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1.
Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278. https://doi.org/10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32. https://doi.org/10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability. 相似文献
2.
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. 相似文献
3.
Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes. 相似文献
4.
Tae-woong Kim Hosung Ahn Gunhui Chung Chulsang Yoo 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(6):705-717
This paper presents a stochastic model to generate daily rainfall occurrences at multiple gauging stations in south Florida.
The model developed in this study is a space–time model that takes into account the spatial as well as temporal dependences
of daily rainfall occurrence based on a chain-dependent process. In the model, a Markovian method was used to represent the
temporal dependence of daily rainfall occurrence and a direct acyclic graph (DAG) method was introduced to encode the spatial
dependence of daily rainfall occurrences among gauging stations. The DAG method provides an optimal sequence of generation
by maximizing the spatial dependence index of daily rainfall occurrences over the region. The proposed space–time model shows
more promising performance in generating rainfall occurrences in time and space than the conventional Markov type model. The
space–time model well represents the temporal as well as the spatial dependence of daily rainfall occurrences, which can reduce
the complexity in the generation of daily rainfall amounts. 相似文献
5.
L. Guenni A. Bárdossy 《Stochastic Environmental Research and Risk Assessment (SERRA)》2002,16(3):188-206
The need for high resolution rainfall data at temporal scales varying from daily to hourly or even minutes is a very important
problem in hydrology. For many locations of the world, rainfall data quality is very poor and reliable measurements are only
available at a coarse time resolution such as monthly. The purpose of this work is to apply a stochastic disaggregation method
of monthly to daily precipitation in two steps: 1. Initialization of the daily rainfall series by using the truncated normal
model as a reference distribution. 2.␣Restructuring of the series according to various time series statistics (autocorrelation
function, scaling properties, seasonality) by using a Markov chain Monte Carlo based algorithm. The method was applied to
a data set from a rainfall network of the central plains of Venezuela, in where rainfall is highly seasonal and data availability
at a daily time scale or even higher temporal resolution is very limited. A detailed analysis was carried out to study the
seasonal and spatial variability of many properties of the daily rainfall as scaling properties and autocorrelation function
in order to incorporate the selected statistics and their annual cycle into an objective function to be minimized in the simulation
procedure. Comparisons between the observed and simulated data suggest the adequacy of the technique in providing rainfall
sequences with consistent statistical properties at a daily time scale given the monthly totals. The methodology, although
highly computationally intensive, needs a moderate number of statistical properties of the daily rainfall. Regionalization
of these statistical properties is an important next step for the application of this technique to regions in where daily
data is not available. 相似文献
6.
Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output 总被引：2，自引：0，他引：2
S. Kannan Subimal Ghosh 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(4):457-474
Conventional statistical downscaling techniques for prediction of multi-site rainfall in a river basin fail to capture the
correlation between multiple sites and thus are inadequate to model the variability of rainfall. The present study addresses
this problem through representation of the pattern of multi-site rainfall using rainfall state in a river basin. A model based
on K-means clustering technique coupled with a supervised data classification technique, namely Classification And Regression
Tree (CART), is used for generation of rainfall states from large-scale atmospheric variables in a river basin. The K-means clustering is used to derive the daily rainfall state from the historical daily multi-site rainfall data. The optimum
number of clusters in the observed rainfall data is obtained after application of various cluster validity measures to the
clustered data. The CART model is then trained to establish relationship between the daily rainfall state of the river basin
and the standardized, dimensionally-reduced National Centers for Environmental Prediction/National Center for Atmospheric
Research (NCEP/NCAR) reanalysis climatic data set. The relationship thus developed is applied to the General Circulation Model
(GCM)-simulated, standardized, bias free large-scale climate variables for prediction of rainfall states in future. Comparisons
of the number of days falling under different rainfall states for the observed period and the future give the change expected
in the river basin due to global warming. The methodology is tested for the Mahanadi river basin in India. 相似文献
7.
L. Meiresonne D. A. Sampson A. S. Kowalski I. A. Janssens N. Nadezhdina J. Cermk J. Van Slycken R. Ceulemans 《Journal of Hydrology》2003,270(3-4):230-252
Four distinct approaches, that vary markedly in the spatial and temporal resolution of their measurement and process-level outputs, are used to investigate the daily and seasonal water vapour exchange in a 70-year-old Belgian Scots pine forest. Transpiration, canopy interception, soil evaporation and evapotranspiration are simulated, using a stand-level process model (SECRETS) and a soil water balance model (WAVE). Simulated transpiration was compared with up-scaled sap flow measurements and simulated evapotranspiration to eddy covariance measurements.
Reasonable agreement in the temporal trends and in the annual water balance between the two models was observed, however daily and weekly predictions often diverged. Most notably, WAVE estimated very low, to no transpiration during late autumn, winter and early spring when incident radiation fell below 50 W m−2 while SECRETS simulated low (0.1–0.4 mm day−1) fluxes during the same period. Both models exhibited similar daily trends in simulated transpiration when compared with sap flow estimates, although simulations from SECRETS were more closely aligned. In contrast, WAVE over-estimated transpiration during periods of no rainfall and under-estimated transpiration during rainfall. Yearly, total evapotranspiration simulated by the models were similar, i.e. 658 mm (1997) and 632 mm (1998) for WAVE and 567 mm (1997) and 619 mm (1998) for SECRETS.
Maximum weekly-average evapotranspiration for WAVE exceeded 5 mm day−1, while SECRETS never exceeded 4 mm day−1. Both models, in general, simulated higher evapotranspiration than that measured with the eddy covariance technique. An impact of the soil water content in the direct relationship between the models and the eddy covariance measurements was found.
The results suggest that: (1) different model formulations can reproduce similar results depending on the scale at which outputs are resolved, (2) SECRETS estimates of transpiration were well correlated with the empirical measurements, and (3) neither model fitted favourably to the eddy covariance technique. 相似文献
8.
A. W. Jayawardena P. C. Xu W. K. Li 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(7):863-877
In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be
treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic
nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature
at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic
and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been
proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and
hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet
tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated
and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale,
the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated
simulated data do show some level of agreement with the observed data. 相似文献
9.
Multivariate time series modeling approaches are known as useful tools for describing, simulating, and forecasting hydrologic variables as well as their changes over the time. These approaches also have temporal and cross-sectional spatial dependence in multiple measurements. Although the application of multivariate linear and nonlinear time series approaches such as vector autoregressive with eXogenous variables (VARX) and multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models are commonly used in financial and economic sciences, these approaches have not been extensively used in hydrology and water resources engineering. This study employed VARX and VARX–MGARCH approaches in modeling mean and conditional heteroscedasticity of daily rainfall and runoff records in the basin of Zarrineh Rood Dam, Iran. Bivariate diagonal VECH (DVECH) model, as a main type of MGARCH, shows how the conditional variance–covariance and conditional correlation structure vary over the time between residuals series of the fitted VARX. For this purpose, five model fits, which consider different combinations of twofold rainfall and runoff, including both upstream and downstream stations, have been investigated in the present study. The VARX model, with different orders, was applied to the daily rainfall–runoff process of the study area in each of these model fits. The Portmanteau test revealed the existence of conditional heteroscedasticity in the twofold residuals of fitted VARX models. Therefore, the VARX–DVECH model is proposed to capture the heteroscedasticity existing in the daily rainfall–runoff process. The bivariate DVECH model indicated both short-run and long-run persistency in the conditional variance–covariance matrix related to the twofold innovations of rainfall–runoff processes. Furthermore, the evaluation criteria for the VARX–DVECH model revealed the improvement of VARX model performance. 相似文献
10.
Spatial-temporal rainfall modelling for flood risk estimation 总被引：4，自引：6，他引：4
H. S. Wheater R. E. Chandler C. J. Onof V. S. Isham E. Bellone C. Yang D. Lekkas G. Lourmas M.-L. Segond 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(6):403-416
Some recent developments in the stochastic modelling of single site and spatial rainfall are summarised. Alternative single
site models based on Poisson cluster processes are introduced, fitting methods are discussed, and performance is compared
for representative UK hourly data. The representation of sub-hourly rainfall is discussed, and results from a temporal disaggregation
scheme are presented. Extension of the Poisson process methods to spatial-temporal rainfall, using radar data, is reported.
Current methods assume spatial and temporal stationarity; work in progress seeks to relax these restrictions. Unlike radar
data, long sequences of daily raingauge data are commonly available, and the use of generalized linear models (GLMs) (which
can represent both temporal and spatial non-stationarity) to represent the spatial structure of daily rainfall based on raingauge
data is illustrated for a network in the North of England. For flood simulation, disaggregation of daily rainfall is required.
A relatively simple methodology is described, in which a single site Poisson process model provides hourly sequences, conditioned
on the observed or GLM-simulated daily data. As a first step, complete spatial dependence is assumed. Results from the River
Lee catchment, near London, are promising. A relatively comprehensive set of methodologies is thus provided for hydrological
application. 相似文献
11.
Harry Pavlopoulos 《Advances in water resources》2011,34(8):990-1011
Spectral multi-scaling postulates a power-law type of scaling of spectral distribution functions of stationary processes of spatial averages, over nested and geometrically similar sub-regions of the spatial parameter space of a given spatio-temporal random field. Presently a new framework is formulated for down-scaling processes of spatial averages, following naturally from the postulate of spectral multi-scaling, and key ingredients required for its implementation are described. Moreover, results from an extensive diagnostic study are presented, seeking statistical evidence supportive of spectral multi-scaling. Such evidence emerges from two sources of data. One is a 13 year long historical record of radar observations of rainfall in southeastern UK (Chenies radar), with high spatial (2 km) and temporal (5 min) resolution. The other is an ensemble of rain rate fields simulated by a spatio-temporal random pulse model fitted to the historical data. The results are consistent between historical and simulated rainfall data, indicating frequency-dependent scaling relationships interpreted as evidence of spectral multi-scaling across a range of spatial scales. 相似文献
12.
Sujeewa Malwila Herath Priyantha Ranjan Sarukkalige Van Thanh Van Nguyen 《水文科学杂志》2013,58(11):2061-2070
ABSTRACTDownscaling of climate projections is the most adapted method to assess the impacts of climate change at regional and local scales. This study utilized both spatial and temporal downscaling approaches to develop intensity–duration–frequency (IDF) relations for sub-daily rainfall extremes in the Perth airport area. A multiple regression-based statistical downscaling model tool was used for spatial downscaling of daily rainfall using general circulation models (GCMs) (Hadley Centre’s GCM and Canadian Global Climate Model) climate variables. A simple scaling regime was identified for 30 minutes to 24 hours duration of observed annual maximum (AM) rainfall. Then, statistical properties of sub-daily AM rainfall were estimated by scaling an invariant model based on the generalized extreme value distribution. RMSE, Nash-Sutcliffe efficiency coefficient and percentage bias values were estimated to check the accuracy of downscaled sub-daily rainfall. This proved the capability of the proposed approach in developing a linkage between large-scale GCM daily variables and extreme sub-daily rainfall events at a given location. Finally IDF curves were developed for future periods, which show similar extreme rainfall decreasing trends for the 2020s, 2050s and 2080s for both GCMs.
Editor M.C. Acreman; Associate editor S. Kanae 相似文献
13.
