All I did here was to augment the effect of cloudiness, and that seems to have brought the model's variability in line with the recorded data's. I changed SolRadGr from SolRad*(1-0.5*cloudy) to SolRad*(1-0.12*cloudy).
2. Check out the sensitivity of the model to changes in the parameter that describes the effect of cloudiness. What happens if the effect of clouds is increased quite dramatically (say, tripled)? How can we avoid some of the unrealistic behavior in the model that we observe in this case?
By multiplying the parameter "cloudy" by three, we get hyper-dominance of cloudiness, such that on rather cloudy days, there's no evaporation:
3. What is more important for the rate of evaporation: the latitude of the site, or the climatic conditions? What changes in climate can compensate the effect of the latitude and vice versa?
Latitude has a relatively minor effect on evaopration:
|LatDeg = 20 (model's minimum, in the tropics)|
|LatDeg = 64 (model's maximum, in the arctic)|
On the other hand, we can see from the inter-dial and inter-seasonal variation that climatic conditions exert a strong effect on evaporation. Although the following graph is crowded, it shows that air temperature is the primary driver of evaporation: