Computing Annual Trends with fasstr

fasstr, the Flow Analysis Summary Statistics Tool for R, is a set of R functions to tidy, summarize, analyze, trend, and visualize streamflow data. This package summarizes continuous daily mean streamflow data into various daily, monthly, annual, and long-term statistics, completes trending and frequency analyses, with outputs in both table and plot formats.

This vignette documents the usage of the compute_annual_trends() function in fasstr. This vignette is a high-level adjunct to the details found in the function documentation (see ?compute_annual_trends()). You’ll learn what arguments to provide to the function to customize your analysis, what analyses are computed, and what outputs are produced.

Overview

Determining trends in streamflow data can provide information on potential changes in hydrological processes over time. The annual trending analysis with fasstr allows for customization of both the inputs and outputs. This function takes 107 annual streamflow metrics (calculated using various annual fasstr functions) and calculates prewhitened, non-parametric trends using the zyp R package. See the zyp documentation for more information on the methods.

Each annual metric/time-series is analyzed for trends using trend-free prewhitening to remove lag-1 correlation (may artificially detect a significant trend). The slope of each metric over time is then estimated using the Theil-Sen approach. If the estimated slope is different from zero, then the data are detrended by the slope and the AR(1) 1s calculated for the detrended time series. The residuals and the trend are combined and then tested for significance using the Mann-Kendall trend test.

The trending function results in a list containing two tibble data frame outputs and, if selected, plot for each metric trended.

1. Annual_Trends_Data - data used for analysis calculated from various annual fasstr functions
2. Annual_Trends_Results - results of the zyp trending analysis, and includes various other statistics
3. ‘Sep_Maximum’ - an example of each of 107 plots produced (one for each metric)

Function and Data Inputs

• compute_annual_trends()

To determine annual trends from a daily streamflow data set, the compute_annual_trends() function will take daily data, either from HYDAT using the station_number argument or your own data frame of data using the data argument to complete the analysis. To complete a custom trends analysis of data please see the zyp functions for more information.

Usage, Options, and Outputs

Analysis Data

This function is provided to calculate trends on a multitude of annual metrics, as calculate by various annual and monthly fasstr functions. The functions will calculate metrics from each of the following functions:

• calc_annual_stats() - calculate annual summary statistics
• calc_annual_cumulative_stats() - calculate annual and seasonal cumulative flows, both volume and yield
• calc_annual_flow_timing() - calculate annual flow timing
• calc_annual_lowflows() - calculate annual lowflows
• calc_annual_outside_normal() - calculate annual days above and below normal
• calc_monthly_stats() - calculate annual monthly summary statistics

While each of the different metrics have default variables for their arguments, many of them can be customized. The following table shows which arguments are used for which statistics and what the defaults are. See the documentation for more information.

Argument Corresponding Function Default
annual_percentiles calc_annual_stats() c(10,90)
monthly_percentiles calc_monthly_stats() c(10,20)
stats_days calc_annual_stats() & calc_monthly_stats() 1
stats_align calc_annual_stats() & calc_monthly_stats() "right"
lowflow_days calc_annual_lowflows() c(1,3,7,30)
lowflow_align calc_annual_lowflows() "right"
timing_percent calc_annual_flow_timing() c(25,33.3,50,75)
normal_percentiles calc_annual_outside_normal() c(25,75)

Examples

Example with default arguments:

compute_annual_trends(station_number = "08NM116",
zyp_method = "yuepilon",
start_year = 1973, end_year = 2013)

Example with custom arguments:

compute_annual_trends(station_number = "08NM116",
zyp_method = "yuepilon",
start_year = 1973, end_year = 2013,
annual_percentiles = c(10,90),
monthly_percentiles = c(10,20),
stats_days = 1,
stats_align = "right",
lowflow_days = c(1,3,7,30),
lowflow_align = "right",
timing_percent = c(25,33,50,75),
normal_percentiles = c(25,75))

This annual data is provided in the Annual_Trends_Data tibble objects. The following is an example of the output, including all the annual metrics and a first few years of data used for the zyp trends analysis:

