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Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially-complete station observations for North America, this study seeks to develop a SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin), and maximum temperature (Tmax) data for 27280 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records and were also used as an assessment benchmark. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming 30 % observations of stations were missing - this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean-value corrections were applied to the final estimates. The median Kling-Gupta efficiency (KGE) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin and Tmax, respectively. The SCDNA is closer to station observations than four benchmark gridded product, and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modelling. The dataset is available at https://doi.org/10.5281/zenodo.3735534 (Tang et al., 2020). [ABSTRACT FROM AUTHOR]

This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes in a regression framework to distribute in situ observations of precipitation and temperature to a grid. The framework enables our understanding of complex atmospheric processes (e.g., orographic precipitation) to be encoded into a statistical model in an easy-to-understand manner. TIER is developed in a modular fashion with key model parameters exposed to the user. This enables the user community to easily explore the impacts of our methodological choices made to distribute sparse, irregularly spaced observations to a grid in a systematic fashion. The modular design allows incorporating new capabilities in TIER. Intermediate processing variables are also output to provide a more complete understanding of the algorithm and any algorithmic changes. The framework also provides uncertainty estimates. This paper presents a brief model evaluation and demonstrates that the TIER algorithm is functioning as expected. Several variations in model parameters and changes in the distributed variables are described. A key conclusion is that seemingly small changes in a model parameter result in large changes to the final distributed fields and their associated uncertainty estimates. [ABSTRACT FROM AUTHOR]

This study presents a gridded meteorology intercomparison using the State of Hawaii as a testbed. This is motivated by the goal to provide the broad user community with knowledge of interproduct differences and the reasons differences exist. More generally, the challenge of generating station-based gridded meteorological surfaces and the difficulties in attributing interproduct differences to specific methodological decisions are demonstrated. Hawaii is a useful testbed because it is traditionally underserved, yet meteorologically interesting and complex. In addition, several climatological and daily gridded meteorology datasets are now available, which are used extensively by the applications modeling community, thus an intercomparison enhances Hawaiian specific capabilities. We compare PRISM climatology and three daily datasets: new datasets from the University of Hawai'i and the National Center for Atmospheric Research, and Daymet version 3 for precipitation and temperature variables only. General conclusions that have emerged are 1) differences in input station data significantly influence the product differences, 2) explicit prediction of precipitation occurrence is crucial across multiple metrics, and 3) attribution of differences to specific methodological choices is difficult and limits the usefulness of intercomparisons. Because generating gridded meteorological fields is an elaborate process with many methodological choices interacting in complex ways, future work should 1) develop modular frameworks that allows users to easily examine the breadth of methodological choices, 2) collate available nontraditional high-quality observational datasets for true out-of-sample validation and make them publicly available, and 3) define benchmarks of acceptable performance for methodological components and products. [ABSTRACT FROM AUTHOR]

It is a major challenge to develop gridded precipitation and temperature estimates that adequately resolve the extreme spatial gradients present in the Hawaiian Islands. The challenge is particularly pronounced because the available station networks are irregularly spaced and sparse, creating large uncertainties in gridded spatial meteorological estimates. Here a 100-member, daily ensemble of precipitation and temperature estimates over the Hawaiian Islands for the period 1990–2014 at 1-km grid resolution is developed. First, an intermediary ensemble estimate of the monthly climatological precipitation and temperature is created, and those climatological surfaces are used to inform daily anomaly interpolation. This climatologically aided interpolation (CAI) method extends our initial ensemble system developed for the continental United States. This study demonstrates that direct interpolation of daily precipitation values is inferior to the CAI methodology, particularly over longer time periods (from years to decades). Daily interpolation performs better for short time periods (e.g., 1 month or less) or when the precipitation distribution substantially diverges from climatology. The CAI ensemble is able to reproduce observed precipitation and temperature patterns, including precipitation occurrence. Leave-one-out cross-validation results illustrate that the ensemble has 1) minimal bias for precipitation and temperature; 2) a mean absolute error of 2.5 mm day−1, 1.0 K, and 2.2 K for precipitation and mean and diurnal temperature, respectively; 3) a mean absolute error of 3.3 mm day−1 for the standard deviation of precipitation; and 4) nearly unbiased probability distributions across multiple thresholds of precipitation intensity. Additionally, the ensemble provides estimates of uncertainty across the distributions with increasing uncertainty for higher percentiles. [ABSTRACT FROM AUTHOR]

