Appendix

Short description of main possible data sources to create the static maps dataset

• CaMa-Flood: Global River Hydrodynamics Model maps The CaMa-Flood (Catchment-based Macro-scale Floodplain) model is a global-scale distributed river model, which is driven by runoff forcing from a land surface model. In order to represent the process of floodplain inundation, the river channel and floodplain topography are represented by sub-grid-scale topographic parameters. The ground elevation, unit-catchment area, channel length and floodplain elevation profile are explicitly derived from fine-resolution flow direction maps and DEMs using the FLOW method.
  Basic map data and sample input data are provided by the model developers, namely: i) the global river map (i.e. river network map, river topography parameters, high-resolution (1 arc min) topography data) based on MERIT Hydro at 15min, 6min, 5min, 3min, 1min resolutions; ii) pre-processed Earth2Observe (E2O) runoff products (i.e. 0.25 degree, daily, 1980-2014, WRR2 version, 7 Land models) and the runoff climatology data; and iii) the river channel parameters, where the channel cross section parameters (i.e. channel width and channel depth) are derived empirically as a function of river discharge due to the lack of global-scale observations of channel cross sections.
  Source: http://hydro.iis.u-tokyo.ac.jp/~yamadai/cama-flood/index.html
  

• MERIT DEM: Multi-Error-Removed Improved-Terrain DEM
  The MERIT DEM: Multi-Error-Removed Improved-Terrain Digital Elevation Model is a high accuracy global DEM at 3 arc second resolution (~90 m at the Equator) covering land area 90N-60S (referenced to EGM96 geoid) with separated absolute bias, stripe noise, speckle noise and tree height bias (achieved by using the existing spaceborne DEMs (SRTM3 v2.1 and AW3D-30m v1) and filtering techniques). It also contains significant improvements in flat regions, where height errors are larger than topography variability, and landscapes such as river networks and hill-valley structures became clearly represented. MERIT DEM was developed by processing the following products as baseline data (all are freely available from their web pages): i) NASA SRTM3 DEM v2.1; ii) JAXA AW3D-30m DEM v1; iii) Viewfinder Panoramas’ DEM. In addition to the baseline DEMs, following products were used as supplementary data: i) NASA-NSIDC ICESat/GLAS GLA14 data; ii) U-Maryland Landsat forest cover data; iii) NASA Global Forest Height Data; iv) JAMSTEC/U-Tokyo G3WBM water body data.
  Initial data consists of 57 GeoTiff files 30x30 degree region each.
  Source: http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/index.html
  

• CORINE Land Cover 2018 CLC2018
  The CORINE (Coordination of Information on the Environment) Land Cove (CLC) inventory for 2018 (CLC2018) is one of the datasets produced within the Corine Land Cover frame programme referring to land cover / land use status of year 2018. It covers 39 countries with a total area of over 5.8 Mkm ² . Satellite imagery provides the geometrical and thematic basis for mapping within situ data as essential ancillary information. The basic technical parameters of CLC (i.e. 44 classes in nomenclature, 25 hectares minimum mapping unit (MMU), and 100 meters minimum mapping width) have not changed since the beginning, therefore the results of the different inventories are comparable. The time period covered by CLC2018 asset is 2017 to 2018.
  Source: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_CORINE_V20_100m#description
  

• Copernicus Global Land Cover Layers: CGLS-LC100 collection 2 The Dynamic Land Cover map CGLS-LC100 is a global land cover map at 100 m spatial resolution. The CGLS provides discrete classes of land cover, and continuous field layers for all basic land cover classes that provide percentage of a grid-cell covered by certain land cover type. Provided maps are derived from the PROBA-V 100 m time-series, a database of high quality land cover training sites and several ancillary datasets, reaching an accuracy of 80% at Level1 over all years.
  Source: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global#description

• Global Lakes and Wetlands Database (GLWD): Large Lake Polygons (Level 1 & 2)
  The Global Lakes and Wetlands Database (GLWD) has been created on the basis of existing maps, data and information, such as the Digital Chart of the World, World Conservation Monitoring Centre (WCMC) and others. It draws upon the best available maps, data and information to display lakes and wetlands on a global scale (1:1 to 1:3 million resolution). The application of GIS functionality enables the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands.
  Level 1 (GLWD-1) comprises the shoreline polygons of the 3067 largest lakes (area ≥ 50 km2) and 654 largest reservoirs (storage capacity ≥ 0.5 km³) worldwide, and includes extensive attribute data. Level 2 (GLWD-2) comprises approximately 250,000 permanent open water bodies polygons (attributed as lakes, reservoirs and rivers) with a surface area ≥ 0.1 km² excluding the water bodies contained in GLWD-1. GLWD-1 and GLWD-2 are delivered with global coverage in shapefile format. Level 3 (GLWD-3) comprises lakes, reservoirs, rivers and different wetland types.
  Source: http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1043160/

• Spatial Production Allocation Model (SPAM) - Global Spatially-Disaggregated Crop Production Statistics Data for 2010 (V 1.0)
  The Spatial Production Allocation Model (SPAM) is an effective way to map detailed patterns of crop production using much less specific (e.g. country, subnational provinces) input data. Using a variety of inputs, SPAM uses a cross-entropy approach to make plausible estimates of crop distribution for 42 crops (such as rice, cassava, potatoes, wheat, maize, etc.) and two production systems (i.e. irrigated and rainfed) within disaggregated units. Knowing where in the world individual crops are cultivated, their production patterns, and whether they are irrigated or rainfed is important for improving spatial understanding of crop production systems, and allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.
  Source: https://nasaharvest.org/news/spam-2010-updated-global-crop-data-aid-food-policy-decisions
  

