Different methods for modelling the areal infiltration of a grass field under heavy precipitation

The areal infiltration behaviour of a grass field is studied using a data set of 78 sprinkler infiltration experiments. The analysis of the experimental data shows a distinct event dependency: once runoff begins, the final infiltration rate increases with increasing rainfall intensity. This behaviour is attributed to the effects of small‐scale variability. Increasing rainfall intensity increases the ponded area and therefore the portion of the plot which infiltrates at maximum rate. To describe the areal infiltration behaviour of the grass field the study uses two different model structures and investigates different approaches for consideration of subgrid variability. It is found that the effective parameter approach is not suited for this purpose. A good representation of the observed behaviour is obtained by using a distribution function approach or a parameterization approach. However, it is not clear how the parameters can be derived for these two approaches without a large measurement campaign. The data analysis and the simulations show the great importance of considering the effects of spatial variability for the infiltration process. This may be significant even at a small scale for a comparatively homogeneous area. The consideration of heterogeneity seems to be more important than the choice of the model type. Furthermore, similar results may be obtained with different modelling approaches. Even the relatively detailed data set does not seem to permit a clear model choice. In view of these results it is questionable to use very complex and detailed simulation models given the approximate nature of the problem. Although the principle processes may be well understood there is a lack of models that represent these processes and, more importantly, there is a lack of techniques to measure and parameterize them. Copyright © 2002 John Wiley & Sons, Ltd.     

A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments

Inverse modelling is a key step in groundwater-related hydrological studies. Several inversion techniques were developed during the last decades, but hardly any comparison between them was presented. We compare seven modern inverse methods for groundwater flow: the Regularised Pilot Points Method (both the estimation, RPPM-CE, and the Monte Carlo (MC) simulation variants, RPPM-CS), the MC variant of the Representer Method (RM), the Sequential Self-Calibration Method (SSC), the Moment Equations Method (MEM), the Zonation Method (ZM) and a non-iterative Semi-Analytical Method (SAM). These methods are applied to a two-dimensional synthetic example, depicting steady-state groundwater flow around a pumping well. Their relative performance is assessed in terms of their ability to characterise the log-transmissivity and hydraulic head fields and to predict the extent of the well catchment, both for a mildly and a strongly heterogeneous transmissivity field. The main conclusions drawn from the comparison are: (1) MC-based methods (RPPM-CS, SSC and RM) yield very similar results, regardless the degree of heterogeneity and despite they use different parameterisation schemes and objective functions; (2) statistical moments of the target quantities provided by MEM and RPPM-CE are similar to those of MC-based methods; (3) ZM and SAM are negatively affected by strong heterogeneity; and (4) in general, observed differences between the performances of all methods are not very large. MC-based inverse methods need considerably more CPU time than the other tested approaches. An advantage of MC-based methods is that they allow computing the posterior probability distribution of the target quantities, which can be directly fed to probabilistic risk-assessment procedures.     

A geomatics‐based approach for the derivation of the spatial distribution of sediment transport processes in periglacial mountain environments

Within this paper modern techniques such as satellite image analysis and tools provided by geographic information systems (GIS) are exploited in order to extend and improve existing techniques for mapping the spatial distribution of sediment transport processes. The processes of interest comprise mass movements such as solifluction, slope wash, dirty avalanches and rock‐ and boulder falls. They differ considerably in nature and therefore different approaches for the derivation of their spatial extent are required. A major challenge is addressing the differences between the comparably coarse resolution of the available satellite data (Landsat TM/ETM+, 30 m × 30 m) and the actual scale of sediment transport in this environment. A three‐stepped approach has been developed which is based on the concept of Geomorphic Process Units (GPUs): parameterization, process area delineation and combination. Parameters include land cover from satellite data and digital elevation model derivatives. Process areas are identified using a hierarchical classification scheme utilizing thresholds and definition of topology. The approach has been developed for the Kärkevagge in Sweden and could be successfully transferred to the Rabotsbekken catchment at Okstindan, Norway using similar input data. Copyright © 2008 John Wiley & Sons, Ltd.     

