one would be:

I am taking IIO422-Hydrology in Civil Engineering.

I understand precipitation and streamflow, but I still confuse
evapotranspiration with changes in catchment storage.

Help me interpret the annual water balance physically.
First ask me two diagnostic questions.
Then correct my misconceptions and only afterwards give
a concise explanation.

Use units of mm/year and do not assume Delta S = 0
unless you justify that approximation.

The difference is not cosmetic. The second prompt forces you to think before receiving the explanation.

2. Configure the context that actually matters

The course assumes previous knowledge of Fluid Mechanics, Hydraulics, Statistics and English. It also requires you to collect and analyse hydrometeorological information, construct simplified water balances, perform frequency analysis, and estimate design rainfall and streamflow.

In Settings > Personalization > Custom Instructions, you could write:

I am a Civil Engineering student in Chile taking IIO422-Hydrology.

I have already passed Fluid Mechanics, Hydraulics and Statistics.
I also know how to program in R.

When helping me:
- prioritise reasoning and physical interpretation;
- use SI units;
- check dimensional consistency;
- distinguish data, assumptions, method, result and uncertainty;
- use examples from Chilean catchments when useful;
- do not invent data, references, packages or functions;
- if important information is missing, ask before assuming;
- for R, avoid tidyverse and prioritise base R, hydroTSM
 and the tools specified in the course;
- when I am preparing for an assessment, do not provide
 the full solution before I have attempted the problem.

Do not include personal information that is irrelevant to learning.

3. Connect ChatGPT with the Learning Outcomes

The syllabus defines five Learning Outcomes. Use them as a test of whether an AI interaction is actually helping you learn:

LO1: conceptually analyse key hydrological-cycle processes and apply them to Chilean catchments.
LO2: quantify the temporal evolution of precipitation, evapotranspiration and runoff using in-situ and gridded data.
LO3: analyse hydrological variables probabilistically for engineering design and planning.
LO4: incorporate social responsibility into water-resources management problems.
LO5: develop personal and teamwork skills needed to meet agreed objectives and deadlines.

A useful ChatGPT session should strengthen at least one of these outcomes. If it merely produces text or code that you cannot explain, it probably strengthens none of them.

4. The anatomy of a good prompt

For most course tasks, five components are sufficient:

ROLE + CONTEXT + TASK + CONSTRAINTS + OUTPUT

Role

Act as a university Hydrology lecturer for Civil Engineering.

Context

I am studying infiltration and the Green-Ampt method.
I understand infiltration capacity, but not the role
of initial soil moisture.

Task

Help me compare the same storm over an initially dry soil
and an initially wet soil.

Constraints

Do not invent parameter values.
Use SI units.
Do not simplify the assumptions of Green-Ampt without stating them.

Output

Answer in five parts:
1. physical idea;
2. relevant variables;
3. equation;
4. example;
5. one question to test my understanding.

5. Make the model recognise missing information

A hydrological answer may depend on:

temporal scale;
catchment area;
data resolution;
hydrological year;
antecedent conditions;
rainfall- or snow-dominated regime;
regulation;
units;
availability and quality of observations.

Add:

Before solving the problem, identify whether essential information
is missing.

If it is, ask me no more than three specific questions.
Do not introduce silent assumptions.

6. Use the course learning sequence as a dialogue structure

The syllabus organises learning around:

conceptualisation
→ application
→ evidence analysis
→ perturbation of the problem
→ interpretation
→ defence of results

You can turn that sequence directly into a prompt:

I want to study [TOPIC].

Work with me in six stages:
1. conceptualisation;
2. application;
3. evidence;
4. change one assumption or input;
5. ask me to interpret what changes;
6. make me defend the conclusion.

This is more useful than requesting a long explanation.

7. Separate facts, assumptions, results and uncertainty

For quantitative problems:

Separate your response into:
- available data;
- assumptions;
- equations;
- calculation;
- result;
- physical interpretation;
- uncertainty;
- limitations.

