ALBERT

Beschreibung: Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing.

Beschreibung: Land–sea contamination observed in Soil Moisture and Ocean Salinity (SMOS) brightness temperature images is found to have two main contributions: the floor error inherent of image reconstruction and a multiplicative error either in the antenna temperature or in the visibility samples measured by the correlator. The origin of this last one is traced down to SMOS calibration parameters to yield a simple correction scheme, which is validated against several geophysical scenarios. Autoconsistency rules in interferometric synthesis together with redundant and complementary calibration procedures provide a robust SMOS calibration scheme.

Beschreibung: This letter proposes a novel algorithm, which is based on the generalized method of moments (GMM), for the estimation and correction of phase errors induced in synthetic aperture radar (SAR) imagery. The GMM algorithm is used to replace the original phase-estimation kernel in the basic structure of the phase-gradient-autofocus algorithm. Since this novel algorithm does not require the observed signal to be a certain distribution model, it is able to estimate arbitrary phase errors. The GMM algorithm has the ability of estimating range-dependent phase errors, which makes it an efficient estimator. As a result, higher accuracy of the estimated phase errors and a better focused image can be achieved. Excellent results have been obtained in autofocusing and imaging experiments on real SAR data.

Beschreibung: Strong clutter reflections of terrain and marine surfaces obscure the contrast between the target-of-interest and clutter (terrain and marine surface reflections) in synthetic aperture radar (SAR) images and consequently hinder the efficiency of image interpretation and analysis. To overcome this problem, this letter proposes an efficient clutter suppression method in SAR images, which is named shedding irrelevant patterns (SIP). The essence is to construct a regression function that can suppress clutter and preserve the target patterns concurrently. We assume that the clutter is irrelevant to the target-of-interest and distinguishable in patterns in terms of image-pixel distribution and intensity (spatial information). Experimental results show the efficiency of the proposed method in both clutter suppression and target pattern preservation.

Beschreibung: A concern in hyperspectral image classification is the high number of required training samples. When traditional classifiers are applied, feature reduction (FR) techniques are the most common approaches to deal with this problem. Subspace-based classifiers, which are developed based on high-dimensional space characteristics, are another way to handle the high dimension of hyperspectral images. In this letter, a novel subspace-based classification approach is proposed and compared with basic and improved subspace-based classifiers. The proposed classifier is also compared with traditional classifiers that are accompanied by an FR technique and the well-known support vector machine classifier. Experimental results prove the efficiency of the proposed method, especially when a limited number of training samples are available. Furthermore, the proposed method has a very high level of automation and simplicity, as it has no parameters to be set.

Beschreibung: To improve the spatial density of measurement points of persistent-scatterer interferometry, distributed scatterer (DS) should be considered and processed. An important procedure in DS interferometry is the phase triangulation (PT). This letter introduces two modified PT algorithms (i.e., equal-weighted PT and coherence-weighted PT) and analyzes the mathematical relations between different published PT methods (i.e., the maximum-likelihood phase estimator, least squares estimator, and eigendecomposition-based phase estimators). The analysis shows that the above five PT methods share very similar mathematical forms with different weight values in the estimation procedure.

Beschreibung: Sparse representation-based classifier and its variants have been widely adopted for hyperspectral image (HSI) classification recently. However, sparse representation is unstable so that similar features might obtain significantly different sparse codes. Despite the instability, we find that the sparse codes follow a class-dependent distribution under the structured dictionary consisting of training samples from all classes. Based on this observation, a novel discriminative feature, sparse code histogram (SCH), is developed for HSI classification. By counting the SCH of each sample from the sparse codes of its spatial neighbors, we can statistically obtain the distribution pattern of sparse codes of the class to which the sample belongs, and then treat the SCH as a new feature for classification. To reduce the possible outliers among the neighbors, a shape-adaptive neighborhood extractor is also employed to enhance the stability of the histogram feature. Experimental results demonstrate that SCH enjoys a strong discriminative power, which can achieve notably better performance than several state-of-the-art methods for HSI classification with limited training samples.

Beschreibung: Several detection statistics have been proposed for detecting fine ground disturbances between two synthetic aperture radar (SAR) images, such as vehicle tracks. The standard method involves estimating a local correlation coefficient between images. Other methods have been proposed using various statistical hypothesis tests. One of these alternative methods is a generalized likelihood ratio test (GLRT), which compares a full-correlation image model to a no-correlation image model. In this letter, we expand the GLRT to polarimetric SAR data and derive the appropriate GLRT detection statistics. Additionally, we explore relaxing the equal variance/equal polarimetric covariance assumptions used in previous results and find improved performance on macroscopic scene changes.

Beschreibung: For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios.

Beschreibung: This letter proposes a signal processing method of passive bistatic radar (PBR) exploiting an uncooperative radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution. Two channels are needed in PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and a simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the uncooperative radar. The procedure of the proposed method is explained in detail, which is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target angle and bistatic range measurement successfully.

Beschreibung: In order to achieve 3-D imaging with an airborne down-looking linear-array synthetic aperture radar (LASAR), a uniform virtual antenna array may be obtained by aperture synthesis of the cross-track sparse multiple-input–multiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by errors in the virtual element position. In this letter, we investigate the effects of these errors on the forms and the degrees of image quality degradation by decomposing the error-related stochastic processes via an orthogonal transform based on discrete Legendre polynomials. It should be noted that these analyses are helpful for designing a LASAR system and providing a reference for specifying the requisite precision of measurement devices and calibration methods. Finally, we briefly consider the use of calibration methods to eliminate the effects of errors.

Beschreibung: In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violate this assumption. In response, we propose a general method by which a neighborhood graph used in SSL is learned using binary classifiers that are trained to predict whether a pair of pixels shares the same label. Working within the framework of semisupervised neural networks (SSNNs), we show that our approach improves on the performance of the SSNN on two HSI data sets.

Beschreibung: In this letter, new models for the spatial correlation of sea clutter texture and intensity are proposed as improved versions of current power law models or exponential decay model. The models for texture have three unknown parameters, and thus can be called triparametric models. The structure of the models is a weighted sum of two components, which can describe the decaying process of the correlation coefficient with spatial lags, as well as the periodic behavior due to the existence of transient coherent structures in sea clutter. Unknown parameters are optimized by the nonlinear least square fit method. Models for sea clutter intensity can be obtained through a linear transform for uncorrelated speckle based on the compound-Gaussian representation of sea clutter. The proposed models are validated and compared with current models using S- and C-band measured sea clutter data. Analysis results indicate the effectiveness of the proposed models in that they can describe the behavior of spatial correlation coefficients with higher accuracy.

Beschreibung: Terrestrial laser scanning (TLS) has become a popular tool for acquiring source data points which can be used to construct digital elevation models (DEMs) for a wide number of applications. A TLS point cloud often has a very fine spatial resolution, which can represent well the spatial variation of a terrain surface. However, the uncertainty in DEMs created from this relatively new type of source data is not well understood, which forms the focus of this letter. TLS survey data representing four terrain surfaces of different characteristics were used to explore the effects of surface complexity and typical TLS data density (in terms of data point spacing) on DEM accuracy. The spatial variation in TLS data can be decomposed into parts corresponding to the signal of spatial variation (of terrain surfaces) and noise due to measurement error. We found a linear relation between the DEM error and the typical TLS data spacings considered (30–100 mm) which arises as a function of the interpolation error, and a constant contribution from the propagated data noise. This letter quantifies these components for each of the four surfaces considered and shows that, for the interpolation method considered here, higher density sampling would not be beneficial.

Beschreibung: A novel way to estimate the live fuel moisture content (LFMC) was explored from the ratio of canopy water content (CWC) and foliage dry biomass (FDB). The CWC was estimated using the PROSAIL (PROSPECT + SAIL) radiative transfer model from the Landsat 8 product. A weak constraint 4-D variational data assimilation method was employed to assimilate the temporally estimated leaf area index into a soil-water-atmosphere-plant (SWAP) model for optimizing the model control variables. Then, the SWAP model was reinitialized with this optimum set of control variables, and better prediction of FDB was obtained. Results showed that a high accuracy level was achieved for the estimated CWC ( $R^{2}=0.91$ , $mbox{RMSE}=84.74 mbox{g/m}^2$ ) and FDB ( $R^2=0.88$ , $mbox{RMSE}=48.54 mbox{g/m} ^2$ ) when compared with in situ measured values. However, the accuracy level of estimated LFMC was poor ( $R^2=0.59$ , $mbox{RMSE} =30.85%$ ) . Further analyses find that the estimated LFMC is reliable for low LFMC but challenged for high LFMC, which indicates that the presented method still makes sense to the assessment of wildfire risk since the wildfire generally occurs when the vegetation is in low LFMC condition.

Beschreibung: In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.

