This dataset represents CLIGEN input parameters for locations in 68 countries. CLIGEN is a point-scale stochastic weather generator that produces long-term weather simulations with daily output. The input parameters are essentially monthly climate statistics that also serve as climate benchmarks. Three unique input parameter sets are differentiated by having been produced from 30-year, 20-year and 10-year minimum record lengths that correspond to 7673, 2336, and 2694 stations, respectively. The primary source of data is the NOAA GHCN-Daily dataset, and due to data gaps, records longer than the three minimum record lengths were often queried to produce the needed number of complete monthly records. The vast majority of stations used at least some data from the 2000's, and temporal coverages are shown in the Excel table for each station. CLIGEN has various applications including being used to force soil erosion models. This dataset may reduce the effort needed in preparing climate inputs for such applications. Revised input files added on 11/16/20. These files were revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months. Second revision input files added on 2/12/20. A formatting error was fixed that affected transition probabilities for 238 stations with zero recorded precipitation for one or more months. The affected stations were predominantly in Australia and Mexico. Resources in this dataset:Resource Title: 30-year input files. File Name: 30-year.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files. File Name: 20-year.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files. File Name: 10-year.zipResource Description: CLIGEN .par input files based on 10-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: Map Layer. File Name: MapLayer.kmzResource Description: Map Layer showing locations of the new CLIGEN stations. This layer may be imported into Google Earth and used to find the station closest to an area of interest.Resource Software Recommended: Google Earth,url: https://www.google.com/earth/ Resource Title: Temporal Ranges of Years Queried. File Name: GHCN-Daily Year Ranges.xlsxResource Description: Excel tables of the first and last years queried from GHCN-Daily when searching for complete monthly records (with no gaps in data). Any ranges in excess of 30 years, 20 years and 10 years, for respective datasets, are due to data gaps.Resource Title: 30-year input files (revised). File Name: 30-year revised.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files (revised). File Name: 20-year revised.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files (revised). File Name: 10-year revised.zipResource Description: CLIGEN .par input files based on 10-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 30-year input files (revised 2). File Name: 30-year revised 2.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files (revised 2). File Name: 20-year revised 2.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files (revised 2). File Name: 10-year revised 2.zipResource Description: CLIGEN *.par input files based on 10-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/

Introduction Preservation and management of semi-arid ecosystems requires understanding of the processes involved in soil erosion and their interaction with plant community. Rainfall simulations on natural plots provide an effective way of obtaining a large amount of erosion data under controlled conditions in a short period of time. This dataset contains hydrological (rainfall, runoff, flow velocity), erosion (sediment concentration and rate), vegetation (plant cover), and other supplementary information from 272 rainfall simulation experiments conducted on 23 rangeland locations in Arizona and Nevada between 2002 and 2013. The dataset advances our understanding of basic hydrological and biological processes that drive soil erosion on arid rangelands. It can be used to quantify runoff, infiltration, and erosion rates on a variety of ecological sites in the Southwestern USA. Inclusion of wildfire and brush treatment locations combined with long term observations makes it important for studying vegetation recovery, ecological transitions, and effect of management. It is also a valuable resource for erosion model parameterization and validation. Instrumentation Rainfall was generated by a portable, computer-controlled, variable intensity simulator (Walnut Gulch Rainfall Simulator). The WGRS can deliver rainfall rates ranging between 13 and 178 mm/h with variability coefficient of 11% across 2 by 6.1 m area. Estimated kinetic energy of simulated rainfall was 204 kJ/ha/mm and drop size ranged from 0.288 to 7.2 mm. Detailed description and design of the simulator is available in Stone and Paige (2003). Prior to each field season the simulator was calibrated over a range of intensities using a set of 56 rain gages. During the experiments windbreaks were setup around the simulator to minimize the effect of wind on rain distribution. On some of the plots, in addition to rainfall only treatment, run-on flow was applied at the top edge of the plot. The purpose of run-on water application was to simulate hydrological processes that occur on longer slopes (>6 m) where upper portion of the slope contributes runoff onto the lower portion. Runoff rate from the plot was measured using a calibrated V-shaped supercritical flume equipped with depth gage. Overland flow velocity on the plots was measured using electrolyte and fluorescent dye solution. Dye moving from the application point at 3.2 m distance to the outlet was timed with stopwatch. Electrolyte transport in the flow was measured by resistivity sensors imbedded in edge of the outlet flume. Maximum flow velocity was defined as velocity of the leading edge of the solution and was determined from beginning of the electrolyte breakthrough curve and verified by visual observation (dye). Mean flow velocity was calculated using mean travel time obtained from the electrolyte solution breakthrough curve using moment equation. Soil loss from the plots was determined from runoff samples collected during each run. Sampling interval was variable and aimed to represent rising and falling limbs of the hydrograph, any changes in runoff rate, and steady state conditions. This resulted in approximately 30 to 50 samples per simulation. Shortly before every simulation plot surface and vegetative cover was measured at 400 point grid using a laser and line-point intercept procedure (Herrick et al., 2005). Vegetative cover was classified as forbs, grass, and shrub. Surface cover was characterized as rock, litter, plant basal area, and bare soil. These 4 metrics were further classified as protected (located under plant canopy) and unprotected (not covered by the canopy). In addition, plant canopy and basal area gaps were measured on the plots over three lengthwise and six crosswise transects. Experimental procedure Four to eight 6.1 m by 2 m replicated rainfall simulation plots were established on each site. The plots were bound by sheet metal borders hammered into the ground on three sides. On the down slope side a collection trough was installed to channel runoff into the measuring flume. If a site was revisited, repeat simulations were always conducted on the same long term plots. The experimental procedure was as follows. First, the plot was subjected to 45 min, 65 mm/h intensity simulated rainfall (dry run) intended to create initial saturated condition that could be replicated across all sites. This was followed by a 45 minute pause and a second simulation with varying intensity (wet run). During wet runs two modes of water application were used as: rainfall or run-on. Rainfall wet runs typically consisted of series of application rates (65, 100, 125, 150, and 180 mm/h) that were increased after runoff had reached steady state for at least five minutes. Runoff samples were collected on the rising and falling limb of the hydrograph and during each steady state (a minimum of 3 samples). Overland flow velocities were measured during each steady state as previously described. When used, run-on wet runs followed the same procedure as rainfall runs, except water application rates varied between 100 and 300 mm/h. In approximately 20% of simulation experiments the wet run was followed by another simulation (wet2 run) after a 45 min pause. Wet2 runs were similar to wet runs and also consisted of series of varying intensity rainfalls and/or run-on inputs. Resulting Data The dataset contains hydrological, erosion, vegetation, and ecological data from 272 rainfall simulation experiments conducted on 12 sq. m plots at 23 rangeland locations in Arizona and Nevada. The experiments were conducted between 2002 and 2013, with some locations being revisited multiple times. Resources in this dataset:Resource Title: Appendix B. Lists of sites and general information. File Name: Rainfall Simulation Sites Summary.xlsxResource Description: The table contains list or rainfall simulation sites and individual plots, their coordinates, topographic, soil, ecological and vegetation characteristics, and dates of simulation experiments. The sites grouped by common geographic area.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix F. Site pictures. File Name: Site photos.zipResource Description: Pictures of rainfall simulation sites and plots.Resource Title: Appendix C. Rainfall simulations. File Name: Rainfall simulation.csvResource Description: Please see Appendix C. Rainfall simulations (revised) for data with errors corrected (11/27/2017). The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experimentsResource Software Recommended: MS Access,url: https://products.office.com/en-us/access Resource Title: Appendix C. Rainfall simulations. File Name: Rainfall simulation.csvResource Description: Please see Appendix C. Rainfall simulations (revised) for data with errors corrected (11/27/2017). The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experimentsResource Software Recommended: MS Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix E. Simulation sites map. File Name: Rainfall Simulator Sites Map.zipResource Description: Map of rainfall simulation sites with embedded images in Google Earth.Resource Software Recommended: Google Earth,url: https://www.google.com/earth/ Resource Title: Appendix D. Ground and vegetation cover. File Name: Plot Ground and Vegetation Cover.csvResource Description: The table contains ground (rock, litter, basal, bare soil) cover, foliar cover, and basal gap on plots immediately prior to simulation experiments. Resource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access Resource Title: Appendix D. Ground and vegetation cover. File Name: Plot Ground and Vegetation Cover.csvResource Description: The table contains ground (rock, litter, basal, bare soil) cover, foliar cover, and basal gap on plots immediately prior to simulation experiments. Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix A. Data dictionary. File Name: Data dictionary.csvResource Description: Explanation of terms and unitsResource Software Recommended: MS Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix A. Data dictionary. File Name: Data dictionary.csvResource Description: Explanation of terms and unitsResource Software Recommended: MS Access,url: https://products.office.com/en-us/access Resource Title: Appendix C. Rainfall simulations (revised). File Name: Rainfall simulation (R11272017).csvResource Description: The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experiments (updated 11/27/2017)Resource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access

About Dataset

This dataset contains a collection of environmental visualizations generated by StormGPT, an AI-assisted environmental intelligence system designed for hydrology, atmospheric science, and stormwater analysis.

The images represent processed outputs derived from publicly available datasets, including NOAA, NASA, USGS, and EPA. They illustrate concepts such as global precipitation patterns, watershed boundaries, stormwater runoff simulations, atmospheric data assimilation, and global environmental sensor coverage.

A companion geospatial Excel file (stormgpt_geospatial_features.xlsx) provides structured metadata for use in machine learning models, GIS workflows, and environmental research.

This dataset is suitable for:

Climate and hydrologic modeling

Atmospheric science research

Data visualization studies

Machine learning training

Educational demonstrations

Geospatial feature engineering

All data elements originate from public environmental sources and were transformed into visual outputs through the StormGPT analytical workflow.

