Decisions

prism

Working towards a robust decision support system (DSS) for managing environmental resources of Espírito Santo

      The dataframes and models developed in PCGAP have demonstrated that they are capable of resolving how the structure of the landscape and climate intersect, to produce patterns of water distribution and water quality. The first step has been to develop detailed, geospatially consistent dataframes from multiple sources. These data represent historical conditions and scenarios of possible future climates and landuse. These dataframes provide the foundation for application of landscape hydrology models, operating at different time and space scales.

     An important aspect of the development to date is not only the technical content, but how the information is presented. An emphasis on visualization makes it much easier to “see” results, especially for the non-specialist. Ready access through an intuitive web portal enables multiple users to examine outcomes. Working towards deployment in the Cloud would greatly facilitate future use (by reducing reliance on specific local computers and administration).

     The next step is to continue building the Phase 1 PCGAP into a robust Decision Support System (DSS). Production of spatial maps of planning scenarios, maps of adaptability, zoning for agricultural activities, and flooding probabilities at short term and longer-term time scales is imminently feasible, as seen in the work to date.

Platform for Evaluating Landuse Scenarios

     A valuable task for a decision support system is to to allow evaluations of future alternatives for landuse, to help guide policy Agricultural activities have a s ignificant impact on regional water balances, where conversion of forest to different crops increases runoff.

What are the impacts of increases in eucalyptus or different agricultural crops on flooding and sediment transfer? What would be the most effective locations to plant buffer strips to reduce sediment flow to the bay of Vitoria?

     PCGAP results, for example, showed that runoff increased substantially from 1997 to 2007. The type of monoculture impacts delivery to the coastal zone, depending on the type of landcover replaced. If eucalyptus were to replace primary forest, differences would likely be small. If it replaced low-lying vegetation, discharge would be reduced and ET increased. If the monocultures were a crop, discharge would increase. Results from the modeling efforts show that forest cover decreases downstream streamflow. Increasing forest cover also reduces the amount of exposed soil susceptible to erosion since forest covers have deeper roots to stabilize soil and reduces the rainfall through the canopy layer.

 

Platform for Monitoring Current and Near-Future Weather on Water Resources

Short –term weather affects a region, though water availability, hydropower , and notice for extreme events, such as floods (immediate) or droughts (longer). Early warning signals could be given. 

With hydrologic modeling based on predictions of regional climate models, what are the hydrological conditions of a near-term future?

     The process of the hydrologic modeling of scenario futures is based explicitly on the predictions of regional (including global downscaled results). Outputs from regional climate models can be easily coupled to VIC (as demonstrated above) by using either the sensitivity method for a back of the envelope calculations or the output from the RCM fed directly into VIC. The caveat for how far to “trust” such models is the uncertainty in the climate models themselves.

     The hydrologic models (VIC, DHSVM) can be used as the robust base model to provide early warnings. At the longer climate scenario time scale (several decades out), the most sensitive regions could be identified, and options for different crops considered. At shorter time scales, drought indices could be provided with a one-month lead-time.

 

Platform for Evaluating Scenarios of Future Climate Change

     A strategy for a region’s response to future climate change is adaptation, by knowing what might happen where. For example, if soil moisture in the lower Rio Doce were reduced by 10%, what crops should be planted? The issues are,

What are possible adaptations of agricultural activities and the management of water in urban areas to different scenarios of climate change?

     Changes in the climate forcings and the sensitivity of (specified) landscapes can be readily computed in VIC. Determining the actual “impact” of these changes in forcing on agriculture and urban areas would be the next step in the process. The sensitivity of, for example coffee, to the changes in precipitation and temperature would have to be addressed specifically. But the environmental conditions to do so are provided.

    

Computer Infrastructure      

The VIC and DHSVM models have been shown to be highly functional in modeling the hydrologic regime of Espírito Santo on both historical conditions and scenarios of the future. In principle these models could absolutely made to be operational, and provide alert capabilities. The core strategy would be to have the model(s) running, and continuously updated, “remembering” what “yesterday’s” conditions were, and needing only “today’s” weather to predict what would happen over the next time steps. 

      A system designed to address very fast time scales floods would operate under a different set of rules than a system addressing drought development. The same model could be used, but climate drivers would be required. A system focusing on floods would need the latest wer predictions, while a system looking at droughts would require weather projections.

      For operational considerations, accuracy is of high importance. The models are currently set up with quite generic parameters. To improve accuracy, the base dataframe ought to be improved (e.g., more realistic values for the rooting depth of eucalyptus, and of soil hydraulic properties).