Beschreibung: Automatic urban area detection in remote sensing images is an important application in the field of earth observation. Most of the existing methods employ feature classifiers and thereby contain a data training process. Moreover, some methods cannot detect urban areas in complex scenes accurately. This letter proposes an automatic urban area detection method that uses multiple features that have different resolutions. First, a downsampled low-resolution image is used to segment the candidate area. After the corner points of the urban area are extracted, a weighted Gaussian voting matrix technique is employed to integrate the corner points into the candidate area. Then, the edge features and homogeneous region are extracted by using the original high-resolution image. Using these results as the input, the processes of guided filtering and contrast enhancement can finally detect accurately the urban areas. This method combines multiple features, such as corner, edge, and regional characteristics, to detect the urban areas. The experimental results show that the proposed method has better detection accuracy for urban areas than the existing algorithms.

Beschreibung: In marine sciences, time series are often nonlinear and nonstationary. Adequate and specific methods are needed to analyze such series. In this letter, an application of the empirical mode decomposition method (EMD) associated to the Hilbert spectral analysis (HSA) is presented. Furthermore, EMD-based time-dependent intrinsic correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series. Four temperature time series obtained from automatic measurements in nearshore waters of the Réunion island are considered, recorded every 10 min from July 2011 to January 2012. The application of the EMD on these series and the estimation of their power spectra using the HSA are illustrated. The authors identify low-frequency tidal waves and display the pattern of correlations at different scales and different locations. By TDIC analysis, it was concluded that the high-frequency modes have small correlation, whereas the trends are perfectly correlated.

Beschreibung: Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information, e.g., road database and accurate target size. We present a method that can detect the vehicles on a 21-MPixel original frame image without accurate scale information within seconds on a laptop single threaded. In addition to the bounding box of the vehicles, we extract also orientation and type (car/truck) information. First, we apply a fast binary detector using integral channel features in a soft-cascade structure. In the next step, we apply a multiclass classifier on the output of the binary detector, which gives the orientation and type of the vehicles. We evaluate our method on a challenging data set of original aerial images over Munich and a data set captured from an unmanned aerial vehicle (UAV).

Beschreibung: We compare five slope correction methods developed by Walter et al. , Montes et al. , Schleppi et al. , España et al. , and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

Beschreibung: In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

Beschreibung: Accurately mapping forest carbon density by combining sample plots and remotely sensed images has become popular because this method provides spatially explicit estimates. However, mixed pixels often impede the improvement of the estimation. In this letter, regression modeling and spectral unmixing analysis were integrated to improve the estimation of forest carbon density for the You County of Hunan, China, using Landsat Thematic Mapper images. Linear spectral unmixing with and without a constraint (LSUWC and LSUWOC) and nonlinear spectral unmixing (NSU) were compared to derive the fractions of five endmembers, particularly forests. Stepwise regression, logistic regression, and polynomial regression (PR) with and without the forest fraction used as an independent variable and the product of the forest fraction image and the map from the best model without the forest fraction were compared. The models were developed using 56 sample plots, and their results were validated using 26 test plots. The decomposition of mixed pixels was assessed using higher spatial resolution SPOT images and a corresponding land cover map. The results showed that 1) LSUWC more accurately estimated the endmember fractions than LSUWOC and NSU, 2) PR had the greatest estimation accuracy of forest carbon, and 3) combining regression modeling and spectral unmixing increased the estimation accuracy by 31%–39%, and introducing the forest fraction into the regressions performed better than the product of forest fraction image and the results from PR without the fraction. This implied that the integrations provided great potential in reducing the impacts of mixed pixels in mapping forest carbon.

Beschreibung: This letter proposes a multiresolution technique to address the high computational cost in remote sensing image registration. The scale-invariant feature transform is applied to detect keypoints and descriptors, and then, global information combined with descriptors is utilized to establish keypoint mappings. Keypoints are first classified according to their octaves. Then, in the lowest resolution, the keypoints of the largest octave are mapped with descriptors and the global information, giving an initial affine transformation $T_0$ . In the next octave, the keypoints of the second largest octave are mapped by employing $T_0$ to narrow the space of matching keypoints. By this means, the process of establishing keypoint correspondences is conducted from one resolution (octave) to the next as the obtained transformation gets finer until we get to the highest resolution. Due to the high computational expense of computing global information, the proposed technique is important for aligning large-size remote sensing imagery. Experimental results show that the proposed method can achieve a comparable registration accuracy but with a less computational cost than directly building keypoint mappings on images of large size.

Beschreibung: In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial–spectral HSI classification for the first time in the literature.

Beschreibung: Compared with airborne laser scanning, terrestrial laser scanning (TLS) offers ground-based point cloud data of trees and provides greater potential to accurately estimate tree and stand parameters. However, there is a lack of effective methods to accurately identify locations of individual trees from TLS point cloud data. It is also unknown whether the estimation accuracy of the parameters, including tree height (H), diameter at breast height (DBH), and so on, using TLS can meet the requirement of forest management and planning. In this letter, a novel method to effectively process point cloud data and further determine the locations of individual trees in a stand based on the central coordinates of point cloud data on a defined grid according to the largest DBH was developed. Moreover, a point-cloud-data-based convex hull algorithm and the cylinder method were, respectively, used to estimate DBH and H of individual trees. This study was conducted in a pure Chinese fir plantation of 45 trees located in Huang-Feng-Qiao forest farm, You County of Hunan, China. The comparison of the estimated and observed values showed that the obtained tree locations had errors of less than 20 cm, and the relative root mean square errors for the estimates of both DBH and H were less than 5%. This implies that TLS is very promising for the retrieval of tree and stand parameters in forest stands. For the applications of these methods to mixed forests with a structure of multilayer canopies, further examination is needed.

Beschreibung: In this letter, a novel algorithm for attitude measurement based on a 3-D electromagnetic model (3-D em-model) is proposed. The 3-D em-model is established offline based on the geometric structure of the target, and it can be used to predict the scattering features at different target attitudes. In order to measure the attitude of the air target, we design a bistatic step frequency radar system. The directions of the two radars' lines of sight (LOSs) relative to the target are acquired by matching the high-resolution range profiles (HRRPs) from the target echoes to the HRRPs generated from the 3-D em-model. Since the directions of two radars' LOSs relative to the Earth are already known, the absolute attitude of the target can be acquired. The innovative contributions of this letter are as follows: 1) A comprehensive theoretical analysis of air target attitude measurement based on its own 3-D em-model is proposed; 2) the method can be applied to different kinds of air targets such as aircraft, satellite, missile, etc.; 3) the proposed attitude measurement method does not require target motion model in advance; and 4) the proposed algorithm can be applied to any kind of step frequency waveforms. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in the chamber verify the validity of the method.

Beschreibung: The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K.

Beschreibung: The daily rainfall-runoff relationship in an experimental watershed was modeled using a statistical method and an artificial neural network method. The estimations were examined and a performance evaluation was done. It was seen that the ANN method, FFBP (Feed Forward Back Propagation), provided closer flow estimations reproducing the shape of the observed hydrograph more realistic. The superiority of FFBP was reflected in the performance evaluation criteria. The extreme flows, i.e., high and low flows, were relatively better approximated by FFBP indicating its promise as a useful tool for hydrologic studies such as flood modeling. The Rational Method was also used, as a conventional tool, to predict the maximum discharge for selected return periods. It was found to be realistic for the forested watershed under consideration when the C coefficient was taken as 0.20 for the 10-year period.

Beschreibung: A procedure for optimal design of District Metered Areas (DMAs) within a water distribution network based on a multilevel balancing and refinement algorithm to partition the network and determine the optimal meter positions, coupled with a pressure driven hydraulic simulator to quantify the hydraulic performance of the districtualized system, is presented. Unlike other procedures based on graph partitioning techniques proposed in the scientific literature, the two main issues involved in the design of the DMAs, namely a) how to partition the nodes into the required number of districts, and b) which pipes linking districts to leave open, and fitted with an assigned number of flow meters, and which to close, are simultaneously resolved. The application of this procedure to a real case shows that this approach provides design solutions well adapted to different numbers of measuring points, yielding superior performance indicator values to similar procedures reported in the literature and used here for comparative purposes.

Beschreibung: Sustainable groundwater management requires approaches to assess the influence of climate and management actions on the evolution of groundwater systems. Traditional approaches that apply continuity to assess groundwater sustainability fail to capture the spatial variability of aquifer responses. To address this gap, our study evaluates groundwater elevation data from the Coachella Valley, California, within a groundwater sustainability framework given the adoption of integrative management strategies in the valley. Our study details an innovative approach employing traditional statistical methods to improve understanding of aquifer responses. In this analysis, we evaluate trends at individual groundwater observation wells and regional groundwater behaviors using field significance. Regional elevation trends identified no significant trends during periods of intense groundwater replenishment, active since 1973, despite spatial variability in individual well trends. Our results illustrate the spatially limited effects of groundwater replenishment occur against a setting of long-term groundwater depletion, raising concerns over the definition of sustainable groundwater management in aquifer systems employing integrative management strategies.

