Hydrology Simulator and Disaggregation

Both probabilistic analysis and scenario analysis require simulation of a range of possible futures. Ensembles of future hydrologic scenarios can be based on the statistical characteristics of the historical record. Reconstructed paleo hydrology can be used to extend the record. (PDSI data can be used to derive sequences of hydrologic states that can be used along with historic flow values.) Climate change projections can generate futures that are not based on the past. The RiverSMART tools for hydrologic simulation can be used for any of these techniques. The library consists of R code and a graphical user interface through which the user specifies the reference sequence, the sampling parameters, the paleo sequence, the conditioning method (Homogeneous Markov or Unhomogeneous Markov), increase or decrease in mean, and other parameters.

KNN Resampling

Ensembles of stochastic hydrologic sequences (traces) are typically based on the statistical characteristics of the historic record. The RiverSMART tool set includes a Hydrology Simulator that generates ensembles of traces based on a non-parametric K-nearest neighbor (KNN) technique (Lall and Sharma, 1996; Prairie et al., 2007). This simple approach is akin to simulating from the conditional PDF without actually fitting it – thus, it has the ability to capture any nonlinear or non-gaussian features present in the data. This resampling can also be used on the paleo record and climate change projections.

Nonparametric Simulation via Conditioning

This resampling technique of high fidelity historic streamflow values can be “conditioned” on the richer variety of sequences found in the paleo record or in climate change projections. We implement the method by Prairie et al. (2008) in which paleo reconstructions are mined for their sequences of states (‘wet’ and ‘dry’) by modeling the state transitions using Markov Chain while the magnitude of streamflow is modeled from the observed flow data. This model can generate a very rich variety of wet and dry spells. They can also be combined with climate change projections to generate flow scenarios that can capture the non-stationarity in the flow variability.

Temporal and Spatial Disaggregation

These techniques are combined with multi-site streamflow simulation method (Prairie et al., 2007; Nowak et al., 2010) to generate monthly flow scenarios at the spatially distributed nodes in the basin.

Bracken, C., B. Rajagopalan, and J. Prairie (2010), A multi-site seasonal ensemble streamflow forecasting       technique, Water Resources Research, 46, W03532, doi:10.1029/2009WR0079652010

Nowak, K., B. Rajagopalan, J. Prairie and U. Lall. (2010). A nonparametric stochastic     approach for multisite disaggregation of annual to daily streamflow, (in press), Water    Resources Research.

Prairie, J., B. Rajagopalan, T. Fulp and E. Zagona, A modified K-NN Model for Generating Stochastic Natural Streamflows, Journal of Hydrologic Engineering, 11(4), 371-378, 2006

Prairie, J., Rajagopalan,  B., Lall, U. and Fulp, T., (2007) "A stochastic nonparametric technique for space-time disaggregation of streamflows." Water Resources Research, 43, W03432, doi:10.1029/2005WR004721.

Prairie, J., Nowak, K. Rajagopalan,  B., Lall, U. and Fulp, T., A stochastic nonparametric approach for streamflow generation combining observational and paleo reconstructed data, Water Resources Research, 44, W06423, doi:10.1029/2007WR006684, 2008.

Towler, E. 2010. Understanding and Modeling the Impacts of Climate change on Source Water Quality and Utility Planning. (Ph.D Thesis, University of Colorado), Boulder, CO

 

 

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