Here we provide an update to global gridded annual and monthly crop datasets. This new dataset uses the crop categories established by the Global Agro-Ecological Zones (GAEZ) Version 3 model, which is based on the Food and Agricultural Organization of the United Nations (FAO) crop production data. We used publicly available data from the FAOSTAT database as well as GAEZ Version 4 global gridded dataset to generate circa 2015 annual crop harvested area, production, and yields by crop production system (irrigated and rainfed) for 26 crops and crop categories globally at 5-minute resolution. We additionally used available data on crop rotations, cropping intensity, and planting and harvest dates to generate monthly gridded cropland data for physical areas for the 26 crops by production system. These data are in standard georeferenced gridded format, and can be used by any global hydrology, land surface, or other earth system model that requires gridded annual or monthly crop data inputs.

Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. The WBM was first published in 1989; here, we describe the first fully open-source WBM version (v.1.0.0). Earlier descriptions of WBM methods provide the foundation for the most recent model version that is detailed here. We present an overview of the model functionality, utility, and evaluation of simulated global river discharge and irrigation water use. This new version adds a novel suite of water source tracking modules that enable the analysis of flow-path histories on water supply. A key feature of WBM v.1.0.0 is the ability to identify the partitioning of sources for each stock or flux within the model. Three different categories of tracking are available: (1) primary inputs of water to the surface of the terrestrial hydrologic cycle (liquid precipitation, snowmelt, glacier melt, and unsustainable groundwater); (2) water that has been extracted for human use and returned to the terrestrial hydrologic system; and (3) runoff originating from user-defined spatial land units. Such component tracking provides a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior. We find that WBM v.1.0.0 simulates global river discharge and irrigation water withdrawals well, even with default parameter settings, and for the first time, we are able to show how the simulation arrives at these fluxes by using the novel tracking functions.

Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. WBM has a long publication history; here we describe the first fully open source WBM version. This version includes a suite of water source tracking modules that enable analysis of flow-path histories on water supply. Earlier descriptions of WBM methods provide the foundation of the most recent model version detailed here. WBM is available here: https://github.com/wsag/WBM. WBM is written in the perl data programming language (PDL), making use of several open-source perl libraries. As a convenience we also provide a Singularity container that simplifies installation of dependencies. We present an overview of the model functionality, utility, and validation of global river discharge and irrigation water use using data from the Global Runoff Data Centre and FAO statistics. A key feature of WBM is the ability to identify the partitioning of sources for each stock or flux within the model. Therefore, users can determine what proportion of any flux consists of each of the primary inputs of water to the surface of the terrestrial hydrologic cycle, previously extracted water for human uses, or runoff generated from any place on the Earth’s surface. Such component tracking provides both a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior, as well as perform model experiments in new ways.