Beschreibung: Extreme rainfall events are among the natural hazards with catastrophic impacts on human society. Trend analysis is important to understand the effects of climate change and variability on extreme rainfalls. In this study, extreme rainfall (i.e., annual maximums) trends were investigated in Victoria (Australia) using data from 23 stations for storm durations of 10 and 30 min, and 1, 3, 6, 12, 24 and 48 h. The Mann-Kendal and Spearman’s Rho tests were employed for detection of temporal trends. Moreover, the spatial variability of extreme rainfall trends was investigated through interpolation of Theil-Sen’s estimator over Victoria. In general, increasing extreme rainfall trends were detected for short storm durations (i.e., 10 and 30 min, and 1 and 3 h), whereas decreasing extreme rainfall trends were found for long storm durations (i.e., 6, 12, 24 and 48 h). Increasing trends for short storm durations were mostly statistically significant, while decreasing trends for long storm durations were statistically insignificant. Trend analysis with respect to the four regions (i.e., Western, Northern, Central and Gippsland) in Victoria showed that increasing trends were present in general in the Northern and east Central Region, whereas decreasing trends were detected in the Western and west Gippsland Regions.

Beschreibung: To make full use of water captured by reservoirs in flood seasons, methods such as forecast-based reservoir flood control (i.e., reservoir operation during flood periods based on precipitation or inflow forecasts) have been developed in China in the past few decades. The success of forecast-based reservoir flood control depends heavily on the precision of the precipitation or inflow forecasts. This study analyzes the sources of uncertainty and quantifies it in the process of reservoir flood control based on forecasts. First, flood inflow series is regarded as a random process with the forecasted flood inflows as its expectation process; the flood inflow error series, which is the difference between the observed inflow process and the forecasted inflow process, is obtained according to the statistical characteristics of the flood forecasts. Second, reservoir flood routing simulations are carried out using the Monte Carlo method with the flood inflow error series as an input to obtain output water level and discharge series. Finally, the frequency distributions of the maximum water level (MWL) reached during the passage of floods are used to evaluate the uncertainties and the corresponding risks associated with forecast-based flood control. These uncertainties and risks are what the operators of the reservoirs care about the most.

Beschreibung: This paper introduces a reservoir design optimization model adapted to incorporate performance norms so that the active storage capacity can be determined assuming that failures may occur during the reservoir operation. The model is able to find the optimal reservoir capacity admitting either a predefined minimum number of failure periods or a maximum failure magnitude, or both. It is formulated as a mixed integer linear program that properly manages reservoir spills and includes evaporation losses. The procedure performs effectively for an example problem and Monte Carlo simulations and shows that lower accepted reliabilities and higher accepted vulnerabilities require less active storage.

Beschreibung: How to produce the difference data of the two temporal images is a crucial factor in image change detection. In this letter, we propose multicontextual mutual information data (MMID) based on the bivariate Gaussian distribution (BGD) for synthetic aperture radar (SAR) image change detection and illustrate their superiorities over the classical difference data. MMID, which are an improved form of image spatial mutual information, are constructed based on the quadrilateral Markov random field (QMRF) and can be factored into the linear combination of the entropies. Then to adapt MMID to the change detection, we construct the 2-D entropies based on the BGD. In this way, MMID are able to capture the intertemporal statistical dependence of the two temporal images and thus can be taken as the feature-level difference data rather than the pixel-level data. The maximum-likelihood method, the automatic threshold method, and the Markov random field method are performed on the MMID of the real two temporal SAR images for the change detection. Experimental results demonstrate the superiorities of MMID over the traditional difference data.

Beschreibung: Spectral unmixing has been a popular technique for analyzing remotely sensed hyperspectral images. The goal of unmixing is to find a collection of pure spectral constituents (called endmembers ) that can explain each (possibly mixed) pixel of the scene as a combination of endmembers, weighted by their coverage fractions in the pixel or abundances . Over the last years, many algorithms have been presented to address the three main parts of the spectral unmixing chain: 1) estimation of the number of endmembers; 2) identification of the endmember signatures; and 3) estimation of the per-pixel fractional abundances. However, to date, there is no standardized tool that integrates these algorithms in a unified framework. In this letter, we present HyperMix, an open-source tool for spectral unmixing that integrates different approaches for spectral unmixing and allows building unmixing chains in graphical fashion, so that the end-user can define one or several spectral unmixing chains in fully configurable mode. HyperMix provides efficient implementations of most of the algorithms used for spectral unmixing, so that the tool automatically recognizes if the computer has a graphics processing unit (GPU) available and optimizes the execution of these algorithms in the GPU. This allows for the execution of spectral unmixing chains on large hyperspectral scenes in computationally efficient fashion. The tool is available online from http://hypercomphypermix.blogspot.com.es and has been validated with real hyperspectral scenes, providing state-of-the-art unmixing results.

Beschreibung: Seismic signals are nonlinear, and the seismic state-space model can be described as a nonlinear system. The particle filter (PF) method, as an effective method for estimating the state of a nonlinear system, can be applied to deal with seismic random noise attenuation. However, PF suffers from sample impoverishment caused by resampling, which results in serious loss of valid seismic information and leads to inaccurate representation of the reflected signal. To address the impoverishment issue and to further improve the particle quality, we propose a novel method to suppress seismic random noise—the adaptive fission particle filter (AFPF). In AFPF, all the particles undergo a fission process and produce “offspring” particles to maintain particle diversity. To implement the adaptation and to monitor the degree of fission, we apply a fission factor, which takes into account weights that indicate the quality of the particles. This leads to significant improvements in the particle quality, i.e., the proportion of highly weighted particles is increased. The effective seismic information provided by the resulting particles reproduces the true signal more reliably, reducing the bias of PF. In addition, we establish a dynamic state-space model suitable for seismic signals. Experimental results on synthetic records and field data illustrate the superior performance of AFPF in noise attenuation and reflected signal preservation compared with the PF.

Beschreibung: Increased groundwater accessibility resulting from the expansion of deep and shallow tube wells helped Bangladesh attain near self-sufficiency in rice, with national output increasing over 15 million tons in the last two decades. However, problems associated with the excessive exploitation of groundwater notably declining water tables, deteriorating water quality, increasing energy costs and carbon emissions are threatening the sustainability of Bangladesh’s groundwater irrigated economy. The forefront challenge, therefore, is to shift the focus from development to management of this precious resource. To ease out pressure on groundwater resources, attention must be diverted to further develop surface water resources. In addition to increasing supplies, water demand also need to be curtailed by increasing water use efficiency through the adoption of water conserving practices such as reduced tillage, raised bed planting, and the right choices of crops. Decreasing water availability both in terms of quantity and quality suggest that the unchecked expansion of dry season boro rice cultivation may not be a long-term option for Bangladesh. Therefore less thirsty wheat and maize crops may be promoted as feasible alternatives to boro . In addition to technical solutions, strong linkage between different institutions will be needed to evaluation strategic options and effective implementation of national policies for the management of groundwater resources.

Beschreibung: The primary objective of the TanDEM-X mission is the generation of a global high-precision digital elevation model (DEM) by using synthetic aperture radar interferometry. This letter presents the developed strategy for estimating the relative height error of the TanDEM-X DEM on a global scale. The mosaicking process of the final DEM combines all acquisitions at full resolution and is expected to be finished by late 2016. On the other hand, global mosaics can be generated starting from quicklook images already available for each single input data take. These downsized mosaics are operationally used to analyze the performance improvement that can be achieved by combining multiple acquisitions over the same ground areas and are a powerful mean for optimizing further acquisition planning. This letter reports the expected global performance of the final TanDEM-X product in advance of the full-resolution DEM. Knowledge of the global status of the TanDEM-X DEM relative height error is fundamental for optimizing the acquisition strategy and, therefore, the final performance and represents a valuable input for the scientific community as well as for selecting suitable areas for further interferometric experiments on a global scale.

Beschreibung: Support vector machines (SVMs) have been applied to land cover classification, and a number of studies have demonstrated their ability to increase classification accuracy. The high correlation between the data set and SVM training model parameters indicates the high performance of the classification model. To improve the correlation, research has focused on the integration of SVMs and other algorithms for data set selection and SVM training model parameter estimation. This letter proposes a novel method, based on a particle filter (PF), of estimating SVM training model parameters according to an observation system. By treating the SVM training function as the observation system of the PF, the new method automatically updates the SVM training model parameters to values that are more appropriate for the data set and can provide a better classification model than can the original model, wherein the parameters are set by trial and error. Various experiments were conducted using Radarsat-2 synthetic aperture radar data from the 2011 Thailand flood. The proposed method provides superior performance and a more accurate analysis compared with the standard SVM.

