Research and Development Office

Bio-physical Integrated Land Atmosphere Water Simulator (BI-LAWS)

Project ID: 19246
Principal Investigator: Michael Tansey
Research Topic: Water Operation Models and Decision Support Systems
Funded Fiscal Years: 2019, 2020 and 2021
Keywords: None

Research Question

Reclamation (2011) identified a need for improved capabilities to simulate crop and native vegetation ET by better representation of plant physiological processes driven by climate and CO2 changes. In FY 2018, Reclamation conducted a comprehensive literature review to evaluate latest advances in understanding of plant physiological responses to climate forcings including CO2 (ongoing S&T project 1858). This project also included an assessment of existing models and determined there exists a need for the development of an open source code model capable of producing ETa, biomass and yield results at a scale suitable for Reclamation Regions. By using the freely available and distributable Python programming language, libraries (eg. Matplotlib, Pandas, Numpy & SciPy) and associated development tools (Anaconda), Reclamation and other developers will be able to modify and extend the BI-LAWS model as new understanding of plant-atmosphere interactions and methods of representing them efficiently in models advance. In addition, Reclamation has identified the need for forecasts, especially ensembles, to characterize uncertainty along with the need to effectively share such information with technical, managerial and stakeholder communities. BI-LAWS will address all of these needs by developing an open source BI-LAWS model; incorporating it into WwET4Cast workflow and providing access to data and BI-LAWS results through the RISE platform.

Need and Benefit

Reclamation (2011) identified the need for improved capabilities to simulate ETa of agricultural crops and native
vegetation by better representation of plant physiological processes driven by climate and CO2 changes. In FY 2018,
a literature review of the recent advances in understanding of plant physiological responses to climate forcings
including CO2 was completed (on-going S&T project 1858). Both the literature review and a sensitivity analysis using
WEAP-PGM, a processed based plant growth model, demonstrated the high likelihood that Reclamation's current
models and methods do not provide reliable estimates of future crop and native vegetation ETa. These deficiencies
could have important consequences for long term planning and development of cost effective adaptation strategies.
The BI-LAWS project will be benefit Reclamation in three primary ways. First it addresses the needs for better
representation of atmospheric effects on plant water use, biomass production and crop yields. Many of the ET
methods currently in use by Reclamation for planning studies depend primarily on temperature and/or solar radiation.
Others such as the ASCE Penman-Montheith and FAO-56 methods as represented in the ET Demands model
(Reclamation, 2015) do not simulate some well-established plant physiologic responses to future climatic changes (eg
CO2 effects on transpiration). These crop coefficient based ETa models are typically calibrated to local conditions
which makes them useful for irrigation scheduling and other short term applications. However, the static crop
coefficents (Kc) used by these models will not adequately simulate long term ETa because to be valid the crop
coefficients (Kc) depend on maintaining a constant ratio between a particular crop's ETa and a reference crop's ETo
typically assumed to be a well water grass (Kc = ETa/ETo). Because there are varying degrees to which particular
plant types respond to various atmospheric forcings, it is expected that Kc values would change as atmospheric
conditions change. BI-LAWS avoids this problem because its parameters represent more fundamental, intransient
plant physiological responses to atmospheric forcings. Simulating ET of native vegetation such as forests and
rangelands is another aspect of BI-LAWS which offers the opportunity for better simulation of changes in ETa over for
longer term climate assessments. There is a considerable and growing body of scientific evidence that changes in
forest and native vegetation water use efficiency (WUE) are affecting regional and global runoff and river flows. While
these changes are more complex than just changes in WUE, a BI-LAWS type of model can be used to better simulate
the transpiration component of the processes affecting watershed runoff.
In addition, the BI-LAWS project addresses two additional needs identified in the FY 2018-2021 Science Priorities for
Water operations and Planning Research Area. These include the Water Supply and Streamflow Forecasting as well
as Open Data Research Categories. In particular, integration of BI-LAWS with the WwET4Cast Network will take
advantage of the WwET4CAST's automated meteorological data acquisition from the GEFS weather model as well
as K-NN statistical methods to provide ensemble based forecasts of daily and seasonal ETo and ETa referenced to

existing agricultural meteorological stations located throughout the Western United States. Finally, integration of BI-
LAWS - WwET4Cast Network with the RISE workflow will provide open data access to the integrated BI-LAWS -

WwET4CAST data and ensemble forecasts to RISE users.
If the BI-LAWS project is not awarded funding, Reclamation will have to continue using existing models for their
limitations for long range planning. Many of these models have a variety of problems including inappropriate scale;
require too many parameters; are proprietary, poorly documented and/or difficult to modify and integrate into other

Contributing Partners

Contact the Principal Investigator for information about partners.

Research Products

Bureau of Reclamation Review

The following documents were reviewed by experts in fields relating to this project's study and findings. The results were determined to be achieved using valid means.

Bio-Physical Crop Land Atmosphere Water Simulator (final, PDF, 7.0MB)
By Dr. Michael Tansey
Report completed on January 31, 2022

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