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Julie E. Kiang

Publications and source records attributed to Julie E. Kiang.

At least 19 recordsLinked to original sources
USGS2022

Projecting flood frequency curves under near-term climate change

Flood-frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate-informed methodology for estimating flood-frequency curves under non-stationary future climate conditions. The methodology develops an asynchronous, semiparametric local-likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log-Pearson Type-3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR-MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR-MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood-frequency quantiles resulting from the ASLLR-MM and GCM members compare well with the flood-frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land-surface conditions and a multi-level model structure that includes other basins in a region could further improve the model performance in arid basins.

Water Resources Research↗
USGS2022

U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data

The Hydrologic Toolbox is a Windows-based desktop software program that provides a graphical and mapping interface for analysis of hydrologic time-series data with a set of widely used and standardized computational methods. The software combines the analytical and statistical functionality provided in the U.S. Geological Survey Groundwater and Surface-Water Toolboxes and provides several enhancements to these programs. The main analytical methods are the computation of hydrologic-frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10); the computation of design flows, including biologically based flows; the computation of flow-duration curves and duration hydrographs; eight computer-programming methods for hydrograph separation of a streamflow time series, including the Base-Flow Index (BFI), HYSEP, PART, and SWAT Bflow methods and Eckhardt’s two-parameter digital-filtering method; and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater-recharge values from streamflow data. Several of the statistical methods provided in the Hydrologic Toolbox are used primarily for computation of critical low-flow statistics. The Hydrologic Toolbox also facilitates retrieval of streamflow and groundwater-level time-series data from the U.S. Geological Survey National Water Information System and outputs text reports that describe their analyses. The Hydrologic Toolbox was developed by use of the DotSpatial geographic information system (GIS) programming library, which is part of the MapWindow project. DotSpatial is a nonproprietary, open-source program written for the .NET framework that includes a spatial data viewer and GIS capabilities. Advantages of the DotSpatial system include its pure .NET implementation for both the user interface and the GIS mapping engine, and thus the DotSpatial system simplifies software deployment and installation. In addition to combining the functionality of the separate Groundwater and Surface-Water Toolboxes, the Hydrologic Toolbox also organizes the functionality by theme (Groundwater Tools, Surface-Water Tools, and general Time-Series Tools). This report provides a description of how to build a Hydrologic Toolbox project and to download and manage hydrologic time-series data. It includes an overview of the analytical and statistical capabilities of the Hydrologic Toolbox and highlights the primary differences between the Hydrologic Toolbox and the Groundwater and Surface-Water Toolboxes. The report supplements information available in an extensive online Help manual and is intended to provide a set of instructions that will allow users to quickly develop skills to use the mapping, data-retrieval, and computational tools of the program.

Techniques and Methods↗
USGS2021

Historical and paleoflood analyses for probabilistic flood-hazard assessments—Approaches and review guidelines

Paleoflood studies are an effective means of providing specific information on the recurrence and magnitude of rare and large floods. Such information can be combined with systematic flood measurements to better assess the frequency of large floods. Paleoflood data also provide valuable information about the linkages among climate, land use, flood-hazard assessments, and channel morphology. This document summarizes methods and techniques for the preparation, gathering, evaluation, and interpretation of paleoflood information, including uncertainties, especially with respect to new statistical approaches available to efficiently use such data. We summarize best practices and strategies for assessing and mitigating uncertainties and provide guidelines on appropriate technical review of paleoflood analyses based on project goals and requirements.

Techniques and Methods↗
USGS2020

Spatial and temporal patterns of low streamflow and precipitation changes in the Chesapeake Bay Watershed

Spatial and temporal patterns in low streamflows were investigated for 183 streamgages located in the Chesapeake Bay Watershed for the period 1939–2013. Metrics that represent different aspects of the frequency and magnitude of low streamflows were examined for trends: (1) the annual time series of seven‐day average minimum streamflow, (2) the scaled average deficit at or below the 2% mean daily streamflow value relative to a base period between 1939 and 1970, and (3) the annual number of days below the 2% threshold. Trends in these statistics showed spatial cohesion, with increasing low streamflow volume at streamgages located in the northern uplands of the Chesapeake Bay Watershed and decreasing low streamflow volume at streamgages in the southern part of the watershed. For a small subset of streamgages (12%), conflicting trend patterns were observed between the seven‐day average minimum streamflow and the below‐threshold time series and these appear to be related to upstream diversions or the influence of reservoir‐influenced streamflows in their contributing watersheds. Using multivariate classification techniques, mean annual precipitation and fraction of precipitation falling as snow appear to be broad controls of increasing and decreasing low‐flow trends. Further investigation of seasonal precipitation patterns shows summer rainfall patterns, driven by the Atlantic Multidecadal Oscillation, as the main driver of low streamflows in the Chesapeake Bay Watershed.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS2020

Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity

Streamflow estimates for floods with an annual exceedance probability of 0.001 or lower are needed to accurately portray risks to critical infrastructure, such as nuclear powerplants and large dams. However, extrapolating flood-frequency curves developed from at-site systematic streamflow records to very low annual exceedance probabilities (less than 0.001) results in large uncertainties in the streamflow estimates. Traditionally, methods for statistically estimating flood frequency have relied on the systematic streamflow record, which provides a time series of annual maximum flood peaks, often including some historical peaks. However, most peak-flow records are less than 100 years, and uncertainties are large when trying to extrapolate magnitudes of very low annual exceedance probability events. Other data may be available that extend the record beyond the systematic dataset. Historical data are defined as data from outside the period of systematic records but within the period of human records. Examples of historical information include flood estimates from other agencies and newspaper accounts that can be translated to flood magnitude point estimates, interval estimates, or perception thresholds (such as a statement that an 1880 flood was the largest since 1869). Paleoflood data, which may also extend the dataset, include a broad range of information about flood occurrence or magnitude from sources like sediment deposits or tree rings. Several assumptions are made in flood-frequency analysis, and an understanding of whether the data conform to these assumptions is desired. A particularly difficult assumption to evaluate for flood-frequency analysis is the underlying assumption that the flood series is stationary—the assumption that a time series of peak flow varies around a constant mean within a particular range of values (constant variance). As the hydrologic community’s understanding of natural systems and anthropogenic effects on streamflows has evolved, the community has come to understand that many surface-water systems exhibit one or more forms of nonstationarity, and thus the stationarity assumption is often violated to some degree. However, there is currently (2020) no consensus among hydrologists regarding the most appropriate flood-frequency-analysis methods for nonstationary systems, and this topic remains an active area of research. A literature review was completed to summarize the state of the science of flood frequency. The literature review highlights tools available to detect nonstationarities and identifies approaches that include external information to inform flood-frequency analysis. To demonstrate methods for initial data analysis and for incorporating historical and paleoflood information in flood-frequency analysis, five sites were selected: the Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada; lower reach, Rapid Creek, South Dakota; Spring Creek, South Dakota; Cherry Creek near Melvin, Colorado; and Escalante River near Escalante, Utah. The sites were chosen for the availability of published historical and paleoflood data and for their geographic diversity and unique characteristics, which highlighted issues such as autocorrelation, change points, trends, outlier peaks, or short periods of record. An initial data analysis that involved examining records for autocorrelation, change points, and trends was completed for all sites. The flood-frequency analysis completed for this study used version 7.2 of the U.S. Geological Survey PeakFQ program. Multiple analyses were done on each site documenting the change in the flood-frequency curve when additional historical or paleoflood data were added. When other flood-frequency studies were available, their results were compared to the results here. The comparisons in some cases simply show the effect of additional years of data, whereas other comparisons show results from probability distributions or fitting methods other than those used in PeakFQ. For the Red River of the North, flood-frequency analysis shows that paleoflood data appear necessary to reasonably estimate very low annual exceedance probabilities. For the analysis of the lower reach of Rapid Creek and Spring Creek, paleoflood information helped put a high outlier from the systematic period in context; however, very low annual exceedance probabilities at these sites still had extraordinarily large confidence bounds. These sites also showed that paleoflood information might be transferred from one site to another, with the caveat that this is a case where we had existing paleoflood data to test the transfer of paleoflood information—this is not the case at many sites, and transferring paleoflood information requires assumptions about the comparability of floods at the sites. The Cherry Creek analysis affirmed the result of an earlier study that showed that the generalized Pareto distribution was not a good distribution for estimating very low annual exceedance probabilities. The Escalante River analysis showed that adding paleoflood information might increase uncertainty for very low annual exceedance probabilities, compared to analysis with the systematic period of record information only, when the paleoflood peaks are of much larger magnitudes than the systematic record.

