Geology topics

C. David Moeser

Publications and source records attributed to C. David Moeser.

11 recordsLinked to original sources

USGS2024

Modeling forest snow using relative canopy structure metrics

Snow and watershed models typically do not account for forest structure and shading; therefore, they display substantial uncertainty when attempting to account for forest change or when comparing hydrological response between forests with varying characteristics. This study collected snow water equivalent (SWE) measurements in a snow-dominated forest in Colorado, the United States, with variable canopy structure. The SWE measurements were integrated with 1 m Lidar derived canopy structure metrics and incoming solar radiation to create empirical SWE offset equations for four canopy structure groupings (forest gaps, south-facing forest edges, north-facing forest edges, and the interior forest) that varied in size compared to an open area. These simple equations indirectly integrate terrain shading and canopy shading and were able to estimate 40 to 70% of SWE variation in a heterogenous forested environment. The equations were then applied to a snow melt model with a 100 m grid size by applying the area-weighted average of SWE offsets from the four canopy structure groupings in each model cell. This tiled model configuration allowed for the model to better represent the subgrid heterogeneity of a forest environment that can be seen through an ensemble or range of potential outputs rather than a singular estimate.

Water↗

USGS2022

Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis

The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.

Upper Rio Grande Basin↗

USGS2021

Streamflow response to potential changes in climate in the Upper Rio Grande Basin

The Rio Grande is a vital water source for the southwestern States of Colorado, New Mexico, and Texas and for northern Mexico. The river serves as the primary source of water for irrigation in the region, has many environmental and recreational uses, and is used by more than 13 million people including those in the Cities of Albuquerque and Las Cruces, New Mexico; El Paso, Texas; and Ciudad Juárez, Chihuahua, Mexico. However, concern is growing over the increasing gap between water supply and demand in the Upper Rio Grande Basin. As populations increase and agricultural crop patterns change, demands for water are increasing, at the same time the region is undergoing a decrease in supply due to drought and climate change. Quantifying the impact of projected climate change on Rio Grande streamflow is difficult because of numerous anthropogenic influences on the hydrologic system. The conveyance and use of surface water in the Upper Rio Grande Basin are achieved through an engineered system of reservoirs, diversions, and irrigation canals designed to deliver water to agricultural, municipal, and industrial users, who greatly reduce the cumulative volume of water in the river. For example, streamflow at Fort Quitman, Tex., the southernmost point of the Upper Rio Grande Basin, has undergone a 95-percent reduction in flow relative to the river’s native state, and some stretches of the river can intermittently go dry. Because streamflow in the basin is highly altered, disentangling the impacts of climate change and changes in streamflow due to anthropogenic influences such as dams, diversions, and other forms of water use is difficult. Therefore, a model of naturalized flow was developed to determine to what degree changes in streamflow can be attributed to potential changes in future temperature and precipitation without quantifying future changes in anthropogenic influences. This study, conducted by the U.S. Geological Survey in cooperation with the South Central Climate Adaptation Science Center and the U.S. Army Corps of Engineers, included the development and calibration of a watershed model of the Upper Rio Grande Basin using the Precipitation-Runoff Modeling System to simulate naturalized streamflow conditions for historical and future time periods.

Colorado, New Mexico, Texas↗

USGS2021

Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests

Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy-mediated processes, we applied a high-resolution energy balance snowpack model at two forested sites at a fine (1 m 2 ) and coarse (100 m 2 ) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine-scale simulations because there is no representation of areas that are near (e.g., <15 m from) trees but with no overhead canopy, which are common in forests of low to medium tree density. Consistent with previous LSM intercomparisons, the coarser simulations predict greater under-canopy radiation (by 30%–80% at our sites), faster snow ablation (by almost 2×), and earlier snow disappearance (by 1–22 days). Many of these biases are reduced dramatically or eliminated when canopy edge environments are considered in the coarser simulations. Furthermore, remaining disagreement between the 100-m and 1-m models can be partially explained by using a combination of tree height, canopy cover, and canopy edginess (which together can explain 46%–96% of remaining model biases). The lack of information about canopy edges and other fine-scale forest structure characteristics in many current LSMs may limit their reliability for simulating forest disturbance.

California, New Mexico↗

USGS2021

Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.

