Quality assessment of groundwater from the south-eastern Arabian Peninsula

Assessment of groundwater quality plays a significant role in the utilization of the scarce water resources globally and especially in arid regions. The increasing abstraction together with man-made contamination and seawater intrusion have strongly affected groundwater quality in the Arabia Peninsula, exemplified by the investigation given here from the United Arab Emirates, where the groundwater is seldom reviewed and assessed. In the aim of assessing current groundwater quality, we here present a comparison of chemical data linked to aquifers types. The results reveal that most of the investigated groundwater is not suitable for drinking, household, and agricultural purposes following the WHO permissible limits. Aquifer composition and climate have vital control on the water quality, with the carbonate aquifers contain the least potable water compared to the ophiolites and Quaternary clastics. Seawater intrusion along coastal regions has deteriorated the water quality and the phenomenon may become more intensive with future warming climate and rising sea level.     

Variability in the phytoplankton community of Kavaratti reef ecosystem (northern Indian Ocean) during peak and waning periods of El Niño 2016

El Niño, an interannual climate event characterized by elevated oceanic temperature, is a prime threat for coral reef ecosystems worldwide, owing to their thermal threshold sensitivity. Phytoplankton plays a crucial role in the sustenance of reef trophodynamics. The cell size of the phytoplankton forms the “master morphological trait” with implications for growth, resource acquisition, and adaptability to nutrients. In the context of a strong El Niño prediction for 2015–2016, the present study was undertaken to evaluate the variations in the size-structured phytoplankton of Kavaratti reef waters, a major coral atoll along the southeast coast of India. The present study witnessed a remarkable change in the physicochemical environment of the reef water and massive coral bleaching with the progression of El Niño 2015–2016 from its peak to waning phase. The fluctuations observed in sea surface temperature, pH, and nutrient concentration of the reef water with the El Niño progression resulted in a remarkable shift in phytoplankton size structure, abundance, and community composition of the reef waters. Though low nutrient concentration of the waning phase resulted in lower phytoplankton biomass and abundance, the diazotroph Trichodesmium erythraeum predominated the reef waters, owing to its capability of the atmospheric nitrogen fixation and dissolved organic phosphate utilization.     

Probabilistic assessment of phenophase-wise agricultural drought risk under different sowing windows: a case study with rainfed soybean

A new methodology for crop-growth stage-specific assessment of agricultural drought risk under a variable sowing window is proposed for the soybean crop. It encompasses three drought indices, which include Crop-Specific Drought Index (CSDI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI). The unique features of crop-growth stage-specific nature and spatial and multi-scalar coverage provide a comprehensive assessment of agricultural drought risk. This study was conducted in 10 major soybean-growing districts of Madhya Pradesh state of India. These areas contribute about 60% of the total soybean production for the country. The phenophase most vulnerable to agricultural drought was identified (germination and flowering in our case) for each district across four sowing windows. The agricultural drought risk was quantified at various severity levels (moderate, severe, and very severe) for each growth stage and sowing window. Validation of the proposed new methodology also yielded results with a high correlation coefficient between percent probability of agricultural drought risk and yield risk (r?=?0.92). Assessment by proximity matrix yielded a similar statistic. Expectations for the proposed methodology are better mitigation-oriented management and improved crop contingency plans for planners and decision makers.     

我国流域水生态完整性评价方法构建

流域水生态完整性评价是指通过对水生态系统中不同水生态指标(生物和非生物)的监测以及由数学方法综合形成的综合评价指数,来反映水生态系统完整性状况。近年来,世界各国水环境管理政策发生了变化,开始强调生态保护,重视水体的生态质量。中国现行的常规理化监测指标(如COD、氨氮、BOD5)很难满足水环境管理的需求,难以全面准确地反映水环境质量变化的趋势。因此,在借鉴欧美发达国家流域水生态完整性评价方法的基础上,结合中国目前监测现状以及流域水环境管理需求,构建了包括物理生境指标、理化指标、水生生物指标在内的流域水生态完整性监测与评价方法,以期为中国流域水质目标管理技术体系的业务化运行提供可资借鉴的技术支撑,实现从单一的化学指标监测转向综合的水生态系统监测,实现流域水生态完整性的监测与评价。     