《水文科学杂志》2013,58(5):917-935
Abstract For urban drainage and urban flood modelling applications, fine spatial and temporal rainfall resolution is required. Simulation methods are developed to overcome the problem of data limitations. Although temporal resolution higher than 10–20 minutes is not well suited for detailed rainfall—runoff modelling for urban drainage networks, in the absence of monitored data, longer time intervals can be used for master planning or similar purposes. A methodology is presented for temporal disaggregation and spatial distribution of hourly rainfall fields, tested on observations for a 10-year period at 16 raingauges in the urban catchment of Dalmuir (UK). Daily rainfall time series are simulated with a generalized linear model (GLM). Next, using a single-site disaggregation model, the daily data of the central gauge in the catchment are downscaled to an hourly time scale. This hourly pattern is then applied linearly in space to disaggregate the daily data into hourly rainfall at all sites. Finally, the spatial rainfall field is obtained using inverse distance weighting (IDW) to interpolate the data over the whole catchment. Results are satisfactory: at individual sites within the region the simulated data preserve properties that match the observed statistics to an acceptable level for practical purposes. 相似文献
14.
Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large‐scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south‐east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI‐ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO‐MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS‐AOM, MIROC‐M, NCAR‐PCM1, IAP and NCAR‐CCSM. However, the reduction in the combined score as we move from the best‐ to the worst‐performing GCMs is gradual, and there is no evident cut‐off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
15.
D.L. De Luca 《水文科学杂志》2013,58(8):1536-1558
AbstractThe spatial and temporal variability of the scaling properties and correlation structure of a data set of rainfall time series, aggregated over different temporal resolutions, and observed in 70 raingauges across the Basilicata and Calabria regions of southern Italy, is investigated. Two types of random cascade model, namely canonical and microcanonical models, were used for each raingauge and selected season. For both models, different hypotheses concerning dependency of parameters on time scale and rainfall height can be adopted. In particular, a new approach is proposed which consists of several combinations of models with a different scale dependence of parameters for different temporal resolutions. The goal is to improve the modelling of the main features of rainfall time series, especially for cases where the variability of rainfall changes irregularly with temporal aggregation. The results obtained with the new methodology showed good agreement with the observed data, in particular, for the summer months. In fact, during this season, rainfall heights aggregated at fine temporal resolutions (from 5 to 20 min) are more similar (relative to the winter season) to the values cumulated on 1 or 3 h (due to convective phenomena) and, consequently, the process of rainfall breakdown is nearly stationary for a range of finer temporal resolutions.
Editor D. Koutsoyiannis; Associate editor A. Montanari 相似文献
16.
Stochastic multi-site generation of daily weather data 总被引：1，自引：1，他引：0
Malika Khalili François Brissette Robert Leconte 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(6):837-849
Spatial autocorrelation is a correlation between the values of a single variable, considering their geographical locations.
This concept has successfully been used for multi-site generation of daily precipitation data (Khalili et al. in J Hydrometeorol
8(3):396–412, 2007). This paper presents an extension of this approach. It aims firstly to obtain an accurate reproduction
of the spatial intermittence property in synthetic precipitation amounts, and then to extend the multi-site approach to the
generation of daily maximum temperature, minimum temperature and solar radiation data. Monthly spatial exponential functions
have been developed for each weather station according to the spatial dependence of the occurrence processes over the watershed,
in order to fulfill the spatial intermittence condition in the synthetic time series of precipitation amounts. As was the
case for the precipitation processes, the multi-site generation of daily maximum temperature, minimum temperature and solar
radiation data is realized using spatially autocorrelated random numbers. These random numbers are incorporated into the weakly
stationary generating process, as with the Richardson weather generator, and with no modifications made. Suitable spatial
autocorrelations of random numbers allow the reproduction of the observed daily spatial autocorrelations and monthly interstation
correlations. The Peribonca River Basin watershed is used to test the performance of the proposed approaches. Results indicate
that the spatial exponential functions succeeded in reproducing an accurate spatial intermittence in the synthetic precipitation
amounts. The multi-site generation approach was successfully applied for the weather data, which were adequately generated,
while maintaining efficient daily spatial autocorrelations and monthly interstation correlations. 相似文献
17.