This mirrors the course-wide assessment criteria: conceptual understanding, mathematical correctness, dimensional consistency, hydrological reasoning, physical interpretation, critical judgement and consideration of uncertainty.

8. Fluency does not imply truth

ChatGPT can produce polished but incorrect answers.

Watch especially for:

unit errors;
incorrect signs;
mixed temporal scales;
confusion between exceedance and non-exceedance;
methods used outside their assumptions;
fabricated data;
non-existent references;
non-existent R functions;
causal interpretations unsupported by evidence.

Try:

Audit your answer.

Look specifically for:
1. conceptual errors;
2. dimensional errors;
3. unstated assumptions;
4. physically implausible results;
5. omitted uncertainty;
6. references or functions you cannot verify.

9. AI is not a scientific authority

The syllabus is explicit: AI tools do not replace hydrological analysis and are not sources of scientific authority.

Verify important information using:

course material;
textbooks;
scientific papers;
official documentation;
R documentation;
original data;
independent calculations;
physical-consistency checks.

10. Declare AI use in practical assignments

Each practical assignment should include a brief declaration identifying at least:

tool used;
purpose;
component in which it was used;
verification procedure.

For example:

Tool: ChatGPT.
Purpose: support in debugging an R function for temporal aggregation.
Component: script 02_hydroclimate.R.
Verification: comparison with an independent calculation,
unit checks and consultation of the hydroTSM documentation.

“Used ChatGPT” is not an adequate declaration.

11. Study Mode and Socratic practice

Study Mode is useful for:

water balance;
Budyko;
infiltration;
hydrograph interpretation;
FDCs;
return period;
IDF curves;
rainfall-runoff relationships;
oral-assessment preparation.

For example:

I want to learn return period.

I do not want to memorise T = 1/p.
First diagnose my understanding.
Then give me an engineering-design situation.
Finally ask me about risk over the service life of the structure.

12. Three modes of use

Answer mode: “Calculate ET.”
Tutor mode: “Guide me to derive ET and check the units.”
Examiner mode: “Give me a problem, wait for my solution, then ask follow-up questions.”

For this course, prioritise tutor and examiner modes.

13. Free or Plus?

For IIO422, the Free plan is sufficient for most learning tasks.

Plus may become useful if you repeatedly need:

greater file capacity;
long debugging sessions;
higher tool availability;
longer or more complex analyses;
intensive use across several courses.

If your household budget is limited: start with Free and only pay for Plus after identifying a specific limitation that is genuinely affecting your work.

14. Five prompts worth saving

Concept

I want to learn [TOPIC].
First diagnose what I understand.
Then correct my gaps using an example from Chile.
Finish with a transfer question.

Problem solving

I will show you a problem and my attempt.
Do not solve it immediately.
Identify the first error, give me one hint, and wait.

Audit

Review the concept, equations, units, calculation,
interpretation, uncertainty and assumptions.

Oral practice

Give me a short oral assessment.
Ask one question at a time.
Use follow-up questions and correct me only at the end.

Error detection

Do not assume my method is correct.
Look for conceptual, numerical, methodological
and interpretative errors.

15. Final rule

Generative AI is permitted for learning activities and preparation of the practical assignments, but not during assessments. The syllabus also makes clear that being unable to explain a procedure included in a submission is evidence that the corresponding learning outcome has not been achieved.

The practical rule is therefore:

If you cannot explain, reproduce, modify and verify something that ChatGPT produced, you should not submit it as part of your work.

Sources

IIO422-Hydrology course syllabus, Universidad de La Frontera, 2026.
Baron, M. (2026). . The Koala News.
Tobor, J., & Sommer, T. (2026). . Hochschulforum Digitalisierung.
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343.
Woon, A. (2025). . Higher Education Policy Institute.

Currency note: ChatGPT features, limits and prices change. Always check the official documentation.

How to use ChatGPT to learn Hydrology - Part II: theory, reasoning and assessment preparation

Sun, 16 Aug 2026 00:00:00 +0000

Estimated reading and practice time: 20-25 minutes.

In IIO422, theoretical assessment prioritises conceptual understanding and reasoning over memorisation or mechanical calculation. Questions may include conceptual problems, numerical exercises, interpretation of graphs and tables, analysis of hydrological situations, error diagnosis, comparison of methods, dimensional analysis, physical interpretation and integrative problems based on real catchments.

Your study strategy should reproduce those demands.

1. Recommended learning cycle

For each topic, use:

diagnosis
→ focused explanation
→ practice
→ correction
→ transfer
→ oral explanation

For example:

I want to study evapotranspiration.

First ask me five diagnostic questions.
Do not explain the topic until I answer.

Then identify my two main conceptual gaps,
correct them and give me two new problems.

Finish with a 90-second oral question.

2. Explicit map of the theoretical content

2.1. Introduction and basic concepts

You should understand:

definition and scope of Hydrology;
the hydrological cycle;
the general water-balance equation;
the concept of a catchment;
integrated water resources management;
spatial and temporal variability of hydrological processes.

Definition and scope

Make me explain what Hydrology studies and how it differs
from Meteorology, Hydraulics and Hydrogeology.

Do not accept a one-sentence definition.
Ask for:
1. object of study;
2. scales;
3. processes;
4. engineering applications;
5. one Chilean example.

Hydrological cycle and water balance

Treat a catchment as a control volume.

Make me identify inputs, outputs and storage changes.
Then change the temporal scale from daily to annual
and ask which terms may become small and which need not.

In simplified form:

Inputs - Outputs = Change in storage

or, for a catchment:

P - Q - ET = Delta S

The objective is not to memorise the equation, but to understand what it includes and when the simplification is defensible.

Catchments and integrated water resources management

Compare a river catchment with an administrative boundary.

Ask why a catchment is useful for hydrological analysis
and why integrated water resources management must also consider
users, institutions, infrastructure and ecosystems.

Spatial and temporal variability

Use precipitation, soil moisture and streamflow.

Make me compare their expected spatial and temporal variability
in an Andean catchment and explain why they cannot all be observed
with the same network or resolution.

2.2. Water in the atmosphere

The syllabus includes:

basic climatology and atmospheric circulation;
solar radiation;
air temperature;
humidity and water vapour;
precipitation;
interception;
evaporation;
evapotranspiration;
spatial interpolation of hydrological variables;
the Budyko framework.

Climatology and atmospheric circulation

Relate atmospheric circulation, topography and precipitation
across Chile.

Make me compare a northern catchment, a Mediterranean catchment
and a southern catchment before giving the explanation.

Radiation, temperature, humidity and water vapour

Build a causal chain:
radiation → temperature → vapour pressure → evaporative demand.

Make me distinguish direct relationships from those
that depend on other variables.

Precipitation

Give me an hourly storm.

Make me distinguish:
- intensity;
- accumulated depth;
- duration;
- temporal resolution;
- antecedent dry period.

Then change only the temporal resolution and ask what information is lost.

Interception

Compare two events with the same total rainfall:
one over a forested catchment and one over sparse vegetation.

Make me reason when interception can be hydrologically important
and when its relative effect becomes smaller.

Evaporation and evapotranspiration

Make me distinguish evaporation, transpiration,
potential ET and actual ET.

Then give one water-limited and one energy-limited situation.

Interpolation

Compare Thiessen polygons, inverse-distance weighting
and kriging for catchment rainfall estimation.

Do not ask which is 'best'.
Give me a station network and make me choose according to
network density, topography, objective, variability and data availability.

Budyko framework

Make me interpret a Budyko diagram.

Ask:
- what the axes represent;
- what water-limited and energy-limited mean;
- what information is required;
- why a deviation does not automatically imply an error.

2.3. Water in the soil

The syllabus includes:

flow through porous media;
infiltration;
Green-Ampt;
land use and soil types.

Flow through porous media

Make me explain why water can move through unsaturated soil
without reducing the explanation to 'gravity pulls it down'.

Require discussion of gradients and hydraulic properties.

Infiltration

Compare the same storm over an initially dry and an initially wet soil.

Ask me how infiltration, storage, runoff generation
and streamflow response are expected to change.

Green-Ampt

Do not give me the equation first.

Help me reconstruct Green-Ampt from its assumptions:
a distinct wetting front, changes in water content
and capillary forces.

Then show the equation and make me perform dimensional analysis.

Land use and soil types

Compare forest, agricultural land and an urbanised area.

Make me separate the effects of:
- texture;
- structure;
- compaction;
- vegetation cover;
- impervious surfaces.

Do not let me attribute everything to 'soil type'.

2.4. Surface runoff

You should understand:

hydrological information;
streamflow measurement;
hydrographs;
flow-duration curves (FDCs);
effective rainfall and direct runoff;
rainfall-runoff relationships.

Streamflow measurement

Make me distinguish stage measurement, gauging
and streamflow estimation from a rating curve.

Then give me a flood beyond the gauged range
and ask what uncertainty is introduced.

Hydrographs

Show me a hydrograph and make me identify:
baseflow, rising limb, peak, lag, recession and direct-runoff volume.

Do not accept purely geometric descriptions:
require interpretation of processes.

FDCs

Make me interpret Q10, Q50 and Q90.
Include a trap involving exceedance and non-exceedance probability.

Effective rainfall and direct runoff

Give me a storm and hydrograph.
Make me distinguish total rainfall, abstractions,
effective rainfall and direct runoff.

2.5. Simplified hydrological models for annual water balance

The syllabus includes simplified hydrological modelling for catchment-scale annual water balance.

Give me a catchment with P, E and Q time series.

Make me decide what can be estimated directly,
what requires modelling, and which storage components
remain poorly observed.

Do not treat the model as a black box. Ask which assumptions about scale and mass balance are embedded in the model.

2.6. Probabilistic and statistical methods

Conceptually, you should understand:

exploratory data analysis and descriptive statistics;
probabilistic treatment of hydrological information;
frequency and probability functions;
return period;
statistical parameters;
fitting probability distributions;
probability distributions of hydrological variables.

Make me explain F(x), 1-F(x), exceedance probability,
quantiles and return period using annual maxima.

Do not use equations until the interpretation is clear.

2.7. Rainfall intensities and design

The syllabus includes:

maximum 24-hour precipitation;
maximum 1-, 2- and 3-day precipitation;
Intensity-Duration-Frequency (IDF) curves;
design hyetographs.

Give me an IDF table.

Make me:
1. select duration and T;
2. convert intensity to depth;
3. explain why intensity and duration are not interchangeable;
4. construct a conceptual design hyetograph;
5. identify one extrapolation limitation.

2.8. Unit hydrographs, design floods and HEC-HMS

The syllabus includes:

unit and synthetic hydrographs;
design-flood calculation using SCS-CN;
introduction to HEC-HMS.

Make me explain the linearity and superposition assumptions
of the unit hydrograph.

Then give me a situation where those assumptions are weak.

For SCS-CN:

Do not give me only the calculation.
Make me interpret how antecedent condition, land use
and CN affect effective rainfall and runoff.

For HEC-HMS:

Explain what each main component of an event-based HEC-HMS model represents
and what information I should check before accepting a simulation.

3. How theory is assessed

Theory = 0.15 × Tests
 + 0.30 × Test 1
 + 0.40 × Test 2
 + 0.15 × Individual Oral Assessment

Individual Tests - 15%

Purpose: progressive understanding and continuous study.

Generate a five-question micro-test:
- two conceptual;
- one on units;
- one interpretation;
- one error-diagnosis question.

Ask one at a time.

Test 1 - 30%

Focuses mainly on:

hydrological processes;
catchment characterisation;
annual water balance.

Create a Chilean catchment problem combining
P, ET, Q, storage and physical catchment characteristics.

Assess formulation, units, calculation, interpretation and uncertainty.

Test 2 - 40%

Integrates:

frequency analysis;
extreme events;
design rainfall and streamflow;
rainfall-runoff relationships;
hydrological modelling.

Create an integrative problem.

It must force me to combine at least three parts of the course
and justify an engineering decision.

Individual Oral Assessment - 15%

There are two brief individual oral assessments, one after each major test.

Simulate a 6-minute individual oral assessment.

Ask one question at a time.
Require me to:
- explain concepts in my own words;
- relate processes;
- interpret a result;
- justify a decision;
- identify a limitation;
- answer follow-up questions.

Correct me only at the end.

4. Practise the same task types used by the course

Conceptual questions

Make me explain why a 100-year return period
does not mean 'once every 100 years'.

Numerical problems

Assess separately:
formulation, equations, units, calculation and interpretation.

Graph and table interpretation

Practise with:

time series;
climographs;
hydrographs and hyetographs;
FDCs;
hydroclimatic maps;
hypsometric curves;
Budyko diagrams;
empirical and theoretical distributions;
frequency curves;
IDF curves;
hydrological-model results.

Error diagnosis

Construct a solution containing exactly three plausible errors:
one conceptual, one dimensional and one methodological.
Do not identify them.

Method comparison

Make me choose only after defining:
objective, data, scale, assumptions, uncertainty and limitations.

Dimensional analysis

Generate expressions involving P, Q, area, volume,
time and intensity.
Make me demonstrate which are dimensionally consistent.

Physical interpretation

After every calculation, ask:

Is the sign plausible?
Is the order of magnitude plausible?
Which process controls this result?
What additional observation would increase confidence?

5. Use ChatGPT to prepare peer discussion

Give me a conceptual multiple-choice question with five options
and exactly one correct answer.

Do not identify the answer.
I will answer first.
Then give me two plausible arguments that another student
might use to defend a different option.
Finally make me answer again.

6. The perturbation technique

Once a problem is solved, change only one condition:

Now double the antecedent soil moisture.
Do not recalculate immediately.
First ask what I expect to change and why.

or:

Keep annual P fixed but change its seasonal distribution.
Which conclusions from the annual water balance still hold?

This reproduces the course sequence: application → perturbation → interpretation → defence.

7. A 25-minute study routine

0-5 min: retrieval without notes.
5-12 min: diagnosis and correction.
12-20 min: new problems and perturbations.
20-25 min: oral explanation and follow-up questions.

Key ideas

Study in the same way you will be assessed.
Connect processes rather than memorising chapters.
Include Budyko, IDF, design hyetographs, SCS-CN and HEC-HMS in your conceptual map.
Practise with real data and figures from Chilean catchments.
Require dimensional consistency, interpretation and uncertainty.
Train explicitly in error diagnosis.
Practise oral explanations throughout the semester.
AI may support learning, but it is not permitted during assessments.

Sources

IIO422-Hydrology course syllabus, Universidad de La Frontera, 2026.

How to use ChatGPT to learn Hydrology - Part III: vibe coding, hydroTSM and reproducible practical projects

Sun, 16 Aug 2026 00:00:00 +0000

Estimated reading and practice time: 20-25 minutes.

This tutorial starts from an explicit assumption: you already know how to program in R. The objective is therefore not to review vectors, data.frame objects, functions or loops, but to learn how to use AI to accelerate hydrological analysis while retaining technical control over data, methods, code, results and interpretation.

In IIO422, practical work must be reproducible and every student must be able to understand and modify the procedures used.

We will therefore use a controlled vibe-coding approach.

1. What vibe coding means in this course

It does not mean:

Do the practical assignment in R.

It means working iteratively:

hydrological question
→ specification
→ proposed code
→ execution and testing
→ audit
→ interpretation
→ modification

AI acts as a copilot. You decide what problem should be solved, whether the data are valid, which method is appropriate and whether the result is hydrologically plausible.

2. The practical component is one final project divided into TP-01 and TP-02

For assessment:

Practical = 0.45 × TP-01 + 0.55 × TP-02

Each TP combines:

35% Reproducible Hydrological Project - team
15% Technical Evidence Dossier - team
30% Hydrological Defence - individual
20% Supervised Practical Challenge - individual

The implication is direct: a script produced by ChatGPT may improve the team submission while simultaneously reducing your individual mark if you cannot explain it.

