TerraClimate adalah set data keseimbangan air iklim dan iklim bulanan untuk permukaan terestrial global. Model ini menggunakan interpolasi yang dibantu iklim, menggabungkan normal klimatologis beresolusi spasial tinggi dari set data WorldClim, dengan resolusi spasial yang lebih kasar, tetapi data yang bervariasi menurut waktu dari CRU Ts4.0 dan Analisis Ulang 55 tahun Jepang (JRA55).
Secara konseptual, prosedur ini menerapkan anomali yang bervariasi menurut waktu yang diinterpolasi
dari CRU Ts4.0/JRA55 ke klimatologi beresolusi spasial tinggi dari
WorldClim untuk membuat set data beresolusi spasial tinggi yang mencakup catatan
temporal yang lebih luas.
Informasi temporal diwarisi dari CRU Ts4.0 untuk sebagian besar permukaan daratan global untuk suhu, curah hujan, dan tekanan uap. Namun,
data JRA55 digunakan untuk wilayah yang tidak memiliki stasiun iklim yang berkontribusi pada data CRU (termasuk seluruh Antartika, dan sebagian Afrika, Amerika Selatan, dan pulau-pulau yang tersebar). Untuk variabel iklim utama seperti suhu, tekanan uap, dan presipitasi, University of Idaho menyediakan data tambahan tentang jumlah stasiun (antara 0 dan 8) yang berkontribusi pada data CRU Ts4.0 yang digunakan oleh TerraClimate. JRA55 digunakan secara eksklusif untuk radiasi matahari dan kecepatan angin.
TerraClimate juga menghasilkan set data keseimbangan air permukaan bulanan menggunakan model keseimbangan air yang menggabungkan evapotranspirasi referensi, presipitasi, suhu, dan kapasitas air tanah yang dapat diekstrak tanaman yang diinterpolasi. Model keseimbangan air iklim Thornthwaite-Mather yang dimodifikasi dan data kapasitas penyimpanan air tanah yang dapat diekstrak digunakan pada petak 0,5° dari Wang-Erlandsson et al. (2016).
Batasan Data:
Tren jangka panjang dalam data diwarisi dari set data induk.
TerraClimate tidak boleh digunakan secara langsung untuk penilaian tren yang independen.
TerraClimate tidak akan merekam variabilitas temporal pada skala yang lebih halus daripada set data induk dan dengan demikian tidak dapat merekam variabilitas dalam rasio dan inversi presipitasi orografis.
Model keseimbangan air sangat sederhana dan tidak memperhitungkan heterogenitas jenis vegetasi atau respons fisiologisnya terhadap perubahan kondisi lingkungan.
Validasi terbatas di wilayah dengan data yang sedikit (misalnya, Antarktika).
Band
Ukuran Piksel 4638,3 meter
Band
Nama
Unit
Min
Maks
Skala
Ukuran Piksel
Deskripsi
aet
mm
0*
3140*
0,1
meter
Evapotranspirasi aktual, yang diperoleh menggunakan model keseimbangan air tanah satu dimensi
def
mm
0*
4548*
0,1
meter
Defisit air iklim, yang diperoleh menggunakan model keseimbangan air tanah satu dimensi
pdsi
-4317*
3418*
0,01
meter
Indeks Keparahan Kekeringan Palmer
pet
mm
0*
4548*
0,1
meter
Evapotranspirasi referensi (ASCE Penman-Montieth)
pr
mm
0*
7245*
meter
Akumulasi presipitasi
ro
mm
0*
12560*
meter
Limpasan, yang diperoleh menggunakan model keseimbangan air tanah satu dimensi
soil
mm
0*
8882*
0,1
meter
Kelembapan tanah, yang diperoleh menggunakan model keseimbangan air tanah satu dimensi
srad
W/m^2
0*
5477*
0,1
meter
Radiasi gelombang pendek permukaan ke bawah
swe
mm
0*
32767*
meter
Setara air salju, yang diperoleh menggunakan model keseimbangan air tanah satu dimensi
tmmn
°C
-770*
387*
0,1
meter
Suhu minimum
tmmx
°C
-670*
576*
0,1
meter
Suhu maksimum
vap
kPa
0*
14749*
0,001
meter
Tekanan uap
vpd
kPa
0*
1113*
0,01
meter
Defisit tekanan uap
vs
m/d
0*
2923*
0,01
meter
Kecepatan angin pada ketinggian 10 m
* perkiraan nilai min atau maks
Properti Gambar
Properti Gambar
Nama
Jenis
Deskripsi
status
STRING
'provisional' atau 'permanent'
Persyaratan Penggunaan
Persyaratan Penggunaan
Set data ini berada dalam domain publik karena dilisensikan berdasarkan lisensi Creative Commons Public Domain (CC0).
Kutipan
Kutipan:
Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018,
Terraclimate, a high-resolution global dataset of monthly climate and
climatic water balance from 1958-2015, Scientific Data 5:170191,
doi:10.1038/sdata.2017.191
TerraClimate adalah set data keseimbangan air iklim dan iklim bulanan untuk permukaan terestrial global. Metode ini menggunakan interpolasi yang dibantu iklim, menggabungkan normal klimatologi beresolusi spasial tinggi dari set data WorldClim, dengan resolusi spasial yang lebih kasar, tetapi data yang bervariasi menurut waktu dari CRU Ts4.0 dan Japanese 55-year Reanalysis (JRA55). Secara konseptual, prosedur ini menerapkan interpolasi …
[null,null,[],[[["\u003cp\u003eTerraClimate provides monthly climate and climatic water balance data for global terrestrial surfaces from 1958 to 2023.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset integrates high-resolution climatological normals from WorldClim with time-varying data from CRU Ts4.0 and JRA55.\u003c/p\u003e\n"],["\u003cp\u003eIt offers various climate variables, including temperature, precipitation, vapor pressure, solar radiation, and wind speeds, alongside derived water balance components like evapotranspiration, runoff, and soil moisture.\u003c/p\u003e\n"],["\u003cp\u003eTerraClimate data is available at a 4638.3 meter resolution and is provided by the University of California Merced.\u003c/p\u003e\n"],["\u003cp\u003eWhile valuable for various climate-related analyses, users should be aware of limitations regarding trend analysis, fine-scale variability, and model simplicity, especially in data-sparse regions.\u003c/p\u003e\n"]]],[],null,["# TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho\n\nDataset Availability\n: 1958-01-01T00:00:00Z--2024-12-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [University of California Merced](http://www.climatologylab.org/terraclimate.html)\n\nCadence\n: 1 Month\n\nTags\n:\n[climate](/earth-engine/datasets/tags/climate) [drought](/earth-engine/datasets/tags/drought) [evapotranspiration](/earth-engine/datasets/tags/evapotranspiration) [geophysical](/earth-engine/datasets/tags/geophysical) [global](/earth-engine/datasets/tags/global) [merced](/earth-engine/datasets/tags/merced) [monthly](/earth-engine/datasets/tags/monthly) [palmer](/earth-engine/datasets/tags/palmer) [pdsi](/earth-engine/datasets/tags/pdsi) [precipitation](/earth-engine/datasets/tags/precipitation) [runoff](/earth-engine/datasets/tags/runoff) [temperature](/earth-engine/datasets/tags/temperature) [vapor](/earth-engine/datasets/tags/vapor) [water-vapor](/earth-engine/datasets/tags/water-vapor) [wind](/earth-engine/datasets/tags/wind) \n\n#### Description\n\nTerraClimate is a dataset of monthly climate and climatic water balance for\nglobal terrestrial surfaces. It uses climatically aided interpolation,\ncombining high-spatial resolution climatological normals from the\n[WorldClim dataset](https://www.worldclim.org/), with coarser spatial\nresolution, but time-varying data from\n[CRU Ts4.0](https://data.ceda.ac.uk/badc/cru/data/cru_ts/) and the\n[Japanese 55-year Reanalysis (JRA55)](https://jra.kishou.go.jp/JRA-55/index_en.html).\nConceptually, the procedure applies interpolated time-varying anomalies\nfrom CRU Ts4.0/JRA55 to the high-spatial resolution climatology of\nWorldClim to create a high-spatial resolution dataset that covers a broader\ntemporal record.\n\nTemporal information is inherited from CRU Ts4.0 for most global land\nsurfaces for temperature, precipitation, and vapor pressure. However,\nJRA55 data is used for regions where CRU data had zero climate stations\ncontributing (including all of Antarctica, and parts of Africa,\nSouth America, and scattered islands). For primary climate variables of\ntemperature, vapor pressure, and precipitation, the University of Idaho\nprovides additional data on the number of stations (between 0 and 8) that\ncontributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used\nexclusively for solar radiation and wind speeds.\n\nTerraClimate additionally produces monthly surface water balance datasets\nusing a water balance model that incorporates reference evapotranspiration,\nprecipitation, temperature, and interpolated plant extractable soil water\ncapacity. A modified Thornthwaite-Mather climatic water-balance model and\nextractable soil water storage capacity data was used at a 0.5° grid from\nWang-Erlandsson et al. (2016).\n\nData Limitations:\n\n1. Long-term trends in data are inherited from parent datasets.\n TerraClimate should not be used directly for independent assessments of\n trends.