The temporal‐spatial resolution of input data‐induced uncertainty in a watershed‐based water quality model, Hydrologic Simulation Program‐FORTRAN (HSPF), is investigated in this study. The temporal resolution‐induced uncertainty is described using the coefficient of variation (CV). The CV is found to decrease with decreasing temporal resolution and follow a log‐normal relation with time interval for temperature data while it exhibits a power‐law relation for rainfall data. The temporal‐scale uncertainties in the temperature and rainfall data follow a general extreme value distribution and a Weibull distribution, respectively. The Nash‐Sutcliffe coefficient (NSC) is employed to represent the spatial resolution induced uncertainty. The spatial resolution uncertainty in the dissolved oxygen and nitrate‐nitrogen concentrations simulated using HSPF is observed to follow a general extreme value distribution and a log‐normal distribution, respectively. The probability density functions (PDF) provide new insights into the effect of temporal‐scale and spatial resolution of input data on uncertainties involved in watershed modelling and total maximum daily load calculations. This study exhibits non‐symmetric distributions of uncertainty in water quality modelling, which simplify weather and water quality monitoring and reducing the cost involved in flow and water quality monitoring. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
18.
Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data. 相似文献
19.
Accurate information of rainfall is needed for sustainable water management and more reliable flood forecasting. The advances in mesoscale numerical weather modelling and modern computing technologies make it possible to provide rainfall simulations and forecasts at increasingly higher resolutions in space and time. However, being one of the most difficult variables to be modelled, the quality of the rainfall products from the numerical weather model remains unsatisfactory for hydrological applications. In this study, the sensitivity of the Weather Research and Forecasting (WRF) model is investigated using different domain settings and various storm types to improve the model performance of rainfall simulation. Eight 24‐h storm events are selected from the Brue catchment, southwest England, with different spatial and temporal distributions of the rainfall intensity. Five domain configuration scenarios designed with gradually changing downscaling ratios are used to run the WRF model with the ECMWF 40‐year reanalysis data for the periods of the eight events. A two‐dimensional verification scheme is proposed to evaluate the amounts and distributions of simulated rainfall in both spatial and temporal dimensions. The verification scheme consists of both categorical and continuous indices for a first‐level assessment and a more quantitative evaluation of the simulated rainfall. The results reveal a general improvement of the model performance as we downscale from the outermost to the innermost domain. Moderate downscaling ratios of 1:7, 1:5 and 1:3 are found to perform better with the WRF model in giving more reasonable results than smaller ratios. For the sensitivity study on different storm types, the model shows the best performance in reproducing the storm events with spatial and temporal evenness of the observed rainfall, whereas the type of events with highly concentrated rainfall in space and time are found to be the trickiest case for WRF to handle. Finally, the efficiencies of several variability indices are verified in categorising the storm events on the basis of the two‐dimensional rainfall evenness, which could provide a more quantitative way for the event classification that facilitates further studies. It is important that similar studies with various storm events are carried out in other catchments with different geographic and climatic conditions, so that more general error patterns can be found and further improvements can be made to the rainfall products from mesoscale numerical weather models. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
20.
This study integrates a simple overland flow module (isochronous cells model) with the river module of MODFLOW such that temporal and spatial interactions between stream flow and groundwater can be simulated using net rainfall data of a watershed. The isochronous cells model is an efficient travel time runoff approach based on geographic information system (GIS) that considers both spatial and temporal variations of net rainfall through hill slope of the watershed. This overland module is easily coupled with MODFLOW river routing module. Specifically, the stream flow from the isochronous cells model is directly assigned to both sides of river cells of the MODFLOW model. Such an integration of MODFLOW and isochronous cells model is especially useful in watersheds where river flow data are limited. The feasibility of this integrated model was demonstrated using a case study in the middle and downstream regions of the Yitong River watershed, China. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献