Modelling Annual Suspended Sediment Yields in Irish River...
Transcript of Modelling Annual Suspended Sediment Yields in Irish River...
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
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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