Data Package Metadata View Summary

Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]

General Information

Data Package:

Local Identifier:

edi.1516.1

Title:

Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]

Alternate Identifier:

DOI PLACE HOLDER

Abstract:

The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable.

The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.

Publication Date:

2023-10-17

For more information:
Visit:	DOI PLACE HOLDER

Time Period

Begin:

1986

End:

2100

People and Organizations
Contact:	Virdis, Salvatore G.P. (Asian Institute of Technology, Associate Professor) [ email ]
Contact:	Kongwarakom, Siwat (Asian Institute of Technology, Research Assistant) [ email ]
Creator:	Virdis, Salvatore G.P. (Asian Institute of Technology, Associate Professor)
Creator:	Kongwarakom, Siwat (Asian Institute of Technology, Research Assistant)
Associate:	Virdis, Salvatore (Asian Institute of Technology, Associate Professor, Data curator)
Associate:	Kongwarakom, Siwat (Asian Institute of Technology, Research Assistant, Data curator)
Associate:	Juneng, Liew (Universiti Kebangsaan Malaysia, Associate Professor, Data curator)
Associate:	Padedda, Bachisio (University of Sassari, Associate Professor, Data curator)
Associate:	Shrestha, Sangam (Asian Institute of Technology, Professor, Data curator)

Data Entities
Data Table Name:	ERA5_MODIS_LSWT_day_mk
Description:	This file presents the results of the original Mann-Kendall test applied to predicted yearly average time series of daytime lake surface water temperatures for 80 lakes located in Southeast Asia during the period from 1986 to 2020. The analysis includes significance levels of 0.01, 0.05, and 0.10.
Data Table Name:	ERA5_MODIS_LSWT_night_mk
Description:	This file presents the results of the original Mann-Kendall test applied to predicted yearly average time series of nighttime lake surface water temperatures for 80 lakes located in Southeast Asia during the period from 1986 to 2020. The analysis includes significance levels of 0.01, 0.05, and 0.10.
Data Table Name:	ERA5_MODIS_LSWT_day_prediction
Description:	Predicted daily time series of daytime lake surface water temperatures (LSWT) for 80 lakes located in Southeast Asia from 1986 to 2020. The predictor variables include all columns from column no. 3 (Latitude) to column no. 12 (Wind Speed (m/s)). The predicted LSWT values are located in columns with the _LSWT suffix.
Data Table Name:	ERA5_MODIS_LSWT_night_prediction
Description:	Predicted daily time series of nighttime lake surface water temperatures (LSWT) for 80 lakes located in Southeast Asia from 1986 to 2020. The predictor variables include all columns from column no. 3 (Latitude) to column no. 12 (Wind Speed (m/s)). The predicted LSWT values are located in columns with the _LSWT suffix.
Data Table Name:	table lake geomorphic features
Description:	Table of lake geomorphic features, with columns 4, 6 through 20 extracted from the HydroLAKES dataset
Data Table Name:	CNRM-CERFACS-CNRM-CM5_RCA4_rcp45_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from CNRM-CERFACS-CNRM-CM5_RCA4 (RCP 4.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	CNRM-CERFACS-CNRM-CM5_RCA4_rcp85_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from CNRM-CERFACS-CNRM-CM5_RCA4 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RCA4_rcp45_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RCA4 (RCP 4.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RCA4_rcp85_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RCA4 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RegCM4-7_rcp26_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RegCM4-7_rcp85_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MPI-M-MPI-ESM-MR_RegCM4-7_rcp26_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from MPI-M-MPI-ESM-MR_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MPI-M-MPI-ESM-MR_RegCM4-7_rcp85_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from MPI-M-MPI-ESM-MR_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	NCC-NorESM1-M_RegCM4-7_rcp26_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from NCC-NorESM1-M_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Data Table Name:	NCC-NorESM1-M_RegCM4-7_rcp85_XGB_day_predicted
Description:	Time series of daytime lake surface water temperature predictions, using climate variables from NCC-NorESM1-M_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	CNRM-CERFACS-CNRM-CM5_RCA4_rcp45_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from CNRM-CERFACS-CNRM-CM5_RCA4 (RCP 4.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	CNRM-CERFACS-CNRM-CM5_RCA4_rcp85_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from CNRM-CERFACS-CNRM-CM5_RCA4 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RCA4_rcp45_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RCA4 (RCP 4.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RCA4_rcp85_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RCA4 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RegCM4-7_rcp26_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MOHC-HadGEM2-ES_RegCM4-7_rcp85_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MOHC-HadGEM2-ES_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MPI-M-MPI-ESM-MR_RegCM4-7_rcp26_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MPI-M-MPI-ESM-MR_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Data Table Name:	MPI-M-MPI-ESM-MR_RegCM4-7_rcp85_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MPI-M-MPI-ESM-MR_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Data Table Name:	NCC-NorESM1-M_RegCM4-7_rcp26_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from NCC-NorESM1-M_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Data Table Name:	NCC-NorESM1-M_RegCM4-7_rcp85_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from NCC-NorESM1-M_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.