A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology

Parameters in a generalized extreme value (GEV) distribution are specified as a function of covariates using a conditional density network (CDN), which is a probabilistic extension of the multilayer perceptron neural network. If the covariate is time or is dependent on time, then the GEV‐CDN model can be used to perform nonlinear, nonstationary GEV analysis of hydrological or climatological time series. Owing to the flexibility of the neural network architecture, the model is capable of representing a wide range of nonstationary relationships. Model parameters are estimated by generalized maximum likelihood, an approach that is tailored to the estimation of GEV parameters from geophysical time series. Model complexity is identified using the Bayesian information criterion and the Akaike information criterion with small sample size correction. Monte Carlo simulations are used to validate GEV‐CDN performance on four simple synthetic problems. The model is then demonstrated on precipitation data from southern California, a series that exhibits nonstationarity due to interannual/interdecadal climatic variability. Copyright © 2009 Her Majesty the Queen in right of Canada. Published by John Wiley & Sons, Ltd.     

The potential of different ANN techniques in evapotranspiration modelling

The potential of three different artificial neural network (ANN) techniques, the multi‐layer perceptrons (MLPs), radial basis neural networks (RBNNs) and generalized regression neural networks (GRNNs), in modelling of reference evapotranspiration (ET₀) is investigated in this paper. Various daily climatic data, that is, solar radiation, air temperature, relative humidity and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN techniques so as to estimate ET₀ obtained using the FAO‐56 Penman–Monteith (PM) equation. In the first part of the study, a comparison is made between the estimates provided by the MLP, RBNN and GRNN and those of the following empirical models: The California Irrigation Management Information System (CIMIS) Penman (1985), Hargreaves (1985) and Ritchie (1990). In this part of the study, the empirical models are calibrated using the standard FAO‐56 PM ET₀ values. The estimates of the ANN techniques are also compared with those of the calibrated empirical models. Mean square errors, mean absolute errors and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it is found that the MLP and RBNN techniques could be employed successfully in modelling the ET₀ process. In the second part of the study, the potential of ANN techniques and the empirical methods in ET₀ estimation using nearby station data is investigated. Among the models, the calibrated Hargreaves model is found to perform better than the others. Copyright © 2007 John Wiley & Sons, Ltd.     

Evaluation of satellite-based rainfall products for hydrological modelling in Morocco

ABSTRACT

Several satellite-based precipitation estimates are becoming available at a global scale, providing new possibilities for water resources modelling, particularly in data-sparse regions and developing countries. This work provides a first validation of five different satellite-based precipitation products (TRMM-3B42 v6 and v7, RFE 2.0, PERSIANN-CDR, CMORPH1.0 version 0.x) in the 1785 km² Makhazine catchment (Morocco). Precipitation products are first compared against ground observations. Ten raingauges and four different interpolation methods (inverse distance, nearest neighbour, ordinary kriging and residual kriging with altitude) were used to compute a set of interpolated precipitation reference fields. Second, a parsimonious conceptual hydrological model is considered, with a simulation approach based on the random generation of model parameters drawn from existing parameter set libraries, to compare the different precipitation inputs. The results indicate that (1) all four interpolation methods, except the nearest neighbour approach, give similar and valid precipitation estimates at the catchment scale; (2) among the different satellite-based precipitation estimates verified, the TRMM-3B42 v7 product is the closest to observed precipitation, and (3) despite poor performance at the daily time step when used in the hydrological model, TRMM-3B42 v7 estimates are found adequate to reproduce monthly dynamics of discharge in the catchment. The results provide valuable perspectives for water resources modelling of data-scarce catchments with satellite-based rainfall data in this region.

Editor M.C. Acreman; Associate editor N. Verhoest     

Assessment of rainfall spatial variability and its influence on runoff modelling: A case study in the Brue catchment,UK

This study explores rainfall spatial variability and its influence on runoff modelling. A novel assessment scheme integrated with coefficient of variance and Moran's I is introduced to describe effective rainfall spatial variability. Coefficient of variance is widely accepted to identify rainfall variability through rainfall intensity, whereas Moran's I reflects rainfall spatial autocorrelation. This new assessment framework combines these two indicators to assess the spatial variability derived from both rainfall intensity and distribution, which are crucial in determining the time and magnitude of runoff generation. Four model structures embedded in the Variable Infiltration Capacity model are adopted for hydrological modelling in the Brue catchment of England. The models are assigned with 1, 3, 8, and 27 hydrological response units, respectively, and diverse rainfall spatial information for 236 events are extracted from 1995. This study investigates the model performance of different partitioning based on rainfall spatial variability through peak volume (Q_p) and time to peak (T_p), along with the rainfall event process. The results show that models associated with dense spatial partitioning are broadly capable of capturing more spatial information with better performance. It is unnecessary to utilize models with high spatial density for simple rainfall events, though they show distinct advantages on complex events. With additional spatial information, Q_p experiences a notable improvement over T_p. Moreover, seasonal patterns signified by the assessment scheme imply the feasibility of seasonal models.     

