Epistemological dimensions of the water–energy–food nexus approach: reply to discussions of “Challenges in operationalizing the water–energy–food nexus”*

ABSTRACT

We thank the authors, Varis and Keskinen, and Nauditt, for their constructive contributions. We endorse their key comments, further referring to recent literature and events, including the UN 2018 High Level Political Forum on sustainable development. Here, we elaborate on the epistemological perspective of the water–energy–food nexus conceptualization, assessment, discourse and operationalization.     

Physico-chemical and nutrient variable stratifications in the water column and in macroalgal thalli as a result of high biomass mats in a non-tidal shallow-water lagoon

A field study to check parameter stratification during high density growth of four opportunistic macroalgae was carried out in Orbetello lagoon (Italy). The effects of macroalgal masses were compared with a seagrass meadow and two lagoon areas with bare bottoms as controls for pH, temperature, dissolved oxygen, salinity, nitrite, nitrate ammonium and orthophosphate. The nutrient content of thalli and sediment redox were measured. Macroalgae showed differences in stratification of thalli nutrient content. Mat with low density and high volume produced stratifications in the water column, but it did not produce nutrient release by sediment. In contrast, high density and low and high volume mats led to sharp falls in dissolved oxygen, with negative values of sediment Eh and anoxic trigger processes that presumably led to release of sediment nutrients. This depended on thallus type: heavy thalli compacted the mat and light ones distributed more widely in the water column.     

Development of FLEMOcs – a new model for the estimation of flood losses in the commercial sector

Abstract

The estimation of flood damage is an important component for risk-oriented flood design, risk mapping, financial appraisals and comparative risk analyses. However, research on flood-loss modelling, especially in the commercial sector, has not gained much attention so far. Therefore, extensive data about flood losses were collected for affected companies via telephone surveys after the floods of 2002, 2005 and 2006 in Germany. Potential loss determining factors were analysed. The new Flood Loss Estimation MOdel for the commercial sector (FLEMOcs) was developed on the basis of 642 loss cases. Losses are estimated depending on water depth, sector and company size as well as precaution and contamination. The model can be applied to the micro-scale, i.e. to single production sites as well as to the meso-scale, i.e. land-use units, thus enabling its countrywide application.

Citation Kreibich, H., Seifert, I., Merz, B. & Thieken, A. H. (2010) Development of FLEMOcs – a new model for the estimation of flood losses in the commercial sector. Hydrol. Sci. J. 55(8), 1302–1314.     

Application and validation of FLEMOcs – a flood-loss estimation model for the commercial sector

Abstract

The estimation of flood loss is difficult, especially in the commercial sector, because of its great inhomogeneity. However, the reliability of loss modelling is fairly unknown, since flood-loss models are scarcely validated. The newly developed Flood Loss Estimation MOdel for the commercial sector (FLEMOcs) was validated on the micro-scale using a leave-one-out cross-validation procedure. Additionally, different meso-scale loss functions were compared. Meso-scale model application was undertaken in 19 municipalities which were affected during the 2002 flood in Germany. Model results were compared with the results of three other loss models, as well as with official loss records. The micro-scale validation shows very good results, with no bias and mean absolute errors between 23 and 31%. The meso-scale validation indicates that FLEMOcs provides good results, especially in large areas with many affected companies where high losses are expected.

Citation Seifert, I., Kreibich, H., Merz, B. & Thieken, A. H. (2010) Application and validation of FLEMOcs – a flood-loss estimation model for the commercial sector. Hydrol. Sci. J. 55(8), 1315–1324.     

Yield–reliability analysis for rural domestic water supply from combined rainwater harvesting and run-of-river abstraction

Abstract

In many rural areas, reticulated water supply from large water resources schemes does not exist and many households obtain water from groundwater (GW), rainwater harvesting (RWH), run-of-river (ROR) flow, or combinations of these. While comprehensive yield–reliability analysis for large water resource systems is the norm, typical rural water supply analysis unrealistically aggregates data into monthly or annual time steps and does not incorporate reliability. A daily time-step simulation of household supply and frequency analysis of the number of days supplied in each year is used herein to: (a) demonstrate a realistic approach of yield–reliability analysis for RWH, ROR and combined RWH and ROR supply; and (b) show how combined utilization of the two can lead to improved supply. Integration of RWH and ROR supply has been found to improve yield and reliability substantially. The limitations of using the simpler mass curve analysis and the effect of applying a monthly time step are demonstrated.

Citation Ndiritu, J., Odiyo, J. O., Makungo, R., Ntuli, C. & Mwaka, B. (2011) Yield–reliability analysis for rural domestic water supply from combined rainwater harvesting and run-of-river abstraction. Hydrol. Sci. J. 56(2), 238–248.     

Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model

Abstract

The accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time.     

Heterogeneity influences on stream water–groundwater interactions in a gravel-dominated floodplain

ABSTRACT

Floodplains are composed of complex depositional patterns of ancient and recent stream sediments, and research is needed to address the manner in which coarse floodplain materials affect stream–groundwater exchange patterns. Efforts to understand the heterogeneity of aquifers have utilized numerous techniques typically focused on point-scale measurements; however, in highly heterogeneous settings, the ability to model heterogeneity is dependent on the data density and spatial distribution. The objective of this research was to investigate the correlation between broad-scale methodologies for detecting heterogeneity and the observed spatial variability in stream/groundwater interactions of gravel-dominated alluvial floodplains. More specifically, this study examined the correlation between electrical resistivity (ER) and alluvial groundwater patterns during a flood event at a site on Barren Fork Creek, in the Ozark ecoregion of Oklahoma, USA, where chert gravels were common both as streambed and as floodplain material. Water table elevations from groundwater monitoring wells for a flood event on 1–5 May 2009 were compared to ER maps at various elevations. Areas with high ER matched areas with lower water table slope at the same elevation. This research demonstrated that ER approaches were capable of indicating heterogeneity in surface water–groundwater interactions, and that these heterogeneities were present even in an aquifer matrix characterized as highly conductive. Portions of gravel-dominated floodplain vadose zones characterized by high hydraulic conductivity features can result in heterogeneous flow patterns when the vadose zone of alluvial floodplains activates during storm events.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR X. Chen     

Effects of soil–structure interaction on the dynamic properties and seismic response of piled structures

This paper presents a simple and stable procedure for the estimation of periods and dampings of piled shear buildings taking soil–structure interaction into account. A substructuring methodology that includes the three-dimensional character of the foundations is used. The structure is analyzed as founded on an elastic homogeneous half-space and excited by vertically incident S waves. The strategies proposed in the literature to estimate the period and damping are revised, and a modified strategy is proposed including crossed impedances and all damping terms. Ready-to-use graphs are presented for the estimation of flexible-base period and damping in terms of their fixed-base values and the system configuration. Maximum shear forces together with base displacement and rocking peak response are also provided. It is shown that cross-coupled impedances and kinematic interaction factors need to be taken into account to obtain accurate results for piled buildings.     

Application du réseau des neurones artificiels à la prévision des débits horaires: Cas du bassin versant de l’Eure,France

RÉSUMÉ

Les modèles pluie–débit sont fortement utilisés dans la gestion des risques hydrologiques et la prévision des crues. Dans cette étude, nous présentons un modèle pluie–débit pour la prévision des débits horaires basé sur la technique des réseaux de neurones artificiels (RNA). Ce modèle a été développé et appliqué sur le bassin versant de l’Eure au Nord-Ouest de la France afin de dépasser les problèmes dus à la non-linéarité de la relation pluie–débit et à l’imprécision des données collectées. La création de ce modèle a nécessité plusieurs étapes pendant lesquelles nous avons pu déterminer les paramètres du modèle permettant la compréhension de la complexité hydrologique et la production des informations nécessaires à la prévision. Elles ont abouti à un modèle de réseau de neurones artificiels capable d’effectuer, en quelques secondes, des prévisions des crues efficaces jusqu’à un horizon de prévision de 48 h. Ces résultats confirment que les modèles RNA peuvent jouer un rôle important dans le domaine de la prévision car capables de modéliser la non-linéarité des relations pluie–débit rencontrées sur certains bassins hydrologiques.

Editor Z.W. Kundzewicz Associate editor E. Gargouri