Hydrological modelling using proxies for gauged precipitation and temperature

Precipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models.     

Catchment-scale observations at the Niwot Ridge long-term ecological research site

The Niwot Ridge and Green Lakes Valley (NWT) long-term ecological research (LTER) site collects environmental observations spanning both alpine and subalpine regimes. The first observations began in 1952 and have since expanded to nearly 300 available datasets over an area of 99 km² within the north-central Colorado Rocky Mountains that include hydrological (n = 101), biological (n = 79), biogeochemical (n = 62), and geographical (n = 56) observations. The NWT LTER database is well suited to support hydrologic investigations that require long-term and interdisciplinary data sets. Experimentation and data collection at the NWT LTER are designed to characterize ecological responses of high-mountain environments to changes in climate, nutrients, and water availability. In addition to the continuation of the many legacy NWT datasets, expansion of the breadth and utility of the NWT LTER database is driven by new initiatives including (a) a catchment-scale sensor network of soil moisture, temperature, humidity, and snow-depth observations to understand hydrologic connectivity and (b) snow-albedo alteration experiments using black sand to evaluate the effects of snow-disappearance on ecosystems. Together, these observational and experimental datasets provide a substantial foundation for hydrologic studies seeking to understand and predict changes to catchment and local-scale process interactions.     

Multi-site bias correction of climate model outputs for hydro-meteorological impact studies: An application over a watershed in China

Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre-processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists between different sites, which could negatively affect simulations from a distributed hydrological model. In this study, three multi-variate bias correction (MBC) methods—initially proposed to correct the inter-variable correlation or multi-variate dependence of climate model outputs—are used to correct biases in distributional properties and spatial dependence at multiple weather stations. To reveal the benefits of correcting spatial dependence, two distribution-based single-site bias correction methods are used for comparison. The effects of multi-site correction on hydro-meteorological extremes are assessed by driving a distributed hydrological model and then evaluating the model performance in terms of several meteorological and hydrological extreme indices. The results show that the multi-site bias correction methods perform well in reducing biases in spatial correlation measures of raw global climate model outputs. In addition, the multi-site methods consistently reproduce watershed-averaged meteorological variables better than single-site methods, especially for extreme values. In terms of representing hydrological extremes, the multi-site methods generally perform better than the single-site methods, although the benefits vary according to the hydrological index. However, when applying the multi-site methods, the original temporal sequence of precipitation occurrence may be altered to some extent. Overall, all multi-site bias correction methods are able to reproduce the spatial correlation of observed meteorological variables over multiple stations, which leads to better hydrological simulations, especially for extremes. This study emphasizes the necessity of considering spatial dependence when applying bias correction to ccc outputs and hydrological impact studies.     

High resolution WRF climatic simulations for the Iberian Peninsula: Model validation

A high resolution atmospheric modelling study was done for a 20-year recent historical period. The dynamic downscaling approach adopted used the Max Planck Institute Earth System Model (MPI-ESM) to drive the WRF running in climate mode. Three online nested domains were used covering part of the North Atlantic and Europe, with a resolution 81 km, and reaching 9 km in the innermost domain which covers the Iberian Peninsula.This paper presents the validation of the WRF configuration, which is based on historic simulations between 1986 and 2005 and observational datasets of near surface temperature and precipitation for the same period. The validation was done in terms of comparison of probability distributions between model results and observations, as daily climatologies, spatially averaged inside subdomains obtained with cluster analysis of the observations, for each of the four seasons. In addition, Taylor diagrams are presented for each of the seasons and subdomains. This validation approach was repeated with the results of a new WRF simulation with the same parameterisations but forced by the ERA-Interim reanalysis. The capacity of the MPI-ESM driven WRF configuration to compare with observations and in a manner similar to the ERA-Interim driven WRF, ensures the capacity of the configuration for climate and climate change studies.Considering the difficulty to simulate extremes in long term simulations, the results showed a comfortable comparison of both models (forced by climate model and reanalysis results) with observations. This provides us confidence on the continuity of using the MPI-ESM driven WRF configuration for climate studies.     