Streamflow records available before and after wildfire in a small, mixed conifer, sub-alpine monsoonal dominated watershed in New Mexico provided a unique opportunity to calibrate a watershed model (PRMS) for pre- and postfire conditions. The calibrated model was then used to simulate the hydrologic effects of fire. Simulated postfire surface runoff averaged 14.7 times greater than prefire for the 29-year simulation period. The relationship between precipitation and streamflow changed dramatically after wildfire, largely from a decreased influence of antecedent soil moisture (ASM) and increased influence of canopy factors (less interception) and soil factors (greater hydrophobicity, less infiltration) in controlling surface runoff. For higher ASM, simulated pre- and postfire streamflow was similarly variable. However, for moderate and lower ASM, soil water storage was too low to contribute baseflow for either prefire or postfire conditions, and thus postfire streamflow maintained a linear, surface runoff-dominated response to precipitation, whereas prefire streamflow showed little response. Postfire streamflow efficiency increased with ASM from a mean of 0.02 at the lowest ASM to 0.30 at the highest ASM, whereas prefire conditions showed no sensitivity to ASM at low to moderate ASM. Postfire streamflow increased (2.1 times greater median flow than prefire), particularly from increased surface runoff (14.7 times greater), which occurred across all ASM conditions. As a result, streamflow shifted from baseflow-dominated to surface runoff-dominated after wildfire. This result indicates that substantial increases in runoff efficiency (20% or more of precipitation volume) can occur across a range of ASM postfire, which may have severe consequences for flooding. This result also indicates that monitoring of soil moisture would enhance raingauge networks for early flood warning.

New Mexico↗

USGS2020

Estimating the effects of forest structure changes from wildfire on snow water resources under varying meteorological conditions

Modeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m 2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction‐based forest structure metrics (aspect‐based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.

New Mexico↗

USGS2020

Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin

The U.S. Geological Survey’s Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic response and streamflow. This study developed, calibrated, and assessed a PRMS model that simulates near-native or naturalized streamflow conditions in the Upper Rio Grande Basin. A PRMS model framework of 1,021 hydrologic response units was constructed for the basin. Subbasins within the larger Upper Rio Grande Basin range from snow-dominated northern basins to monsoon driven southern basins. The 1,021 hydrologic response units were grouped into 133 subareas within the basin, and solar radiation and potential evapotranspiration data were used to calibrate corresponding PRMS parameters in each subarea independently. Nine subbasins with streamgages distributed across the basin were identified as “near-native” subbasins, or those basins with low anthropogenic disturbance. Model parameters that affect streamflow were calibrated for the near-native subbasins, and the calibrated parameters were distributed to the remaining hydrologic response units on the basis of terrain, soil, and vegetation conditions linked to a distribution and weighting algorithm developed for this study. The parameter distribution method was validated in three of the nine near-native subbasins. Calibration results demonstrated that the PRMS model developed in this study with distributed model parameters for the entire Upper Rio Grande Basin was successful in applying local information to improve model performance over the National Hydrologic Model, and that the new model is appropriate to use to simulate near-native conditions throughout the basin. The result is a model that can simulate naturalized flow and other variables that affect the water budget (including soil moisture, evapotranspiration, recharge) at the daily time step for current and future climate conditions, and that can also be used in conjunction with other models developed for the basin.

Upper Rio Grande Basin↗

USGS2020

Snow processes in mountain forests: Interception modeling for coarse-scale applications

Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.

Utah↗

USGS2020

Resolving small-scale forest snow patterns using an energy-balance snow model with a 1-layer canopy

Modelling spatiotemporal dynamics of snow in forests is challenging, as involved processes are strongly dependent on small-scale canopy properties. In this study, we explore how local canopy structure information can be integrated in a medium-complexity energy-balance snow model to replicate observed snow patterns at very high spatial resolutions. Snow depth distributions simulated with the Flexible Snow Model (FSM2) were tested against extensive experimental data acquired in discontinuous subalpine forest stands in Eastern Switzerland over three winters. While the default canopy implementation in FSM2 fails to capture the observed snow depth variability, performance is considerably improved when local canopy cover fraction and hemispherical sky view fraction are additionally accounted for (30% reduction in RMSE). However, realistic snow depth distribution patterns throughout the season are only achieved if effective temperatures of near and distant canopy elements are discerned, and if a mechanism to mimic preferential deposition of snow in canopy gaps is included. We demonstrate that by diversifying the canopy structure input in order to reflect respective portions of the canopy relevant to different processes, even a simple model based on widely used process parametrizations and canopy metrics can be applied for high-resolution simulations of the sub-canopy snow cover with just a few modifications. The presented approaches could be implemented in commonly used land surface models, allowing upscaling experiments and development of sub-grid parametrizations without necessitating complex high-resolution models.

Water Resources Research↗

USGS2020

Spatial variability in seasonal snowpack trends across the Rio Grande headwaters (1984 - 2017)

This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade −1 , and downward trends in winter precipitation were −52 mm decade −1 . Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade −1 , peak SWE decreased by 14% decade −1 , and total snowmelt quantity decreased by 13% decade −1 . These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

Colorado↗

USGS2019

Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico

The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses. A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters. The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods. The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.

New Mexico↗