Given uncertainty in precipitation gauge-based gridded datasets over complex terrain, we use multiple streamflow observations as an additional source of information about precipitation, in order to identify spatial and temporal differences between a gridded precipitation dataset and precipitation inferred from streamflow. We test whether gridded datasets capture across-crest and regional spatial patterns of variability, as well as year-to-year variability and trends in precipitation, in comparison to precipitation inferred from streamflow. We use a Bayesian model calibration routine with multiple lumped hydrologic model structures to infer the most likely basin-mean, water-year total precipitation for 56 basins with long-term (>30 year) streamflow records in the Sierra Nevada mountain range of California. We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century. We find that the long-term average spatial patterns differ: in particular, there is less precipitation in the gridded dataset in higher-elevation basins whose aspect faces prevailing cool-season winds, as compared to precipitation inferred from streamflow. In a few years and basins, there is less gridded precipitation than there is observed streamflow. Lower-elevation, southern, and east-of-crest basins show better agreement between gridded and inferred precipitation. Implied actual evapotranspiration (calculated as precipitation minus streamflow) then also varies between the streamflow-based estimates and the gridded dataset. Absolute uncertainty in precipitation inferred from streamflow is substantial, but the signal of basin-to-basin and year-to-year differences are likely more robust. The findings suggest that considering streamflow when spatially distributing precipitation in complex terrain may improve its representation, particularly for basins whose orientations (e.g., windward-facing) are favored for orographic precipitation enhancement. [ABSTRACT FROM AUTHOR]

Precipitation data in mountain basins are typically sparse and subject to uncertainty due to difficulties in measurement and capturing spatial variability. Streamflow provides indirect information about basin-mean precipitation, but inferring precipitation from streamflow requires assumptions about hydrologic model structure that influence precipitation amounts. In this study, we test the extent to which using both snow and streamflow observations reduces differences in inferred annual total precipitation, compared to inference from streamflow alone. The case study area is the upper Tuolumne River basin in the Sierra Nevada of California, where distributed and basin-mean snow water equivalent (SWE) estimates have been made using LiDAR as part of the NASA Airborne Snow Observatory (ASO). To reconstruct basin-mean SWE for years prior to the ASO campaign, we test for a robust relationship between SWE estimates from ASO and from snow courses and pillows, which have a longer record. Relative to ASO's distributed SWE observations, point SWE measurements in this part of the Sierra Nevada tend to overestimate SWE at a given elevation, but undersample high-elevation areas. We then infer precipitation from snow and streamflow, obtained from multiple hydrologic model structures. When included in precipitation inference, snow data reduce by up to one third the standard deviations of the water year total precipitation between model structures and improve the consistency between structures in terms of the yearly variability in precipitation. We reiterate previous findings that multiple types of hydrologic data improve the consistency of modeled physical processes and help identify the most appropriate model structures. [ABSTRACT FROM AUTHOR]

The Precipitation-Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many. [ABSTRACT FROM AUTHOR]

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation-Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5-3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded. [ABSTRACT FROM AUTHOR]

Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980-2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices. [ABSTRACT FROM AUTHOR]

Point observations and previous basin modeling efforts have suggested that snowmelt may be a significant input of water for runoff during extreme rain-on-snow floods within western U.S. basins. Quantifying snowmelt input over entire basins is difficult given sparse observations of snowmelt. In order to provide a range of snowmelt contributions for water managers, a physically based snow model coupled with an idealized basin representation was evaluated in point simulations and used to quantify the maximum basin-wide input from snowmelt volume during flood events. Maximum snowmelt basin contributions and uncertainty ranges were estimated as 29% (11-47%), 29% (8-37%), and 7% (2-24%) of total rain plus snowmelt input, within the Snoqualmie, East North Fork Feather, and Upper San Joaquin basins, respectively, during historic flooding events between 1980 and 2008. The idealized basin representation revealed that both hypsometry and forest cover of a basin had similar magnitude of impacts on the basin-wide snowmelt totals. However, the characteristics of a given storm (antecedent SWE and available energy for melt) controlled how much hypsometry and forest cover impacted basin-wide snowmelt. These results indicate that for watershed managers, flood forecasting efforts should prioritize rainfall prediction first, but cannot neglect snowmelt contributions in some cases. Efforts to reduce the uncertainty in the above snowmelt simulations should focus on improving the meteorological forcing data (especially air temperature and wind speed) in complex terrain. [ABSTRACT FROM AUTHOR]

