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In this study, a bibliometric analysis technique is used for performance analysis and science mapping of artificial intelligence (AI) applications in streamflow research. This paper examines the current trends in the literature using the Scopus database over the last 37 years. RStudio Bibliometrix software was used to analyse the titles, keywords, abstracts, and full texts of 3000 publications to identify trends in AI models, publication types, journals, citations, authors, countries, and regions. The highest frequency AI-related keyword is “artificial neural networks,” which was used in a total of 25587 times. The most common publication type, at 82.1%, is journal articles, and the highest rate of country production is 25% for China. In recent years, streamflow research studies have significantly increased their use of AI applications. [ABSTRACT FROM AUTHOR]

Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins. [ABSTRACT FROM AUTHOR]

Natural streamflow data is required in many hydrological applications. However, many basins are located in data-scarce regions or are impacted by human construction and activities. In this paper, we explore three machine learning algorithms, namely artificial neural networks, random forest and light gradient boosting machine, to simultaneously estimate all the parameters of the coupled modèle du Génie Rural à 4 paramètres Journaliers (GR4J) and snow accounting routine called CemaNeige model. A database of 675 basins in the USA and Quebec is used to train and test ensembles. After using the estimated parameters in GR4J, the resulting naturalized streamflow series are compared with those obtained by the established drainage area ratio and spatial proximity transfer methods in 11 test basins. The results indicate that the machine learning algorithms outperform the drainage area ratio and spatial proximity transfer methods. Among machine learning algorithms, random forests obtain lower (better) continuous ranked probability scores than the other methods for 10 out of 11 test basins. [ABSTRACT FROM AUTHOR]

This paper investigates how hydrological drought affected suspended sediment yield (SSY) in the Mediterranean Ofanto River basin, southern Italy, and in its five sub-basins from 1951 to 1989. The Standardized Precipitation Evaporation Index over a scale of 12 months (SPEI12) has been used to compute the hydrological drought in the investigated span of time. SPEI12, mean and maximum monthly discharge (Qmean and Qmax), monthly rainfall erosivity, monthly simple daily intensity index and monthly SSY were used to assess the relationships between dry/wet cycles, streamflow and SSY through the Pearson correlation matrix. Qmean and Qmax are significantly correlated with SSY, while SPEI12 and rainfall intensity do not show good correlation with SSY. Furthermore, from the overall analyses it emerges that sediment yield estimations were higher mainly during the wet period following a period of drought or during a drought period. [ABSTRACT FROM AUTHOR]

With climate change, decreases in winter snow storage and increases in precipitation duration and intensities will alter the occurrence of floods in high-latitude countries. The state-of-the-art hydrological climate-impact model chain consists of one or more global climate models, downscaling and bias-correction techniques, and one or more hydrological models. Machine learning offers a complementary approach to hydrological climate-impact modelling by facilitating direct downscaling from large-scale atmospheric variables to streamflow. This paper presents the development of multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks benchmarked against regression tree models for reconstruction of daily streamflow and floods from atmospheric reanalysis data with comparable resolution to global climate model outputs. Catchment-specific, physically-based input features representing the dominant flood drivers were identified for 27 catchments in Norway. Overall, the LSTM obtained the highest accuracy. This article provides a springboard for future research on hydrological climate-impact modelling with neural networks in high-latitude countries. [ABSTRACT FROM AUTHOR]

Rapid climate change is impacting water resources in Afghanistan. The consequences are poorly known. Suitable mitigation and adaptation strategies have not been developed. Thus, this paper summarizes current status of knowledge in relation to Afghan water resources. More than 130 scientific articles, reports and data sources are synthesized to review the potential impacts of climate change on the cryosphere, streamflow, groundwater and hydrological extremes. The available information suggests that Afghanistan is currently witnessing significant increases in temperature, less so precipitation. There is evidence of shifts in the intra-annual distribution of streamflow, with reduced summer flows in non-glaciated basins and increased winter and spring streamflow. However, in the short-term there will be an increase in summer ice melt in glaciated basins, a "glacial subsidy", which sustains summer streamflow, despite reduced snow accumulation. The future prognosis for water resources is likely to be more serious when this glacier subsidy ends. [ABSTRACT FROM AUTHOR]

