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A GNSS Receiver for Small-Sats Enabling Precision POD, Radio Occultations and Reflections
A low cost, low power, and low mass GNSS receiver (called Cion) has been developed and is currently flying on the CICERO cubesats. The receiver was designed in less than a year by JPL for Tyvak and GeoOptics for use in the GeoOptics CICERO constellation and leverages 25 years of JPL GNSS reciever design experience. Cion uses a commercial off-the-shelf (COTS) computer along with existing space qualified RF down-converters, software, and firmware to produce atmospheric Radio Occultation (RO) data. By combining a FPGA with dual core ARM processor and an embedded system controller, the Xilinx Zynq processor is an enabling technology that provides a customizable digital signal processing platform integrated into the computer (System on a chip) and enables off-the-shelf hardware to become the main engine behind this software defined radio. Using Linux for the on-board computer allows for fast development times and liberal use of existing open source software libraries. The parts of the receiver that require realtime implementation are performed in the Field Programmable Gate Array (FPGA), which can also be reprogrammed in flight. While the Zynq is not rad hard, the silicon on insulator (SOI) technology is rad tolerant ‘by accident’, allowing for its use in many space-based applications. Early results show that the Cion is working as designed, has demonstrated the first known GLONASS occultations, and obtains high quality atmospheric profiles with excellent lower troposphere penetration (near Earth’s surface).
IceNode: a buoyant sensor pod for persistent in-situ measurements beneath ice shelves
UNKNOWN
X-ray Inspection of Orbital Tube Weld for Detection of Crack-like Flaws, CCP/NESC POD Study
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Guidebook for Assessing Similarity and Implementing Empirical Transfer Functions for Probability of Detection (POD) Demonstrations for Signal Based Nondestructive Evaluation (NDE) Methods
This document provides guidance for assessing similarity relative to nondestructive evaluation (NDE) detectability based on Probability of Detection demonstrations when comparing different NDE inspection situations. Differences in inspection situations addressed by the similarity assessment methodology herein can include differences in the component being inspected, and differences in the conditions for the inspection. Additional differences in the inspection situation relative to differences in the method or procedure are not addressed in this document but can be assessed using NDE calibration or instrument standardization specimens.
NASA’s Moon to Mars (M2M) Transit Habitat (TH) Refinement Point of Departure (PoD) Design
As NASA prepares for the next human footsteps on the lunar surface, the Agency is already looking ahead to systems that will enable a sustained human presence on the lunar surface and mission to Mars, including a lunar Surface Habitat and Mars Transit Habitat (TH). This paper describes the latest NASA government reference design for the TH and how it will support NASA's Moon to Mars human exploration architecture. First, it will serve as a test and demonstration platform in lunar orbit, demonstrating capabilities required for long-duration microgravity human spaceflight as part of the lunar-Mars analog missions. Then, the TH will also serve as a major Mars habitation exploration element to support the crew during their transit from the lunar orbit to Mars and returning them safely to lunar orbit. This paper will cover several considerations contributing to the latest habitat design refinement, including data on the TH's concept of operations, system functional definition, subsystem assumptions, notional interior layouts, a detailed mass and volume breakdown, and trade studies and analyses required to close identified technology/ development/architecture gaps. In addition to a technical description of the TH, this paper describes how the current TH government reference design will achieve many of the current lunar and Mars mission goals. Additionally, there are many assumed technological advances needed to support the prescribed mission phases leading up to the crewed mission to Mars in the late 2030s. The paper will describe many of the TH systems requiring further technology development and identify architectural solutions to achieve these mass, reliability, autonomy, and crew health targets. As a whole, the data in the paper shows that a TH meeting the 43 metric tons launch mass/trans-Mars injection burn limits specified by the Evolvable Mars Campaign is achievable near the desired timeframe with moderate strategic investments including maintainable life support systems, re-purposable structures and packaging, and lightweight exercise modalities. It also identifies operational and technological options to reduce this mass to less than 41 metric tons, including staging of launch structure/packaging and alternate structural materials. The resulting design detail and data contained in this paper are intended to help teams across NASA and potential commercial, academic, or international partners understand the current performance targets of the Transit Habitat and vehicle interface considerations imposed by the latest Moon to Mars mission scope.
