This paper presents a case study that demonstrates how models can be used to support water management decisions before sufficient data to verify the model are available. In developing areas, and for new reservoirs, ex...This paper presents a case study that demonstrates how models can be used to support water management decisions before sufficient data to verify the model are available. In developing areas, and for new reservoirs, extensive data for model calibration and validation are often not available. As a case study we developed a CE-QUAL-W2 model of Aguamilpa Reservoir using minimal data and used the model to create a data acquisition plan to support early planning decisions. We based the model on a two-year period and compared the model results to data recently collected with our acquisition plan. We present how we developed and used the model to design the data acquisition plan which identifies and collects data to update and calibrate the model to support future decisions. We show that a minimally calibrated model based on scarce data can support management decisions and be the first step in a spiral engineering approach to system management. Spiral engineering uses models and data to both support early decisions and to iteratively improve this information to support subsequent decisions and models. This case study can be used as a guide for developing water quality models with minimal data and uses the models to support early decision requirements.展开更多
文摘This paper presents a case study that demonstrates how models can be used to support water management decisions before sufficient data to verify the model are available. In developing areas, and for new reservoirs, extensive data for model calibration and validation are often not available. As a case study we developed a CE-QUAL-W2 model of Aguamilpa Reservoir using minimal data and used the model to create a data acquisition plan to support early planning decisions. We based the model on a two-year period and compared the model results to data recently collected with our acquisition plan. We present how we developed and used the model to design the data acquisition plan which identifies and collects data to update and calibrate the model to support future decisions. We show that a minimally calibrated model based on scarce data can support management decisions and be the first step in a spiral engineering approach to system management. Spiral engineering uses models and data to both support early decisions and to iteratively improve this information to support subsequent decisions and models. This case study can be used as a guide for developing water quality models with minimal data and uses the models to support early decision requirements.
