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The urban stormwater flood and its ecological effects are becoming increasingly prominent underlying the rapid urbanization of China. Nanning is a typical southern city with frequent flood disasters and an urgent need to conduct quantitative research and explore effective countermeasures. Therefore, the quantitative contributions of rainfall, evapotranspiration (ET), and landscape patterns to urban stormwater runoff are analysed using the Budyko framework in this paper. The results show that the rainfall, landscape pattern, evapotranspiration and runoff in Nanning have significant synergistic interaction. The change of rainfall has a positive effect on runoff increment, accounting for 5.9~12.3% of the annual rainfall. The change of the underlying surface parameter (n) has a negative effect on runoff decrement, accounting for −14.8%. Evidently, the blue (water space), green and grey infrastructure of the underlying surface and its spatial pattern largely determine the quantity and spatial distribution of storm runoff in Nanning City. [ABSTRACT FROM AUTHOR]

In small catchments, the time interval most commonly used for simulation – daily or monthly – may not be sufficient to accurately capture the time distribution of hydrological processes. In this paper, the Soil and Water Assessment Tool (SWAT) was used to perform an hourly long-term streamflow and sediment load simulation in the small (4.8 km2) and forested Aixola catchment (northern Spain). From this simulation, 10 runoff events were tested; the most satisfactory results for streamflow were obtained under wet antecedent conditions. However, simulated sediment load was underestimated during the peaks and remained high towards the end of the event. Furthermore, the influence of the precipitation time step (1–4 h, daily) was not relevant in the streamflow simulation but does influence the sediment simulation. The best results were achieved with the daily step simulations obtained at an hourly time step. This paper shows that sub-daily modelling improves water and especially sediment yield results; however, improvements are still needed in timing-related routines. [ABSTRACT FROM AUTHOR]

This paper proposes runoff models based on machine learning to estimate daily streamflows in Oued Sebaou watershed, a Mediterranean coastal basin located in northern Algeria. Therefore, we applied random forest (RF), artificial neural networks (ANN – under different training algorithms), and locally weighted linear regression (LWLR) using as input combinations of current and past rainfall amounts and previous values of streamflow. We selected streamflow and rainfall records to calibrate and validate the stated approaches. We used root mean square error (RMSE) and correlation coefficient (R) to evaluate the accuracy of the models. Analyses of the results show that RF provided the best outcomes for both training (RMSE = 4.7458 and R = 0.9834) and validation (RMSE = 2.3617 and R = 0.9719). The ANN calibrated with the Levenberg-Marquardt algorithm presented the second-best result, outperforming its counterparts and LWLR. [ABSTRACT FROM AUTHOR]

A tipping bucket is an accurate and reliable device that is used to measure runoff water flows, especially in small-scale catchments. However, the performance of the tipping bucket device depends on the magnitude of the runoff water flow, the dimensions of the tipping bucket and the accuracy of the calibration. This paper discusses technical guidelines that govern the design, construction, field installation, calibration and error minimisation of tipping bucket gauges used for runoff estimation in small-scale catchments under agricultural land-use systems. The proposed tipping bucket system is a cost-effective and simple device that can be fabricated in a basic mechanical workshop. The device proved to be a sensitive and reliable instrument on which accurate measurements can be read out easily with minimal maintenance. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid approach for stormwater runoff modelling and pollutant load estimation in a hilly catchment. The stormwater runoff hydrograph was simulated by developing a conceptual hybrid model (HM) using transfer function. Model parameters were estimated by non-linear optimization. The simulated runoff hydrographs showed an excellent match with the observed data. Next, a relationship was developed between runoff and pollutant load and the developed relationship was used for estimation of pollutant load. A novelty of this hybrid approach is that it could bring out a generic HM. By using the derived model, the pollutant load was successfully predicted from simulated runoff (R2 > 0.75) by considering rainfall as the only input. In addition, assessment, characterization and analysis of the first flush of urban runoff also form part of the study. The HM was tested and validated by applying it to 25 storm events monitored during two monsoon seasons in the Nainital Lake catchment (approx. 1900 m a.m.s.l.), India. [ABSTRACT FROM AUTHOR]

The objective of this paper is to understand how the natural dynamics of a time-varying catchment, i.e. the rainfall pattern, transforms the random component of rainfall and how this transformation influences the river discharge. To this end, this paper develops a rainfall–runoff modelling approach that aims to capture the multiple sources and types of uncertainty in a single framework. The main assumption is that hydrological systems are nonlinear dynamical systems which can be described by stochastic differential equations (SDE). The dynamics of the system is based on the least action principle (LAP) as derived from Noether’s theorem. The inflow process is considered as a sum of deterministic and random components. Using data from the Ouémé River basin (Benin, West Africa), the basic properties for the random component are considered and the triple relationship between the structure of the inflowing rainfall, the corresponding SDE that describes the river basin and the associated Fokker-Planck equations (FPE) is analysed.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR D. Gerten [ABSTRACT FROM PUBLISHER]

