Identification of trends in Malaysian monthly runoff under the scaling hypothesis

Abstract

Statistical tests have been widely used for several decades to identify and test the significance of trends in runoff and other hydrological data. The Mann-Kendall (M-K) trend test is commonly used in trend analysis. The M-K test was originally proposed for random data. Several variations of the M-K test, as well as pre-processing of data for use with it, have been developed and used. The M-K test under the scaling hypothesis has been developed recently. The basic objective of the research presented in this paper is to investigate the trends in Malaysian monthly runoff data. Identification of trends in runoff data is useful for planning water resources projects. Existence of statistically significant trends would also lead to identification of possible effects of climate change. Monthly runoff data for Malaysian rivers from the past three decades are analysed, in both five-year segments and entire data sequences. The five-year segments are analysed to investigate the variability in trends from one segment to another in three steps: (1) the M-K tests are conducted under random and correlation assumptions; (2) the Hurst scaling parameter is estimated and tested for significance; and (3) the M-K test under the scaling hypothesis is conducted. Thus the tests cover both correlation and scaling. The results show that the number of significant segments in Malaysian runoff data would be the same as those found under the assumption that the river flow sequences are random. The results are also the same for entire sequences. Thus, monthly Malaysian runoff data do not have statistically significant trends. Hence there are no indications of climate change in Malaysian runoff data.

Citation Rao, A. R., Azli, M. & Pae, L. J. (2011) Identification of trends in Malaysian monthly runoff under the scaling hypothesis. Hydrol. Sci. J. 56(6), 917–929.     

The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data

Abstract

Kendall's tau (τ) has been widely used as a distribution-free measure of cross-correlation between two variables. It has been previously shown that persistence in the two involved variables results in the inflation of the variance of τ. In this paper, the full null distribution of Kendall's τ for persistent data with multivariate Gaussian dependence is derived, and an approximation to the full distribution is proposed. The effect of the deviation from the multivariate Gaussian dependence model on the distribution of τ is also investigated. As a demonstration, the temporal consistency and field significance of the cross-correlation between the North Hemisphere (NH) temperature time series in the period 1850–1995 and a set of 784 NH tree-ring width (TRW) proxies in addition to 105 NH tree-ring maximum latewood density (MXD) proxies are studied. When persistence is ignored, the original Mann-Kendall test gives temporally inconsistent results between the early half (1850–1922) and the late half (1923–1995) of the record. These temporal inconsistencies are largely eliminated when persistence is accounted for, indicating the spuriousness of a large portion of the identified cross-correlations. Furthermore, the use of the modified test in combination with a field significance test that is robust to spatial correlation indicates the absence of field significant cross-correlation in both halves of the record. These results have serious implications for the use of tree-ring data as temperature proxies, and emphasize the importance of utilizing the correct distribution of Kendall's τ in order to avoid the overestimation of the significance of cross-correlation between data that exhibit significant persistence.

Citation Hamed, K. H. (2011) The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data. Hydrol. Sci. J. 56(5), 841–853.     

Fluctuations in the monthly discharge of Guyana Shield rivers,related to Pacific and Atlantic climate variability

Abstract

The discharge variability of the main rivers that drain the Guyana Shield is analysed over the last 50 years using cross-wavelet, coherence and composite analysis involving oceanic and atmospheric variables. We highlight the overall hydro-climatological homogeneity of this region that allowed us to focus on the longest discharge time series available. Therefore, a wavelet cross-analysis was carried out between monthly and seasonal Maroni River discharge at the Langa Tabiki station and selected climate indices. This confirms a strong relationship between the hydrology of the Guyana Shield and the Pacific sea-surface temperature (SST) fluctuations. There is evidence of intermittent influence, of between inter-annual and near decadal scales, of the Atlantic SST fluctuations, in particular around 1970 and 1990. Finally, we show that the links between oceanic regions and high discharge in the rivers of Guyana are realized through the reinforcement of the Walker and Hadley cells between the Amazon and the adjacent oceans and through decreased trade winds and monsoon flux that favour the persistence of humidity over the Guyana Shield.

Editor Z.W. Kundzewicz; Associate editor D. Hughes

Citation Labat, D., Espinoza, J.-C., Ronchail, J., Cochonneau, G., de Oliveira, E., Doudou, J.C. and Guyot, J.-L., 2012. Fluctuations in the monthly discharge of Guyana Shield rivers, related to Pacific and Atlantic climate variability. Hydrological Sciences Journal, 57 (6), 1081–1091.     

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Abstract

New mathematical programming models are proposed, developed and evaluated in this study for estimating missing precipitation data. These models use nonlinear and mixed integer nonlinear mathematical programming (MINLP) formulations with binary variables. They overcome the limitations associated with spatial interpolation methods relevant to the arbitrary selection of weighting parameters, the number of control points within a neighbourhood, and the size of the neighbourhood itself. The formulations are solved using genetic algorithms. Daily precipitation data obtained from 15 rain gauging stations in a temperate climatic region are used to test and derive conclusions about the efficacy of these methods. The developed methods are compared with some naïve approaches, multiple linear regression, nonlinear least-square optimization, kriging, and global and local trend surface and thin-plate spline models. The results suggest that the proposed new mathematical programming formulations are superior to those obtained from all the other spatial interpolation methods tested in this study.

Editor D. Koutsoyiannis; Associate editor S. Grimaldi

Citation Teegavarapu, R.S.V., 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57 (3), 383–406.     

A) A. I. H. S. / A) A. I. H. S.

Abstract

A new method is presented to generate stationary multi-site hydrological time series. The proposed method can handle flexible time-step length, and it can be applied to both continuous and intermittent input series. The algorithm is a departure from standard decomposition models and the Box-Jenkins approach. It relies instead on the recent advances in statistical science that deal with generation of correlated random variables with arbitrary statistical distribution functions. The proposed method has been tested on 11 historic weekly input series, of which the first seven contain flow data and the last four have precipitation data. The article contains an extensive review of the results.

Editor D. Koutsoyiannis

Citation Ilich, N., 2014. An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series. Hydrological Sciences Journal, 59 (1), 85–98.     

Analysis of trends in reference evapotranspiration data in a humid climate

Abstract

Statistically significant FAO-56 Penman-Monteith (FAO-56 PM) and adjusted Hargreaves (AHARG) reference evapotranspiration (ET₀) trends at monthly, seasonal and annual time scales were analysed by using linear regression, Mann-Kendall and Spearman’s Rho tests at the 1 and 5% significance levels. Meteorological data were used from 12 meteorological stations in Serbia, which has a humid climate, for the period 1980–2010. Web-based software for conducting the trend analyses was developed. All of the trends significant at the 1 and 5% significance levels were increasing. The FAO-56 PM ET₀ trends were almost similar to the AHARG trends. On the seasonal time scale, for the majority of stations significant increasing trends occurred in summer, while no significant positive or negative trends were detected by the trend tests in autumn for the AHARG series. Moreover, 70% of the stations were characterized by significant increasing trends for both annual ET₀ series.

Editor Z.W. Kundzewicz; Associate editor S. Grimaldi

Citation Gocic, M. and Trajkovic, S., 2013. Analysis of trends in reference evapotranspiration data in a humid climate. Hydrological Sciences Journal, 59 (1), 165–180.     

Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables

Abstract

The development of statistical relationships between local hydroclimates and large-scale atmospheric variables enhances the understanding of hydroclimate variability. The rainfall in the study basin (the Upper Chao Phraya River Basin, Thailand) is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. Using correlation analysis and cross-validated multiple regression, the large-scale atmospheric variables, such as temperature, pressure and wind, over given regions are identified. The forecasting models using atmospheric predictors show the capability of long-lead forecasting. The modified k-nearest neighbour (k-nn) model, which is developed using the identified predictors to forecast rainfall, and evaluated by likelihood function, shows a long-lead forecast of monsoon rainfall at 7–9 months. The decreasing performance in forecasting dry-season rainfall is found for both short and long lead times. The developed model also presents better performance in forecasting pre-monsoon season rainfall in dry years compared to wet years, and vice versa for monsoon season rainfall.

Editor Z.W. Kundzewicz

Citation Singhrattna, N., Babel, M.S. and Perret, S.R., 2012. Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables. Hydrological Sciences Journal, 57 (1), 26–41.     

The identification of hydrological indices for the characterization of macroinvertebrate community response to flow regime variability

Abstract

The importance of flow regime variability for maintaining ecological functioning and integrity of river ecosystems has been firmly established in both natural and anthropogenically modified systems. River flow regimes across lowland catchments in eastern England are examined using 47 variables, including those derived using the Indicators of Hydrologic Alteration (IHA) software. A principal component analysis method was used to identify redundant hydrological variables and those that best characterized the hydrological series (1986–2005). A small number of variables (<6) characterized up to 95% of the statistical variability in the flow series. The hydrological processes and conditions that the variables represent were found to be significant in structuring the in-stream macroinvertebrate community Lotic-invertebrate Index for Flow Evaluation (LIFE) scores at both the family and species levels. However, hydrological variables only account for a relatively small proportion of the total ecological variability (typically <10%). The research indicates that a range of other factors, including channel morphology and anthropogenic modification of in-stream habitats, structure riverine macroinvertebrate communities in addition to hydrology. These factors need to be considered in future environmental flow studies to enable the characterization of baseline/reference conditions for management and restoration purposes.

Editor Z.W. Kundzewicz; Guest editor M. Acreman

Citation Worrall, T.P., Dunbar, M.J., Extence, C.A., Laizé, C.L.R., Monk, W.A., and Wood, P.J., 2014. The identification of hydrological indices for the characterization of macroinvertebrate community response to flow regime variability. Hydrological Sciences Journal, 59 (3–4), 645–658.     

Streamflow forecasting using least-squares support vector machines

Abstract

This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of this model, monthly streamflow records from two stations, Tg Tulang and Tg Rambutan of the Kinta River in Perak, Peninsular Malaysia, were used as case studies. The performance of the LSSVM model is compared with the conventional statistical autoregressive integrated moving average (ARIMA), the artificial neural network (ANN) and support vector machine (SVM) models using various statistical measures. The results of the comparison indicate that the LSSVM model is a useful tool and a promising new method for streamflow forecasting.

Editor D. Koutsoyiannis; Associate editor L. See

Citation Shabri, A. and Suhartono, 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 57 (7), 1275–1293.     

Corrected prediction intervals for change detection in paired watershed studies

Abstract

Hydrological data may be temporally autocorrelated requiring autoregressive process parameters to be estimated. Current statistical methods for hydrological change detection in paired watershed studies rely on prediction intervals, but the current form of prediction intervals does not include all appropriate sources of variation. Corrected prediction intervals for the analysis of paired watershed study data that include variation associated with covariance and linear model parameter estimation are presented. We provide an example of their application to data from the Hinkle Creek Paired Watershed Study located in the western Cascade foothills of Southern Oregon, USA. Research implications of using the correct prediction limits and incorporating the estimation uncertainty of autoregressive process parameters are discussed.

Editor D. Koutsoyiannis

Citation Som, N.A., Zégre, N.P., Ganio, L.M. and Skaugset, A.E., 2012. Corrected prediction intervals for change detection in paired watershed studies. Hydrological Sciences Journal, 57 (1), 134–143.     

