Hillslope Hydrology Influences the Spatial and Temporal Patterns of Remotely Sensed Ecosystem Productivity

Abstract

Prediction of ecosystem responses to a changing climate is challenging at the landscape to regional scale, in part because topography creates various habitats and influences ecosystem productivity in complex ways. However, the effects of topography on ecosystem function remain poorly characterized and quantified. To address this knowledge gap, we developed a framework to systematically quantify and evaluate the effects of topographic convergence, elevation, aspect, and forest type on the long-term (1986-2011) average and interannual variability of remotely sensed ecosystem productivity. In a forested watershed in the Rocky Mountains, spanning elevations from 1,800 to 4,000 m, we found a prevalent and positive influence of topographic convergence on long-term productivity. Interannual growing season productivity was positively related to precipitation, with higher sensitivity in low elevation and highly productive areas and lower sensitivity in convergent areas. Our findings highlight the influence of topographic complexity on both long-term and interannual variations of ecosystem productivity and have implications for understanding and prediction of ecosystem dynamics at hillslope to regional scales.

Forest water use is increasingly decoupled from water availability even during severe drought

“…Both Landsat and MODIS analyses identi ed low elevation regions as the most water constrained (Fig. 6), which supports our other ndings that low elevation and downslope portions of the landscape are the most drought sensitive compared to higher elevations or upslope areas (Tai et al, 2020). Low elevation forests below 500m and downslope forests with TWI greater than 10 display the greatest drought sensitivity across sensors and account for 17% and 15%, respectively, of total forested area, underscoring the widespread vulnerability of vegetation due to decreases in lateral ow.…”

“…S1) and the Topographic Wetness Index (TWI) was calculated using the USGS NED as a function of slope and upslope accumulated area (Beven & Kirkby, 1979). The TWI is representative of water availability fed by lateral drainage and has been frequently used to describe hillslope gradients (Hoylman et al, 2018;Hwang et al, 2012;Tai et al, 2020). Lower TWI values correspond to drier upslope landscape positions and higher TWI values correspond to wetter downslope landscape positions.…”

“…Topographic patterns in water availability and microclimate shape landscape vegetation patterns, with increasing LAI and biomass supported by wetter downslope positions compared to drier upslope positions (Hwang, Band, Vose, & Tague, 2012; Hwang et al, 2020;Tai et al, 2020). Further, downslope positions are characterized by tree species that tend to use more water and are more sensitive to drought (Lin et al 2019).…”

Groundwater‐Mediated Memory of Past Climate Controls Water Yield in Snowmelt‐Dominated Catchments

“…Vegetation begins to transpire and plays a major role in the partitioning of snowmelt as soon as water begins to infiltrate soils, even when air temperatures are low and sites remain snow covered (Knowles et al., 2015; Monson et al., 2005). Once transpiration begins, the effects of a changing climate are more straightforward than during the snow covered season, with warmer temperatures increasing ET and decreasing streamflow (Foster et al., 2016; Tai et al., 2020). Catchment‐based approaches at estimating hydrologic partitioning have led to the inference that vegetation develops to use the largest fraction of available precipitation possible (Brooks et al., 2011; Voepel et al., 2011; Zapata‐Rios et al., 2015).…”

Cyclocopula Technique to Study The Relationship Between Two Cyclostationary Time Series With Fractional Brownian Motion Errors

“…The choice of the better model depends on the dependency structure of obligators and is crucial part of the modeling. The applied methods in previous studies include Pearson's correlation coefficient [1][2][3][4][5][6], Spearman's correlation coefficient [7][8][9][10], Kendall's correlation coefficient [11][12][13][14], Sen's slope [15][16][17], cross-correlation function [18][19][20] and copula [21][22][23][24][25][26]. When we face with the relationship of two stationary time series, crosscorrelation function and copula are suggested.…”