Modelling Annual Suspended Sediment Yields in Irish River Catchments

Rymszewicz, A., Bruen, M., O’Sullivan, J.J., Turner, J.N., Lawler, D.M., Harrington, J., Conroy, E., Kelly-Quinn, M.

SILTFLUX Project

Soil Erosion Modelling Workshop, 20-22 March 2017, JRC, Ispra, Italy

• 21 catchments• 51 catchment years• Catchment size: 3.3 - 992.7 km2

• Sediment yields: 2.11 to 48.39 tonnes km-2 year-1

Objective and Study Sites

Objective: Model for annual sediment yields for Irish ungauged catchments• developing sediment database for Irish catchments• exploring controlling factors• considering spatial and temporal variations• model simplicity and inclusion of GIS datasets• minimum number of meaningful predictor variables

Study Sites

Land Cover• Arable (%)• Conifer (%)• Pasture (%)• Natural (%)• Peat_LC (%)

Soil Properties• WD (Well-Drained) (%)• PD (Poorly-Drained) (%)• Alluv (Mineral Alluvium) (%)• Peat_S (%)

Drainage Network• MSL (Mainstream Length) (km)• DRAIND (Drainage Density) (km-1)

• NETLEN (Length of U/S Hydrol. Network) (km)• STMFRQ (Stream Frequency) (dimensionless)• BFISOIL (Baseflow Index) (dimensionless)

Climate• SAAPE (Standard Average Annual Potential

Evapotranspiration) (mm year-1)• SAAR (Standard Period Average Annual Rainfall) (mm year-1)• FLATWET (Index of Wetness) (dimensionless)

Spatial Scale• A (Catchment Area) (km2)

Predictor Variables Explored

Hydrological descriptors• P –Total Annual Rainfall (mm)• Pspring (March – May Rainfall) (mm)• Psummer (June – August Rainfall) (mm)• Pautumn (September – November Rainfall) (mm)• Pwinter (December – February Rainfall) (mm)• R – Rainfall Erosivity Factor (MJ mm ha-1 h-1 year-1)• Q – Mean Annual Discharge (m3 s-1)

• Annual Runoff amount (mm)

Topography• S1085 (m km-1)• ALTBAR (m)• TAYSLO (m km-1)

GIS based spatial predictor variables Hydrological and weather variables

Correlations Between Predictor Variables

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

SS

YS

Y

A P Pspring

Psum

mer

Pautu

mn

Pw

inte

r

R Q Runoff

WD

PD

Allu

vP

eat_

S

Ara

ble

Pastu

reC

onifer

Natu

ral

Peat_

LC

S1085

MS

L

DR

AIN

DA

LT

BA

R

NE

TLE

NS

TM

FR

Q

BF

ISO

ILS

AA

R

FLA

TW

ET

SSYSY

AP

PspringPsummer

PautumnPwinter

RQ

RunoffWD

PDAlluv

Peat_SArable

PastureConifer

NaturalPeat_LC

S1085MSL

DRAINDALTBAR

NETLENSTMFRQ

BFISOILSAAR

FLATWET

Spearman correlation matrix between potential predictor variables

Relationship between catchment area and SSY

Scatter plots between sediment yields and some predictor variables

Model Building

A stepwise regression analysis combining backward and forward inclusion/ exclusion of individual predictors was used to select good combinations of predictor variables

Informed decisions were made to exclude variables that were correlated strongly with others in the regression (e.g. MSL and NETLEN was one such combination)

Multiple linear regression models for predicting SSY and SY were fitted to log transformed data

The coefficients of this model (developed with the logtransformed data) became the exponents in the final non-linear multiple regression model based on the original (notlog transformed) data

Four variable SY model

Five variable SY model

Three variable SY model

Six variable SY model

Models for SY (tonnes year-1)

Measure

of fit

Six parameter SY

model

(Pspring)

Six parameter

SY model

(Pwinter)

Six parameter SY

model

(Pwinter+Pspring)

Five parameter

SY model

Four parameter SY

model

Three

parameter SY

model

R2 0.66 0.67 0.65 0.7 0.63 0.66

adj. R2 0.61 0.62 0.60 0.67 0.6 0.64

BIAS 33.28 45.35 45.42 27.49 55.91 45.94

MAE 463.09 476.76 483.36 448.77 502.96 444.90

SY Model Validation – Split-Sample Tests

Variable Calibration

Dataset A

Validation

Dataset B

Calibration

Dataset B

Validation

Dataset A

N 26 25 25 26

R2 0.64 0.49 0.71 0.74

adjusted R2 0.6 0.42 0.67 0.7

Mean Bias 57.16 156.33 30.12 131.05

Mean Absolute Error 570.65 398.15 345.38 722.23

Model Limitations

• Scale: Catchment size were limited to 3.3 - 992.7 km2

• Land use:Particular focus on Pasture and Tillage

Conclusions

(i) A database of annual values of suspended sediment yield was constructed for Irish catchments: SSY varied between 2.11 and 48.39 tonnes km-2 year-1

(ii) Regression based models for area specific (SSY) and absolute (SY) sediment yield indicate better fits for SY models as indicated by adjusted R2 values of up to 0.58 for SSY models and adjusted R2 value of up to 0.67 for the best SY model.

(iii) Validation of SY model based on the split-sample datasets indicate that the model is relatively robust, however limitations exist (possibly due to the bias of the calibration dataset towards catchments of a smaller size and particular focus on the pasture and arable land use)

Thank You