A spatially distributed Clark’s unit hydrograph based hybrid hydrologic model (Distributed-Clark)

ABSTRACT

A hybrid hydrologic model (Distributed-Clark), which is a lumped conceptual and distributed feature model, was developed based on the combined concept of Clark’s unit hydrograph and its spatial decomposition methods, incorporating refined spatially variable flow dynamics to implement hydrological simulation for spatially distributed rainfall–runoff flow. In Distributed-Clark, the Soil Conservation Service (SCS) curve number method is utilized to estimate spatially distributed runoff depth and a set of separated unit hydrographs is used for runoff routing to obtain a direct runoff flow hydrograph. Case studies (four watersheds in the central part of the USA) using spatially distributed (Thiessen polygon-based) rainfall data of storm events were used to evaluate the model performance. Results demonstrate relatively good fit to observed streamflow, with a Nash-Sutcliffe efficiency (E_NS) of 0.84 and coefficient of determination (R²) of 0.86, as well as a better fit in comparison with outputs of spatially averaged rainfall data simulations for two models including HEC-HMS.     

A three-dimensional,iterative mapping procedure for the implementation of an ionosphere-magnetosphere anisotropic Ohm’s law boundary condition in global magnetohydrodynamic simulations

The mathematical formulation of an iterative procedure for the numerical implementation of an ionosphere-magnetosphere (IM) anisotropic Ohm’s law boundary condition is presented. The procedure may be used in global magnetohydrodynamic (MHD) simulations of the magnetosphere. The basic form of the boundary condition is well known, but a well-defined, simple, explicit method for implementing it in an MHD code has not been presented previously. The boundary condition relates the ionospheric electric field to the magnetic field-aligned current density driven through the ionosphere by the magnetospheric convection electric field, which is orthogonal to the magnetic field B, and maps down into the ionosphere along equipotential magnetic field lines. The source of this electric field is the flow of the solar wind orthogonal to B. The electric field and current density in the ionosphere are connected through an anisotropic conductivity tensor which involves the Hall, Pedersen, and parallel conductivities. Only the height-integrated Hall and Pedersen conductivities (conductances) appear in the final form of the boundary condition, and are assumed to be known functions of position on the spherical surface R=R₁ representing the boundary between the ionosphere and magnetosphere. The implementation presented consists of an iterative mapping of the electrostatic potential , the gradient of which gives the electric field, and the field-aligned current density between the IM boundary at R=R₁ and the inner boundary of an MHD code which is taken to be at R₂>R₁. Given the field-aligned current density on R=R₂, as computed by the MHD simulation, it is mapped down to R=R₁ where it is used to compute by solving the equation that is the IM Ohm’s law boundary condition. Then is mapped out to R=R₂, where it is used to update the electric field and the component of velocity perpendicular to B. The updated electric field and perpendicular velocity serve as new boundary conditions for the MHD simulation which is then used to compute a new field-aligned current density. This process is iterated at each time step. The required Hall and Pedersen conductances may be determined by any method of choice, and may be specified anew at each time step. In this sense the coupling between the ionosphere and magnetosphere may be taken into account in a self-consistent manner.     

A statistical model for karst spring discharge estimation under extensive groundwater development and extreme climate change

ABSTRACT

To acquire better understanding of spring discharge under extreme climate change and extensive groundwater pumping, this study proposed an extreme value statistical decomposition model, in which the spring discharge was decomposed into three items: a long-term trend; periodic variation; and random fluctuation. The long-term trend was fitted by an exponential function, and the periodic variation was fitted by an exponential function whose index was the sum of two sine functions. A general extreme value (GEV) model was used to obtain the return level of extreme random fluctuation. Parameters of the non-linear long-term trend and periodic variation were estimated by the Levenberg-Marquardt algorithm, and the GEV model was estimated by the maximum likelihood method. The extreme value statistical decomposition model was applied to Niangziguan Springs, China to forecast spring discharge. We showed that the modelled spring discharge fitted the observed data very well. Niangziguan Springs discharge is likely to continue declining with fluctuation, and the risk of cessation by August 2046 is 1%. The extreme value decomposition model is a robust method for analysing the nonstationary karst spring discharge under conditions of extensive groundwater development/pumping, and extreme climate changes.

