The Energy Commission has developed this app to quickly and accurately show addresses and locations to determine California’s climate regions. We invite builders and building officials to use this app to determine the climate zones applicable to building projects.Please note:Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.This is explained in the Title 24 energy efficiency standards glossary section:"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year.""The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone."Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is:CZ 1: ArcataCZ 2: Santa RosaCZ 3: OaklandCZ 4: San Jose-ReidCZ 5: Santa MariaCZ 6: TorranceCZ 7: San Diego-LindberghCZ 8: FullertonCZ 9: Burbank-GlendaleCZ10: RiversideCZ11: Red BluffCZ12: SacramentoCZ13: FresnoCZ14: PalmdaleCZ15: Palm Spring-IntlCZ16: Blue CanyonThe original detailed survey definitions of the 16 Climate Zones are found in the 1995 publication, "California Climate Zone Descriptions for New Buildings."

These boundaries define the regions used in by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the National Oceanographic and Atmospheric Administration.

Contained within 3rd Edition (1957) of the Atlas of Canada is a map that shows the division of Canada into climatic regions according to the classification of the climates of the world developed by W. Koppen. Koppen first divided the world into five major divisions to which he assigned the letters A, B, C, D, and E. The letters represent the range of divisions from tropical climate (A) to polar climate (E). There are no A climates in Canada. The descriptions of the four remaining major divisions are given in the map legend. Koppen then divided the large divisions into a number of climatic types in accordance with temperature differences and variations in the amounts and distribution of precipitation, on the basis of which he added certain letters to the initial letter denoting the major division. The definitions of the additional letters which apply in Canada are also given when they first appear in the map legend. Thus b is defined under Csb and the definition is, therefore, not repeated under Cfb, Dfb or Dsb. For this map, the temperature and precipitation criteria established by Koppen have been applied to Canadian data for a standard thirty year period (1921 to 1950 inclusive).

Evaluating multiple signals of climate change across the conterminous United States during three 30-year periods (2010�2039, 2040�2069, 2070�2099) during this century to a baseline period (1980�2009) emphasizes potential changes for growing degree days (GDD), plant hardiness zones (PHZ), and heat zones. These indices were derived using the CCSM4 and GFDL CM3 models under the representative concentration pathways 4.5 and 8.5, respectively, and included in Matthews et al. (2018). Daily temperature was downscaled by Maurer et al. (https://doi.org/10.1029/2007EO470006) at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones. Each of these indices provides unique information about plant health related to changes in climatic conditions that influence establishment, growth, and survival. These data and the calculated changes are provided as 14 individual IMG files for each index to assist with management planning and decision making into the future. For each of the four indices the following are included: two baseline files (1980�2009), three files representing 30-year periods for the scenario CCSM4 under RCP 4.5 along with three files of changes, and three files representing 30-year periods for the scenario GFDL CM3 under RCP 8.5 along with three files of changes.�Plant hardiness zones provide a general indication of the extent of overwinter stress experienced by plants. PHZ are based on the average annual extreme minimum temperatures and have been used by horticulturists to evaluate the cold hardiness of plants. Specifically, the value used here is the absolute minimum temperature achieved for each year and reported as the 30-year mean. Because they reflect cold tolerance for many plant species, including woody ones, hardiness zones are most likely to reflect plant range limits. The zonal variations caused by warming temperatures in the future will therefore be useful to approximately delineate niche constraints of many plant species and hence their future range potential. Plant hardiness zones and subzones were delineated according to the USDA definitions, which break the geography into zones by 10 �F (5.56 �C) increments from zone 1 (-55 to -45.6 �C) to zone 13 (15.7 to 22 �C) of annual extreme minimum temperature. To define the coldest day per year, daily minimum temperatures were identified within the period July 1 to June 30, with the nominal year assigned to the first 6 months of the 12-month period.�Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001

What is the difference between weather and climate? Weather is the present state of the atmosphere in a specific place and time. Climate, however, is the long-term pattern of weather, usually averaged over thirty years. Climate is impacted by a location’s latitude, elevation, proximity to significant landscape features, and ocean and atmospheric circulation patterns. By classifying regions with similar climates, we can build an understanding of a region’s typical conditions throughout one year. These classification systems usually describe the area using key terms as opposed to detailed monthly or seasonal data.The Köppen-Geiger climate classification system was developed by Wladimir Köppen (September 25, 1846-June 22, 1940) in 1900. Wladimir Köppen was a German scientist who studied botany and climatology. His goal was to create a mathematic model that used vegetation to determine terrestrial climatic boundaries. He continued to revise his classification model. Some of the revisions he made were done in collaboration with Rudolph Geiger, whom he met after he moved to Austria in 1927. The two new also edited a five-book Handbuch der Klimatologie (Handbook of Climatology) together - a project Köppen was unable to finish before his death in 1940.This classification system divides the climate into five major types (A, B, C, D, and E). Type A is the warmest and type E is the coolest. Most of the types are defined primarily by temperature. Type B is the exception. It is defined by aridity. Aridity is calculated by measuring evaporation. Evaporation is dependent on precipitation and soil type. During Köppen’s time weather stations did not measure evaporation so he substituted equations that estimated aridity from temperature and precipitation.Each of the five major types are further divided into 30 different sub-classes. The sub-classes add seasonal air temperature and precipitation into the equation as well as upper and lower thresholds. For example, types C and D are broken down into sub-classes based on when during the year the dry season occurs.This classification system is sometimes criticized because it only accounts for two variables in climate, temperature and precipitation. It leaves out other factors critical to plant growth such as sunlight availability and wind. It also glosses over major events such as drought or outlier temperatures such as an extreme cold snap or period of high heat. Despite these limitations it is still useful to see common patterns across the globe.Climate classifications such as the Köppen-Geiger climate classification system change over time as our climate changes. Scientists have continuously updated this classification system since it was first created. As we have built additional weather stations and technology has allowed us to analyze data more quickly, we have been able to create a more refined and accurate picture of climate zones. Additionally, we can model what climate conditions might look like in the future if we continue to allow our planet to warm.This map includes datasets representing a current and future-modeled Köppen-Geiger climate classification system for you to compare. The present datasets (type and sub-class) span the period from 1991 to 2020 and is mapped at a 1-kilometer resolution. This 30-year average captures enough variability to eliminate any extremes during the period. The modeled datasets (type and sub-classes) predict the climate classifications for 2071 to 2099 and are mapped at a 1-kilometer resolution. The mapmakers chose a climate model (Coupled Model Intercomparison Project phase 6) that represents a middle-of-the-road scenario for continued emissions of greenhouse gases. Together, we can compare the climate classifications of the recent past to the future – right around the time you might retire. The swipe tool will help you do this.Note: The layers representing the modeled data are turned off when you initially open the map.You can read more about this data in the paper High-resolution (1 km) Köppen-Geiger maps for 1901-2099 based on constrained CMIP6 projections.

