Abstract
Understanding the combined and separate effects of climate and land use change on the water cycle is necessary to mitigate negative impacts. However, existing methodologies typically divide data into discrete (before and after) periods, implicitly representing climate and land use as step changes when in reality these changes are often gradual. Here, we introduce a new regression-based methodological framework designed to separate climate and land use effects on any hydrological flux of interest continuously through time, and estimate uncertainty in the contribution of these two drivers. We present two applications in the Yahara River Watershed (Wisconsin, USA) demonstrating how our approach can be used to understand synergistic or antagonistic relationships between land use and climate in either the past or the future: (1) historical streamflow, baseflow, and quickflow in an urbanizing subwatershed; and (2) simulated future evapotranspiration, drainage, and direct runoff from a suite of contrasting climate and land use scenarios for the entire watershed. In the historical analysis, we show that ∼60% of recent streamflow changes can be attributed to climate, with approximately equal contributions from quickflow and baseflow. However, our continuous method reveals that baseflow is significantly increasing through time, primarily due to land use change and potentially influenced by long-term increases in groundwater storage. In the simulation of future changes, we show that all components of the future water balance will respond more strongly to changes in climate than land use, with the largest potential land use effects on drainage. These results indicate that diverse land use change trajectories may counteract each other while the effects of climate are more homogeneous at watershed scales. Therefore, management opportunities to counteract climate change effects will likely be more effective at smaller spatial scales, where land use trajectories are unidirectional.
Highlights
While the overall methodological framework we introduce in Section 2.1.1 and 2.1.2 uses partial least squares regression (PLSR) to generate statistical relationships, our approach will work with any regression model capable of accurately predicting hydrological flux (HF) at a monthly resolution
PLSR has a smaller range of predictions during both the validation and prediction period than principal components regression (PCR) and multiple linear regression (MLR)
Based on the results of this comparison, we elected to use PLSR for the remainder of the analysis presented in the manuscript
Summary
While several point-based studies have found significant impacts of land use change on the water balance (Giménez et al, 2016; Nosetto et al, 2012; Scanlon et al, 2005; Twine et al, 2004), most watershedscale studies have found that the impact of climate change on hydrology outweighs that of land use change, where there are strong changes in precipitation (Chawla & Mujumdar, 2015; Jiang et al, 2015; Li et al, 2009; Mango et al, 2011; Pumo et al, 2017; Tao et al, 2014; Wu et al, 2015; Yang et al, 2017). Existing statistical approaches to separate the effects of climate and land use change have three major limitations. Existing statistical methodologies often implicitly treat land use and climate effects as step-changes by dividing datasets into two or more discrete time periods Stephenson, 2018; Xu et al, 2013; Zhang et al, 2016) This assumption may be problematic because land use and climate often change continuously and in tandem, with gradual hydrological impacts (Jiang et al., 2015; Marhaento et al, 2017). There is a need for improved methods to separate the impacts of climate and land use and their interactions in a continuous time series of hydrological data
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