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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]

The identification of homogeneous precipitation regions is essential in the planning, design and management of water resources systems. Regions are identified using a technique that partitions climate sites into groups based on the similarity of their attributes; the procedure is known as regionalization. In this paper the ability of four attribute sets to form large, coherent precipitation zones is assessed in terms of the regional homogeneity of precipitation statistics and computational efficiency. The outcomes provide guidance for effective attribute selection for future studies in Canada. The attributes under consideration include location parameters (latitude, longitude), distance to major water bodies, site elevation and atmospheric variables modelled at different pressure levels. The analysis is conducted in two diverse climate regions within Canada including the Prairie and the Great Lakes–St Lawrence lowlands regions. The method consists of four main steps: (i) formation of the attribute sets; (ii) determination of the preferred number of regions (selection of thec-value) into which the sites are partitioned; (iii) regionalization of climate sites using the fuzzyc-means clustering algorithm; and (iv) validation of regional homogeneity usingL-moment statistics. The results of the attribute formation,c-value selection, regionalization and validation processes are presented and discussed in a comparative analysis. Based on the results it is recommended for both regions to use location parameters including latitude, longitude and distance to water bodies (in the Great Lakes region) to form precipitation regions and to consider atmospheric variables for future (climate change) applications of the regionalization procedure. [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 a neural network model capable of catchment-wide simultaneous prediction of river stages at multiple gauging stations. Thirteen meteorological parameters are considered in the input, which includes rainfall, temperature, mean relative humidity and evaporation. The NARX model is trained with a representative set of hourly data, with optimal time delay for both the input and output. The network trained using 120-day data is able to produce simulations that are in excellent agreement with field observations. We show that for application with one-step-ahead predictions, the loss in network performance is marginal. Inclusion of additional tidal observations does not improve predictions, suggesting that the river stage stations under consideration are not sensitive to tidal backwater effects despite the claim commonly made.EDITOR D. Koutsoyiannis ASSOCIATE EDITOR F. Pappenberger [ABSTRACT FROM PUBLISHER]

Constructed wetlands can be used for reducing nonpoint-source pollution and providing ecological services in a watershed. This paper presents a field monitoring study on water quality improvement in constructed wetlands of five cells in series. The wetland system covers 59.9 ha, or 0.08% of the watershed area; it diverts 7.3 million m3(hm3) water (or 4.3% annual flow) from a degraded river. The results showed that the hydraulic retention times (HRT) of the five cells ranged from 5 to 15 days, 18.4% inflow was lost to seepage and increased evapotranspiration (ET) in the wetlands; the wetlands retained 99.1% total suspended solids (TSS), 60.9% total phosphorus (TP), and 54.4% total nitrogen (TN) from the inflow. Major reductions of TSS and TP were observed in the first two large cells that occupied 57% of the total wetland area. The smaller cells did not show advantages over the bigger ones in pollutant retention as reported in some studies. Although significant water quality benefit can be achieved by the constructed wetlands, the increased water loss may be a concern, particularly in dry regions.EDITOR Z.W. Kundzewicz; ASSOCIATE EDITOR T. Okruszko [ABSTRACT FROM PUBLISHER]

The southern coast of the Caspian Sea in northern Iran is bordered by a mountain range with forested catchments which are susceptible to droughts and floods. This paper examines possible changes to runoff patterns from one of these catchments in response to climate change scenarios. The HEC-HMS rainfall–runoff model was used with downscaled future rainfall and temperature data from 13 global circulation models, and meteorological and hydrometrical data from the Casilian (or “Kassilian”) Catchment. Annual and seasonal predictions of runoff change for three future emissions scenarios were obtained, which suggest significantly higher spring rainfall with increased risk of flooding and significantly lower summer rainfall leading to a higher probability of drought. Flash floods arising from extreme rainfall may become more frequent, occurring at any time of year. These findings indicate a need for strategic planning of water resource management and mitigation measures for increasing flood hazards.EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned [ABSTRACT FROM PUBLISHER]

