CANOPEX: A Canadian hydrometeorological watershed database

Over the past few years, many international initiatives and collaborations were launched to improve and share knowledge in hydrology research. Large databases allowed finding patterns and relationships across regions and scales. This paper introduces the Canadian model parameter experiment (CANOPEX) database, which is adapted from the US MOPEX project data and methods. The CANOPEX database includes meteorological and hydrometric data as well as watershed boundaries for 698 basins. Two sets of basin‐averaged meteorological data (Maximum and minimum temperature and precipitation) are provided. The first dataset is directly taken from Environment Canada's weather stations whereas the second is extracted from the Natural Resources Canada gridded climate data product. Data are provided in MOPEX and Matlab formats. CANOPEX watersheds are well distributed over Canada, which allows investigating a variety of physiological and climatological conditions. The CANOPEX database can be used in a variety of hydrologic research projects such as climate change impact studies, model comparisons, multi‐modelling, ensemble streamflow prediction and model parameter estimation. CANOPEX could be used to generalize findings to other cold climate catchments as well as assess the robustness of research methodologies and procedures. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluating the suitability of three gridded-datasets and their impacts on hydrological simulation at Scotty Creek in the southern Northwest Territories,Canada

In the southern Northwest Territories (NWT), long time series of historical observations of climate and hydrology are scarce. Gridded datasets have been used as an alternative to instrumental observations for climate analysis in this area, but not for driving models to understand hydrological processes in the southern NWT. The suitability of temperature and precipitation from three-gridded datasets (Australian National University Spline [ANUSPLIN], ERA-Interim, and Modern-Era Retrospective Analysis for Research and Application, Version 2 [MERRA-2]) as forcings for hydrological modelling in a small subcatchment in the southern NWT are assessed. Multiple statistical techniques are used to ensure that structural and temporal attributes of the observational datasets are adequately compared. Daily minimum and maximum air temperatures in gridded datasets are more similar to observations than precipitation. The ANUSPLIN temperature time series are more statistically similar to observations, based on population statistics and temporal structure, than either of ERA-Interim or MERRA-2. The gridded datasets capture the seasonal and annual seasonal variability of precipitation but with large biases. ANUSPLIN precipitation compares better with observations than either ERA-Interim or MERRA-2 precipitation. The biases in these gridded datasets affect run-off simulations. The biases in hydrological simulations are predictable from the statistical differences between gridded datasets and observations and can be used to make informed choices about their use.     

Using the Climate Forecast System Reanalysis as weather input data for watershed models

Obtaining representative meteorological data for watershed‐scale hydrological modelling can be difficult and time consuming. Land‐based weather stations do not always adequately represent the weather occurring over a watershed, because they can be far from the watershed of interest and can have gaps in their data series, or recent data are not available. This study presents a method for using the Climate Forecast System Reanalysis (CFSR) global meteorological dataset to obtain historical weather data and demonstrates the application to modelling five watersheds representing different hydroclimate regimes. CFSR data are available globally for each hour since 1979 at a 38‐km resolution. Results show that utilizing the CFSR precipitation and temperature data to force a watershed model provides stream discharge simulations that are as good as or better than models forced using traditional weather gauging stations, especially when stations are more than 10 km from the watershed. These results further demonstrate that adding CFSR data to the suite of watershed modelling tools provides new opportunities for meeting the challenges of modelling un‐gauged watersheds and advancing real‐time hydrological modelling. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

Using reanalysis‐driven regional climate model outputs for hydrology modelling

Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale.     

Gridded climate data products are an alternative to instrumental measurements as inputs to rainfall–runoff models

Rainfall–runoff models are widely used to predict flows using observed (instrumental) time series of air temperature and precipitation as inputs. Poor model performance is often associated with difficulties in estimating catchment‐scale meteorological variables from point observations. Readily available gridded climate products are an underutilized source of temperature and precipitation time series for rainfall–runoff modelling, which may overcome some of the performance issues associated with poor‐quality instrumental data in small headwater monitoring catchments. Here we compare the performance of instrumental measured and E‐OBS gridded temperature and precipitation time series as inputs in the rainfall–runoff models “PERSiST” and “HBV” for flow prediction in six small Swedish catchments. For both models and most catchments, the gridded data produced statistically better simulations than did those obtained using instrumental measurements. Despite the high correspondence between instrumental and gridded temperature, both temperature and precipitation were responsible for the difference. We conclude that (a) gridded climate products such as the E‐OBS dataset could be more widely used as alternative input to rainfall–runoff models, even when instrumental measurements are available, and (b) the processing applied to gridded climate products appears to provide a more realistic approximation of small catchment‐scale temperature and precipitation patterns needed for flow simulations. Further research on this issue is needed and encouraged.     