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation. [ABSTRACT FROM AUTHOR]

Statistical postprocessing techniques have become essential tools for downscaling large-scale information to the point scale, and also for providing a better probabilistic characterization of hydrometeorological variables in simulation and forecasting applications at both short and long time scales. In this paper, the authors assess the utility of statistical postprocessing methods for generating probabilistic estimates of daily precipitation totals, using deterministic high-resolution outputs obtained with the Weather Research and Forecasting (WRF) Model. After a preliminary assessment of WRF simulations over a historical period, the performance of three postprocessing techniques is compared: multinomial logistic regression (MnLR), quantile regression (QR), and Bayesian model averaging (BMA)-all of which use WRF outputs as potential predictors. Results demonstrate that the WRF Model has skill in reproducing observed precipitation events, especially during fall/winter. Furthermore, it is shown that the spatial distribution of skill obtained from statistical postprocessing is closely linked with the quality of WRF precipitation outputs. A detailed comparison of statistical precipitation postprocessing approaches reveals that, although the poorest performance was obtained using MnLR, there is not an overall best technique. While QR should be preferred if skill (i.e., small probability forecast errors) and reliability (i.e., match between forecast probabilities and observed frequencies) are target properties, BMA is recommended in cases when discrimination (i.e., prediction of occurrence versus nonoccurrence) and statistical consistency (i.e., equiprobability of the observations within their ensemble distributions) are desired. Based on the results obtained here, the authors believe that future research should explore frameworks reconciling hierarchical Bayesian models with the use of the extreme value theory for high precipitation events. [ABSTRACT FROM AUTHOR]

Information relevant for most hydrologic applications cannot be obtained directly from the native-scale outputs of climate models. As a result the climate model output must be downscaled, often using statistical methods. The plethora of statistical downscaling methods requires end-users to make a selection. This work is intended to provide end-users with aid in making an informed selection. We assess four commonly used statistical downscaling methods: daily and monthly disaggregated-to-daily Bias Corrected Spatial Disaggregation (BCSDd, BCSDm), Asynchronous Regression (AR), and Bias Corrected Constructed Analog (BCCA) as applied to a continental-scale domain and a regional domain (BCCAr). These methods are applied to the NCEP/NCAR Reanalysis, as a surrogate for a climate model, to downscale precipitation to a 12 km gridded observation data set. Skill is evaluated by comparing precipitation at daily, monthly, and annual temporal resolutions at individual grid cells and at aggregated scales. BCSDd and the BCCA methods overestimate wet day fraction, and underestimate extreme events. The AR method reproduces extreme events and wet day fraction well at the grid-cell scale, but over (under) estimates extreme events (wet day fraction) at aggregated scales. BCSDm reproduces extreme events and wet day fractions well at all space and time scales, but is limited to rescaling current weather patterns. In addition, we analyze the choice of calibration data set by looking at both a 12 km and a 6 km observational data set; the 6 km observed data set has more wet days and smaller extreme events than the 12 km product, the opposite of expected scaling. [ABSTRACT FROM AUTHOR]

Spatially distributed historical meteorological forcings (temperature and precipitation) are commonly incorporated into modeling efforts for long-term natural resources planning. For water management decisions, it is critical to understand the uncertainty associated with the different choices made in hydrologic impact assessments (choice of hydrologic model, choice of forcing dataset, calibration strategy, etc.). This paper evaluates differences among four commonly used historical meteorological datasets and their impacts on streamflow simulations produced using the Variable Infiltration Capacity (VIC) model. The four meteorological datasets examined here have substantial differences, particularly in minimum and maximum temperatures in high-elevation regions such as the Rocky Mountains. The temperature differences among meteorological forcing datasets are generally larger than the differences between calibration and validation periods. Of the four meteorological forcing datasets considered, there are substantial differences in calibrated model parameters and simulations of the water balance. However, no single dataset is superior to the others with respect to VIC simulations of streamflow. Also, optimal calibration parameter values vary across case study watersheds and select meteorological datasets, suggesting that there is enough flexibility in the calibration parameters to compensate for the effects of using select meteorological datasets. Evaluation of runoff sensitivity to changes in climate indicates that the choice of meteorological dataset may be as important in characterizing changes in runoff as climate change, supporting consideration of multiple sources of uncertainty in long-term planning studies. [ABSTRACT FROM AUTHOR]

