1. A MODELING FRAMEWORK FOR IMPROVED AGRICULTURAL WATER- SUPPLY FORECASTING George Leavesley 1, Olaf David 1, David Garen 2, Angus Goodbody 2, Jolyne Lea 2, Jim Marron 2, Tom Perkins 2, Michael Strobel 2, Rashawn Tama 2 1 Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO; 2 National Water and Climate Center, NRCS-USDA, Portland, OR A Modeling Framework for Improved Agricultural Water Supply Forecasting

2. USDA-NRCS National Water and Climate Center (NWCC) Natural Resource Planning Support Provide water supply forecasts Provide water and climate analysis, information, and services for NRCS, partners and customers Data Acquisition and Management Operate the Snowpack Telemetry (SNOTEL) and Soil Climate Analysis Network (SCAN) data collection systems Design and manage an on-line, quality controlled, national database for NRCS and partners in support of farm, watershed and river basin scale planning Technology Innovation Assess and select technologies required to address resource concerns Adapt appropriate technologies

3. Seasonal Water Supply Forecasting Points and Agency Responsibility

4. Water Supply Forecasts Distributed by the NWCC http://www.wcc.nrcs.usda. gov/wsf/wsf.html Combined product of NWCC and National Weather Service Forecasts

5. Forecasts Historically seasonal, regression-equation based forecasts of estimated seasonal streamflow volume Future augment seasonal, regression-equation based water supply forecasts with forecasts based on the use of distributed-parameter, physical process hydrologic models and an Ensemble Streamflow Prediction (ESP) approach.

6. Modeling Program Objectives Development and implementation of a modeling framework, and associated models and tools, to assist in addressing a wide variety of water-user requests for   More information on the volume and timing of water availability,   More timely and frequently updated forecasts,   Improved forecast accuracy, and   Integration with other USDA models and tools

7. MODELING FRAMEWORK Object Modeling System (OMS)  Java-based modular framework  Component-based model design  Library of stand-alone science, control, and database components (modules)  Library of utility modules that provide data management, parameterization, sensitivity analysis, calibration, statistical analysis, and visualization  Scalable multi-processor, cluster to cloud  Open source

8. COMPONENT-BASED MODELING  Main building blocks of simulation models  Typically represent a unique concept in a model like a physical process, a management practice, or a specific data input  Can be hierarchical by containing other, finer grained components  Can be implemented in the Java, Fortran, C, and C++ programming languages.

9. OMS3 Component Design non-invasive for component developers Considered non-invasive for component developers Component = Plain Old Java Object (POJO) + Meta data Meta data provide execution control and connectivity, execution support, and documentation/repository support

10. OMS 3.0 Meta Data @Description @Author @Bibliography @Status @VersionInfo @SourceInfo @Keywords @Label @Description @Unit @In @Out @Range @Role @Bound @Label @Execute @Initialize @Finalize ComponentField Method

11. Meta Data Examples Parameter Declaration @Role(PARAMETER) @Role(PARAMETER) @Description("HRU area, Area of each HRU") @Description("HRU area, Area of each HRU") @Unit("hectare") @Unit("hectare") @Bound ("nhru") @Bound ("nhru") @In public double[] hru_area; @In public double[] hru_area; Input Variable Declaration @Description("Average HRU temperature.") @Description("Average HRU temperature.") @Unit("C") @Unit("C") @ Bound ("nhru") @ Bound ("nhru") @In public double[] tavgc; @In public double[] tavgc;

12. OMS3 Console

13. Visualization Component - Visualization Component - Geospatial Interface Java-Based OS Library + application that combines Geotools (OGC) and WorldWind (NASA)

14. Integrated Forecast System  Model selection  Watershed characterization and parameterization  Data retrieval and update system  Ensemble Streamflow Prediction (ESP)  Post processing forecast analysis (debiasing)  Forecast dissemination and decision making services

15. PRMS Initial model in a planned ensemble modeling approach

16. GIS WEASEL Delineation: Only requires elevation Grid as input Interactively delineate Area of Interest Many kinds of features Streams Elevation bands Landuse Contributing areas Topographic index ……

17. GIS WEASEL Parameterization: 200+ methods available Easily add custom methods Configure recipes Apply to feature maps Exploit many types of data Produce maps and ASCII files of parameters

18. Data Retrieval and Update CurrentlyCurrently NWCC Portland - SNOTEL NOAA – Coop Stations USGS - Streamflow Future Applied Climate Information System (ACIS) – SNOTEL and Coop Stations USGS - Streamflow

19. Calibration Components Spatial Visualization of Model Output (NRMV) Shuffled Complex Evolution Calibration – Modified USGS LUCA Interface Hay and Umemoto, 2006, Multiple-Objective Step-Wise Calibration using Luca: U.S. Geological Survey OFR 2006-1323.

20. ESP Trace Analysis Stand-Alone or Multiple Batch

21. Post-processing Forecast Analysis (Debiasing) Approaches Quantile Mapping Regression Adjustment by a Constant Time Series Modeling

22. Research Components of the Project Evaluation of De-biasing Methods for ESP Forecasts Evaluation of Alternative Climate Scenarios in ESP Evaluation of Alternative Precipitation Distribution Methods Development of an Ensemble of Models for Forecasting

23. Ensemble Modeling Study Viney et al., 2009, Viney et al., 2009, Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions, Advances in Water Resources, 32, pp 147-158.

24. Ensemble Means Performance Viney et al., 2009, Viney et al., 2009, Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions, Advances in Water Resources, 32, pp 147-158.

25. USDA Proposed Data Provisioning and Model Deployment System tools have been developed to manage the configuration and deployment of virtual server images and model services to the computing cloud.

26. Summary OMS Framework Application for Agricultural Water Supply Forecasting Facilitates multi-disciplinary integration of models and tools to address the issues of water availability and agricultural/environmental-resource management. Allows rapid evaluation of the effects of decision and management scenarios. Allows incorporation of continuing advances in physical, social, and economic sciences. Provides an effective means for sharing scientific understanding with stakeholders and decision makers. Open source.

27. MORE INFORMATION http://oms.javaforge.com http://www.wcc.nrcs.usda.gov/wsf