diff --git a/README.md b/README.md index bb7a923b..36e8d9a6 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ Some source files reference tables from the [invest-sample-data](https://bitbuck A clone of `invest-sample-data` must exist in the top level of this repo before you build the documentation. Execute the following command to clone `invest-sample-data` and check out the correct revision: -`make get_sampledata` +`make invest-sample-data` Execute the following command to build HTML documentation from the reStructuredText source: @@ -17,7 +17,7 @@ Then find the html documents in `build/html` and view them in a web browser to e ## Branching & Development Guidelines -Edits to the User Guide that pertain to the currently released version of InVEST can be made directly on this repo's `main` branch. +Edits to the User Guide that pertain to the currently released version of InVEST can be made directly on this repo's `main` branch. Edits that pertain to a yet-to-be-released feature of InVEST should be made on the corresponding `release/X.X` branch in this repo. That branch should be merged into `main` at the same time as the corresponding invest release. diff --git a/source/en/habitat_quality.rst b/source/en/habitat_quality.rst index e94de572..2c03c9a6 100644 --- a/source/en/habitat_quality.rst +++ b/source/en/habitat_quality.rst @@ -66,7 +66,7 @@ The impact of threats on habitat in a grid cell is mediated by four factors. .. math:: i_{rxy}=1-\left( \frac{d_{xy}}{d_{r\ \mathrm{max}}}\right)\ \mathrm{if\ linear} :label: (hq. 1) - + .. math:: i_{rxy}=exp\left(-\left(\frac{2.99}{d_{r\ \mathrm{max}}}\right)d_{xy}\right)\mathrm{if\ exponential} :label: (hq. 2) @@ -112,7 +112,7 @@ and :math:`z` (we hard code :math:`z = 2.5`) and :math:`k` are scaling parameter :align: center :figwidth: 500px -Table 1. Possible degradation sources based on the causes of endangerment for American species classified as threatened or endangered by the US Fish and Wildlife Service. Adapted from Czech et al. 2000. +Table 1. Possible degradation sources based on the causes of endangerment for American species classified as threatened or endangered by the US Fish and Wildlife Service. Adapted from Czech et al. 2000. | @@ -162,7 +162,7 @@ Data Needs - :investspec:`habitat_quality threats_table_path` .. note:: The file system locations for *cur_path*, *base_path* and *fut_path* are relative to the location of the **Threats Table**. For example, if *cur_path* is "threat1.tif", that means that "threat.tif" is located in the same folder as the **Threats Table**. If *cur_path* is "threat_folder/threat1.tif", that means that there is a folder "threat_folder" in the same location as the **Threats Table**, and "threat1.tif" is located inside "threat_folder". You may also provide absolute paths, such as "C:/HabitatQuality/threat_folder/threat1.tif". - + Columns: - :investspec:`habitat_quality threats_table_path.columns.threat` @@ -174,7 +174,7 @@ Data Needs - :investspec:`habitat_quality threats_table_path.columns.fut_path` **Example Study** - + Hypothetical study with three threats for both current and future scenarios. Agriculture (*Agric* in the table) degrades habitat over a larger distance than roads do, and has a greater overall magnitude of impact. Further, paved roads (*Paved_rd*) attract more traffic than dirt roads (*Dirt_rd*) and thus are more destructive to nearby habitat than dirt roads. Filepaths may be absolute or relative to the Threat data table. In this instance the current threats are found in the same directory as the table and the future threats are found in a sub directory adjacent to the Threat data table called *future*. Baseline threat filepaths are left blank because we do not have threat rasters for that scenario OR we have not included the baseline LULC in our model run altogether. ======== ======== ====== =========== ============ ================= ======================= @@ -186,13 +186,13 @@ Data Needs ======== ======== ====== =========== ============ ================= ======================= **Threat Rasters Information** - + GIS raster files of the distribution and intensity of each individual threat, with values between 0 and 1. You will have as many of these maps as you have threats and the raster filepath should be defined in the **Threats data** table. The extent and resolution of these raster datasets does not need to be identical to that of the input LULC maps. In cases where the threats and LULC map resolutions vary, the model will use the resolution and extent of the LULC map. Each cell in the raster contains a value that indicates the density or presence of a threat within it (e.g., area of agriculture, length of roads, or simply a 1 if the grid cell is a road or crop field and 0 otherwise). All threats should be measured in the same scale and units (i.e., all measured in density terms or all measured in presence/absence terms) and not some combination of metrics. Do not leave any area on the threat maps as 'No Data'. If pixels do not contain that threat set the pixels' threat level equal to 0. - + InVEST will not prompt you for these rasters in the tool interface but will instead look for their filepaths in the **Threats data** table under the corresponding scenario columns. The paths may be absolute or relative to the **Threats data** table path. - + Finally, note that we assume that the relative weights of threats and sensitivity of habitat to threats do not change over time, so we only submit one Threat data table and one Habitat sensitivity data table. If you want to change these over time then you will have to run the model multiple times. - + In the sample datasets, threat rasters are stored in the same directory as the Threats data table and are defined in the Threat data table under the appropriate column name as follows: **CUR_PATH**: crops_c.tif; railroad_c.tif; urban_c.tif; timber_c.tif; roads1_c.tif; roads2_c.tif; roads3_c.tif; **FUT_PATH**: crops_f.tif; railroad_f.tif; urban_f.tif; timber_f.tif; roads1_f.tif; roads2_f.tif; roads3_f.tif. When inputting the the baseline and future scenario LULC files found in the sample dataset we are running a habitat quality analysis for the current and future LULC scenario maps. A habitat quality map will not be generated for the baseline map because we have not provided any threat layers for the baseline map and left those columns blank in the Threat data table. The name 'crops' refers to cropland, 'railroad' to train rails, 'urban' to urban, 'timber' to rotation forestry, 'roads1' to primary roads, 'roads2' to secondary roads, and 'roads3' to light roads. - :investspec:`habitat_quality sensitivity_table_path` @@ -216,7 +216,7 @@ Data Needs ==== =============== ======= ======= ========== ========= - :investspec:`habitat_quality access_vector_path` Polygons with minimum accessibility (e.g., strict nature reserves, well protected private lands) are assigned some number less than 1, while polygons with maximum accessibility (e.g., extractive reserves) are assigned a value 1. These polygons can be land management units or a regular array or hexagons or grid squares. - + Field: - :investspec:`habitat_quality access_vector_path.fields.access` @@ -236,19 +236,21 @@ Interpreting Results * **Parameter log**: Each time the model is run, a text (.txt) file will be created in the Workspace. The file will list the parameter values and output messages for that run and will be named according to the service, the date and time. When contacting NatCap about errors in a model run, please include the parameter log. -* **[Workspace]\\output** folder: + * **deg_sum_c_[Suffix].tif** -- Relative level of habitat degradation on the current landscape. A high score in a grid cell means habitat degradation in the cell is high relative to other cells. Grid cells with non-habitat land cover (LULC with :math:`H_j` = 0) get a degradation score of 0. This is a mapping of degradation scores calculated with equation :eq:`(hq. 3)`. + + * **deg_sum_f_[Suffix].tif** -- Relative level of habitat degradation on the future landscape. A high score in a grid cell means habitat degradation in the cell is high relative to other cells. This output is only created if a future LULC map is given as input. Grid cells with non-habitat land cover (LULC with :math:`H_j` = 0) get a degradation score of 0. This is a mapping of degradation scores calculated with equation :eq:`(hq. 3)`. + + * **quality_c_[Suffix].tif** -- Relative level of habitat quality on the current landscape. Higher numbers indicate better habitat quality vis-a-vis the distribution of habitat quality across the rest of the landscape. Areas on the landscape that are not habitat get a quality score of 0. This quality score is unitless and does not refer to any particular biodiversity measure. This is a mapping of habitat qulaity scores calculated with equation :eq:`(hq. 4)`. + + * **quality_f_[Suffix].tif** -- Relative level of habitat quality on the future landscape. Higher numbers indicate better habitat quality vis-a-vis the distribution of habitat quality across the rest of the landscape. This output is only created if a future LULC map is given as input. Areas on the landscape that are not habitat get a quality score of 0. This quality score is unitless and does not refer to any particular biodiversity measure. This is a mapping of habitat qulaity scores calculated with equation :eq:`(hq. 4)`. + + * **rarity_c_[Suffix].tif** -- Relative habitat rarity on the current landscape vis-a-vis the baseline map. This output is only created if a baseline LULC map is given as input. This map gives each grid cell's value of :math:`R_x` (see equation :eq:`(hq. 6)`). The grid cell's values are defined between a range of 0 and 1 where 0.5 indicates no abundance change between the baseline and current or projected map. Values between 0 and 0.5 indicate a habitat is more abundant and the closer the value is to 0 the lesser the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. Values between 0.5 and 1 indicate a habitat is less abundant and the closer the value is to 1 the greater the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. If the LULC habitat type did not appear on the baseline landscape then the grid cell value will be 0. - * **deg_sum_out_c_[Suffix].tif** -- Relative level of habitat degradation on the current landscape. A high score in a grid cell means habitat degradation in the cell is high relative to other cells. Grid cells with non-habitat land cover (LULC with :math:`H_j` = 0) get a degradation score of 0. This is a mapping of degradation scores calculated with equation (3). - - * **deg_sum_out_f_[Suffix].tif** -- Relative level of habitat degradation on the future landscape. A high score in a grid cell means habitat degradation in the cell is high relative to other cells. This output is only created if a future LULC map is given as input. Grid cells with non-habitat land cover (LULC with :math:`H_j` = 0) get a degradation score of 0. This is a mapping of degradation scores calculated with equation (3). + * **rarity_f_[Suffix].tif** -- Relative habitat rarity on the future landscape vis-a-vis the baseline map. This output is only created if both baseline and future LULC maps are given as input. This map gives each grid cell's value of :math:`R_x` (see equation :eq:`(hq. 6)`). The grid cell's values are defined between a range of 0 and 1 where 0.5 indicates no abundance change between the baseline and current or projected map. Values between 0 and 0.5 indicate a habitat is more abundant and the closer the value is to 0 the lesser the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. Values between 0.5 and 1 indicate a habitat is less abundant and the closer the value is to 1 the greater the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. If the LULC habitat type did not appear on the baseline landscape then the grid cell value will be 0. - * **quality_out_c_[Suffix].tif** -- Relative level of habitat quality on the current landscape. Higher numbers indicate better habitat quality vis-a-vis the distribution of habitat quality across the rest of the landscape. Areas on the landscape that are not habitat get a quality score of 0. This quality score is unitless and does not refer to any particular biodiversity measure. This is a mapping of habitat qulaity scores calculated with equation (4). - - * **quality_out_f_[Suffix].tif** -- Relative level of habitat quality on the future landscape. Higher numbers indicate better habitat quality vis-a-vis the distribution of habitat quality across the rest of the landscape. This output is only created if a future LULC map is given as input. Areas on the landscape that are not habitat get a quality score of 0. This quality score is unitless and does not refer to any particular biodiversity measure. This is a mapping of habitat qulaity scores calculated with equation (4). + * **rarity_c_[Suffix].csv** -- Table of relative habitat rarity values by LULC code on the current landscape vis-a-vis the baseline map. This output is only created if a baseline LULC map is given as input. The rarity values are defined between a range of 0 and 1 where 0.5 indicates no abundance change between the baseline and current or projected map. Values between 0 and 0.5 indicate a habitat is more abundant and the closer the value is to 0 the lesser the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. Values between 0.5 and 1 indicate a habitat is less abundant and the closer the value is to 1 the greater the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. If the LULC habitat type did not appear on the baseline landscape then the rarity value will be 0. - * **rarity_c_[Suffix].tif** -- Relative habitat rarity on the current landscape vis-a-vis the baseline map. This output is only created if a baseline LULC map is given as input. This map gives each grid cell's value of :math:`R_x` (see equation (6)). The grid cell's values are defined between a range of 0 and 1 where 0.5 indicates no abundance change between the baseline and current or projected map. Values between 0 and 0.5 indicate a habitat is more abundant and the closer the value is to 0 the lesser the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. Values between 0.5 and 1 indicate a habitat is less abundant and the closer the value is to 1 the greater the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. If the LULC habitat type did not appear on the baseline landscape then the grid cell value will be 0. - - * **rarity_f_[Suffix].tif** -- Relative habitat rarity on the future landscape vis-a-vis the baseline map. This output is only created if both baseline and future LULC maps are given as input. This map gives each grid cell's value of :math:`R_x` (see equation (6)). The grid cell's values are defined between a range of 0 and 1 where 0.5 indicates no abundance change between the baseline and current or projected map. Values between 0 and 0.5 indicate a habitat is more abundant and the closer the value is to 0 the lesser the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. Values between 0.5 and 1 indicate a habitat is less abundant and the closer the value is to 1 the greater the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. If the LULC habitat type did not appear on the baseline landscape then the grid cell value will be 0. + * **rarity_f_[Suffix].csv** -- Table of relative habitat rarity values by LULC code on the future landscape vis-a-vis the baseline map. This output is only created if both baseline and future LULC maps are given as input. The rarity values are defined between a range of 0 and 1 where 0.5 indicates no abundance change between the baseline and current or projected map. Values between 0 and 0.5 indicate a habitat is more abundant and the closer the value is to 0 the lesser the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. Values between 0.5 and 1 indicate a habitat is less abundant and the closer the value is to 1 the greater the likelihood that the preservation of that habitat type on the current or future landscape is important to biodiversity conservation. If the LULC habitat type did not appear on the baseline landscape then the rarity value will be 0. * **[Workspace]\\intermediate** folder: diff --git a/source/en/index.rst b/source/en/index.rst index 88e518a3..f6480f59 100644 --- a/source/en/index.rst +++ b/source/en/index.rst @@ -69,6 +69,7 @@ https://naturalcapitalproject.stanford.edu/software/invest * Conte, Marc * Daily, Gretchen * Davies, Jeremy + * Davis, Emily * Delevaux, Jade * Denu, Douglas * Douglass, James diff --git a/source/es/data_sources.rst b/source/es/data_sources.rst index c374468a..5355c6df 100644 --- a/source/es/data_sources.rst +++ b/source/es/data_sources.rst @@ -1,7 +1,7 @@ .. _data_sources: **************** -Fuentes ed datos +Fuentes de datos **************** Se trata de una recopilación de fuentes de datos y sugerencias para los inputs comunes de los modelos. Esta lista no es definitiva y solo pretende servir de punto de partida. Se recomienda encarecidamente buscar datos más locales y precisos (de fuentes nacionales, estatales, universitarias, bibliográficas, de ONG y otras) y solo utilizar datos globales para los análisis finales si no se dispone de nada más local. Si conoce alguna fuente de datos útil que no figure en esta lista, compártala en el foro. @@ -18,8 +18,8 @@ Los datos globales en bruto de MDE están disponibles de forma gratuita en: * NASA: https://asterweb.jpl.nasa.gov/gdem.asp (30m resolution); y fácil acceso a los datos SRTM: http://dwtkns.com/srtm/ * USGS: https://earthexplorer.usgs.gov/ -La resolución del MDE puede ser un parámetro muy importante dependiendo de los objetivos del proyecto. Por ejemplo, si los responsables de la toma de decisiones necesitan información sobre el impacto de las carreteras en los servicios ecosistémicos, se