Estimation of hydraulic properties in the field is usually small-scale and not cost-effective. This paper proposes an innovative method for estimating hydraulic diffusivity at regional scale. Monthly groundwater storage change over the period from 2003 to 2013 is first estimated from GRACE-derived terrestrial water storage (TWS). Assuming that the aquifer system is unconfined and the hydraulic properties are uniform in a geographical cell, the water balance principle and Darcy’s law are used to establish a relation between groundwater storage and hydraulic diffusivity. The value of hydraulic diffusivity is then adjusted using the generalized least squares and linear correlation method. This GRACE-derived hydraulic diffusivity estimation method, or GHDE method for short, is first verified with a hypothetical case and then applied in the Beishan area with available field-measured hydraulic conductivity data. The hypothetical case study demonstrates that the method works perfectly if the TWS data are error free. The Beishan case study illustrates that the estimated hydraulic diffusivities using the GHDE method correlate reasonably well with field test results, suggesting that this method is applicable. The accuracy of this method is constrained by the resolution of the GRACE-derived TWS data and is most suitable for very large scale groundwater problems due to the current accuracy of the GRACE data.EDITOR A. Castellarin ASSOCIATE EDITOR N. Verhoest [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]

Recession curves are widely used in hydrological studies and projects, such as in rivers, streams or springs. However, no cave drip water has been analysed with recession curves. In this paper, four cave drips were monitored in the Velika Pasica Cave, in order to discover the water flow and storage properties of the epikarst. Various methods were applied in the recession analysis, combining the hydrological characteristics of the four drips: for the slow water in the epikarst, the matching strip method was the identified as the appropriate model for the drip water recession analysis. According to the recession coefficient k, the water flow in the epikarst was divided into fast flow, intermediate flow and slow flow. The volume of water retained in the reservoir (the epikarst storage) could be presented as a function of its specific recession coefficient. EDITOR D.Koutsoyiannis; ASSOCIATE EDITOR X. Chen [ABSTRACT FROM AUTHOR]

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]

There is a lack of suitable methods for creating precipitation scenarios that can be used to realistically estimate peak discharges with very low probabilities. On the one hand, existing methods are methodically questionable when it comes to physical system boundaries. On the other hand, the spatio-temporal representativeness of precipitation patterns as system input is limited. In response, this paper proposes a method of deriving spatio-temporal precipitation patterns and presents a step towards making methodically correct estimations of infrequent floods by using a worst-case approach. A Monte Carlo approach allows for the generation of a wide range of different spatio-temporal distributions of an extreme precipitation event that can be tested with a rainfall–runoff model that generates a hydrograph for each of these distributions. Out of these numerous hydrographs and their corresponding peak discharges, the physically plausible spatio-temporal distributions that lead to the highest peak discharges are identified and can eventually be used for further investigations.Editor A. Castellarin; Associate editor E. Volpi [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]

There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalman-type DA but also provide an important reference to other hydrological DA types. Editor D. Koutsoyiannis; Associate editor F. Pappenberger [ABSTRACT FROM AUTHOR]

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]

Any human intervention or action in karst terrains can unexpectedly, suddenly, strongly and, generally, dangerously change a local and/or a regional hydrological regime. A characteristic example of the Dinaric karst is given in this paper. The operation of two reservoirs in Livanjsko Polje at an altitude of about 702 m above sea level (m a.s.l.) and hydro-electric development of the Cetina River system started in 1973. This year marked a drastic and instantaneous change in the regional hydrological regime. A significant drop in the minimum, mean and maximum annual discharges of two neighbouring karst springs, Rumin Mali and Rumin Veliki, was caused by this anthropogenic construction. The exits of the two analysed karst springs, Rumin Mali and Veliki, are located at altitudes of about 326.8 m a.s.l. and 307.6 m a.s.l. respectively. The areal distance between them is 640 m. Their hydrological regimes have been altered in different ways. The drop in discharges is stronger for the Rumin Veliki than for the Rumin Mali. The analysis shows that the system development influenced the redistribution of the regional karst aquifer and, in this manner, contributed to the reduction of both karst springs’ recharge areas.Editor D. Koutsoyiannis Associate editor E. Rozos [ABSTRACT FROM PUBLISHER]

This paper analyses the composition of surface water and shallow groundwater in the Grande River basin, North-Central Chile, using this information to characterize water interactions. Chemical and isotopic data for surface water and groundwater (7 and 6 sampling locations, respectively) were obtained from three sampling campaigns performed in March-April (autumn), August-September (late winter) and December (early summer) 2012. Precipitation samples were also collected. Data was processed using spatial distribution charts, Piper and Stiff diagrams, and multivariate analysis. In general, the results for each method converge on a high degree of connectivity between surface water and shallow groundwater in the study area. Furthermore, approximately a 10% of groundwater contribution to the surface flow discharge was estimated for a particular reach. This multi-method approach was useful for the characterization of surface water-groundwater interactions in the Grande River basin, and may become a suitable and replicable scheme for studies in arid and semi-arid basins facing similar water management challenges. [ABSTRACT FROM AUTHOR]