In the present paper, we propose a new approach for monthly streamflow prediction based on the extreme learning machine (ELM) and the metaheuristic bat algorithm (Bat-ELM). The performance of the Bat-ELM was compared to that of ELM, support vector regression (SVR), Gaussian process regression (GPR), multilayer perceptron neural network (MLPNN), and generalized regression neural network (GRNN). The proposed models were applied using data from three hydrometric stations located in the Cheliff Basin, Algeria. The results showed that the Bat-ELM was more satisfactory than the standalone models. The Bat-ELM achieved the highest numerical performance with correlation coefficient and Nash-Sutcliffe efficiency ranging from 0.927 to 0.973 and from 0.846 to 0.944, respectively, much higher than the respective values obtained using the MLPNN, GRNN, SVR, GPR and ELM approaches. The obtained results demonstrate that the Bat-ELM is an interesting alternative algorithm for predicting high and extreme streamflow. [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]

This paper presents an evaluation of the parameter sensitivity of a process-based model at the global scale using large-sample data. The analysis was carried out using the HYdrological Prediction of the Environment (HYPE) model, for which soil and snow parameters were evaluated using 187 river flow gauges spread worldwide. As a result, 6 out of 12 soil parameters and 7 out of 10 snow parameters were found to be sensitive. Taking advantage of the global dataset, an additional analysis was used to investigate links between catchment characteristics and parameter sensitivity. Different patterns of sensitivity were observed for different Köppen climate classes, which indicates that parameter regionalization would benefit from calibration based on climate zones. This numerical sensitivity method was compared with the judgement of a set of expert HYPE modellers to understand how numerical results compare with modellers' experience. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a robust accurate streamflow prediction model by balancing the abilities of exploitation and exploration to find the best parameters of a machine learning model. To do so, the simulated annealing (SA) algorithm is integrated with the mayfly optimization algorithm (MOA) as SAMOA to determine the optimal hyper-parameters of support vector regression (SVR) to overcome the exploration weakness of the MOA method. The proposed method is compared with the classical SVR and hybrid SVR-MOA. To examine the accuracy of the selected methods, monthly hydroclimatic data from Jhelum River Basin is used to predict the monthly streamflow on the basis of RMSE, MAE, NSE, and R2 indices. Test results show that the SVR-SAMOA outperformed the SVR-MOA and SVR models. SVR-SAMOA reduced the prediction errors of the SVR-MOA and SVR models by decreasing the RMSE and the MSE from 21.4% to 14.7% and from 21.7% to 15.1%, respectively, in the test stage. [ABSTRACT FROM AUTHOR]

This paper analyses decadal climate phenomena and the phases of the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), and assesses the climate–water–energy nexus at the reservoir system of the São Francisco River Basin (SFRB), Brazil. This study brings a non-traditional approach focused on the link between climatic patterns and hydrological processes and their social-economic consequences, such as their effect on energy production. The methodology used data from the precipitation, natural streamflow, and AMO and PDO series. These variables were evaluated by wavelet transform analysis (WTA), flow duration curve (FDC), streamflow that is equalled or exceed ed 90% of the time (Q90), the reliability index (RI) for different demands and the hydropower generated. The results suggest that the WTA for the SFRB was more significant on the inter-annual scale, and the period with opposite phases of AMO(−) and PDO(+) showed the highest values of FDC, Q90, RI and hydropower generated simulated for the SFRB. [ABSTRACT FROM AUTHOR]

In the current paper, the efficiency of three new standalone data-mining algorithms [M5 Prime (M5P), Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of bagging (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with an autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from the period 1979–2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all of the the data were used (i.e. R and Q – with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, BA-M5P outperformed the other models. [ABSTRACT FROM AUTHOR]

Resampling historical time series remains one of the main approaches used to generate long-term probabilistic streamflow forecasts, while there is a need to develop more flexible approaches taking into account non-stationarities. One possible approach is to use a modelling chain consisting of a stochastic weather generator and a hydrological model. However, the ability of this modelling chain to generate adequate probabilistic streamflows must first be evaluated. The aim of this paper is to compare the performance of a stochastic weather generator against resampling historical meteorological time series in order to produce ensemble streamflow forecasts. The comparison framework is based on 30 years of forecasts for a single Canadian watershed. Forecasts resulting from the two methods are evaluated using the continuous ranked probability score (CRPS) and rank histograms. Results indicate that while there are differences between the methods, they nevertheless perform similarly, thus showing that weather generators can be used as substitutes for resampling the historical past. [ABSTRACT FROM AUTHOR]