A modelling study of hyporheic exchange pattern and the sequence,size, and spacing of stream bedforms in mountain stream networks,Oregon, USA

Studies of hyporheic exchange flows have identified physical features of channels that control exchange flow at the channel unit scale, namely slope breaks in the longitudinal profile of streams that generate subsurface head distributions. We recently completed a field study that suggested channel unit spacing in stream longitudinal profiles can be used to predict the spacing between zones of upwelling (flux of hyporheic water into the stream) and downwelling (flux of stream water into the hyporheic zone) in the beds of mountain streams. Here, we use two‐dimensional groundwater flow and particle tracking models to simulate vertical and longitudinal hyporheic exchange along the longitudinal axis of stream flow in second‐, third‐, and fourth‐order mountain stream reaches. Modelling allowed us to (1) represent visually the effect that the shape of the longitudinal profile has on the flow net beneath streambeds; (2) isolate channel unit sequence and spacing as individual factors controlling the depth that stream water penetrates the hyporheic zone and the length of upwelling and downwelling zones; (3) evaluate the degree to which the effects of regular patterns in bedform size and sequence are masked by irregularities in real streams. We simulated hyporheic exchange in two sets of idealized stream reaches and one set of observed stream reaches. Idealized profiles were constructed using regression equations relating channel form to basin area. The size and length of channel units (step size, pool length, etc.) increased with increasing stream order. Simulations of hyporheic exchange flows in these reaches suggested that upwelling lengths increased (from 2·7 m to 7·6 m), and downwelling lengths increased (from 2·9 m to 6·0 m) with increase in stream order from second to fourth order. Step spacing in the idealized reaches increased from 5·3 m to 13·7 m as stream size increased from second to fourth order. Simulated downwelling lengths increased from 4·3 m in second‐order streams to 9·7 m in fourth‐order streams with a POOL–RIFFLE–STEP channel unit sequence, and increased from 2·5 m to 6·1 m from second‐ to fourth‐order streams with a POOL–STEP–RIFFLE channel unit sequence. Upwelling lengths also increased with stream order in these idealized channels. Our results suggest that channel unit spacing, size, and sequence are all important in determining hyporheic exchange patterns of upwelling and downwelling. Though irregularities in the size and spacing of bedforms caused flow nets to be much more complex in surveyed stream reaches than in idealized stream reaches, similar trends emerged relating the average geomorphic wavelength to the average hyporheic wavelength in both surveyed and idealized reaches. This article replaces a previously published version (Hydrological Processes, 19 (17), 2915–2929 (2005) [ DOI:10.1002/hyp.5790 ]. See also retraction notice DOI:10.1002/hyp.6350 Copyright © 2006 John Wiley & Sons, Ltd.     

A predictive model for Arias intensity at multiple sites and consideration of spatial correlations

Arias intensity, I_a, has been identified as an efficient intensity measure for the estimation of earthquake‐induced losses. In this paper, a new model for the prediction of Arias intensity, which incorporates nonlinear site response through the use of the average shear‐wave velocity and a heteroskedastic variance structure, is proposed. In order to estimate the effects of ground motions on spatially‐distributed systems, it is important to take into account the spatial correlation of the intensity measure. However, existing loss‐estimation models, which use I_a as an input, do not take this aspect of the ground motion into account. Therefore, the potential to model the spatial correlation of Arias intensity is also investigated. The empirical predictive model is developed using recordings from the Pacific Earthquake Engineering Research Center Next Generation of Attenuation database whereas the model for spatial correlation makes use of the well‐recorded events from this database, that is the Northridge and Chi‐Chi earthquakes. Copyright © 2011 John Wiley & Sons, Ltd.     

Hydrological modelling of the Vistula and Odra river basins using SWAT

This paper presents a large-scale application of the SWAT model for water balance and natural streamflow simulation in the entire basins of the Vistula and the Odra, covering most of the territory of Poland. A tailored calibration approach was designed to achieve satisfactory goodness-of-fit across a range of catchment sizes. Model calibration and evaluation driven by high-resolution climate data showed overall good behaviour for 80 benchmark catchments divided into eight clusters, and spatial evaluation for 30 gauges showed that the designed regionalization scheme performed well (median KGE of 0.76). Basin-averaged estimates of blue and green water flow and green water storage estimated using the calibrated model were 185, 517 and 206 mm, respectively. This study provides a basis for future work, such as assessing climate change impacts on hydrology, assessing flow alterations, and water quality simulation. The model output is publicly available through an online research data archive (doi:10.4121/uuid:b8ab4f5f-f692-4c93-a910-2947aea28f42).

EDITOR A. Castellarin

ASSOCIATE EDITOR G. Thirel     

Retracted and replaced: A modelling study of hyporheic exchange pattern and the sequence,size, and spacing of stream bedforms in mountain stream networks,Oregon, USA

This article has been retracted and replaced. See Retraction and Replacement Notice DOI: 10.1002/hyp.6350 Studies of hyporheic exchange flows have identified physical features of channels that control exchange flow at the channel unit scale, namely slope breaks in the longitudinal profile of streams that generate subsurface head distributions. We recently completed a field study that suggested channel unit spacing in stream longitudinal profiles can be used to predict the spacing between zones of upwelling (flux of hyporheic water into the stream) and downwelling (flux of stream water into the hyporheic zone) in the beds of mountain streams. Here, we use two‐dimensional groundwater flow and particle tracking models to simulate vertical and longitudinal hyporheic exchange along the longitudinal axis of stream flow in second‐, third‐, and fourth‐order mountain stream reaches. Modelling allowed us to (1) represent visually the effect that the shape of the longitudinal profile has on the flow net beneath streambeds; (2) isolate channel unit sequence and spacing as individual factors controlling the depth that stream water penetrates the hyporheic zone and the length of upwelling and downwelling zones; (3) evaluate the degree to which the effects of regular patterns in bedform size and sequence are masked by irregularities in real streams. We simulated hyporheic exchange in two sets of idealized stream reaches and one set of observed stream reaches. Idealized profiles were constructed using regression equations relating channel form to basin area. The size and length of channel units (step size, pool length, etc.) increased with increasing stream order. Simulations of hyporheic exchange flows in these reaches suggested that upwelling lengths increased (from 2·7 m to 7·6 m), and downwelling lengths increased (from 2·9 m to 6·0 m) with increase in stream order from second to fourth order. Step spacing in the idealized reaches increased from 5·3 m to 13·7 m as stream size increased from second to fourth order. Simulated upwelling lengths increased from 4·3 m in second‐order streams to 9·7 m in fourth‐order streams with a POOL–RIFFLE–STEP channel unit sequence, and increased from 2·5 m to 6·1 m from second‐ to fourth‐order streams with a POOL–STEP–RIFFLE channel unit sequence. Downwelling lengths also increased with stream order in these idealized channels. Our results suggest that channel unit spacing, size, and sequence are all important in determining hyporheic exchange patterns of upwelling and downwelling. Though irregularities in the size and spacing of bedforms caused flow nets to be much more complex in surveyed stream reaches than in idealized stream reaches, similar trends emerged relating the average geomorphic wavelength to the average hyporheic wavelength in both surveyed and idealized reaches. Copyright © 2005 John Wiley & Sons, Ltd.     

A comparison between different methods for determining grain distribution in coarse channel beds

The determination of grain size distribution in alluvial channels plays a crucial role in understanding fluvial dynamics and processes (e.g., hydraulic resistance, sediment transport and erosion, and habitat suitability). However, to determine an accurate distribution, tremendous field efforts are often required. Traditionally, the grain size distribution of channel beds have been obtained by manually counting a set of randomly selected stones (the“pebble count”). Based on this elementary principle, many authors have proposed different adaptations to overcome weaknesses and problems with the original method; with the development of digital technology, photographic methods have been developed in order to sig-nificantly reduce the time spent in the field. Two of these“image-assisted”methods include Automated Grain Sizing, AGS, and Manual Photo Sieving, MPS. In this study, AGS and MPS were applied under ideal laboratory conditions, to be used as reference, and in two field conditions with different degrees of difficulty in terms of visual determination of the grain size distribution; these included an artificial unlined channel and two natural mountainous streams. The results were compared with those obtained with the pebble-count method. In general, strong agreement between the methods was found when they were applied under favorable conditions (”the laboratory”), and the differences between the image-assisted and pebble count methods were similar to those found in previous studies. Despite being more time consuming, MPS was deemed preferable to AGS when conditions are not optimal;in these cases, the time spent on image elaboration significantly increased in the AGS method (approximately three-fold), but the estimation error of the median grain size decreased by approximately 37%. The use of image-assisted analysis has proven to be robust for characterizing sediment in watercourse beds and reducing fieldwork time, but because field conditions can significantly affect the accuracy of results, the method choice must be carefully considered.     

Implication of evaporative loss estimation methods in discharge and water temperature modelling in cool temperate climates

Evaporative flux is a key component of hydrological budgets. Water loss through evapotranspiration reduces volumes available for run‐off. The transition from liquid to water vapour on open water surfaces requires heat. Consequently, evaporation act as a cooling mechanism during summer. Both river discharge and water temperature simulations are thus influenced by the methods used to model evaporation. In this paper, the impact of evapotranspiration estimation methods on simulated discharge is assessed using a semidistributed model on two Canadian watersheds. The impact of evaporation estimation methods on water temperature simulations is also evaluated. Finally, the validity of using the same formulation to simulate both of these processes is verified. Five well‐known evapotranspiration models and five evaporation models with different wind functions were tested. Results show a large disparity (18–22% of mean annual total evapotranspiration) among the evapotranspiration methods, leading to important differences in simulated discharge (3–25% of observed discharge). Larger differences result from evaporation estimation methods with mean annual divergences of 34–48%. This translates into a difference in mean summer water temperature of 1–15%. Results also show that the choice of model parameter has less influence than the choice of evapotranspiration method in discharge simulations. However, the parameter values influence thermal simulations in the same order of magnitude as the choice of evaporation estimation method. Overall, the results of this study suggest that evapotranspiration and open water evaporation should be represented separately in a hydrological modelling framework, especially when water temperature simulations are required.     

Finding the most appropriate precipitation probability distribution for stochastic weather generation and hydrological modelling in Nordic watersheds

Six precipitation probability distributions (exponential, Gamma, Weibull, skewed normal, mixed exponential and hybrid exponential/Pareto distributions) are evaluated on their ability to reproduce the statistics of the original observed time series. Each probability distribution is also indirectly assessed by looking at its ability to reproduce key hydrological variables after being used as inputs to a lumped hydrological model. Data from 24 weather stations and two watersheds (Chute‐du‐Diable and Yamaska watersheds) in the province of Quebec (Canada) were used for this assessment. Various indices or statistics, such as the mean, variance, frequency distribution and extreme values are used to quantify the performance in simulating the precipitation and discharge. Performance in reproducing key statistics of the precipitation time series is well correlated to the number of parameters of the distribution function, and the three‐parameter precipitation models outperform the other models, with the mixed exponential distribution being the best at simulating daily precipitation. The advantage of using more complex precipitation distributions is not as clear‐cut when the simulated time series are used to drive a hydrological model. Although the advantage of using functions with more parameters is not nearly as obvious, the mixed exponential distribution appears nonetheless as the best candidate for hydrological modelling. The implications of choosing a distribution function with respect to hydrological modelling and climate change impact studies are also discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

Investigating the use of spatial discretization of hydrological processes in conceptual rainfall runoff modelling: a case study for the meso‐scale

In this study a simple modelling approach was applied to identify the need for spatial complexity in representing hydrological processes and their variability over different scales. A data set of 18 basins was used, ranging between 8 and 4011 km² in area, located in the Nahe basin (Germany), with daily discharge values for over 30 years. Two different parsimoniously structured models were applied in lumped as well as in spatially distributed according to two distribution classifications: (1) a simple classification based on the lithology expressed in three permeability types and (2) a more complex classification based on seven dominating runoff production processes. The objective of the study was to compare the performances of the models on a local and on a regional scale as well as between the models with a view to identifying the accuracy in capturing the spatial variability of the rainfall‐runoff relationships. It was shown that the presence of a specific basin characteristic or process of the distribution classification was not related with higher model performance; only a larger basin size promoted higher model performance. The results of this study also indicated that the permeability generally contained more useful information on the spatial heterogeneity of the hydrological behaviour of the natural system than did a more detailed classification on dominating runoff generation processes. Although model performance was slightly lower for the model that used permeability as a distribution classification, consistency in its parameter values was found, which was lacking with the more complex distribution classification. The latter distribution classification had a higher flexibility to optimize towards the variability of the runoff, which resulted in higher performance, however, process representation was applied inconsistently. Copyright © 2007 John Wiley & Sons, Ltd.     