Sentinel-6 Project: Quarterly Technical Status Briefing: JPL POD Validation
No abstract provided
NASA NDE Fracture Critical Detectable Flaw Sizes History and Methodology
NASA requires that NDE methods and inspectors demonstrate 90% Probability of Detection (POD) with 95% confidence for critical flaw sizes when inspecting fracture critical metallic components. NASA addresses the known variability of NDE inspector capability in two ways. The first, Special NDE, requires that every inspector demonstrate the required 90/95 POD, which is resource intensive. The second approach is Standard NDE for which conservative flaw sizes for different NDE methods are provided such that it is expected that most properly trained inspectors will exceed the 90/95 POD requirement. As such, individual POD demonstration testing is not required. The origin of NASA Standard NDE dates to the start of the Space Shuttle Program in the early 1970’s. In the first study to quantitatively assess NDE methods and inspectors, the performance of multiple inspectors was evaluated for different NDE methods using a large set of fatigue cracked specimens. A rudimentary POD analysis was performed to estimate the 90/95 POD flaw size for each inspector for each method. Additionally, the average and standard deviation of the 90/95 POD flaw size across the multiple inspectors was calculated to estimate the flaw size for which 95 percent of inspectors would provide the 90/95 POD capability. These estimated 90/95/95 POD flaw sizes evolved into the NASA Standard NDE flaw sizes still in use for structural analysis five decades later. The methodology for performing Standard NDE POD studies was never documented in NASA requirements. Furthermore, POD analysis methods have significantly evolved since this seminal study. Likewise, NDE methods have improved and there has been a push to reassess Standard NDE flaw sizes for existing methods, and to develop Standard NDE flaw sizes for new methods such as digital radiography. In this study, a Standard NDE POD methodology was developed and baselined using the historical data. This reanalysis of the historical data identified several deficiencies in the original test plan as well as an overall lack of conservatism in the estimated 90/95/95 POD flaw sizes. The results of this historical review and the new methodology are being incorporated into an update of NASA NDE POD requirements.
An Eddy Current Case Study using NASA’s Transfer Function and Limited-Sample Probability of Detection Guidebooks
Probability of detection (POD) requirements for NASA fracture-critical human-rated systems can be met by (1) claiming ‘similarity’ to NASA Standard Nondestructive Evaluation (NDE) flaw sizes or (2) by inspector-specific POD demonstration, known as NASA Special NDE. Standard NDE flaw sizes are intended to represent the detection capability of most qualified inspectors, and were primarily based on POD studies in the development of the Space Shuttle Program Orbiter fracture control plan. However, these Standard NDE flaw sizes were based on fatigue cracks in flat panels of one material alloy over a limited range of panel thicknesses. While the POD study was comprehensive, the inspection conditions are relatively narrow. An evaluation of similarity between the Standard NDE POD study and flight component conditions is required to apply Standard NDE to specific flight component inspections. A similarity evaluation may include differences in materials, surface finish, component geometry (e.g., curvature, corners, welds), and inspection access. Similarity is typically based on a qualitative engineering evaluation. Recently, NASA published a quantitative methodology to assess similarity. If similarity is not supported by test and analysis, then the methodology provides a transfer function of flaw size for Special NDE POD demonstration. NASA recommends that Special POD demonstrations use flaws that are flight-like or more difficult to detect than flight component flaws, which are commonly induced fatigue cracks. Often, it is not feasible to induce fatigue cracks in flight component geometry, and therefore, a simpler representative geometry is used, commonly a flat panel, for POD demonstration. Inducing cracks in simple geometry specimens can be time-consuming and expensive, and thus, minimizing the number of flawed specimens is desired. Traditionally, NASA Special NDE demonstration was limited to MIL-HDBK-1823A methods or the binomial point-estimate method (PEM), commonly known as 29/29. Recently, NASA published a limited-sample POD (LS-POD) methodology for signal-response NDE techniques (e.g., eddy current) that provides POD demonstration with fewer specimens than previous approaches. LS-POD suggests a minimum of 10 flawed specimens, compared to 29 in the PEM and the suggested minimum of 40 from MIL-HDBK-1823A. The methodology also includes new guidance on evaluating the probability of false calls. In this presentation, these new transfer function and LS-POD methodologies are described and illustrated through an eddy current detection capability of far-side flaws on thin welded panels. A geometry-based transfer function is developed using electro-discharge machined (EDM) notches in flight component and simple geometry to estimate the Special NDE flaw size that is representative of the critical initial flaw size required by the fracture analysis. Then, fatigue cracks are induced in simple flat panels of the transferred flaw size, and the LS-POD methodology is used for Special NDE demonstration.