Evaluating weather observations and the Climate Forecast System Reanalysis as inputs for hydrologic modelling in the tropics

Correctly representing weather is critical to hydrological modelling, but scarce or poor quality observations can often compromise model accuracy. Reanalysis datasets may help to address this basic challenge. The Climate Forecast System Reanalysis (CFSR) dataset provides continuous, globally available records, and CFSR data have produced satisfactory hydrological model performance in some temperate and monsoonal locations. However, the use of CFSR for hydrological modelling in tropical and semi‐tropical basins has not been adequately evaluated. Taking advantage of exceptionally high rainfall station density in the catchments of the Rio Grande de Loiza above San Juan, Puerto Rico, we compared model performance based on CFSR records with that based on publicly available weather stations in the Global Historical Climate Network (GHCN, n = 21) and on a dataset of rainfall records maintained by the United States Geological Survey Caribbean Water Science Center (USGS, n = 24). For an implementation of the Soil and Water Assessment Tool (SWAT) with subbasins defined at 11 streamflow gages, uncalibrated measures of Nash–Sutcliffe efficiency (NSE) were >0 at 8 of 11 gages using USGS precipitation data for daily simulations over the period 1998–2012, but were <0 using GHCN weather station records (8 of 11) and CFSR reanalysis data (9 of 11). Autocalibration of individual SWAT models for each of the 11 basins against each of the available weather datasets yielded NSE values > 0 using all precipitation inputs, including CFSR. However, the ground weather station closest to the geographic basin centre produced the highest NSE values in only 5 of 11 cases. The spatially interpolated CFSR data performed as well or better than single ground observations made further than 20–30 km, and sometimes better than individual weather stations <10 km from the basin centroid. In addition to demonstrating the need to evaluate available weather inputs, this research reinforces the value of CFSR data as a means to supplement ground records and consistently determine a baseline for hydrologic model performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Suitability of North American Regional Reanalysis (NARR) output for hydrologic modelling and analysis in mountainous terrain

Meteorological observations at high elevations in mountainous regions are often lacking. One opportunity to fill this data gap is through the use of downscaled output from weather reanalysis models. In this study, we tested the accuracy of downscaled output from the North American Regional Reanalysis (NARR) against high‐elevation surface observations at four ridgetop locations in the southern Coast Mountains of British Columbia, Canada. NARR model output was downscaled to the surface observation locations through three‐dimensional interpolation for air temperature, vapour pressure and wind speed and two‐dimensional interpolation for radiation variables. Accuracy was tested at both the 3‐hourly and daily time scales. Air temperature displayed a high level of agreement, especially at the daily scale, with root mean square error (RMSE) values ranging from 0.98 to 1.21 °C across all sites. Vapour pressure downscaling accuracy was also quite high (RMSE of 0.06 to 0.11 hPa) but displayed some site specific bias. Although NARR overestimated wind speed, there were moderate to strong linear relations (r² from 0.38 to 0.84 for daily means), suggesting that the NARR output could be used as an index and bias‐corrected. NARR output reproduced the seasonal cycle for incoming short‐wave radiation, with Nash–Sutcliffe model efficiencies ranging from 0.78 to 0.87, but accuracy suffered on days with cloud cover, resulting in a positive bias and RMSE ranged from 42 to 46 Wm^{? 2}. Although fewer data were available, incoming long‐wave radiation from NARR had an RMSE of 19 Wm^{? 2} and outperformed common methods for estimating incoming long‐wave radiation. NARR air temperature showed potential to assist in hydrologic analysis and modelling during an atmospheric river storm event, which are characterized by warm and wet air masses with atypical vertical temperature gradients. The incorporation of a synthetic NARR air temperature station to better represent the higher freezing levels resulted in increased predicted peak flows, which better match the observed run‐off during the event. Copyright © 2016 John Wiley & Sons, Ltd.     