This paper describes the development of the first operational seasonal hydrological forecasting service for the UK, the Hydrological Outlook UK (HOUK). Since June 2013, this service has delivered monthly forecasts of streamflow and groundwater levels, with an emphasis on forecasting hydrological conditions over the next three months, accompanied by outlooks over longer time horizons. This system is based on three complementary approaches combined to produce the outlooks: (i) national-scale modelling of streamflow and groundwater levels based on dynamic seasonal rainfall forecasts, (ii) catchment-scale modelling where streamflow and groundwater level models are driven by historical meteorological forcings (i.e. the Ensemble Streamflow Prediction, ESP, approach), and (iii) a catchment-scale statistical method based on persistence and historical analogues. This paper provides the background to the Hydrological Outlook, describes the various component methods in detail and then considers the impact and usefulness of the product. As an example of a multi-method, operational seasonal hydrological forecasting system, it is hoped that this overview provides useful information and context for other forecasting initiatives around the world. [ABSTRACT FROM AUTHOR]

Streamflow prediction is useful for robust water resources engineering and management. This paper introduces a new methodology to generate more effective features for streamflow prediction based on the concept of "interaction effect". The new features (input variables) are derived from the original features in a process called feature generation. It is necessary to select the most efficient input variables for the modelling process. Two feature selection methods, least absolute shrinkage and selection operator (LASSO) and particle swarm optimization-artificial neural networks (PSO-ANN), are used to select the effective features. Principal components analysis (PCA) is used to reduce the dimensions of selected features. Then, optimized support vector regression (SVR) is used for monthly streamflow prediction at the Karaj River in Iran. The proposed method provided accurate prediction results with a root mean square error (RMSE) of 2.79 m3/s and determination coefficient (R2) of 0.92. [ABSTRACT FROM AUTHOR]

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions. [ABSTRACT FROM AUTHOR]

The research paper by Akçay et al. applied innovative polygon trend analysis (IPTA) to derive trend length/volume and trend slope between two consecutive months for rainfall/streamflow in the eastern Black Sea basin, Turkey. Although the trend length/volume equation is correct, the trend slope equation is fundamentally incorrect. A brief discussion is presented to apprise the research community of the correct trend slope equation. [ABSTRACT FROM AUTHOR]

This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models. [ABSTRACT FROM AUTHOR]

Temporary flow patterns remain understudied, despite their wide distribution and their importance for managerial practices and river conservation. This paper explores an advanced procedure for the characterization and definition of temporary flow patterns based on the frequency, duration and magnitude of non-flow (cessation) periods. A detailed analysis of flow patterns was performed on 12 rivers of the Guadiana Basin in Southern Iberia (10 in Spain and 2 in Portugal). An open methodology that can allow managers to better characterize and improve the structure and functioning of those rivers is suggested. This methodology is based on inter- and intra-annual variability analysis and its integration with river ecotypes. Within the methodology, a set of parameters related to ecological features of temporary rivers is proposed for application. This methodology may contribute to a better definition of cessation periods and an integrated understanding of the flow requirements of temporary rivers. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method to estimate flow duration curves (FDCs) for ungauged catchments with permanent flow. It is based on a dataset of 521 catchments located throughout France. The method consists in a three-step procedure called the Quantile Solidarity (QS) approach. First, a regression-based model is built to estimate FDCs in ungauged catchments, linking each flow quantile independently to physical descriptors. The second step involves imposing the continuity of the regression-based model parameters along the quantiles (hence quantile solidarity) to obtain a large reduction in the number of parameters used to estimate the FDC. The last step consists in spatially interpolating the model residuals to further improve the performance of the FDC estimation. The QS approach yields a robust and parsimonious FDC estimation without any loss in simulation efficiency and ensures strictly monotonic FDCs. [ABSTRACT FROM AUTHOR]

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]