Environmental flows in India: towards sustainable water management

Abstract

The strong wet and dry seasons of tropical monsoon hydrology in India necessitate development of storage and flow diversion schemes for utilization of water to meet various social and economic needs. However, the river valley schemes may cause adverse flow-related impacts due to storage, flow diversion, tunnelling and spoil disposal. There may be critical reaches in which altered flows are not able to sustain the river channel ecology and riparian environment that existed prior to implementation of the storage and diversion schemes. In the past, environmental flows in India have usually been understood as the minimum flow to be released downstream from a dam as compensation for riparian rights, without considering the impacts on the river ecosystem. Rivers in India have been significantly influenced by anthropogenic activities over the past 60 years and have great social and religious significance to the vast population. This paper explores various aspects of past, present and future environmental flow assessment (EFA) in India highlighted by case studies from rivers across the nation. It demonstrates that multidisciplinary studies requiring expertise from a range of fields are needed for EFA, and that environmental flows are necessary for aquatic ecosystems to remain in a healthy state and for the sustainable use of water resources. The major focus areas for the development of EFA research in India are the creation of a shareable database for hydrological, ecological and socioeconomic data, developing hydrology–ecology relationships, evaluation of ecosystem services, addressing pollution due to anthropogenic activities and promotion of research on EFA. At the same time, efforts will be needed to develop new methods or refine existing methods for India.

Editor D. Koutsoyiannis; Guest editor M. Acreman

Citation Jain, S.K. and Kumar, P., 2014. Environmental flows in India: towards sustainable water management. Hydrological Sciences Journal, 59 (3–4), 751–769.     

Relations indices de Végétation–Pluie au Burkina Faso: Cas du Bassin Versant du Nakambé/Relationship between Rainfall and Vegetation Indexes in Burkina Faso: A Case Study of the Nakambé Basin

Abstract

This work deals with the problem of the use of remote sensing data derived from NOAA/AVHRR observations for monitoring the West African Sahel climatic variability. NDVI is widely used in hydrological and climatological research, and in the study of global climatic changes. The relationships between NDVI and climatic parameters are not well established yet and are the focus of many studies. The relationships between NDVI and rainfall were studied at a 10-day time step in the Nakambe River basin in Burkina Faso in the Sahelo-Sudanian area over the years 1982–1999. Good correlations were found in the annual evolution of these two variables. The statistical analysis shows a significant relationship between NDVI and the sum of the annual rainfall with determination coefficients greater than 0.80. At the spatial scale of 0.5° × 0.5°, the determination coefficient ranges from 0.91 to 0.96. It was also found that the NDVI is a good indicator of the determination of the beginning and the end of the rainy season. It gives reasonably good results in comparison with the other methods commonly used in the study region.     

Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application

Abstract

The present research study investigates the application of nonlinear normalizing data transformations in conjunction with ordinary kriging (OK) for the accurate prediction of groundwater level spatial variability in a sparsely-gauged basin. We investigate three established normalizing methods, Gaussian anamorphosis, trans-Gaussian kriging and the Box-Cox method to improve the estimation accuracy. The first two are applied for the first time to groundwater level data. All three methods improve the mean absolute prediction error compared to the application of OK to the non-transformed data. In addition, a modified Box-Cox transformation is proposed and applied to normalize the hydraulic heads. The modified Box-Cox transformation in conjunction with OK is found to be the optimal spatial model based on leave-one-out cross-validation. The recently established Spartan semivariogram family provides the optimal model fit to the transformed data. Finally, we present maps of the groundwater level and the kriging variance based on the optimal spatial model.

Editor D. Koutsoyiannis; Associate editor A. Montanari

Citation Varouchakis, E.A., Hristopoulos, D.T., and Karatzas, G.P., 2012. Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application. Hydrological Sciences Journal, 57 (7), 1404–1419.     

Comparison of non-parametric and parametric water temperature models on the Nivelle River,France

Abstract

Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables.     

