1. Introduction
Precipitation and temperature are important for hydrological studies. They are commonly used as meteorological forcings for hydrological models and often come from weather stations. However, weather station coverage is sparse over several regions in the world, and recent years have seen a decline in surface observational networks in most countries.
This results in a shortage of data that sometimes severely limits our ability to conduct hydrological studies. It is therefore important to investigate the capacity to use meteorological data from others sources as proxies of station data, to overcome this deficit of observations.
Several studies have examined the contribution of remote sensing data for hydrological modeling (Bastola and François 2012; Cole and Moore 2009; Sagintayev et al. 2012). Overall, results of these works showed that remote sensing data have potential, but are not precise enough to allow hydrological models to adequately simulate river flows. Indeed, the precipitation rates estimated by radar often contain errors because of the difficulty faced by radars in distinguishing types and diameters of precipitation particles (Hunter 1996). Moreover, the scope and accuracy of radar measurements are often significantly degraded by the obstruction of electromagnetic waves caused, for example, by the rugged topography and trees (Warner et al. 2000; Westrick et al. 1999). Similar problems also plague satellite data since instruments cannot directly measure rainfall, and this necessitates the use of rainfall estimation techniques that have physical limitations (Barrett 1970; Grimes et al. 1999; Kidd et al. 2003; Vicente et al. 2002). Error correction in the remote sensing data still relies mostly on the use of data observed from ground weather stations. Therefore, remote sensing data can only be validated in regions with a dense stations network (Seo 1998; Steiner et al. 1999; Turk et al. 2008).
Meteorological reanalyses constitute another source of meteorological data. They make use of a wide variety of observation databases assimilated in a complex fashion into a numerical weather prediction model to produce a spatially and temporally coherent synthesis of various meteorological variables over the recent historical period. The reanalysis forecast model remains unchanged for consistency of simulated weather data. Data assimilated by reanalysis come from measurements recorded for decades throughout the world; these measurements themselves are derived from different sources. The main sources are terrestrial measurements networks, radiosondes, aircrafts, satellites, and floats (Mesinger et al. 2006; Rienecker et al. 2008; Wang et al. 2011). Terrestrial measurement networks are composed of weather stations, buoys, and ships and provide surface data for variables such as temperature, humidity, pressure, wind direction, and speed. Radiosondes, aircrafts, and satellites provide various atmospheric data, such as radiance, wind, humidity, and pressure at different atmospheric heights. Reanalyses also assimilate data from several autonomous profiling floats (Argo floats) that measure real-time temperature and the salinity of the first 2000 m of ocean water. Although reanalyses are not direct observations, they provide variables throughout the world, including in areas where weather stations are nonexistent or scattered (Bosilovich 2013).
Many studies have compared data from reanalyses to weather station data in several regions of the world (Bosilovich 2013; Lorenz and Kunstmann 2012; Manzanas et al. 2014; Rusticucci et al. 2014; Vose et al. 2012; Zhang et al. 2013). These studies generally conclude that in many cases, reanalyses are comparable to observations. For instance, Nigam and Ruiz-Barradas (2006) showed that the spatial variance of summer and winter precipitation of NARR (Mesinger et al. 2006) was similar to observations over the United States because of NARR’s assimilation of surface precipitation. They also found that in the Great Plains region, summer precipitation in NARR and observations showed similar interannual variability (Ruiz-Barradas and Nigam 2006). Rusticucci et al. (2014) found that the interannual variability of observed precipitation in the southern Central Andes in South America was well represented in ERA-Interim (Dee et al. 2011).
Compared to remote sensing data, the potential of reanalysis data for hydrological modeling studies has been less explored. In general, studies on this topic have been based on a reduced number of watersheds, and their conclusions are therefore difficult to generalize. For instance, Woo and Thorne (2006) used temperature and precipitation data from ERA-40 (Uppala et al. 2005), NCEP–NCAR reanalyses (Kalnay et al. 1996), and NARR to simulate flows in a large subarctic mountain watershed in Canada. They found a cold bias in these reanalyses that produced a late snowmelt. Furthermore, Choi et al. (2009) evaluated the applicability of NARR data for hydrological modeling on three watersheds in northern Manitoba in Canada. They found that river flows simulated from NARR data adequately represented observed hydrographs. Vu et al. (2012) tried to simulate the Dak Bla River discharges in Vietnam using data from the NCEP–NCAR reanalyses and found that simulated discharges significantly differed from those observed. It should be noted that most of the reanalyses examined in these studies have been improved, and their ability to be used as observation proxies for hydrological modeling studies is yet to be investigated.
To address biases present in reanalyses, global forcing datasets have been constructed using postprocessing techniques (e.g., bias correction) based on observations (Sheffield et al. 2006; Weedon et al. 2011, 2014). These global forcing datasets offer long-term consistent time series of near-surface meteorological variables that can be used for the study of seasonal and interannual variability. Some of these datasets are based on older reanalyses, such as NCEP–NCAR reanalyses and ERA-40 (Ngo‐Duc et al. 2005; Sheffield et al. 2006; Weedon et al. 2011), and could, therefore, be less accurate than the ones based on more recent reanalyses, such as ERA-Interim (Weedon et al. 2014). Although a bias-corrected dataset is intended to be more accurate than the reanalyses on which it is based, in regions where weather stations are sparse or nonexistent, bias correction may not bring improvement, and could even introduce additional errors in the corrected data. In addition, since precipitation and temperature are usually postprocessed separately, some coherency between both variables could be lost in the process, with potential adverse effects on impact models.
This study aims to evaluate the use of three global atmospheric reanalyses—ERA-Interim, CFSR, and MERRA—and a regional reanalysis—NARR—for hydrological modeling. The importance of biases and bias correction is further investigated by including the Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI) dataset.
