Data Analytics and Multi-scale Predictions
The Columbia Water Center is a leader in predicting seasonal hydroclimate forecasts and associated risk analyses. These forecasts are used for a variety of applications, including integrating climate forecasts into water allocation procedures for urban, industrial and agricultural consumers.
A key aspect is to develop strategies for adaptation to climate change and variability through climate scenarios for the short and long run that accurately represent uncertainties. Columbia Water Center scientists use numerical and statistical methods that take into account the high dimensionality of the problem. These methods consider spatial variability across a region or the globe and correctly represent the associated uncertainty inherent to data and models. Large-scale simulation and optimization decision making tools have been developed by the Columbia Water Center that introduce novel water allocation and risk management ideas and are supported by quantitative analysis.
Managing Climate Risks through Long Lead Forecasts
The Columbia Water Center, together with the International Research Institute for Climate and Society (IRI) at the Columbia University are major global leaders in Climate Risk Management and Sustainable Development from inter-disciplinary and applied perspectives. Specifically for climate risk management, the research groups at IRI and CWC have pioneered techniques for seasonal to inter-annual climate and hydrologic forecasts. The teams have also developed a participatory and adaptive management system for reservoir operation and planning. This includes a mix of short- and long-term tradable water supply contracts with specified reliability that are derived using seasonal climate forecasts and multi-decadal simulations. These are used as a vehicle for water allocation, and as an insurance mechanism in the event of yield failure. Applications of this approach have been implemented in N. E. Brazil and the Philippines.
CWC research is working to make fundamental advances in our ability to comprehend the complexities of climate by using probabilistic cloud modeling to properly characterize precipitation and solar radiation.
The surface soil moisture state can be inferred by using remote sensing data from Multi-Wavelength Satellite Observations in a process known as retrieval.Neural Network Soil Moisture Retrieval is able to provide global soil moisture estimates at daily or sub-daily resolution.
Comprising a mere 1.57 percent of India’s total geographical area, the state of Punjab produces 12 percent of India’s 234 million tons of food grain, and nearly 40 and 60 percent of the wheat and rice that buffer the nation’s central pool for maintaining food stocks. However, Punjab’s agricultural success is currently threatened by unsustainable irrigation practices and a rapidly dropping water table.
Monsoons drive seasonal variations. Yet climate change threatens to disrupt the regular, alternating periods of rain and arid dryness.Managing water scarcity is a critical challenge for many Asian nations with similar climates.
This project explores opportunities for robust, no regret, decision making for drought standard operating procedures and planning in a practical framework relevant to utilities in the northeastern region of the United States.
The Everglades is one of the largest and most diverse wetland ecosystems in the United States. The pressures of agriculture and development, however, have greatly degraded the system. Today the Everglades is half the size it was 100 years ago, and many keystone species are threatened.
The Ceará allocation project provides new methods for season-ahead climate prediction along with techniques for translating and presenting forecasting information to citizen committees to facilitate the most efficient allocation of water and the greatest possible cooperation between different sectors of the economy.
- China’s water sustainability in the 21st century: a climate-informed water risk assessment covering multi-sector water demandsXi Chen, Naresh Devineni, and Upmanu Lall, Z. Hao, L. Dong, Q. Ju, J. Wang, and S. Wang . Hydrology and Earth System Sciences. , 2014-05-08 [+]
- Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modelingXi Chen, Z. Hao, Naresh Devineni, and Upmanu Lall. Hydrology and Earth System Sciences. , 2014-04-29[+]
- Systematic errors in ground heat flux estimation and their correctionP. Gentine, D. Entekhabi, and B. Heusinkveld, 2012-08-08 [+]
- Multiscale Effects on Spatial Variability Metrics in Global Water Resources DataShama Perveen and L. Allan James | Water Resources Management, 2009 [+]