Flood frequency analysis based on simulated peak discharges

Flood frequency approaches vary from statistical methods, directly applied on the observed annual maximum flood series, to adopting rainfall–runoff simulation models that transform design rainfalls to flood discharges. Reliance on statistical flood frequency analysis depends on several factors such as the selected probability distribution function, estimation of the function parameters, possible outliers, and length of the observed flood series. Through adopting the simulation approach in this paper, watershed-average rainfalls of various occurrence probabilities were transformed into the corresponding peak discharges using a calibrated hydrological model. A Monte Carlo scheme was employed to consider the uncertainties involved in rainfall spatial patterns and antecedent soil moisture condition (AMC). For any given rainfall depth, realizations of rainfall spatial distribution and AMC conditions were entered as inputs to the model. Then, floods of different return periods were simulated by transforming rainfall to runoff. The approach was applied to Tangrah watershed in northeastern Iran. It was deduced that the spatial rainfall distribution and the AMCs exerted a varying influence on the peak discharge of different return periods. Comparing the results of the simulation approach with those of the statistical frequency analysis revealed that, for a given return period, flood quantiles based on the observed series were greater than the corresponding simulated discharges. It is also worthy to note that existence of outliers and the selection of the statistical distribution function has a major effect in increasing the differences between the results of the two approaches.     

Regional hydrologic mapping of flows in stream networks

River low flow (LF) is an important hydrological characteristic used in management of the quantity and quality of water resources. A common way to transpose at-station low flow quantiles to ungaged locations involves development of regional multivariate regression models. In site selection studies of water control facilities, need arises to automate the mapping of LF characteristics onto the stream network in a continuous manner. This would allow estimation of LF at any desired ungaged location. A mapping algorithm is introduced that determines the value of regression model's independent variables (input components) over the drainage area of each stream pixel and calculates the flow (output). The mapping algorithm relies mainly on the digital elevation model (DEM) and its derivatives, such as flow direction and flow accumulation. Furthermore, the contribution of each independent variable of the regional model to the total flow may be plotted to represent the flow profiles along the streams. A case study involving LF mapping in rivers of Gilan province, Iran, where LF quantiles are required for issuing water withdrawal permits as well as maintaining water quality standards, is also described in the paper. Overall, the maps and profiles of flow statistics in the region of interest provide convenient visualization and assessment tools for water resource and environmental engineers. The transposition algorithm may also be applied in regional mapping of other flow characteristics such as flood or average flows.     

基于科学知识图谱的干旱区城镇化文献计量分析

通过CiteSpace对1984—2020年中国知网(CNKI)中干旱区城镇化研究的2703篇文献进行分析,利用发文时间分布、作者与机构共现、关键词共现和突现词检测分析了干旱区城镇化研究的发文趋势、阶段划分、核心作者与机构网络、主题演变与历史热点.相关研究的发文量先升后降,可以划分出萌芽、快速、蓬勃、成熟4个时期.涌现...     

Mineral-Potential Mapping for MVT Deposits with Limited Data Sets Using Landsat Data and Geological Evidence in the Borden Basin, Northern Baffin Island, Nunavut, Canada

Posterior probabilities of occurrence for Zn-Pb Mississippi Valley Type (MVT) mineralization were calculated based on evidence maps derived from regional geology, Landsat-TM, RADARSAT-1, a digital elevation model and aeromagnetic data sets in the Borden Basin of northern Baffin Island, Canada. The vector representation of geological contacts and fault traces were refined according to their characteristics identified in Landsat-TM, RADARSAT-1, DEM, slope, aspect, and shaded relief data layers. Within the study area, there is an association between the occurrence of MVT mineralization and proximity to the contact of platformal carbonates and shale units of the adjacent geological formation. A spatial association also tends to exist between mineralization and proximity to E-W and NW-SE trending faults. The relationships of known MVT occurrences with the geological features were investigated by spatial statistical techniques to generate evidence maps. Supervised classification and filtering were applied to Landsat-TM data to divide the Society Cliffs Formation into major stratigraphic subunits. Because iron oxides have been observed at some of the MVT occurrences within the Borden Basin, Landsat-TM data band ratio (3/1) was calculated to highlight the potential presence of iron-oxides as another evidence map. Processed Landsat-TM data and other derived geological evidence maps provided useful indicators for identifying areas of potential MVT mineralization. Weights of evidence and logistic regression were used independently to integrate and generate posterior probability maps showing areas of potential mineralization based on all derived evidence maps. Results indicate that in spite of the lack of important data sets such as stream or lake sediment geochemistry, Landsat-TM data and regional geological data can be useful for MVT mineral-potential mapping.     

Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions

The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN.     

Assessment of geomorphological bank evolution of the alluvial threshold rivers based on entropy concept parameters

The complex stream bank profiles in alluvial channels and rivers that are formed after reaching equilibrium has been a popular topic of research for many geomorphologists and river engineers. The entropy theory has recently been successfully applied to this problem. However, the existing methods restrict the further application of the entropy parameter to determine the cross-section slope of the river banks. To solve this limitation, we introduce a novel approach in the extraction of the equation based on the calculation of the entropy parameter (λ) and the transverse slope of the bank profile at threshold channel conditions. The effects of different hydraulic and geometric parameters are evaluated on a variation of the entropy parameter. Sensitivity analysis on the parameters affecting the entropy parameter shows that the most effective parameter on the λ-slope multiplier is the maximum slope of the bank profile and the dimensionless lateral distance of the river banks.     

Short communication block lanczos method for dynamic analysis of structures

The simple Lanczos method presented in a recent paper by the writers, with application to single vector loads, is extended to include a more general dynamic loading represented as a linear combination of k vectors (load patterns). The result is a set of orthogonal vectors that is used to transform the equations of motion to a banded form, the half-bandwidth of which becomes k + 1. When k is small relative to the number of equations, this approach provides for a very efficient time-stepping solution.     

Evolution of the near-infrared in rich galaxy clusters

We present the K -band (2.2 μm) luminosity functions (LFs) of the X-ray-luminous clusters MS1054–0321 ( z  = 0.823), MS0451–0305 ( z  = 0.55), Abell 963 ( z  = 0.206), Abell 665 ( z  = 0.182) and Abell 1795 ( z  = 0.063) down to absolute magnitudes M _K  = −20. Our measurements probe fainter absolute magnitudes than do any previous studies of the near-infrared LFs of clusters. All the clusters are found to have similar LFs within the errors, when the galaxy populations are evolved to redshift z  = 0. It is known that the most massive bound systems in the Universe at all redshifts are X-ray-luminous clusters. Therefore, assuming that the clusters in our sample correspond to a single population seen at different redshifts, the results here imply that not only had the stars in present-day ellipticals in rich clusters formed by z  = 0.8, but that they existed in as luminous galaxies then as they do today.   Additionally, the clusters have K -band LFs which appear to be consistent with the K -band field LF in the range −24 <  M _K  < −22, although the uncertainties in both the field and cluster samples are large.     

Coupling snow accumulation and melt rate modules of monthly water balance models with the Jazim monthly water balance model

Several models for simulation of water balance processes in semi-arid mountainous basins were developed by coupling different modules of existing water balance models (WBM). Snow accumulation and snowmelt rate relationships extracted from the McCabe-Markstrom, Guo, Rao-Al Wagdany and WASMOD-M WBMs, originally developed for basins with humid climate, were coupled with the Jazim WBM, primarily developed for arid basins. Karaj Basin, central Iran, with snowy autumn–winter and dry summer periods, was selected to assess model performance. The model parameters were optimized using a genetic algorithm (GA). All coupled models performed better than the non-modified (original) WBMs in the study basin. The coupled Jazim–McCabe-Markstrom model provided the best performance in simulating low and high monthly flows. It estimated the snowmelt runoff values more accurately than other proposed coupled models because the linear relationships used in the snow module of the McCabe-Markstrom model are more compatible with snow variations in the Karaj Basin.