Data Summary: US states grid mask file and NOAA climate regions grid mask file, both compatible with the 12US1 modeling grid domain. Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data. These files can be used with CMAQ-ISAMv5.3 to track state- or region-specific emissions. See Chapter 11 and Appendix B.4 in the CMAQ User's Guide for further information on how to use the ISAM control file with GRIDMASK files. The files can also be used for state or region-specific scaling of emissions using the CMAQv5.3 DESID module. See the DESID Tutorial and Appendix B.4 in the CMAQ User's Guide for further information on how to use the Emission Control File to scale emissions in predetermined geographical areas. File Location and Download Instructions: Link to GRIDMASK files Link to README text file with information on how these files were created File Format: The grid mask are stored as netcdf formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on the model projection and grid structure is contained in the header information of the netcdf file. The output files can be opened and manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other software programs that can read and write netcdf formatted files (e.g. Fortran, R, Python). File descriptions These GRIDMASK files can be used with the 12US1 modeling grid domain (grid origin x = -2556000 m, y = -1728000 m; N columns = 459, N rows = 299). GRIDMASK_STATES_12US1.nc - This file containes 49 variables for the 48 states in the conterminous U.S. plus DC. Each state variable (e.g., AL, AZ, AR, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that state. GRIDMASK_CLIMATE_REGIONS_12US1.nc - This file containes 9 variables for 9 NOAA climate regions based on the Karl and Koss (1984) definition of climate regions. Each climate region variable (e.g., CLIMATE_REGION_1, CLIMATE_REGION_2, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that climate region. NOAA Climate regions: CLIMATE_REGION_1: Northwest (OR, WA, ID) CLIMATE_REGION_2: West (CA, NV) CLIMATE_REGION_3: West North Central (MT, WY, ND, SD, NE) CLIMATE_REGION_4: Southwest (UT, AZ, NM, CO) CLIMATE_REGION_5: South (KS, OK, TX, LA, AR, MS) CLIMATE_REGION_6: Central (MO, IL, IN, KY, TN, OH, WV) CLIMATE_REGION_7: East North Central (MN, IA, WI, MI) CLIMATE_REGION_8: Northeast (MD, DE, NJ, PA, NY, CT, RI, MA, VT, NH, ME) + Washington, D.C.* CLIMATE_REGION_9: Southeast (VA, NC, SC, GA, AL, GA) *Note that Washington, D.C. is not included in any of the climate regions on the website but was included with the “Northeast” region for the generation of this GRIDMASK file.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Climate plays a crucial role in shaping species distribution and evolution over time. Dr. Elisabeth Vrba’s Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These results provide empirical support for the role of historical climate fragmentation and physiography in shaping the distribution and evolution of life on Earth. Methods Climate Data and Classification In this study, we employed the Köppen-Geiger climate classification to categorize climate zones. This system relies on climatic parameters, specifically monthly mean temperature (ºC) and total precipitation (mm), to define climate types (Beck et al., 2018; Köppen, 1884). Given the close correlation between climate and vegetation, these climate zones tend to align closely with global biome patterns (Belda et al., 2014), providing a proxy for examining how climate shapes biome distributions (Mucina, 2019). The Köppen-Geiger climate classification recognises 23 distinct climate regimes, grouped into five major zones: Tropical, Arid, Temperate, Cold, and Polar (Figure 1A). These zones served as the basis for our analysis of the impact of climate change on environmental fragmentation. Climate data for the last 5 million years were obtained from the high-resolution paleoclimate emulator, PALEO-PGEM (Holden et al., 2019). This dataset offers monthly climate information at a spatial resolution of 0.5º and temporal resolution of 1,000 years, beginning from the pre-industrial era (ca. 1760). We reclassified the climate data into the five major climate zones (tropical, arid, temperate, cold, and polar) for each 1,000-year interval following the methodology outlined by Beck et al. (2018). To facilitate computational operations, we introduced a "-99" value for missing data and made specific adjustments to the function 'KoppenGeiger.m' (Beck et al., 2018), as communicated by H. Beck (personal communication, December 18, 2021), to align with defined precipitation thresholds: "Pthreshold = 2×MAT if >70% of precipitation falls in winter, Pthreshold = 2×MAT+28 if >70% of precipitation falls in summer, otherwise Pthreshold = 2×MAT+14 (Galván et al., 2023). This change was made to rectify a previous code typo that prevented the accurate assignment of some pixels to their climate zone. "Pthreshold" refers to the precipitation threshold for determining the aridity of a climate zone. Meanwhile, "MAT" corresponds to Mean Annual Temperature. Geographical Framework This study was conducted on a global scale, to assess whether similar climate zones behaved consistently across different continents. To facilitate these comparisons, we divided the world into three distinct landmasses, hereafter referred to as Americas, Africa, and Eurasia+Oceania (EurOc). The rationale behind this division was to partition our planet into distinct landmasses, each of which would encompass a tropical zone. Upon delimiting the three main landmasses, the different islands were assigned to the nearest landmass in a straight line. This, in turn, corresponds with other biogeographical criteria based on the similarity of flora and fauna. Thus, the three studied landmasses were established as follows: · Americas: This category encompasses continental North, Central, and South America, as well as the Caribbean Islands. In the North (Bering Strait), we have included Aleutian St. Matthew, St. Paul, St. George, and Nunivak Islands. St. Lawrence Island is excluded due to its proximity to Europe. In the West, we encompass the Islands off the Mexican West Coast, the Galápagos Islands, and Easter Island. To the South, the Malvinas Islands are included. In the East, we consider Fernando de Noronha, Atol das Rocas Biological Reserve, and Boi Islands. Greenland is part of this category, while Iceland is excluded. · Africa: This category covers continental Africa and Madagascar. In the West, it includes the Canary, Madeira, and Savage Islands, the Cabo Verde archipelago, St. Helena, Tristan da Cunha, and Ascension Islands. In the East, Socotra, Seychelles, and the Mayotte archipelago, Comoros, and Mascarene Islands are encompassed. · Eurasia + Oceania: This category comprises continental Eurasia, the Arabian Peninsula, Iceland, St. Lawrence Island, Japan, Philippines, Indonesia Australia, New Zealand, and Papua-New Guinea Islands. All the islands of the Pacific Ocean, including the Hawaii Archipelago, are also included. In the Indian Ocean, we consider the Laccadive, Maldives, and Chagos Islands in the West and Ceylon and the Andaman Islands in the East. The French Austral and Antarctic Lands islands that are closer to the Antarctic region were excluded from the study. Measuring Fragmentation To assess climate zone fragmentation, we used the R package landscapemetrics v1.5.4 (Hesselbarth et al., 2019), employing the equal-area Mollweide projection. We applied the “lsm_p_area” function to calculate the number of fragments within each climate zone for each time interval, classifying them based on their area into four size categories: - Small fragments (S): Those with an area of up to 3,000 km2, approximately equivalent to the area of a single pixel under our 0.5º resolution. - Medium fragments (M): Those with an area between 3,000 and 30,000 km2. - Large fragments (L): Those with an area between 30,000 and 600,000 km2. - Extra-large fragments (XL): Those with an area exceeding 600,000 km2. Upon confirming that the number of fragments in the different climate zones followed a normal distribution but did not meet the assumption of variance homogeneity, we conducted the corresponding Welch One-Way ANOVA tests to determine the significance of the results. Given that we were comparing five climate zones, we applied Bonferroni correction to post-hoc results significance. Statistical analyses were conducted using the R library jmv (v2.3.4;53) In addition to quantifying the number of fragments within climate zones at each time in our series, we computed several additional measures to assess fragmentation: -Fragmentation Events: The count of instances when the number of fragments increased compared to the previous point in time. -Fragmentation Strength: The median number of fragments generated in each fragmentation event. -Maximum Fragmentation: The highest number of fragments produced in a single fragmentation event. Higher levels of climatic fragmentation are operationally defined as a prevalence of small (S) and medium (M) fragments, while lower levels of fragmentation are characterised by a greater abundance of larger patches (L and XL). Fragmentation vs. Richness To explore the relationship between climate fragmentation and specialist mammal richness we sourced mammal range maps from IUCN polygons (IUCN, 2022). Terrestrial mammal data was downloaded on 24th January 2022, while freshwater mammal data was obtained on 21th September 2022. We imported these range maps in shapefile format into R using the ‘rgdal’ package version 1.5-28 (Bivand et al., 2021). We excluded polygons associated with certain families such as Delphinidae, Iniidae, Phocidae, Phocoenidae, Platanistidae, Trichechidae, and the possibly extinct Lipotidae, due to their predominantly aquatic habits. We further excluded species range polygons with presence values of 3 (“possibly extant”) and 6 (“presence uncertain”), as well as range values of 3 (“introduced”) and 4 (“vagrant”) to retain only reliable natural range data (Miraldo et al., 2016). Range data for each species were converted into a 0.5º raster using the ‘terra’ R package version 1.5-21 (Hijmans, 2022). Mammal species were classified according to their range into specialists, those species that are restricted to a single climate zone, and generalists, which are found in more than one climate zone. To this end, we considered the current distribution of terrestrial mammal species as a reliable representation of their climatic specificities. We then quantified the richness of specialist and generalist mammal species within each climate zone on every continent. We considered various factors of climate fragmentation, including the total number of fragments categorized by size (S, M, L, and XL), the frequency of fragmentation events, as well as the fragmentation strength and maximum fragmentation within each fragment size, climate zone, and continent. In addition, we calculated the mean annual temperature and mean annual precipitation for each climate zone on each continent. To explore the relationship between these variables and specialist mammal richness, we employed a generalized linear model (GLM). To refine our model and identify the most influential predictors, we employed a bidirectional stepwise regression. This method systematically evaluates interaction terms, ensuring the final model contains only strong predictors or those involved in substantial interactions (Gelman & Hill, 2006). The stepwise regression process continues until no further terms can enhance the model. The selected variables were subsequently evaluated through significance tests, residual analysis, and sensitivity

In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor. Methods Input data

We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover.

The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF).

Preprocessing of vegetation fractional cover data

We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992).

Preprocessing of climate variables data

To process the climate data, we defined a year time step as the months from July of one year to July of the next. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018.

The annual maps of each climate variable were further summarized per pixel, into mean and slope (calculated as the Theil-Sen slope) across one, two, three, four, five, ten-, and 15-year lags.

Estimation of climate potential

We constructed a final multilayer dataset of response and predictor variables for the CONUS including the resulting maps of fractional cover rate of change (four response variables), the mean and slope maps for the climate variables for all the time-lags (70 predictor variables), and the initial percent cover for each lifeform in the year 1986 (four predictor variables).

We evaluated for each pixel in the CONUS which of the predictor variables produced the minimum potential rate of change in fractional cover for each lifeform class. To do that, we first calculated the 100% quantile hull of the distribution of each predictor variable against each response variable.

To calculate the 100% quantile of the predictor variables’ distribution we divided the total range of each predictor variable into equal-sized bins. The size and number of bins were set specifically per variable due to differences in their data distribution. For each of the bins, we calculated the maximum value of the vegetation rate of change, which resulted in a lookup table with the lower and upper boundaries of each bin, and the associated maximum rate of change. We constructed a total of 296 lookup tables, one per lifeform class and predictor variable combination. The resulting lookup tables were used to construct spatially explicit maps of maximum vegetation rate of change from each of the predictor variable input rasters, and the final climate potential maps were constructed by stacking all the resulting maps per lifeform class and selecting for each pixel the minimum predicted rate of change and the predictor variable that produced that rate.

Identifying climate-limited areas

We defined climate-limited areas as the parts of the CONUS with little or no differences between the estimated climate potential and the observed rates of change in fractional cover. To identify these areas, we subtracted the raster of observed rates of change from the raster of climate potential for each lifeform class.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

Abstract This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Köppen's scheme to classify world …Show full descriptionAbstract This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Köppen's scheme to classify world climates was devised in 1918 by Dr Wladimir Köppen of the University of Graz in Austria. Over the decades it has achieved wide acceptance amongst climatologists. However, the scheme has also had its share of critics, who have challenged the scheme's validity on a number of grounds. For example, Köppen's rigid boundary criteria often lead to large discrepancies between climatic subdivisions and features of the natural landscape. Furthermore, whilst some of his boundaries have been chosen largely with natural landscape features in mind, other boundaries have been chosen largely with human experience of climatic features in mind. The present paper presents a modification of Köppen's classification that addresses some of the concerns and illustrates this modification with its application to Australia. A modification of the Köppen classification of world climates has been presented. The extension has been illustrated by its application to Australian climates. Even with the additional complexity, the final classification contains some surprising homogeneity. For example, there is a common classification between the coastal areas of both southern Victoria and southern New South Wales. There is also the identical classification of western and eastern Tasmania. This arises due to the classification not identifying every climate variation because a compromise has to be reached between sacrificing either detail or simplicity. For example, regions with only a slight annual cycle in rainfall distribution do not have that variation so specified in the classification. Similarly, regions with only slightly different mean annual temperatures are sometimes classified as being of the same climate. The classification descriptions need to be concise, for ease of reference. As a result, the descriptions are not always complete. For example, the word "hot" is used in reference to those deserts with the highest annual average temperatures, even though winter nights, even in hot desert climates, can't realistically be described as "hot". In conclusion, the authors see the classification assisting in the selection of new station networks. There is also the potential for undertaking subsequent studies that examine climate change in the terms of shifts in climate classification boundaries by using data from different historical periods, and by using different characteristics to define climate type such as "inter-annual variability of precipitation". In the future, it is planned to prepare climate classification maps on a global scale, as well as on a regional-Australian scale. TABLE 1 Köppen's original scheme New scheme Tropical group Divided into equatorial & tropical groups Monsoon subdivision Becomes rainforest (monsoonal) subdivision Dry group Divided into desert & grassland groups Summer/winter drought subdivisions Now requires 30+mm in wettest month Temperate group Divided into subtropical & temperate groups Cold-snowy-forest group Cold group Dry summer/winter subdivisions Moderately dry winter subdivision added Polar group Maritime subdivision added Frequent fog subdivision Applies now only to the desert group Frequent fog subdivision Becomes high humidity subdivision High-sun dry season subdivision Absorbed into other subdivisions Autumn rainfall max subdivision Absorbed into other subdivisions Other minor subdivisions Absorbed into other subdivisions This dataset has been provided to the BA Programme for use within the programme only. For copyright information go to http://www.bom.gov.au/other/copyright.shtml. Information on how to request a copy of data can be found at www.bom.gov.au/climate/data. Dataset History Trewartha (1943) notes that Köppen's classification has been criticised from "various points of view" (Thornthwaite 1931, Jones 1932, Ackerman, 1941). Rigid boundary criteria often lead to large discrepancies between climatic subdivisions and features of the natural landscape. Some boundaries have been chosen largely with natural landscape features in mind (for example, "rainforest"), whilst other boundaries have been chosen largely with human experience of climatic features in mind (for example, "monsoon"). Trewartha (1943) acknowledges the validity of these criticisms when he writes that "climatic boundaries, as seen on a map, even when precisely defined, are neither better nor worse than the human judgements that selected them, and the wisdom of those selections is always open to debate". He emphasises, however, that such boundaries are always subject to change "with revision of boundary conditions ... (and that) ... such revisions have been made by Köppen himself and by other climatologists as well". Nevertheless, the telling evidence that the Köppen classification's merits outweigh its deficiencies lies in its wide acceptance. Trewartha (1943) observes that "its individual climatic formulas are almost a common language among climatologists and geographers throughout the world ... (and that) ... its basic principles have been ... widely copied (even) by those who have insisted upon making their own empirical classifications". Trewartha's (1943) comments are as relevant today as they were half a century ago (see, for example, Müller (1982); Löhmann et al. (1993)). For the above reasons, in modifying the Köppen classification (Figures 1 and 2), the authors have chosen to depart only slightly from the original. Nevertheless, the additional division of some of the Köppen climates and some recombining of other Köppen climates may better reflect human experience of significant features. In recognition of this, the following changes, which are also summarised in Table 1, have been adopted in this work: The former tropical group is now divided into two new groups, an equatorial group and a new tropical group. The equatorial group corresponds to the former tropical group's isothermal subdivision. The new tropical group corresponds to that remaining of the former tropical group. This is done to distinguish strongly between those climates with a significant annual temperature cycle from those climates without one (although this feature is not as marked in the Australian context, as elsewhere in the world). Under this definition some climates, distant from the equator, are classified as equatorial. This is considered acceptable as that characteristic is typical of climates close to the equator. Figure 1 shows that, in Australia, equatorial climates are confined to the Queensland's Cape York Peninsula and the far north of the Northern Territory. The equatorial and tropical group monsoon subdivisions are re-named as rainforest (monsoonal) subdivisions. This is done because, in these subdivisions, the dry season is so short, and the total rainfall is so great, that the ground remains sufficiently wet throughout the year to support rainforest. Figure 2 shows that, in Australia, rainforest subdivisions are found along parts of the northern part of Queensland's east coast. The former dry group is now divided into two new groups, a desert group and a grassland group. The new groups correspond to the former subdivisions of the dry group with the same name. This is believed necessary because of the significant differences between the types of vegetation found in deserts and grasslands. That there is a part of central Australia covered by the grassland group of climates (Figure 1) is a consequence of the higher rainfall due to the ranges in that region. The new desert and grassland winter drought (summer drought) subdivisions now require the additional criterion that there is more than 30 mm in the wettest summer month (winter month) to be so classified. This change is carried out because drought conditions may be said to prevail throughout the year in climates without at least a few relatively wet months. It should be noted that the original set of Köppen climates employed the phrases "winter drought" and "summer drought" to respectively describe climates that are seasonally dry. Figure 2 shows that the summer drought subdivisions are found in the southern half of the country, whilst the winter drought subdivisions are found in the northern half of the country. The former temperate group is divided into two new groups, a temperate group and a subtropical group. The new subtropical group corresponds to that part of the former temperate group with a mean annual temperature of at least 18°C. The new temperate group corresponds to that part of the former temperate group remaining. This is done because of the significant differences in the vegetation found in areas characterised by the two new groups, and in order that there is continuity in the boundary between the hot and warm desert and grassland climates where they adjoin rainy climates. Figure 1 shows that a large region, covering much of southeast Queensland and some elevated areas further north, is now characterised as subtropical. For simplicity, the former Köppen cold snowy forest group of climates is re-named as the cold group. Figure 1 shows that this climate is not found on the Australian mainland or in Tasmania. For the temperate, subtropical, and the cold groups, the distinctly dry winter subdivision requires the additional criterion of no more than 30 mm in the driest winter month to be so classified. In order that there be consistency between the criteria for the distinctly dry winter and the distinctly dry summer subdivisions, this is thought to be a worthwhile change. Figure 2 shows that, whereas that part of Western Australia characterised

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021 but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).

The changes for v1.1.0.0 HadUK-Grid datasets are as follows:

• The addition of data for calendar year 2021

• The addition of 30 year averages for the new reference period 1991-2020

• An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.

• A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.

Net changes to the input station data used to generate this dataset:

-Total of 122664065 observations

-118464870 (96.5%) unchanged

-4821 (0.004%) modified for this version

-4194374 (3.4%) added in this version

-5887 (0.005%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

The PixBox-S2-CMIX dataset was used as a validation reference within the first Cloud Masking Inter-comparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) in 2019. The PixBox-S2-CMIX pixel collection was existing prior to CMIX and conducted already in 2018.

The overarching idea of PixBox is a quantitative assessment of the quality of a pixel classification which is the result of an automated algorithm/procedure. Pixel classification is defined as assigning a certain number of attributes to an image pixel, such as cloud, clear sky, water, land, inland water, flooded, snow etc. Such pixel classification attributes are typically used to further guide higher level processing.

The PixBox dataset production: trained experienced expert(s) manually classify pixels of an image sensor into a pre-defined detailed set of classes. These are typically different cloud transparencies, cloud shadow, condition of underlying surface (“semi-transparent clouds over snow”, “clouds over bright scattering water”). An average collected dataset includes several 10-thousands of pixels because it has to be representative for all classes, and for various observation and environmental conditions, such as climate zones, sun illumination etc. Quality control of the collected pixels is important in order to detect misclassifications and systematic errors. An auto-associative neural network is trained for this purpose.

The PixBox-S2-CMIX dataset is a pixel collection containing 17,351 pixels manually collected from 29 Sentinel-2 A & B Level 1C products. The dataset is spatially, temporally, and thematically well distributed.

PixBox-S2-CMIX dataset

The PixBox-S2-CMIX dataset consists of two two main ZIP files, one holding the pixel collection and description, and another one with all used Sentinel-2 L1C data. The dataset is structured as follows:

PixBox-S2-CMIX.zip

The collected features (CSV file).

A description to all categories and classes, incl. linkage to the used Sentinel-2 L1C products.

Sentinel-2_L1C.zip

29 zipped Sentinel-2 Level L1C products [1], used to produce the dataset.

Files

pixbox_sentinel2_cmix_20180425.csv - This file contains all collected pixel information in CSV format. All collected classes are stored as integer values. A description of the categories and definition of the integers to class names is given in the additional description file.

pixbox_sentinel2_cmix_20180425_description.txt - This file gives a clear description of the categories and classes. It can be used to convert the class ID numbers, stored in the CSV, to class strings. Additionally, it links the satellite product ID, given in the CSV, to the Sentinel-2 L1C product names.