Editor D. Koutsoyiannis; Associate editor J. Ward     

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.     

Comparative performance of soil water balance models in computing semi-arid aquifer recharge

Abstract

Estimating groundwater recharge is essential to ensure the sustainable use of groundwater resources, particularly in arid and semi-arid regions. Soil water balances have been frequently advocated as valuable tools to estimate groundwater recharge. This article compares the performance of three soil water balance models (Hydrobal, Visual Balan v2.0 and Thornthwaite) in the Ventós-Castellar aquifer, Spain. The models were used to simulate wet and dry years. Recharge estimates were transformed into water table fluctuations by means of a lumped groundwater model. These, in turn, were calibrated against piezometric data. Overall, the Hydrobal model shows the best fit between observed and calculated levels (r² = 0.84), highlighting the role of soil moisture and vegetation in recharge processes.

Editor D. Koutsoyiannis; Associate editor X. Chen

Citation Touhami, I., et al., 2014. Comparative performance of soil water balance models in computing semi-arid aquifer recharge. Hydrological Sciences Journal, 59 (1), 193–203.     

Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency

ABSTRACT

Goodness-of-fit measures are important for an objective evaluation of runoff model performance. The Kling-Gupta efficiency (R_KG), which has been introduced as an improvement of the widely used Nash-Sutcliffe efficiency, considers different types of model errors, namely the error in the mean, the variability, and the dynamics. The calculation of R_KG is implicitly based on the assumptions of data linearity, data normality, and the absence of outliers. In this study, we propose a modification of R_KG as an efficiency measure comprising non-parametric components, i.e. the Spearman rank correlation and the normalized flow–duration curve. The performances of model simulations for 100 catchments using the new measure were compared to those obtained using R_KG based on a number of statistical metrics and hydrological signatures. The new measure resulted overall in better or comparable model performances, and thus it was concluded that efficiency measures with non-parametric components provide a suitable alternative to commonly used measures.     

A new measure for assessing the efficiency of hydrological data-driven forecasting models

Abstract

There is a lack of consistency and generality in assessing the performance of hydrological data-driven forecasting models, and this paper presents a new measure for evaluating that performance. Despite the fact that the objectives of hydrological data-driven forecasting models differ from those of the conventional hydrological simulation models, criteria designed to evaluate the latter models have been used until now to assess the performance of the former. Thus, the objectives of this paper are, firstly, to examine the limitations in applying conventional methods for evaluating the data-driven forecasting model performance, and, secondly, to present new performance evaluation methods that can be used to evaluate hydrological data-driven forecasting models with consistency and objectivity. The relative correlation coefficient (RCC) is used to estimate the forecasting efficiency relative to the naïve model (unchanged situation) in data-driven forecasting. A case study with 12 artificial data sets was performed to assess the evaluation measures of Persistence Index (PI), Nash-Sutcliffe coefficient of efficiency (NSC) and RCC. In particular, for six of the data sets with strong persistence and autocorrelation coefficients of 0.966–0.713 at correlation coefficients of 0.977–0.989, the PIs varied markedly from 0.368 to 0.930 and the NSCs were almost constant in the range 0.943–0.972, irrespective of the autocorrelation coefficients and correlation coefficients. However, the RCCs represented an increase of forecasting efficiency from 2.1% to 37.8% according to the persistence. The study results show that RCC is more useful than conventional evaluation methods as the latter do not provide a metric rating of model improvement relative to naïve models in data-driven forecasting.

Editor D. Koutsoyiannis, Associate editor D. Yang

Citation Hwang, S.H., Ham, D.H., and Kim, J.H., 2012. A new measure for assessing the efficiency of hydrological data-driven forecasting models. Hydrological Sciences Journal, 57 (7), 1257–1274.     

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.     

