Geology topics

Jessica M. Driscoll

Publications and source records attributed to Jessica M. Driscoll.

At least 19 recordsLinked to original sources

USGS2025

U.S. Geological Survey Colorado River Basin science and technology collaboration meetings on drought (2021)—Synthesis of findings

Ongoing, prolonged, and severe drought and water overuse during the first two decades of the 21st century have reduced water supplies of the Colorado River Basin, with effects cascading to ecosystems and human communities throughout the basin. In June and July 2021, the U.S. Geological Survey (USGS) Colorado River Basin Actionable and Strategic Integrated Science and Technology initiative team held a series of 12 collaboration meetings with USGS scientists and managers to discuss complicated, integrated science challenges and solutions related to drought in the Colorado River Basin. These Science and Technology collaboration meetings were structured to identify challenges experienced by meeting participants when working on complex problems, explore opportunities for coproducing scientific information, and envision future collaborative programs that leverage new technology. The 12 meetings were attended by 79 USGS staff representing 43 unique affiliations (for example, USGS science centers, mission areas, and regional offices). Meeting participants submitted 865 individual responses to six general discussion prompt topics (“Challenges,” “Knowledge Gaps,” “Existing Capabilities,” “Strategies and Actions,” “Example Applications,” and “Next Steps”) using a structured online collaboration tool. However, specific questions or tasks from each general discussion prompt varied by meeting topic. Terms from the USGS Thesaurus ( https://apps.usgs.gov/thesaurus/ ) and USGS Data Lifecycle Model ( https://www.usgs.gov/data-management/data-lifecycle ) were used to identify and summarize participant responses relevant to science integration, stakeholder engagement, and information management technology. From these responses, opportunities for the Colorado River Basin Actionable and Strategic Integrated Science and Technology initiative to facilitate science integration in the Colorado River Basin are highlighted in this report, including (a) pursuing specific interdisciplinary research topics that require integrating knowledge across spatial and temporal scales, (b) connecting scientists across disciplines, (c) reducing barriers to stakeholder engagement, (d) identifying new technologies, and (e) facilitating data access. Multiple strategies for designing future Science and Technology collaboration meetings are also outlined in this circular to better collect and analyze participant responses.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2024

Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better

Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.

Colorado↗

USGS2023

U.S. Geological Survey Rocky Mountain Region 2022 science exchange, showcasing interdisciplinary and state-of-the-art USGS science

Introduction The Rocky Mountains and the Colorado River Basin in the Western United States represent complex, interconnected systems that sustain a number of species, including tens of millions of humans. These systems face several challenges, including worsening drought, altered wildfire regimes, climate change, and the spread of invasive species. These factors can exacerbate one another, further contributing to habitat loss and affecting species of conservation concern. Characterizing and managing these challenges require interdisciplinary communities of scientists to develop information and decision-support tools that can inform holistic land and water management solutions. The U.S. Geological Survey (USGS) Rocky Mountain Region 2022 Science Exchange focused on the use of interdisciplinary and state-of-the-art science being conducted by USGS scientists in the region to address these complex problems. The USGS Rocky Mountain Regional Office organized its first Science Exchange in 2017 to share scientific information between leaders and early career scientists throughout the region. Science Exchanges held in 2018 and 2020 focused on drought science relevant to the region and the Earth Monitoring, Analyses, and Prediction (EarthMAP) concept, which is designed to facilitate interdisciplinary, timely, and actionable science related to drought in the Colorado River Basin and other areas. Based on the emerging need for more holistic approaches to address increasingly complex natural resource issues that affect society, the Region hosted a virtual fourth Science Exchange for three days in April 2022. This event focused on barriers and bridges to interdisciplinary science and highlighted studies from the Region to inspire collaboration across disciplines. Presentations described recent and ongoing research that applied collaborative and state-of-the-art methods to address problems in the fields of geology, hydrology, ecology, and natural hazards. Science collaboration and outreach to all levels of stakeholders are vital elements needed for providing timely and actionable data, interpretations, analytical tools, and products. These presentations led to active online chats and panel discussions and showcased interdisciplinary science and advanced methods that may inform and lead to more effective, holistic management decisions as the Western United States adapts to ongoing and future changes.

