Predicting afforestation impacts on monthly streamflow using the DWBM model

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 streamflowobserved. 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.

Forest cover change, climate variability, and hydrological responses

“…Those two case studies clearly demonstrate importance of including both climate and forest changes in assessing hydrological response. This rather holistic approach is also highlighted in several other studies in this special issue (e.g., Zhang, et al, ; Hallema et al, ).…”

“…They found improved water use efficiency in the mixed species as compared to monoculture beech or pine stands and suggest that plantings of pine and beech mixed forest could be an effective climate change adaptation strategy on drought‐prone sites in northern Spain. Zhang, Hickel and Shao () clearly demonstrated the critical role of climate in changing moisture regime, vegetation dynamics, water use efficiency and, consequently, hydrological response to forest disturbance in six large watersheds located in the interior of British Columbia, Canada.…”

Predicting dry‐season flows with a monthly rainfall–runoff model: Performance for gauged and ungauged catchments

“…As suggested above, Monte Carlo runs can be performed to provide confidence intervals around the change in Q min . Additional work on the relationships between catchment characteristics and parameter is in progress with catchments that have pre‐ and post‐afforestation streamflow data (Zhang et al, ). Preliminary results suggest that the relationships hypothesized in Table hold and that regional relationships can be used to predict land‐use change.…”

“…Here, we used dynamic water balance model (DWBM) with a monthly time step (Zhang, Potter, Hickel, Zhang, & Shao, ). The model has four parameters with physical interpretation and was shown to explain flow variations for a large number of catchments in Australia (Zhang et al, ; Zhang, Hickel, & Shao, ). After describing the model and how climate influences its behaviour, we examine the correlations between catchment characteristics and calibrated model parameters.…”

Modeling Hydrological Appraisal of Potential Land Cover Change and Vegetation Dynamics under Environmental Changes in a Forest Basin

“…Land cover change is a global phenomenon and has dramatically altered river basin environments in the past decades [1][2][3][4][5][6]. Although land cover pattern changes certainly provide many socioeconomic benefits, they are also a major driving force in the degradation of natural environments, local river inundation [7], and serious soil degradation [6,8,9].…”