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At least 901 records · Page 50Linked to original sources
USGS2023

Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data

Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.

Earth and Space Science↗
USGS2024

Climate change will impact surface water extents and dynamics across the central United States

Climate change is projected to impact river, lake, and wetland hydrology, with global implications for the condition and productivity of aquatic ecosystems. We integrated Sentinel-1 and Sentinel-2 based algorithms to track monthly surface water extent (2017–2021) for 32 sites across the central United States (U.S.). Median surface water extent was highly variable across sites, ranging from 3.9% to 45.1% of a site. To account for landscape-based differences (e.g., water storage capacity, land use) in the response of surface water extents to meteorological conditions, individual statistical models were developed for each site. Future changes to climate were defined as the difference between 2006–2025 and 2061–2080 using MACA-CMIP5 (MACAv2-METDATA) Global Circulation Models. Time series of climate change adjusted surface water extents were projected. Annually, 19 of the 32 sites under RCP4.5 and 22 of the 32 sites under RCP8.5 were projected to show an average decline in surface water extent, with drying most consistent across the southeast central, southwest central, and midwest central U.S. Projected declines under surface water dry conditions at these sites suggest greater impacts of drought events are likely in the future. Projected changes were seasonally variable, with the greatest decline in surface water extent expected in summer and fall seasons. In contrast, many north central sites showed a projected increase in surface water in most seasons, relative to the 2017–2021 period, likely attributable to projected increases in winter and spring precipitation exceeding increases in projected temperature.

Earth's Future↗
USGS2024

Incorporating intensity distance attenuation into PLUM ground-motion-based earthquake early warning in the United States: The APPLES configuration

We develop Attenuated ProPagation of Local Earthquake Shaking (APPLES), a new configuration for the United States West Coast version of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm that incorporates attenuation into its ground-motion prediction procedures. Under APPLES, instead of using a fixed radius to forward-predict observed peak ground shaking to the area surrounding a seismic station, the forward-predicted intensity at a location depends on the distance from the station using an intensity prediction relationship. We conduct conceptual tests of maximum intensity distribution predictions in APPLES and PLUM using a catalog of ShakeMaps to confirm that the attenuation relationship in APPLES is appropriately modeling shaking distributions for West Coast earthquakes. Then, we run APPLES and PLUM in simulated real-time tests to determine warning time performance. Finally, we compare real-time alert behavior during the 2022 M 6.4 Ferndale, California, earthquake and other recent events. We find that APPLES presents two potential improvements to PLUM by reducing over-alerting during smaller magnitude earthquakes and by increasing warning times in some locations during larger earthquakes. APPLES can produce missed and late alerts in locations that experience shaking intensities close to the level used to issue alerts, so preferred alerting strategies with APPLES would use alert thresholds that are lower than the intensities targeted for EEW alerts. We find alerts using APPLES are also similar to those for the source-based approaches currently used in the ShakeAlert EEW system, which will make APPLES easier to integrate into the system.

California, Oregon, Washington↗
USGS2023

Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc

Alaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra-producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska-Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska-Aleutian arc, specifically its pre-Holocene spatiotemporal distribution.

Alaska↗
USGS2023

VogCast: A framework for modeling volcanic air pollution and its application to the 2022 eruption of Mauna Loa Volcano, Hawai'i

Volcanic activity and the associated gas emissions into the atmosphere often result in adverse air quality conditions and present a hazard to human health and the environment. Building on a decade-long effort to provide operational surface sulfur dioxide and sulfate aerosol forecasts for the State of Hawai'i, we present an air quality modeling framework called VogCast. VogCast is designed to simplify ensemble air quality prediction on a regional scale by linking together multiple state-of-the-art models of meteorology, emissions, and dispersion. The framework is open-source and introduces a new dynamic plume-rise algorithm for distributing pollutants vertically. Using radar and satellite data, we demonstrate that VogCast reasonably captured the mean injection height, the location, and the general envelope of the vog plume during Mauna Loa's 2022 eruption. The results suggest that during the 12-day eruption period model performance varied between days with trade and non-trade wind conditions. Our findings also highlight the importance of sulfur dioxide emission rate and vent parameter inputs for improving forecast accuracy. The broad goal of this work is to better our understanding of vog dispersion and improve air quality prediction for impacted communities.