\n\n2. TerraClimate will not capture temporal variability at finer scales than\n parent datasets and thus is not able to capture variability in\n orographic precipitation ratios and inversions.\n\n3. The water balance model is very simple and does not account for\n heterogeneity in vegetation types or their physiological response to\n changing environmental conditions.\n\n4. Limited validation in data-sparse regions (e.g., Antarctica).\n\n### Bands\n\n\n**Pixel Size**\n\n4638.3 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Scale | Pixel Size | Description |\n|--------|--------|---------|---------|-------|------------|-------------------------------------------------------------------------------------|\n| `aet` | mm | 0\\* | 3140\\* | 0.1 | meters | Actual evapotranspiration, derived using a one-dimensional soil water balance model |\n| `def` | mm | 0\\* | 4548\\* | 0.1 | meters | Climate water deficit, derived using a one-dimensional soil water balance model |\n| `pdsi` | | -4317\\* | 3418\\* | 0.01 | meters | Palmer Drought Severity Index |\n| `pet` | mm | 0\\* | 4548\\* | 0.1 | meters | Reference evapotranspiration (ASCE Penman-Montieth) |\n| `pr` | mm | 0\\* | 7245\\* | | meters | Precipitation accumulation |\n| `ro` | mm | 0\\* | 12560\\* | | meters | Runoff, derived using a one-dimensional soil water balance model |\n| `soil` | mm | 0\\* | 8882\\* | 0.1 | meters | Soil moisture, derived using a one-dimensional soil water balance model |\n| `srad` | W/m\\^2 | 0\\* | 5477\\* | 0.1 | meters | Downward surface shortwave radiation |\n| `swe` | mm | 0\\* | 32767\\* | | meters | Snow water equivalent, derived using a one-dimensional soil water balance model |\n| `tmmn` | °C | -770\\* | 387\\* | 0.1 | meters | Minimum temperature |\n| `tmmx` | °C | -670\\* | 576\\* | 0.1 | meters | Maximum temperature |\n| `vap` | kPa | 0\\* | 14749\\* | 0.001 | meters | Vapor pressure |\n| `vpd` | kPa | 0\\* | 1113\\* | 0.01 | meters | Vapor pressure deficit |\n| `vs` | m/s | 0\\* | 2923\\* | 0.01 | meters | Wind-speed at 10m |\n\n\\* estimated min or max value\n\n### Image Properties\n\n**Image Properties**\n\n| Name | Type | Description |\n|--------|--------|------------------------------|\n| status | STRING | 'provisional' or 'permanent' |\n\n### Terms of Use\n\n**Terms of Use**\n\nThe data set is in the public domain as licensed under the Creative Commons\nPublic Domain (CC0) license.\n\n### Citations\n\nCitations:\n\n- Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018,\n Terraclimate, a high-resolution global dataset of monthly climate and\n climatic water balance from 1958-2015, Scientific Data 5:170191,\n [doi:10.1038/sdata.2017.191](https://doi.org/10.1038/sdata.2017.191)\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')\n .filter(ee.Filter.date('2017-07-01', '2017-08-01'));\nvar maximumTemperature = dataset.select('tmmx');\nvar maximumTemperatureVis = {\n min: -300.0,\n max: 300.0,\n palette: [\n '1a3678', '2955bc', '5699ff', '8dbae9', 'acd1ff', 'caebff', 'e5f9ff',\n 'fdffb4', 'ffe6a2', 'ffc969', 'ffa12d', 'ff7c1f', 'ca531a', 'ff0000',\n 'ab0000'\n ],\n};\nMap.setCenter(71.72, 52.48, 3);\nMap.addLayer(maximumTemperature, maximumTemperatureVis, 'Maximum Temperature');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\ndataset = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE').filter(\n ee.Filter.date('2017-07-01', '2017-08-01')\n)\nmaximum_temperature = dataset.select('tmmx')\nmaximum_temperature_vis = {\n 'min': -300.0,\n 'max': 300.0,\n 'palette': [\n '1a3678',\n '2955bc',\n '5699ff',\n '8dbae9',\n 'acd1ff',\n 'caebff',\n 'e5f9ff',\n 'fdffb4',\n 'ffe6a2',\n 'ffc969',\n 'ffa12d',\n 'ff7c1f',\n 'ca531a',\n 'ff0000',\n 'ab0000',\n ],\n}\n\nm = geemap.Map()\nm.set_center(71.72, 52.48, 3)\nm.add_layer(\n maximum_temperature, maximum_temperature_vis, 'Maximum Temperature'\n)\nm\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IDAHO_EPSCOR/IDAHO_EPSCOR_TERRACLIMATE) \n[TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho](/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE) \nTerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces. It uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55). Conceptually, the procedure applies interpolated ... \nIDAHO_EPSCOR/TERRACLIMATE, climate,drought,evapotranspiration,geophysical,global,merced,monthly,palmer,pdsi,precipitation,runoff,temperature,vapor,water-vapor,wind \n1958-01-01T00:00:00Z/2024-12-01T00:00:00Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/http://www.climatologylab.org/terraclimate.html)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE)"]]