Assessing Reliability of NDE Flaw Detection Using Smaller Number of Demonstration Data Points
The paper provides an engineering analysis approach for assessing reliability of NDE flaw detection using smaller number of demonstration data points. It explores dependence of probability of detection (POD), probability of false positive (POF), on contrast-to-noise ratio, and net decision threshold-to-noise ratio in a simulated data; and draws some generically applicable inferences to devise the approach. ASTM nondestructive evaluation standards provide requirements on signal-to-noise ratio and/or contrast-to-noise ratio in order to provide reliable flaw detection and limit false positive calls. POD analysis of inspection test data results in an estimated flaw size, denoted by 𝑎90/95. This flaw size has 90% POD and minimum 95% confidence. POF is also estimated in the analysis. POD demonstration requires specimens with flaws of known size. In many situations, it is very expensive to produce the large number of flaws required for the POD analysis. In some situations, only real flaws can truly represent the flaws for demonstration. Real flaws of correct size and location in part configuration specimen may be difficult to produce, if not impossible. Here, an engineering analysis approach is devised using simulation to assess reliability of NDE technique when a limited number of flaws are available for demonstration. In this simulation, a technique is considered reliable, if it provides flaw detectability size equal to or better than the theoretical 𝑎90𝑡ℎ used in simulation and also provides a POF less than or equal to a chosen value. The paper uses simulated signal response versus flaw size data to devise the approach. Linear correlation is used between the signal response data and flaw size. POD software mh1823 uses generalized linear model (GLM) in POD analysis after transforming the flaw size and signal response, if needed, using logarithm. Therefore, this approach is in agreement with the linear signal correlation used in mh1823. Using the POD analysis of data, generic conditions on contrast-to-noise ratio and net decision threshold-to-noise ratio are derived for reliable flaw detection. In order to assess technique reliability using the engineering approach, signal response-to-flaw size correlation about the flaw size of concern is needed. In addition, measurement of noise is also needed. If the technique meets the above requirements, assumption of linear signal-to-flaw size correlation and conditions on noise, then the technique can be assessed using this analysis as it fits the underlying POD model used here. The approach is conservative and is designed to provide a larger flaw size compared to the POD approach. Such NDE technique assessment approach, although, not as rigorous as POD, can be cost effective if the larger flaw size can be tolerated. Typically, this is a situation for all quality control NDE inspections. Here, an NDE technique needs to be reliable and 𝑎90/95 is not estimated, but the assessed flaw size is assumed to be larger than the unknown a90 due to conservative factors or margins. Applicability of the approach for assessing reliability of flaw detection in x-ray radiography and 2D imaging in general is also explored.
Fluid-Driven Mass Transfer During Retrograde Metamorphism and Exhumation of the UHP Western Gneiss Region Terrane, Norway
Dehydration reactions within subducted oceanic crust are important for fluid-mediated element transfer within the subducting plate and potentially to the mantle wedge. The effects of metamorphic reactions and fluid flow on element recycling that occur during retrogression and exhumation of subducted continental crust from mantle depths are poorly understood. We study two metabasite pods with fresh eclogite cores and retrogressed amphibolite-facies rims and surrounding host gneiss within the Western Gneiss Region (WGR), Norway, to better understand element mobility and mass transfer during exhumation of subducted continental crust. Bulk-rock data were collected from samples taken across the pod and into the host gneiss. Phengite breakdown in eclogite and epidote recrystallization in veins and/or gneiss within pod cores contributed large ion lithophile elements and REE to retrogressed eclogite closest to the pod cores. In gneiss hosting the pods, phengite and epidote breakdown provided fluid that mediated elemental transfer and redistribution to the pod rim or tail. Compared to the studied pod in the southern WGR, the pod in the northern WGR underwent higher P-T conditions, partial melting and higher strain rates. This resulted in the infiltration of external fluid farther into the pod interior from the rim and facilitated larger mass gain in trace elements in the amphibolite tail of the pod relative to fresh eclogite in the core. The results show clear evidence for retrogression dehydration reactions driving significant fluid-mediated element redistribution as observed on the outcrop scale during exhumation following ultrahigh-pressure metamorphism, which directly impacts element signatures within the exhuming crust.
