Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors

Three downscaling models, namely the Statistical Down‐Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS‐WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS‐WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS‐WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry‐spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry‐spell lengths of the LARS‐WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessment of future precipitation indices in the Shikoku region using a statistical downscaling model

In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0–15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios.     

An improved statistical analogue downscaling procedure for seasonal precipitation forecast

Seasonal forecasting can be highly valuable for water resources management. Hydrological models (either lumped conceptual rainfall-runoff models or physically based distributed models) can be used to simulate streamflows and update catchment conditions (e.g. soil moisture status) using rainfall records and other catchment data. However, in order to use any hydrological model for skillful seasonal forecasting, rainfall forecast at relevant spatial and/or temporal scales is required. Together with downscaling, general circulation models are probably the only tools for making such seasonal predictions. The Predictive Ocean Atmosphere Model for Australia (POAMA) is a state-of-the-art seasonal climate forecast system developed by the Australian Bureau of Meteorology. Based on the preliminary assessment on the performance of existing statistical downscaling methods used in Australia, this paper is devoted to develop an analogue downscaling method by modifying the Euclidian distance in the selection of similar weather pattern. Such a modification consists of multivariate Box–Cox transformation and then standardization to make the resulted POAMA and observed climate pattern more similar. For the predictors used in Timbal and Fernadez (CAWCR Technical Report No. 004, 2008), we also considered whether the POAMA precipitation provides useful information in the analogue method. Using the high quality station data in the Murray Darling Basin of Australia, we found that the modified analogue method has potential to improve the seasonal precipitation forecast using POAMA outputs. Finally, we found that in the analogue method, the precipitation from POAMA should not be used in the calculation of similarity. The findings would then help to improve the seasonal forecast of streamflows in Australia.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Intercomparison of soil pore water extraction methods for stable isotope analysis

Measurements of δ²H and δ¹⁸O composition of pore waters in saturated and unsaturated soil samples are routinely performed in hydrological studies. A variety of in‐situ and lab‐based pore water extraction methods for the analysis of the stable isotopes of water now exist. While some have been used for decades (e.g. cryogenic vacuum extraction) others are relatively new, such as direct vapour equilibration or the microwave extraction technique. Despite their broad range of application, a formal and comprehensive intercomparison of soil water extraction methods for stable isotope analysis is lacking and long overdue. Here we present an intercomparison among five commonly used lab‐based pore water extraction techniques (high pressure mechanical squeezing, centrifugation, direct vapour equilibration, microwave extraction, and cryogenic extraction). We applied these extraction methods to two physicochemically different soil types that were dried and rewetted with water of known isotopic composition at three different water contents. Our results showed that the extraction approach can have a significant effect on pore water isotopic composition as all methods exhibited significant deviations from the spiked reference water, depending secondarily on the soil type and soil water content. Most pronounced, cryogenic water extraction showed large deviations from the spiked reference water, whereas mechanical squeezing and centrifugation provided results closest to the spiked water for both soil types. We also compared results for each extraction method – where liquid water was obtained – on both an OA‐ICOS and IRMS. Differences between these two analytical instruments were negligible for these organic compound‐free waters. We suggest that users of soil water extraction approaches carefully choose an extraction technique that is suitable for the specific research question, adapted to the dominant soil type and water content of the study. Copyright © 2016 John Wiley & Sons, Ltd.     

Statistical downscaling model for late-winter rainfall over Southwest China

A statistical downscaling model is built for the late-winter rainfall over Southwest China(SWC).A partial-correlation method is used for selecting factors.The results show that the selected factors for late-winter rainfall in SWC are sea level pressure in Western Europe(SNAO)and sea surface temperature in Western Pacific(WPT).SNAO is related to the southern pole of North Atlantic Oscillation(NAO)and excites Southern Eurasian teleconnection,which influences the development of the southern branch trough and the water vapor transport to SWC.WPT indicates the variability of ENSO in the tropical Western Pacific.WPT excites Pacific-East Asia teleconnection and an anticyclone(cyclone)is formed in the southern part of China and suppresses(enhances)rainfall over SWC.A regression statistical downscaling model using SNAO and WPT shows good performance in fitting the variability of late-winter rainfall in the whole SWC region and every observation station,and the model also shows strong robustness in the independent validation.The statistical model can be used for downscaling output from seasonal forecast numerical models and improve the SWC late winter rainfall prediction in the future.     