Alternative climate data sources for distributed hydrological modelling on a daily time step

Two major criteria in choosing climate data for use in hydrological modelling are the period of record of the data set and the proximity of the collection platform(s) to the basin under study. Conventional data sets are derived from weather stations; however, in many cases there are no weather stations sufficiently close to a basin to be representative of climate conditions in that basin. In addition, it is often the case either that the period of record for the weather station(s) does not cover the period of the proposed simulation or that there are gaps in the data. Therefore, the objectives of this study are to investigate alternative climate data sources for use in hydrological modelling and to develop a protocol for creating hydrological data sets that are spatially and temporally harmonized. The methods we used for constructing daily, spatially distributed, climatic data sets of precipitation, maximum and minimum temperature, wind speed, solar radiation, potential evapotranspiration, and relative humidity are described. The model used in this study was the Soil and Water Assessment Tool implemented on the Mimbres River Basin located in southwestern New Mexico, USA, for the period 2003–2006. Our hydrological simulations showed that two events in January and February 2005 were missed, while an event in August 2006 was well simulated. We have also investigated the usefulness of several other precipitation data sets and compared the simulation results. Copyright © 2010 John Wiley & Sons, Ltd.     

Validation of a new meteorological forcing data in analysis of spatial and temporal variability of precipitation in India

During the past two decades, numerous datasets have been developed for global/regional hydrological assessment and modeling, but these datasets often show differences in their spatial and temporal distributions of precipitation, which is one of the most critical input variables in global/regional hydrological modeling. This paper is aimed to explore the precipitation characteristics of the Water and Global Change (WATCH) forcing data (WFD) and compare these with the corresponding characteristics derived from satellite-gauge data (TRMM 3B42 and GPCP 1DD) and rain gauge data. It compared the consistency and difference between the WFD and satellite-gauge data in India and examined whether the pattern of seasonal (winter, pre-monsoon, monsoon and post-monsoon) precipitation over six regions [e.g. North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI)] of India agrees well for the gridded data to be useful in precipitation variability analyses. The multi-time scale of precipitation in India was analysed by wavelet transformation method using gauged and WFD precipitation data. In general, precipitation from WFD is larger than that from satellite-gauge data in NMI and Western Ghats region whereas it is smaller in the dry region of NWI. Both WFD and satellite-gauge datasets underestimate precipitation compared to the measured data but the precipitation from WFD is better estimated than that from satellite-gauge data. It was found that the wavelet power spectrum of precipitation based on WFD is reasonably close to that of measured precipitation in NWI and NCI, while slightly different in NMI. It is felt that the WFD data can be used as a potential dataset for hydrological study in India.     

Climate change analysis on historical watershed‐scale precipitation by means of long‐term dynamical downscaling

A methodology based on long‐term dynamical downscaling to analyse climate change effects on watershed‐scale precipitation during a historical period is proposed in this study. The reliability and applicability of the methodology were investigated based on the long‐term dynamical downscaling results. For an application of the proposed methodology, two study watersheds in Northern California were selected: the Upper Feather River watershed and the Yuba River watershed. Then, precipitation was reconstructed at 3‐km spatial resolution and hourly intervals over the study watersheds for 141 water years from 1 October 1871 to 30 September 2012 by dynamically downscaling a long‐term atmospheric reanalysis dataset, 20th century global reanalysis version 2 by means of a regional climate model. The reconstructed precipitation was compared against observed precipitation, in order to assess the applicability of the proposed methodology for the reconstruction of watershed‐scale precipitation and to validate this methodology. The validation shows that the reconstructed precipitation is in good agreement with observation data. Moreover, the differences between the reconstructed precipitation and the corresponding observations do not significantly change through the historical period. After the validation, climate change analysis was conducted based on the reconstructed precipitation. Through this analysis, it was found that basin‐average precipitation has increased significantly over both of the study watersheds during the historical period. An upward trend in monthly basin‐average precipitation is not significant in wet months except February while it is significant in dry months of the year. Furthermore, peak values of basin‐average precipitation are also on an upward trend over the study watersheds. The upward trend in peak basin‐average precipitation is more significant during a shorter duration. Copyright © 2016 John Wiley & Sons, Ltd.     

