Detecting Land Subsidence Due to Groundwater Withdrawal in Aliabad Plain,Iran, Using ESA Sentinel-1 Satellite Data

Natural Resources Research - In the present study, ground subsidence in the Aliabad plain (central Iran) was investigated using differential synthetic aperture radar interferometry. The data used...     

3D Structural,Facies and Petrophysical Modeling of C Member of Six Hills Formation,Komombo Basin,Upper Egypt

Natural Resources Research - Two main reservoirs are producing in Komombo Basin: the first one belongs to the C Member of the Six Hills Formation, and the second belongs to the Albian/Cenomanian...     

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

巴丹吉林沙漠北缘拐子湖流沙下垫面近地层湍流强度和陆面过程特征

利用巴丹吉林沙漠北缘拐子湖流沙下垫面2013年7、10月和2014年1、4月的湍流通量资料，计算并分析了研究区近地层湍流强度，同时针对风速分量、温度、水汽和CO₂归一化标准差随稳定度的变化关系和总体输送系数等陆面过程特征进行分析。结果表明：（1）风速各分量的湍流强度均随风速的增加逐渐减小，风速处于2 m·s^-1以下时湍流发展最为旺盛。湍流强度主要由水平方向风速分量决定，垂直方向风速的作用较小，且近中性和不稳定层结利于湍流的发展。与其他地区相比，平坦且没有建筑物的沙漠地区，机械湍流较弱，湍流强度相应较小。（2）风速各分量的归一化标准差与稳定度（z/L）均满足1/3次方函数规律，其中垂直方向风速分量的拟合曲线方程较好。（3）动量输送系数C_d具有明显的夏季高、冬季低的变化状态且各月的日变化形态均呈夜间低、日间高的循环形态。热量输送系数C_h的不同月份日变化间并没有明显的排列次序，且日出日落前后具有明显的波动。不稳定层结时，C_d和C_h均随风速的增加逐渐减小；稳定层结时，C_d和C_h均随着风速的增加逐渐上升。     

塔克拉玛干荒漠-绿洲过渡带尘卷风活动特征——以肖塘为例

利用1992-2011年塔克拉玛干沙漠北缘荒漠-绿洲过渡带肖塘气象站的观测资料，分析了该地区尘卷风的年、月变化规律及其与气象因子的关系。结果表明：（1）1992-2011年尘卷风发生日数总体呈波动递减趋势；尘卷风主要发生在3-9月，占全年总日数的90.9%，其中4-7月占全年总日数的70%左右。（2）尘卷风月发生日数随月平均地表与1.5 m高处温差的增大而线性增加（r=0.875，P<0.01）。（3）尘卷风月发生日数随着月平均风速的增大而幂函数增加（r=0.89，P<0.01）。（4）尘卷风月发生日数随月平均相对湿度的增大而线性减少（r=-0.869，P<0.01）。     

Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds(Case study: the Navrood watershed in Iran)

In watersheds that have not sufficient meteorological and hydrometric data for simulating rainfall-runoff events, using geomorphologic and geomorphoclimatic characteristics of watershed is a conventional method for the simulation. A number of rainfall-runoff models utilize these characteristics such as Nash-IUH, Clark-IUH, Geomorphologic Instantaneous Unit Hydrograph(GIUH), Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH), GIUH-based Nash(GIUH-Nash) and GcIUH-based Clark(GcIUH-Clark). But all these models are not appropriate for mountainous watersheds. Therefore, the objective of this study is to select the best of them for the simulation. The procedure of this study is: a) selecting appropriate rainfall-runoff events for calibration and validation of six hybrid models, b) distinguishing the best model based on different performance criteria(Percentage Error in Volume(PEV); Percentage Error in Peak(PEP); Percentage Error in Time to Peak(PETP); Root Mean Square Error(RMSE) and Nash-Sutcliffe model efficiency coefficient(ENS)), c) Sensitivity analysis for determination of the most effective parameter at each model, d) Uncertainty determination of different parameters in each model and confirmation of the obtained results by application of the performance criteria. For application of this procedure, the Navrood watershed in the north of Iran as a mountainous watershed has been considered. The results showed that the ClarkIUH and GcIUH-Clark are suitable models for simulation of flood hydrographs, while other models cannot simulate flood hydrographs appropriately. The sensitivity analysis shows that the most sensitive parameters are the infiltration constant rate and time of concentration in the Clark-IUH model. Also, the most sensitive parameters include the infiltration constant rate and storage coefficient in the GcIUHClark model. The Clark-IUH and GcIUH-Clark models are more sensitive to their parameters. The Latin Hypercube Sampling(LHS) on Monte Carlo(MC) simulation method was used for evaluation of uncertainty of data in rainfall-runoff models. In this method 500 sets of data values are produced and then the peak discharge of flood hydrographs for each produced data set is simulated with rainfall-runoff models. The uncertainty of data changes the value of simulated peak discharge of flood hydrograph. The uncertainty analysis shows that the observed peak discharges of different rainfall-runoff events are within the range of values of simulated by the six hybrid rainfall-runoff models and IUH that inputs of these models were the produced data sets. The range of the produced peak discharge of flood hydrographs by the Clark-IUH and GcIUH-Clark models is wider than those of other models.     

