Modelling wetland connectivity during overbank flooding in a tropical floodplain in north Queensland,Australia

Hydrological connectivity between floodplain wetlands and rivers is one of the principal driving mechanisms for the diversity, productivity and interactions of the major biota in river–floodplain systems. This article describes a method of quantifying flood‐induced overbank connectivity using a hydrodynamic model (MIKE 21) to calculate the timing, the duration and the spatial extent of the connections between several floodplain wetlands and rivers in the Tully–Murray catchment, north Queensland, Australia. Areal photogrammetry and field surveyed stream cross data were used to reproduce floodplain topography and rivers in the model. Laser altimetry (LiDAR)–derived fine resolution elevation data, for the central floodplain, were added to the topography model to improve the resolution of key features including wetlands, flow pathways and natural and artificial flow barriers. The hydrodynamic model was calibrated using a combination of in‐stream and floodplain gauge records. A range of off‐stream wetlands including natural and artificial, small and large were investigated for their connectivity with two main rivers (Tully and Murray) flowing over the floodplain for flood events of 1‐, 20‐ and 50‐year recurrence intervals. The duration of the connection of individual wetlands varied from 1 to 12 days, depending on flood magnitude and location in the floodplain, with some wetlands only connected during large floods. All of the wetlands studied were connected to the Tully River for shorter periods than they were to the Murray River because of the higher bank heights and levees on the Tully River and wetland proximity to the Murray River. Other than hydrology, land relief, riverbank elevation and levee banks along the river were found key factors controlling the degree of connectivity. These variations in wetland connectivity could have important implications for aquatic biota that move between rivers and off‐stream habitats during floods. Copyright © 2011 John Wiley & Sons, Ltd.     

Effect of Cold Plasma Injection on Whistler Mode Instability Triggered by Perpendicular Ac Electric Field at Uranus

The effect of cold plasma injection on whistler mode instability has been studied separately for a bi-Maxwellian and a loss-cone hackground plasma with perpendicular AC electric field. The cold plasma is described by a simple Maxwellian distribution, whereas a generalized distribution function with index j that reduces to a bi-Maxwellian for j = 0 and to a loss-cone for j = 1 has been derived for a plasma in the presence of a perpendicular AC electric field, to form a hot/warm background. The dispersion relation is obtained using the method of characteristic solutions and kinetic approach. An expression for the growth rate of a system with added cold plasma injection has been calculated. Results of sample theoretical calculations for representative values of parameters suited to the magnetosphere of Uranus has been obtained. The salient features of the analysis and the results obtained in both cases have been compared and discussed. It is inferred that it is not the magnitude but the frequency of the AC field which influences the growth rate and a loss-cone background plasma has a triggering effect on the growth rate, increasing the value of the real frequency and maximum growth rate by an order of magnitude. These results may go a long way to enable one to get a better understanding of whistlers and diagnostics of plasma parameters in the Uranian magnetosphere.     

FAO 56 Model and Remote Sensing for the Estimation of Crop-Water Requirement in Main Branch Canal of the Bhadra Command area, Karnataka State

Remote sensing and FAO 56 crop water model are used for estimating crop water requirement for paddy crop located in the main branch canal of Bhadra Command Area in Karnataka, India. The estimation of crop-water requirement depends on the meteorological factors, soil type and crop coefficients. The result obtained showed that water requirements of rabi crops higher than those of the kariff crops. The total irrigated area estimated from the IRS image is 29,353 ha. It is found that the total paddy crop acreage is 18,257 ha covering 62 % in the total irrigated area of the command area, Arecanut 20 %, coconut 15 % and sugarcane with other crops 3 %. The water requirement for paddy is 1180.4 mm for its entire growth period. The total water requirement for irrigation supply for crops in the entire command area is 5,790 at a demand of 0.10501 cusecs per ha.     

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.     

Joint frequency analysis and uncertainty estimation of coupled rainfall–runoff series relying on historical and simulated data

ABSTRACT

Joint frequency analysis and quantile estimation of extreme rainfall and runoff (ERR) are crucial for hydrological engineering designs. The joint quantile estimation of the historical ERR events is subject to uncertainty due to the errors that exist with flow height measurements. This study is motivated by the interest in introducing the advantages of using Hydrologic Simulation Program-Fortran (HSPF) simulations to reduce the uncertainties of the joint ERR quantile estimations in Taleghan watershed. Bivariate ERR quantile estimation was first applied on P_AMS-Q_SIM pairs and the results were compared against the historical rainfall–runoff data (P_AMS-Q_obs). Student’s t and Frank copulas with respectively Gaussian-P3 and Gaussian-LN3 marginal distributions well suited to fit the P_AMS-Q_obs and P_AMS-Q_SIM pairs. Results revealed that confidence regions (CRs) around the p levels become wider for P_AMS-Q_obs compared to P_AMS-Q_SIM, indicating the lower sampling uncertainties of HSPF simulations compared to the historical observations for bivariate ERR frequency analysis.     

