Testing the assumptions in JULES: chalk soils Nataliya Bulygina, Christina Bakopoulou, Adrian Butler, and Neil McIntyre

Outline 1)Assumptions 2)Methods Data Models 3)Results 4)Summary and conclusions 5)Ways forward

Assumptions A1) The default parameter values are applicable at the point scale A2) Free drainage lower boundary condition A3) No subsurface lateral interactions A4) No groundwater representation

Methods A1) The default parameterisation is applicable at the point scale JULES outputs are compared with observations of: soil moisture measurements, and EA fluxes. A2) Free drainage lower boundary condition Lower boundary effect investigation using a 1D Richards’ equation model A3) No subsurface lateral interactions Subsurface fluxes estimation using a 2D Richards’ equation model A4) No groundwater representation See A2) A 2D Richards’ equation model simulated discharge vs. no GW routing A 2D Richards’ equation model estimated EA fluxes from UZ and GW

Methods ▪Data Grimsbury Wood (Pang catchment) (and other sites to be decided) 1 Jan, 2005 – 30 Jun, 2005 Met. data (30-min) AE fluxes from HYDRA Soil moisture probes (.1,.2,.3,.4,.6, and 1 m) Pang and Lambourne long term data Jan, 1961 – Mar, 2003 Met. Data (daily)

Methods ▪Models 1)1D Richards’ equation-based model (CUZ model of Ireson et al, 2009) 2)2D Richards’ equation-based model for a hillslope (2D CUZ of Ireson) Hillslope topography Mesh used in the hillslope 2D model

Results A1) The default parameterisation is applicable at the point scale Soil moisture for the layer 1 exhibits the best agreement Soil moisture in the layer 2 and 3 is under-estimated

Results A1) The default parameterisation is applicable at the point scale JULES under-estimates AE in winter-spring, and over-estimates in summer

Results A2) Free drainage lower boundary condition Free drainage condition at the bottom of 3m soil column (JULES type) vs. water table condition at the bottom of 40m column (the original CUZ setup) The JULES column is drier: 4.5% more water drainage & EA is 1.2% lower. More variable soil moisture in the JULES column (especially, in the deeper layers).

Results A3) No subsurface lateral interactions Total net and lateral net fluxes in the UZ located in the middle of a hillslope Lateral fluxes in the UZ are close to 0.

Results A4) No groundwater representation UZ drainage routed via GW vs. directly delivered to the river (no routing – as in JULES) No GW routing leads to flushy response in rainy periods & flow underestimation in dry periods.

Results A4) No groundwater representation EA fluxes from UZ and SZ EA from the SZ is close to 0, and is negligible relative to EA from the UZ.

Summary and conclusions In the context of these tests on chalk soils: The default JULES parameterisation is questionable. Improper boundary condition leads to inadequate soil moisture variability. Lower boundary condition has a little effect on EA rates. Lateral fluxes between columns can be neglected. Lack of groundwater leads to significantly different discharge patterns. EA extracted from the groundwater is negligible. Therefore Other parameter estimation options are to be explored, and Groundwater representation and its interaction with the soil column can significantly improve soil moisture and discharge estimation in JULES, whilst it might only have minor effects on EA estimation. But wider range of sites need to be considered

Ways forward Is JULES model structure, or the default parameterisation flawed? Parameter sensitivity analysis Model structure supported prediction ranges Model calibration JULES assumptions for low permeability soils in the Thames (ANY suggestions about locations / data?) Consider coupling with a groundwater model ZOOM (BGS) Various simplifications