Shoreline seasonality measurements from Landsat satellite imagery for California beaches, 2000-2022

Frequently anticipated questions:

• What does this data set describe?
  1. How might this data set be cited?
  2. What geographic area does the data set cover?
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
  5. What is the general form of this data set?
  6. How does the data set represent geographic features?
  7. How does the data set describe geographic features?
• Who produced the data set?
  1. Who are the originators of the data set?
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
• Why was the data set created?
• How was the data set created?
  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
  3. What similar or related data should the user be aware of?
• How reliable are the data; what problems remain in the data set?
  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
  5. How consistent are the relationships among the data, including topology?
• How can someone get a copy of the data set?
  1. Are there legal restrictions on access or use of the data?
  2. Who distributes the data?
  3. What's the catalog number I need to order this data set?
  4. What legal disclaimers am I supposed to read?
  5. How can I download or order the data?
• Who wrote the metadata?

What does this data set describe?

1. How might this data set be cited?
   Warrick, Jonathan A., and Buscombe, Daniel, 20250116, Shoreline seasonality measurements from Landsat satellite imagery for California beaches, 2000-2022: data release DOI:10.5066/P14WWHOJ, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

   Online Links:

   • https://doi.org/10.5066/P14WWHOJ

   This is part of the following larger work.
   Warrick, Jonathan A., and Buscombe, Daniel, 2025, Data to Support Analyses of Shoreline Seasonal Cycles for Beaches of California: data release DOI:10.5066/P14WWHOJ, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

   Online Links:

   • https://doi.org/10.5066/P14WWHOJ

   Other_Citation_Details:

   Suggested Citation: Warrick, J.A. and Buscombe, D., 2025, Data to Support Analyses of Shoreline Seasonal Cycles for Beaches of California: U.S. Geological Survey data release, https://doi.org/10.5066/P14WWHOJ.

2. What geographic area does the data set cover?

   West_Bounding_Coordinate: -124.403
   East_Bounding_Coordinate: -117.124
   North_Bounding_Coordinate: 42.013
   South_Bounding_Coordinate: 32.526
   

3. What does it look like?

   https://www.sciencebase.gov/catalog/file/get/6635585ad34edc29f409e2f5?name=BrowseGraphic1_Calif_StudyArea.jpg&allowOpen=true (JPEG)

   Map of the CoastSat transects included in this dataset.

   https://www.sciencebase.gov/catalog/file/get/6635585ad34edc29f409e2f5?name=BrowseGraphic2_Workflow_Diagram.jpg&allowOpen=true (JPEG)

   Workflow diagram of the data processing used to convert raw CoastSat data of Vos (2023) into metrics of the shoreline seasonality.

4. Does the data set describe conditions during a particular time period?

   Beginning_Date: 01-Jan-2000

   Ending_Date: 31-Dec-2022

   Currentness_Reference:

   The date range from which Landsat imagery were processed for shoreline analyses.

5. What is the general form of this data set?

   Geospatial_Data_Presentation_Form: comma-delimited text
   

6. How does the data set represent geographic features?
   1. How are geographic features stored in the data set?
      This is a Point data set. It contains the following vector data types (SDTS terminology):
      • Point (7777)
   2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.00000001. Longitudes are given to the nearest 0.0000001. Latitude and longitude values are specified in Decimal degrees. The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.0000.
      The flattening of the ellipsoid used is 1/298.2572.
      
7. How does the data set describe geographic features?

   CoastSat_Calif_SeasonalCycleData.csv

   Table containing attribute information associated with the dataset (Source: Producer defined)

   CoastSat_Section

   There are 309 distinct coastal sections in the CoastSat shoreline database for Califonia beaches, ranging from the U.S.-Mexico border (section 1) to the California-Oregon border (section 309). This attribute provides these CoastSat section numbers according to the data published in Vos (2023). (Source: CoastSat database (Vos, 2023))

