Frontal dynamics boost primary production in the summer stratified Mediterranean sea

Bio-physical glider measurements from a unique process-oriented experiment in the Eastern Alboran Sea (AlborEx) allowed us to observe the distribution of the deep chlorophyll maximum (DCM) across an intense density front, with a resolution (～ 400 m) suitable for investigating sub-mesoscale dynamics. This front, at the interface between Atlantic and Mediterranean waters, had a sharp density gradient (Δρ ～ 1 kg/m³ in ～ 10 km) and showed imprints of (sub-)mesoscale phenomena on tracer distributions. Specifically, the chlorophyll-a concentration within the DCM showed a disrupted pattern along isopycnal surfaces, with patches bearing a relationship to the stratification (buoyancy frequency) at depths between 30 and 60 m. In order to estimate the primary production (PP) rate within the chlorophyll patches observed at the sub-surface, we applied the Morel and Andrè (J Geophys Res 96:685–698 1991) bio-optical model using the photosynthetic active radiation (PAR) from Argo profiles collected simultaneously with glider data. The highest production was located concurrently with domed isopycnals on the fresh side of the front, suggestive that (sub-)mesoscale upwelling is carrying phytoplankton patches from less to more illuminated levels, with a contemporaneous delivering of nutrients. Integrated estimations of PP (1.3 g C m^?2d^?1) along the glider path are two to four times larger than the estimations obtained from satellite-based algorithms, i.e., derived from the 8-day composite fields extracted over the glider trip path. Despite the differences in spatial and temporal sampling between instruments, the differences in PP estimations are mainly due to the inability of the satellite to measure DCM patches responsible for the high production. The deepest (depth > 60 m) chlorophyll patches are almost unproductive and probably transported passively (subducted) from upper productive layers. Finally, the relationship between primary production and oxygen is also investigated. The logarithm of the primary production in the DCM interior (chlorophyll (Chl) > 0.5 mg/m³) shows a linear negative relationship with the apparent oxygen utilization, confirming that high chlorophyll patches are productive. The slope of this relationship is different for Atlantic, mixed interface waters and Mediterranean waters, suggesting the presence of differences in planktonic communities (whether physiological, population, or community level should be object of further investigation) on the different sides of the front. In addition, the ratio of optical backscatter to Chl is high within the intermediate (mixed) waters, which is suggestive of large phytoplankton cells, and lower within the core of the Atlantic and Mediterranean waters. These observations highlight the relevance of fronts in triggering primary production at DCM level and shaping the characteristic patchiness of the pelagic domain. This gains further relevance considering the inadequacy of optical satellite sensors to observe DCM concentrations at such fine scales.     

Assessment of earthquake hazard for the northwestern Vietnam from geological and geophysical data using an original program package

The highest seismic activity in Vietnam is observed in the northwest of the country, hence the practical significance of more accurate assessment of the earthquake hazard for the area. The worldwide experience of seismicity, in particular, the recent Tohoku mega-earthquake (March 11, 2011, M _w = 9.0, Japan) shows that instrumental and historical data alone are insufficient to reliably estimate earthquake hazard. This is all the more relevant in relation to Vietnam where the period of instrumental observation is short and historical evidence is nearly lacking. In this connection we made an attempt to construct maps of earthquake hazard based on known seismicity data using the available geological and geophysical data and the method of G.I. Reisner and his associates for classification of areas by seismic potential. Since the question of what geological and geophysical parameters are to be used and with what weights remains unresolved, we developed a program package to estimate Mmax based on different options in the use of geological and geophysical data. In this paper we discuss the first results and the promise held by this program package.     

Quantifying differences between reservoir inflows and dam site floods using frequency and risk analysis methods

Reservoirs are the most important constructions for water resources management and flood control. Great concern has been paid to the effects of reservoir on downstream area and the differences between inflows and dam site floods due to the changes of upstream flow generation and concentration conditions after reservoir’s impoundment. These differences result in inconsistency between inflow quantiles and the reservoir design criteria derived by dam site flood series, which can be a potential risk and must be quantificationally evaluated. In this study, flood frequency analysis (FFA) and flood control risk analysis (FCRA) methods are used with the long reservoir inflow series derived from a multiple inputs and single output model and a copula-based inflow estimation model. The results of FFA and FCRA are compared and the influences on reservoir flood management are also discussed. The Three Gorges Reservoir (TGR) in China is selected as a case study. Results show that the differences between the TGR inflow and dam site floods are significant which result in changes on its flood control risk rates. The mean values of TGR’s annual maximum inflow peak discharge and 3 days flood volume have increased 5.58 and 3.85% than the dam site ones, while declined by 1.82 and 1.72% for the annual maximum 7 and 15 days flood volumes. The flood control risk rates of middle and small flood events are increased while extreme flood events are declined. It is shown that the TGR can satisfy the flood control task under current hydrologic regime and the results can offer references for better management of the TGR.     

Simulating rainfall time-series: how to account for statistical variability at multiple scales?

Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events.     

Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.     

Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling

Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds.     

BARENTS16: a 1-D velocity model for the western Barents Sea

A minimum 1-D seismic velocity model for routine seismic event location purposes was determined for the area of the western Barents Sea, using a modified version of the VELEST code. The resulting model, BARENTS16, and corresponding station corrections were produced using data from stations at regional distances, the vast majority located in the periphery of the recorded seismic activity, due to the unfavorable land–sea distribution. Recorded seismicity is approached through the listings of a joint bulletin, resulting from the merging of several international and regional bulletins for the region, as well as additional parametric data from temporary deployments. We discuss the challenges posed by this extreme network-seismicity geometry in terms of velocity estimation resolution and result stability. Although the conditions do not facilitate the estimation of meaningful station corrections at the farthermost stations, and even well-resolved corrections do not have a convincing contribution, we show that the process can still converge to a stable velocity average for the crust and upper mantle, in good agreement with a priori information about the regional structure and geology, which reduces adequately errors in event location estimates.     

Allocating river water in a cooperative way: a case study of the Dongjiang River Basin,South China

Water resources provide the foundation for human development and environmental sustainability. Water shortage occurs more or less in some regions, which usually causes sluggish economic activities, degraded ecology, and even conflicts and disputes over water use sectors. Game theory can better reflect the behaviors of involved stakeholders and has been increasingly employed in water resources management. This paper presents a framework for the allocation of river basin water in a cooperative way. The proposed framework applies the TOPSIS model combined with the entropy weight to determine stakeholders’ initial water share, reallocating water and net benefit by using four solution concepts for crisp and fuzzy games. Finally, the Fallback bargaining model was employed to achieve unanimous agreement over the four solution concepts. The framework was demonstrated with an application to the Dongjiang River Basin, South China. The results showed that, overall, the whole basin gained more total benefits when the players participated in fuzzy coalitions rather than in crisp coalitions, and \(\left\{ {NHS_{Fuzzy} \,and\, SV_{Crisp} } \right\}\) could better distribute the total benefit of the whole basin to each player. This study tested the effectiveness of this framework for the water allocation decision-making in the context of water management in river basins. The results provide technical support for water right trade among the stakeholders at basin scale and have the potential to relieve water use conflicts of the entire basin.     

Estimation of carbon stock for greenhouse gas emissions from hydropower reservoirs

The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO₂eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO₂eq./kWh and 167.70 Gg C year^?1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions.