---
title: "Introduction to ET0models (Temperature-Based)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to ET0models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```

## Overview

The `ET0models` package provides 10 temperature-based empirical models for
estimating daily reference evapotranspiration (ET0). The FAO Penman-Monteith
method is included as the standard reference against which the temperature-based
models are compared and evaluated.

Temperature-based models are particularly valuable in data-scarce environments
because they require only readily available temperature data (plus humidity,
day of year, and location in some cases), unlike methods that require wind
speed or solar radiation measurements.

### Temperature-Based Models

| # | Model | Function | Primary Inputs |
|---|-------|----------|----------------|
| 1 | Blaney-Criddle (1950) | `et0_blaney_criddle()` | Tmean |
| 2 | Schendel (Bormann, 2011) | `et0_schendel()` | Tmean, RH |
| 3 | Hargreaves-Samani (1985) | `et0_hargreaves_samani()` | Tmin, Tmax, J, lat |
| 4 | Linacre (1977) | `et0_linacre()` | Tmean, Tmin, Tmax, RH, z, lat |
| 5 | Tabari-Talaee 1 (2011) | `et0_tabari_talee1()` | Tmin, Tmax, J, lat |
| 6 | Tabari-Talaee 2 (2011) | `et0_tabari_talee2()` | Tmin, Tmax, J, lat |
| 7 | Droogers-Allen (2002) | `et0_droogers_allen()` | Tmin, Tmax, J, lat |
| 8 | Berti et al. (2014) | `et0_berti()` | Tmin, Tmax, J, lat |
| 9 | Dorji et al. (2016) | `et0_dorji()` | Tmin, Tmax, J, lat |
| 10 | Baier-Robertson (1965) | `et0_baier_robertson()` | Tmin, Tmax, J, lat |

## Quick Start

### Loading the Package and Data

```{r load}
library(ET0TempModels)
data(jhansi_weather)
head(jhansi_weather)
```

### Computing ET0 for a Single Day

```{r single-day}
# FAO Penman-Monteith (reference)
et0_fao_pm(Tmin = 15, Tmax = 30, RH_morning = 90, RH_evening = 40,
           u2 = 1.5, n = 8, J = 172, lat = 25.43, z = 216)

# Blaney-Criddle (needs only Tmean)
et0_blaney_criddle(Tmean = 22.5)

# Hargreaves-Samani (needs Tmin, Tmax, day of year, latitude)
et0_hargreaves_samani(Tmin = 15, Tmax = 30, J = 172, lat = 25.43)
```

### Computing All 10 Temperature-Based Models at Once

The `et0_temp_all()` function runs all 10 temperature-based models plus the
FAO-PM reference in a single call:

```{r all-models}
result <- et0_temp_all(
  Tmin       = jhansi_weather$Tmin[1:5],
  Tmax       = jhansi_weather$Tmax[1:5],
  RH_morning = jhansi_weather$RH_morning[1:5],
  RH_evening = jhansi_weather$RH_evening[1:5],
  u2         = jhansi_weather$WS[1:5],
  n          = jhansi_weather$SSH[1:5],
  J          = jhansi_weather$J[1:5],
  lat        = 25.43,
  z          = 216
)
round(result, 2)
```

## Evaluating Model Performance Against FAO-PM

The package includes 9 statistical metrics for comparing temperature-based
model performance against the FAO-PM reference: NSE, d, MSE, RMSE, NRMSE,
MAE, MBE, r, and R-squared.

```{r evaluation}
# Compute ET0 for the full year
full_result <- et0_temp_all(
  Tmin       = jhansi_weather$Tmin,
  Tmax       = jhansi_weather$Tmax,
  RH_morning = jhansi_weather$RH_morning,
  RH_evening = jhansi_weather$RH_evening,
  u2         = jhansi_weather$WS,
  n          = jhansi_weather$SSH,
  J          = jhansi_weather$J,
  lat        = 25.43,
  z          = 216
)

# FAO-PM is the reference (observed)
obs   <- full_result$FAO_PM
preds <- full_result[, c("Blaney_Criddle", "Hargreaves_Samani",
                          "Linacre", "Droogers_Allen")]
metrics <- evaluate_models(obs, preds)
print(metrics)
```

## Visualization

### Scatter Plot

```{r scatter, fig.width=6, fig.height=6}
plot_scatter(obs, preds,
             main = "Temperature-Based Models vs FAO-PM")
```

### Taylor Diagram

```{r taylor, fig.width=7, fig.height=7}
plot_taylor(obs, preds,
            main = "Taylor Diagram - Temperature-Based Models")
```

## Helper Functions

The package exports helper functions for computing intermediate meteorological
variables used internally by the models:

```{r helpers}
# Extraterrestrial radiation for June 21 at 25.43 N
extraterrestrial_radiation(J = 172, lat = 25.43)

# Saturation vapor pressure at 25 degrees C
saturation_vapor_pressure(25)

# Daylight hours for June 21 at 25.43 N
daylight_hours(J = 172, lat = 25.43)
```

## References

- Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop
  evapotranspiration: Guidelines for computing crop water requirements.
  FAO Irrigation and Drainage Paper No. 56.
- Satpute, A.N., Barman, S., Singh, A.K., Ghosh, A., & Gupta, G. (2026).
  A multi-scale evaluation of 30 empirical models for reference
  evapotranspiration estimation in a data-scarce semi-arid region:
  A case study of Jhansi, India.
  *Journal of Hydrology: Regional Studies*.
  https://doi.org/10.1016/j.ejrh.2026.103925