Marin County Wildfires Ii: Improving Fire Suppression Modeling to Inform Fire Prevention and Suppression Decisions in Marin County, Ca
A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning
Binomial Test Method for Determining Probability of Detection Capability for Fracture Critical Applications
The capability of an inspection system is established by applications of various methodologies to determine the probability of detection (POD). One accepted metric of an adequate inspection system is that for a minimum flaw size and all greater flaw sizes, there is 0.90 probability of detection with 95% confidence (90/95 POD). Directed design of experiments for probability of detection (DOEPOD) has been developed to provide an efficient and accurate methodology that yields estimates of POD and confidence bounds for both Hit-Miss or signal amplitude testing, where signal amplitudes are reduced to Hit-Miss by using a signal threshold Directed DOEPOD uses a nonparametric approach for the analysis or inspection data that does require any assumptions about the particular functional form of a POD function. The DOEPOD procedure identifies, for a given sample set whether or not the minimum requirement of 0.90 probability of detection with 95% confidence is demonstrated for a minimum flaw size and for all greater flaw sizes (90/95 POD). The DOEPOD procedures are sequentially executed in order to minimize the number of samples needed to demonstrate that there is a 90/95 POD lower confidence bound at a given flaw size and that the POD is monotonic for flaw sizes exceeding that 90/95 POD flaw size. The conservativeness of the DOEPOD methodology results is discussed. Validated guidelines for binomial estimation of POD for fracture critical inspection are established.
Interrelationships Between Receiver/Relative Operating Characteristics Display, Binomial, Logit, and Bayes' Rule Probability of Detection Methodologies
Unknown risks are introduced into failure critical systems when probability of detection (POD) capabilities are accepted without a complete understanding of the statistical method applied and the interpretation of the statistical results. The presence of this risk in the nondestructive evaluation (NDE) community is revealed in common statements about POD. These statements are often interpreted in a variety of ways and therefore, the very existence of the statements identifies the need for a more comprehensive understanding of POD methodologies. Statistical methodologies have data requirements to be met, procedures to be followed, and requirements for validation or demonstration of adequacy of the POD estimates. Risks are further enhanced due to the wide range of statistical methodologies used for determining the POD capability. Receiver/Relative Operating Characteristics (ROC) Display, simple binomial, logistic regression, and Bayes' rule POD methodologies are widely used in determining POD capability. This work focuses on Hit-Miss data to reveal the framework of the interrelationships between Receiver/Relative Operating Characteristics Display, simple binomial, logistic regression, and Bayes' Rule methodologies as they are applied to POD. Knowledge of these interrelationships leads to an intuitive and global understanding of the statistical data, procedural and validation requirements for establishing credible POD estimates.
ICESat-2 Precision Orbit Determination
The ICESat-2 Precision Orbit Determination (POD) system computes the precise position of the laser altimeter instrument in inertial space, a measurement necessary for accurate geolocation of individual photon surface returns. ICESat-2 POD solutions are generated by the reduction of Global Positioning System (GPS) double-difference carrier phase observable residuals with NASA Goddard Space Flight Center's GEODYN software. Independent satellite laser ranging (SLR) measurements are withheld from the POD solutions and used as an independent arbiter of POD performance. Measurement model updates in the form of estimated GPS antenna phase center and satellite laser ranging retro-reflector tracking point offset vectors as well as a GPS antenna phase center variation map have led to significant improvement in POD performance. GPS residual analysis and orbit precision analysis show no significant anomalous temporal or geographic POD performance issues. The independent SLR residual analysis indicates that POD solutions have achieved a radial orbit accuracy just below 1.5 cm, thus exceeding the mission radial orbit accuracy requirement of 3.0 cm by more than a factor of 2. Future POD processing updates have been identified and will lead to even further improvements in POD performance.
Offline Maximizing Minimally Invasive Proper Orthogonal Decomposition for Reduced-Order Modeling of S n Radiation Transport
Deterministic solutions to the Sn radiation transport equation can be computationally expensive to calculate. Reduced-order modeling enables efficient approximation of the full-order model (FOM) solution. We propose a novel method for constructing reduced-order models (ROMs) of the S n radiation transport equation, offline maximizing minimally invasive (OMMI) proper orthogonal decomposition (POD). POD uses the method of snapshots to create a reduced-order basis for constructing an ROM. Minimally invasive POD leverages the sweep infrastructure existing in deterministic transport codes to create a POD-based ROM, even when infeasible by traditional methods. Offline maximizing minimally invasive proper orthogonal decomposition (OMMI-POD) extends minimally invasive POD by performing sweeps offline, therefore maximizing the potential speedup. OMMI-POD does so by creating a library of reduced systems from a training set. This library of reduced systems is then interpolated to provide a rapid approximate solution of the S n radiation transport equation. The model is evaluated on a set of test problems, achieving a low error with a 466 times speedup over the FOM. Also presented is a study of the effect of sampling method on the performance of OMMI-POD, specifically comparing naive uniform sampling to the more accurate and computationally expensive greedy sampling.
HarDWR - Harmonized Water Rights Records
A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category
Assessing Risk Due to Small Sample Size in Probability of Detection Analysis Using Tolerance Intervals
Small sample size (e.g.6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.