Aubrey Dugger

Streamflow observations play a critical role in hydrologic modeling, supporting a wide range of applications. Although increasing the density of streamflow measurements is desirable, the installation and maintenance of new streamgauges can be time intensive and cost prohibitive. Strategic placement of new observation sites is desired both in terms of cost associated with their operation, and also the desire to make the benefits of the observation optimal for its applications. This study explores the application of observing system simulation experiments (OSSEs) to guide the placement of streamgauges within the Delaware River Basin. The results show that OSSEs can help guide selection of candidate locations for new streamgauges within an existing observing network. This study also assesses the impact of existing U.S. Geological Survey streamgauges, particularly those deployed as part of the Next Generation Water Observing System (NGWOS) in improving hydrologic model performance through data assimilation (DA). The HydroDART framework that couples the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) with the Data Assimilation Research Testbed (DART) is used here to evaluate the impact of these gauges. Findings indicate that applying DA using non‐NGWOS gauges improves the median Kling‐Gupta efficiency (KGE) from 0.42 to 0.71 for high‐flow events. Inclusion of NGWOS gauges further increases the median KGE value to 0.76. Using advanced data assimilation methods such as the hybrid ensemble Kalman filter‐optimal interpolation (EnKF‐OI) substantially improves the prediction skill emphasizing the importance of blending in climatological information, in addition to the flow‐dependent ensemble, to make the most of available observations.

Wildfire-induced changes to vegetation and soil properties can alter snowpack dynamics and hydrologic processes in ways that complicate predictions of post-wildfire water availability. To better understand these effects, we coupled the Parameter ESTimation in C++ (PEST++) software with the Weather Research & Forecasting Hydrologic model (WRF-Hydro) across three burned, snow-dominated watersheds in the western United States. We incorporated three burn-severity-based land cover categories into WRF-Hydro and used an iterative Ensemble Smoother (iES) within PEST++ to estimate model parameters controlling snow accumulation and melt, infiltration, and streamflow generation. This framework supported three complementary objectives: (1) assessing parameter sensitivities under pre- and post-wildfire conditions, (2) comparing pre- and post-wildfire parameter ensembles to identify wildfire-driven shifts in hydrologic processes, and (3) using counterfactual simulations to isolate the hydrologic response to wildfire from interannual climate variability. Our findings indicate that model parameter sensitivity varied by domain and shifted following wildfire. Maximum snow albedo and snow albedo decay were most influential in the higher-elevation East Troublesome domain, while the rain-snow partitioning temperature threshold dominated in the lower-elevation Chippy Creek and Las Conchas domains. Following wildfire, streamflow sensitivity shifted away from snowpack parameters toward soil hydraulic parameters, consistent with wildfire-driven reductions in evapotranspiration and increases in soil moisture. Incorporating wildfire effects led to complex changes in simulated snowpack accumulation and streamflow timing, generally leading to earlier snowmelt freshet initiation and cessation and reduced late season flows. Simulations were more sensitive to interannual climate variations than wildfire land cover changes. These results highlight both the potential and challenges of ensemble-based inverse modeling for understanding post-wildfire hydrologic change in snow-dominated watersheds.

Determining the age distribution of water exiting a catchment is important for understanding groundwater storage and mixing. New water‐tagging capabilities within models track precipitation events as they move through simulated storages, yet forward modeling of individual events may not systematically capture the full transit time distribution (TTD). Here, we present a “sequential precipitation input tagging” (SPIT) framework to tag all input precipitation at regular intervals during extended model simulations. Monthly tags over 7 years were applied at six National Ecological Observatory Network sites to calculate TTDs and derive mean virtual tracer age, , fractions of young water, F _yw , and hydrologic tracer concentrations (water isotopes δ ¹⁸ O and δ ² H) within a tagging enabled version of the Weather Research and Forecast hydrologic model (WRF‐Hydro). Throughout seven simulation years, the fraction of simulated discharge derived from tagged events, F _tag , increased each year, with the final year's F _tag ranging from 66% to 100% and highlights the need to apply SPIT over many years to understand TTDs. When the F _tag was >75%, simulated ranged 179–923 days and F _yw 0.6%–23.9%, with daily values exhibiting a power‐law relationship with precipitation, discharge, and groundwater. Through implementation of SPIT, we find this hydrologic model configuration performs poorly in estimation of and F _yw (root mean squared error of 469 days and 14.4% respectively), suggesting it misrepresents subsurface mixing. Thus, the SPIT framework provides a reproducible approach to calculate watershed transit times within tagging enabled models and thereby assess and improve representation of hydrologic processes.

The WRF‐Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open‐source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF‐Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, in particular we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.