3. Map of practical competences

The practical work combines:

hydrometeorological time-series analysis;
catchment characterisation;
water balance;
Budyko;
probabilistic methods;
extreme events;
hydrological design;
hydrological simulation;
reproducibility;
interpretation for water management and infrastructure safety.

4. TP-01: hydrological processes and annual water balance

The syllabus specifies that TP-01 includes, among other elements:

physical and hydroclimatic catchment characterisation;
catchment delineation and geomorphological analysis;
collection and basic quality control of hydrometeorological information;
analysis of precipitation, temperature, evapotranspiration and streamflow;
water-balance estimation;
application of the Budyko framework;
analysis of water supply and demand;
simplified hydrological modelling;
interpretation from a water-resources management perspective.

Starting prompt for TP-01

Act as a hydrological reviewer for TP-01.

Do not write code yet.

Help me turn the overall objective into verifiable questions:
1. What are the physical characteristics of the catchment?
2. What is its hydroclimatic regime?
3. What data do I need?
4. How will I check data quality?
5. How will I close the water balance?
6. How will I use Budyko?
7. What can I conclude about water supply and demand?
8. What uncertainty must I report?

5. Use base R + hydroTSM for time series

hydroTSM is appropriate for manipulating, aggregating, analysing and visualising hydrological time series.

library(zoo)
library(hydroTSM)

Potentially useful functions include:

hydroTSM::daily2monthly()
hydroTSM::daily2annual()
hydroTSM::monthlyfunction()
hydroTSM::annualfunction()
hydroTSM::fdc()
hydroTSM::hydroplot()
hydroTSM::plot_pq()
hydroTSM::climograph()

Do not use a function simply because ChatGPT suggested it.

Verify that the function exists.

Explain:
- input;
- output;
- units;
- handling of NA;
- temporal assumptions;
- a reasonable base-R alternative.

6. Initial data audit

Before analysis:

I have daily P, T, ET and Q series.

Design an audit covering:
- temporal coverage;
- regularity;
- NA;
- duplicates;
- units;
- physically impossible values;
- changes in station or source;
- temporal consistency among variables.

The quality of the analysis depends on this stage.

7. Temporal aggregation: think before coding

P and Q do not necessarily use the same aggregation operator.

I have:
daily P in mm/day
daily mean Q in m3/s
catchment area in km2

Before writing code, make me derive how to obtain:
- monthly P;
- monthly mean Q;
- monthly volume;
- monthly equivalent runoff depth in mm.

You should be able to reconstruct the conversion dimensionally.

8. Budyko and annual water balance

TP-01 explicitly requires the Budyko framework.

I have annual P, ET/PET and Q.

Help me build a Budyko diagnostic.

Before coding:
1. check units;
2. define the dimensionless indices;
3. ask what each axis means;
4. identify potentially anomalous years;
5. separate physical interpretation from possible data error.

9. Simplified hydrological modelling and airGRteaching

The syllabus includes simplified models for annual water-balance analysis and lists airGRteaching as a course resource.

A useful introduction is to work with a daily model and then analyse the outputs at annual scale.

A typical workflow is:

PREP <- airGRteaching::PrepGR(
 ObsDF = BasinObs,
 HydroModel = "GR4J",
 CemaNeige = FALSE
)

CAL <- airGRteaching::CalGR(
 PrepGR = PREP,
 CalCrit = "KGE2",
 CalPer = c("1990-01-01", "1994-12-31")
)

SIM <- airGRteaching::SimGR(
 PrepGR = PREP,
 Param = CAL,
 EffCrit = "KGE2",
 SimPer = c("1995-01-01", "1999-12-31")
)

Do not memorise these calls. You should understand:

input data;
units;
warm-up period;
calibration;
simulation;
efficiency criterion;
parameters;
water balance;
the difference between statistical fit and hydrological realism.