Fact Sheet↗

USGS2023

High resolution SnowModel simulations reveal future elevation-dependent snow loss and earlier, flashier surface water input for the Upper Colorado River Basin

Continued climate warming is reducing seasonal snowpacks in the western United States, where >50% of historical water supplies were snowmelt-derived. In the Upper Colorado River Basin, declining snow water equivalent (SWE) and altered surface water input (SWI, rainfall and snowmelt available to enter the soil) timing and magnitude affect streamflow generation and water availability. To adapt effectively to future conditions, we need to understand current spatiotemporal distributions of SWE and SWI and how they may change in future decades. We developed 100-m SnowModel simulations for water years 2001–2013 and two scenarios: control (CTL) and pseudo-global-warming (PGW). The PGW fraction of precipitation falling as snow was lower relative to CTL, except for November–April at high elevations. PGW peak SWE was lower for low (−45%) and mid elevations (−14%), while the date of peak SWE was uniformly earlier in the year for all elevations (17–23 days). Currently unmonitored high elevation snow represented a greater fraction of total PGW SWE. PGW peak daily SWI was higher for all elevations (30%–42%), while the dates of SWI peaks and centroids were earlier in the year for all elevations under PGW. PGW displayed elevated winter SWI, lower summer SWI, and changes in spring SWI timing were elevation-dependent. Although PGW peak SWI was elevated and earlier compared to CTL, SWI was more evenly distributed throughout the year for PGW. These simulated shifts in the timing and magnitude of SWE and SWI have broad implications for water management in dry, snow-dominated regions.

Colorado↗

USGS2022

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.

Frontiers in Earth Sciences↗

USGS2022

Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA

Building continental-scale hydrologic models in data-sparse regions requires an understanding of spatial variation in hydrologic processes. Extending these models to ungaged locations requires techniques to group ungaged locations with gaged ones to make process importance and model parameter transfer decisions to ungaged locations. This analysis (1) tested the utility of fundamental streamflow statistics (FDSS) in defining hydrologic regions across Alaska, USA; (2) evaluated if the hydrologic regions represented different hydrologic processes; and (3) tested the ability of random forest and direct assignment techniques, informed by statistically estimated FDSS (FDSSest) and basin characteristics (BCs), to correctly assign ungaged locations to hydrologic regions. Six hydrologic regions were identified across the domain using FDSS. Differences in mean flow, phase shift of the seasonal cycle, and skewness were the primary characteristics defining each region. Two regions represented arctic and continental climates, generally in the northern portion of the domain; four regions represented the southern, maritime portion of the domain. Random forest modeling with BCs (67% success rate) outperformed FDSSest (58% success rate) suggesting that no statistically estimated streamflow was needed to assign ungaged locations to a region. For regions with many sites, most region assignment techniques performed similarly. Random forest modeling performance declined when BCs and FDSSest were both used to predict region membership, suggesting FDSSest had little information in addition to BCs. This analysis demonstrated that FDSS-based hydrologic regions discern process differences across a data-sparse and hydrologically diverse landscape. Process importance rankings from random forest-derived BCs provided model-independent information for making modeling decisions.

Alaska↗

USGS2022

Strength and memory of precipitation's control over streamflow across the conterminous United States

How precipitation (P) is translated into streamflow (Q) and over what timescales (i.e., “memory”) is difficult to predict without calibration of site-specific models or using geochemical approaches, posing barriers to prediction in ungauged basins or advancement of general theories. Here, we used a data-driven approach to identify regional patterns and exogenous controls on P–Q interactions. We applied an information flow analysis, which quantifies uncertainty reduction, to a daily time series of P and Q from 671 watersheds across the conterminous United States. We first demonstrated that information transfer from P to Q primarily reflects the quickflow component of water-budgets, based on a watershed model. Readily quantifiable information flows show a functional relationship with model parameters, suggesting utility for model calibration. Second, applied to real watersheds, P–Q information flows exhibit seasonally varying behavior within regions in a manner consistent with dominant runoff generation mechanisms. However, the timing and the magnitude of information flows also reflect considerable subregional heterogeneity, likely attributable to differences in watershed size, baseflow contributions, and variation in aerial coverage of preferential flow paths. A regression analysis showed that a combination of climate and watershed characteristics are predictive of P–Q information flows. Though information flows cannot, in most cases, uniquely determine dominant runoff mechanisms, they provide a means to quantify the heterogeneous outcomes of those mechanisms within regions, thereby serving as a benchmarking tool for models developed at the regional scale. Last, information flows characterize regionally specific ways in which catchment connectivity changes from the wet to dry season.

conterminous United States↗

USGS2021

Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates

Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation ( P ), actual evapotranspiration (ET), runoff ( R ), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P , 14 % to 26 % for ET, 28 % to 82 % for R , 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R , 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.

