Mountainous watersheds are characterized by variability in functional traits, including vegetation, topography, geology, and geomorphology, which together determine nitrogen (N) retention, and release. Coal Creek and East River are two contrasting catchments within the Upper Colorado River Basin that differ markedly in total nitrate (NO3-) export. The East River has a diverse vegetation cover, sinuous floodplains, and is underlain by N-rich marine shale, resulting in a three to twelve times greater total NO3- export relative to the conifer-dominated Coal Creek. While this can partly be explained by the larger size of the East River, the distinct watershed traits of these two catchments imply different mechanisms controlling the aggregate N-export signal. A causality analysis shows biogenic and geogenic processes were critical in determining NO3- export from the East River catchment. Stable isotope ratios of NO3- (δ15NNO3 and δ18ONO3) show the East River catchment is a strong hotspot for biogeochemical processing of NO3- at the soil-saprolite interface and within the floodplain prior to export. By contrast, the conifer-dominated Coal Creek retained nearly all (~97 %) atmospherically-deposited NO3-, and its export was controlled by catchment hydrological traits (i.e., snowmelt periods and water table depth). The conservative N-cycle within Coal Creek is likely due to the abundance of conifer trees, and a smaller riparian region, retaining more NO3- overall and reduced processing prior to export. This study highlights the value of integrating isotope systematics to link watershed functional traits to mechanisms of watershed element retention and release.

Understanding the partitioning of snow and rain contributing to either catchment streamflow or evapotranspiration (ET) is of critical relevance for water management in response to climate change. To investigate this partitioning, we use endmember splitting and mixing analyses based on stable isotope (18O) data from nine headwater catchments in the East River, Colorado. Our results show that one third of the snow partitions to ET and 13% of the snowmelt sustains summer streamflow. Only 8% of the rainfall contributes to the summer streamflow, because most of the rain (67%) partitions to ET. The spatial variability of precipitation partitioning is mainly driven by aspect and tree density across the sub-catchments. Catchments with higher tree density have a higher share of snow becoming ET, resulting in less snow in summer streamflow. Summer streamflow did not contain more rain with higher rainfall sums, but more rain was taken up in ET.

As a result of climate change, California is experiencing the impact of more extreme weather patterns including longer drought periods and atmospheric rivers resulting in extreme snow pack and heavy flood flows. CA faces a significant challenge to mitigate these impacts while simultaneously providing resilient sources of water under uncertain future conditions. One approach that addresses both flood mitigation and water storage is the use of Managed Aquifer Recharge (MAR). Ventura County Waterworks District #1 (VCWWD) is designing a MAR recharge facility to divert flood flows in the adjacent Arroyo Las Posas to a series of engineered basins, where water will infiltrate and replenish the local aquifer (estimated recharge: 3000 acre-feet annually). However, large uncertainties in percolation rates and an inability to predict or improve percolation (measured: 5 and 16 cm/day) places large uncertainties on the facility’s ultimate performance (and impact) on VCWWD’s overall strategy for sustainable groundwater management. The goals of this project are to use a suite of geophysical techniques, point sensors and novel modeling approaches to measure the basin(s) spatial recharge rates, where and how the water is infiltrating (fast paths) and how will basin modification improve recharge rates. Selected basins will first be characterized using electromagnetic methods and electrical resistivity tomography (ERT) coupled with soil cores to estimate the distribution of subsurface permeability in order to design the infiltration monitoring layout. During managed flooding events Spontaneous Potential will be used to monitor subsurface leakage from the basins back into the river. Within a basin, novel vertical Distributed Temperature Profiling sensors will measure diurnal temperature fluxes to calculate spatially distributed 1-D vertical recharge rates and 3D time-lapse ERT to monitor and measure the spatially dynamic recharge. ERT results will be coupled with multi-point geostatistical simulations to estimate soil permeability field scenarios and with novel joint inversion codes to estimate volumetric recharge and rates, offering a powerful suite of tools for water managers to quantify, and potentially improve basin recharge rates and develop operational and maintenance plans to maximize recharge.

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.