29 Sentinel-2 L1C products in ZIP format.

References

[1] Copernicus Sentinel data 2017/2018

The dataset summarizes areas of Central Valley wetland and cropland waterbird habitats available for each of 17 projected scenarios by each month (August–December and following January–March). The dataset also includes relatively recent (year 2005) area of existing habitat (i.e., “existing area”) for comparison with habitat areas based on scenarios. Cropland habitats are defined as winter-flooded rice, unplowed dry rice, winter-flooded corn, unplowed dry corn, and other winter-flooded cropland (in Tulare basin). Wetlands are defined as summer-irrigated seasonal wetland, seasonal wetland that is not summer-irrigated, and semipermanent wetland (combines semipermanent and permanent wetland types). Thus, data on availability of waterbird habitats is summarized by scenario, habitat, and month in 1 metric (area of available habitat in km2). These data are used to support the following publication: Matchett EL, Fleskes JP (2017) Projected Impacts of Climate, Urbanization, Water Management, and Wetland Restoration on Waterbird Habitat in California’s Central Valley. PLoS ONE 12(1): e0169780. doi:10.1371/journal.pone.0169780

The dataset accompanies Figures 2–4 of Matchett and Fleskes (2018) and therein the subject data are referenced as "Table A1". Data summarize peak abundance (km2) of Central Valley waterbird habitats (i.e., wetland and flooded cropland types) that are available between August and April (of the following year) for each of 17 projected scenarios by planning basin, scenario, and habitat. Area of each habitat for each scenario-basin combination is provided for the month when the most area of the respective habitat is typically flooded and available for waterbird use (i.e., January for all wetlands and winter-flooded rice and corn, and September for other winter-flooded crops in Tulare Basin). The dataset also includes relatively recent (year 2005) area of habitat (i.e., “existing_km2”) for comparison with habitat areas based on scenarios. Flooded cropland habitats are defined as winter-flooded rice, winter-flooded corn, and other winter-flooded cropland (in Tulare basin). Wetlands are defined as summer-irrigated seasonal wetland, seasonal wetland that is not summer irrigated, and semipermanent wetland (combines semipermanent and permanent wetland types). This dataset includes results for eight of nine basins defined by the Central Valley Joint Venture in the Central Valley Joint Venture Implementation Plan (2006); subject basins are the Colusa, Butte, Sutter, American, Yolo, Delta, San Joaquin, and Tulare Basins (Suisun Basin excluded). Data on availability of waterbird habitats is summarized by planning basin, scenario, habitat, and month in 5 metrics (in addition to recent historical area): the least available area; areas available in ≥ 25%, ≥ 50%, and ≥ 75% of years; and maximum available area. These data support the following publication: Matchett EL, Fleskes JP. 2018. Waterbird habitat in California’s Central Valley basins under climate, urbanization, and water management scenarios. Journal of Fish and Wildlife Management. Online early. doi:10.3996/122016-JFWM-095

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This metadata contain data for AWRA-L (Australian Water Resource Assessments - Landscape) modelling used for the GLO subregion. The metadata contain the workflow, processes, inputs and outputs data. The workflow pptx file under the top folder provides the top level summary of the modelling framework, including three slides. The first slide explains how to generate global definition file; the second slide outlines the calibration and simulation for AWRA-L model run; the third slide shows AWRA-L model post-processing for getting streamflow under baseline and coal mine resources development.

Gridded datasets were used as inputs to model surface water using the AWRA methodology. See https://publications.csiro.au/rpr/download?pid=csiro:EP162100&dsid=DS1 for a technical description of the process.

The exactable model framework is under the Application subfolder

Other subfolders, including model calibration, model simulation, post processing, contain the associated used for model calibration, simulation and post processing, respectively.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA GLO 2.6.1.3 and 2.6.1.4 products (Zhang et al., 2016).

References

Zhang Y Q, Viney N R, Peeters L J M, Wang B, Yang A, Li L T, McVicar T R, Marvanek S P, Rachakonda P K, Shi X G, Pagendam D E and Singh R M (2016) Surface water numerical modelling for the Gloucester subregion. Product 2.6.1 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia.

Purpose

To quantify the impacts of mining development on hydrological response variables for the Gloucester subregion

Dataset History

The directories within contain the input and output data of the Hunter AWRA-L model for model calibration, simulation and post-processing. Input data are linked to to the parent datasets in the lineage and outputs are results from running the model. Other model files were generated for the modelling procedure.

The calibration folder contains the input and output subfolders used for two model calibration schemes: lowflow and normal. The lowflow model calibration puts more weights on median and low streamflow; the normal model calibration put more weights on high streamflow.

The simulation folder contains only one replicate of model input and output as an example.

The post-processing folder contains three subfolders: inputs, outputs and scripts used for generating streamflow under the baseline and coal mine resources development conditions.

Input and output files are the daily data covering the period of 1970 to 2102, with the first 23 years (1970-1982) for model spin-up.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA GLO 2.6.1.3 and 2.6.1.4 products.

Data details are in below

Model calibrations

1. Climate forcings are under '... AWRAL_Metadata\model calibration\inputs\Climate\'

2. Lowflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs\lowflow\'

3. Higflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs
   ormal\'

4. Observed streamflow data used for model calibrations are under '... AWRAL_Metadata\model calibration\inputs\Streamflow\'

Model simulations

1. Climate forcings are under '... AWRAL_Metadata\model simulation\inputs\Climate\'

2. Global definition file used in csv output mode data is under '... AWRAL_Metadata\model simulation\inputs\csv_Model_1\'

3. Global definition file used in netcdf output mode data is under '... AWRAL_Metadata\model simulation\inputs\Netcdf_Model_1\'

4. Output files used in csv output mode data contain Dd, dgw, E0, Qg, Qtot, Rain, Sg outputs, which is used for AWRA-R model input and is under '... AWRAL_Metadata\model simulation\outputs\csv_Model_1\'

5. Output files used in netcdf output mode data contain Qg and Qtot outputs, which is used for AWRA-L postprocessing and is under '... AWRAL_Metadata\model simulation\outputs\Netcdf_Model_1\'

Post-processing

1. Input data include AWRA-L streamflow, ground water baseflow input and mine footprint data, stored at '... AWRAL_Metadata\post processing\Inputs\'

2. Output data include streamflow outputs under crdp and baseline for the HUN and MTL subregions, stored at '... AWRAL_Metadata\post processing\Outputs\'

3. Scripts for use for post-processing AWRA-L streamflow and ground water baseflow, is under '... AWRAL_Metadata\model simulation\post processing\Scripts\'

Dataset Citation

Bioregional Assessment Programme (2015) GLO AWRA Model v02. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/018bfc12-6b9f-4ccc-83e4-e002cfd72b6a.