Specifically, this study has two objectives: 1) compare temperature and precipitation datasets from these datasets to an observationally based gridded dataset and 2) test their ability to serve as inputs for the hydrological modeling of 370 watersheds of the Model Parameter Estimation Experiment (MOPEX) database located in different climatic regions of the contiguous United States (CONUS). These watersheds were selected because of their relatively high density of weather stations. This study will be useful in validating the use of reanalyses for hydrological modeling in regions with relatively abundant surface weather stations. If it is successful, the next step will be to evaluate the potential use of reanalysis data in regions with sparse or low density of stations. Ultimately, the main interest of reanalyses for hydrological studies is twofold: to provide proxy data in regions not well covered with surface weather stations (e.g., Northern Canada, Africa) and to provide additional variables less commonly measured (e.g., wind, humidity, real evapotranspiration). The inclusion of the WFDEI dataset will allow the impacts of bias correction on the performance of ERA-Interim to be assessed for hydrological modeling. Contrasting the performance of bias correction in regions with dense weather networks (eastern United States) versus that of regions less well covered (Midwest) should yield important information as to the applicability of reanalysis in remote regions.
2. Region of interest and datasets
a. Region of interest
This study was conducted over the CONUS, and the selected watersheds for hydrological simulations were derived from the MOPEX database (Duan et al. 2006). A total of 370 watersheds in five climatic regions (Fig. 1) according to the Köppen–Geiger climate classification (Kottek et al. 2006) were used. The watershed areas range between 104 and 10 325 km2. The daily mean precipitation, temperature, and discharge of the watersheds from each climatic region are presented in Table 1.
The 370 watersheds in the region of interest. The different colors show all watersheds from the same climatic zones.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
Range of watershed-averaged daily mean precipitation, temperature, and discharge for each climate zone.
b. Datasets
This study covers the 1979–2003 period, which is the longest common period of all the databases. All datasets are based on a daily sample.
1) Reanalysis and WFDEI datasets
(i) ERA-Interim
ERA-Interim is the latest global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF; Dee et al. 2011). It covers the period from 1979 to the present and is produced by the December 2006 integrated forecast model of ECMWF [Integrated Forecast System (IFS) cy31r2]. ERA-Interim uses a four-dimensional variational data assimilation (4DVAR) approach. The observations assimilated before 2002 come mainly from the data used for ERA-40 (Uppala et al. 2005). ERA-Interim is updated in near–real time, using data from the operational ECMWF forecast system (Dee et al. 2011). ERA-Interim temperature results from the assimilated surface temperature, while precipitation is produced by the weather forecast model. The horizontal resolution of ERA-Interim is 0.75° × 0.75°. The ERA-Interim dataset is available for free online (http://apps.ecmwf.int/datasets/).
(ii) CFSR
The global CFSR is produced by NCEP from a coupled climate system atmosphere–ocean–land surface with an interactive sea ice component. It covers the period from 1979 to the present and uses a three-dimensional variational data assimilation approach (Saha et al. 2010). CFSR assimilates satellite radiance rather than estimated temperature and humidity values (Wang et al. 2011). Estimates of greenhouse gas concentration changes, aerosols, and solar variations are used as forcings in CFSR; CFSR also assimilates hydrological quantities of a land surface parallel model forced by the NOAA/Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) (Xie and Arkin 1997) and the CPC unified daily gauge analysis (Wang et al. 2011). The horizontal resolution of CFSR is 0.313° (longitude) × 0.312° (latitude), and the CFSR dataset is available for free online (http://cfs.ncep.noaa.gov/cfsr/).
(iii) MERRA
The global MERRA is developed by the Global Modeling and Assimilation Office (GMAO) of the National Aeronautics and Space Administration (NASA) in order to allow the use of the GMAO satellite observations in a climate context and to improve the hydrological cycle represented in the first generation of reanalyses (Rienecker et al. 2011). MERRA covers the satellite era (from 1979 to present) and is generated from the Goddard Earth Observing System Model, version 5.2.0 (GEOS-5.2.0), and a data assimilation system based on a three-dimensional variational approach (3DVAR). The Data Assimilation System (DAS); the input data flux; and their sources, observations, and error statistics are well documented in Suarez et al. (2008). The primary performance drivers for the GEOS DAS are temperature, humidity, and wind fields (Schubert et al. 1993). The horizontal resolution of MERRA is ⅔° (longitude) × ½° (latitude). The datasets are available for free online (http://disc.sci.gsfc.nasa.gov/mdisc/overview/index.shtml).
(iv) NARR
The NARR is a product of NCEP, developed to produce high-resolution data for North America. NARR was developed from major improvements of the global NCEP–NCAR reanalyses (Kalnay et al. 1996; Kistler et al. 2001), both in terms of resolution and precision. In light of these improvements, NARR adequately represents extreme events such as droughts and floods. For more details about these improvements, see Mesinger et al. (2006). NARR covers the period from 1979 to the present. NARR initially covered the 1979–2003 period. A real-time extension of the NARR called the Regional Climate Data Assimilation System (R-CDAS) covers the more recent period. The NARR system uses the Eta 32-km atmospheric model with 45 vertical layers and a three-dimensional variational data assimilation approach (Mesinger et al. 2006). That model uses the convection scheme of Betts–Miller–Janjić (BMJ) (Betts and Miller 1986; Janjić 1994). Surface precipitation is assimilated in NARR as latent heat profiles (Mesinger et al. 2006). Precipitation data used for assimilation come from different sources. A 1° rain gauge analysis is used for Mexico and Canada. A ⅛° daily rain gauge data analysis from the CPC is used for the CONUS (Shafran et al. 2004); CONUS is orographically adjusted using the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) approach (Daly et al. 1994). Over oceans south of 27.5°N and land south of Mexico, the CMAP global 2.5° analysis is used, and no data are assimilated for oceans north of 43.5°N (Mesinger et al. 2006). NARR also updates the simulated snowpack using the daily global snow depth analysis model (SNODEP) of the U.S. Air Force Weather Agency (Kopp and Kiess 1996). The horizontal resolution of NARR is 32 km × 32 km. The datasets are available for free online (
(v) WFDEI
WFDEI is a global meteorological forcing dataset produced using the WATCH Forcing Data methodology (Weedon et al. 2011) applied to ERA-Interim data. It covers the period 1979–2012 and contains eight meteorological variables at a 3-hourly time step. Bias correction was performed on a monthly basis. For two of the variables—rainfall rate and snowfall rate—biases were corrected using Climatic Research Unit Time Series, version 3.101 (CRU TS3.101) (TS3.21 for 2010–12; Harris et al. 2014; New et al. 1999, 2000), and GPCC, version 5 (version 6 for 2010; Rudolf and Schneider 2005; Schneider et al. 2014). The horizontal resolution of the WFDEI datasets is ½° × ½°. The WFDEI dataset is available online (
2) Observations datasets
(i) Gridded datasets of Santa Clara
The Santa Clara observed gridded dataset was produced at the University of Washington by interpolating observed data from NOAA weather cooperative station (average of one station per 700 km2; Maurer et al. 2002). The Synergraphic Mapping System (SYMAP) algorithm of Shepard (1984) implemented by Widmann and Bretherton (2000) was used for the interpolation. The Santa Clara gridded precipitation was scaled to match the long-term average precipitation of the PRISM (Daly et al. 1994, 1997). The Santa Clara gridded dataset covers the 1949–2010 period, and its horizontal resolution is ⅛° × ⅛° (available online at http://hydro.engr.scu.edu/files/gridded_obs/daily/ncfiles_2010).
(ii) Discharge datasets of the MOPEX database
The MOPEX database contains daily mean precipitation and temperature (minimum and maximum) for 400 watersheds. Streamflows at each watershed outlet are also provided. The watershed-averaged precipitation and temperature data are derived from the National Climatic Data Center (NCDC) weather stations (Duan et al. 2006). An inverse distance weighting method was implemented to estimate the final MOPEX climate data. A detailed description of this data source is available in Schaake et al. (2006). Only time series of length greater than 10 years were admitted in the database. The MOPEX database covers the 1949–2003 period (available online at
Table 2 summarizes general information on all the databases described above.
Description of all databases used in this study.
3. Methodology
Initially, the quality of precipitation and temperature from all datasets was assessed through a comparison against observations, as represented by the Santa Clara gridded dataset. In a second step, hydrological simulations were performed with all datasets to further assess the ability of the different reanalyses to capture the complex precipitation–temperature interactions needed to adequately simulate watershed hydrology.
a. Data comparison: Temperature and precipitation
Prior to the comparison, the Santa Clara gridded dataset was aggregated to the resolution of each reanalysis and WFDEI datasets. This aggregation was achieved by averaging the data from the Santa Clara grid toward each target grid (spatial average).
The statistics used for comparison include the bias, root-mean-square error (RMSE), variances ratio, and correlation using daily time series. The mean annual cycles were also calculated and compared for each climate region. The bias is the difference between a dataset mean precipitation (or temperature) over a given period and that of the corresponding observations. It indicates how much a given dataset overestimates or underestimates the observed data. Thus, a null bias indicates a perfect fit, while a positive (or negative) bias corresponds to an overestimation (or an underestimation). The RMSE is a measure of the absolute fit between each dataset and observations. Low RMSE values indicate a better fit. The variance ratio compares the dataset variability to that of observations, and thus, a ratio of 1 indicates equal variability. The temporal correlation coefficient shows the intensity of the link between daily time series from each dataset and observed data. A zero correlation coefficient corresponds to an absence of correlation, while a correlation coefficient of 1 (or −1) indicates a perfect positive (or negative) dependence between the time series.
Results are presented for each season: winter (DJF), spring (MAM), summer (JJA), and autumn (SON). Extreme values are not analyzed because they are out of the main scope of this study.
b. Hydrological modeling: Input data and model calibration
The sizes of the watersheds considered in this study are relatively small. Therefore, the lumped conceptual hydrological model, HSAMI (Fortin 2000; Minville et al. 2008), is used to simulate discharges. HSAMI has been used to predict the hourly and daily flows of more than 100 watersheds in Quebec. It has also been used operationally by Hydro-Québec over 100 watersheds for more than 30 years, as well as in climate change impact projects (Chen et al. 2012; Poulin et al. 2011). HSAMI simulates the main hydrological cycle processes, such as vertical and horizontal water transfers, evapotranspiration, snowmelt, and soil freezing. HSAMI has 23 calibration parameters: 10 for the different production function processes, 5 for the horizontal transfer through reservoir-type soil layers, 2 for evapotranspiration, and 6 for snow-related processes. There are four interconnected reservoirs that contribute to the vertical water transfer balance: snow on ground, surface runoff, saturated soil layer, and unsaturated soil layer. The horizontal water transfer is based on two unit hydrographs (one for surface runoff and one for delayed runoff) and one linear reservoir for groundwater flows. HSAMI requires spatially averaged daily minimum and maximum temperatures as well as daily rainfall and snowfall depths. Precipitation and temperature data from all databases were averaged over each watershed using the Thiessen polygon method (Thiessen 1911). Other methods were tested (e.g., weighting by the inverse of the distance) but had no impact on the conclusions of this study.
Because of the large number of watersheds used in this study, an automatic optimization algorithm was used to calibrate the hydrological model. Arsenault and Brissette (2014) showed that the Covariance Matrix Adaptation Evolution Strategy (CMAES) algorithm (Hansen and Ostermeier 1996, 2001) was the optimal choice for calibrating HSAMI. Thus, the CMAES optimization algorithm was used to perform all calibrations in this study.
The Nash–Sutcliffe efficiency (NSE) metric (Nash and Sutcliffe 1970) was used to evaluate the performance of the different databases. Other performance metrics could have been used, but the NSE is by far the most widely used in hydrology, and it was deemed adequate for the needs of this study.