Beschreibung: Maize is a widely planted crop in China and in other areas of the world and plays an important role in grain production. Monitoring the growth status of maize using remote sensing technology is an important component of precision agriculture and height, as a crucial growth indicator for maize, can be retrieved from light detection and ranging (LIDAR) data. However, height extraction for crops, such as maize using airborne laser scanning point clouds results in a great number of uncertainties and challenges. Here, airborne full-waveform LIDAR data were used to extract maize height. In the first step, a workflow was designed based on the Gold deconvolution algorithm combined with a basic data process technique. The method was then tested and was determined to be effective for capturing the portion of the waveform interacting with the tops of vegetation, characterized by lower amplitude stemming from the ground. Therefore, the number of second returns from point clouds was dramatically increased. During the experiment, the number of point clouds increased nearly 50% for three of the four maize plots, as compared with the original point clouds. Compared with the commonly used Gaussian fitting algorithm, the deconvolution algorithm had the advantage of extracting an accurate position for overlapping weak signals. The height percentiles indicated that the original and Gaussian decomposition derived point clouds data underestimated and deconvolution algorithm can accurately reflect the true height of maize, particularly for the 75% and 95% height percentiles.

Beschreibung: The Moderate Resolution Imaging Spectroradiometer (MODIS) is an important data set in global burned-area mapping. The MODIS global burned-area product has a coarse spatial resolution at approximately 500 m, which often introduces errors to the mapped burned areas. In this letter, a novel subpixel mapping (SPM) approach was proposed to produce burned-area maps at the fine spatial resolution similar to Landsat imagery, by exploring the spectral and spatial information provided by the second and fifth bands of MODIS. The proposed SPM approach aims to refine the estimate of burned areas, which have been detected by the MODIS global burned-area product. The performance of the proposed SPM approach was assessed with an experiment area containing six burned areas, by comparing with the MODIS burned-area product MCD45. The result shows that the average omission error decreased from 52.26% for MCD45 to 16.74% for SPM, and the average commission error decreased from 21.76% for MCD45 to 12.54% for SPM. The kappa value increased from 0.5583 for MCD45 to 0.8756 for SPM, indicating that the proposed SPM approach is effective in reducing the influence of the coarse spatial resolution of MODIS imagery in mapping a burned area and refining existing global burned-area products.

Beschreibung: Radiometric distortions caused by rugged terrain make the classification of forest types from satellite imagery a challenge. Various band-specific topographic normalization models are expected to eliminate or reduce these effects. The quality of these models also depends on the approach to estimate empirical parameters. Generally, a global estimation of these parameters from a whole satellite image is simple, but it may tend to overcorrection, particularly for larger areas. A land-cover-specific method usually performs better, but it requires obtaining a priori land classification, which presents another challenge in many cases. Empirical parameters can be directly estimated from local pixels in a given window. In this letter, we propose and evaluate a central-pixel-based parameter estimation method for topographic normalization using local window pixels. We tested the method with Landsat 8 imagery and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) in very rough terrain with diverse forest types. Visual comparison and statistical analyses showed that the proposed method performed better at a range of window sizes compared with an uncorrected image or with a global parameter estimation approach. The intraclass spectral variability of each forest type has been reduced significantly, and it can yield higher accuracy of forest type classification. The proposed method does not require the a priori knowledge of land covers. Its simplicity and robustness suggest that this method has the potential to be a standard preprocessing approach for optical satellite imagery, particularly for rough terrain.

Beschreibung: Many methods have been developed to detect damaged buildings due to earthquake. However, little attention has been paid to analyze slightly affected buildings. In this letter, an unsupervised method is presented to detect earthquake-triggered “ roof-holes ” on rural houses from unmanned aerial vehicle (UAV) images. First, both orthomosaic and gradient images are generated from a set of UAV images. Then, a modified Chinese restaurant franchise model is used to learn an unsupervised model of the geo-object classes in the area by fusing both oversegmented orthomosaic and gradient images. Finally, “roof-holes” on rural houses are detected using the learned model. The performance of the proposed method is evaluated in terms of both qualitative and quantitative indexes.

Beschreibung: Automatic target generation process (ATGP) has been used in a wide range of applications in hyperspectral image analysis. It performs a sequence of orthogonal subspace projections to extract potential targets of interest. This letter presents a recursive version of the ATGP, which is referred to as the recursive ATGP (RATGP) and has three advantages over the ATGP as follows: 1) there is no need of inverting a matrix as the ATGP does for finding each new target; 2) there is a significant reduction in the computational complexity in the hardware design due to its recursive structure; and 3) there is an automatic stopping rule that can be derived by the Neyman–Pearson detection theory to terminate the algorithm.

Beschreibung: In this letter, we propose a novel automatic algorithm for road extraction from remote sensing images. The algorithm includes low- and high-level processing. In the low-level processing, we determine a normalized second derivative map of road profiles of a generalized bar shape, which is width invariant and contrast proportional, and accordingly obtain initial road center pixels. In the high-level processing, using the map and initial center pixels, we initially determine road segments. The segments are then locally refined using their orientation randomness and length-to-width ratio and further refined via global graph-cut optimization. A final road network is thereby extracted in a robust manner. Experimental results demonstrate that the proposed algorithm provides noticeably more robust and higher road extraction performance in various images compared with the existing algorithms.

Beschreibung: Remote sensing images often need to be coded and/or transmitted with constrained computational resources. Among other features, such images commonly have high spatial, spectral, and bit-depth resolution, which may render difficult their handling. This letter introduces an embedded quantization scheme based on two-step scalar deadzone quantization (2SDQ) that enhances the quality of transmitted images when coded with a constrained number of bits. The proposed scheme is devised for use in JPEG2000. It is named cell-based 2SDQ since it uses cells, i.e., small sets of wavelet coefficients within the codeblocks defined by JPEG2000. Cells permit a finer discrimination of coefficients in which to apply the proposed quantizer. Experimental results indicate that the proposed scheme is especially beneficial for high bit-depth hyperspectral images.

Beschreibung: High suspended solid (SS) concentrations in coastal waters are aesthetically undesirable, and adversely affect fisheries and coastal ecosystems. Environmental agencies usually require frequent measurements of SS over coastal regions at a spatially detailed level for water quality assessment and control. To develop a method for SS estimation in the complex coastal waters of Hong Kong, an archive of 57 Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and HJ-1 A/B Charged Couple Device (CCD) images over a 13-year period from January 2000 to December 2012 was used. Atmospherically corrected Landsat TM/ETM+ and HJ-1 A/B CCD bands 1–4 along with 240 in situ field samples of SS concentration collected within 2 h of image acquisition, were used to develop and validate regression models over a wide range of SS concentrations from 0.5–56.0 mg/L. The best representation of actual SS concentrations was given by the log-transformed combination of Band 2 (Green, 0.52–0.60 $mumbox{m}$ ) and Band 3 (Red, 0.63–0.69 $mumbox{m} $ ), with correlation coefficient (R) of 0.85, root-mean-square error of 2.60 mg/L and mean absolute error of 2.04 mg/L. This is attributed to the sensitivity of SS to green and red wavelengths specific to the characteristic refractive index and grain size of SS found in Hong Kong waters. This letter is considered more robust than previous studies, due to the much larger number of images and in situ samples used for model development and validation, as well as the different times of year and wide range of SS concentrations investigated.

Beschreibung: The quantitative estimation of the fractional cover of carbonate rock (CR) is critical for natural resource management and ecological conservation in karst areas. Based on the analysis of spectral properties of CR together with other land cover types, we proposed two CR indices (CRIs) and established the model that represents the relationships between the CRIs and the fractional cover of CR. Then, the fractional cover of CR was estimated by using the developed model. Experimental results on Landsat-8 Operational Land Imager images acquired at Southwestern China demonstrated the effectiveness of the developed model. Compared with other indices, the proposed CRIs show the highest correlations with the fractional cover of CR.

Beschreibung: Since 2001, a rising water table occurred in the eastern part of the city of Naples (Italy), leading to flooding of the foundations of many buildings and of the underground infrastructures (garages, subway tracks, etc.). This resulted from the reduction in the exploitation of water for industrial use and drinking water in the central part of the groundwater body of the eastern plain of Naples: the groundwater withdrawal decreased from up to 3 m 3 /s in 1990 to a few hundred litres per second in 1998. The variation of piezometric levels led to flooding of foundations of many buildings and of underground infrastructures, this is a big amount of water lost by the aquifer system and that should be evaluated. In order to assess aquifer hydraulic features and the groundwater quantitative status, a 3D hydrostratigraphic model reconstruction was developed to support the groundwater flow modeling and the evaluation of the groundwater balance. The 3D hydrostratigraphic model reconstructed the subsoil of the study area: layers of tuff and lenses of peat, which play an important hydrogeologic role as aquitard and aquiclude, are faithfully defined. Indeed, the model allows the depiction of the horizontal extension and of the thickness of each hydrofacies, even if strongly heteropic. The groundwater flow model was defined using the 3D hydrostratigraphic model in the construction of the geometry and assignment of hydrodynamic parameters. The calibration and sensitivity analysis prove the goodness of the assigned hydraulic conductivity values. The model, together with a detailed computation of the paved areas with reduced infiltration, evaluated the recharge in 0.101 m 3 /s. The model calibration assessed an evapotranspiration rate lower than the prefixed value, probably due to the absence of vegetation that restricts the phenomenon almost exclusively to evaporation. Moreover, the calibration of the model confirmed: − the assumed groundwater flow towards the sea in 0.313 m 3 /s; − the hypothesized amount of the subsurface inflow from upstream in 0.504 m 3 /s. A considerable amount of water lost by the aquifer system, about 0.315 m 3 /s, could be used by Municipality for different public uses.