This paper presents the results of a hydrological study aimed at characterizing flood-prone areas in the urban growth zone in the city of Manizales based on the potential effects of melting of the Nevado del Ruiz glacier, in Caldas, Colombia. These results constitute a basis for decision making regarding possible urban growth zones in Andean areas that face risks from volcanic eruptions producing lahars and floods caused by glacier melt. Conservative estimates of extreme flows in the Chinchiná River in the urban growth area of El Rosario can be obtained by considering the effects of rain triggered by airborne particulate material following a volcanic explosion combined with the effects of glacier melt. The effects of global warming on tropical glaciers contribute to their retreat, leading to their disappearance. Therefore, the worst scenario would take place if these events occurred in the short term as glacier volume decreases with time. [ABSTRACT FROM AUTHOR]

Following the June 2013 disaster in the Uttarakhand Himalayas, many discussions are ongoing with regard to how climate change is seeking revenge on mankind by endowing us with disasters! The event was mostly linked with the occurrence of an extreme event due to climate change. In view of this, an attempt has been made in this paper to analyse the extreme rainfall events experienced by the Uttarakhand during 1901-2013 using more than 100 stations' daily rainfall data. The study revealed that during the 113-year period, the highest numbers of extreme events were recorded during the decade 1961-1970, and to some extent in the decade 1981-1990. Thereafter, there is a decrease in extreme rainfall events. The comparative study of extreme events prior to 1901 showed that on 17-18 September 1880, a rainstorm which occurred in close vicinity to Uttarakhand caused serious floods and damage to lives and properties. The extreme rainfall recorded by some stations during this unprecedented rainstorm has not been surpassed to date. [ABSTRACT FROM AUTHOR]

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]

This paper evaluates the sensitivity of hydrological projections to the choice of potential evapotranspiration formulas on two natural sub-catchments, in Canada and Germany. Twenty-four equations, representing a large range of options, are applied for calibration over the whole observation time series and for future conditions. The modelling chain is composed of dynamically downscaled climatic projections and a 20-member (ensemble) hydrological model, along with a snow module. The roots of the sensitivity and its propagation within the hydrological chain are evaluated to show influences on climate change impact conclusions. Results show large differences between the 24 simulated potential evapotranspiration time series. However, these discrepancies only moderately affect the calibration efficiency of hydrological models as a result of adaptation of parameters. Choice of formula influences hydrological projections and climate change conclusions for both catchments in terms of simulated and projected values, and also in the magnitude of changes during important dynamic periods such as spring and autumn high flows and summer low flows. Spread of the hydrological response is lower for the combinational formulas than for temperature-based or radiation-based equations. All the results reveal the importance of testing a large spectrum of potential evapotranspiration formulas in a decision-making context, such as water resources management. [ABSTRACT FROM AUTHOR]

The aim of the present paper was to improve understanding of the rainfall dynamics in Bas-Congo and Kinshasa provinces, in Democratic Republic of Congo (DRC). The first objective of the study was achieved by analysing the spatial correlations of monthly, seasonal, annual and individual monthly rainfall amounts of Kinshasa and Bas-Congo. The second objective was achieved through investigating and quantifying the temporal trends and their spatial variations. The results demonstrated notably high average inter-station correlation of +0.63 for dry season series, followed by monthly rainfall series with an average inter-station correlation of +0.58. However, there was no station with a stable monthly rainfall regime, i.e. with mean precipitation concentration index lower than 10% (it varies between 14.2 and 21.9%). Moreover, Kinshasa experienced an increase of rainfall with an average annual rate of change of +4.59 mm/year for the period 1961–2006. The results will be helpful for efficient water resources management and for mitigating the adverse impacts of future extreme drought or flood occurrences.Editor M.C. Acreman Associate editor N. Verhoest [ABSTRACT FROM PUBLISHER]

