Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (L) and volumetric soil water content (θ) on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of L and θ. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of L, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in L and decline in θ. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests.

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1Soil properties and terrain characteristics influence spatiotemporal patterns of soil 2 moisture across a watershed. To improve the predictive power of landscape hydrology models, it 3 is essential to consider both soil and terrain attributes when stratifying a catchment into similar 4 hydrologic functional units. In this study, we developed and validated a new catchment-scale 5 stratification scheme for the Shale Hills watershed by combining soil and terrain attributes in an 6 attempt to delineate soil-landscape units with similar soil moisture dynamics. Terrain was 7 combined with soils information by first using a Random Forest supervised classification 8 algorithm to predict a detailed soil map using 47 field soil samples and terrain variables derived 9 from 1-m LiDAR. A slope class map generated from the LiDAR-derived digital elevation model 10 (DEM) was overlaid on the predicted soil map to delineate areas of similar slope value across the 11 hillslope. We compared the performance of this new stratification scheme with two classical 12 stratification schemes, a soil map developed from detailed field survey and a landform unit map 13 based on the DEM, for estimating soil moisture time-series across the forested watershed. The 14 combined soil-terrain method outperformed classical stratification schemes in estimating soil 15 moisture time-series over a 4-year period. Our results demonstrate that combining soil and 16 terrain attributes can help improve the stratification of a catchment into similar soil hydrologic 17 functional units, which is valuable to distributed hydrology modeling and other applications. 18 19Soil type 21 Understanding the link between soil moisture patterns and landscape features is critical to 23 improving landscape hydrologic modeling (Band et al., 1993; Pauwels et al., 2001; Yu et al., 24 2014). A common assumption in catchment hydrology is that terrain places a dominant control 25 on hydrologic functions (Beven and Kirkby, 1979; Winter, 2001). This assumption leads many 26 researchers to parameterize hydrologic models based on landforms or sub-catchment units using 27 terrain alone. Since topographic information in the form of digital elevation models (DEM) has 28 been increasingly available, stratifying catchments into similar hydrologic functioning units with 29 terrain has been widespread (Moore et al., 1991; Bloschl and Sivapalan, 1995; Winter, 2001). 30However, field-based soil properties are often not directly included in these stratification 31 schemes, and terrain is assumed to be a proxy for inferring soil properties. These assumptions 32 remain largely unchallenged, since many catchment hydrologic studies do not validate terrain-33 based sub-catchment units using in situ collected soil moisture data or compare model 34 performance with actual soil distributions. 35Topographically-based stratification approaches have been continuously improved over 36 time with advancements in GIS and remote sensing technologies. Following the conceptual 37 work by Beven and Kirkby ...

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