Main findings from the analysis were:
- Budyko in its simplicity, worked well to partition P into Q and AET, even in this small headwater catchments.
-WS4 was most sensitive to changes in P than to changes in PET (Sensitivity coefficient = 1.8 ; PET’s sensitivity coeffitient = 0.8).
- Calibrated n value (1.7406) is similar to reported in the literature for global forest (n=1.8) (Choudhury 1999).
- Budyko’s AETc performs well in finding means, and it shows similar variability to the one reported in the literature.
What is next? This analysis was useful to understand how a sensitivity analysis can be carried out using equations and how the Budyko framework would work in a small head water catchment. Further studies will:
- Look deeper into the results for the Fernow.
-Consider other sensitivity analysis to understand the what approaches are appropriate.
-Study the mysterious “n” landscape parameter.
- Apply the approach to different reference catchments in the US and Canada across a wide range of ecosystems.
- Budyko in its simplicity, worked well to partition P into Q and AET, even in this small headwater catchments.
-WS4 was most sensitive to changes in P than to changes in PET (Sensitivity coefficient = 1.8 ; PET’s sensitivity coeffitient = 0.8).
- Calibrated n value (1.7406) is similar to reported in the literature for global forest (n=1.8) (Choudhury 1999).
- Budyko’s AETc performs well in finding means, and it shows similar variability to the one reported in the literature.
What is next? This analysis was useful to understand how a sensitivity analysis can be carried out using equations and how the Budyko framework would work in a small head water catchment. Further studies will:
- Look deeper into the results for the Fernow.
-Consider other sensitivity analysis to understand the what approaches are appropriate.
-Study the mysterious “n” landscape parameter.
- Apply the approach to different reference catchments in the US and Canada across a wide range of ecosystems.