Summer and winter daily heavy precipitation events (events above the 97.5th percentile) are analyzed in regional climate simulations with 36-, 12-, and 4-km horizontal grid spacing over the headwaters of the Colorado River. Multiscale evaluations are useful to understand differences across horizontal scales and to evaluate the effects of upscaling finescale processes to coarser-scale features associated with precipitating systems. Only the 4-km model is able to correctly simulate precipitation totals of heavy summertime events. For winter events, results from the 4- and 12-km grid models are similar and outperform the 36-km simulation. The main advantages of the 4-km simulation are the improved spatial mesoscale patterns of heavy precipitation (below ~100 km). However, the 4-km simulation also slightly improves larger-scale patterns of heavy precipitation. [ABSTRACT FROM AUTHOR]

Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50-200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November-May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate. [ABSTRACT FROM AUTHOR]

Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested. The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method. [ABSTRACT FROM AUTHOR]

Hydrological scientists develop perceptual models of the catchments they study, using field measurements and observations to build an understanding of the dominant processes controlling the hydrological response. However, conceptual and numerical models used to simulate catchment behaviour often fail to take advantage of this knowledge. It is common instead to use a pre-defined model structure which can only be fitted to the catchment via parameter calibration. In this article, we suggest an alternative approach where different sources of field data are used to build a synthesis of dominant hydrological processes and hence provide recommendations for representing those processes in a time-stepping simulation model. Using analysis of precipitation, flow and soil moisture data, recommendations are made for a comprehensive set of modelling decisions, including Evapotranspiration (ET) parameterization, vertical drainage threshold and behaviour, depth and water holding capacity of the active soil zone, unsaturated and saturated zone model architecture and deep groundwater flow behaviour. The second article in this two-part series implements those recommendations and tests the capability of different model sub-components to represent the observed hydrological processes. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The current generation of hydrological models has been widely criticized for their inability to adequately simulate hydrological processes. In this study, we evaluate competing model representations of hydrological processes with respect to their capability to simulate observed processes in the Mahurangi River basin in Northland, New Zealand. In the first part of this two-part series, the precipitation, soil moisture, and flow data in the Mahurangi were used to estimate the dominant hydrological processes and explore several options for their suitable mathematical representation. In this paper, diagnostic tests are applied to gain several insights for model selection. The analysis highlights dominant hydrological processes (e.g. the importance of vertical drainage and baseflow compared to sub-surface stormflow), provides guidance for the choice of modelling approaches (e.g. implicitly representing sub-grid heterogeneity in soils), and helps infer appropriate values for model parameters. The approach used in this paper demonstrates the benefits of flexible model structures in the context of hypothesis testing, in particular, supporting a more systematic exploration of current ambiguities in hydrological process representation. The challenge for the hydrological community is to make better use of the available data, not only to estimate parameter values but also to diagnostically identify more scientifically defensible model structures. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

The article reports on rain-on-snow events in the western United States. It is noted that rain-on-snow floods are considered as interesting hydrometereological phenomenon. The severity of these floods are greatly dependent not only on the magnitude of the precipitation, but also on the elevation of the freezing level and the water equivalent and areal boundary of the antecedent snowpack. These causative factors are essential in predicting floods and flood risk assessment. It is stated that improving forecast of rain-on-snow floods is limited by the inadequacy of general knowledge about the character of rain-on-snow events in the western United States.

Abstract: This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy of streamflow simulations near the end of the snowmelt season. The small effect from SCA assimilation is initially surprising. It can be explained both because a substantial portion of snowmelts before any bare ground is exposed, and because the transition from 100% to 0% snow coverage occurs fairly quickly. Both of these factors are basin-dependent. Satellite SCA information is expected to be most useful in basins where snow cover is ephemeral. The data assimilation strategy presented in this study improved the accuracy of the streamflow simulation, indicating that SCA is a useful source of independent information that can be used as part of an integrated data assimilation strategy. [Copyright &y& Elsevier]