Hydrology and Earth System Sciences↗

USGS2020

Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey

The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation’s greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km 2 . Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as “Integrated Water Science Basins”; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.

contiguous United States↗

USGS2020

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of subgrid or subwatershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) represents the subgrid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-average SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation (CV) values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the subgrid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as increases in the subgrid SWE CV by 1.0 resulted in decreases in runoff ratio by as much as 12 percent in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water limited dryness index were associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modeling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modeling applications.

Hydrological Processes↗

USGS2020

Spatiotemporal variability of modeled watershed scale surface-depression storage and runoff for the conterminous United States

This study uses the explores the viability of a proxy model calibration strategy through assessment of the spatiotemporal variability of surface-depression storage and runoff generated with the U.S. Geological Survey’s National Hydrologic Model (NHM) infrastructure for hydrologic response units (HRUs; n=109,951) across the conterminous United States (CONUS). Simulated values for each HRU of daily surface-depression storage (treated as a decimal fraction of total possible volume) and monthly normalized runoff (0 to 1) values were calculated using Spearman’s rho at monthly and annual aggregations. Locations where values are correlated show where previously-developed proxy calibration strategies are likely to be effective. In addition, differences in the correlation for monthly and annual time scale aggregations show which time scale drives surface-depression storage processes in the NHM. Results show overall long-term (annual) correlation is more common than short-term (monthly) correlation over the CONUS; however, summary statistics for eighty-six ecoregions show five with higher ranges of monthly relative to annual Spearman’s rank coefficient values. This landscape-scale analysis shows simulations aggregated to an annual time scale are generally more dominant for the CONUS; however, simulations aggregated to monthly, short-term time scales are more dominant in focused areas where surface-depression storage processes are investigated.

Journal of the American Water Resources Associatio↗

USGS2019

Estimation of base flow by optimal hydrograph separation for the conterminous United States and implications for national-extent hydrologic models

Optimal hydrograph separation (OHS) uses a two-parameter recursive digital filter that applies specific conductance mass-balance constraints to estimate the base flow contribution to total streamflow at stream gages where discharge and specific conductance are measured. OHS was applied to U.S. Geological Survey (USGS) stream gages across the conterminous United States to examine the range/distribution of base flow inputs and the utility of this method to build a hydrologic model calibration dataset. OHS models with acceptable goodness-of-fit criteria were insensitive to drainage area, stream density, watershed slope, elevation, agricultural or perennial snow/ice land cover, average annual precipitation, runoff, or evapotranspiration, implying that OHS results are a viable calibration dataset applicable in diverse watersheds. OHS-estimated base flow contribution was compared to base flow-like model components from the USGS National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS). The NHM-PRMS variable gwres_flow is most conceptually like a base flow component of streamflow but the gwres_flow contribution to total streamflow is generally smaller than the OHS-estimated base flow contribution. The NHM-PRMS variable slow_flow , added to gwres_flow , produced similar or greater estimates of base flow contributions to total streamflow than the OHS-estimated base flow contribution but was dependent on the total flow magnitude.

conterminous United States↗

USGS2019

Simulation of water availability in the Southeastern United States for historical and potential future climate and land-cover conditions