Beschreibung: Structural and operational management methods are used to meet water demands in watersheds around the world. Most river systems are affected by reservoirs, dams, or other engineering structures, and decisions regarding their construction and operation are made in advance of knowing what water demands will be. Numerical models are used to predict future water needs and evaluate the effectiveness of water management strategies. It is important to consider a variety of management methods and future environmental conditions to ensure future demands can be met. In this work, a coupled surface water operations and hydrologic model of the Lower Republican River Basin in portions of Nebraska and Kansas, USA is used to evaluate the ability of several water management strategies, including structural and operational, to meet future demands of a water-stressed agricultural basin under a variety of future climate scenarios. Simulations indicate recent administrative and operational changes to the distribution of water between Nebraska and Kansas have significantly decreased water shortages for irrigation districts in Kansas and will continue to do so. Simulations also indicate that structural alternative of reservoir expansion is most effective at minimizing shortages to demands under a repeat of historical climate conditions. However, an operational alternative of increasing water supplies for Kansas' exclusive use, such as those historically purchased under the Warren Act (US Code 43 Section 523–524), is most effective at minimizing shortages to demands under a hotter and drier climate, demonstrating how optimal water management strategies can vary significantly depending upon climate scenario.

Beschreibung: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Beschreibung: In this article we propose a new method - the Most Probable Precipitation Method (MPPM) - for estimating the precipitation at regional scale. Comparisons with the Thiessen polygons methods (TPM), inverse distance weighting interpolation (IDW) and ordinary kriging (OK) on annual, monthly, seasonal and annual maximum monthly precipitation are provided. In all cases MPPM performs better than IDW and OK, and in most of them, better than TPM.

Beschreibung: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Beschreibung: As various technologies are used for water and wastewater treatment, a global strategy with the objective of comparing and containing the price of water in the future is required. A study was carried out for this reason on production cost component of the price of water. This paper extends the concept of previously reported Water Price Index (WPI) as a metric for the cost of water production that can be adapted to water and wastewater treatment plants to evaluate their performance. Equations that define the contribution of capital and operational expenditure to the WPI are presented. Case studies from Eraring Power Station, Marquette Water Filtration Plant, Perth Desalination II and a theoretically modelled plant demonstrated the applicability of WPI and the benefit it brings to water practitioners.

Beschreibung: Forecasting flow in rivers has special significance in surface water management, especially in agricultural planning and risk reduction of floods and droughts. In recent years, studies have shown the superiority of forecasting models based on artificial intelligence, using artificial neural networks (ANN) and genetic programming (GP), over time-series models. In this paper, continuous and discrete historical flow records are used for monthly river flow forecasting of the Saeed-Abad river in East Azarbaijan province, Iran. Auto regressive moving average with exogenous inputs (ARMAX), ANN, and GP models are used in both continuous and discrete flow series. For both flow series, results of the ARMAX, ANN, and GP models are then compared and results of each method are evaluated relative to each other. Two quantitative standard statistical performance evaluation measures, coefficient of determination (R 2 ) and root mean square error (RMSE), are employed to evaluate the performance of the aforementioned models. Results show that for the two methods, the GP model is more effective with respect to accuracy than ARMAX and ANN. For continuous time-series forecasting, GP is a more precise model ( R 2  = 0.7 and RMSE  = 0.172) than either ANN ( R 2  = 0.627 and RMSE  = 0.193) or ARMAX ( R 2  = 0.595 and RMSE  = 0.243). For discrete time-series forecasting, the superiority of the GP model is evident in most months. For monthly flow forecasting, results indicate that the discrete time-series forecasting method is superior to the continuous time-series forecasting method.

Beschreibung: The variability of the relationship between precipitation and runoff is of vital importance to study the characteristics of regional water cycle and water resource management and planning. In this study, in order to explore the relationship between precipitation and runoff of the different hydrological patterns, the analysis of bivariate precipitation and runoff distributions and the simultaneous occurrence probability was analyzed by employing the Archimedean copula, based on the monthly runoff and precipitation data during 1960 ~ 2000 in the upper Huai river basin, China. The results indicated that: (I) the study region could be classified into four hydrological patterns, namely the Huai river valley zone, Huaibei plain zone, Huainan mountain zone and Huaibei mountain zone, with Xixian, Zhuanqiao, Meishan and Zhaopingtai as the maximal loading subbasin respectively. (II) There were positive dependence structure between precipitation and runoff in the study area, and the bivariate frequency distributions could be fitted best by the Gumbel-Hougaard copula. (III) The simultaneous occurrence probability of bivariate drought events was remarkable higher than that of corresponding classification of bivariate flood events; besides, the simultaneous occurrence probability of bivariate extreme events was maximum in the Huainan mountain zone, followed by the Huai river valley zone and Huaibei mountain zone, while that was minimum in the Huaibei plain zone. These results would be of essential guiding significance for water resource management and planning, flood and drought control and layout optimization of water conservancy projects in the study region.

Beschreibung: In this study a novel configuration of the Water Level Difference Error method is introduced to speed up the error sharing in the context of Model Predictive Control (MPC). The potential application of the controller is examined. The main objective of this controller is fair distribution of water between upstream and downstream users in main canals suffering from water shortages. The scheme uses the Integrator-Delay (ID) model for canal pool responses in a model predictive controller. The designed controller is tested on an accurate simulation model of a large canal system, using four test scenarios. The scenarios suffer from limited water supply conditions that are imposed by a limitation on the canal inflow. The results show fast reactions in equitable sharing of water level deviations from target throughout the canal. Since, all the pools are involved in optimally managing the water shortage, significant improvements in operational performance of the canal are achieved. In addition, the operational performance of the designed controller is remarkably improved by applying a new strategy of target-bands instead of target-levels in the canal pools as it increases the flexibility of the controller in making appropriate decisions.

Beschreibung: The importance of the groundwater management of karst aquifers relatively to their complexity requires the knowledge of the subsurface flow and storage behavior. In this study, a methodological approach based on the exploitation of daily spring’s discharge data was developed and tested. The methodology makes use of the hydrograph recession curves, the correlograms output, and the logarithmically structured duration curves. This methodological approach was applied to the complex karst system of Louros basin. The Louros karst system consists of individual karst units discharged by respective springs which are distributed on three levels and form three easily distinguishable groups. The application results revealed a well organized karst system with conduits of slow and fast flow. It also revealed the uniformity and the complexity of the different units, as well as the properties, such as the storativity and the evolutionary process. This approach demonstrates the benefits of interpreting different methods in a hydrologically meaningful way for the recharge data evaluation.

Beschreibung: The ecological flow requirement (EFR) during special life stages of species, for instance the fish spawning period, concerns not only the flow rate, but also daily changes in the flow rate. Therefore, it is more appropriate to optimize ecologically-friendly reservoir operation on a daily base. Directly formulating and solving a daily-based optimization model would involve a large number of decision variables as well as constraints, which may lead to unfavourable time consumption and unreliable solutions. This study proposes a time-nested approach to derive an optimal daily reservoir operation scheme with consideration of the downstream ecological hydrograph. It scales down the decision variables from monthly-base to 10-day base and finally to daily-base. The proposed method was applied to two cascaded reservoirs in the Yalong River in southwest China, where a daily ecological flow is required to conserve the habitats of an indigenous fish Schizothorax chongi ( S. chongi ). The results showed that the developed method could efficiently derive a daily optimal operational scheme with the consideration of downstream EFR for fish habitat conservation. In addition, the method greatly improves global searching ability in dealing with complex optimization problems.