Creator: Daniel Guzman Project: Stormwater Intelligence / StormGPT-V3

The data provided is comprised of essential inputs utilized for constructing and executing the soil water assessment tool (SWAT) model. The SWAT model was constructed for the Baffin Bay watershed. Both temporal and spatial data are included in the form of text/excel files, shapefiles, and raster images. The excel/text files encompass the Global Precipitation Measurement Mission (GPM) precipitation data for 116 stations, along with data from eighteen National Oceanic and Atmospheric Administration (NOAA) temperature stations (minimum and maximum temperature). The locations of these stations are provided in a separate Excel file. A Digital Elevation Model (DEM) with a 7-meter spatial resolution was incorporated as a raster file (.tif). Additionally, the land-use land-cover map from the National Land Cover Database (U.S. Geological Survey) of the watershed was included as a raster file. The soil data for the watershed were sourced from two different databases: 1) the Soil Survey Geographic Database (SSURGO), and 2) The Digital General Soil Map of the United States (STATSGO2). These soil data are available in raster and shapefile formats respectively. For model calibration, three flow gauges from the USGS and two sub-basins outputs from the Texas Water Development Board model were utilized. These calibration data are provided as excel files. Nutrient data were acquired from the TCEQ database. Model output is available in related datasets HI.x847.000:0017 (doi: 10.7266/fmz6065v, entire watershed) and HI.x847.000:0018 (doi: 10.7266/vwry1w58, sub-basins of the watershed).

Reptiles make up one-third of tetrapods, however they are often omitted from global conservation analyses. Understanding the determinants of reptile distribution is the foundation for reptile conservation research. We assembled a dataset on the distribution of 231 reptile species (nearly 50% of recorded species in China). We then investigated the association of species range filling (the proportion of observed ranges compared to species potential climate distributions) with climate, range size, topography, and human activity, using three regression methods. At the species level, we found the most primary factors influencing the recent distribution pattern of reptiles across China were the mean annual precipitation (MAP) and the mean annual temperature (MAT). In contrast, human activity came in last. Similarly, at aspatial level, MAP and MAT were still the most important factors. Geographically, the south and east of China support the highest reptile diversity, partially due to high prec..., 614 pieces of literature for reptile occurrence records in China Results of this analysis, , # Data from: Precipitation and temperature primarily determine the reptile distributions in China

https://doi.org/10.5061/dryad.x0k6djhtp

Description of the data and file structure

The first excel offer 614 pieces of literature for reptile occurrence records in China, the second excel offer results of analysis. Our data description is in excel files.

'NA' value in the results represent so that there's not not applicable.

Sharing/Access information

The data that support the findings of this study are also available in supplementary material of this paper, refer 10.1111/ecog.07005.

The Bangladesh Historical Rainfall Dataset (1948-2014) provides historical rainfall data 🌧️ for Bangladesh 🇧🇩, covering the period from 1948 to 2014. The dataset includes monthly and daily rainfall data collected from multiple weather stations 🏞️ across Bangladesh. This dataset is organized by station, year, and month, making it a valuable resource for climate studies, environmental research, and agricultural forecasting. The data has been cleaned to ensure the accuracy and consistency of the records, with missing or erroneous values replaced with zero. This makes it a reliable source for conducting various analytical studies. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F996bf3433ceb3a4d470661fce3af1591%2FScreenshot%20(81).png?generation=1739795092825853&alt=media" alt=""> Key Features: Stations 🏠: The dataset includes data from numerous weather stations across Bangladesh, including prominent locations like Bogra, Dhaka, Chittagong, Sylhet, and more. Years 📆: The data spans from 1948 to 2014, providing over six decades of historical rainfall data. Months 📅: Rainfall data is available for each month, enabling the study of seasonal variations in rainfall patterns. Daily Data 📊: Detailed daily rainfall records are included for every station, offering granular insight into the rainfall distribution over time. Data Quality ✅: The dataset has been cleaned to remove anomalies, and missing values have been appropriately handled, ensuring reliability and consistency. Potential Uses: Climate Research 🌡️: Researchers can analyze long-term climate trends, such as changes in the average rainfall over decades, the frequency of extreme rainfall events, and the impact of climate change on the region. Agricultural Planning 🌾: Farmers, agronomists, and agricultural agencies can use the data to predict crop yield based on rainfall trends, determine planting seasons, and plan for drought or flood risks. Disaster Management 🌪️: Government agencies and disaster management teams can use this data to understand flooding patterns and plan mitigation measures for heavy rain periods or drought conditions. Water Resource Management 💧: This data is essential for authorities working on water conservation, irrigation planning, and hydroelectric power generation to evaluate water availability in different regions of Bangladesh. Environmental Studies 🌍: Environmentalists and organizations can use this data to study the impact of rainfall on ecosystems, soil erosion, and the overall health of the environment. Urban Planning 🏙️: Urban planners can use historical rainfall data to design drainage systems and flood protection infrastructure, helping cities manage heavy rains and prevent flooding. Who Can Use This Dataset: Researchers and Academics 🎓: Researchers in the fields of climatology, environmental science, and meteorology can analyze long-term trends, study seasonal rainfall patterns, and understand the impact of climate change on the region. Farmers and Agricultural Agencies 🌾: Farmers and agricultural consultants can use the rainfall data to determine optimal planting schedules, irrigation requirements, and predict weather-related risks to crops. Private Sector Companies 💼: Businesses in the insurance, construction, and energy sectors can use this dataset to assess risk factors such as floods, droughts, and water availability. Environmental NGOs 🌳: Non-governmental organizations focused on environmental conservation and climate change adaptation can use the dataset for studies on ecosystems and environmental resilience. How to Use This Dataset: Data Analysis 📊: Use tools like Python, R, or Excel to analyze trends in rainfall over time. This can involve time-series analysis, seasonal variation studies, and examining the frequency of extreme weather events. Visualization 📈: Visualize the data using graphs such as line plots, bar charts, heatmaps, and pie charts to represent rainfall patterns by month, station, or year. Modeling 🤖: Use machine learning models to predict future rainfall based on historical data. This can aid in forecasting seasonal weather patterns, extreme rainfall events, or drought conditions. Reports and Presentations 📑: Present the findings in reports, articles, or presentations for policy-makers, farmers, and planners to help them understand rainfall patterns and make better decisions. Source: This dataset was sourced from the official data.gov.bd 💻. You can access the original dataset at http://data.gov.bd/dataset/daily-total-rainfall-till-jun-2014.

Credit to the original author: The dataset was originally published here

Weather prediction dataset

A dataset for teaching machine learning and deep learning

Hands-on teaching of modern machine learning and deep learning techniques heavily relies on the use of well-suited datasets. The "weather prediction dataset" is a novel tabular dataset that was specifically created for teaching machine learning and deep learning to an academic audience. The dataset contains intuitively accessible weather observations from 18 locations in Europe. It was designed to be suitable for a large variety of different training goals, many of which are not easily giving way to unrealistically high prediction accuracy. Teachers or instructors thus can chose the difficulty of the training goals and thereby match it with the respective learner audience or lesson objective. The compact size and complexity of the dataset make it possible to quickly train common machine learning and deep learning models on a standard laptop so that they can be used in live hands-on sessions.

The dataset can be found in the `\dataset` folder and be downloaded from zenodo: https://doi.org/10.5281/zenodo.4980359

References

If you make use of this dataset, in particular if this is in form of an academic contribution, then please cite the following two references:

• Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. of Climatol., 22, 1441-1453. Data and metadata available at http://www.ecad.eu
• Florian Huber, Dafne van Kuppevelt, Peter Steinbach, Colin Sauze, Yang Liu, Berend Weel, "Will the sun shine? – An accessible dataset for teaching machine learning and deep learning", DOI TO BE ADDED!

Map of the locations of the 18 weather stations from which data was collected

Climate change directly affects the intensity of surface erosion and the sediment load in rivers. Currently, there are many climate models in use in Europe, and their forecasts differ to some extent. This means that the selection of models and their ensembles in environmental analyzes will directly translate into the predicted sediment load, which is important for mitigating the siltation of rivers and dammed reservoirs. Using the SWAT model [1,2,3], we performed research on the impact of adopting various climate models [4] on the sediment load flowing into the dammed reservoir. We included three types of comparison: A. We adopted point forecasts for the nearest large city – Kraków [5] and areal forecasts for the catchment. B. We compared the adopted reference period, which is a source of precipitation change for the climate scenarios. C. We have put together various types of ensembles - consisting of wet and dry models [6]. We present the research results in individual sheets of the included Excel file. The file contains four sheets: 1.Location: We present a graphic of the location of the calculation point for which the research was carried out. It contains a map of Poland and the Carpathian Mountains with the studied catchment marked (red). The next drawing highlights the analyzed catchment area of the upper Raba River flowing into the dammed reservoir (Dobczyce). The graphics also include data source setup for which point and areal approach scenarios were performed. The third figure on the graphic is the results of modeling average monthly sediment load for base scenario against the average precipitation monthly sums. 2. Climate change predictions: First, the reader should pay attention to the graphics. They present all implemented scenarios (outer circle with corresponding colors) and the way they are named (inside the circle). It also includes which scenarios belong to the point (P) and areal (A) approaches, and what was the reference period (10 or 30 years). The table in this sheet presents the changes of precipitation introduced in the model using the delta change tool in the model SWAT. 3. Scenarios Database - Av.: The spreadsheet presents the modeling results as average monthly sediment loads for each scenario. The data are presented in a table and a chart enabling quick comparison of annual cycle trends and their changes in various scenarios. 4. Scenarios Database - Y: The last sheet presents the full monthly sediment load data sets for all scenarios. The reader's attention is focused on the interactive pivot chart. The user should select the scenarios of interest, years or months. It is then possible to thoroughly analyze changes in the modeling results of individual monthly values and time shifts resulting from the introduction of various types of ensemble climate models. In the background there are values that can be used by the reader for their own comparative analyses, provided that the source of the data is properly cited.