KALMOD,a stochastic-deterministic model for simulating groundwater flow with Kalman filtering

Abstract

This paper presents the program KALMOD which has been developed to enable the execution of the integration of the Kalman filtering and the numerical groundwater flow model MODFLOW on microcomputers. The program can be applied to quantify and reduce the uncertainty of the groundwater flow model, and to analyse and design groundwater monitoring networks. KALMOD consists of a preprocessor, a processor and a postprocessor. The preprocessor acts as an interface between the user and the processor. The processor manipulates the measurement processes and carries out the filtering tasks. The filtering algorithm is implemented so that it is relatively efficient with respect to computer memory and execution time. The postprocessor was designed to present the model results in graphics. The program is suitable for small scale problems and for educational purposes.     

A combined rotated general regression neural network method for river flow forecasting

ABSTRACT

This study focused on the performance of the rotated general regression neural network (RGRNN), as an enhancement of the general regression neural network (GRNN), in monthly-mean river flow forecasting. The study of forecasting of monthly mean river flows in Heihe River, China, was divided into two steps: first, the performance of the RGRNN model was compared with the GRNN model, the feed-forward error back-propagation (FFBP) model and the soil moisture accounting and routing (SMAR) model in their initial model forms; then, by incorporating the corresponding outputs of the SMAR model as an extra input, the combined RGRNN model was compared with the combined FFBP and combined GRNN models. In terms of model efficiency index, R², and normalized root mean squared error, NRMSE, the performances of all three combined models were generally better than those of the four initial models, and the RGRNN model performed better than the GRNN model in both steps, while the FFBP and the SMAR were consistently the worst two models. The results indicate that the combined RGRNN model could be a useful river flow forecasting tool for the chosen arid and semi-arid region in China.

Editor D. Koutsoyiannis; Associate editor not assigned     

Improving long-range hydrological forecasts with extended Kalman filters

Abstract

There is a continuing effort to advance the skill of long-range hydrological forecasts to support water resources decision making. The present study investigates the potential of an extended Kalman filter approach to perform supervised training of a recurrent multilayer perceptron (RMLP) to forecast up to 12-month-ahead lake water levels and streamflows in Canada. The performance of the RMLP was compared with the conventional multilayer perceptron (MLP) using suites of diagnostic measures. The results of the forecasting experiment showed that the RMLP model was able to provide a robust modelling framework capable of describing complex dynamics of the hydrological processes, thereby yielding more accurate and realistic forecasts than the MLP model. The performance of the method in the present study is very promising; however, further investigation is required to ascertain the versatility of the approach in characterizing different water resources and environmental problems.

Citation Muluye, G. Y. (2011) Improving long-range hydrological forecasts with extended Kalman filters. Hydrol. Sci. J. 56(7), 1118–1128.     

Riparian responses to reduced flood flows: comparing and contrasting narrowleaf and broadleaf cottonwoods

Abstract

To enable assessment of risks of water management to riparian ecosystems at a regional scale, we developed a quantile-regression model of abundance of broadleaf cottonwoods (Populus deltoides and P. fremontii) as a function of flood flow attenuation. To test whether this model was transferrable to narrowleaf cottonwood (Populus angustifolia), we measured narrowleaf abundance along 39 river reaches in northwestern Colorado, USA. The model performed well for narrowleaf in all 32 reaches where reservoir storage was <75% of mean annual flow. Field data did not fit the model at four of seven reaches where reservoir storage was >90% of mean annual flow. In these four reaches, narrowleaf was abundant despite peak flow attenuation of 45–61%. Poor model performance in these four reaches may be explained in part by a pulse of narrowleaf cottonwood expansion as a response to channel narrowing and in part by differences between narrowleaf and broadleaf cottonwood response to floods and drought.

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

Citation Wilding, T.K., Sanderson, J.S., Merritt, D.M., Rood, S.B., and Poff, N.L., 2014. Riparian responses to reduced flood flows: comparing and contrasting narrowleaf and broadleaf cottonwoods. Hydrological Sciences Journal, 59 (3–4), 605–617.     