Previous research has suggested that a general inverse relation exists between winter precipitation in the southwestern United States (US) and summer monsoon precipitation. In addition, it has been suggested that this inverse relation between winter precipitation and the magnitude of the southwestern US monsoon breaks down under certain climatic conditions that override the regional winter/monsoon precipitation relations. Results from this new study indicate that the winter/monsoon precipitation relations do not break down, but rather shift location through time. The strength of winter/monsoon precipitation relations, as indexed by 20-year moving correlations between winter precipitation and monsoon precipitation, decreased in Arizona after about 1970, but increased in New Mexico. The changes in these correlations appear to be related to an eastward shift in the location of monsoon precipitation in the southwestern US. This eastward shift in monsoon precipitation and the changes in correlations with winter precipitation also appear to be related to an eastward shift in July/August atmospheric circulation over the southwestern US that resulted in increased monsoon precipitation in New Mexico. Results also indicate that decreases in sea-surface temperatures (SSTs) in the central North Pacific Ocean also may be associated with the changes in correlations between winter and monsoon precipitation. Copyright © 2006 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

A snow data assimilation study was undertaken in which real data were used to update a conceptual model, SNOW-17. The aim of this study is to improve the model's estimate of snow water equivalent (SWE) by merging the uncertainties associated with meteorological forcing data and SWE observations within the model. This is done with a view to aiding the estimation of snowpack initial conditions for the ultimate objective of streamflow forecasting via a distributed hydrologic model. To provide a test of this methodology, the authors performed experiments at 53 stations in Colorado. In each case the situation of an unobserved location is mimicked, using the data at any given station only for validation; essentially, these are withholding experiments. Both ensembles of model forcing data and assimilated data were derived via interpolation and stochastic modeling of data from surrounding sources. Through a process of cross validation the error for the ensemble of model forcing data and assimilated observations is explicitly estimated. An ensemble square root Kalman filter is applied to perform assimilation on a 5-day cycle. Improvements in the resulting SWE are most evident during the early accumulation season and late melt period. However, the large temporal correlation inherent in a snowpack results in a less than optimal assimilation and the increased skill is marginal. Once this temporal persistence is removed from both model and assimilated observations during the update cycle, a result is produced that is, within the limits of available information, consistently superior to either the model or interpolated observations. [ABSTRACT FROM AUTHOR]

This paper describes a flexible method to generate ensemble gridded fields of precipitation in complex terrain. The method is based on locally weighted regression, in which spatial attributes from station locations are used as explanatory variables to predict spatial variability in precipitation. For each time step, regression models are used to estimate the conditional cumulative distribution function (cdf) of precipitation at each grid cell (conditional on daily precipitation totals from a sparse station network), and ensembles are generated by using realizations from correlated random fields to extract values from the gridded precipitation cdfs. Daily high-resolution precipitation ensembles are generated for a 300 km × 300 km section of western Colorado (dx = 2 km) for the period 1980–2003. The ensemble precipitation grids reproduce the climatological precipitation gradients and observed spatial correlation structure. Probabilistic verification shows that the precipitation estimates are reliable, in the sense that there is close agreement between the frequency of occurrence of specific precipitation events in different probability categories and the probability that is estimated from the ensemble. The probabilistic estimates have good discrimination in the sense that the estimated probabilities differ significantly between cases when specific precipitation events occur and when they do not. The method may be improved by merging the gauge-based precipitation ensembles with remotely sensed precipitation estimates from ground-based radar and satellites, or with precipitation and wind fields from numerical weather prediction models. The stochastic modeling framework developed in this study is flexible and can easily accommodate additional modifications and improvements. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

The timing of snowmelt runoff (SMR) for 84 rivers in the western United States is examined to understand the character of SMR variability and the climate processes that may be driving changes in SMR timing. Results indicate that the timing of SMR for many rivers in the western United States has shifted to earlier in the snowmelt season. This shift occurred as a step change during the mid-1980s in conjunction with a step increase in spring and early-summer atmospheric pressures and temperatures over the western United States. The cause of the step change has not yet been determined. [ABSTRACT FROM AUTHOR]

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 snow-pack, 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. *Joint Institute for the Study of the Atmosphere and the Ocean Contribution Number 1073 [ABSTRACT FROM AUTHOR]

