AGU Chapman Conference on Seasonal to Interannual Hydroclimate Forecasts and Water Management

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AGU Chapman Conference on Seasonal to Interannual Hydroclimate Forecasts and Water Management

Recent advances in seasonal to interannual hydroclimate predictions provide an opportunity for developing a proactive approach towards water management. These advances include the wider availability of weather-to-climate scale ensemble predictions, new techniques for downscaling and forecast calibration, the advent of hindcasting within major forecast production centers such as NCEP and ECMWF, and the steady improvement of hydrologic and other land surface models, greater exploitation of remote sensing datasets to constrain hydrologic simulation, and movement toward the use of high performance computing and cyber-infrastructure for earth science applications. It is therefore an opportune time for hydroclimate forecasting experts and water managers to come together for a Chapman conference focusing on various topics relevant to both communities. The primary objective is to convene climate scientists, hydrologists, forecasting agencies, water utilities, reservoir operators and water management agencies together for understanding the challenges and opportunities in developing hydroclimate forecasts that are relevant to water resources management. Over four days, the participants will focus on various issues related to hydroclimate forecasts and water management by addressing the following science questions:

  1. What are the key sources of uncertainties that challenge development of skillful hydroclimate forecasts at daily, seasonal and interannual time scales?
  2. How best do we reduce the uncertainty and improve reliability in downscaling large-scale climate information for developing regional hydroclimate forecasts?
  3. What are the key challenges in using probabilistic streamflow information in operational water resource management models and decision tools?
  4. What are the limitations in applying streamflow forecasts for real-time applications?
  5. How can we bridge the gaps between forecast producers (agencies, research institutions) and forecast consumers (water resource managers, operational agencies) for improving forecast applications in water management?

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