Abstract: I came up with the question: With all of these flooding events, what have we learned from them? What can we do so that when extreme precipitation occurs, what can we do to prepare? Research: I collected data from the Kentucky Mesonet after giving them dates that I found significant precipitation in. After that, I compiled excel spreadsheets, took text files that were given to me with the date, time, station names (75), latitudes, longitudes, and precipitation totals in 5 minute increments. I summed up these precipitation totals, put them in other excel sheets that contained lat and longs, and that were alphabetized by station name. Hypothesis: We can look at the data collected and see how those areas are affected. From there, we can see what areas are more susceptible to flooding, or are located in floodplains. I believe that we could prepare better for these events and have news outlets not become too hysterical when said events occur as to not cause fear throughout the communities. Experiment: I looked at the data and analyzed it. Share your results: The precip totals are posted on the map, and from there, they are shared on the story map. I hope you enjoy, and learned something new.

Excel Application Tool for Agricultural Water Use Data 1998 - 2005 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 1998 – 2005 agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using California Ag Water Use model for updating the information in the California Water Plan Updates-2003 & 2009. Therefore, this current Excel application tool just covers agricultural water use data from the period of 1998 - 2005 water years. It should also be mentioned that there are 2 other similar Excel application tools that cover 2006 - 2010 and 2011 - 2015 agricultural water use data for the California Water plan Updates - 2013 and 2018, respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 1998 – 2005 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu.
Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets were included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised.

Following are definitions of terminology and listing of 20 crop categories used in this Excel application.

1. Study Area Maps The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR),
   The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation.

2. Irrigated Crop Area (ICA) in acres The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres)

3. Multi-cropping (MC) in acres A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field.

4. Evapotranspiration (ET) Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity.

5. Reference Evapotranspiration (ETo) Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988).

6. Penman-Monteith Equation (PM) Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo.

7. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet) Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep.

8. Crop Coefficient (Kc) Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration.

9. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet) Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation

10. Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet) Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw.

11. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet) Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency.

12. Consumed Fraction (CF) in percentage (%) An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year.

13. Crop category numbers and descriptions
    Crop Category Crop category description.

1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay)
2 Rice (rice, rice_wild, rice_flooded, rice-upland)
3 Cotton
4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early)
5 Corn
6 Dry beans
7 Safflower
8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane
9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual)
10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue)
11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc)
12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc)
13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon)
14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic)
15 Potatoes (potatoes, potatoes_sweet)
16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower)
17 Almond & pistachios
18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis)
19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba)
20 Vineyards (grape_table, grape_raisin, grape_wine)

Excel Application Tool for Agricultural Water Use Data 2011 - 2015 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2011 – 2015 agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using Cal_simetaw model for updating the information in the California Water Plan Updates-2018. Therefore, this current Excel application tool just covers agricultural water use data from the period of 2011-2015 water years. It should also be mentioned that there are 2 other similar Excel applications that cover 1998 - 2005 and 2006 - 2010 agricultural water use data for the California Water plan Updates 2003/2009 and 2013, respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2011 – 2015 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu. Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised. Following are definitions of terminology and listing of 20 crop categories used in this Excel application. Study Area Maps The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), the next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the DAU or PA level, and the results are aggregated into hydrologic regions for presentation. Irrigated Crop Area (ICA) in acres The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres) Multi-cropping (MC) in acres A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field. Evapotranspiration (ET) Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity. Reference Evapotranspiration (ETo) Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988). Penman-Monteith Equation (PM) Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet) Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep. Crop Coefficient (Kc) Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet) Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet) Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet) Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency. Consumed Fraction (CF) in percentage (%) An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year. Crop category numbers and descriptions Crop Category Crop category description. 1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay) 2 Rice (rice, rice_wild, rice_flooded, rice-upland) 3 Cotton 4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early) 5 Corn 6 Dry beans 7 Safflower 8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane 9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual) 10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue) 11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc) 12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc) 13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon) 14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic) 15 Potatoes (potatoes, potatoes_sweet) 16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower) 17 Almond & pistachios 18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis) 19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba) 20 Vineyards (grape_table, grape_raisin, grape_wine)

The dataset for this study was assembled by collecting all peer-reviewed publications from March 2000 to April 2024 using the Web of Science, SCOPUS, and China National Knowledge Infrastructure databases. The bibliographic retrieval process preferred reporting items for systematic reviews and meta-analyses (PRISMA), with specific search terms used titles, keywords, and abstracts: ("NBPT" OR "N-(n-butyl) thiophosphoric triamide") AND ("DMPP" OR "3,4-dimethylepyrazole phosphate" OR "DCD" OR "dicyandiamide"). Based on the inclusion criteria, 261 experimental sites from 41 countries were selected for meta-analysis (Fig. 1). Â The selection criteria were based on the following: (1) inclusion of only field observations, excluding pot and laboratory experiments; (2) experiments using urea as the base fertilizer; (3) comparison of the efficacy of individual inhibitors with combination inhibitors to determine cost-effective; (4) inclusion of treatment replicates (a minimum of three); (5) measurem..., , , # Global meta-analysis of individual and combined nitrogen inhibitors: enhancing plant productivity and reducing environmental losses

https://doi.org/10.5061/dryad.1vhhmgr4d

This dataset serves the article "Global meta-analysis of individual and combined nitrogen inhibitors: enhancing plant productivity and reducing environmental losses" from the Global Change Biology, containing data of the inhibitor use effects under 285 different treatments extracted from 41 studies. The meanings represented by different abbreviations are shown in the “Abbreviation†.

The “Data†table contains data on pH type, soil texture type, nitrification inhibitors type, urease inhibitors type, N application rate type, cropping system classified type, soil organic carbon type and environmental impact variables, including mean annual temperature (MAT) and local mean annual precipitation (MAP) extracted from 41 studies.

“Data sources†table are the 41 studies from whic...

This dataset consists of point intercept data, sampled with a point frame, from three 1 ha sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between late May and mid July, 2019. The lowest elevation site ('wbs1', 1,425 m) was vegetated by shrub steppe dominated Wyoming big sage (Artemisia tridentata ssp. wyomingensis). Vegetation at the middle elevation site ('los1', 1,680 m) was shrub steppe dominated by low sage (Artemisia arbuscula). Shrub steppe at the highest elevation site ('mbs1', 2,110 m) was dominated by mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). At each site 30 randomly located square 1 m^2 plots were sampled. The plots were oriented with one axis randomly chosen from 45, 90, 135, 180, 225, 270, 315 and 360 degrees north azimuth. A point frame of 20 pins was orientated perpendicular to the azimuth and each pin was lowered through the canopy and each contact was recorded to species or other plant material category. Whether the contacted material was photosynthetic (coded as a '+') or non-photosynthetic (coded as '-') was also recorded. Last seasons senesced plant material that is alive but not photosynthetic is coded as '.'. There may be 0, 1, 2 or more canopy hits for each pin (numbered 1 through n with 1 being the top-most canopy hit). A final basal hit is recorded for each pin and coded as hit 0. The point frame was moved so that a total of 5 rows were recorded for a total of 100 pins for each plot. The plant species codes used follow the USDA Plants Database. Resources in this dataset:Resource Title: Data from: UAS imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient . File Name: point_frame_2019_reynoldscreek.xlsxResource Description: This dataset consists of point frame data from three 1 ha sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between late May and mid July, 2019. The lowest site's ('wbs1', 1,425 m) characteristic dominant shrub is Wyoming big sage (Artemisia tridentata ssp. wyomingensis). The middle elevation site's ('los1', 1,680 m) dominant shrub is low sage (Artemisia arbuscula). The highest elevation site's ('mbs1', 2,110 m) dominant shrubs are mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). At each site 30 randomly located square 1 m^2 plots were sampled. The plots were oriented with one axis randomly chosen from 45, 90, 135, 180, 225, 270, 315 and 360 degrees north azimuth. A point frame of 20 pins was orientated perpendicular to the azimuth and each pin was lowered through the canopy and each contact was recorded to species or other plant material category. Whether the contacted material was photosynthetic (coded as a '+') or non-photosynthetic (coded as '-') was also recorded. Last seasons senesced plant material that is alive but not photosynthetic is coded as '.'. There may be 0, 1, 2 or more canopy hits for each pin (numbered 1 through n with 1 being the top-most canopy hit). A final basal hit is recorded for each pin and coded as hit 0. The point frame was moved so that a total of rows rows were recorded for a total of 100 pins for each plot. The plant species codes used follow the USDA Plants Database.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: ReynoldsCrkExpWtrshdGeoJSON.json

This report describes and presents all the data gathered on Brown Glacier during the 2000 field season. The goal of the study (ASAC project 1158) was to characterize one of the Heard Island glaciers that has a relatively simple geometry and is easy to work on. A land terminating glacier was chosen because of the larger expected response to climate change. The plan was to collect data on the dynamic characteristics, mass balance, sub-glacial topography and recent fluctuations of a Heard Island glacier. The data will ultimately be used to construct and validate a numerical model of the glacier. The model will be used to simulate the possible cause of the glacier fluctuations, and help to derive a climate-proxy record for this remote and sensitive area.

The study of the morphology, dynamics and mass balance of the Brown Glacier on the northeastern side of Heard Island was undertaken between mid-October and late November 2000. An ANARE field party, operating from a small field camp near the foot of the glacier, comprised three glaciologists (M.T., A.R. and D.T.) until early November, with two staying for the full period (M.T. and D.T.). This group was independent of two other ANARE field camps located at Atlas Cove and Spit Bay.