   Range of values
   Minimum:	1
   Maximum:	309

   CoastSat_Transect

   Each section of the CoastSat database has beach transects numbered from zero (0) to n-1, where n is the total number of transects in the section. Transects are spaced every 100 meters along the shoreline. This attribute provides these CoastSat transect numbers according to the data published in Vos (2023). (Source: CoastSat database (Vos, 2023))

   Range of values
   Minimum:	0
   Maximum:	258

   Lat_AvShoreline_WGS84

   This is the latitude of the average shoreline position for each transect in the CoastSat database. Average shoreline position was computed as the mean shoreline position from the 2000-2021 CoastSat records. These across-shore positions (in meters from the transect origin) were transformed to latitude and longitude using the transect origin point from Vos (2023), the transect direction (from the ShoreOrientation_deg attribute below), and a UTM-to-Decimal degrees calculator. (Source: CoastSat database (Vos, 2023))

   Range of values
   Minimum:	32.52579102
   Maximum:	42.01239218
   Units:	Decimal degrees

   Lon_AvShoreline_WGS84

   This is the longitude of the average shoreline position for each transect in the CoastSat database. Average shoreline position was computed as the mean shoreline position from the 2000-2021 CoastSat records. These across-shore positions (in meters from the transect origin) were transformed to latitude and longitude using the transect origin point from Vos (2023), the transect direction (from the ShoreOrientation_deg attribute below), and a UTM-to-Decimal degrees calculator. (Source: CoastSat database (Vos, 2023))

   Range of values
   Minimum:	-124.4026317
   Maximum:	-117.1240128
   Units:	Decimal degrees

   ShoreOrientation_deg

   The orientation for each transect of the CoastSat database was computed using the bearing computed between the origin and terminal ends of the transects. These were computed by converting the WGS positions in decimal degrees to UTM to produce distance units of meters, and the orientation was computed from the arctangent function and the total distance in the northing and easting directions. (Source: CoastSat database (Vos, 2023))

   Range of values
   Minimum:	0.1
   Maximum:	353.2
   Units:	Degrees from North

   Lat_Backbeach_WGS84

   The geographic position of the backbeach was derived from the interection of each transect and a hand-digitized line that represented the first landward position of: (i) the base of the cliff, (ii) an urbanized structure, (iii) a mid-backwater channel, or (iv) a well-vegetated dune or marsh area. (Source: Google's 2015 satellite-based map data in Q-GIS and CoastSat database (Vos, 2023))

   Range of values
   Minimum:	32.52580357
   Maximum:	42.01295728
   Units:	Decimal degrees

   Lon_Backbeach_WGS84

   The geographic position of the backbeach was derived from the interection of each transect and a hand-digitized line that represented the first landward position of: (i) the base of the cliff, (ii) an urbanized structure, (iii) a mid-backwater channel, or (iv) a well-vegetated dune or marsh area. (Source: Google's 2015 satellite-based map data in Q-GIS and CoastSat database (Vos, 2023))

   Range of values
   Minimum:	-124.4020462
   Maximum:	-117.1226493
   Units:	Decimal degrees

   BeachWidth_m

   The along-transect distance between the backbeach point as characterized by the following attributes (Lat_Backbeach_WGS84, Lon_Backbeach_WGS84) and the average shoreline position characterized by these attributes (Lat_AvShoreline_WGS84, Lon_AvShoreline_WGS84). To compute these distances, the geographic positions in decimal degrees were transformed to UTM in meters. (Source: CoastSat database (Vos, 2023))

   Range of values
   Minimum:	0.0
   Maximum:	520.9
   Units:	Meters

   STL_SeasonalExcurs_m

   Using the seasonal-trend decomposition with LOESS (STL) technique of Cleveland and others (1990), the median monthly detrended shoreline positions were computed. The seasonal excursion distance is defined to be the difference between the maximum and minimum median monthly shoreline position. However, if the STL results were not found to be significant (p>0.05) with respect to either (i) a student's t-test between the complete set of values for the month with the maximum and the month with the minimum shoreline position values, or (ii) a Fischer's g-statistic test for the spectral periodogram at the annual frequency, the transect was deemed to be 'nonseasonal' and the seasonal excursion distance was set to zero meters. (Source: U.S. Geological Survey)