Scientists and water users are concerned about the potential impact on water resources, particularly during low-flow periods, of freshwater withdrawals for hydraulic fracturing (fracking). Therefore, the objective of this paper is to assess the potential impact of hydraulic fracturing on water resources in the Muskingum watershed of Eastern Ohio, USA, especially due to the trend of increased withdrawals for hydraulic fracking during drought years. The Statistical Downscaling Model (SDSM) was used to generate 30 years of plausible future daily weather series in order to capture the possible dry periods. The data generated were incorporated in the Soil and Water Assessment Tool (SWAT) to examine the level of impact due to fracking at various scales. Analyses showed that water withdrawal due to hydraulic fracking had a noticeable impact, especially during low-flow periods. Clear changes in the 7-day minimum flows were detected among baseline, current and future scenarios when the worst-case scenario was implemented. The headwater streams in the sub-watersheds were highly affected, with significant decrease in 7-day low flows. The flow alteration in hydrologically-based (7Q10, i.e. 7-day 10-year low flow) or biologically-based (4B3 and 1B3) design flows due to hydraulic fracking increased with decrease in the drainage area, indicating that the relative impact may not be as great for higher order streams. Nevertheless, change in the annual mean flow was limited to 10%. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the Hungry Horse Dam on streamflow dynamics in the South Fork of the Flathead River, Montana, USA. To this end, pre- and post-dam periods of raw and naturalized streamflow data were analysed. Pettitt’s change point analysis indicated a significant change point in streamflow dynamics due to dam construction. Complexities in the pre- and post-dam periods were evaluated by sample and multi-scale entropy analyses, and the entropies of the post-dam period were found to be higher than those of the pre-dam period. Possible reasons for this, unrelated to the natural hydrological cycle caused by the dam, were analysed using wavelet analyses. The wavelet analyses showed a clear change in the phase relationship between precipitation and streamflow. Finally, weak positive trends found in the hydrological variables indicated the effects of human activities (e.g. dam construction). The results also revealed distorted lead times, which can improve the streamflow forecasts for different lead times. [ABSTRACT FROM PUBLISHER]

Different statistical methodologies can be employed to identify possible trend components in any hydro-meteorological time series. A pre-whitening (P-W) procedure has been suggested to reduce the serial correlation effect on Mann-Kendall (M-K) trend analysis. In this paper, instead of P-W, an over-whitening (O-W) procedure is suggested, which generates serially independent series with the same trend slope value. Analytically necessary formulations for O-W are presented with a non-parametric but simple innovative trend assessment procedure, which are supported by extensive simulation studies. The applications of the methodology are presented for eight factual time series records from tropical, temperate and arid regions including temperature, rainfall, streamflow, relative humidity and CO2concentrations for different short and long durations. Relative humidity and CO2records are monthly time series and, hence, there are trend and periodicity components. It is noticed in all cases that the natural trends remain as they were after the O-W procedure, thus providing an opportunity to determine reliably the trends embedded even in the serially dependent series. The O-W procedure is applicable even in the cases of periodicity in the original records. [ABSTRACT FROM PUBLISHER]

Storm runoff from the Marikina River Basin frequently causes flood events in the Philippine capital region Metro Manila. This paper presents and evaluates a system to predict short-term runoff from the upper part of that basin (380 km2). It was designed as a possible component of an operational warning system yet to be installed. For the purpose of forecast verification, hindcasts of streamflow were generated for a period of 15 months with a time-continuous, conceptual hydrological model. The latter was fed with real-time observations of rainfall. Both ground observations and weather radar data were tested as rainfall forcings. The radar-based precipitation estimates clearly outperformed the raingauge-based estimates in the hydrological verification. Nevertheless, the quality of the deterministic short-term runoff forecasts was found to be limited. For the radar-based predictions, the reduction of variance for lead times of 1, 2 and 3 hours was 0.61, 0.62 and 0.54, respectively, with reference to a “no-forecast” scenario, i.e. persistence. The probability of detection for major increases in streamflow was typically less than 0.5. Given the significance of flood events in the Marikina Basin, more effort needs to be put into the reduction of forecast errors and the quantification of remaining uncertainties. [ABSTRACT FROM PUBLISHER]

This paper presents an analysis of three common hydrological regionalization methods (multiple linear regression, spatial proximity and physical similarity) in a virtual-world setting, using a 15 km resolution regional climate model to eliminate uncertainty due to measurement errors and missing data. It was found that in many cases the best donor is neither the most similar nor the closest watershed to the ungauged site, indicating a need for better hydrologically relevant catchment descriptors. Results show that using the closest donors yields satisfactory results only if they share similar characteristics with the ungauged basin, confirming that the proximity method is a good proxy only if there is reason to believe that the basins are physically similar. It was also shown that the ability to predict whether a method will succeed or fail is limited by the quality of catchment descriptors and the inherent probabilistic nature of the problem. A method to determine whether a regionalization method will fail or succeed based on the ungauged catchment’s characteristics failed to recognize a successful candidate 20% of the time, whereas it incorrectly classified a poor candidate in 30% of cases. The results indicate that there are unknown properties or processes that contribute to the hydrological behaviour of ungauged basins.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR F. Pappenberger [ABSTRACT FROM PUBLISHER]

