Modelling Annual Suspended Sediment Yields in Irish River Catchments

Transcription

1 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, March 2017, JRC, Ispra, Italy

2 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 21 catchments 51 catchment years Catchment size: km 2 Sediment yields: 2.11 to tonnes km -2 year -1

3 Predictor Variables Explored GIS based spatial predictor variables Hydrological and weather variables Spatial Scale A (Catchment Area) (km 2 ) Land Cover Arable (%) Conifer (%) Pasture (%) Natural (%) Peat_LC (%) Soil Properties WD (Well-Drained) (%) PD (Poorly-Drained) (%) Alluv (Mineral Alluvium) (%) Peat_S (%) Topography S1085 (m km -1 ) ALTBAR (m) TAYSLO (m km -1 ) 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) 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 (m 3 s -1 ) Annual Runoff amount (mm)

4 SSY SY A P Pspring Psummer Pautumn Pwinter R Q Runoff WD PD Alluv Peat_S Arable Pasture Conifer Natural Peat_LC S1085 MSL DRAIND ALTBAR NETLEN STMFRQ BFISOIL SAAR FLATWET Correlations Between Predictor Variables Relationship between catchment area and SSY SSY SY A P Pspring Psummer Pautumn Pwinter R Q Runoff WD PD Alluv Peat_S Arable Pasture Conifer Natural Peat_LC S1085 MSL DRAIND ALTBAR NETLEN STMFRQ BFISOIL SAAR FLATWET Scatter plots between sediment yields and some predictor variables Spearman correlation matrix between potential predictor variables

5 Model Building Multiple linear regression models for predicting SSY and SY were fitted to log transformed data 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) The coefficients of this model (developed with the log transformed data) became the exponents in the final nonlinear multiple regression model based on the original (not log transformed) data

6 Models for SY (tonnes year -1 ) Three variable SY model Four variable SY model Five variable SY model Six variable SY model 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 R adj. R BIAS MAE

7 SY Model Validation Split-Sample Tests Variable Calibration Dataset A Validation Dataset B Calibration Dataset B Validation Dataset A N R adjusted R Mean Bias Mean Absolute Error

8 Model Limitations Scale: Catchment size were limited to km 2 Land use: Particular focus on Pasture and Tillage

9 Conclusions (i) (ii) (iii) A database of annual values of suspended sediment yield was constructed for Irish catchments: SSY varied between 2.11 and tonnes km -2 year -1 Regression based models for area specific (SSY) and absolute (SY) sediment yield indicate better fits for SY models as indicated by adjusted R 2 values of up to 0.58 for SSY models and adjusted R 2 value of up to 0.67 for the best SY model. 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)

10

11 Thank You