Scientific Investigations Report↗
USGS2019

Updating estimates of low-streamflow statistics to account for possible trends

Accurate estimators of streamflow statistics are critical to the design, planning, and management of water resources. Given increasing evidence of trends in low-streamflow, new approaches to estimating low-streamflow statistics are needed. Here we investigate simple approaches to select a recent subset of the low-flow record to update the commonly used statistic of 7 Q 10, the annual minimum 7-day streamflow exceeded in 9 out of 10 years on average. Informed by low-streamflow records at 174 US Geological Survey streamgages, Monte Carlo simulation experiments evaluate competing approaches. We find that a strategy which estimates 7 Q 10 using the most recent 30 years of record when a trend is detected, reduces error and bias in 7 Q 10 estimators compared to use of the full record. This simple rule-based approach has potential as the basis for a framework for updating frequency-based statistics in the context of possible trends.

Hydrologic Sciences Journal↗
USGS2019

Regionalization of surface-water statistics using multiple linear regression

This report serves as a reference document in support of the regionalization of surface-water statistics using multiple linear regression. Streamflow statistics are quantitative characterizations of hydrology and are often derived from observed streamflow records. In the absence of observed streamflow records, as at unmonitored or ungaged locations, other techniques are required. Multiple linear regression is one tool that is widely used to regionalize or transfer information from gaged to ungaged locations. This report provides the background to support regression-based regionalization of streamflow statistics. This background includes tools for data assembly, exploratory data analysis, model estimation in a least-squares framework, and model evaluation.

Techniques and Methods↗
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↗
USGS2018

Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves

In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 September 2013, are used to explore a method for rescaling simulated streamflow to correct the distributional bias. Based on an existing approach that separates the simulated streamflow into components of temporal structure and magnitude, the temporal structure is converted to simulated nonexceedance probabilities and the magnitudes are rescaled using an independently estimated flow duration curve (FDC) derived from regional regression. In this study, this method is applied to a pooled ordinary kriging simulation of daily streamflow coupled with FDCs estimated by regional regression on basin characteristics. The improvement in the representation of high and low streamflows is correlated with the accuracy and unbiasedness of the estimated FDC. The method is verified by using an idealized case; however, with the introduction of regionally regressed FDCs developed for this study, the method is only useful overall for the upper tails, which are more accurately and unbiasedly estimated than the lower tails. It remains for future work to determine how accurate the estimated FDCs need to be to be useful for bias correction without unduly reducing accuracy. In addition to its potential efficacy for distributional bias correction, this particular instance of the methodology also represents a generalization of nonlinear spatial interpolation of daily streamflow using FDCs. Rather than relying on single index stations, as is commonly done to reflect streamflow timing, this approach to simulation leverages geostatistical tools to allow a region of neighbors to reflect streamflow timing.

Hydrology and Earth System Sciences↗
USGS2018

A comparison of methods for streamflow uncertainty estimation

Streamflow time series are commonly derived from stage‐discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage‐discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage‐discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time‐varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.

Water Resources Research↗
USGS2018

Continuing progress toward a national assessment of water availability and use

Executive Summary The Omnibus Public Land Management Act of 2009 (Public Law 111—11) was passed into law on March 30, 2009. Subtitle F, also known as the SECURE Water Act, calls for the establishment of a “national water availability and use assessment program” within the U.S. Geological Survey (USGS). The USGS issued the first report on the program in 2013. Program progress over the period 2013–17 is reported herein to fulfill the requirement to inform Congress on implementation of the national water availability and use assessment program, also referred to as the USGS National Water Census (the Water Census). Much work has been accomplished during 2013–17 on producing water budgets for the nation, a goal USGS outlined in its first report on program progress to Congress. The USGS has completed three geographic focus area studies and has begun three others. Work has advanced on nationwide efforts in streamflow analysis, groundwater assessment and research, evapotranspiration studies, water use, environmental water science, and drought science. The USGS works with Federal and non-Federal agencies, universities, and other organizations to ensure that the information can be aggregated with other types of water-availability and socioeconomic information, such as data on food and energy production. The USGS has also made great strides in measures for delivering data and information on the Water Census to stakeholders and the public. Much work remains to be accomplished for the Nation to have a comprehensive, ongoing Water Census. In this report, the USGS lays out activities to be accomplished in the next 5 years (2017–22), based upon current funding levels. These include selecting new focus area studies, conducting hydrologic modeling to complete water budgets for the conterminous United States, expanding groundwater modeling efforts, mapping a national classification system for environmental water science, and developing an inventory of interbasin water transfers. All of these steps are necessary in order for the Water Census to achieve the goals outlined by Congress in the SECURE Water Act.