High-resolution data on the spatial pattern of water use are a prerequisite for appropriate and sustainable water management. Based on one well-validated hydrological model, the Distributed Time Variant Gains Model (DTVGM), this paper obtains reliable high-resolution spatial patterns of irrigation, industrial and domestic water use in continental China. During the validation periods, ranges of correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) coefficient are 0.67–0.96 and 0.51–0.84, respectively, between the observed and simulated streamflow of six hydrological stations, indicating model applicability to simulate the distribution of water use. The simulated water use quantities have relative errors (RE) less than 5% compared with the observed. In addition, the changes in streamflow discharge were also correctly simulated by our model, such as the Zhangjiafen station in the Hai River basin with a dramatic decrease in streamflow, and the Makou station in the Pearl River basin with no significant changes. These changes are combined results of basin available water resources and water use. The obtained high-resolution spatial pattern of water use could decrease uncertainty of hydrological simulation and guide water management efficiently.Editor M.C. Acreman; Associate editor X. Fang [ABSTRACT FROM PUBLISHER]

This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models.Editor D. Koutsoyiannis; Associate editor not assigned [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]

Series of observed flood intervals, defined as the time intervals between successive flood peaks over a threshold, were extracted directly from 11 approximately 100-year streamflow datasets from Queensland, Australia. A range of discharge thresholds were analysed that correspond to return periods of approximately 3.7 months to 6.3 years. Flood interval histograms at South East Queensland gauges were consistently unimodal whereas those of the North and Central Queensland sites were often multimodal. The exponential probability distribution (pd) is often used to describe interval exceedence probabilities, but fitting utilizing the Anderson-Darling statistic found little evidence that it is the most suitable. The fatigue life pd dominated subyear return periods (<1 year), often transitioning to a log Pearson 3 pd at above-year return periods. Fatigue life pd is used in analysis of the lifetime to structural failure when a threshold is exceeded, and this paper demonstrates its relevance also to the elapsed time between above-threshold floods. At most sites, the interval medians were substantially less than the means for sub-year return periods. Statistically the median is a better measure of the central tendency of skewed distributions but the mean is generally used in practice to describe the classical concept of flood return period. [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]

Seasonal river flow forecasting methods are currently being developed for country-wide application in the United Kingdom, using several different techniques. In this paper, methods based on persistence and historical flow analogues are presented. New 1- and 3-month forecasts are made each month using monthly river flows at 93 stations with records at least 30 years long. The method that performs best is selected for each separate month, catchment and forecast duration. The forecasts based on persistence of the previous month’s flow generally outperform the analogues approach, particularly for slowly responding catchments (mainly in the southeast) with large underground water storage in aquifers. Historical analogues make a useful contribution to the forecasts in the northwest of the country. Correlations between hindcasts and observations that exceed 0.23 and are significant at the 5% level for a one-sided test are found for 81% (70%) of the station–month combinations for the 1-month (3-month) forecast.Editor Z. W. Kundzewicz Associate editor Not assigned [ABSTRACT FROM AUTHOR]

Spearman’s rho, a distribution-free statistic, has been suggested in the literature for testing the significance of trend in time series data. Although the use of the test based on Spearman’s rho (also known as the Daniels test) is less widespread than that based on Kendall’s tau (the Mann-Kendall test), the two tests have been shown in the literature to be equivalent for time series with independent observations. The distribution of the Mann-Kendall trend statistic for persistent data has been previously addressed in the literature. In this paper, the distribution of Spearman’s rho as a trend test statistic for persistent data is studied. Following the same procedures used for Kendall’s tau in earlier work, an exact expression for the variance of Spearman’s rho for persistent data with multivariate Gaussian dependence is derived, and a method for calculating the exact full distribution of rho for small sample sizes is also outlined. Approximations for moderate and large sample sizes are also discussed. A case study of testing the significance of trends in a group of world river flow station data using both Kendall’s tau and Spearman’s rho is presented. Both the theoretical results and those of the case study confirm the equivalence of trend testing based on Spearman’s rho and Kendall’s tau for persistent hydrologic data.Editor Z. W. Kundzewicz; Associate editor S. Grimaldi [ABSTRACT FROM AUTHOR]