Beschreibung: Bidirectional inter-basin water transfer is a special form of inter-basin water transfer, which can create a win-win outcome between two basins by utilizing the differences in flow regime from different basins. The objective of the research is to develop operating rules that consider both water transfer and water supply of water distribution system simultaneously for guiding the operation of multi-reservoir system in bidirectional inter-basin water transfer-supply system. The proposed rules include threefold: (1) triggering mechanisms for water transfer based on the storages of two equivalent reservoirs are presented for regulating the bidirectional water transfer between two basins, (2) water supply policy in the form of operating rule curves coupled to multiple hedging rules is proposed for guiding the water supply operation of multi-reservoir system, and (3) allocation approach of system release based on different priority demands is developed for determining the release from each reservoir. To obtain the optimal operating rules, the quantum-behaved particle swarm optimization algorithm (QPSO) is applied to a bidirectional inter-basin water transfer-supply system located in Liaoning province of northeast China. The results demonstrate that the obtained operating rules can guide the joint operation of multi-reservoir in bidirectional inter-basin water transfer-supply system in a mutually beneficial way.

Beschreibung: This paper develops a new method for real-time operation of reservoir systems. Genetic programming (GP) and a developed fixed length gene GP (FLGGP) are applied and compared in two approaches of static and dynamic operation rules with the aim of hydroelectric supply of Karun3 reservoir in Iran. Results are compared with those of genetic algorithm (GA) and nonlinear programming (NLP) method, indicating that GP and FLGGP have a higher efficiency (on average, 5 %) than GA and NLP operation methods. In addition, results showed that the FLGGP method is a powerful and efficient tool without the limitations of GP and can be used as a suitable replacement to GP. Comparison of two approaches of static and dynamic operation rules demonstrated the superiority of dynamic operation rules and this approach has an average superiority of 10 % to static operation rules in all methods.

Beschreibung: Drought is a slow and creeping phenomenon that occurs more frequently in arid and semi-arid regions. In recent decades, among natural disasters influencing human societies the number or frequency of drought event has increased more than others. In this context, the prediction of drought intensity, duration and frequency can help to take the necessary precautions and reduce drought risk. This study employs Markov chains of different orders (0, 1, 2 and 3) to analyze hydrological drought characteristics. Hydrological drought is identified based on the streamflow drought index (SDI) at 3-, 6-, 9- and 12-month time scales using data from 21 hydrometric stations located in the Karkheh River Basin in Iran. According to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), first-order Markov chain models are adequate to reproduce the statistical structure of SDI-based hydrological droughts. Moreover, the steady state probabilities and expected residence time of drought severity for all time scales increase as the degree of severity decreases. The results also indicate that the expected frequency of drought occurrence is higher for smaller time scales (i.e., 3-month and 6-month).

Beschreibung: This study proposed an inexact double-hand-side chance-constrained programming (IDCCP) method for nonpoint-source water quality management within an agricultural system. The IDCCP model can express interval parameters and double-hand-side random variables (e.g. nitrogen requirement of crop and nitrogen content of manure) simultaneously. To handle the double-sided random variables, a linear form of sufficient conditions for the IDCCP was deduced and proved. The performance of IDCCP was testified in the water quality management study case at three individual probabilities ( θ i  = 0.1, 0.05 and 0.01), and compared with the corresponding ILP model. The results demonstrated that, the net benefit of the agriculture system decrease with a decreasing violating probability ( θ i ) , an increasing satisfaction of constraint (1- θ i ), or a lower risk of the system. Although it is the first application of the IDCCP method to nonpoint-resource water quality management, the IDCCP could also be applied to other environmental issues under such uncertainties.

Beschreibung: Natural and reconstructed monthly streamflow records from 172 river basins in Romania (143 with full records and 29 with less than 10 % missing values) have been analyzed for trends with the nonparametric Mann-Kendall test for the period 1956–2005. The statistical significance of trends was tested for each station on a monthly basis. Changes in the streamflow regime in Romania are demonstrated. The monthly flow presents upward trends from August to January, and predominantly downward trends from February to June. The most important changes are the increasing streamflow trends from September till November, which are well explained by the increase in autumn precipitation. The annual streamflow shows a dependence on latitude, with increasing trends in the northern part, and decreasing in the south. Strong negative correlations between the North Atlantic Oscillation and the mean annual streamflow have been found in western and southern Romania, highlighting the influence of the large-scale atmospheric circulation on Romanian annual streamflow in these areas, as well as the important orographic role of the Carpathian Mountains.

Beschreibung: The nonlinear form of the Muskingum model has been widely applied to river flood routing. There are four variants of the nonlinear Muskingum model based on alternative formulations of the nonlinear storage equation. This paper proposes a new Muskingum model with an improved, seven-parameter, nonlinear storage equation. The proposed model provides more degrees of freedom in fitting observed hydraulic data than other nonlinear Muskingum models. The proper estimation of the proposed Muskingum nonlinear model’s parameters is essential to achieve accurate flood-routing predictions. This paper introduces a hybrid method for the estimation of Muskingum parameters. The parameter-estimation method combines the shuffled frog leaping algorithm (SFLA) and the Nelder-Mead simplex (NMS). The proposed Muskingum model and parameter estimation method were applied to the routing of several hydrographs. Our results indicate improved performance of the methodology described in this work when compared with those of other Muskingum models.

Beschreibung: An integrated geophysical investigation viz., Vertical Electrical Sounding (VES), Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) were carried out in a coastal terrain at Vizag Steel Plant (VSP), Visakhapatnam, Andhra Pradesh., India for locating fresh water locations. The entire area was underlain by recent alluvial of varying thickness consists of sand, clay and silt with basement as Kondalite rock formation consisting of saline water in some parts of the area. Locating favorable groundwater locations in such a terrain is a difficult task. The area consists of saturated clay, saturated silt and saline water has the same resistivity signature and misleads resistivity data interpretation for identifying and recommending the suitable groundwater locations in a coastal terrain. To overcome these uncertainties, a second geophysical approach or combinations of geophysical methods become necessary to resolve these resistivity ranges in a more reliable and better fruitful interpretation of sub-surface layers. In order to pin-point the groundwater locations at VSP; VES, ERT and GPR investigations were carried out. Interpreted results of VES, ERT pseudo-section and GPR images (radargram) were correlated with each other to ascertain confidently the geophysical signature of the sub-surface. Combination of geophysical methods gives better resolution or interpretation of sub-surface information before made any recommendations for drilling of borewell. Based on integrated geophysical investigation few borewell sites were recommended and drilled. The observed drilled lithologs was well correlated with the VES, ERT and GPR data. Besides, this hydrochemical analysis of water samples was carried out from the existing borewells and dugwells. Total Dissolved Solids (TDS) and chloride concentration were ranges from 189 to 3398 mg/l and 5 to 1610 mg/l. The eastern and north-eastern part of the area has elevated concentration of TDS and chloride indicating intrusion of saline water. TDS concentration of drilled borewell was found to range from 400 to 500 mg/l. The observed yield of drilled borewells ranges from 104 to 3623 gal/hr.

Beschreibung: The most important piece of legislation towards integrated water management in Europe is the Water Framework Directive 2000/60/EC (WFD) based on the river basin. During its implementation each Member State (MS) should prepare a River Basin Management Plan (RBMP) accompanied with the Programme of Measures (PoMs) aiming at achieving good quality for all water bodies. The paper aims at presenting a summary of the progress regarding the RBMPs developed by each EU27 MS and special focus is given in Greece. The paper attempts a comparative analysis of the 12 Greek RBMPs highlighting the problems occurred and the drawbacks identified. Special focus is given in the economic analysis assessment where different methodologies are used to estimate the full water cost. Finally suggestions are given that can be taken into consideration during the second WFD implementation cycle in Greece.

Beschreibung: EPANET 2 has been used previously to simulate pressure-deficient operating conditions in water distribution systems by: (a) executing the algorithm repetitively until convergence is achieved; (b) modifying the source code to cater for pressure-dependent outflows; or (c) incorporating artificial elements e.g., reservoirs in the data input file. This paper describes a modelling approach that enables operating conditions with insufficient pressure to be simulated in a single execution of EPANET 2 without modifying the source code. This is achieved by connecting a check valve, a flow control valve and an emitter to the demand nodes. Thus the modelling approach proposed enhances an earlier formulation by obviating the need for an artificial reservoir at the nodes with insufficient pressure. Consequently the connecting pipe for the artificial reservoir (for which additional data must be provided) is not required. Also, we removed a previous limitation in the modelling of pressure-dependent nodal flows to better reflect the performance of the nodes with insufficient flow and pressure. This yields improved estimates of the available nodal flow and is achieved by simulating pressure-deficient nodal flows with emitters. The emitter discharge equation enables the nodal head-flow relationship to be varied to reflect the characteristics of any network. The procedure lends itself to extended period simulation, especially when carried out with the EPANET toolkit. The merits of the methodology are illustrated on several networks from the literature one of which has 2465 pipes. The results suggest the procedure is robust, reliable and fast enough for regular use.