Field survey and modelling of irrigation water quality indices in a Mediterranean island catchment: a comparison between spatial interpolation methods

ABSTRACT

A biannual survey of physico-chemical quality indices of 104 irrigation-water wells located in a cultivated plain of a Mediterranean island catchment was conducted using a multi-parameter probe. The campaign was planned so as to differentiate between the dry and wet seasons. The acquired data constituted the test bed for evaluating the results and the features of four spatial interpolation methods, i.e. ordinary kriging, universal kriging, inverse distance weighted and nearest neighbours, against those of the recently introduced bilinear surface smoothing (BSS). In several cases, BSS outperformed the other interpolation methods, especially during the two-fold cross-validation procedure. The study emphasizes the fact that both in situ measurements and good mathematical techniques for studying the spatial distribution of water quality indices are pivotal to agricultural practice management. In the specific case studied, the spatio-temporal variability of water quality parameters and the need for monitoring were evident, as low irrigation water quality was encountered throughout the study area.     

Evaluation of remote‐sensing‐based rainfall products through predictive capability in hydrological runoff modelling

The emergence of regional and global satellite‐based rainfall products with high spatial and temporal resolution has opened up new large‐scale hydrological applications in data‐sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite‐based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub‐catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC‐FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC‐FEWS, produced good results with values of R²_NS between 0·84 and 0·87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products. Copyright © 2009 John Wiley & Sons, Ltd.     

A Reynolds-averaged turbulence modelling approach to the maintenance of the Venus superrotation

Abstract

A maintenance mechanism of an approximately linear velocity profile of the Venus zonal flow or superrotation is explored, with the aid of a Reynolds-averaged turbulence modelling approach. The basic framework is similar to that of Gierasch (Meridional circulation and maintenance of the Venus atmospheric rotation. J. Atmos. Sci. 1975, 32, 1038–1044) in the sense that the mechanism is examined under a given meridional circulation. The profile mimicking the observations of the flow is initially assumed, and its maintenance mechanism in the presence of turbulence effects is investigated from a viewpoint of the suppression of energy cascade. In the present work, the turbulent viscosity is regarded as an indicator of the intensity of the cascade. A novelty of this formalism is the use of the isotropic turbulent viscosity based on a non-local time scale linked to a large-scale flow structure. The mechanism is first discussed qualitatively. On the basis of these discussions, the two-dimensional numerical simulation of the proposed model is performed, with an initially assumed superrotation, and the fast zonal flow is shown to be maintained, compared with the turbulent viscosity lacking the non-local time scale. The relationship of the present model with the current general circulation model simulation is discussed in light of a crucial role of the vertical viscosity.     

Regional‐scale investigation of the spatial distribution and origin of soluble salts in central north Queensland

A method for regional assessment of the distribution of saline outbreaks is demonstrated for a large area (68 000 km²) in north Queensland, Australia. Soil samples were used in conjunction with a digital elevation model and a map of potentially saline discharge zones to examine the landscape distribution of soluble salts in the region. The hypothesis of atmospheric accession of salt was tested for the topographically defined catchment regions feeding into each potentially saline discharge area. Most catchments showed a salt distribution consistent with this hypothesis, i.e. %TSS was large near the discharge areas and decreased rapidly with distance uphill from the discharge areas. In some catchments, however, local saline outbreaks were apparent at significant distances uphill from discharge areas. The possibility of geological sources of this salt was examined by comparing random point distributions with the location of saline points with distance downhill from geological units (excluding points near discharge zones). The distribution of some saline outbreaks was consistent with the occurrence of Cambro‐Ordovician metasediments, Devonian limestone, Upper Devonian–Lower Carboniferous volcanics, and Triassic sediments. Copyright © 2000 John Wiley & Sons, Ltd.     

Influence of spatial distribution of sensors and observation accuracy on the assimilation of distributed streamflow data in hydrological modelling

The aim of this study is to assess the influence of sensor locations and varying observation accuracy on the assimilation of distributed streamflow observations, also taking into account different structures of semi-distributed hydrological models. An ensemble Kalman filter is used to update a semi-distributed hydrological model as a response to measured streamflow. Various scenarios of sensor locations and observation accuracy are introduced. The methodology is tested on the Brue basin during five flood events. The results of this work demonstrate that the assimilation of streamflow observations at interior points of the basin can improve the hydrological models according to the particular location of the sensors and hydrological model structure. It is also found that appropriate definition of the observation accuracy can affect model performance and consequent flood forecasting. These findings can be used as criteria to develop methods for streamflow monitoring network design.