MODELLING APPROACHES - SOIL-MOISTURE BALANCE MODELLING

During yesterday's lecture on environmental modelling, Pf. Richard Taylor demonstrated an accounting model of groundwater recharge. His talk was very informative and entertaining, building up from the basics of groundwater systems to reach some profound conclusions, such as the tendency for global climate models (GCM) to overlook subsurface water flow and where that philosophy might originate. 

I'd like to expand on many of the modelling themes that he touched on. The accounting model was based on a 55yr dataset from Uganda, the details of which can be found here: Taylor, R.G., Todd, M., Kongola, L., Nahozya, E., Maurice, L., Sanga, H. and MacDonald, A., Evidence of the dependence of groundwater resources on extreme rainfall in East Africa. Nature Climate Change, Vol. 3, pp.374-378 (2013).

The accounting model looks at the water level of the saturated zone. A groundwater recharge event raises the water table. From the UK Groundwater Forum.


Taylor first  mentioned the WaterGAP model, as used in Portmann, F. et al., Impact of climate change on renewable groundwater resources: assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections. Environmental Research Letters, Vol.8 (2013). Broadly speaking, the model was used by Portmann et al. to project future groundwater recharge (GWR). The relevant point here is that the GWR estimates were not calibrated.

In my post on the Oreskes paper it was seen that it is often difficult to calibrate components of complex models, due to small inputs generalized into large model inputs, feedback with other components, and selective or non-existent data on model inputs. In this case, no global observational data of GWR exists, making it impossible to calibrate WaterGAP's predictions. 

There is another serious shortcoming in the WaterGAP's GWR forecast - it determines a priori that greater precipitation leads to greater surface runoff, so it cannot be used to test this hypothesis. This arises because the model uses a simple cutoff threshold, whereby excess precipitation over this threshold goes straight to runoff. This precludes the possibility of alternate phenomena such as increased GWR.

As the Taylor et al. paper shows, the evidence from Uganda presents a different view. GWR only significantly occurs during extreme precipitation events. For example, the Makutapora record shows the 4-month 1997-98 ENSO contributed 25% of the total GWR in the 55 year record. Drawing two conclusions about modelling in the talk, Taylor (paraphrased) stated that models should "utilise historical data to [disprove] hypotheses" and "properly structured GWR models give better predictions", echoing the general sentiment from Oreskes et al.

Recalling an unfortunately futile effort by some hydrologists for representation in the IPCC's 5th assessment report, Taylor noted that GCMs tended to use a landsurface with no subsurface component. This reduced familiarity may lead to GCM modellers being less confident linking the subsurface component with climate change, misrepresenting GWR changes. 

Returning to the Taylor et al. model, Taylor outlined how a less complex accounting model based on local data was developed. It was built "from the ground up", using the empirical data. The model's simplicity came in part from the absence of physical knowledge of the flow of groundwater in that part of Uganda. The observed GWR was much more rapid than the most appropriate physical law, Richard's equation, would predict. It thus did not model process, but the water content in different parts of the ground over time (hence the "accounting" moniker).

In outlaying how the model extrapolated from the data into 2070, Taylor noted that most GCMs were trained by looking at past daily rainfall and distributing it in the most statistically appropriate way in the future (a "delta approach"). However, since a warmer world has less frequent and more intense precipitation (e.g. Pall et al., Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO₂ warming. Climate Dynamics, Volume 28, Issue 4, pp 351-363 (March 2007)), the delta approach underestimates GWR from intense precipitation in a warmer world. This is especially true if focus recharge, such as from floods, become more common as a result of higher precipitation - another aspect that GCMs neglect.

Taylor made two other points that resonated with my physics background. In justifying Excel for modelling, he pointed out that "few colleagues have Matlab licenses or R experience, whereas everyone has Excel" and "simple models with calibrated variables are better than complex models with broad, uncalibrated components". A models' end-users, the modelling community, as well as Occam's Razor, will be considered further on this blog in the future.