   Range of values
   Minimum:	0.0
   Maximum:	76.6
   Units:	Meters

   STL_Tmin_mo

   Using the seasonal-trend decomposition with LOESS (STL) technique of Cleveland and others (1990), the median monthly detrended shoreline positions were computed. The month of the minimum shoreline excursion distance was found and is defined to be 'Tmin.' However, if the STL results were not found to be significant (p>0.05) with respect to either (i) a student's t-test between the complete set of values for the month with the maximum and the month with the minimum shoreline position values, or (ii) a Fischer's g-statistic test for the spectral periodogram at the annual frequency, the transect was deemed to be 'nonseasonal' and the Tmin was set to 'NaN'. 'NaN' indicates that no value was calculated. (Source: U.S. Geological Survey.)

   Range of values
   Minimum:	1
   Maximum:	12
   Units:	Month of the year (1=Jan, 2=Feb, ... 12=Dec)

   STL_Tmax_mo

   Using the seasonal-trend decomposition with LOESS (STL) technique of Cleveland and others (1990), the median monthly detrended shoreline positions were computed. The month of the maximum shoreline excursion distance was found and is defined to be 'Tmax.' However, if the STL results were not found to be significant (p>0.05) with respect to either (i) a student's t-test between the complete set of values for the month with the maximum and the month with the minimum shoreline position values, or (ii) a Fischer's g-statistic test for the spectral periodogram at the annual frequency, the transect was deemed to be 'nonseasonal' and the Tmax was set to 'NaN'. 'NaN' indicates that no value was calculated. (Source: U.S. Geological Survey)

   Range of values
   Minimum:	1
   Maximum:	12
   Units:	Month of the year (1=Jan, 2=Feb, ... 12=Dec)

   STL_pval_tTest

   Using the seasonal-trend decomposition with LOESS (STL) technique of Cleveland and others (1990), the median monthly detrended shoreline positions were computed. The significant of these STL results were tested with two analyses. This attribute provides the results of one of these analyses, the p-value of a student's t-test between the complete set of detrended shoreline values for the month with the maximum and the month with the minimum shoreline position values. For our analyses, transects with p-values greater than 0.05 were determined to be 'nonseasonal.' (Source: U.S. Geological Survey)

   Range of values
   Minimum:	0.0
   Maximum:	0.61962
   Units:	Unitless probability from 0 to 1

   fft_SeasonalExcurs_m

   A fast Fourier transform (FFT) was conducted on the 22-year detrended monthly shoreline position time series of each transect. The FFT-derived seasonal shoreline excursion distance was computed as twice the FFT amplitude for the annual frequency, where the amplitude was calculated to be the absolute value of the complex FFT magnitude for the annual frequency divided by the number of frequencies assessed in the single-sided FFT, which was 132 for our analyses. (Source: U.S. Geological Survey)

   Range of values
   Minimum:	2.0
   Maximum:	73.6
   Units:	Meters

   Periodogram_pval_annual

   To evaluate whether the seasonal cycles in the detrended shoreline positions were significant, the power spectral density (PSD) of the monthly shoreline positions were estimated with a periodogram. An assessment of whether the annual frequency cycle was statistically significant was made using Fischer’s g-statistic. Here we follow general recommendations that the first term of the Fisher series is adequate to calculate the p-value. (Source: U.S. Geological Survey)

   Range of values
   Minimum:	0.0
   Maximum:	1.0
   Units:	Unitless probability from 0 to 1

   k_Means_Cluster

   A k-means clustering analysis was conducted on the entire set of standardized monthly median shoreline positions from each transect. The number of clusters was set to 11, as determined by a peak in the 'gap statistic' that characterizes change in within cluster dispersion verses change that would occur from a reference null distribution. Clusters were ordered by the timing of the seasonal response, from '1' for the earliest winter-narrow conditions to '10' for the latest summer-narrow conditions. The final cluster ('11') included all 'nonseasonal' transects. (Source: U.S. Geological Survey)

   Range of values
   Minimum:	1
   Maximum:	11
   Units:	Unitless

   Entity_and_Attribute_Overview:

   There is one header line in the csv file that provides the attribute labels.