Rainfall uncertainty for extreme events in NWP downscaling model

Limited area numerical weather predication (NWP) models such as MM5 have become a popular method for generating rainfall estimates for hydrological analysis, particularly for catchments where rainfall data are sparse. Although several studies have been undertaken to investigate the appropriateness of MM5 parameterization schemes for hydrological applications, the size of the nested domains and the distance between them have been overlooked as a source of uncertainty in model precipitation estimates for hydrological purposes. This study examines the uncertainty of model rainfall estimates derived from MM5 by varying the domain size and the distance between the domains. The results from this study show that domain size and buffer zone have a significant impact on model rainfall estimates, which should not be overlooked by hydrologists. Copyright © 2010 John Wiley & Sons, Ltd.     

Dynamical downscaling of regional climate: A review of methods and limitations

The traditional dynamical downscaling (TDD) method employs continuous integration of regional climate models (RCM) with the general circulation model (GCM) providing the initial and lateral boundary conditions. Dynamical downscaling simulations are constrained by physical principles and can generate a full set of climate information, providing one of the important approaches to projecting fine spatial-scale future climate information. However, the systematic biases of climate models often degrade the TDD simulations and hinder the application of dynamical downscaling in the climate-change related studies. New methods developed over past decades improve the performance of dynamical downscaling simulations. These methods can be divided into four groups: the TDD method, the pseudo global warming method, dynamical downscaling with GCM bias corrections, and dynamical downscaling with both GCM and RCM bias corrections. These dynamical downscaling methods are reviewed and compared in this paper. The merits and limitations of each dynamical downscaling method are also discussed. In addition, the challenges and potential directions in progressing dynamical downscaling methods are stated.     

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada

This study compares three linear models and one non-linear model, specifically multiple linear regression (MLR) with ordinary least squares (OLS) estimates, robust regression, ridge regression, and artificial neural networks (ANNs), to identify an appropriate transfer function in statistical downscaling (SD) models for the daily maximum and minimum temperatures (T_max and T_min) and daily precipitation occurrence and amounts (P_occ and P_amount). This comparison was made over twenty-five observation sites located in five different Canadian provinces (British Columbia, Saskatchewan, Manitoba, Ontario, and Québec). Reanalysis data were employed as atmospheric predictor variables of SD models. Predictors of linear transfer functions and ANN were selected by linear correlations coefficient and mutual information, respectively. For each downscaled case, annual and monthly models were developed and analysed. The monthly MLR, annual ANN, annual ANN, and annual MLR yielded the best performance for T_max, T_min, P_occ and P_amont according to the modified Akaike information criterion (AICu). A monthly MLR is recommended for the transfer functions of the four predictands because it can provide a better performance for the T_max and as good performance as the annual MLR for the T_min, P_occ, and P_amount. Furthermore, a monthly MLR can provide a slightly better performance than an annual MLR for extreme events. An annual MLR approach is also equivalently recommended for the transfer functions of the four predictands because it showed as good a performance as monthly MLR in spite of its mathematical simplicity. Robust and ridge regressions are not recommended because the data used in this study are not greatly affected by outlier data and multicollinearity problems. An annual ANN is recommended only for the T_min, based on the best performance among the models in terms of both the RMSE and AICu.     

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station‐scale hydrological variables from large‐scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K‐nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue‐type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices

ABSTRACT

This work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.

Editor D. Koutsoyiannis; Associate editor K. Hamed     

Projection of future rainfall for the North China Plain using two statistical downscaling models and its hydrological implications