Climate change will accelerate the hydrological cycle, altering rainfall, and the magnitude and timing of runoff. The purpose of this paper is to assess the impacts of climate change on streamflow and sediment yield from the Gorganroud river basin in the North of Iran. To study the effects of climatic variations, the SWAT model was implemented to simulate the hydrological regime and the SUFI-2 algorithm was used for parameter optimization. The climate change scenarios were constructed using the outcomes of three general circulation models for three emission scenarios. The study results for 2040–2069 showed an increase in annual streamflow of 5.8%, 2.8% and 9.5% and an increase in sediment yield of 47.7%, 44.5% and 35.9% for the A1F1, A2 and B1 emission scenarios, respectively. This implies that the impact of climate change on sediment yield is greater than on streamflow. Monthly variations show that the increase in discharge and sediment yield is more pronounced in wet seasons and the decrease is more pronounced in summer (July–September). The results highlighted the strong impact of climate change and reflected the importance of incorporating such analysis into adaptive management.Editor Z.W. Kundzewicz Associate editor Not assigned [ABSTRACT FROM AUTHOR]

Today, more than ever, there is a need to implement robust statistical methods to ensure the proper evaluation of water resources data to support decision makers in water resources planning and management. Graphing or mapping data for visualization is the easiest way to communicate trends, especially to a non-technical audience. This paper describes the use of an approach that combines the Mann-Kendall test, Sen slope test and principal component analysis to detect and map the monthly trends of piezometric time series and their magnitude in the period 1979–2008. The data were obtained in 23 shallow wells in the alluvial aquifers of the Elqui River basin in central Chile, an area characterized by scarce water resources and intense agricultural and mining activities. The results show significant downward trends at the majority of the wells. Because groundwater in these shallow wells is highly dependent on the water in the river and its tributaries, the reasons for these downward trends are mainly related to a decrease of streamflow observed in the Elqui River. The streamflow is derived from mountain snowmelt rather than from rainfall, which showed no flow trend during the same period. [ABSTRACT FROM AUTHOR]

A significant decrease in mean river flow as well as shifts in flood regimes have been reported at several locations along the River Niger. These changes are the combined effect of persistent droughts, damming and increased consumption of water. Moreover, it is believed that climate change will impact on the hydrological regime of the river in the next decades and exacerbate existing problems. While decision makers and stakeholders are aware of these issues, it is hard for them to figure out what actions should be taken without a quantitative estimate of future changes. In this paper, a Soil and Water Assessment Tool (SWAT) model of the Niger River watershed at Koulikoro was successfully calibrated, then forced with the climate time series of variable length generated by nine regional climate models (RCMs) from the AMMA-ENSEMBLES experiment. The RCMs were run under the SRES A1B emissions scenario. A combination of quantile-quantile transformation and nearest-neighbour search was used to correct biases in the distributions of RCM outputs. Streamflow time series were generated for the 2026–2050 period (all nine RCMs), and for the 2051–2075 and 2076–2100 periods (three out of nine RCMs) based on the availability of RCM simulations. It was found that the quantile-quantile transformation improved the simulation of both precipitation extremes and ratio of monthly dry days/wet days. All RCMs predicted an increase in temperature and solar radiation, and a decrease in average annual relative humidity in all three future periods relative to the 1981–1989 period, but there was no consensus among them about the direction of change of annual average wind speed, precipitation and streamflow. When all model projections were averaged, mean annual precipitation was projected to decrease, while the total precipitation in the flood season (August, September, October) increased, driving the mean annual flow up by 6.9% (2026–2050), 0.9% (2051–2075) and 5.6% (2076–2100). At-test showed that changes in multi-model annual mean flow and annual maximum monthly flow between all four periods were not statistically significant at the 95% confidence level. [ABSTRACT FROM PUBLISHER]

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]

Available data from nearby gauging stations can provide a great source of hydrometric information that is potentially transferable to an ungauged site. Furthermore, streamflow measurements may even be available for the ungauged site. This paper explores the potential of four distance-based regionalization methods to simulate daily hydrographs at almost ungauged pollution-control sites. Two methods use only the hydrological information provided by neighbouring catchments; the other two are new regionalization methods parameterized with a limited number of streamflow data available at the site of interest. Based on a network of 149 streamgauges and 21 pollution-control sites located in the Upper Rhine-Meuse area, the comparative assessment demonstrates the benefit of making available point streamflow measurements at the location of interest for improving quantitative streamflow prediction. The advantage is moderate for the prediction of flow types (stormflow, recession flow, baseflow) and pulse shape (duration of rising limb and falling limb). Editor Z.W. Kundzewicz; Associate editor A. Viglione [ABSTRACT FROM AUTHOR]

We thank van Emmerik et al. for their discussion of our opinion paper. We do not fully agree that publication of negative results will be very effective in promoting experimental hydrology. Instead, we think that experimental hydrology is considered to be more risky than modelling studies because of difficulties in publishing inconclusive results and the potential for failure. [ABSTRACT FROM AUTHOR]