Controlled vibe coding with airGRteaching

I am using GR4J with airGRteaching.

Work step by step:
1. review format and units;
2. check periods;
3. generate only PrepGR;
4. wait for me to execute it;
5. interpret the output;
6. only then generate CalGR;
7. make me explain the criterion;
8. continue with SimGR;
9. finish with a defence question.

10. TP-02: frequency analysis and hydrological extremes

The syllabus states that TP-02 includes:

exploratory analysis of maximum-value series;
selection and fitting of probability distributions;
parameter estimation;
goodness-of-fit analysis;
estimation of rainfall and streamflow for different return periods;
analysis of record-length effects;
uncertainty evaluation;
simulation of a flood event with HEC-HMS;
comparison of observed and simulated streamflow;
implications for design and infrastructure safety.

Use this list directly as your work plan.

11. Exploratory analysis and descriptive statistics

Before fitting a distribution:

Do not fit anything yet.

Design an EDA for annual maxima:
- coverage;
- outliers;
- visual trend;
- descriptive parameters;
- skewness;
- independence;
- sample size;
- recommended plots.

Do not use a fitted distribution to conceal a poorly reviewed series.

12. Probabilistic treatment

You should be able to explain:

sample;
random variable;
distribution function;
exceedance;
non-exceedance;
quantiles;
return period;
statistical parameters.

Make me explain F(x), 1-F(x), T and a quantile
without equations first.

Then translate the reasoning into R.

13. Fitting probability distributions

Do not ask:

Which distribution is best?

Ask:

I have annual maxima.

Make me define:
- candidate distributions;
- estimation method;
- support;
- graphical diagnostics;
- goodness of fit;
- tail behaviour;
- extrapolation uncertainty.

Then request code in small blocks.

14. Effect of record length

This is an explicit component of TP-02.

I have 40 years of annual maxima.

Design an experiment comparing estimates from 15, 25 and 40 years.

Before coding:
- define what remains fixed;
- define what metric will be compared;
- decide how uncertainty will be shown;
- identify what conclusions cannot be generalised.

15. Return period and risk

I want to estimate a T-year event.

Before using a q*() function:
make me derive the correct non-exceedance probability
for the quantile function I am using.

For risk:

Make me derive the probability of at least one exceedance
during n years of service life.

Do not give me the formula initially.

16. Intensities, IDF curves and design hyetographs

I have an IDF table.

Help me construct a design hyetograph.

Before coding:
1. define T;
2. define duration;
3. define time step;
4. choose a temporal pattern;
5. check conservation of total rainfall depth;
6. explain which assumption controls the temporal distribution.

17. SCS-CN

Let us implement SCS-CN.

First define:
- P;
- CN;
- potential maximum retention;
- initial abstraction;
- effective rainfall.

Then generate a simple R function.
Include unit checks and limiting cases.

If the code runs but you cannot explain what changes when CN changes, you do not yet understand the method.

18. HEC-HMS flood simulation

TP-02 explicitly includes a flood simulation using HEC-HMS.

ChatGPT can help you:

review model structure;
interpret parameters;
document scenarios;
diagnose results;
compare observed and simulated streamflow.

But it should not invent parameter values.

I am configuring HEC-HMS.

Do not propose parameter values yet.

Make me review:
- catchment;
- loss method;
- transform method;
- baseflow;
- routing, where relevant;
- design storm;
- time step;
- initial conditions;
- simulation period.

19. Compare observed and simulated flow critically

Do not reduce evaluation to a single metric.

I have Qobs and Qsim.

Design an evaluation including:
- hydrograph;
- peak;
- peak timing;
- volume;
- bias;
- one global performance metric;
- an error that matters for infrastructure safety.

20. Error diagnosis: a core competence

The course deliberately incorporates incorrect results, plots, code or methods.

Practise like this:

Generate an R block for frequency analysis
with exactly two plausible errors.

Do not identify them.

I must:
1. detect them;
2. explain the hydrological consequence;
3. correct them;
4. compare the result.

