Variation in the Climate Change Signal of the Discharge for the River Mitano Part 3: Hydrological Model, Downscaling and Lack of Data as Sources of Uncertainty

Uncertainty in hydrological model structure and calibration, and parameterisation uncertainty

Uncertainty in the hydrological model can result during its calibration. For example, Kingston and Taylor (2010) experienced occasionally very poor month-to-month performance of the model, with substantial monthly flow events either missing or erroneously introduced in the modelled time series. They attributed this to discrepancies between the observed (station-based) precipitation data within the Mitano Basin and the CRU TS 3.0 data set used for calibration of hydrological model – in other words, the gridded rainfall data over the Mitano Basin that the model was run with was of dubious accuracy (Kingston and Taylor, 2010). This poor month-to-month performance remained following autocalibration routines and manual calibration of the most sensitive parameters, despite close matching of the flow duration curves (Kingston and Taylor, 2010). Even in the context of possible errors in the observed discharge data of +/- 15% (Mileham et al., 2008), the errors produced were large, showing that some uncertainty pertaining to model calibration will always remain.

Uncertainty in the parameters of the hydrological model (i.e. model parameterisation) is a possible cause for additional uncertainty in climate change projections of freshwater. However, Kingston and Taylor (2010) undertook an uncertainty analysis through manually varying the 10 most sensitive parameters, including evapotranspiration and soil water capacity, in their hydrological model for the Mitano Basin. This indicated that the effects of hydrological model parameter uncertainty on simulated runoff changes are small when compared to those associated with choice of GCM, climate scenario and PET algorithm, in agreement with much literature (Cisneros et al., 2014; e.g. Arnell, 2011; Lawrence and Haddeland, 2011). However this was a limited and subjective assessment only performed on the HadCM3 GCM, so greater parameter uncertainty may be present for GCMs with more extreme climate scenarios.

Uncertainty in downscaling

The estimated impacts of climate change on freshwater resources can be very dependent on the approach used to downscale the climate model data, as demonstrated through systematic evaluations of different methods (Quintana Segui et al., 2010). In fact the range in projected change between downscaling approaches can be as large as the range between different climate models (Chen et al., 2011). Downscaling methods may also encompass assumptions, for example, Kingston and Taylor (2010) downscaled monthly climate data to a daily scale suing the ClimGen weather generator, which is based on the assumption that the pattern of climate change is relatively constant for a given GCM (Arnell and Osborn, 2006) – yet it is questionable if this is always the case.

Uncertainty due to lack of data

As mentioned previously with PET estimation, the projections of freshwater availability are limited by the quality of input data. In the case of Kingston and Taylor (2010), neither temperature nor evaporations data were available for the Mitano basin, so that the closest available observations were from the town of Mbarara, approximately 50 km to the East of the catchment. This would have introduced uncertainty into their models. Furthermore, the Mitano hydrological model was calibrated for the 1961-90 baseline period using daily river discharge data from only a single gauging station within the catchment – where data from more gauging stations would have been preferable (Kingston and Taylor, 2010). Lastly, in validating the hydrological model, the closest rainfall station to the Mitano River Basin in the CRU TS 3.0 data set became unavailable from 1996 onwards, with the new nearest available rainfall station located approximately 115 km from the basin – potentially being the cause of poorer performance of the validation period for the hydrological model compared to the calibration period (Kingston and Taylor, 2010).

Conclusions

This review includes the evaluation of uncertainty due to choice of GCM, estimation of PET, hydrological model structure and calibration, and limitations of the data used. The main outcome is the major dependence on the GCM used, with estimation of PET being another major source of uncertainty. Climate sensitivity is another source of uncertainty (Kingston and Taylor, 2010) which has not been explored in this post, although Cisneros et al. (2014) argue that the relative weightings given to different emissions scenarios are typically less important in determining the distribution of future impacts than the initial selection of GCMs considered. For the near future at least, uncertainty in modelling future precipitation and freshwater resources under climate change will always have some uncertainty, so climate change scenario runs should always be evaluated within the context of all this uncertainty.