necesita una resolución fina. Los aspectos hidrológicos del MDE utilizado en el modelo deben ser correctos. La mayoría de los datos en bruto del MDE tienen errores, por lo que es probable que haya que rellenar el MDE para eliminar los sumideros. Múltiples pasadas de la herramienta de relleno de ArcGIS, o del algoritmo de relleno de QGIS Wang & Liu (biblioteca SAGA) han dado buenos resultados. - +La resolución del MDE puede ser un parámetro muy importante dependiendo de los objetivos del proyecto. Por ejemplo, si los responsables de la toma de decisiones necesitan información sobre el impacto de las carreteras en los servicios ecosistémicos, se necesita una resolución fina. Los aspectos hidrológicos del MDE utilizado en el modelo deben ser correctos. La mayoría de los datos en bruto del MDE tienen errores, por lo que es probable que haya que rellenar el MDE para eliminar los sumideros. Múltiples pasadas de la herramienta de relleno de ArcGIS, o del algoritmo de relleno de QGIS Wang & Liu (biblioteca SAGA) han dado buenos resultados. + En el caso de los modelos hidrológicos que generan corrientes a partir del MDE, hay que fijarse bien en el resultado del ráster de la red de corrientes. Si las corrientes no son continuas, sino que están divididas en trozos, el MDE todavía tiene sumideros que deben ser llenados. Si al llenar los sumideros varias veces no se crea una red de corrientes continua, tal vez haya que probar con otro MDE. Si los resultados muestran un patrón de red inesperado, puede deberse a la reproyección del MDE con un método de interpolación "vecino más cercano" en lugar de "bilineal" o "cúbico". En este caso, vuelva a los datos en bruto del MDE y reproyéctelos utilizando el método "bilineal" o "cúbico". Consulte también la sección Trabajo con el MDE de esta guía de uso para obtener más detalles y orientación sobre el procesamiento de MDE. @@ -201,7 +201,7 @@ Una forma sencilla de determinar la evapotranspiración de referencia es la ecua .. math:: ET_0 = 0.0013\times 0.408\times RA\times (T_{av}+17)\times (TD-0.0123 P)^{0.76} -El método de 'Hargreaves modificado' utiliza el promedio de las temperaturas máximas diarias medias y mínimas diarias medias para cada mes ('Tavg' en grados Celsius), la diferencia entre las máximas diarias medias y las mínimas diarias medias para cada mes ('TD'), radiación extraterrestre (:math:`RA` en :math:`\mathrm{MJm^{-2}d^{-1}}`) y precipitación (:math:`P` en mm por mes), todo lo cual puede obtenerse con relativa facilidad. Los datos de temperatura y precipitación suelen estar disponibles en gráficos regionales, mediciones directas o conjuntos de datos nacionales o mundiales. Los datos de radiación, por otro lado, son mucho más costosos de medir directamente, pero se pueden estimar de manera confiable a partir de herramientas, tablas o ecuaciones en línea. FAO Irrigation Drainage Paper 56 (Allan (1998)) proporciona datos mensuales de radiación en el Anexo 2. Seleccione valores para la latitud más cercana a su área de estudio. Otra opción es usar una herramienta SIG para calcular la radiación solar para su área de estudio específica y usar esta capa espacial como input para el cálculo de Hargreaves modificado. +El método de 'Hargreaves modificado' utiliza el promedio de las temperaturas máximas diarias medias y mínimas diarias medias para cada mes ('Tavg' en grados Celsius), la diferencia entre las máximas diarias medias y las mínimas diarias medias para cada mes ('TD'), radiación extraterrestre (:math:`RA` en :math:`\mathrm{MJm^{-2}d^{-1}}`) y precipitación (:math:`P` en mm por mes), todo lo cual puede obtenerse con relativa facilidad. Los datos de temperatura y precipitación suelen estar disponibles en gráficos regionales, mediciones directas o conjuntos de datos nacionales o mundiales. Los datos de radiación, por otro lado, son mucho más costosos de medir directamente, pero se pueden estimar de manera confiable a partir de herramientas, tablas o ecuaciones en línea. FAO Irrigation Drainage Paper 56 (Allan (1998)) proporciona datos mensuales de radiación en el Anexo 2. Seleccione valores para la latitud más cercana a su área de estudio. Otra opción es usar una herramienta SIG para calcular la radiación solar para su área de estudio específica y usar esta capa espacial como input para el cálculo de Hargreaves modificado. La evapotranspiración de referencia también se puede calcular mensual y anualmente utilizando la ecuación de Hamon (Hamon 1961, Wolock y McCabe 1999): @@ -335,7 +335,7 @@ Los mapas de infraestructura construida se pueden obtener de la muncipalidad o d Referencias ----------- -Allan, Richard, Pereira, L. y Smith, Martin. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. +Allan, Richard, Pereira, L. y Smith, Martin. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. "Hydrologic Soil Groups. "National Engineering Handbook, United States Department of Agriculture, National Resources Conservation Service, 2007, www.nrcs.usda.gov/wps/portal/nrcs/detailfull/national/water/?cid=stelprdb1043063. diff --git a/source/es/index.rst b/source/es/index.rst index 8b76a2f0..ff263e93 100644 --- a/source/es/index.rst +++ b/source/es/index.rst @@ -39,6 +39,7 @@ Resilience Centre. * Conte, Marc * Daily, Gretchen * Davies, Jeremy + * Davis, Emily * Delevaux, Jade * Denu, Douglas * Douglass, James @@ -128,7 +129,7 @@ Introducción y puesta en marcha data_sources -Modelos InVEST +Modelos InVEST -------------- Servicios ecosistémicos de apoyo y finales diff --git a/source/es/scenario_gen_proximity.rst b/source/es/scenario_gen_proximity.rst index a29f6290..c4591a94 100644 --- a/source/es/scenario_gen_proximity.rst +++ b/source/es/scenario_gen_proximity.rst @@ -1,4 +1,4 @@ -Generador de escenarios: basado en lq proximidad +Generador de escenarios: basado en la proximidad ================================================ Resumen @@ -19,10 +19,10 @@ La herramienta puede generar dos escenarios a la vez (el más cercano al borde y Cómo funciona ~~~~~~~~~~~~~ -Se deben definir tres tipos de cobertura del suelo: 1) *Cobertura del suelo* *focal* es la(s) cobertura(s) terrestre(s) que establece(n) las reglas de proximidad a partir de las cuales se determinarán los escenarios. El generador de escenarios convertirá el hábitat desde el borde o hacia el borde de parches de este tipo de cobertura. Esto no +Se deben definir tres tipos de cobertura del suelo: 1) *Cobertura del suelo* *focal* es la(s) cobertura(s) terrestre(s) que establece(n) las reglas de proximidad a partir de las cuales se determinarán los escenarios. El generador de escenarios convertirá el hábitat desde el borde o hacia el borde de parches de este tipo de cobertura. Esto no significa que convertirá estas cubiertas terrestres, solo que medirá distancia hacia o desde los bordes al designar dónde sucederá la conversión. 2) *Cobertura del suelo convertible* es la(s) cobertura(s) terrestre(s) que puede(n) ser convertidas. Podrían ser las mismas que la(s) cobertura(s) terrestre(s) focal(es), un subconjunto, o completamente diferentes. 3) *Cobertura del suelo de reemplazo* es el tipo de cobertura al que se convertirán las coberturas convertibles. Solo puede haber un tipo de cobertura del suelo por ejecución del modelo. -Se pueden ejecutar dos escenarios a la vez: 1) *Más cercano al borde* significa que los tipos de cobertura de suelo convertibles más cercanos a los bordes de las coberturas de suelo focales se convertirán en la cobertura de reemplazo. 2) *Más alejado del borde* significa que los tipos de cobertura convertibles más alejados de los bordes de la zona focal +Se pueden ejecutar dos escenarios a la vez: 1) *Más cercano al borde* significa que los tipos de cobertura de suelo convertibles más cercanos a los bordes de las coberturas de suelo focales se convertirán en la cobertura de reemplazo. 2) *Más alejado del borde* significa que los tipos de cobertura convertibles más alejados de los bordes de la zona focal se convertirán a la cobertura de reemplazo. Si este escenario es elegido, usted puede designar en cuántos pasos debe ocurrir la conversión. Esto es relevante si la cobertura terrestre focal es igual que la cobertura convertible porque la conversión de la cobertura focal creará nuevos bordes y, por lo tanto, afectará la distancia calculada a partir del borde de esa cobertura. Si lo desea, la conversión puede ocurrir en varios pasos, cada vez convirtiendo el más alejado del borde de la cobertura terrestre focal, causando un patrón fragmentario. A continuación se muestran algunos ejemplos de los tipos de escenarios que se pueden generar manipulando estos inputs básicos, utilizando la cobertura terrestre en los datos de diff --git a/source/zh/index.rst b/source/zh/index.rst index 4e755662..0c8d3ade 100644 --- a/source/zh/index.rst +++ b/source/zh/index.rst @@ -33,6 +33,7 @@ Resilience Centre. * Conte, Marc * Daily, Gretchen * Davies, Jeremy + * Davis, Emily * Delevaux, Jade * Denu, Douglas * Douglass, James