Spatial variability of rainfall has been recognised as an important factor controlling the hydrological response of catchments. However, gauged daily rainfall data are often available at scattered locations over the catchments. This paper looks into how to capitalise on the spatial structure of radar rainfall data for improving kriging interpolation of limited gauge data over catchments at the 1-km2grid scale, using for the case study 117 gauged stations within the 128 km × 128 km region of the Mt Stapylton weather radar field (near Brisbane, Australia). Correlograms were developed using a Fast Fourier Transform method on the Gaussianised radar and gauged data. It is observed that the correlograms vary from day to day and display significant anisotropy. For the radar data, locally varying anisotropy (LVA) was examined by developing the correlogram centred on each pixel and for different radial distances. Cross-validation was carried out using the empirical correlogram tables, as well as different fitting strategies of a two-dimensional exponential distribution for both the gauged and the radar data. The results indicate that the correlograms based on the radar data outperform the gauged ones as judged by statistical measures including root mean square error, mean bias, mean absolute bias, mean standard deviation and mean inter-quartile range. While the radar data display significant LVA, it was observed that LVA did not significantly improve the estimates compared with the global anisotropy. This was also confirmed by conditional simulation of 120 rainfields using different options of correlogram development.EDITOR M.C. Acreman; ASSOCIATE EDITOR Q. Zhang [ABSTRACT FROM AUTHOR]

Somalia has frequently been affected by droughts, famines and water-related humanitarian crises. Water is scarce and the only perennial streams, the Juba and Shabelle rivers, are trans-boundary with river flows mainly originating from the Ethiopian highlands. In both riparian countries water demands are projected to increase. This paper reveals the impact of rising regional water abstractions on stream flows by illustrating sectoral demands and joining them into scenarios of medium and high population and economic growth. These scenarios are associated to the time horizons of 2035 and 2055, respectively. The scenarios disclose alarming trends especially for the Shabelle River: in the medium and high growth scenarios, water demands surpass the available river flows by 200 and 3500 hm3, respectively. The calculated deficits partly derive from conflicting assumptions about river flows by the two main riparian countries, an obstacle to any integrated planning efforts and sustained regional development.EDITOR Z.W. Kundzewicz; ASSOCIATE EDITOR F. Hattermann [ABSTRACT FROM AUTHOR]

Terrain variables are the main factors affecting the spatial distribution of snow cover. This paper aims to find a relationship between snow-cover area (SCA) and topographic variables (elevation, slope and aspect), using MODIS Terra data (MOD09A1) in parts of the Chenab basin, western Himalayas. The inter-annual variability of SCA% for each month has been analysed for the years 2000 to 2011. The analysis reveals that mean annual SCA value was maximum (37.89%) in 2005 and minimum (32.07%) in 2001. The slope classes with maximum and minimum SCA% are 5°–10° and 30°–35°, respectively. Among the 16 aspect classes, the ESE-facing slope evinces maximum SCA%. During the snow accumulation period, the expanse at 3600–4300 m elevation, and in the depletion period, 4300–5000 m elevation are found to have maximum rate of change in SCA% per 100 m rise in elevation, i.e. 3.37% and 3.67%, respectively.EDITOR Z.W. Kundzewicz; ASSOCIATE EDITOR not assigned [ABSTRACT FROM AUTHOR]

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]

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs. [ABSTRACT FROM AUTHOR]

We greatly enjoyed reading the paper by Liu et al., which is both timely and rich in insight, as it discusses the challenges in operationalizing the water-energy-food security (WEF) nexus. The nexus approach is gaining increasing attention, both in research and in policy documents, as reflected in the number and content of published documents in the past years and highlighted by the authors. [ABSTRACT FROM AUTHOR]

The opinion paper by Kreibich et al. provides interesting insights into the interplay between floods and droughts, which are often ignored. The structure of the paper is original and the findings of high interest. It may however be difficult to obtain the required data to pursue the research agenda in actual catchments. This discussion paper suggests various ways in which the linkages between floods and droughts can be explored even if recorded data of sufficient detail is not available. [ABSTRACT FROM AUTHOR]

Turbulence is considered to generate and drive most geophysical processes. The simplest case is isotropic turbulence. In this paper, the most common three-dimensional power-spectrum-based models of isotropic turbulence are studied in terms of their stochastic properties. Such models often have a high order of complexity, lack stochastic interpretation and violate basic stochastic asymptotic properties, such as the theoretical limits of the Hurst coefficient, when Hurst-Kolmogorov behaviour is observed. A simpler and robust model (which incorporates self-similarity structures, e.g. fractal dimension and Hurst coefficient) is proposed using a climacogram-based stochastic framework and tested over high-resolution observational data of laboratory scale as well as hydro-meteorological observations of wind speed and precipitation intensities. Expressions of other stochastic tools such as the autocovariance and power spectrum are also produced from the model and show agreement with data. Finally, uncertainty, discretization and bias related errors are estimated for each stochastic tool, showing lower errors for the climacogram-based ones and larger for power spectrum ones. [ABSTRACT FROM AUTHOR]