The calculation of runoff and infiltration parameters is a fundamental task for water budget estimation and water resources management. The "Kenessey method" allows these values to be calculated and its use is widespread among water scientists and practitioners. Due to language barriers and inefficient translation in past decades, the method has undergone some misinterpretations and is now far from its original usefulness and purpose. The use of the modified method can give unreal results for specific climatic zones. The purpose of this paper is to point out these methodological variations and to return to the original approach, improving it with modern technology. [ABSTRACT FROM AUTHOR]

Spatial and seasonal variations of curve number (CN) and initial abstraction ratio (λ) in a watershed can result in inaccurate runoff volume estimations when using the US Natural Resources Conservation Service (SCS-CN) method with constant values for these parameters. In this paper, parameters of CN and λ are considered as calibration parameters and the sensitivity of estimated runoff to these parameters using the SCS-CN method is scrutinized. To incorporate the uncertainty associated with CN and λ, fuzzy linear regression (FLR) is applied to derive the relationships of CN and λ with rainfall depth (P) by employing a large dataset of storm events from four watersheds in Iran. Results indicate that the proposed approach provides more accuracy in estimation of runoff volume compared to the SCS method with constant values of CN and λ, and gives a straightforward technique for evaluating the hydrological effects of CN, λ, and P on runoff volume. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA.  The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes. [ABSTRACT FROM PUBLISHER]

Baseflows have declined for decades in the Lesser Himalaya but the causes are still debated. This paper compares variations in streamflow response over three years for two similar headwater catchments in northwest India with largely undisturbed (Arnigad) and highly degraded (Bansigad) oak forest. Hydrograph analysis suggested no catchment leakage, thereby allowing meaningful comparisons. The mean annual runoff coefficient for Arnigad was 54% (range 44–61%) against 62% (53–69%) at Bansigad. Despite greater total runoffQt(by 250 mm year–1), baseflow at Bansigad ceased by March, but was perennial at Arnigad (making up 90% ofQtvs. 51% at Bansigad). Arnigad storm flows,Qs, were modest (8–11% ofQt) and occurred mostly during monsoons (78–98%), whileQsat Bansigad was 49% ofQtand occurred also during post-monsoon seasons. Our results underscore the importance of maintaining soil water retention capacity after forest removal to maintain baseflow levels.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR D. Gerten [ABSTRACT FROM AUTHOR]

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions. [ABSTRACT FROM PUBLISHER]

This paper assesses the possibility of using multi-model averaging techniques for continuous streamflow prediction in ungauged basins. Three hydrological models were calibrated on the Nash-Sutcliffe Efficiency metric and were used as members of four multi-model averaging schemes. Model weights were estimated through optimization on the donor catchments. The averaging methods were tested on 267 catchments in the province of Québec, Canada, in a leave-one-out cross-validation approach. It was found that the best hydrological model was practically always better than the others used individually or in a multi-model framework, thus no averaging scheme performed statistically better than the best single member. It was also found that the robustness and adaptability of the models were highly influential on the models’ performance in cross-verification. The results show that multi-model averaging techniques are not necessarily suited for regionalization applications, and that models selected in such studies must be chosen carefully to be as robust as possible on the study site.Editor M.C. Acreman; Associate editor S. Grimaldi [ABSTRACT FROM PUBLISHER]

Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR E. Toth [ABSTRACT FROM AUTHOR]

The application of artificial neural networks (ANNs) has been widely used recently in streamflow forecasting because of their flexible mathematical structure. However, several researchers have indicated that using ANNs in streamflow forecasting often produces a timing lag between observed and simulated time series. In addition, ANNs under- or overestimate a number of peak flows. In this paper, we proposed three data-processing techniques to improve ANN prediction and deal with its weaknesses. The Wilson-Hilferty transformation (WH) and two methods of baseflow separation (one parameter digital filter, OPDF, and recursive digital filter, RDF) were coupled with ANNs to build three hybrid models: ANN-WH, ANN-OPDF and ANN-RDF. The network behaviour was quantitatively evaluated by examining the differences between model output and observed variables. The results show that even following the guidelines of the Wilson-Hilferty transformation, which significantly reduces the effect of local variations, it was found that the ANN-WH model has shown no significant improvement of peak flow estimation or of timing error. However, combining baseflow with streamflow and rainfall provides important information to ANN models concerning the flow process operating in the aquifer and the watershed systems. The model produced excellent performance in terms of various statistical indices where timing error was totally eradicated and peak flow estimation significantly improved.Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei [ABSTRACT FROM AUTHOR]

