Identification of appropriate temporal scales of dominant low flow indicators in the Main River, Germany

Models incorporating the appropriate temporal scales of dominant indicators for low flows are assumed to perform better than models with arbitrary selected temporal scales. In this paper, we investigate appropriate temporal scales of dominant low flow indicators: precipitation (P), evapotranspiration (ET) and the standardized groundwater storage index (G). This analysis is done in the context of low flow forecasting with a lead time of 14 days in the Main River, a tributary of the Rhine River, located in Germany. Correlation coefficients (i.e. Pearson, Kendall and Spearman) are used to reveal the appropriate temporal scales of dominant low flow indicators at different time lags between low flows and indicators and different support scales of indicators. The results are presented for lag values and support scales, which result in correlation coefficients between low flows and dominant indicators falling into the maximum 10% percentile range. P has a maximum Spearman correlation coefficient (ρ) of 0.38 (p = 0.95) at a support scale of 336 days and a lag of zero days. ET has a maximum ρ of –0.60 (p = 0.95) at a support scale of 280 days and a lag of 56 days and G has a maximum ρ of 0.69 (p = 0.95) at a support scale of 7 days and a lag of 3 days. The identified appropriate support scales and lags can be used for low flow forecasting with a lead time of 14 days

Hydrologic homogeneous regions using monthly Streamflow in Turkey

Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the characterization of hydrologic systems. In this paper we applied the hierarchical cluster analysis to the task of objectively classifying streamflow data into regions encompassing similar streamflow patterns over Turkey. The performance of three standardization techniques was also tested, and standardizing by range was found better than standardizing with zero mean and unit variance. Clustering was carried out using Ward’s minimum variance method which became prominent in managing water resources with squared Euclidean dissimilarity measures on 80 streamflow stations. The stations have natural flow regimes where no intensive river regulation had occurred. A general conclusion drawn is that the zones having similar streamflow pattern were not be overlapped well with the conventional climate zones of Turkey; however, they are coherent with the climate zones of Turkey recently redefined by the cluster analysis to total precipitation data as well as homogenous streamflow zones of Turkey determined by the rotated principal component analysis. The regional streamflow information in this study can significantly improve the accuracy of flow predictions in ungauged watersheds

Identification of an appropriate low flow forecast model\ud for the Meuse River

This study investigates the selection of an appropriate low flow forecast model for the Meuse\ud River based on the comparison of output uncertainties of different models. For this purpose, three data\ud driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression\ud model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to\ud be represented by the difference between observed and simulated discharge. The results show that the ANN\ud low flow forecast model with one or two input variables(s) performed slightly better than the other statistical\ud models when forecasting low flows for a lead time of seven days. The approach for the selection of an\ud appropriate low flow forecast model adopted in this study can be used for other lead times and river basins\ud as well

Discussion of “clustering on dissimilarity Representations for detecting mislabelled Seismic signals at Nevado del Ruiz Volcano” by Mauricio Orozco-Alzate, and César Germán Castellanos-Domínguez

The authors are to be congratulated for a systematic investigationof the accurate and non subjective classifying approach in seismic research. The authors have conducted several clustering algorithms to the seismic event records from Volcanological and SeismologicalObservatory at Manizales. Their objective was to improve the grouping of seismic data (i.e., volcano-tectonic earthquakes, long-period earthquakes and icequakes) digitized at 100.16 Hz sampling frequency.Their study seems adding new approach to their previous work of Langer et al. (2006) who applied different classification techniques to seismic data

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.</jats:p

Monte-Carlo simulation of neutron transmission through nanocomposite materials for neutron-optics applications

Nanocomposites enable us to tune parameters that are crucial for use of such materials for neutron-optics applications such as diffraction gratings by careful choice of properties such as species (isotope) and concentration of contained nanoparticles. Nanocomposites for neutron optics have so far successfully been deployed in protonated form, containing high amounts of $^1$H atoms, which exhibit rather strong neutron absorption and incoherent scattering. At a future stage of development, chemicals containing $^1$H could be replaced by components with more favourable isotopes, such as $^2$H or $^{19}$F. In this note, we present results of Monte-Carlo simulations of the transmissivity of various nanocomposite materials for thermal and very-cold neutron spectra. The results are compared to experimental transmission data. Our simulation results for deuterated and fluorinated nanocomposite materials predict a decrease of absorption- and scattering-losses down to about 2 % for very-cold neutrons.Comment: submitted to NIM

Reply to Andersen et al.(2016) "Assumptions behind size-based ecosystem models are realistic"

In a recent publication (Froese et al., ICES Journal of Marine Science; doi:10.1093/icesjms/fsv122), we presented a critique of the balanced harvesting (BH) approach to fishing. A short section dealt with the size-spectrum models used to justify BH, wherein we pointed out the lack of realism of these models, which mostly represented ecosystems as consisting of a single cannibalistic species. Andersen et al. (ICES Journal of Marine Science; doi:10.1093/icesjms/fsv211) commented on our paper and suggested that we criticized size-spectrum models in general and that we supposedly made several erroneous statements. We stress that we only referred to the size-spectrum models that we cited, and we respond to each supposedly erroneous statement. We still believe that the size-spectrum models used to justify BH were highly unrealistic and not suitable for evaluating real-world fishing strategies. We agree with Andersen et al. that BH is unlikely to be a useful guiding principle for ecosystem-based fisheries management, for many reasons. The use of unrealistic models is one of them

A critique of the balanced harvesting approach to fishing

The approach to fisheries termed “balanced harvesting” (BH) calls for fishing across the widest possible range of species, stocks, and sizes in an ecosystem, in proportion to their natural productivity, so that the relative size and species composition is maintained. Such fishing is proposed to result in higher catches with less negative impact on exploited populations and ecosystems. This study examines the models and the empirical evidence put forward in support of BH. It finds that the models used unrealistic settings with regard to life history (peak of cohort biomass at small sizes), response to fishing (strong compensation of fishing mortality by reduced natural mortality), and economics (uniform high cost of fishing and same ex-vessel price for all species and sizes), and that empirical evidence of BH is scarce and questionable. It concludes that evolutionary theory, population dynamics theory, ecosystem models with realistic assumptions and settings, and widespread empirical evidence do not support the BH proposal. Rather, this body of evidence suggests that BH will not help but will hinder the policy changes needed for the rebuilding of ecosystems, healthy fish populations, and sustainable fisheries