Beschreibung: The cold river water inflow often plunges below the ambient dam reservoir water and becomes density underflow through the reservoir. The hydrodynamics of density currents and plunging are difficult to study in the natural environment and laboratory condition due to small-scale, entrainment and turbulent flows. Numerical modeling of plunging flow and defining of the plunging depth can provide valuable insights for the dam reservoir sedimentation and water quality problem. In this study, an adaptive neuro-fuzzy (NF) approach is proposed to estimate plunging flow depth in dam reservoir. The results of the NF model are compared with two-dimensional hydrodynamic model, artificial neural network (ANN), and multi linear regression (MLR) model results. The two-dimensional model is adapted to simulate density plunging flow simulation through a reservoir with sloping bottom. The model is developed using nonlinear and unsteady continuity, momentum, energy and k-ε turbulence model equations in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results. Mean square errors (MSE), mean absolute errors (MAE) and determination coefficient (R 2 ) statistics are used as comparing criteria for the evaluation of the models’ performances. The NF model approach for the data yields the small MSE (1.18 cm), MAE (0.86 cm), and high determination coefficient (0.95–0.98). Based on the comparisons, it was found that the NF computing technique performs better than the other models in plunging flow depth estimation for the particular data sets used in this study.

Beschreibung: The paper presents semi-analytical mathematical model to estimate unsteady groundwater recharge resulting from variable depth of water in a large water body, influenced by time variant inflows and outflows. The model has been derived by integrating Hantush’s ( 1967 ) analytical expression for water table rise due to recharge from a rectangular spreading basin into the water balance equation of the water body. The model has been applied to a test study site in Raipur (India) for assessing viability of Managed Aquifer Recharge (MAR) from a lake located on an area dominated by the massive limestone formation. The components of the water balance equation have been carried out by the comprehensive analysis of the hydrological and hydrogeological aspects of the lake. The hydrological components include analysis of rainfall-runoff, evaporation rate, lake water quality and the hydrogeological components include aquifer characterization, parameters estimate, ambient groundwater level and quality. The time variant depth of water resulting from the interaction of water balance components, computed using the model, has been compared with the measured data and found a satisfactory match, as revealed from RMSE analysis. Compared to the inflows and lake storages, the recharge rates from the lake found very less, which ranged between 3.75 and 4.82 mm/day for depth of water ranged between 2.5 and 3.36 m. The lake water quality indicated contamination by bacteriological parameters (viz. Fecal coliform and Total coliform), turbidity and COD, exceeding the permissible limit of drinking water standards ( IS-10500:2012 ). The aquifer formations below the lakebed and around possess thick limestone formation - a limiting factor for MAR-ASTR proposition, and hence no engineered hydrogeological intervention has been found viable to enhance the recharge rate.

Beschreibung: The inverse problem of synthetic aperture imaging radiometers (SAIRs) has been demonstrated to be not well posed. The regularization methods are crucial for providing unique and stable solutions in the reconstruction of radiometric brightness temperature (BT) maps. Different to deterministic ones, a new approach is presented by referring to the rule of Bayesian inference, providing a probability model of regularized constraints to combat the ill-posedness of finite-dimensional discrete inverse problems. In addition, the SAIR inverse problem can be converted into the probability estimation of the reconstructed BT. Furthermore, in application to both uniformly and nonuniformly spaced arrays, our method can obtain the optimal solution adaptively and avoid the dilemma of choosing the optimal regularization parameter. Finally, simulation results illustrating the effectiveness and performance of the proposed method are provided.

Beschreibung: A range-cell-focusing algorithm is proposed in order to improve the quality of the target image. In a synthetic aperture radar (SAR) imaging system, the range resolution depends on the frequency bandwidth and determines the ability to distinguish between targets that are very close to each other. In cases where the resolution and the SNR from the environment are not adequate, targets cannot be accurately visualized. In order to successively classify targets that are close, we are combining an enhanced-multiple-signal-classification spectrum as a weighting function to reproduce the raw data. The proposed algorithm improves classification and separation for close targets while suppressing artifacts in the final images. The targets of interest are stationary point scatterers. The results are obtained from both simulated and experimental data to demonstrate that the proposed algorithm provides better performance than a conventional SAR imaging algorithm, the range migration algorithm.

Beschreibung: This letter presents a multiscale edge detection method for multilook polarimetric synthetic aperture radar (PolSAR) images based on the nonsubsampled contourlet transform (NSCT). The NSCT can provide flexible multiscale and directional decomposition. In the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the NSCT are calculated via maximizing the polarimetric contrast between the adjacent subband levels, instead of using the difference of the adjacent subbands as used in the additive noise model. By this way, we make the NSCT applicable to PolSAR data and multiband data. Then, the edges are detected in the NSCT domain based on a fusion of the directional subband coefficients at different scales. Experimental results with both simulated and real PolSAR data show that the present approach is robust to noise and the extracted edges are complete and continuous.

Beschreibung: A method for defining the spatial resolution of a Global Navigation Satellite System reflectometry delay–Doppler map (DDM) and of any derived geophysical product is proposed. An effective spatial resolution is derived as a function of measurement geometry and delay–Doppler (DD) interval, and as a more appropriate representation of resolution than the geometric resolution previously used in the literature. The definition more accurately accounts for variations in the scattered power across different pixels of the DDM and more accurately includes the power spreading effect caused by the Woodward ambiguity function. The dependence of the effective resolution on incidence angle, receiver altitude, and DD interval is analyzed and compared with the dependence of the geometric resolution with similar parameters.

Beschreibung: Laser scanner-captured 3-D point cloud data analysis is becoming more commonly used for remote sensing and plant science applications. Because of nonrigidity and complexity, reconstructing a 3-D model of a plant is extremely challenging. Existing algorithms often fail to find correct correspondences for plantlike thin structures. We address the problem of finding 3-D junction points in plant point cloud data as a first step of this correspondence matching process. Temporarily, we transform the 3-D problem into 2-D by performing appropriate coordinate transformations to the neighborhood of each 3-D point. Our proposed method has two steps. First, a statistical dip test of multimodality is performed to detect the nonlinearity of the local 2D structure. Then, each branch is approximated by sequential random-sample-consensus line fitting and a Euclidean clustering technique. The straight line parameters of each branch are extracted using total-least-squares estimation. Finally, the straight line equations are solved to determine if they intersect in the local neighborhood. Such junction points are good candidates for subsequent correspondence algorithms. Using these detected junction points, we formulate a correspondence algorithm as a subgraph matching problem and show that, without using traditional descriptor similarity-based matching, good correspondences can be obtained by simply considering geodesic distances among graph nodes. Experiments on synthetic and real ( Arabidopsis plant) data show that the proposed method outperforms the state of the art.

Beschreibung: Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms< 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.

Beschreibung: Extracting ships from complex backgrounds is the bottleneck of ship detection in high-resolution optical satellite images. In this letter, we propose a nearly closed-form ship rotated bounding box space used for ship detection and design a method to generate a small number of highly potential candidates based on this space. We first analyze the possibility of accurately covering all ships by labeling rotated bounding boxes. Moreover, to reduce search space, we construct a nearly closed-form ship rotated bounding box space. Then, by scoring for each latent candidate in the space using a two-cascaded linear model followed by binary linear programming, we select a small number of highly potential candidates. Moreover, we also propose a fast version of our method. Experiments on our data set validate the effectiveness of our method and the efficiency of its fast version, which achieves a close detection rate in near real time.

Beschreibung: In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority.

Beschreibung: High-resolution wide-swath synthetic aperture radar (SAR) systems are very attractive for the observation of dynamic processes on the Earth's surface, but they require the downlink of a huge volume of data. In order to comply with azimuth ambiguity requirements, in fact, a pulse repetition frequency much higher than the required processed Doppler bandwidth is often desirable. The volume of downlinked data, however, can be drastically reduced by performing Doppler filtering and decimation on board. A finite-impulse-response filter with a relatively small number of taps suffices to suppress the additional ambiguous components and to recover the original impulse response. This strategy is of special relevance for staggered SAR systems, which are typically characterized by a high oversampling factor. The proposed data reduction technique is also baseline for Tandem-L, where onboard Doppler filtering, resampling, and decimation will be jointly implemented.

Beschreibung: In this letter, we generate anisotropic bicontinuous media with different vertical and horizontal correlation functions. With the computer-generated bicontinuous medium, we then use numerical solutions of Maxwell equations in 3-dimensions (NMM3D) to calculate the anisotropic effective permittivities and the effective propagation constants of V and H polarizations. The copolarization phase difference (CPD) of VV and HH is then derived. The CPDs have recently been applied to the retrieval of snow water equivalent, snow depth, and anisotropy. The NMM3D simulation results are also compared with the results of the strong permittivity fluctuations in the low frequency limit and compared against the Maxwell–Garnett mixing formula.