Development and Testing of 2D Finite Difference Model in Open Channels

This study develops a depth-averaged two-dimensional (2D) numerical model using a finite difference method (FDM) on a staggered grid. The governing equations were solved using the Marker and Cell method that was developed at the Los Alamos laboratories by Harlow and Welch in 1965. In the paper, an explicit FDM was used to solve the governing equations. A first-order approximation was used for the temporal derivative. Second-order central difference approximations were used for space discretization. The time step is limited by the Courant–Friedrichs–Lewy (CFL) condition. The time step used in this study depends on the grid spacing and velocity components in the x- and y-directions. The study is divided into two steps: the first step is to develop a depth-averaged 2D numerical model to simulate the flow process. The second constructs a module to calculate the bed load transport and simulate the river morphology in the areas that have steep slopes, torrents, and mountain rivers. Developed model was applied to the artificial channel and a flood event in the Asungjun River section of the mountainous Yangyang Namdae River (South Korea). General simulation results showed that the developed model was in good agreement with the observed data.     

A Novel Formulation for Designing a Monitoring Strategy: Application to the Design of a River Quality Monitoring System

In this work, we propose a technique to automatically optimize the monitoring of any distributed indicator (concentration of a substance along a river, blood pressure of a patient over time, etc.) for which a reliable estimate is previously available. From a mathematical point of view, the problem is based on obtaining a reliable estimate of the chosen indicator (e.g., by numerical simulation), and then solving a multi-objective optimization problem (with mixed real and integer variables) whose solution must provide an efficient and satisfactory monitoring strategy. As an illustrative case, we show the steps to follow in order to implement that strategy when designing a system for monitoring water quality in a river. Finally, we present and analyze the results when applying the proposed technique to study a real case in the Neuse River (North Carolina, USA).     

A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling

Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.     

Application of Adjoint Approach to Oil Spill Problems

Adjoint method is applied to various oil spill problems. A three-dimensional model for describing the dispersion of a quasi-passive substance (a pollutant or a nutrient) and its adjoint model are considered in a limited sea region. Direct and adjoint estimates are used to get dual (equivalent) estimates of the mean concentration of the substance in important zones of the region. The role of dual estimates is illustrated with a few examples. They include such oil spill problems as the search of the most dangerous point of the oil tanker route, the oil dispersion with a climatic velocity, and the dependence of the oil concentration estimates on the oil spill rate. One more example is the application of optimal bioremediation strategy for cleaning a few zones polluted by oil. In this case, instead of oil, the model describes the dispersion of a nutrient released to marine environment. Balanced, unconditionally stable second-order finite-difference schemes based on the splitting method for the solution of the dispersion model and its adjoint are suggested. The main and adjoint difference schemes are compatible in the sense that at every fractional step of the splitting algorithm, the one-dimensional split operators of both schemes satisfy a discrete form of Lagrange identity. In the special unforced and non-dissipative case, each scheme has two conservation laws. Every split one-dimensional problem is solved by Thomas’ factorization method.     

Marine Reserve Design with Ocean Currents and Multiple Objectives

There is a growing tendency to consider marine reserves as a management and conservation tool. We investigate a spatial bio-economic model to determine fishery profits under conservation efforts. Rather than imposing a marine reserve on our model, we ask, “When and where should a marine reserve be implemented ?” For one-dimensional habitat, we determine conditions under which marine reserves emerge as a part of the optimal policy. Depending upon the size of the habitat, the optimal strategy is either to avoid fishing or to fish at maximum rate. The effect of ocean currents is analyzed through numerical simulations. We find that in the presence of strong currents, a marine reserve may become ineffective. If the currents are at low rate, a marine reserve could emerge as a variable management tool.