A study was conducted by the U.S. Geological Survey (USGS), in cooperation with the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC) and the Department of the Interior Southeast Climate Adaptation Science Center, to evaluate the hydrologic response of a daily time step hydrologic model to historical observations and projections of potential climate and land-cover change for the period 1952–2099. The model simulations were used to compute the potential changes in hydrologic response and streamflow statistics across the Southeastern United States, using historical observations of climate and streamflow. Thirteen downscaled general circulation models with four representative concentration pathways were used to represent a range of potential future changes in climate (a total of 45 future simulations) from the Coupled Model Intercomparison Project Phase 5. The streamflow statistics were selected to describe streamflow conditions that may be most useful in defining the suitability for each river or stream to support sustaining populations of priority aquatic species across the GCPO LCC. An application of the Precipitation-Runoff Modeling System (included as part of the USGS National Hydrologic Model) was used to develop the hydrologic simulations. The results showed increases in air temperature across the study area, with the highest increases occurring in the northern part of the study area during July to September. The results showed a mix of increases and decreases in precipitation accumulation across the study area and across seasons, with decreases in precipitation accumulation across all seasons for the southwestern part of the study area. Actual evapotranspiration decreased for the southeastern part of the study area and increased for the northwestern part of the study area. The results showed general decreases in runoff across the study area, with increases in runoff in areas surrounding large metropolitan regions where potential future increases in impervious area occur. Results from a statistical analysis (Kolmogorov-Smirnov test) showed that the downscaled general circulation models generally have more skill in producing historical streamflow statistics in the duration and magnitude categories and less skill in producing historical streamflow statistics in the frequency, rate of change, and timing categories for this study area. The potential changes in the streamflow statistics and the results of the Kolmogorov-Smirnov test are available through the GCPO LCC Conservation Planning Atlas, an online science-based mapping platform built specifically for land managers and planners.

Alabama, Arkansas, Florida, Georgia, Illinois, Kan↗

USGS2019

GRACE storage change characteristics (2003–2016) over major surface basins and principal aquifers in the Conterminous United States

In this research, we characterized the changes in Gravity Recovery and Climate Experiment’s (GRACE) monthly total water storage anomaly (TWSA) in 18 surface basins and 12 principal aquifers in the Conterminous United States (CONUS) over 2003–2016. Regions with high variability in storage were identified. Ten basins and 4 aquifers showed significant change in storage. Eight surface basins and 8 aquifers were found to show decadal stability in storage. A pixel-based analysis of storage showed that New England basin and North Atlantic Coastal Plain aquifer showed the largest area under positive storage change. Whereas, the Lower Colorado basin and California aquifers showed largest area under negative change. This study found that historically wetter regions (with more storage) are becoming wetter and dryer regions (with less storage) are becoming dryer. Fourier analysis of the GRACE data showed that while all basins exhibited prominent annual periodicities, significant sub-annual and multi-annual cycles also exist in some basins. The storage turnover period was estimated to range between 6 to 12 months. The primary explanatory variable (PEV) of TWSA was identified for each region. This study provides new insights on several aspects of basin or aquifer storage that are important for understanding basin/aquifer hydrology.

Remote Sensing↗

USGS2019

The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States

The ability to effectively manage water resources to meet present and future human and environmental needs is essential. Such an ability necessitates a comprehensive understanding of hydrologic processes that affect streamflow at a watershed scale. In the United States, water-resources management at scales ranging from local to national can benefit from a nationally consistent, process-based watershed modeling capability to provide the requisite understanding. The National Hydrologic Model (NHM) infrastructure, which was developed by the U.S. Geological Survey to support coordinated, comprehensive, and consistent hydrologic modeling at multiple scales for the conterminous United States, provides this essential capability. NHM-based applications provide information to enable more effective water-resources planning and management, fill knowledge gaps in ungaged areas, and support basic scientific inquiry. In the future, as process algorithms and data sets improve, the NHM infrastructure will continue to evolve to better support the nation's water-resources research and management needs.

Conterminous United States↗

USGS2018

Event-response ellipses: A method to quantify and compare the role of dynamic storage at the catchment scale in snowmelt-dominated systems

A method for quantifying the role of dynamic storage as a physical buffer between snowmelt and streamflow at the catchment scale is introduced in this paper. The method describes a quantitative relation between hydrologic events (e.g., snowmelt) and responses (e.g., streamflow) by generating event-response ellipses that can be used to (a) characterize and compare catchment-scale dynamic storage processes, and (b) assess the closure of the water balance. Event-response ellipses allow for the role of dynamic, short-term storage to be quantified and compared between seasons and between catchments. This method is presented as an idealization of the system: a time series of a snowmelt event as a portion of a sinusoidal wave function. The event function is then related to a response function, which is the original event function modified mathematically through phase and magnitude shifts to represent the streamflow response. The direct relation of these two functions creates an event-response ellipse with measurable characteristics (e.g., eccentricity, angle). The ellipse characteristics integrate the timing and magnitude difference between the hydrologic event and response to quantify physical buffering through dynamic storage. Next, method is applied to eleven snowmelt seasons in two well-instrumented headwater snowmelt-dominated catchments with known differences in storage capacities. Results show the time-period average daily values produce different event-response ellipse characteristics for the two catchments. Event-response ellipses were also generated for individual snowmelt seasons; however, these annual applications of the method show more scatter relative to the time period averaged values. The event-response ellipse method provides a method to compare and evaluate the connectivity between snowmelt and streamflow as well as assumptions of water balance.