   Entity_and_Attribute_Detail_Citation: U.S. Geological Survey
   

Who produced the data set?

1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
   • Jonathan A. Warrick
   • Daniel Buscombe
2. Who also contributed to the data set?
3. To whom should users address questions about the data?
   U.S. Geological Survey, Pacific Coastal and Marine Science Center

   Attn: PCMSC Science Data Coordinator

   2885 Mission Street

   Santa Cruz, CA
   

   831-427-4747 (voice)

   pcmsc_data@usgs.gov

Why was the data set created?

How was the data set created?

1. From what previous works were the data drawn?

   Vos (2023) (source 1 of 1)

   Vos, Kilian, 2023, Time-series of shoreline change along the Pacific Rim, v1.4: Zenodo, online.

   Online Links:

   • https://doi.org/10.5281/zenodo.7758183

   Type_of_Source_Media: online database
   Source_Contribution:

   Time-series of coastal shoreline positions derived from remote sensing analyses of imagery from 1984-2022, version 1.4.

2. How were the data generated, processed, and modified?

   Date: 09-Feb-2024 (process 1 of 6)

   The first processing step involved the Seasonal Trend decomposition with LOESS (STL) techniques of Cleveland and others (1990) to measure the magnitude and timing of shoreline seasonal patterns. For each CoastSat transect, the 2000-2022 raw shorelines were used to compute monthly median shoreline positions for each year of the record. Standard STL techniques were then used to remove the time-dependent trend in the resulting data using the single-pass LOESS function with the standard 2nd order polynomial fit and tri-cube weight function (Cleveland and others 1990). The detrended data were then used to compute the median monthly shoreline positions of the entire record, from which the range of these median values was assigned to be the ‘shoreline seasonal excursion distance’ and the months of the minimum and maximum shoreline positions were assigned to be ‘tmin’ and ‘tmax.’ The twelve median monthly shoreline position values were used to conduct k-mean clustering as noted below. The complete set of shoreline positions for the months of tmin and tmax were compared using a student’s t-test p-values, the results of which were used to assess whether the range of values from these two months were significantly different. These statistical results were used to assess whether the STL seasonality results were statistically significant as noted below. Data sources used in this process:

   • Vos (2023)

   Date: 09-Feb-2024 (process 2 of 6)

   The second processing step involved spectral analyses of the 22-year monthly median shoreline position values for each transect. First, we computed the periodograms of the monthly shoreline positions over a range of frequencies associated with periodicity of 61 days to 42 years to assess whether annual frequency cycles were statistically significant using the Fischer’s g-statistic. We computed p-values from each g-statistic using the first term of the Fischer’s series, and these p-values were used to assess the significance of the seasonality patterns as discussed below. Second, spectra peaks and a spectra-derived seasonal shoreline excursion distances were computed using a fast Fourier transform (FFT) of the monthly shoreline position time-series for the purpose of having an independent excursion distance measurement to compare with the STL results. We did not use zero padding because there were enough samples to adequately characterize the annual frequency results, and windowing techniques were unnecessary because the time series length (exactly 22-yr) was chosen to be an integer multiple of the primary, i.e., the annual, frequency. The FFT-derived seasonal shoreline excursion distances were computed as twice the single-sided FFT amplitude for the annual frequency, where the amplitude was calculated to be the absolute value of the complex FFT magnitude for the annual frequency divided by the number of frequencies assessed in the FFT, which was 132 in our analyses. Data sources used in this process:

   • Vos (2023)

   Date: 09-Feb-2024 (process 3 of 6)

   The third processing step consisted of combining the STL and spectral analyses to evaluate whether the seasonal patterns were statistically significant for each transect. The p-values from both the STL t-test and the periodgrams (see above) were used to evaluate statistical significance at p<0.05. If either of these p-values were greater than this threshold, the transect was determined to be ‘nonseasonal’ and the STL-derived values of seasonal excursion distance were changed to zero meters, and the tmin and tmax were changed into ‘NaN’ values (not a number) to denote that a significant seasonal cycle could not be measured. Data sources used in this process:

   • Vos (2023)

   Date: 09-Feb-2024 (process 4 of 6)

   The fourth processing step was an unsupervised classification of the STL results using k-means clustering. For this analysis, the twelve-monthly values of median shoreline position for each transect were used. Transects deemed ‘nonseasonal’ by the statistical tests denoted above were given shoreline position values of zero for each of the twelve months. Prior to clustering, the monthly median shoreline positions were standardized by subtracting the mean and dividing by the standard deviation for each transect. The number of k-means clusters was defined using the gap statistic technique that characterizes the change in within-cluster dispersion with respect to changes that would occur from a reference null distribution, which was assigned to be a random distribution over the range of the source values. This analysis showed peaks in the gaps at 5 and 11 clusters over a range of zero to 50 clusters, and the larger of these values (11) was used for our analyses to capture a broader range of seasonal timing groups. Cluster distances were computed as squared Euclidean distances, and the final cluster groups were organized by the month of the narrowest beach conditions, starting in the early winter (cluster 1) and ending in the summer (cluster 10), with the final cluster given to the nonseasonal group (cluster 11). Data sources used in this process:

   • Vos (2023)

   Date: 09-Feb-2024 (process 5 of 6)

   The fifth processing step included summary and geographical information about each transect. First, the mean position and orientation of the shoreline were computed using the initial and final transect points defined and tabulated in Vos (2023). The ‘back-beach position’ was derived from a non-erodible shoreline for California mapped and used by Vitousek and others (2023) that was modified in places to reflect the differences in perspectives of our goals of defining the recent active beach and Vitousek and others’ goals of defining the inland limit of future coastal erosion under sea-level rise scenarios. These changes consisted primarily of altering the back-beach lines of broad coastal plains and dune settings from the full inland extent of the low elevation landscape to the initial densely vegetated areas. The positions of this back beach and the active beach widths were found by intersecting the CoastSat transects with the back beach location. Data sources used in this process:

   • Vos (2023)

   Date: 21-Jan-2025 (process 6 of 6)

   The citation for the accompanying journal article was added to the Cross Reference section of this metadata. No data were changed (mau@usgs.gov)

3. What similar or related data should the user be aware of?
   Warrick, Jonathan, Buscombe, D., Vos, K., Kenyon, H., Ritchie, A.C., Harley, M.D., Janda, C., L'Heureux, J., and Vitousek, S., 2025, Shoreline Seasonality of California's Beaches.

   Online Links:

   • https://doi.org/10.1029/2024JF007836

   Other_Citation_Details:

   Warrick, J.A, Buscombe, D., Vos, K., Kenyon, H., Ritchie, A.C., Harley, M.D., Janda, C., L'Heureux, J., and Vitousek, S., 2025, Shoreline Seasonality of California's Beaches. Journal of Geophysical Research-Earth Surface. https://doi.org/10.1029/2024JF007836.

   Vos, K., Splinter, K.D., Palomar-Vázquez, J., Pardo-Pascual, J.E., Almonacid-Caballer, J., Cabezas-Rabadán, C., Kras, E.C., Luijendijk, A.P., Kalkoen, F., L.P., Almeida., Pais, D., Klein, A.H.F., Mao, Y., Harris, D., B., Castelle, Buscome, D., and Vitousek, S., 2023, Benchmarking satellite-derived shoreline mapping algorithms.

   Other_Citation_Details:

   Vos K., Splinter, K.D., Palomar-Vázquez, J., Pardo-Pascual, J.E., Almonacid-Caballer, J., Cabezas-Rabadán, C., Kras, E.C., Luijendijk, A.P., Kalkoen, F., Almeida., L.P., Pais, D., Klein, A.H.F., Mao, Y., Harris, D., Castelle B., Buscome, D., and Vitousek, S., Benchmarking satellite-derived shoreline mapping algorithms. Communications Earth & Environment, 4:345. doi: 10.1038/s43247-023-01001-2.

