Recent U.S. Geological Survey water-use report suggests that increasing water-use efficiency could mitigate the supply-and-demand imbalance arising from changing climate and growing population. However, this rich data have neither analyzed to understand the underlying patterns, nor have been investigated to identify the factors contributing to this increased efficiency. A national-scale synthesis of public supply withdrawals ("withdrawals") reveals a strong North-south gradient in public supply water use with the increasing population in the South contributing to increased withdrawal. Contrastingly, a reverse South-north gradient exists in per capita withdrawals ("efficiency"), with northern states consistently improving the efficiency, while the southern states' efficiency declined. Our analyses of spatial patterns of per capita withdrawals further demonstrate that urban counties exhibit improved efficiency over rural counties. Improved efficiency is also demonstrated over high-income and well-educated counties. Given the potential implications of the findings in developing long-term water conservation measures (i.e., increasing block rates), we argue the need for frequent updates, perhaps monthly to annual, of water-use data for identifying effective strategies that control the water-use efficiency in various geographic settings under a changing climate.

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.

Future changes in monthly precipitation are typically evaluated by estimating the shift in the long-term mean/variability or based on the change in the marginal distribution. General circulation model (GCM) precipitation projections deviate across various models and emission scenarios and hence provide no consensus on the expected future change. The current study proposes a rank/percentile-based multimodel combination approach to account for the fact that alternate model projections do not share a common time indexing. The approach is evaluated using 10 GCM historical runs for the current period and is validated by comparing with two approaches: equal weighting and a non-percentile-based optimal weighting. The percentile-based optimal combination exhibits lower values of RMSE in estimating precipitation terciles. Future (2000–49) multimodel projections show that January and July precipitation exhibit an increase in simulated monthly extremes (25th and 75th percentiles) over many climate regions of the conterminous United States.

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Earlier researches have proposed algorithms to quantify the measurement uncertainty in rating curves and found that the magnitude of the uncertainty can be significant enough to impact hydrologic modeling. Therefore, they suggested frameworks to include measurement uncertainty in the rating curve to make it robust. Despite their efforts, a robust rating curve is often ignored in traditional practices, considering the investment of time and money as well as the resulting benefit from it. In the current research, we are interested in understanding the role of the measurement error variance in real-time streamflow forecasting. Our objectives are (i) to employ a state-of-the-art statistical forecasting model that can handle measurement uncertainty in daily streamflow and (ii) to understand the trade-off in forecasting performance when substantial knowledge regarding the measurement uncertainty is provided to the modeler. We apply the Bayesian dynamic hierarchical model (BDHM) on four gauging sites in the United States. Results show that the BDHM performs better than the daily climatology and local linear regression model. Also, the forecast variance changes proportionally with the change in the error variance as an input in the observation equation. Following this, we design a simulation-based study, which assigns the measurement error in the reported streamflow to obtain multiple realizations of the true streamflow. The inclusion of substantial knowledge about the true error improves the BDHM's performance by lowering the CRPS (continuous rank probability score) values. However, the inclusion increases the forecast variance to bring the true streamflow within the sampling variability of the forecasted streamflow. Overall, an improved trade-off between the success rate of forecasts and the forecast variance can be achieved by including the measurement error in the BDHM for rivers that witness less dispersed streamflow data.Plain Language Summary Rating curve uncertainty is one of the major sources of measurement uncertainty in reported streamflow. It occurs when a precalibrated stage-discharge relationship curve is used to indirectly measure the streamflow volume based on river stage measurement. Considering the cost and time involved to include the uncertainty in reported streamflow, rating curve uncertainty is often ignored in traditional streamflow modeling practices. The current study adopts a state-of-the-art statistical model that can include rating curve uncertainty while issuing daily streamflow forecasts 1 to 3 days in advance. The study found that a proper knowledge of rating curve uncertainty at a gauge site significantly improves the forecasting of true streamflow.