              Statistic          1973          1974          1975
1        Annual_Maximum        37.700        66.000        48.700
2           Annual_Mean         3.331         8.430         5.483
3         Annual_Median         0.980         1.340         1.540
4        Annual_Minimum         0.025         0.447         0.320
5            Annual_P10         0.549         0.709         0.580
6            Annual_P90         8.832        32.980        19.580
7             Min_1_Day         0.025         0.447         0.320
8         Min_1_Day_DoY       293.000       333.000        11.000
9             Min_3_Day         0.045         0.533         0.378
10        Min_3_Day_DoY       295.000       334.000        39.000
11            Min_7_Day         0.194         0.602         0.416
12        Min_7_Day_DoY       299.000       346.000        41.000
13           Min_30_Day         0.574         0.665         0.494
14       Min_30_Day_DoY       249.000       358.000        58.000
15      Total_Volume_m3 105036393.421 265854182.477 172900396.854
16    Jan-Jun_Volume_m3  84993926.226 223662988.720 136045958.439
17    Jul-Dec_Volume_m3  20042467.195  42191193.757  36854438.415
18    Jan-Mar_Volume_m3   5734540.834   8368012.779   5258217.605
19    Apr-Jun_Volume_m3  79259385.392 215294975.941 130787740.834
20    Jul-Sep_Volume_m3   7503580.782  35216640.131  24657350.460
21    Oct-Dec_Volume_m3  12538886.413   6974553.626  12197087.955
22       Total_Yield_mm       132.121       334.408       217.485
23     Jan-Jun_Yield_mm       106.911       281.337       171.127
24     Jul-Dec_Yield_mm        25.211        53.071        46.358
25     Jan-Mar_Yield_mm         7.213        10.526         6.614
26     Apr-Jun_Yield_mm        99.697       270.811       164.513
27     Jul-Sep_Yield_mm         9.438        44.298        31.016
28     Oct-Dec_Yield_mm        15.772         8.773        15.342
29     DoY_25pct_TotalQ       138.000       135.000       146.000
30     DoY_33pct_TotalQ       141.000       146.000       153.000
31     DoY_50pct_TotalQ       151.000       158.000       162.000
32     DoY_75pct_TotalQ       172.000       173.000       177.000
33    Days_Below_Normal       230.000        69.000       129.000
34    Days_Above_Normal        15.000        75.000        30.000
35  Days_Outside_Normal       245.000       144.000       159.000
36             Jan_Mean         0.730         1.023         0.625
37           Jan_Median         0.705         1.020         0.595
38          Jan_Maximum         0.963         1.260         1.050
39          Jan_Minimum         0.595         0.864         0.320
40              Jan_P10         0.626         0.906         0.507
41              Jan_P20         0.643         0.934         0.538
42             Feb_Mean         0.670         0.985         0.490
43           Feb_Median         0.676         0.984         0.483
44          Feb_Maximum         0.728         1.060         0.614
45          Feb_Minimum         0.595         0.830         0.368
46              Feb_P10         0.619         0.944         0.407
47              Feb_P20         0.632         0.957         0.449
48             Mar_Mean         0.806         1.211         0.896
49           Mar_Median         0.799         1.120         0.943
50          Mar_Maximum         0.951         2.140         1.390
51          Mar_Minimum         0.685         0.855         0.677
52              Mar_P10         0.708         0.937         0.694
53              Mar_P20         0.736         0.983         0.716
