@article{bhowmik_sankarasubramanian_sinha_patskoski_mahinthakumar_kunkel_2017, title={Multivariate Downscaling Approach Preserving Cross Correlations across Climate Variables for Projecting Hydrologic Fluxes}, volume={18}, ISSN={1525-755X 1525-7541}, url={http://dx.doi.org/10.1175/JHM-D-16-0160.1}, DOI={10.1175/jhm-d-16-0160.1}, abstractNote={Abstract
               Most of the currently employed procedures for bias correction and statistical downscaling primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature, ignoring the interdependency between the two variables. In this study, a multivariate approach, asynchronous canonical correlation analysis (ACCA), is proposed and applied to global climate model (GCM) historic simulations and hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to downscale monthly precipitation and temperature over the conterminous United States. ACCA is first applied to the CNRM-CM5 GCM historical simulations for the period 1950–99 and compared with the bias-corrected dataset based on quantile mapping from the Bureau of Reclamation. ACCA is also applied to CNRM-CM5 hindcasts and compared with univariate asynchronous regression (ASR), which applies regular regression to sorted GCM and observed variables. ACCA performs better than ASR and quantile mapping in preserving the cross correlation at grid points where the observed cross correlations are significant while reducing fractional biases in mean and standard deviation. Results also show that preservation of cross correlation increases the bias in standard deviation slightly, but estimates observed precipitation and temperature with increased likelihood, particularly for months exhibiting significant cross correlation. ACCA also better estimates the joint likelihood of observed precipitation and temperature under hindcasts since hindcasts estimate the observed variability in precipitation better. Implications of preserving cross correlations across climate variables for projecting runoff and other land surface fluxes are also discussed.}, number={8}, journal={Journal of Hydrometeorology}, publisher={American Meteorological Society}, author={Bhowmik, Rajarshi Das and Sankarasubramanian, A. and Sinha, Tushar and Patskoski, Jason and Mahinthakumar, G. and Kunkel, Kenneth E.}, year={2017}, month={Aug}, pages={2187–2205} }
 @article{patskoski_sankarasubramanian_2015, title={Improved reservoir sizing utilizing observed and reconstructed streamflows within a Bayesian combination framework}, volume={51}, ISSN={0043-1397 1944-7973}, url={http://dx.doi.org/10.1002/2014WR016189}, DOI={10.1002/2014wr016189}, abstractNote={AbstractReservoir sizing is a critical task as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, sequent peak algorithm (SQP) is used with observed streamflow to obtain reservoir storage estimates. To overcome the limited sample length of observed streamflow, synthetic streamflow traces estimated from observed streamflow characteristics are provided with SQP to estimate the distribution of storage. However, the parameters in the stochastic streamflow generation model are derived from the observed record and are still unrepresentative of the long‐term drought records. Paleo‐streamflow time series, usually reconstructed using tree‐ring chronologies, span for a longer period than the observed streamflow and provide additional insight into the preinstrumental drought record. This study investigates the capability of reconstructed streamflow records in reducing the uncertainty in reservoir storage estimation. For this purpose, we propose a Bayesian framework that combines observed and reconstructed streamflow for estimating the parameters of the stochastic streamflow generation model. By utilizing reconstructed streamflow records from two potential stations over the Southeastern U.S., the distribution of storage estimated using the combined streamflows is compared with the distribution of storage estimated using observed streamflow alone based on split‐sample validation. Results show that combining observed and reconstructed streamflow yield stochastic streamflow generation parameters more representative of the longer streamflow record resulting in improved reservoir storage estimates. We also generalize the findings through a synthetic experiment by generating reconstructed streamflow records of different sample length and skill. The analysis shows that uncertainty in storage estimates reduces by incorporating reconstruction records with higher skill and longer sample lengths. Potential applications of the proposed methodology are also discussed.}, number={7}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Patskoski, Jason and Sankarasubramanian, A.}, year={2015}, month={Jul}, pages={5677–5697} }
 @article{patskoski_sankarasubramanian_wang_2015, title={Reconstructed streamflow using SST and tree-ring chronologies over the southeastern United States}, volume={527}, ISSN={["1879-2707"]}, DOI={10.1016/j.jhydrol.2015.05.041}, abstractNote={A hybrid approach that considers both tree-ring chronologies and sea surface temperature (SST) data for reconstructing annual streamflow is proposed. The most common approach to reconstruct annual streamflow is to develop statistical regression relationships between principal components of tree rings and observed annual flow values and then extend the relationship to estimate annual streamflow values over the period for which tree-ring chronology is available. The primary limitation of this approach is in estimating high flow values since tree-ring growth reaches its potential limit during wet years. The proposed hybrid approach overcomes this limitation by using SST conditions from the tropical Pacific and tree-ring chronologies from the watershed for reconstructing annual streamflows. For this purpose, we considered eight virgin watersheds having long tree-ring chronologies over the southeastern United States. Given the role of El Nino Southern Oscillation (ENSO) in influencing the hydroclimatology of the southeastern United States, we estimated the periodic component of streamflow using Nino3.4 – an index representing ENSO – and the non-periodic component of streamflow using the non-periodic component of tree rings that represent interannual variability of moisture supply within the region. We employed Singular Spectrum Analysis (SSA) for extracting periodic and non-periodic components from tree-ring chronologies, Nino3.4 and streamflow data. The proposed tree ring and SST hybrid approach was compared with the traditional principal component regression (PCR) approach based on cross-validation. Results show that inclusion of SST provided better reconstructed flow values during high flow years but also resulted in overestimation of flow during low flow years. Combination of annual streamflow estimates from the two models – PCR and the hybrid approach – resulted in improved estimates of reconstructed annual streamflow for the selected eight watersheds. Potential applications for such improved reconstructed streamflow estimates is also discussed.}, journal={JOURNAL OF HYDROLOGY}, author={Patskoski, Jason and Sankarasubramanian, A. and Wang, Hui}, year={2015}, month={Aug}, pages={761–775} }