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.     

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.     

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.     

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.     

Precipitation Changes in High Southern Latitudes from Global Reanalyses: A Cautionary Tale

The temporal consistency of the moisture fields (precipitation, evaporation and total precipitable water) from five global reanalyses is examined over Antarctica and the Southern Ocean during 1989?C2009. This concern is important given that (1) global reanalyses are known to be prone to inhomogeneities and artificial trends caused by changes in the observing system, and (2) the period of study has seen a dramatic increase in the volume of satellite observations available for data assimilation. In particular, the study aims to determine whether the recent reanalyses are suitable for investigating changes in Antarctic surface mass balance. The datasets investigated consist of NCEP-2, JRA-25, ERA-Interim, MERRA and CFSR. Strong evidence of spurious changes is found in NCEP-2, JRA-25, MERRA and CFSR, although the magnitude, spatial patterns and timing of these artifacts vary between the reanalyses. MERRA exhibits a jump in Antarctic precipitation-minus-evaporation (P?CE) and in Southern Ocean precipitation in the late 1990s. This jump is related to the introduction of sounding radiances from the Advanced Microwave Sounding Unit (AMSU). The impact of AMSU is also discernible, albeit less pronounced, in CFSR data. It is shown that ERA-Interim likely provides the most realistic depiction of the interannual variability and overall change in Antarctic P?CE since 1989. We conclude that the presence of spurious changes is not a solved problem in recent global reanalyses. Caution should continue to be exercised when using these datasets for trend analyses in general, particularly in high southern latitudes.     

Impact of the spatial variability of daily precipitation on hydrological projections: A comparison of GCM‐ and RCM‐driven cases in the Han River basin,Korea

In this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes.     

Recent trends of extreme precipitation indices in the Iberian Peninsula using observations and WRF model results

Spatial and temporal distributions of the trends of extreme precipitation indices were analysed between 1986 and 2005, over the Iberian Peninsula (IP). The knowledge of the patterns of extreme precipitation is important for impacts assessment, development of adaptation and mitigation strategies. As such, there is a growing need for a more detailed knowledge of precipitation climate change.This analysis was performed for Portuguese and Spanish observational datasets and results performed by the Weather Research and Forecast (WRF) model forced by the ERA-Interim reanalysis. Extreme precipitation indices recommended by the Expert Team for Climate Change Detection Monitoring and Indices were computed, by year and season. Then, annual and seasonal trends of the indices were estimated by Theil-Sen method and their significance was tested by the Mann-Kendal test. Additionally, a second simulation forced by the Max Planck Institute Earth System Model (MPI-ESM), was considered. This second modelling configuration was created in order to assess its performance when simulating extremes of precipitation.The annual trends estimated for the 1986–2005, from the observational datasets and from the ERA-driven simulation reveal: 1) negative statistically significant trends of the CWD index in the Galicia and in the centre of the IP; 2) positive statistically significant trends of the CDD index over the south of the IP and negative statistically significant trends in Galicia, north and centre of Portugal; 3) positive statistically significant trends of the R75p index in some regions of the north of the IP; 4) positive statistically significant trends in the R95pTOT index in the Central Mountains Chain, Leon Mountains and in the north of Portugal.Seasonally, negative statistically significant trends of the CWD index were found in Galicia, in winter and in the south of the IP, in summer. Positive statistically significant trends of the CWD index were identified in the Leon Mountains, in spring, and in Galicia, in autumn. For the CDD index, negative statistically significant trends were seen in Valencia, in the spring, and, in Galicia and Portugal (north and centre), in summer. Positive statistically significant trends of the CDD index were found: in the east of the IP, in the winter; in the Cantabrian Mountain, in the spring; and, in the south of the IP, in summer. Regarding to the R75p index, negative statistically significant trends were found in Galicia, in winter and positive statistically significant trends in the north of Portugal, in spring and in the Central Mountains Chain and north of Portugal, in autumn. For the R95pTOT index, negative statistically significant trends were found over the Sierra Cuenca and Sierra Cazorla, in winter and positive statistically significant trends were found over the Sierra Cebollera, in winter and in Castile-la Mancha region, in spring.The results of the annual and seasonal trends of the extreme precipitation indices performed for observational datasets and the simulation forced by ERA-Interim, are similar. The results obtained for the simulation forced by MPI-ESM are not satisfactory, and can be a source of criticism for the use of simulation forced by MPI-ESM in this type of climate change studies. Even for the relatively short period used, the WRF model, when properly forced is a useful tool due to the similar results of Portuguese and Spanish observational datasets and the simulation forced by ERA-Interim.     

Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the Sefidrood River Basin,Iran

ABSTRACT

Hydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Arti?cial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).     

Precipitation variability and its relationship to hydrologic variability in Alberta

By applying wavelet‐based empirical orthogonal function (WEOF) analysis to gridded precipitation (P) and empirical orthogonal function (EOF) analysis to gridded air temperature (T), potential evapotranspiration (PET), net precipitation (P‐PET) and runoff (Q), this paper examines the spatial, temporal and frequency patterns of Alberta's climate variability. It was found that only WEOF‐based precipitation patterns, possibly modulated by El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation(PDO), delineated Alberta into four major regions which geographically represent northern Alberta Boreal forests, southern Alberta grasslands and Aspen Parklands and the Rocky Mountains and Foothills. The leading mode of wavelet‐based precipitation variability WPC1 showed that between 1900 and 2000, a wet climate dominated northern Alberta with significant 4–8, 11 and 25‐year periodic cycles, while the second mode WPC2 showed that between 1960 and 2000, southern Alberta grasslands were characterized by decreasing precipitation, dominated by 11‐year cycles, and the last two modes WPC3 and WPC4 were characterized by 4–7 and 25‐year cycles and both delineated regions where moisture from the Pacific Ocean penetrated the Rocky Mountains, accounted for much of the sub‐alpine climate. These results show that WEOF is superior to EOF in delineating Alberta precipitation variability to sub‐regions that more closely agree with its eco‐climate regions. Further, it was found that while WPC2 could not explain runoff variations in southern Alberta, WPC1, WPC3 and WPC4 accounted for runoff variability in their respective sub‐regions. Copyright © 2009 John Wiley & Sons, Ltd.     

Assessment of terrestrial water contributions to polar motion from GRACE and hydrological models

The hydrological contribution to polar motion is a major challenge in explaining the observed geodetic residual of non-atmospheric and non-oceanic excitations since hydrological models have limited input of comprehensive global direct observations. Although global terrestrial water storage (TWS) estimated from the Gravity Recovery and Climate Experiment (GRACE) provides a new opportunity to study the hydrological excitation of polar motion, the GRACE gridded data are subject to the post-processing de-striping algorithm, spatial gridded mapping and filter smoothing effects as well as aliasing errors. In this paper, the hydrological contributions to polar motion are investigated and evaluated at seasonal and intra-seasonal time scales using the recovered degree-2 harmonic coefficients from all GRACE spherical harmonic coefficients and hydrological models data with the same filter smoothing and recovering methods, including the Global Land Data Assimilation Systems (GLDAS) model, Climate Prediction Center (CPC) model, the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis products and European Center for Medium-Range Weather Forecasts (ECMWF) operational model (opECMWF). It is shown that GRACE is better in explaining the geodetic residual of non-atmospheric and non-oceanic polar motion excitations at the annual period, while the models give worse estimates with a larger phase shift or amplitude bias. At the semi-annual period, the GRACE estimates are also generally closer to the geodetic residual, but with some biases in phase or amplitude due mainly to some aliasing errors at near semi-annual period from geophysical models. For periods less than 1-year, the hydrological models and GRACE are generally worse in explaining the intraseasonal polar motion excitations.     

Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts

The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or GCM/RCM – Global/Regional Climate Model pairing, this paper analyses the relationship between complexity and robustness of three distribution‐based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty of groundwater head and stream discharge given the various DBS methods. A unique metric is devised, which allows for comparison of spatial variability in climate model bias and projected change in precipitation. It is found that the spatial variability in climate model bias is larger than in the climate change signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables that integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological simulations forced by the least parameterized DBS approach show the highest error in mean and maximum groundwater heads; however, the most highly parameterised DBS approach shows less robustness in future periods compared with the reference period it was trained in. For hydrological impacts studies, choice of bias correction method should depend on the spatial scale at which hydrological impacts variables are required and whether CM initial bias is spatially uniform or spatially varying. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

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.     

The role of rainfall temporal and spatial averaging in seasonal simulations of the terrestrial water balance

The partitioning of rainfall into surface runoff and infiltration influences many other aspects of the hydrologic cycle including evapotranspiration, deep drainage and soil moisture. This partitioning is an instantaneous non-linear process that is strongly dependent on rainfall rate, soil moisture and soil hydraulic properties. Though all rainfall datasets involve some degree of spatial or temporal averaging, it is not understood how this averaging affects simulated partitioning and the land surface water balance across a wide range of soil and climate types. We used a one-dimensional physics-based model of the near-surface unsaturated zone to compare the effects of different rainfall discretization (5-min point-scale; hourly point-scale; hourly 0.125° gridded) on the simulated partitioning of rainfall for many locations across the United States. Coarser temporal resolution rainfall data underpredicted seasonal surface runoff for all soil types except those with very high infiltration capacities (i.e., sand, loamy sand). Soils with intermediate infiltration capacities (i.e., loam, sandy loam) were the most affected, with less than half of the expected surface runoff produced in most soil types when the gridded rainfall dataset was used as input. The impact of averaging on the water balance was less extreme but non-negligible, with the hourly point-scale predictions exhibiting median evapotranspiration, drainage and soil moisture values within 10% of those predicted using the higher resolution 5-min rainfall. Water balance impacts were greater using the gridded hourly dataset, with average underpredictions of ET up to 27% in fine-grained soils. The results suggest that “hyperresolution” modelling at continental to global scales may produce inaccurate predictions if there is not parallel effort to produce higher resolution precipitation inputs or sub-grid precipitation parameterizations.     

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.     

Decoupling the effects of deforestation and climate variability in the Tapajós river basin in the Brazilian Amazon

Tropical river basins are experiencing major hydrological alterations as a result of climate variability and deforestation. These drivers of flow changes are often difficult to isolate in large basins based on either observations or experiments; however, combining these methods with numerical models can help identify the contribution of climate and deforestation to hydrological alterations. This paper presents a study carried out in the Tapaj?s River (Brazil), a 477,000 km² basin in South‐eastern Amazonia, in which we analysed the role of annual land cover change on daily river flows. Analysis of observed spatial and temporal trends in rainfall, forest cover, and river flow metrics for 1976 to 2008 indicates a significant shortening of the wet season and reduction in river flows through most of the basin despite no significant trend in annual precipitation. Coincident with seasonal trends over the past 4 decades, over 35% of the original forest (140,000 out of 400,000 km²) was cleared. In order to determine the effects of land clearing and rainfall variability to trends in river flows, we conducted hindcast simulations with ED2 + R, a terrestrial biosphere model incorporating fine scale ecosystem heterogeneity arising from annual land‐use change and linked to a flow routing scheme. The simulations indicated basin‐wide increases in dry season flows caused by land cover transitions beginning in the early 1990s when forest cover dropped to 80% of its original extent. Simulations of historical potential vegetation in the absence of land cover transitions indicate that reduction in rainfall during the dry season (mean of ?9 mm per month) would have had an opposite and larger magnitude effect than deforestation (maximum of +4 mm/month), leading to the overall net negative trend in river flows. In light of the expected increase in future climate variability and water infrastructure development in the Amazon and other tropical basins, this study presents an approach for analysing how multiple drivers of change are altering regional hydrology and water resources management.