Spatial analysis of water quality and its suitability in farming giant freshwater prawn (Macrobrachium rosenbergii) in Negeri Sembilan region,Peninsular Malaysia

Finding potential sites for resilient prawn production in the tropical environment that also prevents wastage of natural resources is not an easy task. The purpose of this study is to evaluate water quality suitability for prawn farming in Negeri Sembilan of Peninsular Malaysia based on Geographic Information System (GIS). To achieve this goal, numerous criteria including sources of water, water temperature, water pH, sources of pollution, salinity, soil texture and availability of phytoplankton criteria were considered for the modelling process. Analytic Hierarchy Process (AHP) technique was performed to standardize the criteria and the weighting process. The weighted overlay of indicators and results were accomplished by applying the Multi‐Criteria Decision Analysis (MCDA) method in GIS. It was indicated that the Negeri Sembilan area has potential for prawn farming. The results showed that about 25 per cent (163 056.93 ha) of the area was most suitable for prawn farming, about 58 per cent (384 656.88 ha) was considered moderately suitable, while 18 per cent (117 633.49 ha) was regarded as least suitable. The study concluded that the multi‐criteria decision analysis of water quality for prawn farming is vital for regional economic planning in the Negeri Sembilan area and also significant when establishing a model for aquaculture development.     

Factors associated with unnecessary requests for an ambulance by non-traumatic patients after the acute earthquake responding phase: a qualitative content analysis

The pre-hospital emergency staff played a key role in transferring the injured patients to health centers. Usually, they reported changes in their decisions on the transfer of non-traumatic patients to hospitals. So, this study was aimed to explore the reasons for unnecessarily requesting an ambulance by non-traumatic patients after the acute responding-to-earthquake phase. This study was a qualitative study that data were analyzed by content analysis approach. Participants were eleven pre-hospital emergency technicians. Data were collected by three sessions of focus group discussion. Data analysis was led to emergence of a main theme: “feeling urgency due to turmoil and uncertainty.” This theme illustrates the basic approach of the inhabitants of the earthquake-stricken region when unnecessarily requesting an ambulance. This theme was derived from two main categories of “turbulent and uncertain conditions” and “psychological turmoil.” The category of “turbulent and uncertain conditions” was comprised of three subcategories: “unreliable care,” “inadequate facilities” and “turbulent living conditions.” The category of “psychological turmoil” was comprised of three subcategories: “psychological turmoil in survivors,” “healthcare providers deciding under pressure” and “turmoil in providing psychological and psychiatric services.” Ambulance dispatch may be unnecessarily performed owing to turbulent and unsure conditions and psychological turmoil in earthquake-stricken people and pre-hospital emergency staff. Providing earthquake-stricken people with psycho-medical services in their place of residence can significantly reduce the workload of pre-hospital emergency staff and consequently that of hospital staff and therefore save time and treatment costs and increase the quality of health services provided for the injured.

     

Compressive and rarefactive double layers in non-uniform plasma with $$-nonextensive distributed electrons

Electrostatic solitary waves and double layers (DLs) formed by the coupled ion acoustic (IA) and drift waves have been investigated in non-uniform plasma using \(q\)-nonextensive distribution function for the electrons and assuming ions to be cold \(T_{i}< T_{e}\). It is found that both compressive and rarefactive nonlinear structures (solitary waves and DLs) are possible in such a system. The steeper gradients are supportive for compressive solitary (and double layers) and destructive for rarefactive ones. The \(q\)-nonextensivity parameter \(q\) and the magnitudes of gradient scale lengths of density and temperature have significant effects on the amplitude of the double layers (and double layers) as well as on the speed of these structures. This theoretical model is general which has been applied here to the \(F\)-region ionosphere for illustration.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.