The following field measurements were successfully completed:

A differential GPS survey of the surface elevation along the centre-line of the glacier (from 1 100 m elevation to the snout below 200 m) and across five transverse sections,

A differential GPS survey of the location of the present snout of the glacier, and the 1947 lateral moraines,

Bathymetric measurements of the lagoon formed by retreat of the glacier since the 1950's,

Detailed ice thickness measurements (with a portable radio echo-sounder) across a transverse profile of the glacier at 500 m elevation, and spot thickness measurements across three other profiles,

Surface ice velocity measurements along the centre-line of the glacier (10 locations) and along two transverse sections at 500 m and 400 m elevation (five and four locations respectively). Most of these sites were measured over two epochs, and time varying velocity measurements were made for nearly four weeks at one site,

Surface mass balance measurements during November at the location of all velocity measurements. Other members of the ANARE party made additional measurements in mid-January,

Temperature and density measurements, and snow sampling from a pit and a crevasse,

Detailed meteorological measurements from a temporary automatic weather station (AWS) on the glacier surface (at 500 m elevation) during November, and ongoing satellite-relayed meteorological measurements from a larger AWS on rock adjoining the Brown Glacier (~550 m elevation). The glacier surface measurements included half-hourly surface height measurements with an acoustic ranger to record short time scale changes in surface mass budget.

Data Files available:

DEM Folder contents
dem.ascHeard DEM in ASCII format, obtained from the AAD
allprof.datall kinematic GPS profiles
brownarea.datHeard DEM cropped to the Brown Glacier area
brownpoints.datPoints surveyed with static GPS methods
dem.datHeard DEM without header
newdem.datBrown Glacier area DEM corrected with GPS profiles
oldglacier.dat1947 Glacier outline
outline.datGlacier outline, digitized from newdem.dat
.mvarious Matlab programs to adjust the DEM
createdem.mMatlab program used for final DEM adjustment
dem.mMatlab program to display and crop the Heard DEM
drawdem.mMatlab program to display a map (with various options)
icelost.mMatlab program to calculate volume change
dem.prjProjection parameters for Heard Island DEM
.profkinematic GPS profiles in ASCII format
Glacier velocities folder contents
daily.*ASCII and Excel file of daily velocities measured at BG35
GPSpoints.*ASCII and Excel file of index points surveyed with static GPS
index.mMatlab program to display velocities
indexpoints.txtFile with velocity data used in index.m
Isotope Analysis folder contents
Del018.xlsExcel file containing Oxygen isotope data from BG35 and crevasse
kinematic profiles folder contents
.txtall the kinematic GPS profiles in ASCII format
Lagoon bathymetry folder contents
lagoon.mMatlab program to reduce bathymetry data in bathy.txt
lagoon.figMatlab figure showing the bathymetry data
bathy.txtASCII data file for use in lagoon.m
shore.txtASCII data file for the surveyed shore line
bathymetry.xlsExcel file with bathymetry data
Matlab folder contents
areasize.mprogram to calculate the area enclosed in a polygon
chkload.mprogram to load a file with additional options
circle.mfunction to draw a circle (used in index.m)
RES folder contents
bg.datRES raw data (format is specified in RES.m help)
bgbottom.datDigitized bed from RES returns
bg*.resprocessed RES files (format specified in RES.m help)
RES.mMatlab program to reduce RES data
drwelip.m
fitline.mMatlab programs used by RES.m
Weather folder contents
rock_aws.xlsoriginal weather data files from the rock AWS
glacier_aws.xlsoriginal weather data files from the glacier AWS
snow_surface.xlssnow surface height measurements from the bamboo poles at the
index sites (longitudinal and transverse).

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

As the main body of terrestrial ecosystems, forests account for over 65% of the fixed carbon content each year, and forest biomass accounts for about 90% of the total biomass of terrestrial ecosystems. They play an important role in regulating global carbon balance and slowing down the rise of greenhouse gas concentrations. Tianshan spruce, as an important forest resource in Xinjiang, has significant ecological and economic value. Constructing its biomass spatiotemporal dataset can provide basic data for the assessment of regional carbon sequestration potential, and provide scientific basis for the protection and sustainable management of Tianshan spruce forests. The dataset includes text data and image data, among which Excel text data collects the structural data of the dense area of Xinjiang Tianshan spruce in 2002, 2007, 2012, and 2017 for each sample plot; Grid image data includes terrain data such as altitude, remote sensing data such as normalized vegetation index, meteorological data such as annual average precipitation, annual average runoff depth, annual average easterly wind speed, and biomass distribution map of spruce dense areas. The comprehensiveness of these data is crucial for revealing the growth trends and changes of spruce in the Tianshan Mountains. It not only has important scientific value and practical application potential in the fields of ecological protection and climate change research in the Tianshan Mountains of Xinjiang, but also provides valuable data resources for researchers in the area of ecosystem management and related fields.

