The high computational cost of large-scale, process-based hydrological simulations can be approached using variable resolution meshes, where only the region around significant topographic features is refined. However, generating quality variable resolution meshes from digital elevation data is non-trivial. In literature, usually a slope or curvature-based criterion is defined to detect regions of refinement. These techniques often involve a number of free parameters that control the finest and coarsest resolutions, and the transition between fine resolution to coarse resolution. The influence of these parameters on the resulting mesh is usually not well-understood. In order to overcome the large number of free parameters involved, we propose to carry out the Mallat decomposition of the digital elevation data using the Haar wavelet. This gives a nested multilevel representation of the elevation data, split into average coefficients and detail coefficients. Applying hard-thresholding to these detail coefficients assigns a required level of refinement to each data point. This reduces the number of free parameters to exactly one: the acceptable error threshold. In this presentation, we focus on identifying which geomorphometric parameter(s) should be used to steer mesh refinement. We compare zero-inertia model simulation runs on meshes generated by decomposing elevation and slope. We hypothesize that because of the form of the Haar wavelet, the first mesh refinement essentially is using the gradient information, while the latter is using the curvature as refinement criterion. Our results suggest that in high-elevation catchments the curvature of the topography is a far better indicator for refinement than the slope. Using the Mallat decomposition on the tensor of the first derivative of the bed elevation (i.e., bed slope) for mesh refinement yields better agreement in the hydrograph compared to the decomposition of the bed elevation. We present surface runoff results for the Lower Triangle catchment, CO, USA, to illustrate the performance of the wavelet-based local mesh refinement.

A multi-scale understanding of processes controlling the nitrogen budget is essential for predicting how nitrogen loads will be affected by climate-induced disturbances. Recent studies in snowmelt-dominated catchments have documented changes in nitrogen retention over time, such as declines in watershed exports of nitrogen, though there is a limited understanding of the controlling processes driving these trends. Working in the mountainous headwater East River Colorado watershed, our study aims to refine this process-based understanding by exploring the effects of riparian hollows as nitrogen cycling hotspots. The objectives of this study are to (1) quantify the influence of riparian hollows on nitrogen retention in snowmelt-dominated catchments, (2) understand how disturbances (i.e. early snowmelt, long summer droughts) and heterogeneities affect the nitrogen-retention capacity of riparian hollows, and (3) quantify the relative contribution of riparian hollows to the watershed nitrogen budget using high-resolution LIDAR watershed data. We used a multi-component flow and reactive transport model, MIN3P, to simulate the biogeochemical kinetics of riparian hollows, using data from the East River watershed to parameterize, constrain, and validate the model. Several hydrological, biogeochemical, and geological perturbations were then imposed across simulations to assess the effects of abrupt and gradual perturbations on riparian hollow hydrobiogeochemical dynamics. Topographic position and wetness indices were used to scale the net yearly storage and flux terms from riparian hollows, and reveal the significant impacts hollows can have on aggregated watershed biogeochemistry. Initial model results suggest that riparian hollows serve as significant nitrogen sinks, and that earlier snowmelt and extended dry season considerably limit denitrifying processes. Our work linking remote sensing and empirical scaling techniques to numerical biogeochemical simulations is an important first-step in assessing nitrogen-retaining features relative to the watershed nitrogen budget.

Patterns of watershed nitrogen (N) retention and loss are shaped by how watershed biogeochemical processes retain, biogeochemically transform, and lose incoming atmospheric deposition of N. Loss patterns represented by concentration, discharge, and their associated stream exports are important indicators of watershed N retention patterns because they reveal hysteresis patterns (i.e. return to initial state) or one-way transition patterns (i.e. new steady state) that provide insight into watershed conditions driving long term stream trends. We examined the degree to which Continental U.S. (CONUS) scale deposition patterns (wet and dry atmospheric deposition), vegetation trends, and stream trends can be potential indicators of watershed N-saturation and retention conditions, and how watershed N retention and losses vary over space and time. By synthesizing changes and modalities in watershed nitrogen loss patterns based on stream data from 2200 U.S. watersheds over a 50 year record, our work characterized a new hysteresis conceptual model based on factors driving watershed N-retention and loss, including hydrology, atmospheric inputs, land-use, stream temperature, elevation, and vegetation. Our results show that atmospheric deposition and vegetation productivity groups that have strong positive or negative trends over time are associated with patterns of stream loss that uniquely indicate the stage of watershed N-saturation and reveal unique characteristics of watershed N-retention hysteresis patterns. In particular, regions with increasing atmospheric deposition and increasing vegetation health/biomass patterns have the highest N-retention capacity, become increasingly N-saturated over time, and are associated with the strongest declines in stream N exports—a pattern that is consistent across all land cover categories. In particular, the second largest factor explaining watershed N-retention was in-stream temperature and dissolved organic carbon concentration trends, while land-use explained the least amount of variability in watershed N-retention. Our CONUS scale investigation supports an updated hysteresis conceptual model of watershed N-retention and loss, providing great value to using long-term stream monitoring data as indicators of watershed N hysteresis patterns.

We numerically study how the interactions between surface, subsurface, and rainfall create complex runoff behavior in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to understand the effect of surface--subsurface interactions on the hydrological response, we compare model variations with different soil permeability. We compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the domain. Our results show that changing surface--subsurface interactions trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Increased surface--subsurface interaction leads to a higher degree of connectivity in the catchment. Temporal dynamics of both distributed and aggregated hydrological response did not change significantly, which may be because the structural connectivity of the catchment remained untouched. Our results indicate that in higher order catchments, the effect of changes in distributed hydrological response may not always be observable through aggregated hydrological signatures.