In calibration, the NSE was calculated based on the even years, with cross validation on odd years, and vice versa. This allows different climatic trends to be taken into account (e.g., natural decadal or multidecadal variability). However, this method has a disadvantage because the hydrological model has to be executed over the entire study period to select the odd years or pairs to calculate the NSE. This therefore doubles the computational cost. For each watershed, 10 calibrations in the even/odd approach and 10 calibrations in the odd/even approach were achieved for a total of 20 calibrations. This approach reduces the likelihood of the calibration algorithm not converging during a single optimization process. For each watershed, only the best parameter set was selected.
HSAMI was calibrated particularly to each specific dataset. The nonparametric Wilcoxon test was performed to test the null hypothesis of equal NSE median values of simulated discharges, between the Santa Clara gridded database and each reanalysis at the 95% confidence level (Rakotomalala 2008). To avoid any issues due to equifinality and overfitting, all results presented in the next section cover only the validation period.
4. Results
a. Data comparison: Temperature and precipitation
1) Temperature
The spatial distributions of the mean temperature biases are similar from one reanalysis to another, especially in spring and autumn (Figs. 2a–d). In general, all reanalyses tend to overestimate the observed temperatures, which results in a warm bias over most of the United States. NARR biases are relatively low in the eastern United States. Over the western United States, NARR warm biases are more important. In winter, NARR displays a cold bias in the Midwest. During winter and autumn, ERA-Interim overestimates the temperature over most of the United States, except in Florida. In summer, ERA-Interim is cooler than observations in the southern United States.
The 1979–2003 mean seasonal temperature difference (°C) between reanalyses and the observed gridded dataset from Santa Clara.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
In spring, in the northeastern United States, ERA-Interim agrees well with observations (bias between −0.5° and 0.5°C). CFSR is warmer than observations in the South and Midwest, regardless of the season. MERRA is warmer than observations in the western United States, but cooler in the Midwest and New England during winter and in the South during summer.
In the case of WFDEI, the spatial distribution of the mean temperature biases is similar for all seasons. In general, these biases are between −0.5° and 1°C in the eastern United States (Fig. 2e). They are warmer in the northwestern United States (particularly during winter, bias > 2°C) and cooler in the southwestern United States (particularly during spring and summer). Overall, results show that bias correction reduced the biases of the mean temperature compared to ERA-Interim. This is particularly true in the eastern United States, likely because of a denser network of weather stations.
In general, RMSE values indicate that the gaps between reanalysis mean temperatures and observations are higher in winter (Figs. 3a–d). In the eastern United States, the RMSE values of NARR are less than 2°C and are lower than in the western part. Furthermore, in the Midwest and the Rockies, the RMSE values of ERA-Interim, CFSR, and MERRA are higher than in the South and the East.
The 1979–2003 mean seasonal temperature RMSE (°C) between the reanalyses and the observed gridded dataset from Santa Clara.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
Most of the RMSE values of WFDEI are between 1° and 1.5°C in the eastern United States, except in winter (Fig. 3e). In the western United States, the RMSE values are higher (RMSE > 1.5°C) especially during winter in the Midwest where RMSE > 2°C. Overall, the RMSE values of the mean temperature from WFDEI are lower than their ERA-Interim counterparts.
The variances of daily temperature from the reanalyses are generally similar to the observations, particularly in the humid continental and subtropical regions (Figs. 4a–d). Compared to precipitation (shown below), temperature variance is generally lower and is more easily simulated by the reanalyses. The four reanalyses tend to show less variance than observed during winter in the western United States. The highest variance ratios (ratio >1.5) are obtained during the summer in the northwestern United States for NARR and the western United States for ERA-Interim and MERRA. CFSR temperature variance is greater than observations over most of the United States during summer. Reanalysis model uncertainty plays an important role in the representation of the temperature variance (Willett et al. 2012). That uncertainty is higher in summer and leads to a larger difference between reanalyses and observed temperature variance.
The 1979–2003 ratio of variance of mean daily temperature between reanalyses and the observed gridded dataset from Santa Clara for each season.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
The variances of WFDEI daily temperature are generally very similar to the observations in the eastern United States during spring and autumn, with variance ratios varying between 0.75 and 1.25 (Fig. 4e). The patterns of the variance ratios of WFDEI and ERA-Interim are similar. However, ratios are lower with WFDEI than with ERA-Interim.
Correlations between reanalysis daily temperature time series and observations are much larger than for precipitation. During spring and fall, reanalysis correlation coefficients are higher than 0.9 throughout the United States (Figs. 5a–d). In winter, the correlations are lower in the Rockies. Lower correlation coefficients are observed in the southern United States during summer. In general, correlation is higher between observations and NARR. Of the three global reanalyses, ERA-Interim temperature has the best overall correlation with observations. Indeed, the mean area-average correlation coefficients in winter, spring, summer, and autumn are 0.92, 0.97, 0.87, and 0.98 for ERA-Interim; 0.91, 0.94, 0.89, and 0.97 for CFSR; and 0.89, 0.96, 0.85, and 0.97 for MERRA, respectively. This is likely due to the assimilation of land surface temperatures in ERA-Interim (Dee et al. 2011; Simmons et al. 2010). The correlation spatial patterns of WFDEI and ERA-Interim daily temperature time series are similar (Fig. 5e). However, correlation coefficients are higher for WFDEI.
Daily temporal correlation between reanalyses and Santa Clara temperature. Results are shown by season and are based on daily data for the 1979–2003 period.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
Overall, reanalyses and WFDEI adequately reproduce the mean annual cycle of observed temperatures in the five climate regions of this study (figure not shown). This result is not surprising because reanalysis temperature is well correlated with observed temperature, and their biases are low.
In summary, the representation of temperature in reanalyses is robust, probably because the atmospheric temperatures from radiosondes and satellites are regularly assimilated in the reanalysis systems. Bias-correcting reanalysis (as represented by the WFDEI dataset) results in improved values of all considered statistical criteria. The absolute improvement is, however, relatively small, since reanalysis performs quite well with respect to temperature.
2) Precipitation
Differences between the mean seasonal precipitation of reanalyses and observations (Figs. 6a–d) show that NARR data are much closer to the observations compared to the other datasets, including WFDEI. This is because, unlike other reanalyses, the NARR atmospheric model precipitation is forced by observed precipitation through latent heat profiles. In general, NARR is slightly drier than observations for each season over the United States, except in the Midwest region, where NARR precipitation biases are relatively low (±10%). ERA-Interim and MERRA tend to be drier than observations in the Southeast and the West Coast in the winter, spring, and fall, but they are wetter in the northern high plains, especially in winter. Overall, ERA-Interim wet biases are higher than those of MERRA, while MERRA dry biases are higher than those of ERA-Interim. In summer, these two reanalyses are wetter than observations in the southern United States, but are drier in the Midwest. Bosilovich (2013) obtained similar results while studying the ability of reanalyses to reproduce changes in summer precipitation and temperature in the United States. CFSR shows similar biases as ERA-Interim and MERRA. However, in the Midwest and the western United States, CFSR wet biases are significantly higher in winter (bias > 130%), and its dry biases are considerably higher in the summer (bias < −40%).
The 1979–2003 mean seasonal precipitation relative difference between reanalyses and observed gridded dataset from Santa Clara.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
Differences between the mean seasonal precipitation of the WFDEI-forced GPCC (WFDEI-GPCC) or WFDEI-forced CRU TS (WFDEI-CRU) and observations are shown in Figs. 6e and 6f. Results show that the precipitation of WFDEI is similar to that of observations in the eastern United States during spring, summer, and autumn and in the southeastern United States during winter (bias ±10%). In the western United States, WFDEI-GPCC and WFDEI-CRU are drier than observations. Overall, bias correction seems to have improved the precipitation from ERA-Interim only in the eastern United States, possibly because of the higher density of weather stations in this region.
Overall, the RMSE spatial distributions of the reanalyses are similar to one another (Figs. 7a–d). In general, RMSE values are high (RMSE > 6 mm day−1) in the southeastern United States, where land–atmosphere interactions strongly affect the reanalysis forecast model. In fact, the land–atmosphere interactions influence the physical parameterizations in the forecast model (Bosilovich 2013). Moreover, the atmospheric moisture fluxes and the land surface soil moisture affect local precipitation (Wei et al. 2016). In the southeastern United States, there is a strong humidity gradient and an intense moisture flux, which increases the influence of land–atmosphere interactions on the forecast model of the reanalysis. Conversely, in the western half of the United States, RMSE values are lower (RMSE < 2 mm day−1). However, in the ocean and Mediterranean regions, where precipitation is abundant, regular, and largely influenced by the Pacific Ocean, reanalyses face difficulties in adequately estimating quantities of precipitation in the winter, spring, and autumn (RMSE > 5 mm day−1). In general, NARR and MERRA RMSE values are lower than those of the other two reanalysis. The highest RMSE values are obtained with CFSR (RMSE > 9 mm day−1 in the southern United States).
The 1979–2003 seasonal precipitation RMSE (mm day−1) between reanalyses and observed gridded dataset from Santa Clara.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
The pattern of the RMSE values for WFDEI-GPCC and WFDEI-CRU is similar to that of ERA-Interim. However, in the southeastern United States—and in the Midwest during summer—the RMSE values of precipitation from WFDEI are slightly higher than those of ERA-Interim (Figs. 7e,f). In this region, bias correction of ERA-Interim has not resulted in any improvement.
NARR precipitation variance is globally similar to the observations (variance ratios between 0.8 and 1.2; Fig. 8a). In general, ERA-Interim and MERRA precipitation variances are higher than observed values, except in the southern United States (Figs. 8b–d). Over most of the United States, CFSR precipitation variance is higher than that of the observations.
Variance ratios of daily precipitation between reanalyses and observed gridded dataset from Santa Clara for each season.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
The variance of WFDEI precipitation is higher than observed values in the eastern United States during winter, spring, and autumn and lower in the western United States, particularly in the Midwest during winter (Figs. 8e,f). In summer, WFDEI and ERA-Interim perform similarly.
Daily precipitation series from reanalysis generally correlate well with the observations over the United States (Figs. 9a–d). The lowest correlations are obtained in the summer because, during that season, reanalysis models have a lower predictive ability, which is explained by local and stochastic weather conditions (Bosilovich 2013; Bosilovich et al. 2009). Compared to the other reanalyses, NARR displays the best correlations with coefficients above 0.8 over the entire United States, with the exception of the central part, where the correspondence with observations is lower (correlation coefficient <0.6). Fuller (2012) also observed a low correlation between NARR and observed precipitation from two weather stations in the central United States. During winter, spring, and fall, ERA-Interim, MERRA, and CFSR correlate well with observations in the eastern United States and in the mountainous regions of the western United States (correlation coefficient >0.7). In the southern and central United States, these three reanalyses are less well correlated with observations. During winter, the atmospheric dynamics over the United States generally plays an important role for precipitation, and there is a high correlation between precipitation from reanalyses and observations, except in the lower Mississippi River valley region in the southern United States, where precipitation is mainly convective in winter. However, in summer, the correlation between the precipitation from reanalyses and observations is lower than in winter because convection processes are more important because of the strong land–atmosphere interactions (Higgins et al. 2010). Despite its coarser resolution, ERA-Interim correlates better with the observations as compared to the other two global reanalyses. Furthermore, despite its higher resolution, CFSR displays the lowest correlations.