Dataset Ancestors

• Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW)

• Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014

• Derived From GLO SW Receptors 20150828 withRivers&CatchmentAreas

• Derived From Groundwater Economic Assets GLO 20150326

• Derived From Gloucester digitised coal mine boundaries

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From Geology 100K NSW selected mapsheets for Sydney Bioregion

• Derived From GLO Geological Model Extracted Horizons Final Grid XYZ V01

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From GLO SW receptor total catchment areas V01

• Derived From Groundwater Modelling Report for Rocky Hill Coal Project

• Derived From Asset database for the Gloucester subregion on 12 September 2014

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From BA SYD GLO - AGL Avon Wards River alluvium extent v1

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From GLO Receptors 20150518

• Derived From GLO climate data stats summary

• Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved

• Derived From GLO MFdmax v01

• Derived From R-scripts for uncertainty analysis v01

• Derived From New South Wales 2 kilometers Residential Exclusions Zone

• Derived From Geofabric Surface Cartography - V2.1

• Derived From BA SYD selected GA TOPO 250K data plus added map features

• Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From EIS Gloucester Coal 2010

• Derived From GLO Rocky Hill Open Cut Pits v01

• Derived From [Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset is an extract for the Hunter subregion of the soil thickness data from the ASRIS Continental-scale soil property predictions 2001. The source data are the Surface of predicted Thickness of soil layer 1 (A Horizon - top-soil) surface for the intensive agricultural areas of Australia. Data modelled from area based observations made by soil agencies both State and CSIRO and presented as .0.01 degree grid cells.

The dataset consists of statistics for soils depths (MIN, MAX, RANGE, MEAN, STD, MEDIAN) for each of the simulation catchments in the AWRA-L model. The soil thickness data were resampled to the model grid (BILO cells - 0.05 degree grid cells) and the catchments are defined by the BILO cells which fall within them. The gauging station ID in the spreadsheet defines the gauges which were used to define the upstream catchment area.

Purpose

Used to define soils thickness in the AWRA-L model.

Dataset History

The soil thickness data were resampled to the model grid (BILO cells - 0.05 degree grid cells). Statistics for soils depths (MIN, MAX, RANGE, MEAN, STD, MEDIAN) for each of the simulation catchments in the AWRA-L model were calculated using the Zonal Statistics as Table tool within ArcGIS with the simulation catchments used as the zone dataset. The output table was used to populate the excel spreadsheet with the Station ID and catchments areas added.

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-L ASRIS soil properties v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d8091c0a-5fdc-4f6a-8b61-b1e6cc7c3ace.

Dataset Ancestors

• Derived From HUN AWRA-R simulation nodes v01

• Derived From Bioregional Assessment areas v06

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Bioregional Assessment areas v04

• Derived From Gippsland Project boundary

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From BA All Regions BILO cells in subregions shapefile

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From National Surface Water sites Hydstra

• Derived From ASRIS Continental-scale soil property predictions 2001

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Victoria - Seamless Geology 2014

• Derived From HUN AWRA-R simulation catchments v01

• Derived From Climate model 0.05x0.05 cells and cell centroids

Parts of the envelope of ski areas where natural snow is guaranteed, according to different scenarios of global warming. The current guaranteed natural snow zone is empirically defined “expert” in the light of past and current snow conditions (1980-2010). Its lower limit fluctuates with guidance (N, E, SE, S, SO, O). It is fixed for all the massifs of Haute-Savoie (the massifs Haut-Savoyards having a relatively homogeneous character from a meteorological point of view, and therefore logically, from a nivological point of view) at an altitude of 1,500 meters on the north facing and goes back to 1,800 meters facing south. Within the area, the snow is “guaranteed”: ski area operators and skiers can count from year to year on sufficient snow cover from mid-December to mid-April with a probability close to 1.

This concept of guaranteed natural snow makes it possible to assess the degree of exposure to snow hazards in the ski areas of Haute-Savoie. It was used as part of a reflection on the potential interactions between climate change and snow resources in winter sports stations. In a forward-looking approach, DDT74 attempted to assess the future likelihood of snow in the ski areas of Haute-Savoie by simulating the consequences of global warming on the current guaranteed natural snow zone. These simulations result in a rise of the lower limit of the current zone of 150 metres per additional degree Celsius. Three warming scenarios were applied to the current guaranteed snow zone (+ 1 °C, + 2 °C and + 4 °C), to define three new “fictitious” areas of guaranteed natural snow.

Abstract

This dataset includes the current boundary data required for the bioregional assessment impact analysis for the Gloucester subregion. These data are (1) the current Preliminary Assessment Extent (PAE), (2) the Analysis Extent (AE) and (3) the Analysis Domain Extent (ADE).

The PAE is defined and explained in the BA submethodology (1.3 Description of the water-dependent asset register) and, specifically for the Gloucester subregion in product 1.3 Water-dependent asset register for the Gloucester subregion. The Analysis Extent (AE) is defined as the geographic area that encompasses all the possible areas that may be reported as part of the impact analysis component of a bioregional assessment, specifically, the subregion boundary, the PAE and the relevant groundwater and surface water model domains. For Gloucester, the AE consists of the PAE boundary plus some minor extensions to include the alluvium groundwater model domain, particularly adjacent to the Duralie mine. The Analysis Domain Extent (ADE) is defined as the geographic area used for geoprocessing and data preparation purposes that encompasses the Analysis Extent plus additional areas sufficient to ensure all relevant data is included for the impact analysis component of a bioregional assessment. For Gloucester, at least an additional 1.5 km was added to the AE boundary and, in places further extensions were required to include all of the groundwater model domain.