Hawaii↗
USGS2024

Quantifying compound and nonlinear effects of hurricane-induced flooding using a dynamically coupled hydrological-ocean model

We recently developed a dynamically coupled hydrological-ocean modeling system that provides seamless coverage across the land-ocean continuum during hurricane-induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF-Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain.

Water Resources Research↗
USGS2024

Classifying plant communities in the North American Coastal Plain with PRISMA spaceborne hyperspectral imagery and the spectral mixture residual

The effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak-enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end-members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR-transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR-based classifications across all four landscapes. The MR-based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near-infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions.

JGR Biogeosciences↗
USGS2024

Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach

Elastic continuum mechanical models are widely used to compute deformations due to pressure changes in buried cavities, such as magma reservoirs. In general, analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software. To overcome these limitations, we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity but >1,000× greater computational efficiency. The emulators are based on generalized nondimensional forms of governing equations for finite non‐dipping spheroidal cavities in elastic halfspaces. Either cavity volume change or uniform pressure change boundary conditions can be specified, and the models predict both surface displacements and cavity (pore) compressibility. Because of their computational efficiency, using the emulators as numerical model surrogates can greatly accelerate data inversion algorithms such as those employing Bayesian Markov chain Monte Carlo sampling. The emulators also permit a comprehensive evaluation of how displacements and cavity compressibility vary with geometry and material properties, revealing the limitations of analytical models. Our open‐source emulator code can be utilized without finite element software, is suitable for a wide range of cavity geometries and depths, includes an estimate of uncertainties associated with emulation, and can be used to train new emulators for different source geometries.

JGR Machine Learning and Computation↗
USGS2024

Integrating depth measurements from gaging stations with image archives for spectrally based remote sensing of river bathymetry

Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R 2 ( p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.

Willamette River basin, Delaware River basin↗
USGS2024

Ensemble methods for parameter estimation of WRF-Hydro

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.

Water Resources Research↗
USGS2026

Short-term estuarine phytoplankton dynamics in response to hurricanes along the Gulf Coast of America: A Variational Autoencoder (VAE) approach with satellite and bio-optical observations

Hurricanes drive diverse estuarine phytoplankton responses and can trigger cascading ecological and physicochemical impacts. Capturing these short-term dynamics requires high spatiotemporal resolution. Here, we applied a globally-applicable coastal ocean color algorithm, Variational Autoencoder (VAE), to Sentinel-2 MSI imagery for chlorophyll- a (Chl- a ) estimation and validated its strong performance across the northern Gulf coast of America (GoA) estuaries, including Galveston Bay (TX), Barataria-Terrebonne Estuary (LA), Apalachicola Estuary (FL) and Tampa Bay (FL). The test set showed strong performance (MAE: 1.44 mg m −3 ; RMSE: 17.7 mg m −3 ; slope: 0.86; median symmetric accuracy: 30.33%). The validated VAE was then applied to 76 Sentinel-2 MSI images to assess phytoplankton biomass responses to hurricanes Harvey (2017), Michael (2018), Ida (2021), Francine (2024), Helene (2024), and Milton (2024) in the GoA estuaries. Results showed that hurricane disturbances on Chl- a typically lasted 3–5 weeks. Estuarine waters west (left) of hurricane tracks showed a rapid decline in Chl- a (∼5 mg m −3 ) due to elevated turbidity from heavy rainfall, and wind-driven flushing in the estuary, followed by a rebound over about two weeks, with Chl- a increasing approximately 10–15 mg m −3 above pre-storm levels. In contrast, right-side waters showed a slower response, likely from oligotrophic seawater intrusion driven by the hurricane's counterclockwise rotation. Post-storm observations showed increased freshwater phytoplankton like chlorophytes and cyanobacteria dominating estuaries, while shelf-waters exhibited elevated dinoflagellates (e.g., Karenia brevis bloom after Hurricane Milton). These results highlight the spatial heterogeneity of hurricane impacts on estuarine phytoplankton dynamics, which may trigger cascading effects on biogeochemical cycling and food webs, potentially prolonging ecosystem recovery.