• RiceAtlas, a spatial database of global rice calendars and production Version 3
  The RiceAtlas dataset has collected data on the start, peak, and end dates of sowing or transplanting, and the start, peak, and end dates of harvesting of rice for all seasons in all rice-growing countries. In cases where peak planting and harvesting dates were not available, dates were estimated to be at the midpoint between the start and end dates. Planting in a region is not done on a single date but the length of the planting window varies between regions. In the absence of information, the planting window was set to 30 days. Where available, additional data such as crop establishment method and seedling age for transplanted rice were recorded. Data is delivered with global coverage in shapefile format.
  Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5448352/
  

• Copernicus Global Land Service LAI Collection Version 2
  The Copernicus Global Land Service (CGLS) LAI Collection 1km Version 2 product is derived from SPOT/VEGETATION and PROBA-V data. The product is provided every 10 days, with a temporal basis for compositing between ±15 and ±60 days depending on the number of available valid observations. The version 2 product is derived from top of canopy daily (S1-TOC) reflectance. The compositing step is performed at the biophysical variable level. This allows reducing sensitivity to missing observations and avoiding the use of a BRDF model. Smoothing and gap filling is achieved over a compositing temporal window that may be dissymmetric depending on the number of valid daily estimates. This version 2 product provides an improved continuity (no missing data in Version 2 due to climatological gap filling) and smoothness and include a Near Real Time computation. Version 2 product is delivered with global coverage in netCDF4 CF-1.6 format.
  Source: https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_PUM_LAI1km-V2_I1.33.pdf
  

• SoilGrids250m 2017
  SoilGrids is a system for automated soil mapping based on global soil profile and covariate data (Hengl et al. 2017. SoilGrids250m is a collection of soil property and class maps of the world (ca. 300 GeoTiffs) produced using machine learning and statistics. SoilGrids predictions are a global soil data product generated at ISRIC.
  Source: https://www.isric.org/explore/soilgrids/faq-soilgrids-2017
  

• FAO Irrigation and Drainage Paper No. 56: Crop Evapotranspiration (guidelines for computing crop water requirements) by Richard G. ALLEN, Luis S. PEREIRA, Dirk RAES, Martin SMITH
  FAO Irrigation and Drainage Paper No. 56: Crop Evapotranspiration (guidelines for computing crop water requirements) presents an updated procedure for calculating reference and crop evapotranspiration from meteorological data and crop coefficients. Publication contains several pieces of information that were used in this research: i) Lengths of crop development stages for various planting periods and climatic regions (days) (Table 11); ii) Single (time-averaged) crop coefficients (Kc), and mean maximum plant heights for non stressed, well-managed crops in subhumid climates (RHmin ≈ 45%, u2 ≈ 2 m/s) (Table 12); iii) Ranges of maximum effective rooting depth (Zr), and soil water depletion fraction for no stress (p), for common crops (Table 22).
  Source: https://www.researchgate.net/publication/284300773_FAO_Irrigation_and_drainage_paper_No_56
  

• System description of the Wofost 6.0 crop simulation model implemented in CGMS. Volume 1: Theory and Algorithms by I. Supit, A.A. Hoojer and C.A. Van Diepen
  System description of the Wofost 6.0 crop simulation model implemented in CGMS. Volume 1: Theory and Algorithms present results from the “Development, validation of crop specific agrometeorological simulation models” project. Projects objectives were “to develop, validate and test new or already existing agrometeorological simulation models for 10-day routine quantitative forecasting of national and NUTS-1 yields and for 10-day wise (regional), but qualitative monitoring of agricultural season conditions over the whole of the EC and for each of the following crops: wheat (spring and winter; hard and soft), barley (spring and winter), oats, maize (grain), rice, potato, sugar beet, pulses (human consumption), soybean, oilseed rape, sunflower, tobacco and cotton.” Project has adapted the already existing WOrld FOod STudies (WOFOST) crop growth model to achieve its objectives. From this publication we used following information: i) Soil water depletion fraction (p) as a function of potential evapotranspiration of a closed crop canopy for different crop groups (Table 6.1, p. 87); ii) Example of crops in the different crop groups (Table 6.2, p. 87).
  Source: System description of the Wofost 6.0 crop simulation model implemented in CGMS. Volume 1: Theory and Algorithms by I. Supit, A.A. Hoojer and C.A. Van Diepen
  

• OPEN-CHANNEL HYDRAULICS by Ven Te Chow
  OPEN-CHANNEL HYDRAULICS presents the knowledge of open-channel hydraulics, which is essential to the design of many hydraulic structures: Part I - basic principles, the type of flow in open channels is classified according to the variation in the parameters of flow with respect to space and time; Part II - on uniform flow; Part III - on gradually varied flow; Part IV - on rapidly varied flow; Part V - on unsteady flow. From this publication we used following information: Values of the roughness coefficient n (Table 5-6, p. 110).
  Source: http://web.ipb.ac.id/~erizal/hidrolika/Chow%20-%20OPEN%20CHANNEL%20HYDRAULICS.pdf