In this paper a universal multifractals comparison of the outputs of two types of collocated optical disdrometers installed on the roof of the Ecole des Ponts ParisTech is performed. A Campbell Scientific PWS100 which analyses the light scattered by the hydrometeors and an OTT Parsivel2which analyses the portion of occluded light are deployed. Both devices provide a binned distribution of drops according to their size and velocity. Various fields are studied across a range of scales: rain rate (R), liquid water content (ρ), polarimetric weather radar quantities such the horizontal reflectivity (Zh) and the specific differential phase (Kdp), and drop size distribution (DSD) parameters such as the total drop concentration (Nt) and the mass-weighted diameter (Dm). For both devices, good scaling is retrieved on the whole range of available scales (2 h–30 s), except for the DSD parameters for which the scaling only holds down to few minutes. ForR, the universal multifractal parameters are found to equal 1.5 and 0.2 forαandC1, respectively. Results are interpreted with the help of the classicalZh–RandR–Kdpradar relations.Editor D. Koutsoyiannis; Associate editor E. Volpi [ABSTRACT FROM AUTHOR]

This paper presents a discussion of some of the issues associated with the multiple sources of uncertainty and non-stationarity in the analysis and modelling of hydrological systems. Different forms of aleatory, epistemic, semantic, and ontological uncertainty are defined. The potential for epistemic uncertainties to induce disinformation in calibration data and arbitrary non-stationarities in model error characteristics, and surprises in predicting the future, are discussed in the context of other forms of non-stationarity. It is suggested that a condition tree is used to be explicit about the assumptions that underlie any assessment of uncertainty. This also provides an audit trail for providing evidence to decision makers.Editor D. Koutsoyiannis; Associate editor S. Weijs [ABSTRACT FROM AUTHOR]

This article is a concept paper, which discusses the definition of randomness, and the sources of randomness in the mathematical system as well as in the physical system (the Universe). We document that randomness is an inherited property of mathematics and of the physical world, shaping all observed forms and structures, and we discuss its role.Editor D. Koutsoyiannis; Guest editor E. Volpi [ABSTRACT FROM AUTHOR]

This paper deals with the question of whether a lumped hydrological model driven with lumped daily precipitation time series from a univariate single-site weather generator can produce equally good results compared to using a multivariate multi-site weather generator, where synthetic precipitation is first generated at multiple sites and subsequently lumped. Three different weather generators were tested: a univariate “Richardson type” model, an adapted univariate Richardson type model with an improved reproduction of the autocorrelation of precipitation amounts and a semi-parametric multi-site weather generator. The three modelling systems were evaluated in two Alpine study areas by comparing the hydrological output with respect to monthly and daily statistics as well as extreme design flows. The application of a univariate Richardson type weather generator to lumped precipitation time series requires additional attention. Established parametric distribution functions for single-site precipitation turned out to be unsuitable for lumped precipitation time series and led to a large bias in the hydrological simulations. Combining a multi-site weather generator with a hydrological model produced the least bias. [ABSTRACT FROM PUBLISHER]

This paper presents hydrochemical data of an AMD stream, Poderosa Creek, in the Iberian Pyrite Belt, obtained between its source, in the Poderosa Mine portal, and its confluence with the Odiel River. The main objective is to establish potential interdependent relationships between sulphate and metal loads and the following physico-chemical variables: pH, electrical conductivity (EC), redox potential (EH) and dissolved oxygen (DO). All the parameters show an overall increasing tendency from the tunnel exit to the confluence at the Odiel River. The TDS and EC are two relevant exceptions. They behave similarly, showing a decreasing trend and a strong inflection that describes a minimum immediately after the discharging point. Spatial analysis combined with statistical tools reveal typical AMD processes and the respective physico-chemical implications. Inputs with distinctive hydrochemical signatures impose relevant modifications in the Poderosa Creek waters. This indicates low hydrochemical inertia and high vulnerability to external stimuli.Editor D. Koutsoyiannis; Associate editor not assigned [ABSTRACT FROM PUBLISHER]

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.Editor D. Koutsoyiannis; Associate editor H. Aksoy [ABSTRACT FROM PUBLISHER]

In 2013, the International Association of Hydrological Sciences (IAHS) launched the hydrological decade 2013–2022 with the theme “Panta Rhei: Change in Hydrology and Society”. The decade recognizes the urgency of hydrological research to understand and predict the interactions of society and water, to support sustainable water resource use under changing climatic and environmental conditions. This paper reports on the first Panta Rhei biennium 2013–2015, providing a comprehensive resource that describes the scope and direction of Panta Rhei. We bring together the knowledge of all the Panta Rhei working groups, to summarize the most pressing research questions and how the hydrological community is progressing towards those goals. We draw out interconnections between different strands of research, and reflect on the need to take a global view on hydrology in the current era of human impacts and environmental change. Finally, we look back to the six driving science questions identified at the outset of Panta Rhei, to quantify progress towards those aims.Editor D. Koutsoyiannis; Associate editor not assigned [ABSTRACT FROM PUBLISHER]

“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned [ABSTRACT FROM PUBLISHER]

In recent years there has been a surge in land investments, primarily in the African continent, but also in Asia and Latin America. This increase in land investment was driven by the food pricing crisis of 2007–2008. Land investors can be identified from a variety of sectors, with actors ranging from hedge funds to national companies. Many water-scarce countries in the Middle East and North Africa (MENA) are among these financiers, and primarily invest in Africa. Recognizing the potential for “outsourcing” their food security (and thereby also partly their water security), Middle Eastern countries such as Jordan, Qatar and the United Arab Emirates have invested in land for food production in Africa. The extent to which this is happening is still unclear, as many contracts are not yet official and the extent of the leases is vague. This paper investigates the land investments and acquisitions by Middle Eastern countries. It also seeks to analyse what effect, if any, these investments can have on the potential for conflict reduction and subsequent peacebuilding in the Middle East region as the activity removes pressure from transboundary water resources. EDITOR D. Koutsoyiannis ASSOCIATE EDITOR K. Aggestam [ABSTRACT FROM PUBLISHER]

The Bhagirathi River that originates from the snout of Gangotri Glacier at an elevation of about 4000 m is traditionally considered as the source of Ganga River. In June 2013, a multi-day cloudburst centred on the North Indian state of Uttarakhand caused devastating floods and landslides which resulted in massive loss of lives and property. This unusual rain event occurred during the tourist season in June, consequently trapping a large number of tourists and vendors in this area. Sudden release of stored water generated floods that created havoc downstream of most of the rivers in this area. This paper presents a description of the meteorological conditions which led to the catastrophic hydrological situation and flash flood as experienced in the large part of Bhagirathi basin in June 2013. The hydro-meteorological and discharge data for 14–18 June 2013 collected at an observatory near the snout of the Gangotri Glacier are analysed. This area normally receives less rainfall; the maximum monthly rainfall for June in previous years hardly exceeded 93 mm, while the total rainfall during this 5-day storm was 178 mm. A sudden increase of water discharge in the river resulted in flooding downstream. Flows at the gauging site peaked at 163 m3s-1on 16 June 2013. The mean runoff depths for June varied between 12 and 14 mm, whereas runoff depths recorded up to the study site were much higher, being highest on 16 June 2013.Editor Z.W. Kundzewicz; Associate editor A. Viglione [ABSTRACT FROM PUBLISHER]

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]

Dynamic programming (DP) has been among the most popular techniques for solving multireservoir problems since the early 1960s. However, DP and DP-based methods suffer from two serious issues: namely, the curses of modelling and dimensionality. Later, reinforcement learning (RL) was introduced to overcome some deficiencies in the traditional DP mainly related to the curse of modelling, but it still encounters the curse of dimensionality in larger systems. Recently, the artificial neural network has emerged as an effective approach to solve stochastic optimization of reservoir systems with high flexibility. In this paper, we develop a single-step evolving artificial neural network (SENN) model that overcomes the curses of modelling and dimensionality in a multireservoir system. Furthermore, a novel efficient allocation technique is developed to ease the allocation of water among different users. A two-reservoir system in Karkheh Basin, Iran, is applied to derive and test the methods. Elitist-mutated particle swarm optimization is used to train the network. A comparison of the results with Q-learning shows the superiority of the SENN, especially during drought periods. Moreover, the SENN performs better in producing more hydropower energy in the system. Thus, the main contributions of this research are (1) development of SENN applications to multireservoir systems, (2) a comparative analysis between SENN and Q-learning especially in prolonged drought conditions and (3) a proposed efficient optimal allocation technique using the simulation method. Editor D. Koutsoyiannis; Associate editor E. Toth [ABSTRACT FROM AUTHOR]