Alabama, Florida, Louisiana, Mississippi, Texas↗
USGS2026

Rapid seismic and infrasound assessment of large landslides: A case study from Denali National Park and Preserve (Alaska)

Large, rapid landslides are a global hazard that can occur in remote, mountainous areas. Eyewitness reports of landslides and satellite imagery can often be limited or delayed, particularly during inclement weather. However, landslide-generated seismic and infrasound (low-frequency atmospheric sound) waves can be remotely detected in near real-time. This information can significantly expedite characterization and possible landslide response activities. Here, we highlight these capabilities using a > 4 million m³ ice–rock avalanche in Denali National Park and Preserve (Alaska). This event was detected via a landslide-specific seismic location and volume estimation algorithm deployed in Alaska, and — notably — by standard earthquake monitoring systems. Following rapid detection of this event, we combined its seismic and infrasound dataset with optical, synthetic aperture radar, and oblique aerial imagery, multitemporal digital elevation models, and a numerical flow model to reconstruct its failure timeline and dynamics. We apply array processing to infrasound signals traveling > 250 km and find that two precursory events occurred minutes prior to the main failure. We use long-period seismic signals to infer the force exerted by the landslide on the Earth and constrain the rheological parameters of our numerical flow simulation with this result and deposit morphology. The main failure produced a steeply-dipping impulsive initial downward force and reached speeds exceeding 60 m/s. This impulsive force generated relatively strong seismic body waves, which contributed to the earthquake system detection. This large, remote Alaska landslide underscores the key value of seismic and infrasound analysis for rapid landslide assessment and motivates efforts to further operationalize these approaches.

Alaska↗
USGS2010

Solute transport with multiple equilibrium-controlled or kinetically controlled chemical reactions

A new approach is applied to the problem of modeling solute transport accompanied by many chemical reactions. The approach, based on concepts of the concentration space and its stoichiometric subspaces, uses elements of the subspaces as primary dependent variables. It is shown that the resulting model equations are compact in form, isolate the chemical reaction expressions from flow expressions, and can be used for either equilibrium or kinetically controlled reactions. The implications of the results on numerical algorithms for solving the equations are discussed. The application of the theory is illustrated throughout with examples involving a simple but broadly representative set of reactions previously considered in the literature. Numerical results are presented for four interconnected reactions: a homogeneous complexation reaction, two sorption reactions, and a dissolution/precipitation reaction. Three cases are considered: (1) four kinetically controlled reactions, (2) four equilibrium-controlled reactions, and (3) a system with two kinetically controlled reactions and two equilibrium-controlled reactions.

Water Resources Research↗
USGS2010

A finite-volume Eulerian-Lagrangian Localized Adjoint Method for solution of the advection-dispersion equation

A new mass-conservative method for solution of the one-dimensional advection-dispersion equation is derived and discussed. Test results demonstrate that the finite-volume Eulerian-Lagrangian localized adjoint method (FVELLAM) outperforms standard finite-difference methods, in terms of accuracy and efficiency, for solute transport problems that are dominated by advection. For dispersion-dominated problems, the performance of the method is similar to that of standard methods. Like previous ELLAM formulations, FVELLAM systematically conserves mass globally with all types of boundary conditions. FVELLAM differs from other ELLAM approaches in that integrated finite differences, instead of finite elements, are used to approximate the governing equation. This approach, in conjunction with a forward tracking scheme, greatly facilitates mass conservation. The mass storage integral is numerically evaluated at the current time level, and quadrature points are then tracked forward in time to the next level. Forward tracking permits straightforward treatment of inflow boundaries, thus avoiding the inherent problem in backtracking, as used by most characteristic methods, of characteristic lines intersecting inflow boundaries. FVELLAM extends previous ELLAM results by obtaining mass conservation locally on Lagrangian space-time elements. Details of the integration, tracking, and boundary algorithms are presented. Test results are given for problems in Cartesian and radial coordinates.