Daily correlation between reanalyses and Santa Clara precipitation. Results are shown by season and are based on daily data for the 1979–2003 period.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
The correlation between WFDEI precipitation and observations is similar to that between ERA-Interim and observations (Figs. 9e,f), except in the western United States during winter, spring, and summer, where WFDEI is slightly worse. Overall, bias-correcting ERA-Interim precipitation has not improved its correlation against observations.
The mean annual cycle of precipitation from all datasets is similar to the one observed in the West Coast and Mediterranean regions, especially during the summer and autumn (Figs. 10a,b). This is consistent with previous results. Indeed, for these regions in the summer and autumn, reanalysis precipitation correlates well with observations, and their biases are relatively low. Results also show that in these climate regions, CFSR precipitation amounts are greater than observations during the winter and spring. ERA-Interim and MERRA precipitation is lower than observations during the winter and autumn. In the semiarid region (Fig. 10c), the reanalysis annual cycles are similar to the observed one. In the humid continental region, CFSR weakly reproduces the annual observed cycle (Fig. 10d); the same is seen in the humid subtropical region for all three global reanalyses (Fig. 10e). Unlike these global reanalyses, NARR adequately simulates the annual precipitation cycle in this region. This result is not surprising since NARR assimilates surface precipitation. Bukovsky and Karoly (2007) also obtained similar results for NARR. Compared to ERA-Interim, WFDEI adequately simulates the annual precipitation cycle in the humid subtropical region. This implies that precipitation biases present in the ERA-Interim dataset have been adequately corrected in that climate region, as discussed earlier.
The 1979–2003 mean annual cycle of precipitation for the five climatic zones in which the 370 watersheds are distributed.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
b. Hydrological simulations: Performance statistics
The validation performance of different precipitation and temperature combinations are shown in Fig. 11. Results show that precipitation from NARR, combined with the temperature of any of the reanalyses—except MERRA—leads to a similar performance. The same is true for the precipitation of other reanalyses. This indicates that NARR, ERA-Interim, and CFSR temperatures are equally good with respect to hydrological modeling. In other words, the differences existing between these reanalysis temperature datasets are not significant. Results also show that the use of the MERRA temperature leads to a slight statistically significant drop in performance. In almost all the cases, the drop in performance appears to be significant, according to the Wilcoxon statistical test performed, and stands at a 95% level of significance. This result is consistent with the temperature comparison results, which showed that the MERRA temperature deviates the most from observations. It also appears from the results presented in Fig. 11 that reanalysis performance in hydrological simulation is mainly determined by the quality of its precipitation field.
Distribution of the NSE values of the different datasets for 370 watersheds. Results are based on daily discharges simulated in the validation period. The lower and upper limits of each box plot represent the 25th and 75th percentiles, respectively. The middle line represents the median (50th percentile). The ends of the whiskers represent extreme values.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
Generally, the hydrological performance of observations (Santa Clara) is slightly superior to that of NARR (Fig. 12a). The NSE median values are 0.784 for observations and 0.764 for NARR when considering all of the 370 watersheds. Performances obtained from the global reanalyses and WFDEI are significantly lower than those of NARR. Their NSE median values are equal to 0.512 for ERA-Interim, 0.496 for CFSR, 0.441 for MERRA, 0.590 for WFDEI-GPCC, and 0.519 for WFDEI-CRU. Hydrologists generally consider 0.6 as an acceptable NSE value (Chiew et al. 2009; Kouame et al. 2013; Kralisch et al. 2007; Pappenberger and Buizza 2009). The performances of the global reanalyses and those of the global WFDEI data are below that threshold for at least 73% of the watersheds, with the exception of WFDEI-GPCC (53% of the watersheds). Individual comparisons show that the NSE values of WFDEI-GPCC and WFDEI-CRU are superior to those of ERA-Interim for 74% and 52%, respectively, of the 370 watersheds. A more complex portrait emerges when each of the five climate regions is considered (Figs. 12b–f).
Distribution of performances based on daily discharges simulated in the validation period.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
Hydrological performances are also compared by climate regions. Results show that in both the humid continental and humid subtropical regions (Figs. 12b,c), the global reanalysis performance is particularly low. For example, from the 213 catchments considered in the humid continental region, 78% display an NSE value lower than 0.6 for ERA-Interim, 86% for CFSR, and 90% for MERRA. In that climatic zone, the NSE values of WFDEI-GPCC (median NSE = 0.554) and WFDEI-CRU (median NSE = 0.490) are superior to those of ERA-Interim (median NSE = 0.512) for 72% and 44% of the watersheds, respectively. Thus, WFDEI-GPCC is significantly better than ERA-Interim, whereas ERA-Interim is slightly better than WFDEI-CRU for most of the watersheds in the humid continental region. In the humid subtropical region, both WFDEI-GPCC (median NSE = 0.601) and WFDEI-CRU (median NSE =0.530) are also significantly better than ERA-Interim (median NSE = 0.445).
On the other hand, all the reanalyses performed very well over the West Coast, Mediterranean, and semiarid regions. The NSE median value of each reanalysis is greater than 0.7 in these climate regions, and their performances are similar to (and sometimes better than) those obtained from observations. WFDEI-GPCC and WFDEI-CRU perform similarly to ERA-Interim, except in the semiarid region, where ERA-Interim (median NSE = 0.768) performs significantly better than those of WFDEI-GPCC (median NSE = 0.630) and WFDEI-CRU (median NSE = 0.580). Thus, in the semiarid region, bias correction made ERA-Interim worse, possibly because of the relatively sparse network of weather stations in that region.
As mentioned previously in the data comparison section, the three global reanalyses do not adequately reproduce the observed annual cycle of precipitation in the humid continental and subtropical regions of the United States (see Figs. 10d,e). These same behaviors have been observed at the watershed scale. The inadequate representation of the seasonal precipitation cycle is the main cause leading to the poor performance of these reanalyses for hydrological modeling over both the continental and humid subtropical regions. Indeed, the hydrological model is not able to adequately simulate flows observed when precipitation seasonality is not well represented in input datasets.