This study projected the future rainfall (2046–2065 and 2081–2100) for the North China Plain (NCP) using two stochastic statistical downscaling models, the non-homogeneous hidden Markov model and the generalized linear model for daily climate time series, conditioned by the large-scale atmospheric predictors from six general circulation models for three emission scenarios (A1B, A2 and B1). The results indicated that the annual total rainfall, the extreme daily rainfall and the maximum length of consecutive wet/dry days would decline, while the number of annual rainfall days would slightly increase (correspondingly rainfall intensity would decrease) in the NCP, in comparison with the base period (1961–2010). Moreover, the summer monsoon rainfall, which accounted for 50–75 % of the total annual rainfalls in NCP, was projected to decrease in the latter half of twenty-first century. The spatial patterns of change showed generally north–south gradients with relatively larger magnitude decrease in the northern NCP and less decrease (or even slightly increase) in the southern NCP. This could result in decline of the annual runoff by ?5.5 % (A1B), ?3.3 % (A2) and ?4.1 % (B1) for 2046–2065 and ?5.3 % (A1B), ?4.6 % (A2) and ?1.9 % (B1) decrease for 2081–2100. These rainfall changes, combined with the warming temperature, could lead to drier catchment soil profiles and further reduce runoff potential, would hence provide valuable references for the water availability and related climate change adaption in the NCP.     

Intercomparison of 3D pore-scale flow and solute transport simulation methods

Multiple numerical approaches have been developed to simulate porous media fluid flow and solute transport at the pore scale. These include 1) methods that explicitly model the three-dimensional geometry of pore spaces and 2) methods that conceptualize the pore space as a topologically consistent set of stylized pore bodies and pore throats. In previous work we validated a model of the first type, using computational fluid dynamics (CFD) codes employing a standard finite volume method (FVM), against magnetic resonance velocimetry (MRV) measurements of pore-scale velocities. Here we expand that validation to include additional models of the first type based on the lattice Boltzmann method (LBM) and smoothed particle hydrodynamics (SPH), as well as a model of the second type, a pore-network model (PNM). The PNM approach used in the current study was recently improved and demonstrated to accurately simulate solute transport in a two-dimensional experiment. While the PNM approach is computationally much less demanding than direct numerical simulation methods, the effect of conceptualizing complex three-dimensional pore geometries on solute transport in the manner of PNMs has not been fully determined. We apply all four approaches (FVM-based CFD, LBM, SPH and PNM) to simulate pore-scale velocity distributions and (for capable codes) nonreactive solute transport, and intercompare the model results. Comparisons are drawn both in terms of macroscopic variables (e.g., permeability, solute breakthrough curves) and microscopic variables (e.g., local velocities and concentrations). Generally good agreement was achieved among the various approaches, but some differences were observed depending on the model context. The intercomparison work was challenging because of variable capabilities of the codes, and inspired some code enhancements to allow consistent comparison of flow and transport simulations across the full suite of methods. This study provides support for confidence in a variety of pore-scale modeling methods and motivates further development and application of pore-scale simulation methods.     

Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea

In this paper, cluster analysis (CA), principal component analysis (PCA) and the fuzzy logic approach were employed to evaluate the trophic status of water quality for 12 monitoring stations in Daya Bay in 2003. CA grouped the four seasons into four groups (winter, spring, summer and autumn) and the sampling sites into two groups (cluster DA: S1, S2, S4-S7, S9 and S12 and cluster DB: S3, S8, S10 and S11). PCA identified the temporal and spatial characteristics of trophic status in Daya Bay. Cluster DB, with higher concentrations of TP and DIN, is located in the western and northern parts of Daya Bay. Cluster DA, with the low Secchi, is located in the southern and eastern parts of Daya Bay. The fuzzy logic approach revealed more information about the temporal and spatial patterns of the trophic status of water quality. Chlorophyll a, TP and Secchi may be major factors for deteriorating water quality.     

Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output

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.     

Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method

In this study, the applicability of the statistical downscaling model (SDSM) in downscaling precipitation in the Yangtze River basin, China was investigated. The investigation includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP/NCAR reanalysis data, the validation of the model using independent period of the NCEP/NCAR reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios. Selected as climate variables for downscaling were measured daily precipitation data (1961–2000) from 136 weather stations in the Yangtze River basin. The results showed that: (1) there existed good relationship between the observed and simulated precipitation during the calibration period of 1961–1990 as well as the validation period of 1991–2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The average R ² values between the simulated and observed monthly and seasonal precipitation for the validation period were 0.78 and 0.91 respectively for the whole basin, which showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under both scenarios A2 and B2, during the prediction period of 2010–2099, the change of annual mean precipitation in the Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a surplus of precipitation in the 2080s as compared to the mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2 and increase by about 5.33% under scenario B2 in the 2080s. The winter and autumn might be the more distinct seasons with more predicted changes of precipitation than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean precipitation in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.