A database of longterm monitoring of lake ice phenology

A database of longterm monitoring of lake ice phenology

Collaborators and Contributors

• Sapna Sharma
• Thi Nguyen,
• Alessandro Filazzola
• M. Arshad Imrit
• Kevin Blagrave
• Damien Bouffard
• Julia Daly
• Harley Feldman
• Natalie Felsine
• Harrie-Jan Hendricks-Franssen
• Nikolay Granin
• Richard Hecock
• Jan Henning L’Abée-Lund
• Ed Hopkins
• Tom Hoverstad
• Neil Howk
• Paulette Janssen
• Johanna Korhonen
• Hilmar J. Malmquist
• Wlodzimierz Marszelewski
• Shin-Ichiro Matsuzaki
• Yuichi Miyabara
• Kiyoshi Miyasaka
• Alexander Mills
• Joe Norton
• Lolita Olson
• Ted Peters
• David C. Richardson
• Dale Robertson
• Lars Rudstam
• Tom Skramstad
• Larry Smisek
• Danielle Wain
• Holly Waterfield
• Gesa Weyhenmeyer
• Brendan Wiltse
• Huaxia Yao
• Andry Zhdanov
• John J. Magnuson

Description

Lake ice is an important resource supporting water quality, local biodiversity, arctic transportation, and regional economies (Knoll et al. 2019). However, many studies have identified the threat of climate change on the long-term persistence of lake ice (Magnuson et al. 2000; Livingstone et al. 2009; Sharma et al. 2019; Filazzola et al. 2020; Sharma et al. 2021). Long-term surveys of lake ice collected in situ are the gold standard for monitoring in environmental science (Filazzola & Cahill 2021). Here, we present ice phenology records for 78 lakes in the Northern Hemisphere spanning up to 578 years. These surveys include 12 different countries across North America and Eurasia collected by researchers, community scientists, priests, and digital observations. This database includes information about ice phenology (ice-on dates, ice-off dates, ice cover duration), lake characteristics (e.g., size, location, names), and meta-data about each data source (e.g., how the data was collected). Our intention is that this database may be used to understand factors driving lake ice patterns and the biological or socio-economic consequences.

Data available in the database

## Load libraries
library(tidyverse)
library(DT)

## Load data
lakeChar <- read.csv("data//LakeCharacteristics.csv")
icePheno <- read.csv("data//PhenologyData.csv")

datatable(lakeChar, caption="Lake Characteristics")

datatable(lakeChar, caption="Lake Ice Phenology")

Data

The data within this database are separated into three main files - PhenologyData.csv: has the lake ice phenology for all 478 lakes. - LakeCharacteristics.csv: has the physical characteristics and coordinates of the lakes in the database. - Definitions.csv: the meta-data associated with each lake including the range of the time series, number of missing observations, and definitions of ice-on/ice-off.

Scripts

The qaqc.r file was used for converting 78_lakes_ts_minimal.csv into “long” format where only one each column represents ice on and ice off dates. The qaqc.r file also performs some basic quality control and assurance of the dataset. There are two source files, create_lake_ice_time_series.py and additional_functions.py that consolated lake names, conduct some quality control, and were responsible for the original data aggregation across multiple files.

General patterns in data

library(tidyverse)

lakeChar %>% 
  select(lake = lakename, ManuscriptName) %>% 
  right_join(icePheno) %>% 
  group_by(ManuscriptName, lake) %>% 
  summarize(minYear = min(start_year),
            maxYear = max(start_year),
            totalTimeseries = length(iceOn),
            missingIceOn = sum(is.na(iceOn)),
            missingIceOff = sum(is.na(iceOff)))

## # A tibble: 78 x 7
## # Groups:   ManuscriptName [78]
##    ManuscriptName       lake        minYear maxYear totalTimeseries missingIceOn
##    <chr>                <chr>         <int>   <int>           <int>        <int>
##  1 Cazenovia Lake       cazenovia      1838    2018             191           14
##  2 China Lake           china          1873    2018             101          101
##  3 Christmas Lake       christmas      1886    2017             131          128
##  4 Clear Lake           clear          1873    2019             128          126
##  5 Cobbosseecontee Lake cobbossee      1839    2018             178          178
##  6 Damariscotta Lake    damariscot~    1836    2018             182          182
##  7 Detroit Lake         detroit        1892    2019             128           18
##  8 Geneva Lake          geneva         1862    2018             157            6
##  9 Grand Traverse Bay   grand_trav~    1850    2016             166           44
## 10 Green Lake           green          1896    2015              91           21
## # ... with 68 more rows, and 1 more variable: missingIceOff <int>