一套格点化的中国区域逐日观测资料及与其它资料的对比

为高分辨率气候模式检验等的需要,基于2400余个中国地面气象台站的观测资料,通过插值建立了一套0.25°×0.25°经纬度分辨率的格点化数据集(CN05.1).CN05.1包括日平均和最高、最低气温,以及降水4个变量.插值通过常用的"距平逼近"方法实现,首先将计算得到的气候平均场使用薄板样条方法进行插值,随后使用"角距权重法"对距平场进行插值,然后将两者叠加,得到最终的数据集.将CN05.1与CN05、EA05和APHRO三种日气温和降水资料(四种资料的分析时段统一为1961-2005年)进行对比,分析了它们对气候平均态和极端事件描述上的不同,结果表明几者总体来说在中国东部观测台站密集的地方差别较小,而在台站稀疏的西部差别较大,相差最大的是青藏高原北部至昆仑山西段等地形起伏较大而很少或没有观测台站的地方,反映了格点化数据在这些地区的不确定性,在使用中应予以注意.     

Distributed hydrological modelling using weather radar in gauged and ungauged basins

Distributed hydrological modelling using space–time estimates of rainfall from weather radar provides a natural approach to area-wide flood forecasting and warning at any location, whether gauged or ungauged. However, radar estimates of rainfall may lack consistent, quantitative accuracy. Also, the formulation of hydrological models in distributed form may be problematic due to process complexity and scaling issues. Here, the aim is to first explore ways of improving radar rainfall accuracy through combination with raingauge network data via integrated multiquadric methods. When the resulting gridded rainfall estimates are employed as input to hydrological models, the simulated river flows show marked improvements when compared to using radar data alone. Secondly, simple forms of physical–conceptual distributed hydrological model are considered, capable of exploiting spatial datasets on topography and, where necessary, land-cover, soil and geology properties. The simplest Grid-to-Grid model uses only digital terrain data to delineate flow pathways and to control runoff production, the latter by invoking a probability-distributed relation linking terrain slope to soil absorption capacity. Model performance is assessed over nested river basins in northwest England, employing a lumped model as a reference. When the distributed model is used with the gridded radar-based rainfall estimators, it shows particular benefits for forecasting at ungauged locations.     

Finding the most appropriate precipitation probability distribution for stochastic weather generation and hydrological modelling in Nordic watersheds

Six precipitation probability distributions (exponential, Gamma, Weibull, skewed normal, mixed exponential and hybrid exponential/Pareto distributions) are evaluated on their ability to reproduce the statistics of the original observed time series. Each probability distribution is also indirectly assessed by looking at its ability to reproduce key hydrological variables after being used as inputs to a lumped hydrological model. Data from 24 weather stations and two watersheds (Chute‐du‐Diable and Yamaska watersheds) in the province of Quebec (Canada) were used for this assessment. Various indices or statistics, such as the mean, variance, frequency distribution and extreme values are used to quantify the performance in simulating the precipitation and discharge. Performance in reproducing key statistics of the precipitation time series is well correlated to the number of parameters of the distribution function, and the three‐parameter precipitation models outperform the other models, with the mixed exponential distribution being the best at simulating daily precipitation. The advantage of using more complex precipitation distributions is not as clear‐cut when the simulated time series are used to drive a hydrological model. Although the advantage of using functions with more parameters is not nearly as obvious, the mixed exponential distribution appears nonetheless as the best candidate for hydrological modelling. The implications of choosing a distribution function with respect to hydrological modelling and climate change impact studies are also discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatio-temporal evaluation of global gridded precipitation datasets across Iran

ABSTRACT

In this work, the accuracy of four gridded precipitation datasets – Climatic Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), PERSIANN-Climate Data Record (PCDR) and University of Delaware (UDEL) – is evaluated across Iran to find an alternative source of precipitation data. Monthly, seasonal and annual precipitation data from 85 synoptic stations for the period 1984–2013 were used as the basis for the evaluations. Our results indicate that all datasets underestimate and overestimate precipitation in stations with annual precipitation greater than 600 and less than 100 mm, respectively. However, all datasets correctly recognize regimes of precipitation, but with a bias in amount of precipitation. Our spatio-temporal assessments show that GPCC is the most suitable dataset to be used over Iran. Both UDEL and CRU can be considered as the second and third most suitable datasets, while PCDR showed the weakest performance among the studied datasets.     

