Nonstationary Hydrologic Behavior in Forested Watersheds Is Mediated by Climate‐Induced Changes in Growing Season Length and Subsequent Vegetation Growth

Abstract

Forested watersheds provide important ecosystem services through the provision of high quality freshwater, mitigation of floods, and maintenance of base flows. How alteration of these services under ongoing climate change is mediated by vegetation dynamics is not fully understood. Combining independent remote sensing based vegetation information and distributed hydrological modeling, we investigated the impact of climate‐induced vegetation dynamics on long‐term non‐stationary hydrologic behavior in two forested watersheds in the southern Appalachians. We found significant increases in precipitation‐runoff deficit (defined as annual precipitation minus annual runoff), equivalent to annual evapotranspiration plus storage changes, over the last three decades. This non‐stationary hydrologic behavior was significantly correlated with long‐term and interannual changes in growing season length and subsequent vegetation growth. These patterns in vegetation phenology were attributed primarily to minimum temperature regimes, which showed steeper and more consistent increases than temperature maxima. Using a distributed modeling framework, we also found that the long‐term non‐stationary hydrologic behavior could not be simulated unless full vegetation dynamics, including vegetation phenology and long‐term growth, were incorporated into the model. Incorporating seasonal vegetation dynamics also led to the improved simulation in streamflow dynamics, while its effect spread out through the following dormant seasons. Our study indicates that non‐stationary hydrologic behavior has been closely mediated by long‐term seasonal and structural forest canopy interaction with climate variables rather than directly driven by climatic variables. This study emphasizes the importance of understanding the ecosystem responses to ongoing climate change for predictions of future freshwater regimes.

Twenty-First Century Streamflow and Climate Change in Forest Catchments of the Central Appalachian Mountains Region, US

“…Because our analysis focused on HCDN catchments, we assumed that catchment properties (parameter n) do not change in the future, thereby setting the n sensitivity coefficient and dn to 0. We recognize that this assumption is likely an oversimplification of future landscape conditions, particularly in light of changes in forest structure, age, productivity, and growing season length [32,68,69] in relatively undisturbed catchments throughout the region. Future analysis should consider dn to more thoroughly account for ecosystem changes important to the partition of P into E and Q.…”

“…Since 1951, the monthly Q throughout the region has increased by 1 to 2 mm/year, with the largest increases during the fall and winter seasons due to increases in precipitation [29]. Coincidentally, the forest growing season length throughout the region has increased on average by 22 days since 1983 [30] as has E, which has increased between 0.4 to 4 mm/year [30][31][32].…”

Modelling the interaction of climate, forest ecosystem, and hydrology to estimate catchment dissolved organic carbon export

“…Previous research has demonstrated that incorporation of dynamic vegetation phenology in ecohydrologic models improves streamflow (Kim et al, 2018) and vegetation water use/growth (Hwang et al, ) predictions. Our study shows that inter‐annual variability of vegetation phenology improves timing and magnitude of stream DOC prediction in a forested watershed.…”

“…For example, Kim et al (2018) showed that accounting for inter‐annual phenology variation in a watershed model for Harvard Forest watershed in the north‐eastern United States improved predictions of ET/GPP and catchment discharge in both growing seasons and following dormant seasons. Accounting for climate‐dependent variable phenology will likely be more important under future climate conditions, as inter‐annual climate variability is expected to increase, accompanied by a shift in the seasonal pattern of vegetation phenology (Hayhoe et al, , Hwang et al, ).…”

“…In this study, we used the predefined threshold values of each GSI (minimum temperature, VPD, and photo period) from Jolly et al (2005). Our modelling study added additional validation of GSI (Hayhoe et al, 2007, Hwang et al, 2018.…”