Detailed Metadata

Data Entities

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/eac6240137c30ae408f5a4d3a0da542c
Name:	ERA5_MODIS_LSWT_day_mk
Description:	This file presents the results of the original Mann-Kendall test applied to predicted yearly average time series of daytime lake surface water temperatures for 80 lakes located in Southeast Asia during the period from 1986 to 2020. The analysis includes significance levels of 0.01, 0.05, and 0.10.
Number of Records:	80
Number of Columns:	16

Table Structure

Object Name:

ERA5_MODIS_LSWT_day_mk.csv

Size:

16478 byte

Authentication:

250fa34b157f07259e8d21b37a01f479 Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Latitude

Longitude

trend_10

trend_05

trend_01

h_10

h_05

h_01

p

z

Tau

s

var_s

slope

intercept

Column Name:

ID

Latitude

Longitude

trend_10

trend_05

trend_01

h_10

h_05

h_01

p

z

Tau

s

var_s

slope

intercept

Definition:

HydroLAKES ID

Latitude in decimal degrees

Longitude in decimal degrees

The Mann-Kendall test results determine whether or not there is a monotonic upward or downward trend.

The Mann-Kendall test results determine whether or not there is a monotonic upward or downward trend.

The Mann-Kendall test results determine whether or not there is a monotonic upward or downward trend.

The Mann-Kendall test results determine the presence or absence of a trend.

The Mann-Kendall test results determine the presence or absence of a trend.

The Mann-Kendall test results determine the presence or absence of a trend.

p-value of the significance test

Normalized test statistics

Kendall Tau

Mann-Kendal's score

Variance S

Theil-Sen estimator/slope

Intercept of Kendall-Theil Robust Line

Storage Type:

string

float

float

string

string

string

string

string

string

float

float

float

float

float

float

float

Measurement Type:

nominal

ratio

ratio

nominal

nominal

nominal

nominal

nominal

nominal

ratio

ratio

ratio

ratio

ratio

ratio

ratio

Measurement Values Domain:

Definition

text

Unit	degree
Type	real

Unit	degree
Type	real

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	increasing
Definition	Lake surface water temperature is increasing over time (significance level = 0.10)
Source

Code Definition

Code	no trend
Definition	No trend for lake surface water temperature (significance level = 0.10)
Source

Code Definition

Code	decreasing
Definition	Lake surface water temperature is decreasing over time (significance level = 0.10)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	increasing
Definition	Lake surface water temperature is increasing over time (significance level = 0.05)
Source

Code Definition

Code	no trend
Definition	No trend for lake surface water temperature (significance level = 0.05)
Source

Code Definition

Code	decreasing
Definition	Lake surface water temperature is decreasing over time (significance level = 0.05)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	increasing
Definition	Lake surface water temperature is increasing over time (significance level = 0.01)
Source

Code Definition

Code	no trend
Definition	No trend for lake surface water temperature (significance level = 0.01)
Source

Code Definition

Code	decreasing
Definition	Lake surface water temperature is decreasing over time (significance level = 0.01)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	False
Definition	Trend is absent (significance level = 0.10)
Source

Code Definition

Code	True
Definition	Trend is present (significance level = 0.10)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	False
Definition	Trend is absent (significance level = 0.05)
Source