   R.B., Cleveland, W.S., Cleveland, J.E., McRae, and I., Terpenning, 1990, STL: A seasonal-trend decomposition procedure based on loess.

   Other_Citation_Details:

   Reference: Cleveland R.B., Cleveland W.S., McRae J.E., Terpenning I., 1990, STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6:3–73

   Vitousek, S., Vos, K., Splinter, K.D., Erikson, L., and Barnard, P.L., 2023, A Model Integrating Satellite?Derived Shoreline Observations for Predicting Fine-Scale Shoreline Response to Waves and Sea-Level Rise Across Large Coastal Regions..

   Other_Citation_Details:

   Reference: Vitousek, S., Vos, K., Splinter, K.D., Erikson, L., Barnard, P.L. 2023. A Model Integrating Satellite?Derived Shoreline Observations for Predicting Fine-Scale Shoreline Response to Waves and Sea-Level Rise Across Large Coastal Regions. Journal of Geophysical Research: Earth Surface, doi: 10.1029/2022JF006936

How reliable are the data; what problems remain in the data set?

1. How well have the observations been checked?
   Analyses of the raw CoastSat shoreline position data has been conducted by Vos and others (2023). This showed that horizontal root-mean-square errors (r.m.s.e.) for each derived shoreline position are between 7 and 14 meters, depending on location. The computation of monthly, seasonal, or multi-annual metrics reduces the overall variance in shoreline time series due to averaging, thereby increasing measurement precision, and as a result Vos and others (2023) suggest that monthly composites of shoreline positions have errors of approximately 10 m. Compilation of these raw data into 22-year seasonal excursion distances resulted in minimum detection limits of 5 meters.
2. How accurate are the geographic locations?
   Coordinates for each transect were derived from the average shoreline position of the CoastSat raw output along predetermined transects spaced 100-meters along the shoreline. These transects were used to extract relative shoreline information from Landsat imagery, and they were not assessed for accuracy with any field measurements or other remotely sensed data.
3. How accurate are the heights or depths?
   
4. Where are the gaps in the data? What is missing?
   The shoreline file is complete and contains all 7777 shoreline transects in the CoastSat database that were derived from the mainland coast of California and did not have erroneous data from piers, cliff shadows, or offshore rocks. These data represented the shoreline seasonal patterns over the 2000-2022 record of interest.
5. How consistent are the relationships among the observations, including topology?
   Data were checked for logical consistency by querying the range of numerical values to ensure that they were within a reasonable range for each field. We performed outlier checks by plotting all numerical values on maps using the geospatial coordinates of each transect location in QGIS. Site coordinates were checked in QGIS.

How can someone get a copy of the data set?

1. Who distributes the data set? (Distributor 1 of 1)
   
   U.S. Geological Survey - ScienceBase

   Denver Federal Center, Building 810, Mail Stop 302

   Denver, CO
   

   1-888-275-8747 (voice)

   sciencebase@usgs.gov
2. What's the catalog number I need to order this data set? These data are available is csv format contained in a single file (CoastSat_Calif_SeasonalCycleData.csv) and accompanied by CSDGM FGDC-compliant metadata
3. What legal disclaimers am I supposed to read?
   Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
4. How can I download or order the data?
   • Availability in digital form:

     Data format:	the csv file contains the csv table in format comma-delimited text Size: 1.132
     Network links:	https://www.sciencebase.gov/catalog/file/get/6635585ad34edc29f409e2f5 https://www.sciencebase.gov/catalog/item/6635585ad34edc29f409e2f5 https://doi.org/10.5066/P14WWHOJ

   • Cost to order the data: None.

5. What hardware or software do I need in order to use the data set?
   These data can be viewed with any spreadsheet or text editing software.

Who wrote the metadata?

Dates:

Last modified: 21-Jan-2025

Metadata author:

U.S. Geological Survey, Pacific Coastal and Marine Science Center

Attn: PCMSC Science Data Coordinator

2885 Mission Street

Santa Cruz, CA

831-427-4747 (voice)

pcmsc_data@usgs.gov

Metadata standard:

Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)