The performance of the reanalyses was also assessed on the basis of simulated mean monthly flows over the calibration–validation period. The distribution of performances is similar to those shown in Fig. 12, but with slightly improved NSE values. This implies that the monthly bias structure of the reanalyses, not deficiencies at the daily scale, is the main obstacle to hydrological modeling.
The measured and simulated average annual hydrographs computed on the basis of mean daily flows over the calibration–validation period were compared in the humid continental and humid subtropical regions. Results are shown for two rivers: the East Fork White River at Columbus located in Indiana (in the humid continental region; Fig. 13a) and the Flint River at Montezuma located in Georgia (in the humid subtropical region; Fig. 13b). The simulated discharge NSE values (over the validation period) for the East Fork White River and the Flint River are 0.791 and 0.828 for Santa Clara, 0.776 and 0.813 for NARR, 0.583 and 0.455 for ERA-Interim, 0.464 and 0.472 for CFSR, 0.547 and 0.376 for MERRA, 0.613 and 0.634 for WFDEI-PGCC, and 0.592 and 0.628 for WFDEI-CRU, respectively. These results are typical of most watersheds in each of the two humid regions. For both rivers, the dry biases of global reanalyses in winter led to a significant underestimation of discharges, whereas the wet biases in summer led to a considerable overestimation of discharge. In general, the simulated discharges using the Santa Clara dataset also led to an underestimation of winter/spring discharges and an overestimation of summer/autumn discharges, but to a much lesser extent than those due to global reanalyses. This explains the low performances of global reanalyses for both rivers and, more generally, the low performances of global reanalyses in both humid continental and subtropical regions.
Comparison of the measured and simulated mean annual cycle hydrographs of the (a) East Fork White River and (b) Flint River. Hydrographs are based on daily mean discharges during 1979–2003.
Citation: Journal of Hydrometeorology 17, 7; 10.1175/JHM-D-15-0138.1
5. Discussion
Precipitation and temperature are the two principal meteorological inputs for hydrological modeling. These data are sourced mainly from weather stations, although many regions in the world have a sparse network of monitoring stations, resulting in a severe limitation of hydrological studies.
In regions with sparse weather station coverage, reanalyses may offer a good alternative to station data; reanalyses in fact offer global coverage and may be good proxies in the absence of surface observations, since they rely on global observations from multiple sources that are assimilated in a weather forecast model. However, the spatial resolution of reanalyses is relatively coarse, and the quality of their precipitation and temperature has to be validated in detail before being used for hydrological modeling.
To investigate the potential of reanalysis for use as proxies of surface observations of precipitation and temperature, four different atmospheric reanalyses were evaluated and compared to observations. In this work, observations are represented by the Santa Clara gridded dataset. A comparison of precipitation from reanalyses to the gridded dataset showed that reanalyses are generally biased, especially in the Midwest and humid subtropical regions. Temperature biases from reanalyses vary from season to season and from one reanalysis to another. Generally, temperatures exhibits smaller biases than precipitation, but MERRA temperature biases are consistently high in the western United States during summer.
Overall, differences between reanalyses and observed gridded precipitation and temperature were judged to be sufficiently small to allow reanalysis outputs to be used directly for hydrological modeling, without any sort of bias correction needed. To that end, temperature and precipitation from reanalyses were averaged at the watershed scale on 370 watersheds in the United States. HSAMI was then calibrated to each dataset (reanalyses and gridded observations), and the river flow simulated by the hydrological model was evaluated against observed flows over a validation period. This approach provides an indirect validation of reanalyses that are used to force the hydrological model. It measures the differences between reanalyses and observations by taking into account the consistency between precipitation and temperature, which is key for hydrological modeling. Proceeding without bias correction implies that all the differences between the observed and reanalysis fields are small enough to be taken into account through the adjustment of the hydrological model parameters. It also recognizes the fact that differences between gridded observations and reanalyses may be the result of biases in the reanalyses, gridded observations, or a combination of both. While weather surface observations are commonly recognized as constituting the most accurate representation of reality, they do suffer from biases, especially at the watershed scale, due to observational errors and, more importantly, to inhomogeneous coverage of weather stations, especially in the case of mountainous watersheds.
The results showed that adequately representing precipitation seasonality is critical and that simulated river flows using NARR forcing are similar to the simulated streamflows using the gridded observations. This is linked to the NARR surface precipitation assimilation in its atmospheric model (Mesinger et al. 2006; Sheffield et al. 2012). Although this assimilation is done indirectly through latent heat profiles, it seems to be effective. It should be mentioned that the good capacity of NARR over the continental United States does not extend to Canada, where weather station coverage is much lower, especially in northern Canada (Bukovsky and Karoly 2007; Langlois et al. 2009).
In the humid continental and subtropical regions, the precipitation from ERA-Interim, CFSR, and MERRA is significantly different from the gridded observation and NARR. These reanalyses do not assimilate surface precipitation data and rely on the physics of their weather forecast models to simulate precipitation, which they often do rather poorly, especially in the summer (Bosilovich 2013; Higgins et al. 2010). Indeed, in these climatic regions, summers are hot and humid because of the tropical atmospheric flow from the Gulf of Mexico. Most rainfall occurs as convective storms in the summer. These local events are not well simulated in global reanalyses, mainly because of their coarse resolutions. Moreover, precipitation is unevenly distributed over the year in both climatic regions, and their seasonality is highly sensitive to daily precipitation because of a weak mean annual cycle. For these reasons, global reanalyses are unable to adequately reproduce the seasonality of precipitation in humid continental and subtropical regions. This explains the relatively poor ability of the three global reanalyses to produce an adequate simulation of the river flow by the hydrological model, in both the humid continental and subtropical regions. Despite a specific calibration to each dataset, the hydrological model’s parameters were not able to compensate for the inadequate representation of the seasonality of precipitation by the global reanalyses.