Development of a global flood risk index based on natural and socio-economic factors

Abstract

A global flood risk index (FRI) is established, based on both natural and social factors. The advanced flood risk index (AFRI) is the expectation of damage in the case of a single flood occurrence, estimated by a linear regression-based approach as a function of hazard and vulnerability metrics. The resulting equations are used to predict potential flood damage given gridded global data for independent variables. It is new in the aspect that it targets floods by units of events, instead of a long-term trend. Moreover, the value of the AFRI is that it can express relative potential flood risk with the process of flood damage occurrence considered. The significance of this study is that not only the hazard parameters which contribute directly to flood occurrence, but vulnerability parameters which reflect the conditions of the region where flood occurred, including its residential and social characteristics, were shown quantitatively to affect flood damage.

Citation Okazawa, Y., Yeh, P., Kanae, S. & Oki, T. (2011) Development of a global flood risk index based on natural and socioeconomic factors. Hydrol. Sci. J. 56(5), 789–804.     

Nonlinear response of runoff to atmospheric freezing level height variation based on hybrid prediction models

ABSTRACT

We use data on the freezing level height (FLH) and summer runoff in the Hotan River, China, from 1960 to 2013, to analyse the nonlinear relationships of atmospheric and hydrological factors at different time scales, by employing three nonlinear decomposition methods. Six hybrid prediction models are established by combining linear regression and back-propagation artificial neural network (BPANN) models. The decomposition results by three nonlinear methods are compared, indicating that the extreme-point symmetric mode decomposition (ESMD) method ensures the best prediction capacity. The runoff and FLH have periods of 3 and 6 years, respectively, at the inter-annual scale, which pass the significance test of 0.05 (P < 0.05) by using the Monte Carlo method, although there were slight differences in the periods at the inter-decadal scale. Among the six models, ESMD-BPANN exhibits the highest accuracy, with good reliability and resolution, according to several performance indicators. The ESMD-BPANN model is thus selected for the simulation and prediction of runoff.     

2.—FORECAST OF SUMMER FLOOD WAVES OF THE SIÓ AND KAPOS RIVERS FROM PRECIPITATION

Abstract

Flood forecasts for the Sió and Kapos Rivers in Hungary have been made using gage relations. In order to increase the forecast time advantage procedures have been developed for predicting maximum stage using precipitation data and other variables including soil moisture index.

The paper describes the methods for developing forecasting relations involving three variables and four or more variables. The procedures are restricted to flood events caused by summer and autumn-precipitation (ice and snow-melt floods excluded).     

Virtual California: Fault Model, Frictional Parameters, Applications

Virtual California is a topologically realistic simulation of the interacting earthquake faults in California. Inputs to the model arise from field data, and typically include realistic fault system topologies, realistic long-term slip rates, and realistic frictional parameters. Outputs from the simulations include synthetic earthquake sequences and space-time patterns together with associated surface deformation and strain patterns that are similar to those seen in nature. Here we describe details of the data assimilation procedure we use to construct the fault model and to assign frictional properties. In addition, by analyzing the statistical physics of the simulations, we can show that that the frictional failure physics, which includes a simple representation of a dynamic stress intensity factor, leads to self-organization of the statistical dynamics, and produces empirical statistical distributions (probability density functions: PDFs) that characterize the activity. One type of distribution that can be constructed from empirical measurements of simulation data are PDFs for recurrence intervals on selected faults. Inputs to simulation dynamics are based on the use of time-averaged event-frequency data, and outputs include PDFs representing measurements of dynamical variability arising from fault interactions and space-time correlations. As a first step for productively using model-based methods for earthquake forecasting, we propose that simulations be used to generate the PDFs for recurrence intervals instead of the usual practice of basing the PDFs on standard forms (Gaussian, Log-Normal, Pareto, Brownian Passage Time, and so forth). Subsequent development of simulation-based methods should include model enhancement, data assimilation and data mining methods, and analysis techniques based on statistical physics.