Abstract

The availability of long-term gridded datasets of precipitation, temperature, and other surface meteorological variables offers the potential for deriving a range of land surface conditions that have not been directly observed. These include, for instance, soil moisture, snow water equivalent, evapotranspiration, runoff, and subsurface moisture transport. However, gridding procedures can themselves introduce artificial trends due to incorporation of stations with different record lengths and locations. Hence, existing gridded datasets are in general not appropriate for estimation of long-term trends. Methods are described here for adjustment of gridded daily precipitation and temperature maxima and minima over the continental United States based on newly available (in electronic form) U.S. Cooperative Observer station data archived at the National Climatic Data Center from the early 1900s on. The intent is to produce gridded meteorological datasets that can be used, in conjunction with hydrologic modeling, for long-term trend analysis of simulated hydrologic variables.

Abstract

This study examines the influences of state and local political affiliation and local exposure to weather-related impacts on local government climate change adaptation efforts in 88 U.S. cities. Although climate adaptation takes place when cities replace critical infrastructure damaged by severe weather events, little is known about the influence of political affiliation and severe weather events on climate adaptation in a broader sense. Using multiple linear regression models, this study analyzes variations in local government climate adaptation efforts as a function of local gross domestic product (as a control variable), historical weather-related factors [i.e., number of extreme weather events, weather-related economic impact due to property damage, and weather-related human impact (injuries and fatalities)], and state and local political affiliation. The findings of this study indicate that local political affiliation significantly influences local government climate adaptation efforts; however, state political affiliation does not. Further, local weather-related impacts do not appear to affect the likelihood of local government to engage in climate adaptation efforts, even when accounting for potential interactions with local political affiliation. These results support the hypothesis that local political affiliation is a strong and robust predictor of local climate adaptation in U.S. cities. This study contributes to literature aimed at addressing the widely acknowledged need for understanding key barriers to U.S. climate adaptation, as well as the role of politics in moderating climate action.

Abstract

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r ², Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region.

Abstract

A physically based hydrology model is used to produce time series for the period 1916–2003 of evapotranspiration (ET), runoff, and soil moisture (SM) over the western United States from which long-term trends are evaluated. The results show that trends in ET in spring and summer are determined primarily by trends in precipitation and snowmelt that determine water availability. From April to June, ET trends are mostly positive due primarily to earlier snowmelt and earlier emergence of snow-free ground, and secondarily to increasing trends in spring precipitation. From July to September trends in ET are more strongly influenced by precipitation trends, with the exception of areas (most notably California) that receive little summer precipitation and have experienced large changes in snowmelt timing. Trends in the seasonal timing of ET are modest, but during the period 1947–2003 when temperature trends are large, they reflect a shift of ET from midsummer to early summer and late spring. As in other studies, it is found that runoff is occurring earlier in spring, a trend that is related primarily to increasing temperature, and is most apparent during 1947–2003. Trends in the annual runoff ratio, a variable critical to western water management, are determined primarily by trends in cool season precipitation, rather than changes in the timing of runoff or ET. It was found that the signature of temperature-related trends in runoff and SM is strongly keyed to mean midwinter [December–February (DJF)] temperatures. Areas with warmer winter temperatures show increasing trends in the runoff fraction as early as February, and colder areas as late as June. Trends toward earlier spring SM recharge are apparent and increasing trends in SM on 1 April are evident over much of the region. The 1 July SM trends are less affected by snowmelt changes and are controlled more by precipitation trends.

Abstract

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse ( °) and fine ( °) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In particular, the adequacy of VIC simulations in groundwater- versus runoff-dominated watersheds using a range of flow metrics relevant for water supply and aquatic habitat was examined. These flow metrics were 1) total annual streamflow; 2) total fall, winter, spring, and summer season streamflows; and 3) 5th, 25th, 50th, 75th, and 95th flow percentiles. The effect of climate on model performance was also evaluated by comparing the observed and simulated streamflow sensitivities to temperature and precipitation. Model performance was evaluated using four quantitative statistics: nonparametric rank correlation ρ, normalized Nash–Sutcliffe efficiency NNSE, root-mean-square error RMSE, and percent bias PBIAS. The VIC model captured the sensitivity of streamflow for temperature better than for precipitation and was in poor agreement with the corresponding temperature and precipitation sensitivities derived from observed streamflow. The model was able to capture the hydrologic behavior of the study watersheds with reasonable accuracy. Both total streamflow and flow percentiles, however, are subject to strong systematic model bias. For example, summer streamflows were underpredicted (PBIAS = −13%) in groundwater-dominated watersheds and overpredicted (PBIAS = 48%) in runoff-dominated watersheds. Similarly, the 5th flow percentile was underpredicted (PBIAS = −51%) in groundwater-dominated watersheds and overpredicted (PBIAS = 19%) in runoff-dominated watersheds. These results provide a foundation for improving model parameterization and calibration in ungauged basins.

Natural fluctuations in the atmosphere–ocean system related to the El Niño–Southern Oscillation (ENSO) induce climate variability over many parts of the world that is potentially predictable with lead times from seasons to decades. This study examines the potential of using a model nesting approach to provide seasonal climate and streamflow forecasts suitable for water resources management. Two ensembles of perpetual January simulations were performed with a regional climate model driven by a general circulation model (GCM), using observed climatological sea surface temperature (SST) and the mean SST of the warm ENSO years between 1950 and 1994. The climate simulations were then used to drive a macroscale hydrology model to simulate streamflow. The differences between the two ensembles of simulations are defined as the warm ENSO signals.

The simulated hydroclimate signals were compared with observations. The analyses focus on the Columbia River basin in the Pacific Northwest. Results show that the global and regional models simulated a warming over the Pacific Northwest that is quite close to the observations. The models also correctly captured the strong wet signal over California and the weak dry signal over the Pacific Northwest during warm ENSO years. The regional climate model consistently performed better than the GCM in simulating the spatial distribution of regional climate and climate signals. When the climate simulations were used to drive a macroscale hydrology model at the Columbia River basin, the simulated streamflow signal resembles that derived from hydrological simulations driven by observed climate. The streamflow simulations were considerably improved when a simple bias correction scheme was applied to the climate simulations. The coupled regional climate and macroscale hydrologic simulations demonstrate the prospect for generating and utilizing seasonal climate forecasts for managing reservoirs.

In western North America, snow provides crucial storage of winter precipitation, effectively transferring water from the relatively wet winter season to the typically dry summers. Manual and telemetered measurements of spring snowpack, corroborated by a physically based hydrologic model, are examined here for climate-driven fluctuations and trends during the period of 1916–2002. Much of the mountain West has experienced declines in spring snowpack, especially since midcentury, despite increases in winter precipitation in many places. Analysis and modeling show that climatic trends are the dominant factor, not changes in land use, forest canopy, or other factors. The largest decreases have occurred where winter temperatures are mild, especially in the Cascade Mountains and northern California. In most mountain ranges, relative declines grow from minimal at ridgetop to substantial at snow line. Taken together, these results emphasize that the West's snow resources are already declining as earth's climate warms.

Abstract

Recent studies have shown substantial declines in snow water equivalent (SWE) over much of the western United States in the last half century, as well as trends toward earlier spring snowmelt and peak spring streamflows. These trends are influenced both by interannual and decadal-scale climate variability, and also by temperature trends at longer time scales that are generally consistent with observations of global warming over the twentieth century. In this study, the linear trends in 1 April SWE over the western United States are examined, as simulated by the Variable Infiltration Capacity hydrologic model implemented at 1/8° latitude–longitude spatial resolution, and driven by a carefully quality controlled gridded daily precipitation and temperature dataset for the period 1915–2003. The long simulations of snowpack are used as surrogates for observations and are the basis for an analysis of regional trends in snowpack over the western United States and southern British Columbia, Canada. By isolating the trends due to temperature and precipitation in separate simulations, the influence of temperature and precipitation variability on the overall trends in SWE is evaluated. Downward trends in 1 April SWE over the western United States from 1916 to 2003 and 1947 to 2003, and for a time series constructed using two warm Pacific decadal oscillation (PDO) epochs concatenated together, are shown to be primarily due to widespread warming. These temperature-related trends are not well explained by decadal climate variability associated with the PDO. Trends in SWE associated with precipitation trends, however, are very different in different time periods and are apparently largely controlled by decadal variability rather than longer-term trends in climate.

Abstract

Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth–area–duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity–area–duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena.

Abstract

The data required to drive distributed hydrological models are significantly limited within mountainous terrain because of a scarcity of observations. This study evaluated three common configurations of forcing data: 1) one low-elevation station, combined with empirical techniques; 2) gridded output from the Weather Research and Forecasting Model (WRF); and 3) a combination of the two. Each configuration was evaluated within the heavily instrumented North Fork American River basin in California during October–June 2000–10. Simulations of streamflow and snowpack using the Distributed Hydrology Soil and Vegetation Model (DHSVM) highlighted precipitation and radiation as variables whose sources resulted in significant differences. The best source of precipitation data varied between years. On average, the WRF performed as well as the single station distributed using the Parameter Regression on Independent Slopes Model (PRISM). The average percent biases in simulated streamflow were 3% and 1%, for configurations 1 and 2, respectively, even though precipitation compared directly with gauge measurements was biased high by 6% and 17%, suggesting that gauge undercatch may explain part of the bias. Simulations of snowpack using empirically estimated longwave irradiance resulted in melt rates lower than those observed at high-elevation sites, while at lower elevations the same forcing caused significant midwinter melt that was not observed. These results highlight the complexity of how forcing data sources impact hydrology over different areas (high- versus low-elevation snow) and different time periods. Overall, results support the use of output from the WRF model over empirical techniques in regions with limited station data.