Code Definition

Code	True
Definition	Trend is present (significance level = 0.05)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	False
Definition	Trend is absent (significance level = 0.01)
Source

Code Definition

Code	True
Definition	Trend is present (significance level = 0.01)
Source

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Degrees Celsius per Year
Type	real

Unit	Unitless
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/02b14ebe0ffe9ed537be4e5cc0baea2e
Name:	ERA5_MODIS_LSWT_night_mk
Description:	This file presents the results of the original Mann-Kendall test applied to predicted yearly average time series of nighttime lake surface water temperatures for 80 lakes located in Southeast Asia during the period from 1986 to 2020. The analysis includes significance levels of 0.01, 0.05, and 0.10.
Number of Records:	80
Number of Columns:	16

Table Structure

Object Name:

ERA5_MODIS_LSWT_night_mk.csv

Size:

16473 byte

Authentication:

9df91e9674882d049d4ed73d5da397d9 Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Latitude

Longitude

trend_10

trend_05

trend_01

h_10

h_05

h_01

p

z

Tau

s

var_s

slope

intercept

Column Name:

ID

Latitude

Longitude

trend_10

trend_05

trend_01

h_10

h_05

h_01

p

z

Tau

s

var_s

slope

intercept

Definition:

HydroLAKES ID

Latitude in decimal degrees

Longitude in decimal degrees

The Mann-Kendall test results determine whether or not there is a monotonic upward or downward trend.

The Mann-Kendall test results determine whether or not there is a monotonic upward or downward trend.

The Mann-Kendall test results determine whether or not there is a monotonic upward or downward trend.

The Mann-Kendall test results determine the presence or absence of a trend.

The Mann-Kendall test results determine the presence or absence of a trend.

The Mann-Kendall test results determine the presence or absence of a trend.

p-value of the significance test

Normalized test statistics

Kendall Tau

Mann-Kendal's score

Variance S

Theil-Sen estimator/slope

Intercept of Kendall-Theil Robust Line

Storage Type:

string

float

float

string

string

string

string

string

string

float

float

float

float

float

float

float

Measurement Type:

nominal

ratio

ratio

nominal

nominal

nominal

nominal

nominal

nominal

ratio

ratio

ratio

ratio

ratio

ratio

ratio

Measurement Values Domain:

Definition

text

Unit	degree
Type	real

Unit	degree
Type	real

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	increasing
Definition	Lake surface water temperature is increasing over time (significance level = 0.10)
Source

Code Definition

Code	no trend
Definition	No trend for lake surface water temperature (significance level = 0.10)
Source

Code Definition

Code	decreasing
Definition	Lake surface water temperature is decreasing over time (significance level = 0.10)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	increasing
Definition	Lake surface water temperature is increasing over time (significance level = 0.05)
Source

Code Definition

Code	no trend
Definition	No trend for lake surface water temperature (significance level = 0.05)
Source

Code Definition

Code	decreasing
Definition	Lake surface water temperature is decreasing over time (significance level = 0.05)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	increasing
Definition	Lake surface water temperature is increasing over time (significance level = 0.01)
Source

Code Definition

Code	no trend
Definition	No trend for lake surface water temperature (significance level = 0.01)
Source

Code Definition

Code	decreasing
Definition	Lake surface water temperature is decreasing over time (significance level = 0.01)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	False
Definition	Trend is absent (significance level = 0.10)
Source

Code Definition

Code	True
Definition	Trend is present (significance level = 0.10)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	False
Definition	Trend is absent (significance level = 0.05)
Source

Code Definition

Code	True
Definition	Trend is present (significance level = 0.05)
Source

Allowed Values and Definitions

Enumerated Domain

Code Definition

Code	False
Definition	Trend is absent (significance level = 0.01)
Source

Code Definition

Code	True
Definition	Trend is present (significance level = 0.01)
Source

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Unitless
Type	real

Unit	Degrees Celsius per Year
Type	real

Unit	Unitless
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/297b49cb5553c597209bf138d6a3172b
Name:	ERA5_MODIS_LSWT_day_prediction
Description:	Predicted daily time series of daytime lake surface water temperatures (LSWT) for 80 lakes located in Southeast Asia from 1986 to 2020. The predictor variables include all columns from column no. 3 (Latitude) to column no. 12 (Wind Speed (m/s)). The predicted LSWT values are located in columns with the _LSWT suffix.
Number of Records:	1022720
Number of Columns:	17