54             Apr_Mean         1.774         7.761         1.789
55           Apr_Median         1.510         4.910         1.780
56          Apr_Maximum         3.450        28.300         3.400
57          Apr_Minimum         0.852         1.850         0.949
58              Apr_P10         0.963         1.919         0.984
59              Apr_P20         1.116         2.070         1.036
60             May_Mean        16.395        29.845        16.274
61           May_Median        15.200        30.300        16.800
62          May_Maximum        37.700        50.400        32.300
63          May_Minimum         3.450        15.900         3.340
64              May_P10         4.420        17.500         5.210
65              May_P20         5.950        20.100         6.820
66             Jun_Mean        11.863        44.460        31.853
67           Jun_Median        10.350        44.900        30.850
68          Jun_Maximum        26.900        66.000        48.700
69          Jun_Minimum         3.880        20.600        13.000
70              Jun_P10         6.104        25.610        17.240
71              Jun_P20         7.204        33.280        23.820
72             Jul_Mean         1.422         9.966         4.922
73           Jul_Median         0.926         8.210         3.450
74          Jul_Maximum         4.980        24.800        10.900
75          Jul_Minimum         0.462         2.420         1.180
76              Jul_P10         0.518         3.790         1.250
77              Jul_P20         0.561         5.380         1.460
78             Aug_Mean         0.615         1.761         2.274
79           Aug_Median         0.564         1.610         1.680
80          Aug_Maximum         1.100         3.710         5.920
81          Aug_Minimum         0.326         0.960         0.736
82              Aug_P10         0.453         1.200         0.974
83              Aug_P20         0.476         1.280         1.180
84             Sep_Mean         0.789         1.468         2.076
85           Sep_Median         0.685         1.395         1.750
86          Sep_Maximum         2.270         2.100         5.100
87          Sep_Minimum         0.399         1.130         1.390
88              Sep_P10         0.470         1.199         1.460
89              Sep_P20         0.496         1.246         1.536
90             Oct_Mean         1.630         1.231         1.845
91           Oct_Median         0.867         1.120         1.840
92          Oct_Maximum         8.070         2.080         2.210
93          Oct_Minimum         0.025         0.838         1.510
94              Oct_P10         0.113         0.867         1.650
95              Oct_P20         0.416         0.903         1.740
96             Nov_Mean         1.760         0.710         1.488
97           Nov_Median         1.485         0.711         1.400
98          Nov_Maximum         3.060         0.943         2.940
99          Nov_Minimum         1.220         0.447         0.906
100             Nov_P10         1.327         0.616         1.047
101             Nov_P20         1.350         0.633         1.128
102            Dec_Mean         1.349         0.686         1.268
103          Dec_Median         1.400         0.680         1.290
104         Dec_Maximum         1.830         0.850         1.590
105         Dec_Minimum         0.977         0.541         0.991
106             Dec_P10         1.020         0.609         1.050
107             Dec_P20         1.210         0.637         1.120

