Bedload yields for sand‐bed streams in the Ngarradj Creek catchment,Northern Territory,Australia

Bedload yields were calculated by 39 methods at the East Tributary gauge, nine methods at Upper Swift Creek gauge and 11 methods at Swift Creek gauge in the Ngarradj Creek catchment in northern Australia. These methods involved combining various significant bedload rating curves determined for a measured bedload data set for a 4‐year period with either the hourly or daily hydrographs or flow duration curves for the same period, 1 September 1998 to 31 August 2002. Bedload ratings were both statistically significant (ρ ≤ 0.05) and explained at least 60% of the variance in bedload flux. Bias corrections were used with all methods based on log₁₀‐transformed ratings. Estimated mean annual bedload yields varied by three orders of magnitude at the East Tributary gauge and by two orders of magnitude at Upper Swift Creek and Swift Creek gauges. Hourly discharges usually produced higher estimated yields than daily discharges. The bedload rating‐flow duration curve technique overestimates yields and bias correction methods always produce even higher yields. Ratings using both immersed bedload weight and adjusted immersed bedload weight always under‐predicted yields because they contain an implicit threshold of motion condition that is at least four times greater than that predicted by Bagnold's threshold equation. Such a result questions the applicability of Bagnold's threshold equation to the Ngarradj Creek catchment. The best estimates of mean annual bedload yield at East Tributary, Upper Swift Creek and Swift Creek gauges are 600 ± 170 (SE), 1065 ± 150 and 1795 ± 270 t/year, respectively. © 2015 Commonwealth of Australia. Hydrological Processes © 2015 John Wiley & Sons Ltd.     

Predictability of stemflow in a species‐rich tropical forest

Numerous studies investigated the influence of abiotic (meteorological conditions) and biotic factors (tree characteristics) on stemflow generation. Although these studies identified the variables that influence stemflow volumes in simply structured forests, the combination of tree characteristics that allows a robust prediction of stemflow volumes in species‐rich forests is not well known. Many hydrological applications, however, require at least a rough estimate of stemflow volumes based on the characteristics of a forest stand. The need for robust predictions of stemflow motivated us to investigate the relationships between tree characteristics and stemflow volumes in a species‐rich tropical forest located in central Panama. Based on a sampling setup consisting of ten rainfall collectors, 300 throughfall samplers and 60 stemflow collectors and cumulated data comprising 26 rain events, we derive three main findings. Firstly, stemflow represents a minor hydrological component in the studied 1‐ha forest patch (1.0% of cumulated rainfall). Secondly, in the studied species‐rich forest, single tree characteristics are only weakly related to stemflow volumes. The influence of multiple tree parameters (e.g. crown diameter, presence of large epiphytes and inclination of branches) and the dependencies among these parameters require a multivariate approach to understand the generation of stemflow. Thirdly, predicting stemflow in species‐rich forests based on tree parameters is a difficult task. Although our best model can capture the variation in stemflow to some degree, a critical validation reveals that the model cannot provide robust predictions of stemflow. A reanalysis of data from previous studies in species‐rich forests corroborates this finding. Based on these results and considering that for most hydrological applications, stemflow is only one parameter among others to estimate, we advocate using the base model, i.e. the mean of the stemflow data, to quantify stemflow volumes for a given study area. Studies in species‐rich forests that wish to obtain predictions of stemflow based on tree parameters probably need to conduct a much more extensive sampling than currently implemented by most studies. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Study of discontinuities in hydrological data using catastrophe theory

Abstract

Modelling and prediction of hydrological processes (e.g. rainfall–runoff) can be influenced by discontinuities in observed data, and one particular case may arise when the time scale (i.e. resolution) is coarse (e.g. monthly). This study investigates the application of catastrophe theory to examine its suitability to identify possible discontinuities in the rainfall–runoff process. A stochastic cusp catastrophe model is used to study possible discontinuities in the monthly rainfall–runoff process at the Aji River basin in Azerbaijan, Iran. Monthly-averaged rainfall and flow data observed over a period of 20 years (1981–2000) are analysed using the Cuspfit program. In this model, rainfall serves as a control variable and runoff as a behavioural variable. The performance of this model is evaluated using four measures: correlation coefficient, log-likelihood, Akaike information criterion (AIC) and Bayesian information criterion (BIC). The results indicate the presence of discontinuities in the rainfall–runoff process, with a significant sudden jump in flow (cusp signal) when rainfall reaches a threshold value. The performance of the model is also found to be better than that of linear and logistic models. The present results, though preliminary, are promising in the sense that catastrophe theory can play a possible role in the study of hydrological systems and processes, especially when the data are noisy.

Citation Ghorbani, M. A., Khatibi, R., Sivakumar, B. & Cobb, L. (2010) Study of discontinuities in hydrological data using catastrophe theory. Hydrol. Sci. J. 55(7), 1137–1151.     

Estimation of rainfall distribution for the southwestern region of Saudi Arabia

Abstract

Rainfall is the most important input parameter for water resource planning and hydrological studies because flood risk assessment, rainfall harvesting and runoff estimation depend on the rainfall distribution within a region. Due to practical and economic factors, it is not possible to site rainfall stations everywhere, so representative rainfall stations are sited at specific locations. Rainfall distribution is then estimated from such stations. In this study, rainfall distribution in the southwestern region of Saudi Arabia was estimated using kriging, co-kriging and inverse distance weighted (IDW) methods. Historical records of rainfall from 47 stations for the period 1965–2010 and the altitude of these stations were used. The study shows that co-kriging is a better interpolator than the kriging and IDW methods, with a better correlation between actual and estimated monthly average rainfall for the region.     

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.     

Incorporating groundwater recharge and discharge functions into an existing monthly rainfall–runoff model/Incorporation de fonctions de recharge et de vidange superficielle de nappes au sein d’un modèle pluie-débit mensuel existant

Abstract

Abstract There is an urgent need for an integrated surface water and groundwater modelling tool that is suitable for southern African conditions and can be applied at various basin scales for broad strategic water resource planning purposes. The paper describes two new components (recharge and groundwater discharge) that have been added to an existing monthly time-step rainfall–runoff model that is widely used in the southern African subcontinent. The new components are relatively simple, consistent with the existing model formulation, but based on accepted groundwater flow principles and well understood groundwater parameters. The application of the revised model on two basins in southern Africa with quite different baseflow characteristics has demonstrated that the new components have a great deal of potential, even if the improvement is only to be able to simulate the groundwater baseflow component of total runoff more explicitly. More comprehensive testing and comparison of the results with existing groundwater and geohydrological data is required, while some extensions to the new components need to be considered to ensure that the model can be considered applicable to a wide range of basin and climate types.     

Rainfall interception in a lower montane forest in Ecuador: effects of canopy properties

Rainfall interception in forests is influenced by properties of the canopy that tend to vary over small distances. Our objectives were: (i) to determine the variables needed to model the interception loss of the canopy of a lower montane forest in south Ecuador, i.e. the storage capacity of the leaves S and of the trunks and branches S_t, and the fractions of direct throughfall p and stemflow p_t; (ii) to assess the influence of canopy density and epiphyte coverage of trees on the interception of rainfall and subsequent evaporation losses. The study site was located on the eastern slope of the eastern cordillera in the south Ecuadorian Andes at 1900–2000 m above sea level. We monitored incident rainfall, throughfall, and stemflow between April 1998 and April 2001. In 2001, the leaf area index (LAI), inferred from light transmission, and epiphyte coverage was determined. The mean annual incident rainfall at three gauging stations ranged between 2319 and 2561 mm. The mean annual interception loss at five study transects in the forest varied between 591 and 1321 mm, i.e. between 25 and 52% of the incident rainfall. Mean S was estimated at 1·91 mm for relatively dry weeks with a regression model and at 2·46 mm for all weeks with the analytical Gash model; the respective estimates of mean S_t were 0·04 mm and 0·09 mm, of mean p were 0·42 and 0·63, and of mean p_t were 0·003 and 0·012. The LAI ranged from 5·19 to 9·32. Epiphytes, mostly bryophytes, covered up to 80% of the trunk and branch surfaces. The fraction of direct throughfall p and the LAI correlated significantly with interception loss (Pearson's correlation coefficient r = −0·77 and 0·35 respectively, n = 40). Bryophyte and lichen coverage tended to decrease S_t and vascular epiphytes tended to increase it, although there was no significant correlation between epiphyte coverage and interception loss. Our results demonstrate that canopy density influences interception loss but only explains part of the total variation in interception loss. Copyright © 2004 John Wiley & Sons, Ltd.     

Analysis of agricultural drought in Iiuni,Eastern Kenya: application of a Markov model

In semi‐arid Kenya, episodes of agricultural droughts of varying severity and duration occur. The occurrence of these agricultural droughts is associated with seasonal rainfall variability and can be reflected by seasonal soil moisture deficits that significantly affect crop performance and yield. The objective of this study was to stochastically simulate the behaviour of dry and wet spells and rainfall amounts in Iiuni watershed, Kenya. The stochastic behaviour of the longest dry and wet spells (runs) and largest rainfall amounts were simulated using a Markov (order 1) model. There were eight raingauge stations within the watershed. The entire analysis was carried out using probability parameters, i.e. mean, variance, simple and conditional probabilities of dry and rain days. An analysis of variance test (ANOVA ) was used to establish significant differences in rainfall characteristics between the eight stations. An analysis of the number of rain days and rainfall amount per rain day was done on a monthly basis to establish the distribution and reliability of seasonal rainfall. The graphic comparison of simulated cumulative distribution functions (Cdfs) of the longest spells and largest rainfall amounts showed Markovian dependence or persistence. The longest dry spells could extend to 24 days in the long rainy season and 12 in the short rainy season. At 50% (median) probability level, the largest rainfall amounts were 91 mm for the long rainy season and 136 mm for the short rainy season. The short rains were more reliable for crop production than the long rains. The Markov model performed well and gave adequate simulations of the spells and rainfall amounts under semi‐arid conditions. Copyright © 2004 John Wiley & Sons, Ltd.     

Selecting the best IDF model by using the multifractal approach

In this work, the multifractal properties of hourly rainfall data recorded at a location in Southern Spain have been related to the scale properties of the corresponding intensity–duration–frequency (IDF) curves. Four parametric models for the IDF curves have been fitted to the quantiles of rainfall obtained using the generalized Pareto frequency distribution function with the extreme data series obtained for the same place. The scaling of the rainfall intensity moments has been analysed, and the empirical moments scaling exponent function has been obtained. The corresponding values of q₁ and γ₁ have been empirical and theoretically calculated and compared with some characteristics of the different IDF models. Thus, the scaling behaviour of IDF curves has been analysed, and the best model has been selected. Copyright © 2012 John Wiley & Sons, Ltd.