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 estimation of drought at certain temporal and spatial scales is useful for research on climate change and global warming. Greece is often affected by droughts, which are widespread spatially and temporally due to the complex topography. Within the Greek territory, various complex microclimates are created, linked with the spatial variances in drought phenomena. In this paper an estimation of drought in the Sperchios River basin was conducted using the Aridity Index (AI). Additionally, a seasonal analysis of drought was performed. Meteorological data from the Hellenic National Meteorological Service (HNMS) were used as inputs for the AI equation. Spatial interpolation of AI for the Sperchios River basin was performed using a kriging method by the application of ArcGIS 9.3. In order to produce required input data, several models (EmPEst, RayMan) and techniques (linear regression, interpolation) were combined. Finally, the meteorological data series were randomly separated into two periods and AI was estimated for these sub-periods, in order to test the effectiveness of the drought index used. The results indicate that the conditions prevailing in the area are humid, mostly affected by increased rainfall occurring in the mountainous section of the basin. Broadly, the humid environment in the upstream of Sperchios River prevents drought occurring in the lowlands of Sperchios River valley. Nevertheless, some differentiation appeared during the summer period, to which special attention needs to be given in order to prevent drought conditions.Editor Z. W. Kundzewicz Associate editor not assigned [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyse, for the Senegal River basin, the evolution of four classes of daily precipitation, the number of rainy days and the duration of the rainy season. The approach is to apply statistical tests of homogeneity to the series of annual rainfall over the period 1950–1998 to detect abrupt changes and to characterize the evolution of these six variables on each side of the change dates. The time series present an abrupt change (break) between 1966 and 1970. Deficits of rainy days in the period after the break compared to before vary between 6.2% at Kédougou and 38.6% at Saint Louis. The class of rain of more than 50 mm (P4) has the largest deficit, ranging from 14.8% at Mamou station to 66.6% at Nioro du Sahel.Editeur Z. W. Kundzewicz; Edithur Associé E. Gargouri [ABSTRACT FROM AUTHOR]

There is an implicit assumption in most work that the parameters calibrated based on observations remain valid for future climatic conditions. However, this might not be true due to parameter instability. This paper investigates the uncertainty and transferability of parameters in a hydrological model under climate change. Parameter transferability is investigated with three parameter sets identified for different climatic conditions, which are: wet, intermediate and dry. A parameter set based on the baseline period (1961–1990) is also investigated for comparison. For uncertainty analysis, ak-simulation set approach is proposed instead of employing the traditional optimization method which uses a single best-fit parameter set. The results show that the parameter set from the wet sub-period performs the best when transferred into wet climate condition, while the parameter set from the baseline period is the most appropriate when transferred into dry climate condition. The largest uncertainty of simulated daily high flows for 2011–2040 is from the parameter set trained in the dry sub-period, while that of simulated daily medium and low flows lies in the parameter set from the intermediate calibration sub-period. For annual changes in the future period, the uncertainty with the parameter set from the intermediate sub-period is the largest, followed by the wet sub-period and dry sub-period. Compared with high and medium flows/runoffs, the uncertainty of low flows/runoffs is much smaller for both simulated daily flows and annual runoffs. For seasonal runoffs, the largest uncertainty is from the intermediate sub-period, while the smallest is from the dry sub-period. Apart from that, the largest uncertainty can be observed for spring runoffs and the lowest one for autumn runoffs. Compared with the traditional optimization method, thek-simulation set approach shows many more advantages, particularly being able to provide uncertainty information to decision support for watershed management under climate change. EDITOR Z.W. Kundzewicz ASSOCIATE EDITOR not assigned [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the most important components in the hydrological cycle, and a key variable in hydrological modelling and water resources management. However, understanding the impacts of spatial variability in ET and the appropriate scale at which ET data should be incorporated into hydrological models, particularly at the regional scale, is often overlooked. This is in contrast to dealing with the spatial variability in rainfall data where existing guidance is widely available. This paper assesses the impacts of scale on the estimation of reference ET (ETo) by comparing data from individual weather stations against values derived from three national datasets, at varying resolutions. These include the UK Climate Impacts Programme 50 km climatology (UKCP50), the UK Met Office 5 km climatology (UKMO5) and the regional values published in the Agricultural Climate of England and Wales (ACEW). The national datasets were compared against the individual weather station data and the UKMO5was shown to provide the best estimate of ETo at a given site. The potential impacts on catchment modelling were then considered by mapping variance in ETo to show how geographical location and catchment size can have a major impact, with small lowland catchments having much higher variance than those with much larger areas or in the uplands. Some important implications for catchment hydrological modelling are highlighted.Editor D. Koutsoyiannis; Associate editor L. Ruiz [ABSTRACT FROM PUBLISHER]