To provide examples of the outputs, an analysis will be completed on a Mission Creek HYDAT station from 1973 to 2013. The argument zyp_method is described below in the Analysis Results section:

trends_analysis <- compute_annual_trends(station_number = "08NM116",
zyp_method = "yuepilon",
start_year = 1973, end_year = 2013)

The following is an example of the outputted Annual_Trends_Data tibble:

trends_analysis$Annual_Trends_Data # A tibble: 107 x 43 STATION_NUMBER Statistic 1973 1974 1975 1976 1977 1978 1979 <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 08NM116 Annual_M~ 3.77e+1 66 48.7 71.1 36 44.5 43 2 08NM116 Annual_M~ 3.33e+0 8.43 5.48 8.18 4.38 6.75 4.40 3 08NM116 Annual_M~ 9.80e-1 1.34 1.54 3.84 1.26 3.28 1.56 4 08NM116 Annual_M~ 2.50e-2 0.447 0.320 0.736 0.564 0.532 0.411 5 08NM116 Annual_P~ 5.49e-1 0.709 0.580 0.884 0.776 0.828 0.618 6 08NM116 Annual_P~ 8.83e+0 33.0 19.6 25.6 17.2 19.7 15.9 7 08NM116 Min_1_Day 2.50e-2 0.447 0.320 0.736 0.564 0.532 0.411 8 08NM116 Min_1_Da~ 2.93e+2 333 11 38 73 55 268 9 08NM116 Min_3_Day 4.53e-2 0.533 0.378 0.741 0.627 0.630 0.416 10 08NM116 Min_3_Da~ 2.95e+2 334 39 63 252 3 269 # ... with 97 more rows, and 34 more variables: 1980 <dbl>, 1981 <dbl>, # 1982 <dbl>, 1983 <dbl>, 1984 <dbl>, 1985 <dbl>, 1986 <dbl>, # 1987 <dbl>, 1988 <dbl>, 1989 <dbl>, 1990 <dbl>, 1991 <dbl>, # 1992 <dbl>, 1993 <dbl>, 1994 <dbl>, 1995 <dbl>, 1996 <dbl>, # 1997 <dbl>, 1998 <dbl>, 1999 <dbl>, 2000 <dbl>, 2001 <dbl>, # 2002 <dbl>, 2003 <dbl>, 2004 <dbl>, 2005 <dbl>, 2006 <dbl>, # 2007 <dbl>, 2008 <dbl>, 2009 <dbl>, 2010 <dbl>, 2011 <dbl>, # 2012 <dbl>, 2013 <dbl> Analysis Results To complete a trends analysis, a variable to the zyp_method argument must be provided, either "yuepilon" or "zhang", designated the two different approaches to analyzing data for trends. See the zyp documentation for more information on the differences between the two methods. After running the function, the results of the trending analysis will be outputted in the Annual_Trends_Results tibble data frame. See the zyp documentation for how to interpret the results. The results tibble contains the following columns: Column Name Description Statistic the annual statistic used for trending lbound the lower bound of the trend’s confidence interval (zyp) trend the Sens’ slope (trend) per unit time (zyp) trendp the Sen’s slope (trend) over the time period (zyp) ubound the upper bound of the trend’s confidence interval (zyp) tau Kendall’s tau statistic computed on the final detrended timeseries (zyp) sig Kendall’s P-value computed for the final detrended timeseries (zyp) nruns the number of runs required to converge upon a trend (zyp) autocor the autocorrelation of the final detrended timeseries (zyp) valid_frac the fraction of the data which is valid (not NA) once autocorrelation is removed (zyp) linear the least squares fit trend on the same data (zyp) intercept the intercept of the Sen’s slope (trend) (zyp) min_year the minimum year used in the trending max_year the maximum year used in the trending n_years the number of years with data for trending mean the mean of all values used for trending median the median of all values used for trending min the minimum of all values used for trending max the maximum of all values used for trending The following is an example of the outputted Annual_Trends_Results tibble from the Mission Creek HYDAT station from 1973 to 2013: trends_analysis$Annual_Trends_Results
# A tibble: 107 x 20
STATION_NUMBER Statistic   lbound    trend  trendp  ubound      tau    sig
<chr>          <fct>        <dbl>    <dbl>   <dbl>   <dbl>    <dbl>  <dbl>
1 08NM116        Annual_M~ -0.368    5.71e-2  2.34   0.517    0.00513 0.972
2 08NM116        Annual_M~ -0.0608  -4.47e-3 -0.183  0.0476  -0.0718  0.522
3 08NM116        Annual_M~ -0.0297  -7.45e-3 -0.305  0.0100  -0.138   0.213
4 08NM116        Annual_M~ -0.00480 -2.97e-4 -0.0122 0.00450 -0.0487  0.666
5 08NM116        Annual_P~ -0.0102  -4.19e-3 -0.172  0.00300 -0.105   0.345
6 08NM116        Annual_P~ -0.211    2.05e-3  0.0839 0.218   -0.0308  0.789
7 08NM116        Min_1_Day -0.00480 -2.97e-4 -0.0122 0.00450 -0.0487  0.666
8 08NM116        Min_1_Da~ -0.5      1.74e+0 71.3    6.55     0.285   0.0100
9 08NM116        Min_3_Day -0.00460  3.45e-4  0.0142 0.00529 -0.0282  0.807
10 08NM116        Min_3_Da~ -1.65     7.57e-1 31.0    3        0.131   0.239
# ... with 97 more rows, and 12 more variables: nruns <dbl>, autocor <dbl>,
#   valid_frac <dbl>, linear <dbl>, intercept <dbl>, min_year <dbl>,
#   max_year <dbl>, n_years <int>, mean <dbl>, median <dbl>, min <dbl>,
#   max <dbl>