All data are in the Australian Albers coordinate system (EPSG 3577).

Purpose

The purpose of this dataset is to provide the boundaries needed for the impact analysis component of the BA.

Dataset History

This dataset includes the current boundary data required for the bioregional assessment impact analysis for the Gloucester subregion. These data are (1) the current Preliminary Assessment Extent (PAE), (2) the Analysis Extent (AE) and (3) the Analysis Domain Extent (ADE). All data were transformed to the Australian Albers coordinate system (EPSG 3577).

The PAE is defined and explained in the BA submethodology (1.3 Description of the water-dependent asset register) and, specifically for the Gloucester subregion in product 1.3 Water-dependent asset register for the Gloucester subregion. For Gloucester, the original PAE contained a 'hole' in the southern end. For the purposes of the impact analysis, this 'hole' has been removed. Due to this change, it is possible that there may be water-related assets in the location of the 'hole' that were not included in the asset database and asset register. This should be noted in any assessment reports.

The Analysis Extent (AE) is defined as the geographic area that encompasses all the possible areas that may be reported as part of the impact analysis component of a bioregional assessment, specifically, the subregion boundary, the PAE and the relevant groundwater and surface water model domains. For Gloucester, the AE consists of the PAE boundary plus some minor extensions to include the alluvium groundwater model domain, particularly adjacent to the Duralie mine. Due to these extensions, it is possible that there may be water-related assets in the location of the extensions that were not included in the asset database and asset register. This should be noted in any assessment reports.

The Analysis Domain Extent (ADE) is defined as the geographic area used for geoprocessing and data preparation purposes that encompasses the Analysis Extent plus additional areas sufficient to ensure all relevant data is included for the impact analysis component of a bioregional assessment. For Gloucester, at least an additional 1.5 km was added to the AE boundary and, in places further extensions were required to include all of the groundwater model domain.

Dataset Citation

Bioregional Assessment Programme (2016) GLO subregion boundaries for Impact and Risk Analysis 20160712 v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/b1fa8214-ceec-47d8-b074-8539e94f728f.

Dataset Ancestors

• Derived From Bioregional Assessment areas v06

• Derived From Bioregional Assessment areas v04

• Derived From Gippsland Project boundary

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From Australian Geological Provinces, v02

• Derived From Geofabric Hydrology Reporting Catchments - V2.1

• Derived From Geological Provinces - Full Extent

• Derived From GLO Preliminary Assessment Extent

• Derived From Australian Coal Basins

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From BA ALL mean annual flow for NSW - Choudhury implementation of Budyko runoff v01

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Gloucester Coal Basin

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Subcatchment boundaries within and nearby the Gloucester subregion

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Victoria - Seamless Geology 2014

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset contains 10,000 replicates of AWRA model pre-processing outputs (streamflow Qtot and baseflow Qb), used for calculating additional coal resources development impacts on hydrological response variables in 30 simulation nodes (Zhang et al., 2016). References Zhang Y Q, Viney N R, Peeters L J M, Wang B, Yang A, Li L T, McVicar T R, Marvanek S P, Rachakonda P K, Shi X G, Pagendam D E and Singh R M (2016) Surface water numerical modelling for the Gloucester subregion. Product 2.6.1 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., http://data.bioregionalassessments.gov.au/product/NSB/GLO/2.6.1. Purpose This pre-processing data is used for estimating AWRA post-processing streamflow outputs under CRDP and baseline conditions, respectively. Dataset History The dataset has all files and scripts necessary to execute the 10,000 runs on the linux platform of the CSIRO High Performance Cluster computers. The AWRA-L model version 4.5 has been used for all BA surface water simulations. The application is developed with the C# language. All execution and class (dll) files can be found at \OSM-07-CDC.it.csiro.au\OSM_CBR_LW_BA_working\Disciplines\SurfaceWater\Modelling\AWRA-LG\Bin. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 work stations. The AWRA preprocessing data are the inputs for estimating AWRA post-processing model outputs (GUID: http://data.bioregionalassessments.gov.au/dataset/15ca8f9d-84b4-4395-87db-ab4ff15b9f07). The dataset was uploaded to \lw-osm-01-cdc.it.csiro.au\OSM_CBR_LW_BAModelRuns_app\GLO\AWRA_ScalingChange_rerun on 03 September 2016 This dataset were further used to compute daily streamflow post-processing outputs under CRDP and baseline conditions, respectively. Dataset Citation Bioregional Assessment Programme (XXXX) GLO AWRA Model Pre-Processing Data v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/51079bcc-96a8-409d-a951-3671fbbad6a2. Dataset Ancestors Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW) Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014 Derived From GLO SW Receptors 20150828 withRivers&CatchmentAreas Derived From Groundwater Economic Assets GLO 20150326 Derived From Gloucester digitised coal mine boundaries Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From GLO SW receptor total catchment areas V01 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Gloucester subregion on 12 September 2014 Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From National Groundwater Information System (NGIS) v1.1 Derived From GLO Receptors 20150518 Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved Derived From Natural Resource Management (NRM) Regions 2010 Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From EIS Gloucester Coal 2010 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From GEODATA TOPO 250K Series 3 Derived From Asset database for the Gloucester subregion on 28 May 2015 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Geofabric Surface Cartography - V2.1 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014 Derived From EIS for Rocky Hill Coal Project 2013 Derived From Bioregional Assessment areas v03 Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Asset database for the Gloucester subregion on 8 April 2015 Derived From Gloucester - Additional assets from local councils Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Asset database for the Gloucester subregion on 29 August 2014 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From Groundwater Modelling Report for Stratford Coal Mine Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Freshwater Fish Biodiversity Hotspots Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014 Derived From GLO climate data stats summary Derived From Australia - Species of National Environmental Significance Database Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From GLO Receptors 20150828 Derived From Report for Director Generals Requirement Rocky Hill Project 2012 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)