Gaussian process classification: singly versus doubly stochastic models, and new computational schemes

The aim of this paper is to compare four different methods for binary classification with an underlying Gaussian process with respect to theoretical consistency and practical performance. Two of the inference schemes, namely classical indicator kriging and simplicial indicator kriging, are analytically tractable and fast. However, these methods rely on simplifying assumptions which are inappropriate for categorical class labels. A consistent and previously described model extension involves a doubly stochastic process. There, the unknown posterior class probability f(·) is considered a realization of a spatially correlated Gaussian process that has been squashed to the unit interval, and a label at position x is considered an independent Bernoulli realization with success parameter f(x). Unfortunately, inference for this model is not known to be analytically tractable. In this paper, we propose two new computational schemes for the inference in this doubly stochastic model, namely the “Aitchison Maximum Posterior” and the “Doubly Stochastic Gaussian Quadrature”. Both methods are analytical up to a final step where optimization or integration must be carried out numerically. For the comparison of practical performance, the methods are applied to storm forecasts for the Spanish coast based on wave heights in the Mediterranean Sea. While the error rate of the doubly stochastic models is slightly lower, their computational cost is much higher.     

Hydrological impact and potential flooding of convective rain cells in a semi-arid environment

Abstract

t Spatio-temporal storm properties have a large impact on catchment hydrological response. The sensitivity of simulated flash floods to convective rain-cell characteristics is examined for an extreme storm event over a 94 km² semi-arid catchment in southern Israel. High space–time resolution weather radar data were used to derive and model convective rain cells that then served as input into a hydrological model. Based on alterations of location, direction and speed of a major rain cell, identified as the flooding cell for this case, the impacts on catchment rainfall and generated flood were examined. Global sensitivity analysis was applied to identify the most important factors affecting the flash flood peak discharge at the catchment outlet. We found that the flood peak discharge could be increased three-fold by relatively small changes in rain-cell characteristics. We assessed that the maximum flash flood magnitude that this single rain cell can produce is 175 m³/s, and, taking into account the rest of the rain cells, the flash flood peak discharge can reach 260 m³/s.

Editor Z.W. Kundzewicz; Guest editor R.J. Moore

Citation Morin, E. and Yakir, H., 2013. Hydrological impact and potential flooding of convective rain cells in a semi-arid environment. Hydrological Sciences Journal, 59 (7), 1275–1284. http://dx.doi.org/10.1080/02626667.2013.841315     

A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows

Abstract

A wavelet-neural network (WNN) hybrid modelling approach for monthly river flow estimation and prediction is developed. This approach integrates discrete wavelet multi-resolution decomposition and a back-propagation (BP) feed-forward multilayer perceptron (FFML) artificial neural network (ANN). The Levenberg-Marquardt (LM) algorithm and the Bayesian regularization (BR) algorithm were employed to perform the network modelling. Monthly flow data from three gauges in the Weihe River in China were used for network training and testing for 48-month-ahead prediction. The comparison of results of the WNN hybrid model with those of the single ANN model show that the former is able to significantly increase the prediction accuracy.

Editor D. Koutsoyiannis; Associate editor H. Aksoy

Citation Wei, S., Yang, H., Song, J.X., Abbaspour, K., and Xu, Z.X., 2013. A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58 (2), 374–389.     

Rainfall–runoff simulation using a normalized antecedent precipitation index

Abstract

The normalized antecedent precipitation index (NAPI) model by Heggen for the prediction of runoff yield is analytically derived from the water balance equation. Heggen's model has been simplified further to a rational form and its performance verified with the Soil Conservation Service Curve Number (SCS-CN) model. The simplified model has three coefficients specific to a watershed, and requires two inputs: rainfall and the derived parameter, NAPI. The characteristic behaviour of the NAPI has resonance with the curve number (CN) of the SCS model. The proposed NAPI model was applied to three watersheds in the semi-arid region of India to simulate runoff yield. The model showed improved correlation between the observed and predicted runoff data compared to the SCS-CN model. The F test and paired t test also confirmed the reliability of the model with significance levels of 0.01 and 0.001%, respectively. The proposed model could be used successfully for rainfall–runoff modelling in a watershed.

