knitr::opts_chunk$set(echo = TRUE)
options(knitr.kable.NA = '')
library(dplyr)
library(smires)
library(knitr)
library(lubridate)

General definitions

In our working group we agreed on the following definitions, most of them are already incorporated in the R package smires.

Here, the function smires() is used to demonstrate the meaning of major and minor intervals. The argument major = 91 specifies that the major interval (the hydrological year) starts on the 91^st^ day of the year, which is April 1^st^. Per default no minor interval is used. The argument minor = intervals$month makes use of the list intervals containing frequently used minor intervals like r smires:::.itemize_text(sQuote(names(intervals))). The functions returns at table of spells, which we can ignore for the time being. The function is just used for the side effect of plotting the stream-flow permanence of the river 'Ampney Brook' at station 'Ampney St Peter' based on a threshold of 1 l/s.

ampney <- char_binary(ampneyBrook, major = 91, minor = intervals$month, rule = "onset", plot = TRUE)

The first record of the data set exists for the date r ampneyBrook$time[1] which starts the r smires:::.written_suffix(as.numeric(ampney$minor[1])) month of our hydrological year r ampney$major[1]. The data set covers in total r (tot <- nlevels(ampney$major)) years, of which r (no <- length(unique(ampney$major[ampney$state=="no-flow"]))) show a no-flow event.

Flow/No-flow spell characteristics

A characteristic that can be calculated for no-flow spells can also be calculated for flow spells. The following sections demonstrate just the calculation of no-flow spell characteristics.

General characteristics

Each of the following sections start with a table, showing all metrics attributed to this certain section. This section 'General characteristics' comprises just one characteristic, the proportion of no-flow years $f_0$ .

tbl <- metrics() %>% 
  filter(section == "General characteristics") %>%
  select(Name, Acronym, Function, Description)
kable(tbl)

Using the same example as before this metric is defined as

$$f_0 = \frac{\textrm{Number of no-flow years}}{\textrm{Number of years in study period}} = \frac{r no}{r tot} = r no/tot= r round(no/tot*100, 1) \%$$

and can be calculated using the function:

no_flow_years(ampneyBrook)



Number of no-flow days

tbl <- metrics() %>% 
  filter(section == "Number of no-flow days") %>%
  select(Name, Measure, Acronym, Function, Description)
kable(tbl)



Duration

tbl <- metrics() %>% 
  filter(section == "Duration") %>%
  select(Name, Measure, Acronym, Function, Description)
kable(tbl)
MAMD(ampneyBrook)
FAMD(ampneyBrook)



Timing and seasonality

tbl <- metrics() %>% 
  filter(section == "Timing and seasonality") %>%
  select(Name, Measure, Acronym, Function, Description)
kable(tbl)
tau0(ampneyBrook)



Rate of change before/after no-flow event

tbl <- metrics() %>% 
  filter(section == "Rate of change before/after no-flow event") %>%
  select(Name, Measure, Acronym, Function, Description)
kable(tbl)
k(ampneyBrook)


mundl/smires documentation built on May 23, 2019, 8:22 a.m.