For the other three climatic regions, the streamflows simulated using the global reanalyses were similar to those obtained from the gridded observations. This suggests that surface precipitation assimilation is not always essential for a good river flow simulation by hydrological modeling forced by global reanalyses. In the three western U.S. climatic regions, the frequency and intensity of precipitation are both lower than in the eastern climatic regions. In particular, for the oceanic and Mediterranean regions, precipitation is influenced by the proximity to the Pacific Ocean. Moreover, precipitation-generating weather systems in the western United States during the cold/wet season are much more dynamic; that is, precipitation is more strongly forced and likely more predictable. For these reasons, despite their coarse resolutions, global reanalyses manage to adequately represent precipitation seasonality and therefore lead to river flow simulations that are comparable to when gridded observations are used to force hydrological models.
Overall, the results confirm the potential of reanalyses as adequate forcings to hydrological models, despite some known weaknesses, such as their coarse resolutions and nonclosure of the water budget due to the mixture of model data and observed data during each analysis process performed about every 12 h (Lorenz and Kunstmann 2012; Trenberth et al. 2011). In addition, the separate analysis of the surface, the atmosphere, and the ocean, as well as the change in time and space of the quantity and quality of assimilated data (Poli et al. 2010; Wang et al. 2011), may introduce false variabilities, sudden changes, and trends in the reanalysis datasets. However, reanalyses will continue to improve in the future and should result in even better accuracy for river flow simulations from hydrological models.
The goal of this paper was to present a comprehensive evaluation of reanalysis precipitation and temperature for streamflow simulations from hydrological models. The continental United States was chosen because of its overall relatively good station coverage, thus allowing a robust validation benchmark for reanalysis temperature and precipitation. The real interest of reanalyses for hydrological studies lies in regions not well covered with surface weather stations, such as northern Canada and the Arctic (Lindsay et al. 2014). Thus, further work should compare the accuracy of simulated streamflow using reanalyses to those using gridded observations, as a function of the density of surface weather stations. It is expected that the accuracy of simulated streamflows using gridded observations will decrease with the reduction of the density of the weather stations. On the other hand, global reanalyses should be less affected by the lack of weather stations since they simulate their own precipitation and rely on global data from different sources in their assimilation process.
For regions where precipitation in the global reanalyses are known to be not very good, a combination of reanalyses with the few available observations may be an interesting approach to develop better datasets. Such global datasets have been developed by postprocessing (bias correction) global reanalysis with global observation-derived gridded datasets (Sheffield et al. 2006; Weedon et al. 2011, 2014). However, global observation datasets also contain spatially dependent biases (Adam and Lettenmaier 2003; Cherry et al. 2007; Goodison et al. 1998). In addition, bias-correcting precipitation and temperature independently can impact the spatial and temporal correlation between those variables (Li et al. 2014). Therefore, uncertainties also exist in these global forcing data.
Indeed, the comparison of ERA-Interim to its bias-corrected counterpart (WFDEI) shows that in the western United States, ERA-Interim was just as good as or better than WFDEI. That is likely because, in that region, precipitation is more dynamic and thus well reproduced by reanalyses. Moreover, the relatively low density of weather stations in the semiarid region might have reduced the efficiency of bias correction and even possibly introduced errors leading to a degraded performance compared to the original ERA-Interim dataset.
Overall, results suggest that postprocessing (bias correction) global reanalyses with global observation-derived gridded datasets will not automatically result in improved river flow simulation. This suggests that the quality of the underlying observational dataset is critical. This has important implications for the use of such datasets in remote regions.
Further work should compare the accuracy of simulated streamflow using reanalyses to those using global forcing data (e.g., compare ERA-Interim to WFDEI) associated over regions with sparse weather stations, such as northern Canada.
A key advantage of reanalyses (ERA-Interim, CFSR, and MERRA) is that they are updated on a regular basis (in near–real time in some cases), which is important for many water resources management applications, which is not the case for global forcing databases (e.g., WFDEI does not extend beyond 2012).
6. Conclusions
In this study, precipitation and temperature data from NARR and from global reanalyses ERA-Interim, CFSR, and MERRA were compared to gridded Santa Clara observations over the CONUS. The potential use of precipitation and temperature data from reanalyses as direct inputs for hydrological modeling was investigated. Precipitation and temperature series were used to calibrate a lumped hydrological model and to simulate river flows over 370 watersheds located in five climatic regions over the CONUS. The Nash–Sutcliffe values of simulated river flows using reanalysis forcings were compared against simulated streamflows using gridded observations.
Results showed that the temperatures from reanalyses are generally comparable to those observed over the CONUS, except in the western United States during summer for MERRA. Furthermore, there were some notable differences between precipitation and observations in the reanalyses, especially in the summer and winter.
Hydrological simulation was then used to indirectly validate the reanalyses. Over the five chosen climatic regions, the simulated river flows using the NARR forcing were as good as when the gridded observations were used. Overall, the Nash–Sutcliffe values of the simulated river flows using the global reanalyses were equal to those of the simulated river flows using the gridded observations, with the exception of the humid continental and subtropical regions, where precipitation seasonality is not well reproduced.
This study shows that reanalyses have a strong potential for use as proxies to weather station data, despite various differences between reanalyses and gridded observations. This potential is particularly promising in regions where weather station coverage is limited.
Acknowledgments
This work was funded through a Natural Science and Engineering Research Council collaborative research grant (NSERC-CRD) with Hydro-Québec, Rio-Tinto-Alcan, Ontario Power Generation, and the Ouranos consortium on regional climatology and adaptation to climate change as industrial partners. These industrial research partners are greatly acknowledged for their direct and indirect contributions to this work. We also sincerely thank all the individuals and institutions that developed the datasets used in this work and made them available to the scientific community. We hope that this work is a small contribution to their important effort.
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