Citation Ali, S., Ghosh, N. C. & Singh, R. (2010) Rainfall–runoff simulation using a normalized antecedent precipitation index. Hydrol. Sci. J. 55(2), 266–274.     

Inversion of waveforms from. Xiangtang borehole seismic array for soil dynamic property

In order to understand the site soil response of the Xiangtang borehole seismic array under real strong ground motion, reveal the site response, verify the technique of borehole exploration, and improve the precision of in-situ test and laboratory test, this paper presents a new approach, which is composed of two methods. One is the layered site seismic response method, whose layer transform matrix is always real. The other is a global-local optimization technique, which uses genetic algorithm (GA)-simplex method. An inversion of multi-component waveforms of P, SV and SH wave is carried out simultaneously. By inverting the records of three moderate and small earthquakes obtained from the Xiangtang borehole array (2# ) site, the soil dynamic characteristic parameters, including P velocity, damping ratio and frequency-dependent coefficient b, which has not been given in previous literatures, are calculated. The results show that the soil S wave velocity of the Xiangtang 2# borehole is generally greater than that obtained from the 1994 in-situ test, and is close to the velocity of the 3# borehole, which is more than 200 m away from the 2# borehole. Meanwhile, perceptible soil nonlinear behavior under peak ground motion of about 60×10-2m/s2 is detected by the inversion analysis. The presented method can be used for studying the soil response of other borehole array sites.     

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.     

Performance of the COSERO precipitation–runoff model under non-stationary conditions in basins with different climates

Abstract

This study is a contribution to a model intercomparison experiment initiated during a workshop at the 2013 IAHS conference in Göteborg, Sweden. We present discharge simulations with the conceptual precipitation–runoff model COSERO in 11 basins located under different climates in Europe, Africa and Australia. All of the basins exhibit some form of non-stationary conditions, due, for example, to warming, droughts or land-cover change. The evaluation of the daily discharge simulations focuses on the overall model performance and its decomposition into three components measuring temporal dynamics, mean flow volume and distribution of flows. Calibration performance is similarly high as in previous COSERO applications. However, when looking at evaluation periods independent of the calibration, the model performance drops considerably, mainly due to severely biased discharge simulations in semi-arid basins with strong non-stationarity in rainfall. Simulations are more robust in European basins with humid climates. This highlights the fact that hydrological models frequently fail when simulations are required outside of calibration conditions in basins with non-stationary conditions. As a consequence, calibration periods should be sufficiently long to include both wet and dry periods, which should yield more robust predictions.     

Contribution to hydrological modelling of small Amazonian catchments: application of rainfall–runoff models to simulate flow duration curves

Abstract

The objective of this study is to analyse three rainfall–runoff hydrological models applied in two small catchments in the Amazon region to simulate flow duration curves (FDCs). The simple linear model (SLM) considers the rainfall–runoff process as an input–output time-invariant system. However, the rainfall–runoff process is nonlinear; thus, a modification is applied to the SLM based on the residual relationship between the simulated and observed discharges, generating the modified linear model (MLM). In the third model (SVM), the nonlinearity due to infiltration and evapotranspiration is incorporated into the system through the sigmoid variable gain factor. The performance criteria adopted were a distance metric (δ) and the Nash-Sutcliffe coefficient (R²) determined between simulated and observed flows. The good results of the models, mainly the MLM and SVM, showed that they could be applied to simulate FDCs in small catchments in the Amazon region.

Editor D. Koutsoyiannis; Associate editor A. Montanari

Citation Blanco, C.J.C., Santos, S.S.M., Quintas, M.C., Vinagre, M.V.A., and Mesquita, A.L.A., 2013. Contribution to hydrological modelling of small Amazonian catchments: application of rainfall–runoff models to simulate flow duration curves. Hydrological Sciences Journal, 58 (7), 1–11.