Predicting afforestation impacts on monthly streamflow using the DWBM model. Issue 2 (16th February 2017)

• Record Type: Journal Article
• Authors: Zhang, Lu; Hickel, Klaus; Shao, Quanxi
• Other Names: Wei Xiaohua guestEditor.; Winkler Rita guestEditor.; Sun Ge guestEditor.
• Is Part Of: Ecohydrology. Volume 10:Issue 2(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-16
• Subjects: afforestation -- streamflow -- water balance model; Ecohydrology -- Periodicals; Hydrology -- Periodicals; Water -- Environmental aspects -- Periodicals; 577.6
• DOI: 10.1002/eco.1821
• Languages: English
• ISSNs: 1936-0584

Abstract

Abstract: Understanding afforestation impacts on streamflow is important for water resources management. This study presents a predictive method for determining afforestation impacts on streamflow using data from four Australian experimental catchments with considerable forest cover change. Monthly values of rainfall, potential evapotranspiration, and streamflow are available for these catchments, as well as other data including plant available water capacity, minimum and maximum elevations, and index of valley bottom flatness. The proposed method is based on a dynamic water balance model (DWBM) with parameter values estimated from climate and catchment characteristics using projection pursuit regression (PPR). To predict the impacts of afforestation on monthly streamflow, the DWBM model was calibrated for pretreatment conditions and afforestation‐induced changes in the model parameters were determined by adjusting the calibrated model parameter values with PPR predicted model parameter values under pretreatment and posttreatment conditions. Predicted monthly streamflow agreed well with measured streamflow for the period following afforestation. The success of the method indicates that the DWBM model appropriately represents the key catchment processes and characteristics. On the basis of the assessment of model parameter changes, increased storage capacity and evapotranspiration efficiency are the key factors responsible for the reduced monthly streamflow observed. The degree of change in the model parameters due to afforestation is also influenced by other characteristics of the catchments and the local climatic conditions. This study demonstrated the strength of the DWBM model and its ability to predict afforestation impacts on monthly streamflow when combined with the PPR for estimating model parameters.