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Diagnostics for Model Structure: Improving Hydrological Models using Data from Experimental Basins Hilary McMillan 1 *, Martyn Clark 1, Guillermo Martinez.

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Presentation on theme: "Diagnostics for Model Structure: Improving Hydrological Models using Data from Experimental Basins Hilary McMillan 1 *, Martyn Clark 1, Guillermo Martinez."— Presentation transcript:

1 Diagnostics for Model Structure: Improving Hydrological Models using Data from Experimental Basins Hilary McMillan 1 *, Martyn Clark 1, Guillermo Martinez 2, Dave Goodrich 3, MS Srinivasan 1, Maurice Duncan 1, Ross Woods 1, Andrew Western 4 1 National Institute of Water and Atmospheric Research, Christchurch, New Zealand 2 Department of Hydrology and Water Resources, University of Arizona, USA 3 US Department of Agriculture, Agricultural Research Service, Tucson, Arizona, USA 4 Department of Civil & Environmental Engineering, University of Melbourne, Australia *h.mcmillan@niwa.co.nz Mahurangi Experimental Catchment, Northland, New Zealand 29 flow gauges (circles) 13 rain gauges (triangles) Satellite Sub-catchment - Intensive monitoring of soil moisture Our aim: Using data to inform modelling decisions Aerial view of Satellite Sub- catchment ? Our research aims to use experimental data collected at Mahurangi to inform the structure(s) of a national hydrological model for New Zealand. We tested many different model structures using a modular modelling framework. All gave good Nash-Sutcliffe fits to the hydrograph. We want to identify structures which give “the right answers for the right reasons”. Examples of modelling decisions: Upper Zone Architecture Lower Zone Architecture ET-available water Productions mechanisms for saturation excess / interflow Recession Analysis Analysis of streamflow recessions can be used to give insight into the storage:discharge behaviour of a watershed, and hence to appropriate lower- zone representations. We study the relationship between flow (Q) and its derivative dQ/dt. A weir used to measure flow in Satellite Catchment Figure: Recession Analysis by season at Satellite Catchment Conclusions No single Q vs dQ/dt relationship exists, so a single baseflow reservoir formulation (e.g. TopModel) is rejected Season controls the proportions of storage in each reservoir, by influencing the recharge history of the catchment Synthetic experiments show that multiple linear perennial reservoirs or a non-linear baseflow reservoir are required to reproduce low-flow behaviour Soil Moisture Analysis In the satellite sub-catchment we have direct measurements of soil moisture. Although there is a clear relationship between soil moisture and flow (figure below), surprisingly there is no relationship between time-derivative of soil moisture and flow. This is consistent with tracer studies at the catchment which suggest that flow is controlled by a deeper reservoir with residence times of months-years, and suggests interflow is less important in this catchment. Soil moisture measurements were made at upper, middle and lower locations on this hillslope, for upper and lower soil layers Soil moisture sensor There is a seasonally-varying threshold in the soil moisture/ flow relationship (left). We suggest that field capacity and wilting point vary with season due to soil cracking. Average soil moisture over storm events reveals strong connectivity between upper and lower soil layers. The need for multiple reservoirs (see recession analysis) probably reflects deeper aquifer systems.


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