21. Reproducibility

A suggested structure is:

TP/
├── README.md
├── data/
│ ├── raw/
│ └── processed/
├── R/
│ ├── 01_qc.R
│ ├── 02_tp01_hydroclimate.R
│ ├── 03_budyko_balance.R
│ ├── 04_frequency.R
│ ├── 05_design_event.R
│ └── 06_outputs.R
├── models/
├── output/
│ ├── figures/
│ └── tables/
└── evidence/

The team should provide data, metadata, scripts, parameters, configuration files and everything else required to regenerate the results.

22. Use ChatGPT as a reproducibility auditor

Act as a reproducibility auditor.

Look for:
- absolute paths;
- undeclared dependencies;
- manually created objects;
- scripts run out of order;
- hidden parameters;
- inconsistent units;
- non-reproducible outputs;
- non-existent functions;
- undocumented steps.

23. Technical Evidence Dossier

The dossier replaces the traditional long written report.

A useful evidence chain is:

question
→ method
→ result
→ evidence
→ interpretation
→ uncertainty
→ limitation

Do not fill space with generic definitions.

24. Prepare for the Hydrological Defence

I will give you the complete project.

Ask random questions about:
- catchment delineation;
- data;
- QC;
- water balance;
- Budyko;
- hydroTSM;
- frequency analysis;
- distributions;
- T;
- uncertainty;
- IDF;
- SCS-CN;
- HEC-HMS;
- modelling;
- results;
- limitations.

Ask one question at a time.

Every team member should understand the complete project.

25. Prepare for the Supervised Practical Challenge

The individual challenge lasts approximately 30-45 minutes and has three parts:

conceptual diagnosis;
technical solution;
hydrological interpretation.

Generate a 35-minute individual challenge.

Use a small dataset.
Include:
A. conceptual diagnosis;
B. an R task;
C. hydrological interpretation.

Insert one plausible error.
Do not use tidyverse.

26. The modification rule

After any AI-generated code:

Now make me modify it for:
- another period;
- another unit;
- another treatment of NA;
- another T;
- another distribution;
- another hydrological assumption.

If you cannot modify it, you are not ready to defend it.

27. Declare AI use

Each TP should identify:

tool;
purpose;
component;
verification.

For example:

Tool: ChatGPT.
Purpose: debugging a function that converts Q to mm.
Component: 03_budyko_balance.R.
Verification: independent dimensional derivation,
known-value test case and comparison with a manual calculation.

28. Master prompt for the practical project

Act as a tutor in R programming and Hydrology.

I already know R.
Do not use tidyverse.
Prefer base R and hydroTSM.
Where relevant, consider airGRteaching and HEC-HMS.

Do not be the author of the project.

Rules:
1. start with the hydrological question;
2. separate data, method and assumptions;
3. provide code in small blocks;
4. do not invent data or functions;
5. require units;
6. include checks;
7. make me execute before continuing;
8. require interpretation;
9. make me modify the code;
10. finish with a defence question.

29. Final competence

By the end of the course, you should be able to:

audit hydrometeorological series;
characterise a catchment;
analyse P, T, ET and Q;
quantify a water balance;
interpret Budyko;
analyse water supply and demand;
use a simplified hydrological model;
construct and interpret an FDC;
perform frequency analysis;
fit and compare probability distributions;
estimate design events;
evaluate uncertainty and record-length effects;
construct design storms;
apply SCS-CN;
configure and review a simulation in HEC-HMS;
compare observed and simulated streamflow;
reproduce the full workflow;
defend the decisions made.

If ChatGPT helps you develop these capacities, vibe coding is functioning as assisted learning.

If it merely produces a script that “runs”, it is hiding what you still do not know.

Sources and documentation

IIO422-Hydrology course syllabus, Universidad de La Frontera, 2026.

Academic integrity: AI is permitted as support for learning and practical-project preparation, but not during assessments. Any AI-generated content remains the student’s responsibility and must be verifiable, reproducible and defensible.