In this paper, a mid- to long-term runoff forecast model is developed using an ideal point fuzzy neural network–Markov (NFNN-MKV) hybrid algorithm to improve the forecasting precision. Combining the advantages of the new fuzzy neural network and the Markov prediction model, this model can solve the problem of stationary or volatile strong random processes. Defined error statistics algorithms are used to evaluate the performance of models. A runoff prediction for the Si Quan Reservoir is made by utilizing the modelling method and the historical runoff data, with a comprehensive consideration of various runoff-impacting factors such as rainfall. Compared with the traditional fuzzy neural networks and Markov prediction models, the results show that the NFNN-MKV hybrid algorithm has good performance in faster convergence, better forecasting accuracy and significant improvement of neural network generalization. The absolute percentage error of the NFNN-MKV hybrid algorithm is less than 7.0%, MSE is less than 3.9, and qualification rate reaches 100%. For further comparison of the proposed model, the NFNN-MKV model is employed to estimate (training and testing for 120-month-ahead prediction) and predict river discharge for 156 months at Weijiabao on the Weihe River in China. Comparisons among the results of the NFNN-MKV model, the WNN model and the SVR model indicate that the NFNN-MKV model is able to significantly increase prediction accuracy.Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei [ABSTRACT FROM PUBLISHER]

The Wei River Basin is a typical arid and semi-arid area of the Yellow River Basin. Quantitative estimates of the contributions of human activities and climate changes in this region will enhance our understanding of the local hydrological mechanism and provide an effective reference for other arid and semi-arid areas of the world in local water resource planning and management. The heuristic segmentation method was used to detect the inflection points of the annual runoff. The slope change ratio of accumulative quantity (SCRAQ) method was applied to compute the relative contributions of human activities and climate changes to the decreasing runoff in the whole basin and the basin above Linjiacun. For the whole basin, when 1960–1969 is selected as the baseline, the contributions of climate changes and human activities in 1970–1993 are 26.47% and 73.53%, respectively, those in 1993–2005 are 23.33% and 76.67%, respectively. When 1970–1993 is selected as the baseline, the contributions of climate and human impacts in 1994–2005 are 18.88% and 81.12%, respectively. The results imply that human activities are the dominant driving factors of runoff reduction, whose effect is increasingly intensifying. Furthermore, in order to verify the contributions of human activities and climate changes based on the SCRAQ method, an approach based on the Budyko hypothesis was used in this paper. The results indicate that the contributions of human activities and climate changes based on the SCRAQ method are consistent with those based on the sensitivity-based method.Editor Z.W. Kundzewicz; Associate editor D. Yang [ABSTRACT FROM PUBLISHER]

The first objective of this paper is to analyse the trends and change points in the hydroclimatic time series of five representative sub-catchments of the Macta basin, which lies in western Algeria. The second objective is to quantify the role of climate on the trends observed in annual flow time series. This is achieved using hydrological modelling at the multi-annual time step using the Schreiber formulation. The results showed no significant trends on annual rainfall in the 1975–2005 period, a significant increase of temperature and different flow responses to the latter, depending on the catchment considered. Two out of five catchments considered presented a significant flow decrease in the 1975–2005 period with a change point at the beginning of the 1990s. Modelling results suggest that the increase of air temperature is not the sole factor explaining the decrease of annual flow time series in these two catchments. [ABSTRACT FROM AUTHOR]

This paper quantifies the impacts of bushfire and climate variability on streamflow from three southeast Australian catchments where bushfires occurred in February 1983. Three hydrological models (AWRA-L, Xinanjiang and GR4J) were first calibrated against streamflow data from the pre-bushfire period and then used to simulate runoff for the post-bushfire period with the calibrated parameters. The difference in simulated streamflow between pre- and post-bushfire periods provides an estimate of the impact of climate variability on streamflow. The impact of bushfire on streamflow is quantified by removing the climate variability impact from the difference in mean annual observed streamflow between post- and pre-bushfire periods. For the first 15 years after the 1983 bushfires, the results from hydrological models for the three catchments indicate that there is a substantial increase in streamflow; this is attributed to initial decreases in evapotranspiration and soil infiltration rates resulting from the fires, followed by logging activity. After 15 years, streamflow dynamics are more heavily influenced by climate effects, although some impact from fire and logging regeneration may still occur. The results show that hydrological models provide reasonably consistent estimates of bushfire and climate impacts on streamflow for the three catchments. The models can be used to quantify relative contributions of forest disturbance (bushfire, logging and other forest management) and climate variability. The results presented can also help forest managers understand the relationship between bushfire and climate variability impacts on water yield in the context of climate variability. [ABSTRACT FROM AUTHOR]

Large errors in peak discharge estimates at catchment scales can be ascribed to errors in the estimation of catchment response time. The time parameters most frequently used to express catchment response time are the time of concentration (TC), lag time (TL) and time to peak (TP). This paper presents a review of the time parameter estimation methods used internationally, with selected comparisons in medium and large catchments in the C5 secondary drainage region in South Africa. The comparison of different time parameter estimation methods with recommended methods used in South Africa confirmed that the application of empirical methods, with no local correction factors, beyond their original developmental regions, must be avoided. TheTCis recognized as the most frequently used time parameter, followed byTL. In acknowledging this, as well as the basic assumptions of the approximationsTL = 0.6TCandTC≈TP, along with the similarity between the definitions of theTPand the conceptualTC, it was evident that the latter two time parameters should be further investigated to develop an alternative approach to estimate representative response times that result in improved estimates of peak discharge at these catchment scales.Editor Z.W. Kundzewicz; Associate editor Qiang Zhang [ABSTRACT FROM AUTHOR]