Water↗

USGS2018

Description of the National Hydrologic Model for use with the Precipitation-Runoff Modeling System (PRMS)

This report documents several components of the U.S. Geological Survey National Hydrologic Model of the conterminous United States for use with the Precipitation-Runoff Modeling System (PRMS). It provides descriptions of the (1) National Hydrologic Model, (2) Geospatial Fabric for National Hydrologic Modeling, (3) PRMS hydrologic simulation code, (4) parameters and estimation methods used to compute spatially and temporally distributed default values as required by PRMS, (5) National Hydrologic Model Parameter Database, and (6) model extraction tool named Bandit. The National Hydrologic Model Parameter Database contains values for all PRMS parameters used in the National Hydrologic Model. The methods and national datasets used to estimate all the PRMS parameters are described. Some parameter values are derived from characteristics of topography, land cover, soils, geology, and hydrography using traditional Geographic Information System methods. Other parameters are set to long-established default values and computation of initial values. Additionally, methods (statistical, sensitivity, calibration, and algebraic) were developed to compute parameter values on the basis of a variety of nationally-consistent datasets. Values in the National Hydrologic Model Parameter Database can periodically be updated on the basis of new parameter estimation methods and as additional national datasets become available. A companion ScienceBase resource provides a set of static parameter values as well as images of spatially-distributed parameters associated with PRMS states and fluxes for each Hydrologic Response Unit across the conterminuous United States.

Techniques and Methods↗

USGS2017

Land subsidence and recovery in the Albuquerque Basin, New Mexico, 1993–2014

The Albuquerque Bernalillo County Water Utility Authority (ABCWUA) drinking water supply was almost exclusively sourced from groundwater from within the Albuquerque Basin before 2008. In 2008, the San Juan-Chama Drinking Water Project (SJCDWP) provided surface-water resources to augment the groundwater supply, allowing for a reduction in groundwater pumping in the Albuquerque Basin. In 2013, the U.S. Geological Survey, in cooperation with the ABCWUA, began a study to measure and compare aquifer-system and land-surface elevation change before and after the SJCDWP in 2008. Three methods of data collection with different temporal and spatial resolutions were used for this study: (1) aquifer-system compaction data collected continuously at a single extensometer from 1994 to 2013; (2) land-surface elevation change from Global Positioning System (GPS) surveys of a network of monuments collected in 1994–95, 2005, and 2014; and (3) spatially distributed Interferometric Synthetic Aperture Radar (InSAR) satellite data from 1993 to 2010. Collection of extensometer data allows for direct and continuous measurement of aquifer-system compaction at the extensometer location. The GPS surveys of a network of monuments allow for periodic measurements of land-surface elevation change at monument locations. Interferograms are limited in time by lifespan of the satellite, orbital pattern, and data quality but allow for measurement of gridded land-surface elevation change over the study area. Each of these methods was employed to provide a better understanding of aquifer-system compaction and land-surface elevation change for the Albuquerque Basin. Results do not show large magnitudes of subsidence in the Albuquerque Basin. High temporal-resolution but low spatial-resolution data measurements of aquifer-system compaction at the Albuquerque extensometer show elastic aquifer-system response to recovering groundwater levels. Results from the GPS survey of the network of monuments show inconsistent land-surface elevation changes over the Albuquerque Basin, likely because of the lack of significant change and the complexity of subsurface stratigraphy in addition to the spatial and temporal heterogeneity of groundwater withdrawals over the study period. Results from the InSAR analysis show areas of land-surface elevation increase after 2008, which could be attributed to elastic recovery of the aquifer system. The spatial extent to which elastic recovery of the aquifer system has resulted in recovery of land-surface elevation is limited to the in-situ measurements at the extensometer. Examination of spatially distributed InSAR data relative to limited spatial extent of the complex heterogeneity subsurface stratigraphy may explain some of the heterogeneity of land-surface elevation changes over this study period.

New Mexico↗