# Characterizing Trends in the Variability

Many factors can produce nonstationarity in annual maximum series (AMS) data, including changes in climate, land use/cover, and water management. This vignette demonstrates how to use the `ffaframework` to check for evidence of nonstationarity in the variability of a time series.

## List of Tests

Mean Trend Tests

Function | Purpose
--- | ---
`eda_mk_test` | Tests for a monotonic trend (Mann-Kendall test).
`eda_bbmk_test` | Tests for a monotonic trend under serial correlation (BBMK test).

Stationarity Tests

Function | Purpose
--- | ---
`eda_spearman_test` | Tests for serial correlation (Spearman test).
`eda_kpss_test` | Tests for a stochastic trend (KPSS test).
`eda_pp_test` | Tests for a deterministic trend (Phillips-Perron test).

Variability Trend Tests

Function | Purpose
--- | ---
MW-MK Test | Tests for a trend in the variability (MWMK test)
`eda_white_test` | Tests for time-dependence in the variability (White test).

Trend Estimation (Mean & Variability)

Function | Purpose
--- | ---
`eda_sens_trend` | Estimates slope and intercept of a linear trend (Sen’s trend estimator).
`eda_runs_test` | Evaluates residuals’ structure under linear model assumptions (runs test).

## Case Study

This vignette will explore data from the Chilliwack River at Chilliwack Lake (08MH016) hydrological monitoring station. The remoteness of this station means that trends annual maxima are caused by changes in climate as opposed to changes in land use or cover. Data for this station is provided as `CAN-08MH016.csv` in the `ffaframework` package.

    library(ffaframework)

    df <- data_local("CAN-08MH016.csv")
    head(df)
    #>   year  max
    #> 1 1922 62.9
    #> 2 1923 74.5
    #> 3 1924 79.0
    #> 4 1925 35.1
    #> 5 1926 62.3
    #> 6 1927 65.7

    plot_ams_data(df$max, df$year, title = "Chilliwack River at Chilliwack Lake (08MH016)")

## Assessing Trends in the Variance

### MWMK Test

The MWMK test is used to detect trends in the variability of a time series. First, a moving-window algorithm is used to estimate the variability of the AMS data. Then, the Mann-Kendall test is applied to the series of standard deviations to check for a trend.

The `data_mw_variability` estimates the moving-window standard deviations and the `eda_mk_test` function performs the Mann-Kendall test.

    mw <- data_mw_variability(df$max, df$year)
    mwmk_test <- eda_mk_test(mw$std)
    print(mwmk_test$p_value)
    #> [1] 0.001463998

Conclusion: At a p-value of \(0.0015\), we reject the null hypothesis. There is evidence of a linear trend in the variability in the data.

### White Test

The White test checks for heteroskedasticity, or general time-dependence in the variability. The null hypothesis is homoskedasticity, or constant variability in the data.

    white_test <- eda_white_test(df$max, df$year)
    print(white_test$p_value)
    #> [1] 0.1175955

Conclusion: At a p-value of \(0.1176\), we fail to reject the null hypothesis. There is no statistical evidence of heteroskedasticity.

### Sen’s Trend Estimator

While the previous tests provide evidence for a monotonic trend in the variability, they do not estimate the slope or intercept of this trend. We can estimate the monotonic trend using Sen’s trend estimator, which uses a nonparametric approach that is robust to outliers.

`eda_sens_trend()` takes two arguments: the data (either an annual maximum series or vector of standard deviations) and the corresponding vector of years. Alternatively, `plot_ams_data()` can be used to run Sen’s trend estimator on the data and/or variability series and then plot the results. It takes the optional arguments `plot_mean` and `plot_variability` in addition to the `data` and `years`.

    plot_ams_data(
        df$max,
        df$year,
        plot_mean = "Constant",
        plot_variability = "Trend",
        title = "Sen's Trend Estimator (AMS Variability)"
    )

Note: The covariate is computed using the formula \((\text{years} - 1900) / 100\).

### Runs Test

Sen’s trend estimator assumed that the nonstationarity is linear. The runs test assess the feasibility of the linearity assumption by checking the residuals for randomness. If the residuals are random (the null hypothesis), there is evidence that the underlying trend is linear. The `eda_runs_test()` function takes the residuals from `eda_sens_trend()` as the first argument and the observation years as the second argument.

    sens_trend <- eda_sens_trend(mw$std, mw$year)

    runs_test <- eda_runs_test(sens_trend$residuals, mw$year)

    print(runs_test$p_value)
    #> [1] 0.3311375

    plot_runs_test(runs_test, title = "Runs Test (AMS Variability)")

Conclusion: At a p-value of \(0.3311\), we fail to reject the null hypothesis. There is evidence that the residuals are random and a linear trend is suitable for the data.

## Conclusion

The MWMK and White tests find evidence of nonstationarity in the variability of the AMS. The runs test confirms that a linear model is suitable for the nonstationarity. Flood frequency analysis of this dataset requires a time-dependent probability model.

Recommendation: Use NS-FFA.