Table Structure

Object Name:

ERA5_MODIS_LSWT_day_prediction.csv

Size:

123631933 byte

Authentication:

fe1b07c36eeaffab34c25d7299407630 Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Date

Latitude

Longitude

Cloud Fraction (%)

Shortwave Radiation (w/m^2)

Relative Humidity (%)

Rainfall (mm)

Max. Temperature (C)

Min. Temperature (C)

Mean Temperature (C)

Wind Speed (m/s)

MLR_Predicted_LSWT

RF_Predicted_LSWT

SVR_Predicted_LSWT

MLP_Predicted_LSWT

XGB_Predicted_LSWT

Column Name:

ID

Date

Latitude

Longitude

Cloud Fraction (%)

Shortwave Radiation (w/m^2)

Relative Humidity (%)

Rainfall (mm)

Max. Temperature (C)

Min. Temperature (C)

Mean Temperature (C)

Wind Speed (m/s)

MLR_Predicted_LSWT

RF_Predicted_LSWT

SVR_Predicted_LSWT

MLP_Predicted_LSWT

XGB_Predicted_LSWT

Definition:

HydroLAKES ID

Date in YYYY-MM-DD format

Latitude in decimal degrees

Longitude in decimal degrees

Cloud fraction

Shortwave radiation

Relative humidity

Rainfall

Maximum air temperature

Minimum air temperature

Mean air temperature

Wind speed

Predicted lake surface water temperature using a multiple linear regression model

Predicted lake surface water temperature using a random forest model

Predicted lake surface water temperature using a support vector regression model

Predicted lake surface water temperature using a multilayer perceptron model

Predicted lake surface water temperature using an extreme gradient boosting model

Storage Type:

string

dateTime

float

float

float

float

float

float

float

float

float

float

float

float

float

float

float

Measurement Type:

nominal

dateTime

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

Measurement Values Domain:

Definition

text

Format	YYYY-MM-DD
Precision

Unit	degree
Type	real

Unit	degree
Type	real

Unit	percent
Type	real

Unit	wattPerMeterSquared
Type	real

Unit	percent
Type	real

Unit	millimeter
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	meterPerSecond
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/c7dc9448c4d3f1231b7a8dcd8f638894
Name:	ERA5_MODIS_LSWT_night_prediction
Description:	Predicted daily time series of nighttime lake surface water temperatures (LSWT) for 80 lakes located in Southeast Asia from 1986 to 2020. The predictor variables include all columns from column no. 3 (Latitude) to column no. 12 (Wind Speed (m/s)). The predicted LSWT values are located in columns with the _LSWT suffix.
Number of Records:	1022720
Number of Columns:	17

Table Structure

Object Name:

ERA5_MODIS_LSWT_night_prediction.csv

Size:

123631333 byte

Authentication:

0a353dd149a3fa68c66f65141a438d3e Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Date

Latitude

Longitude

Cloud Fraction (%)

Shortwave Radiation (w/m^2)

Relative Humidity (%)

Rainfall (mm)

Max. Temperature (C)

Min. Temperature (C)

Mean Temperature (C)

Wind Speed (m/s)

MLR_Predicted_LSWT

RF_Predicted_LSWT

SVR_Predicted_LSWT

MLP_Predicted_LSWT

XGB_Predicted_LSWT

Column Name:

ID

Date

Latitude

Longitude

Cloud Fraction (%)

Shortwave Radiation (w/m^2)

Relative Humidity (%)

Rainfall (mm)

Max. Temperature (C)

Min. Temperature (C)

Mean Temperature (C)

Wind Speed (m/s)

MLR_Predicted_LSWT

RF_Predicted_LSWT

SVR_Predicted_LSWT

MLP_Predicted_LSWT

XGB_Predicted_LSWT

Definition:

HydroLAKES ID

Date in YYYY-MM-DD format

Latitude in decimal degrees

Longitude in decimal degrees

Cloud fraction

Shortwave radiation

Relative humidity

Rainfall

Maximum air temperature

Minimum air temperature

Mean air temperature

Wind speed

Predicted lake surface water temperature using a multiple linear regression model

Predicted lake surface water temperature using a random forest model

Predicted lake surface water temperature using a support vector regression model

Predicted lake surface water temperature using a multilayer perceptron model

Predicted lake surface water temperature using an extreme gradient boosting model

Storage Type:

string

dateTime

float

float

float

float

float

float

float

float

float

float

float

float

float

float

float

Measurement Type:

nominal

dateTime

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

ratio

Measurement Values Domain:

Definition

text

Format	YYYY-MM-DD
Precision

Unit	degree
Type	real

Unit	degree
Type	real

Unit	percent
Type	real

Unit	wattPerMeterSquared
Type	real

Unit	percent
Type	real

Unit	millimeter
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	meterPerSecond
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Unit	celsius
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/899132406139461ccb628cdac83898ea
Name:	table lake geomorphic features
Description:	Table of lake geomorphic features, with columns 4, 6 through 20 extracted from the HydroLAKES dataset
Number of Records:	80
Number of Columns:	21

Table Structure

Object Name:

table lake geomorphic features.csv

Size:

9574 byte

Authentication:

61fea5c6febab45b62cfbff075479eab Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

Hylak_id

Latitude

Longitude

Lake_name

Type

Country

Continent

Lake_type

Lake_area

Shore_len

Shore_dev

Vol_total

Vol_res

Vol_src

Depth_avg

Dis_avg

Res_time

Elevation

Slope_100

Wshd_area

Regulated

Column Name:

Hylak_id

Latitude

Longitude

Lake_name

Type

Country

Continent

Lake_type

Lake_area

Shore_len

Shore_dev

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Wshd_area

Regulated

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Latitude in decimal degrees

Longitude in decimal degrees

Lake name

Lake type (dam, lake, reservoir)

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Continent

Lake type

Lake area

Length of shoreline

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Definition

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Code Definition

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Code Definition

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Predicted lake surface water temperature

Storage Type:

string

dateTime

float

Measurement Type:

nominal

dateTime

ratio

Measurement Values Domain:

Definition

text

Format	YYYY-MM-DD
Precision

Unit	celsius
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/c4bfd52607e0a0ea0d4c426f1718c66a
Name:	MPI-M-MPI-ESM-MR_RegCM4-7_rcp26_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MPI-M-MPI-ESM-MR_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Number of Records:	2746640
Number of Columns:	3

Table Structure

Object Name:

MPI-M-MPI-ESM-MR_RegCM4-7_rcp26_XGB_night_predicted.csv

Size:

65299961 byte

Authentication:

af96d028c98391961cf04675da6c6a58 Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Date

Predicted LSWT

Column Name:

ID

Date

Predicted LSWT

Definition:

HydroLAKES ID

Date in YYYY-MM-DD format

Predicted lake surface water temperature

Storage Type:

string

dateTime

float

Measurement Type:

nominal

dateTime

ratio

Measurement Values Domain:

Definition

text

Format	YYYY-MM-DD
Precision

Unit	celsius
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/014820567a5b939ea21f54c9e502dcf7
Name:	MPI-M-MPI-ESM-MR_RegCM4-7_rcp85_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from MPI-M-MPI-ESM-MR_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Number of Records:	2746640
Number of Columns:	3

Table Structure

Object Name:

MPI-M-MPI-ESM-MR_RegCM4-7_rcp85_XGB_night_predicted.csv

Size:

65300267 byte

Authentication:

21c526295a1bc22259906f9cdee4271f Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Date

Predicted LSWT

Column Name:

ID

Date

Predicted LSWT

Definition:

HydroLAKES ID

Date in YYYY-MM-DD format

Predicted lake surface water temperature

Storage Type:

string

dateTime

float

Measurement Type:

nominal

dateTime

ratio

Measurement Values Domain:

Definition

text

Format	YYYY-MM-DD
Precision

Unit	celsius
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/ade2c791ce045c9d9166416e70e4e237
Name:	NCC-NorESM1-M_RegCM4-7_rcp26_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from NCC-NorESM1-M_RegCM4-7 (RCP 2.6) as predictor variables with an extreme gradient boosting model.
Number of Records:	2744720
Number of Columns:	3

Table Structure

Object Name:

NCC-NorESM1-M_RegCM4-7_rcp26_XGB_night_predicted.csv

Size:

65253381 byte

Authentication:

aa9d42884bac7ea09f36bdd235d499a8 Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Date

Predicted LSWT

Column Name:

ID

Date

Predicted LSWT

Definition:

HydroLAKES ID

Date in YYYY-MM-DD format

Predicted lake surface water temperature

Storage Type:

string

dateTime

float

Measurement Type:

nominal

dateTime

ratio

Measurement Values Domain:

Definition

text

Format	YYYY-MM-DD
Precision

Unit	celsius
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Table


Data:	https://pasta-s.lternet.edu/package/data/eml/edi/1516/1/2bda623b97cb7c0590b2fe9c98371b6c
Name:	NCC-NorESM1-M_RegCM4-7_rcp85_XGB_night_predicted
Description:	Time series of nighttime lake surface water temperature predictions, using climate variables from NCC-NorESM1-M_RegCM4-7 (RCP 8.5) as predictor variables with an extreme gradient boosting model.
Number of Records:	2744800
Number of Columns:	3

Table Structure

Object Name:

NCC-NorESM1-M_RegCM4-7_rcp85_XGB_night_predicted.csv

Size:

65256102 byte

Authentication:

ec9969aa99ae66631ac352b186bef2a3 Calculated By MD5

Text Format:

Number of Header Lines:

1

Record Delimiter:

\r\n

Orientation:

column

Simple Delimited:

Field Delimiter:	,
Quote Character:	"

Table Column Descriptions

ID

Date

Predicted LSWT

Column Name:

ID

Date

Predicted LSWT

Definition:

HydroLAKES ID

Date in YYYY-MM-DD format

Predicted lake surface water temperature

Storage Type:

string

dateTime

float

Measurement Type:

nominal

dateTime

ratio

Measurement Values Domain:

Definition

text

Format	YYYY-MM-DD
Precision

Unit	celsius
Type	real

Missing Value Code:

Accuracy Report:

Accuracy Assessment:

Coverage:

Methods:

Data Package Usage Rights

This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data ("Data User" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is." The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you.

Keywords

By Thesaurus:
LTER Controlled Vocabulary	climate change
(No thesaurus)	lake, trend, Lake Surface Water Temperature (LSWT), machine and deep learning, prediction

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

Methods and protocols used in the collection of this data package

Description:

We used a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable.

Trend Analysis: To assess trends, we applied the Mann-Kendall test and Sen's slope estimator. The input time series is yearly average LSWT.

People and Organizations

Publishers:

Organization:

Environmental Data Initiative

Email Address:

info@edirepository.org

Web Address:

https://edirepository.org

Id:

https://ror.org/0330j0z60

Creators:

Individual:

Salvatore G.P. Virdis

Organization:

Asian Institute of Technology

Position:

Associate Professor

Address:

P.O. Box 4; 58 Moo 9, Km. 42, Paholyothin Highway, Klong Luang, Pathum Thani, 12120, Thailand,

Khlong Luang, 12120 Thailand

Email Address:

virdis@ait.ac.th

Web Address:

https://ait.ac.th/people/dr-salvatore-g-p-virdis/

Id:

https://orcid.org/0000-0003-3927-9494

Individual:

Siwat Kongwarakom

Organization:

Asian Institute of Technology

Position:

Research Assistant

Email Address:

siwatkongwarakom@gmail.com

Contacts:

Individual:

Salvatore G.P. Virdis

Organization:

Asian Institute of Technology

Position:

Associate Professor

Address:

P.O. Box 4; 58 Moo 9, Km. 42, Paholyothin Highway, Klong Luang, Pathum Thani, 12120, Thailand,

Klong Luang, 12120 Thailand

Phone:

+66 2 524 6196 (voice)

Email Address:

virdis@ait.ac.th

Web Address:

https://ait.ac.th/people/dr-salvatore-g-p-virdis/

Id:

https://orcid.org/0000-0003-3927-9494

Individual:

Siwat Kongwarakom

Organization:

Asian Institute of Technology

Position:

Research Assistant

Email Address:

siwatkongwarakom@gmail.com

Associated Parties:

Individual:

Salvatore Virdis

Organization:

Asian Institute of Technology

Position:

Associate Professor

Email Address:

virdis@ait.ac.th

Role:

Data curator

Individual:

Siwat Kongwarakom

Organization:

Asian Institute of Technology

Position:

Research Assistant

Email Address:

siwatkongwarakom@gmail.com

Role:

Data curator

Individual:

Liew Juneng

Organization:

Universiti Kebangsaan Malaysia

Position:

Associate Professor

Email Address:

Role:

Data curator

Individual:

Bachisio Padedda

Organization:

University of Sassari

Position:

Associate Professor

Email Address:

bmpadedda@uniss.it

Role:

Data curator

Individual:

Sangam Shrestha

Organization:

Asian Institute of Technology

Position:

Professor

Email Address:

sangam@ait.ac.th

Role:

Data curator

Metadata Providers:

Individual:

Salvatore Virdis

Organization:

Asian Institute of Technology

Position:

Associate Professor

Email Address:

virdis@ait.ac.th

Id:

https://orcid.org/0000-0003-3927-9494

Individual:

Siwat Kongwarakom

Organization:

Asian Institute of Technology

Position:

Research Assistant

Email Address:

siwatkongwarakom@gmail.com

Temporal, Geographic and Taxonomic Coverage

Temporal, Geographic and/or Taxonomic information that applies to all data in this dataset:

Time Period

Begin:

1986

End:

2100

Geographic Region:

Description:

Mainland Southeast Asia

Bounding Coordinates:

Northern:	35.0	Southern:	0.8
Western:	86.0	Eastern:	117.0

Project

Parent Project Information:

Title:

Climate Change Risk Assessment for Southeast Asian Lakes (CCRASEAL)

Personnel:

Individual:

Salvatore G.P. Virdis

Organization:

Asian Institute of Technology

Position:

Associate Professor

Email Address:

virdis@ait.ac.th

Id:

https://orcid.org/0000-0003-3927-9494

Role:

Principal Investigator

Abstract:

Southeast Asian lakes provide several ecosystem services and are an important natural resource for water supplies, industry, agriculture, shipping, fishing, and recreation. It is demonstrated that they are highly vulnerable to anthropogenic and climate threats. Scientific studies clearly demonstrated that climate change has already significantly affected the SEA region and that these impacts will continue and expand as the pace of climate change accelerates. However, a deep understanding of “if” and “how” climate change, as well as the intensification of land uses may exacerbate those impacts on such vulnerable ecosystems across the whole region is lacking.

CCRASEAL will try to detect possible linking between observed alterations to multiple-threats, to understand if, when and where threats overlap and will define and choose metrics that best quantify the effects of multiple threats and their changes under future scenarios of climate and land uses. CCRASEAL will thus design a regional-scale approach for filling existing knowledge gaps and will provide guidance for addressing the urgent management challenges posed by multiple threats in freshwater ecosystems.

Interdisciplinary in nature, the project has a strategic approach and transdisciplinary outlook to guarantee that the linkage between science and policy at the regional level will be strengthened by actively engaging academic and government partners from 5 different countries in the Indo-Burma region.

Additional Award Information:

Funder:	Asia-Pacific Network for Global Change Research (APN)
Number:	CRRP2020-02MY-Virdis
Title:	Collaborative Regional Research Programme (CRRP)
URL:	https://www.apn-gcr.org/project/climate-change-risk-assessment-for-southeast-asian-lakes-ccraseal/

Maintenance

Maintenance:

Description:	Version 1.0 completed and uploaded on 10 October 2023
Frequency:	asNeeded

Other Metadata

Additional Metadata

additionalMetadata
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Additional Metadata

additionalMetadata
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