A coupled model for heating and hydratation in unsaturated clays

Knowledge of transport processes of heat and moisture in soils of arid zones is vital to understanding the environmental and economic impacts of many activities: agriculture, waste disposal, geoenvironmental practices and earth sciences. Through extensive review and study on the different aspects of coupled transfer processes in swelling porous media, a general mathematical model for coupled heat, moisture, air flow and deformation problems in clayey soils is proposed in a consistent and unified manner. The model is characterized by the presence of a deformable solid matrix filled with two fluid phases (liquid water and air). In the proposed model, both pore water and air transfers are assumed to be governed by the generalized Darcy’s law. Fully coupled, non-linear partial differential equations are established and then solved by using a Galerkin weighted residual approach in space domain and an implicit integrating scheme in time domain. The obtained model has been finally validated by means of some case tests for the prediction of the thermo-hydro-mechanical behaviour of unsaturated swelling soils. The calculated relative errors between experimental and numerical results are 3% for temperature and 7% for stresses. Consequently, the developed numerical model predicts satisfactory results, compared to experimental test measures. The model is applicable to two-dimensional problems with various initial and boundary conditions; non-linear soil parameters can be easily included in this model.     

Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference

Calibration of hydrologic models is very difficult because of measurement errors in input and response, errors in model structure, and the large number of non-identifiable parameters of distributed models. The difficulties even increase in arid regions with high seasonal variation of precipitation, where the modelled residuals often exhibit high heteroscedasticity and autocorrelation. On the other hand, support of water management by hydrologic models is important in arid regions, particularly if there is increasing water demand due to urbanization. The use and assessment of model results for this purpose require a careful calibration and uncertainty analysis. Extending earlier work in this field, we developed a procedure to overcome (i) the problem of non-identifiability of distributed parameters by introducing aggregate parameters and using Bayesian inference, (ii) the problem of heteroscedasticity of errors by combining a Box–Cox transformation of results and data with seasonally dependent error variances, (iii) the problems of autocorrelated errors, missing data and outlier omission with a continuous-time autoregressive error model, and (iv) the problem of the seasonal variation of error correlations with seasonally dependent characteristic correlation times. The technique was tested with the calibration of the hydrologic sub-model of the Soil and Water Assessment Tool (SWAT) in the Chaohe Basin in North China. The results demonstrated the good performance of this approach to uncertainty analysis, particularly with respect to the fulfilment of statistical assumptions of the error model. A comparison with an independent error model and with error models that only considered a subset of the suggested techniques clearly showed the superiority of the approach based on all the features (i)–(iv) mentioned above.     

The effect of causal parameter bounds in PSHA‐based ground motion selection

In this paper the effect of causal parameter bounds (e.g. magnitude, source‐to‐site distance, and site condition) on ground motion selection, based on probabilistic seismic hazard analysis (PSHA) results, is investigated. Despite the prevalent application of causal parameter bounds in ground motion selection, present literature on the topic is cast in the context of a scenario earthquake of interest, and thus specific bounds for use in ground motion selection based on PSHA, and the implications of such bounds, is yet to be examined. Thirty‐six PSHA cases, which cover a wide range of causal rupture deaggregation distributions and site conditions, are considered to empirically investigate the effects of various causal parameter bounds on the characteristics of selected ground motions based on the generalized conditional intensity measure (GCIM) approach. It is demonstrated that the application of relatively ‘wide’ bounds on causal parameters effectively removes ground motions with drastically different characteristics with respect to the target seismic hazard and results in an improved representation of the target causal parameters. In contrast, the use of excessively ‘narrow’ bounds can lead to ground motion ensembles with a poor representation of the target intensity measure distributions, typically as a result of an insufficient number of prospective ground motions. Quantitative criteria for specifying bounds for general PSHA cases are provided, which are expected to be sufficient in the majority of problems encountered in ground motion selection for seismic demand analyses. Copyright © 2016 John Wiley & Sons, Ltd.