Fig 1.
Simplified schematic of the RHEAS software architecture.
Fig 2.
Unified Modeling Language (UML) diagram describing the components (i.e. modules) within RHEAS.
Table 1.
Structure of PostGIS tables representing each RHEAS dataset.
Fig 3.
Example database performance with multiple processors querying different number of years (10, 20, and 30).
Table 2.
List of data products available in the RHEAS database.
Table 3.
Code snippet (URL shortened) defining the fetch function for the CHIRPS dataset module.
Table 4.
Example configuration for downloading multiple datasets using RHEAS.
Fig 4.
UML observation-class diagram.
UML diagram of classes representing observational datasets that are assimilated (the SMAP class is omitted for visualization purposes).
Fig 5.
Sequence diagram for the nowcasting mode of RHEAS.
Fig 6.
Sequence diagram for the forecasting mode of RHEAS.
Table 5.
Example RHEAS configuration file for a nowcast simulation.
Table 6.
Execution times and domain size for each of the case-study simulations (using a 3-GHz 8-core Intel Xeon E5 processor).
Fig 7.
Maps of the 3-month Standardized Precipitation Index (left) and agricultural drought severity (right) on 1 July 2014 over the Sacramento/San Joaquin basin.
Fig 8.
Map of uncertainty (derived from ensemble 1σ) in soil moisture over Sacramento/San Joaquin river basin on 31 August 2014.
Fig 9.
Time series of forecasted (open-loop and assimilated) and observed streamflow at Taylor River with forecasts initialized on 1 April 2009. Forecasts are bounded by the 25th and 75th percentile of the ensemble.
Fig 10.
Map of maize yield over Kenya in 2011 (first planting season) dissagregated to county level.
Fig 11.
Comparison of simulated and observed maize yields over the Nzoia River basin during the earlier growing season of 2000-2006.