The non-parametric mathematical framework of bilinear surface smoothing (BSS) methodology provides flexible means for spatial (two dimensional) interpolation of variables. As presented in a companion paper, interpolation is accomplished by means of fitting consecutive bilinear surface into a regression model with known break points and adjustable smoothing terms defined by means of angles formed by those bilinear surface. Additionally, the second version of the methodology (BSSE) incorporates, in an objective manner, the influence of an explanatory variable available at a considerably denser dataset. In the present study, both versions are explored and illustrated using both synthesized and real world (hydrological) data, and practical aspects of their application are discussed. Also, comparison and validation against the results of commonly used spatial interpolation methods (inverse distance weighted, spline, ordinary kriging and ordinary cokriging) are performed in the context of the real world application. In every case, the method’s efficiency to perform interpolation between data points that are interrelated in a complicated manner was confirmed. Especially during the validation procedure presented in the real world case study, BSSE yielded very good results, outperforming those of the other interpolation methods. Given the simplicity of the approach, the proposed mathematical framework’s overall performance is quite satisfactory, indicating its applicability for diverse tasks of scientific and engineering hydrology and beyond.Editor Z. W. Kundzewicz; Associate editor A. Carsteanu [ABSTRACT FROM PUBLISHER]

Bilinear surface smoothing is an alternative concept that provides flexible means for spatial interpolation. Interpolation is accomplished by means of fitting a bilinear surface into a regression model with known break points and adjustable smoothing terms. Additionally, as an option, the incorporation, in an objective manner, of the influence of an explanatory variable available at a considerable denser dataset is possible. The parameters involved in each case (with or without an explanatory variable) are determined by a nonparametric approach based on the generalized cross-validation (GCV) methodology. A convenient search technique for the smoothing parameters was achieved by transforming them in terms of tension parameters, with values restricted in the interval [0, 1). The mathematical framework, the computational implementation and details concerning both versions of the methodology, as well as practical aspects of their application are presented and discussed. In a companion paper, examples using both synthesized and real-world (hydrological) data are presented to illustrate the methodology. The proposed mathematical framework constitutes a simple alternative to existing spatial interpolation methodologies.Editor Z.W. Kundzewicz; Associate editor A. Carsteanu [ABSTRACT FROM PUBLISHER]

In this paper we develop a coupled analytical model for salinity and tidal propagation in estuaries where the cross-sectional area varies exponentially. A simple analytical model for tidal dynamics has been used to estimate the tidal excursion, which has an important influence on the salt intrusion process since it determines the extreme salinities (i.e. salinity distribution for high water slack and low water slack). The objective of the coupling is to reduce the number of calibration parameters, which subsequently strengthens the reliability of the salt intrusion model. Moreover, the coupling enables us to assess the potential impacts of external changes, both human-induced interventions (e.g. dredging) and natural changes (e.g. global sea level rise), on the salt intrusion process. In addition, the fully analytical solution for hydrodynamics allows immediate estimation of the tidally averaged depth and friction coefficient for given water level recordings and salinity measurements. This is particularly useful when a geometric survey is not available. The coupled model has been applied to six previously unsurveyed estuaries in Malaysia and the results show that the correspondence between analytical estimations and observations is very good. Thus, the coupled model proves to be a useful tool to obtain estimates of salt intrusion in estuaries based on a minimum amount of information required and for assessing the effect of human-induced or natural changes. EDITOR D. Koutsoyiannis ASSOCIATE EDITOR B. Dewals [ABSTRACT FROM AUTHOR]