Evaluation of the North American Regional Reanalysis (NARR) precipitation fields in a topographically complex domain

ABSTRACT

The North American Regional Reanalysis (NARR) precipitation product was evaluated using station observations and catchment water yield in British Columbia (BC), Canada, at inter-annual, monthly, and daily time scales. A structural break occurred in 2003, associated with exclusion of Canadian precipitation gauge data from NARR’s data assimilation process beginning in that year. The NARR product under-predicted precipitation in mountainous regions, over-predicted in the northern region of BC’s Interior Plateau, and catchment-averaged NARR precipitation was less than observed water yield in coastal BC. The product was unable to reproduce even the seasonal pattern at three stations. This study highlights uncertainties associated with the NARR precipitation product, and presents a cautionary tale that is likely relevant not only to the application of NARR in BC, but may be relevant for other re-analysis products and other regions with complex topography and sparse station networks.     

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.     

Validation of hydrological models for climate scenario simulation: the case of Saguenay watershed in Quebec

This paper presents the results of an investigation into the problems associated with using downscaled meteorological data for hydrological simulations of climate scenarios. The influence of both the hydrological models and the meteorological inputs driving these models on climate scenario simulation studies are investigated. A regression‐based statistical tool (SDSM) is used to downscale the daily precipitation and temperature data based on climate predictors derived from the Canadian global climate model (CGCM1), and two types of hydrological model, namely the physically based watershed model WatFlood and the lumped‐conceptual modelling system HBV‐96, are used to simulate the flow regimes in the major rivers of the Saguenay watershed in Quebec. The models are validated with meteorological inputs from both the historical records and the statistically downscaled outputs. Although the two hydrological models demonstrated satisfactory performances in simulating stream flows in most of the rivers when provided with historic precipitation and temperature records, both performed less well and responded differently when provided with downscaled precipitation and temperature data. By demonstrating the problems in accurately simulating river flows based on downscaled data for the current climate, we discuss the difficulties associated with downscaling and hydrological models used in estimating the possible hydrological impact of climate change scenarios. Copyright © 2007 John Wiley & Sons, Ltd.     

Sensitivity analysis and uncertainty assessment in water budgets simulated by the variable infiltration capacity model for Canadian subarctic watersheds

In this study, we evaluate uncertainties propagated through different climate data sets in seasonal and annual hydrological simulations over 10 subarctic watersheds of northern Manitoba, Canada, using the variable infiltration capacity (VIC) model. Further, we perform a comprehensive sensitivity and uncertainty analysis of the VIC model using a robust and state-of-the-art approach. The VIC model simulations utilize the recently developed variogram analysis of response surfaces (VARS) technique that requires in this application more than 6,000 model simulations for a 30-year (1981–2010) study period. The method seeks parameter sensitivity, identifies influential parameters, and showcases streamflow sensitivity to parameter uncertainty at seasonal and annual timescales. Results suggest that the Ensemble VIC simulations match observed streamflow closest, whereas global reanalysis products yield high flows (0.5–3.0 mm day⁻¹) against observations and an overestimation (10–60%) in seasonal and annual water balance terms. VIC parameters exhibit seasonal importance in VARS, and the choice of input data and performance metrics substantially affect sensitivity analysis. Uncertainty propagation due to input forcing selection in each water balance term (i.e., total runoff, soil moisture, and evapotranspiration) is examined separately to show both time and space dimensionality in available forcing data at seasonal and annual timescales. Reliable input forcing, the most influential model parameters, and the uncertainty envelope in streamflow prediction are presented for the VIC model. These results, along with some specific recommendations, are expected to assist the broader VIC modelling community and other users of VARS and land surface schemes, to enhance their modelling applications.     

中国区域夏季再分析资料高空变量可信度的检验

利用全球探空资料(IGRA)对1989—2008年美国国家环境预报中心(NCEP)和大气研究中心(NCAR)再分析资料、NCEP和美国能源部(DOE)再分析资料、NCEP气候预测系统再分析资料(CFSR)、日本气象厅25年再分析资料(JRA-25)、欧洲数值预报中心再分析资料(ERA-Interim)和美国国家航空航天局(NASA)现代回顾性再分析资料(MERRA)的高空变量在中国地区对流层中高层的可信度进行了初步的检验.分析结果表明:再分析资料对中高层位势高度和温度的夏季平均气候态具有较好的再现能力,其EOF的时空变化特征与观测吻合也较好;再分析资料的绝对湿度值较观测结果要偏大,其中MERRA与观测最为接近.再分析资料不能很好地反映经向风的夏季平均气候态及年际变化特征,EOF的时空模态和观测偏离也较大.总体而言,NCEP/NCAR、NCEP/DOE及NCEP/CFSR对这些变量的再现能力较JRA-25、ERA-Interim和MERRA弱.