Circular↗
USGS2018

Guidelines for determining flood flow frequency — Bulletin 17C

Accurate estimates of flood frequency and magnitude are a key component of any effective nationwide flood risk management and flood damage abatement program. In addition to accuracy, methods for estimating flood risk must be uniformly and consistently applied because management of the Nation’s water and related land resources is a collaborative effort involving multiple actors including most levels of government and the private sector. Flood frequency guidelines have been published in the United States since 1967, and have undergone periodic revisions. In 1967, the U.S. Water Resources Council presented a coherent approach to flood frequency with Bulletin 15, “A Uniform Technique for Determining Flood Flow Frequencies.” The method it recommended involved fitting the log-Pearson Type III distribution to annual peak flow data by the method of moments. The first extension and update of Bulletin 15 was published in 1976 as Bulletin 17, “Guidelines for Determining Flood Flow Frequency” (Guidelines). It extended the Bulletin 15 procedures by introducing methods for dealing with outliers, historical flood information, and regional skew. Bulletin 17A was published the following year to clarify the computation of weighted skew. The next revision of the Bulletin, the Bulletin 17B, provided a host of improvements and new techniques designed to address situations that often arise in practice, including better methods for estimating and using regional skew, weighting station and regional skew, detection of outliers, and use of the conditional probability adjustment. The current version of these Guidelines are presented in this document, denoted Bulletin 17C. It incorporates changes motivated by four of the items listed as “Future Work” in Bulletin 17B and 30 years of post-17B research on flood processes and statistical methods. The updates include: adoption of a generalized representation of flood data that allows for interval and censored data types; a new method, called the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval data; a generalized approach to identification of low outliers in flood data; and an improved method for computing confidence intervals. Federal agencies are requested to use these Guidelines in all planning activities involving water and related land resources. State, local, and private organizations are encouraged to use these Guidelines to assure uniformity in the flood frequency estimates that all agencies concerned with flood risk should use for Federal planning decisions. This revision is adopted with the knowledge and understanding that review of these procedures will be ongoing. Updated methods will be adopted when warranted by experience and by examination and testing of new techniques.

Georgia↗
USGS2018

SWToolbox: A surface-water tool-box for statistical analysis of streamflow time series

This report is a user guide for the low-flow analysis methods provided with version 1.0 of the Surface Water Toolbox (SWToolbox) computer program. The software combines functionality from two software programs—U.S. Geological Survey (USGS) SWSTAT and U.S. Environmental Protection Agency (EPA) DFLOW. Both of these programs have been used primarily for computation of critical low-flow statistics. The main analysis methods are the computation of hydrologic frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10), computation of design flows including biologically based flows, and computation of flow-duration curves and duration hydrographs. Other annual, monthly, and seasonal statistics can also be computed. The interface facilitates retrieval of streamflow discharge data from the USGS National Water Information System and outputs text reports for a record of the analysis. Tools for graphing data and screening tests are available to assist the analyst in conducting the analysis.

Techniques and Methods↗
USGS2018

Improving predictions of hydrological low-flow indices in ungaged basins using machine learning

We compare the ability of eight machine-learning models (elastic net, gradient boosting, kernel-k-nearest neighbors, two variants of support vector machines , M5-cubist, random forest, and a meta-learning ensemble M5-cubist model) and four baseline models (ordinary kriging , a unit area discharge model, and two variants of censored regression) to generate estimates of the annual minimum 7-day mean streamflow with an annual exceedance probability of 90% (7Q10) at 224 unregulated sites in South Carolina, Georgia, and Alabama, USA. The machine-learning models produced substantially lower cross validation errors compared to the baseline models. The meta-learning M5-cubist model had the lowest root-mean-squared-error of 26.72 cubic feet per second. Partial dependence plots show that 7Q10s are likely moderated by late summer and early fall precipitation and the infiltration capacity of basin soils.

