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.
Reservoir 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.
Studies focusing on national/global water scarcity require water availability and water use to quantify the imbalance. In this regard, annual irrigation withdrawal data reported by the USGS every 5 years provide a valuable information on the water use patterns over the United States. This study develops an empirical model to estimate annual irrigation water withdrawal using irrigated area, climate information, and population as predictors. Given the hierarchy in the data sources, we propose a predictive linear hierarchical regression model to develop annual irrigation water withdrawal models using varying intercepts (VI) and varying intercepts and slopes (VIS) approaches. Estimates from hierarchical models are compared with pooled and unpooled classical regression models. Overall, both hierarchical models outperform the classical models with the adjusted R2 between USGS‐reported and modeled withdrawal estimates being above 0.6 in most states using county and climate division level data. However, due to the spatial difference between the supply (rural areas) and demand (urban areas) for agriculture products, climate division level estimates exhibit a higher adjusted R2 than county level estimates. The VIS model is able to capture local effects better, particularly for states whose irrigation withdrawal patterns significantly differ from the national pattern. The performance of the models is also validated by leaving out the entire nation's water‐use data out (i.e., leave‐one‐out cross‐validation) to ensure the reported skill is not due to overfitting. Split‐sample validation in predicting 2010 irrigation withdrawal also shows the potential of the developed hierarchical model in estimating the annual irrigation withdrawals for the years with no data within the once in 5 year USGS database.
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