Water Resources Research↗
USGS1997

Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data

A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. The land cover classification was developed by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly normalized difference vegetation index (NDVI) image composites (April-September 1992). Quantitative areal proportions of the major boreal forest components were determined for a 821 km ?? 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, 1 km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.

Journal of Geophysical Research D: Atmospheres↗
USGS2012

Direct computation of the sensible heat flux

An algorithm to determine the sensible heat flux from simple field measurements (wind speed, air and ground temperatures) has been developed. It provides a direct solution, in parametric form, which can be displayed graphically or tabularly. This method has an advantage over the previous iterative solution in that the computation is fast. It also provides a clearer understanding of the drag coefficient, its variation and response to different conditions. A simple example illustrated that at low wind speeds the drag coefficient cannot be treated as a constant. Both the computational speed and analysis of the drag coefficient can be important for remote-sensing applications involving thermal scanner data.

Geophysical Research Letters↗
USGS2012

A teleseismic analysis of the New Brunswick earthquake of January 9, 1982

The analysis of the New Brunswick earthquake of January 9, 1982, has important implications for the evaluation of seismic hazards in eastern North America. Although moderate in size ( m b 5.7), it was well-recorded teleseismically. Source characteristics of this earthquake have been determined from analysis of data that were digitally recorded by the Global Digital Seismograph Network. From broadband displacement and velocity records of P waves, we have obtained a dynamic description of the rupture process as well as conventional static properties of the source. The depth of the hypocenter is estimated to be 9 km from depth phases. The focal mechanism determined from the broadband data corresponds to predominantly thrust faulting. From the variation in the waveforms the direction of slip is inferred to be updip on a west dipping NNE striking fault plane. The steep dip of the inferred fault plane suggests that the earthquake occurred on a preexisting fault that was at one time a normal fault. From an inversion of bodywave pulse durations, the estimated rupture length is 5.5 km. Average properties of the rupture process were examined by a moment tensor analysis of long-period P and SH body waves. The long-period moment of this earthquake was 5.3 × 10 24 dyne cm. The static and dynamic stress drops are 41 and 65 bars, respectively, similar to those of many earthquakes with similar moment in regions that are more seismically active. The joint epicenter determination algorithm was used to locate, relative to the mainshock, the three teleseismically recorded aftershocks that occurred through March 31, 1982. The relocated hypocenters of the aftershocks are significantly different from each other and from that of the mainshock; they provide additional support for the source dimensions inferred from the waveform analysis.

Journal of Geophysical Research Solid Earth↗
USGS2008

The effect of glaciers on streamflow variations

The effect of temperate glaciers on runoff variations is examined for the North Cascade Mountains of Washington State. The principal influences of glaciers on streamflow are often unexpected contributions to streamflow volume, a delay of the maximum seasonal flow, and a decrease in annual and monthly variation of runoff. The delay of maximum flow is caused by temporary englacial storage of spring meltwater and by peak meltwater production occurring in midsummer. The englacial storage, for one case, is 54% of the potential May runoff. An algorithm is presented that calculates the coefficient of variation of runoff for any arbitrary glacier cover. The results suggest that a minimum in year-to-year variation occurs for basins about 36% glacierized. On a month-to-month basis, maximum variation occurs in July and August for basins with less than 10% glacier cover but is a minimum for basins with glacier covers greater than 30%.

Water Resources Research↗