Relationship between Ocean-Atmospheric Climate Variables and Regional Streamflow of the Conterminous United States

Understanding the interconnections between oceanic-atmospheric climate variables and regional streamflow of the conterminous United States may aid in improving regional long lead-time streamflow forecasting. The current research evaluates the time-lagged relationship between streamflow of six geographical regions defined from National Climate Assessment and sea surface temperature (SST), 500-mbar geopotential height (Z500), 500-mbar specific humidity (SH500), and 500-mbar east-west wind (U500) of the Pacific and the Atlantic Ocean using singular value decomposition (SVD). The spatio-temporal correlation between streamflow and SST was developed first from SVD and thus obtained correlation was later associated with Z500, SH500, and U500 separately to evaluate the coupled interconnections between the climate variables. Furthermore, the associations between regional streamflow and the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation, and Atlantic Multidecadal Oscillation were evaluated using the derivatives of continuous wavelet transform. Regional SVD analysis revealed significant teleconnection between several regions and climate variables. The warm phase of equatorial SST had shown a stronger correlation with the majority of streamflow. Both SVD and wavelet analyses concluded that the streamflow variability of the regions in close proximity to the Pacific Ocean was strongly associated with the ENSO. Improved knowledge of teleconnection of climate variables with regional streamflow variability may help in regional water management and streamflow prediction studies

Detection, attribution, and sensitivity of trends toward earlier streamflow in the Sierra Nevada

Observed changes in the timing of snowmelt dominated streamflow in the western United States are often linked to anthropogenic or other external causes. We assess whether observed streamflow timing changes can be statistically attributed to external forcing, or whether they still lie within the bounds of natural (internal) variability for four large Sierra Nevada (CA) basins, at inflow points to major reservoirs. Streamflow timing is measured by “center timing” (CT), the day when half the annual flow has passed a given point. We use a physically based hydrology model driven by meteorological input from a global climate model to quantify the natural variability in CT trends. Estimated 50-year trends in CT due to natural climate variability often exceed estimated actual CT trends from 1950 to 1999. Thus, although observed trends in CT to date may be statistically significant, they cannot yet be statistically attributed to external influences on climate. We estimate that projected CT changes at the four major reservoir inflows will, with 90% confidence, exceed those from natural variability within 1–4 decades or 4–8 decades, depending on rates of future greenhouse gas emissions. To identify areas most likely to exhibit CT changes in response to rising temperatures, we calculate changes in CT under temperature increases from 1 to 5°. We find that areas with average winter temperatures between −2°C and −4°C are most likely to respond with significant CT shifts. Correspondingly, elevations from 2000 to 2800 m are most sensitive to temperature increases, with CT changes exceeding 45 days (earlier) relative to 1961–1990

Uncertainty in projections of streamflow changes due to climate change in California

Understanding the uncertainty in the projected impacts of climate change on hydrology will help decision-makers interpret the confidence in different projected future hydrologic impacts. We focus on California, which is vulnerable to hydrologic impacts of climate change. We statistically bias correct and downscale temperature and precipitation projections from 10 GCMs participating in the Coupled Model Intercomparison Project. These GCM simulations include a control period (unchanging CO2 and other forcing) and perturbed period (1%/year CO2 increase). We force a hydrologic model with the downscaled GCM data to generate streamflow at strategic points. While the different GCMs predict significantly different regional climate responses to increasing atmospheric CO2, hydrological responses are robust across models: decreases in summer low flows and increases in winter flows, and a shift of flow to earlier in the year. Summer flow decreases become consistent across models at lower levels of greenhouse gases than increases in winter flows do

Impacts of Climate Change and Climate Variability on Hydropower Potential in Data-Scarce Regions Subjected to Multi-decadal variability

To achieve sustainable development of hydroelectric resources, it is necessary to understand their availability, variability, and the expected impacts of climate change. Current research has mainly focused on estimating hydropower potential or determining the optimal locations for hydropower projects without considering the variability and historical trends of the resources. Herein, the hydropower potential variability from reconstructed streamflow series estimated with a non-parametric gap-filling method and geographic information systems (GIS) techniques are analyzed. The relationships between hydropower and large-scale climate variability, expressed by sea surface temperature, are explored. Finally, we project hydropower potential through 2050 using 15 global circulation models with representative concentration pathway (RCP) 4.5. We used four watersheds in central Chile as a case study. The results show significant interannual and inter-basin hydropower potential variability, with decreasing trends over time modulated by alternating positive and negative decadal trends; these modulations exhibit greater intensities than the general trends and are attributable to climatic oscillations such as El Niño. Future scenarios indicate high hydropower availability and a possible over-investment in hydroelectric plants in two of the four studied watersheds. Results show the need to improve the current policies that promote hydropower development including hydropower resource variability in order to achieve optimal, sustainable hydropower development worldwide

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼  200 km) to 1∕8° ( ∼  12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼  50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis

Toward improved streamflow forecasts: Value of semidistributed modeling

The focus of this study is to assess the performance improvements of semidistributed applications of the U.S. National Weather Service Sacramento Soil Moisture Accounting model on a watershed using radar-based remotely sensed precipitation data. Specifically, performance comparisons are made within an automated multicriteria calibration framework to evaluate the benefit of "spatial distribution" of the model input (precipitation), structural components (soil moisture and streamflow routing computations), and surface characteristics (parameters). A comparison of these results is made with those obtained through manual calibration. Results indicate that for the study watershed, there are performance improvements associated with semidistributed model applications when the watershed is partitioned into three subwatersheds; however, no additional benefit is gained from increasing the number of subwatersheds from three to eight. Improvements in model performance are demonstrably related to the spatial distribution of the model input and streamflow routing. Surprisingly, there is no improvement associated with the distribution of the surface characteristics (model parameters)