Alabama, Georgia, South Carolina↗
USGS2017

How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications

Streamflow data are used for important environmental and economic decisions, such as specifying and regulating minimum flows, managing water supplies, and planning for flood hazards. Despite significant uncertainty in most flow data, the flow series for these applications are often communicated and used without uncertainty information. In this commentary, we argue that proper analysis of uncertainty in river flow data can reduce costs and promote robust conclusions in water management applications. We substantiate our argument by providing case studies from Norway and New Zealand where streamflow uncertainty analysis has uncovered economic costs in the hydropower industry, improved public acceptance of a controversial water management policy, and tested the accuracy of water quality trends. We discuss the need for practical uncertainty assessment tools that generate multiple flow series realizations rather than simple error bounds. Although examples of such tools are in development, considerable barriers for uncertainty analysis and communication still exist for practitioners, and future research must aim to provide easier access and usability of uncertainty estimates. We conclude that flow uncertainty analysis is critical for good water management decisions.

Water Resources Research↗
USGS2017

Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities

The U.S. Geological Survey (USGS), in cooperation with the U.S. Nuclear Regulatory Commission, has investigated statistical methods for probabilistic flood hazard assessment to provide guidance on very low annual exceedance probability (AEP) estimation of peak-streamflow frequency and the quantification of corresponding uncertainties using streamgage-specific data. The term “very low AEP” implies exceptionally rare events defined as those having AEPs less than about 0.001 (or 1 × 10 –3 in scientific notation or for brevity 10 –3 ). Such low AEPs are of great interest to those involved with peak-streamflow frequency analyses for critical infrastructure, such as nuclear power plants. Flood frequency analyses at streamgages are most commonly based on annual instantaneous peak streamflow data and a probability distribution fit to these data. The fitted distribution provides a means to extrapolate to very low AEPs. Within the United States, the Pearson type III probability distribution, when fit to the base-10 logarithms of streamflow, is widely used, but other distribution choices exist. The USGS-PeakFQ software, implementing the Pearson type III within the Federal agency guidelines of Bulletin 17B (method of moments) and updates to the expected moments algorithm (EMA), was specially adapted for an “Extended Output” user option to provide estimates at selected AEPs from 10 –3 to 10 –6 . Parameter estimation methods, in addition to product moments and EMA, include L-moments, maximum likelihood, and maximum product of spacings (maximum spacing estimation). This study comprehensively investigates multiple distributions and parameter estimation methods for two USGS streamgages (01400500 Raritan River at Manville, New Jersey, and 01638500 Potomac River at Point of Rocks, Maryland). The results of this study specifically involve the four methods for parameter estimation and up to nine probability distributions, including the generalized extreme value, generalized log-normal, generalized Pareto, and Weibull. Uncertainties in streamflow estimates for corresponding AEP are depicted and quantified as two primary forms: quantile (aleatoric [random sampling] uncertainty) and distribution-choice (epistemic [model] uncertainty). Sampling uncertainties of a given distribution are relatively straightforward to compute from analytical or Monte Carlo-based approaches. Distribution-choice uncertainty stems from choices of potentially applicable probability distributions for which divergence among the choices increases as AEP decreases. Conventional goodness-of-fit statistics, such as Cramér-von Mises, and L-moment ratio diagrams are demonstrated in order to hone distribution choice. The results generally show that distribution choice uncertainty is larger than sampling uncertainty for very low AEP values.

Scientific Investigations Report↗
USGS2016

Rating curve uncertainty: A comparison of estimation methods

The USGS is engaged in both internal development and collaborative efforts to evaluate existing methods for characterizing the uncertainty of streamflow measurements (gaugings), stage-discharge relations (ratings), and, ultimately, the streamflow records derived from them. This paper provides a brief overview of two candidate methods that may be used to characterize the uncertainty of ratings, and illustrates the results of their application to the ratings of the two USGS streamgages.

Conference Paper↗