This data contains location information for 1 of Ontario’s snow monitoring networks:Surface Water Monitoring Centre (SWMC)Snow course data is collected by:conservation authoritiesOntario Power GenerationMinistry of Natural Resources (MNR) districtsData is collected twice a month from November 15 until May 15. The Surface Water Monitoring Centre uses this data to assess:current snow coverfrozen ground conditionssnowpackpotential snowmeltcontributions to streamflowThe snow data is located in a corporate water and climate database. This data helps MNR and conservation authorities assess the potential for flood at the local and provincial scale. Additional DocumentationOntario Snow Survey location and data - Data Dictionary (Excel) Historic and Current Snow Survey Metadata (1933-2024) (CSV) StatusPlanned: fixed date has been established upon or by which the data will be created or updated Maintenance and Update FrequencyAnnually: data is updated every year ContactSurface Water Monitoring Centre, surface.water@ontario.ca

This collection contains data files which contain "plot-ready" (that is, at the final processing stage) data used to generate figures in the 2024 State of the Climate report, and are made available as an addendum to the report. These plot-ready files are in common, easily-read file formats (text, csv, Excel file and netCDF) and are organised by chapter and then by figure. Lineage: Data production varies based on the figure and chapters. For most figures, data from observations (including weather stations, ocean floats, river streamflow gauges, tide gauges and satellites) are used to generate a high-quality data product, which are used as input for data analysis (these products are listed in the online references for the report). From these products, trends in climate and weather-related indices are generated, and provide the data in this collection.

For the future climate projection plot, a range of CMIP6 climate projections and simulations of the historical climate are analysed to determine a range of simulated Australian temperature anomalies since 1910, and extend this range of simulated temperature anomalies forward to 2040 under different emissions scenarios.

The database contains results of the modeling study performed for the Raba River basin (Carpathian Mts., Poland) on the sediment yields. Using the calibrated and verified basin created in the DNS/SWAT Macromodel as a baseline scenario, subsequently the climate and land use scenarios were superimposed. The climate change scenarios were prepared using temperature and precipitation projections from the EuroCORDEX regional climate [https://euro-cordex.net/060378/index.php.en] and CMIP5 general circulation models [https://esgf-node.llnl.gov/projects/cmip5/][http://climateimpact.sggw.pl/map/proj/]. The land use change projections were based on the FORECOM project [http://www.gis.geo.uj.edu.pl/forecom/]. To track the sediment yields on the sub-basin level the Raba River basin was divided into 36 units. Impacts of single (hypothetical) and combined climate change and land use scenarios on sediment yield (t/ha and %) for each of these units (sub-basins) are displayed through the selection of database filters. The database consists of six Excel sheets: ‘scenarios’ - a list of scenarios prepared for the needs of the research together with their descriptions, whose impact on sediment yield in each sub-basin can be traced in the next sheets ‘results_plot’ - results of the individual scenarios impact on the sediment yield in the selected sub-basins and/or seasons the data is presented in the form of a pivot column chart. By using the "Select sub-basin or/and season" buttons user can filter any sub-basin and season values ‘changes_plot (t/ha)’ - sediment yield change for each scenario in comparison to baseline scenario in t/ha (functionality like previous sheet) ‘changes_plot (%)’ - sediment yield change for each scenario sediment yield change for each scenario in t/ha in comparison to baseline scenario in % (functionality like previous sheet) ‘sub-basins features’ - location of individual sub-basins, their numbers in the Raba River basin and main features: land use, slopes and soils classes share in the sub-basin area ‘data’ - output from the model for all scenarios (see sheet ‘scenarios’), average values of sediment yield for each season of data from 2005-2017 for 36 sub-basins in t/month

IntroductionMany tools support housing decisions for older adults but often overlook mobility patterns and social health. We explored these factors in older Canadians living at home to inform housing decisions.MethodsWe conducted a mixed-methods study with 20 older adults (65+) from Quebec and Alberta living independently or in senior residences with outdoor mobility. Data collection included sociodemographic information, GPS tracking, walking interviews, daily journals, and in-depth interviews. Data from interviews, which explored physical and social assets and barriers to social health and mobility, were analyzed using deductive content analysis in NVivo 12. GPS data were subjected to spatial analysis in QGIS (Quantum Geographic Information System) to map activity spaces and mobility patterns by the number and distance of activities, activity types, and modes of transportation. Daily journals were transcribed into an Excel spreadsheet and compared with GPS data. Overall analysis was guided hierarchically by qualitative data, utilizing verbatim narratives and visualization (activity space maps) to illustrate data convergence.ResultsAmong 20 participants, 14 completed all activities, including GPS trackers. GPS maps showed participants mostly left home to drive for shopping or walking. Over 14 days, participants made an average of 10.4 (±5.8) trips and traveled 186.9 km (±130.4), averaging 16.8 km (±29.8) per day. Transportation modes included car (n=9), walking (n=5), and bus (n=2). Daily journals revealed that participants typically traveled alone. Interviews identified physical assets as libraries and supermarkets (n=10), while social assets were family support when desired (n=13) neighborhood familiarity (n=14), both contributing to social health. Winter weather was the most cited mobility barrier (n=13).ConclusionsThese findings provide actionable insights to guide the development of user-informed decision support tools tailored to the housing decisions of Canadian older adults.