This paper provides a detailed description of the relationship between spring snow mass in the mountain areas of the western United States and summertime precipitation in the southwestern United States associated with the North American monsoon system and examines the hypothesis that antecedent spring snow mass can modulate monsoon rains through effects on land surface energy balance. Analysis of spring snow water equivalent (SWE) and July–August (JA) precipitation for the period of 1948–97 confirms the inverse snow–monsoon relationship noted in previous studies. Examination of regional difference in SWE–JA precipitation associations shows that although JA precipitation in New Mexico is significantly correlated with SWE over much larger areas than in Arizona, the overall strength of the correlations are just as strong in Arizona as in New Mexico. Results from this study also illustrate that the snow–monsoon relationship is unstable over time. In New Mexico, the relationship is strongest during 1965–92 and is weaker outside that period. By contrast, Arizona shows strongest snow–monsoon associations before 1970. The temporal coincidence between stronger snow–monsoon associations over Arizona and weaker snow–monsoon associations over New Mexico (and vice versa) suggests a common forcing mechanism and that the variations in the strength of snow–monsoon associations are more than just climate noise. There is a need to understand how other factors modulate monsoonal rainfall before realistic predictions of summertime precipitation in the Southwest can be made. [ABSTRACT FROM AUTHOR]

Monthly data from 206 stations for the period 1947--93 are used to examine characteristics of snowfall over the eastern half of the United States and relationships with precipitation and the maximum temperature on precipitation days. Linkages between snowfall and modes of low-frequency circulation variability are diagnosed through composite analyses, based on results from a rotated Principal Component Analysis (PCA) of monthly 500-hPa geopotential height. Results are examined for the 2-month windows of November--December, January-- February, and March--April. The three dominant PCAs for each window capture regional components of the Pacific--North American (PNA), Tropical-Northern Hemisphere (TNH), and east Pacific (EP) teleconnection patterns. Two general snowfall regimes are identified: 1) the dry and cold upper midwest, Nebraska and Kansas, where snowfall is strongly a function of precipitation; and 2) the Midwest, southeast, and northeast, where snowfall is more closely tied to the mean maximum temperature on precipitation days. The PNA (the dominant circulation mode) and the EP pattern are both associated with strong snowfall signals, best expressed for November-- December and January--February. Snowfall for the PNA over the southeast, midwest, and mid-Atlantic states increases (decreases) under positive (negative) extremes, when the eastern United States is dominated by a strong 500-hPa trough (zonal flow or weak ridge) with associated lower (higher) precipitation-day temperatures. Snowfall signals are more extensive under positive PNA extremes where the lower temperatures have a greater impact on precipitation phase. An opposing precipitation-controlled snowfall signal is found over the upper Midwest. The positive phase of the EP pattern, describing a western ridge--eastern trough, is associated with negative snowfall signals clustered over the midwest and upper midwest. Opposing signals are found under the midwestern trough--eastern ridge. [ABSTRACT FROM AUTHOR]

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely used since its release in 2014. IMERG V06 provides global rainfall and snowfall data beginning from 2000. This study comprehensively analyzes the quality of the IMERG product at daily and hourly scales in China from 2000 to 2018 with special attention paid to snowfall estimates. The performance of IMERG is compared with nine satellite and reanalysis products (TRMM 3B42, CMORPH, PERSIANN-CDR, GSMaP, CHIRPS, SM2RAIN, ERA5, ERA-Interim, and MERRA2). Results show that the IMERG product outperforms other datasets, except the Global Satellite Mapping of Precipitation (GSMaP), which uses daily-scale station data to adjust satellite precipitation estimates. The monthly-scale station data adjustment used by IMERG naturally has a limited impact on estimates of precipitation occurrence and intensity at the daily and hourly time scales. The quality of IMERG has improved over time, attributed to the increasing number of passive microwave samples. SM2RAIN, ERA5, and MERRA2 also exhibit increasing accuracy with time that may cause variable performance in climatological studies. Even relying on monthly station data adjustments, IMERG shows good performance in both accuracy metrics at hourly time scales and the representation of diurnal cycles. In contrast, although ERA5 is acceptable at the daily scale, it degrades at the hourly scale due to the limitation in reproducing the peak time, magnitude and variation of diurnal cycles. IMERG underestimates snowfall compared with gauge and reanalysis data. The triple collocation analysis suggests that IMERG snowfall is worse than reanalysis and gauge data, which partly results in the degraded quality of IMERG in cold climates. This study demonstrates new findings on the uncertainties of various precipitation products and identifies potential directions for algorithm improvement. The results of this study will be useful for both developers and users of satellite rainfall products. • GPM IMERG and nine precipitation products are evaluated from 2000 to 2018. • IMERG shows higher accuracy by years due to increasing passive microwave samples. • Some new findings of various precipitation datasets are demonstrated and discussed. • The accuracy, distributions, and trends of snowfall in China are revealed. • Reanalysis products exceed IMERG and even gauge data in snowfall estimation. [ABSTRACT FROM AUTHOR]