The hydrologic community uses geochemical tracers to determine the age distribution of water exiting a catchment, with transit time distributions (TTDs) important for understanding groundwater storage and mixing. New water-tagging capabilities within models track precipitation events as they move through simulated storages. Here, we present a ‘sequential precipitation input tagging’ (SPIT) framework to tag all input precipitation events at regular intervals over an extended period (monthly tags over seven years). SPIT is applied at six National Ecological Observatory Network sites to calculate TTDs and derive from these mean transit times (MTT), fractions of young water (Fyw), and hydrologic tracer concentrations (δQ-δ18O and δ2H) within a water-tagging enabled version of the Weather Research and Forecast hydrologic model. Throughout seven simulation years, the fraction of simulated discharge derived from tagged events increased each year, with the final year’s tagged stream water fraction (TSWF) ranging 21% to 100%. When the TSWF was ≥75%, simulated MTTs range 190 days to 850 days and Fyw 1% to 24%, with a root mean squared error (RMSE) of 456 days and 14.5%. The RMSE for δ18O is 1.08‰ and δ2H 6.58‰. Low TSWF values early in the simulation period highlights the need to apply SPIT over many years to fully understand the TTD. At daily timescales, model MTT and Fyw exhibit a power-law relationship with precipitation, discharge, and groundwater. The successful implementation of SPIT within a tracer-enabled version of an operational hydrologic model allows for a reproducible approach to calculate water transit times and hydrologic tracers.

Most current land models approximate terrestrial hydrological processes as one‐dimensional vertical flow, neglecting lateral water movement from ridges to valleys. Such lateral flow is fundamental at catchment scales and becomes crucial for finer‐scale land models. To test the effect of incorporating lateral flow toward three‐dimensional representations of hydrological processes in the next generation land models, we integrate a water tracer model into the WRF‐Hydro framework to track water movement from precipitation to discharge and evapotranspiration. This hydrologic‐tracer integrated system allows us to identify the key mechanisms by which lateral flow affects the flow paths and transit times in WRF‐Hydro. By comparing modeling experiments with and without lateral routing in two contrasting catchments, we determine the impacts of lateral flow on the transit times of precipitation event‐water. Results show that with limited hydrologic connectivity, lateral flow extends the transit times by reducing (increasing) event‐water drainage loss (accumulation) in ridges (valleys) and allowing reinfiltration of infiltration‐excess flow, which is missing in most land models. On the contrary with high hydrologic connectivity, lateral flow can effectively accelerate the water release to streams and reduce the transit time. However, the transit times are substantially underestimated by the model compared with isotope‐derived estimates, indicating model limitations in representing flow paths and transit times. This study provides some insights on the fundamental differences in terrestrial hydrology simulated by land models with and without lateral flow representation.

Terrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances.

The National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) provides analyses and predictions of hydrologic variables relevant to drought monitoring and forecasts at fine time and space scales (hourly, 0.25–1 km). We present results exploring the potential for NWM soil moisture and streamflow analyses to inform operational drought monitoring. Both agricultural and hydrologic drought monitoring rely either explicitly or implicitly on an accurate representation of anomalous soil moisture values. Much of our analysis focuses on comparisons of soil moisture anomalies in the NWM to those from in‐situ observations. To establish benchmarks for NWM soil moisture skill, we also include other gridded data sets currently used to inform the US Drought Monitor, specifically those from the North American Land Data Assimilation System phase 2 (NLDAS‐2) land surface models. We then compare NWM streamflow low flows with ∼500 stream gauges from the United States Geological Survey (USGS) Hydro‐Climatic Data Network of undisturbed basins. The NWM soil moisture simulation’s skill parallels that from NLDAS‐2. The accuracy of drought condition identification from NWM streamflow exceeds that based on soil moisture as determined by Critical Success Index scores for extreme dry percentiles. Different meteorological forcings are used in the operational NWM cycles than those used in this retrospective analysis. This forcing disconnect, together with concerns about current‐generation land surface model soil moisture‐transport schemes, inhibit its current operational use for drought monitoring.

Biodiversity collections are experiencing a renaissance fueled by the intersection of informatics, emerging technologies, and the extended use and interpretation of specimens and archived databases. In this article, we explore the potential for transformative research in ecology integrating biodiversity collections, stable isotope analysis (SIA), and environmental informatics. Like genomic DNA, SIA provides a common currency interpreted in the context of biogeochemical principles. Integration of SIA data across collections allows for evaluation of long-term ecological change at local to continental scales. Challenges including the analysis of sparse samples, a lack of information about baseline isotopic composition, and the effects of preservation remain, but none of these challenges is insurmountable. The proposed research framework interfaces with existing databases and observatories to provide benchmarks for retrospective studies and ecological forecasting. Collections and SIA add historical context to fundamental questions in freshwater ecological research, reference points for ecosystem monitoring, and a means of quantitative assessment for ecosystem restoration.