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Beschreibung: Most of the transboundary river basins are contested due to the overlapping water demands of their riparian countries. Hence, these border crossing river basins are under immense pressure from the rising water demand. Thus, most of these essential fresh water resources could face water bankruptcy scenario in the future. The Nile river basin is one of these contested river basins. The demand for the river’s water is rising rapidly. Research studies indicated that the river basin could become water bankrupt in the near future. In this article the authors applied the classical bankruptcy water allocation rules for allocating the predicted available water of the river basin. In addition, the authors proposed an innovative way of accounting the water contribution of riparian states and also a mechanism for weighing the water deficit allotted to them. Generally, the authors hope that this article shades some light on allocation of water under water scarcity in the Nile river basin and in other border crossing river basins which could help for avoiding water conflicts and ensuring the sustainability of these crucial freshwater resources.

Beschreibung: Predictive control is one of the most commonly used control methods in a variety of application areas, including hydraulic processes such as water distribution canals for irrigation. This article presents the design and application of predictive control for the water discharge entering into an irrigation canal located in Spain. First, a discrete time linear model of the process is described and its parameters are experimentally identified. The model is well validated within the usual canal operating range and is used to formulate a predictive control law with an incremental formulation. Finally, experimental and simulation results are presented in which predictive control has shown better performance than a well-tuned proportional, integral and derivative controller to automatically manage demanded water discharges.

Beschreibung: This paper deals with uncertainty estimation and knowledge enhancement in water distribution networks (WDNs). A new three steps data assimilation approach is introduced, which in combination with multi-objective optimization, allows selecting effective and affordable monitoring networks. An innovative cascade of Ensemble Kalman Filters is used to assimilate the information deriving from sensors measuring pressure heads, flow in pipes and demands, with the objective of increasing knowledge while preserving at the same time the structural relationships among state variables. Selection of the most appropriate and economically affordable measurement network, is then based on the derivation of a Pareto front using the NSGA-II algorithm in conjunction with the data assimilation approach. The front is obtained by compromising between the overall sensors cost and the uncertainty reduction (or knowledge enhancement), which is expressed as a function of the Total Variance of state variables. The operational use of the proposed data assimilation approach as well as the effectiveness of the chosen observation network is also demonstrated by showing the reduction of uncertainty deriving from successive assimilations of real-time observations.

Beschreibung: The assessment of the suspended sediment (SS) amount in rivers has an importance because it specifically affects the design and operation of numerous hydraulic structures such as dams, bridges, etc. This paper proposes an adaptive neuro-fuzzy embedded fuzzy c-means clustering (ANFIS-FCM) approach for estimating SS concentration. The accuracy of ANFIS-FCM models was compared with classical ANFIS, artificial neural networks (ANNs) and sediment rating curve (SRC). Daily streamflow and SS data from two stations, Muddy Creek near Vaughn and Muddy Creek at Vaughn, operated by the United States Geological Survey were used in the study. Applied models were compared with each other based on root mean square errors and correlation coefficient. Based on comparison, ANFIS-FCM performed superior to the other two models for modeling complex non-linear behavior of the suspended sediment concentration. The ANFIS-FCM model increased the performance (RMSE) of the optimal MLP model by 10 % and 16 % in estimating SSC for the downstream and upstream stations, separately. ANFIS-FCM model provided improvements in performance and parsimonious and took lesser time in calibration than the classical ANFIS model.

Beschreibung: Accurate and reliable stream-flow forecasting has a key role in water resources planning and management. Most recently, soft computing approaches have become progressively prevalent in modelling hydrological variables and most specifically stream-flows. This is due to their ability to capture the non-linearity and non-stationarity characteristics of the hydrological variables with minimum information requirements. Despite this, they present several challenges in the modelling architecture, as there is a need to establish a suitable pre-processing method for the stream-flow data and an appropriate optimization model has to be integrated in order re-adjust the weights and biases associated with the model structure. On top of that, artificial intelligent models require “trial and error” procedures in order to be properly tuned (number of hidden layers, number of neurons within the hidden layers and the type of the transfer function). However, soft computing approach experienced several problems while calibration such as over-fitting. In this research, the Response Surface Method (RSM) is improved based on high-order polynomial functions for forecasting the river stream-flow namely; High-Order Response Surface (HORS) method. Several higher orders have been examined, second, third, fourth and fifth polynomial functions in order to figure out the best fit that able to mimic the pattern of stream-flow. In order to demonstrate the effectiveness of the proposed model, monthly stream-flow time series data located in Aswan High Dam (AHD) has been examined. A detailed analysis of the overall statistical indicators revealed that the proposed method showed outstanding performance for monthly stream-flow forecasting at AHD. It could be concluded that the fifth order polynomial function outperforms the other orders of the polynomial functions especially with May model who achieved minimum MAE 0.12, NRMSE 0.07, MSE 0.03 and maximum SF and R 2 (0.97, 0.99) respectively.

Beschreibung: This study focuses on assessing trends of reference evapotranspiration (ET o ) considering aridity. Weather data sets of 54–62 years of Inner Mongolia, a Chinese Province where climate varies from hyper-arid in the West to wet sub-humid in the East, were used. Trends were analyzed for ET o computed with the FAO Penman-Monteith method (PM-ET o ) using full data sets of maximum and minimum temperature (T max and T min ), sunshine duration (SD) used to compute net radiation, relative humidity (RH) and wind speed (WS). Trends were also assessed for ET o computed with the Hargreaves-Samani temperature eq. (ET o HS ) and the Penman-Monteith equation with temperature estimates of solar radiation and actual vapour pressure (ET o PMT ). In addition, trends relative to T max , T min , SD, RH and WS were assessed. Trends for PM-ET o show to vary with aridity, with decreasing trends in the areas marked by aridity in the West and increased trends in less arid and sub-humid areas in the East. The detected trends are well explained by the trends in weather variables which consist of large increasing trends of T max and T min and of decreasing trends for SD, RH and WS. Therefore, negative trends of ET o occur where impacts of increases in temperature and decreases in RH are smaller than impacts of declining SD and WS; otherwise, when warming influences are larger it results a positive trend for ET o . Trends were coherent when considering seasonality influences. Contrarily, results for the temperature methods, ET o PMT and ET o HS, always identified increased trends for ET o due to warming effects. These results show that it is inappropriate to assess ET o trends when using simplified temperature methods.

Beschreibung: Accurate prediction of surface runoff is critical to watershed management. In this research a semi-analytical model was adopted to solve the kinematic wave equation based on the assumption that the rate of overland-flow depth change is proportional to the rainfall excess. Simulations were compared with the results from laboratory experiments at various rain intensities. Parameters of infiltration rate and Manning’s roughness coefficient were determined. The accuracy of the semi-analytical model was evaluated by numerical simulations. The predicted outflow rates from the numerical simulations agreed well with the observed data. Further, our study indicated that the ratio ( c ) of the overland-flow depth change to the rainfall excess was a power function of the rain intensity. The depth and velocity of water flow at any time and distance could be calculated with the semi-analytical model. Hydraulic parameters including Reynolds number, Froude number, hydraulic shear stress, stream power and Darcy-Weisbach friction factor characterizing the dynamic features of overland flow of rainwater were calculated based on calculated overland-flow depth and velocity. The proposed analytical method can provide a new way to predict infiltration and runoff over sloped land.

Beschreibung: The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.

Beschreibung: Water scarcity and the poor quality of water resources are leading to a wider diffusion of desalination plants using the Reverse Osmosis (RO) process. Unfortunately, the cost of a cubic meter of fresh water produced by an RO plants is still high and many efforts are in progress to increase the efficiency of the membranes used in osmotic plants and to limit the energy required by the process. A further reduction of the energy cost could be obtained by an optimal operation of the desalination plant so reducing the hourly energy cost, or by coupling the RO plant with an energy production plant based on direct osmosis (Pressure Retarded Osmosis PRO). The economic viability of the desalination process has been analyzed until now without accounting for the integration of the RO plant with the existing water network. This analysis is developed in the present paper with reference to a hypothetical change of water supply in a real network, where a desalination plant is used to satisfy the fresh water demand. Several scenarios will be analyzed to assess the minimum cost of fresh water production and water supply to the network, including the use of energy recovery systems, such as an integrated use of RO and PRO processes, or the regulation of pressure at the network intake by a micro hydro power plant.

Beschreibung: Quantifying reference evapotranspiration (ET 0 ) is essential in water resources management. Although, many methods have been developed with different level of accuracy, in this study, two new equations were developed and optimized for estimating ET 0 using Honey-Bee Mating Optimization (HBMO) algorithm. The first eq. estimates ET 0 from extraterrestrial radiation (R a ), relative humidity (RH) and mean daily temperature (T mean ), while the second uses the same parameters except that mean daily temperatures is replaced with maximum daily air temperature (T max ). Both equations were developed using climatic data from eight weather stations in Western Australia and subsequently verified using data from ten sites across Australia. The estimated ET 0 values from both equations versus the FAO56-Penman-Monteith have a coefficient of determination, R 2 , of larger than 0.96. Moreover, the performance of six commonly used methods of estimating ET 0 including Hargreaves-Samani, Thornthwaith, Hamon, Mc Guinness-Bordne, Irmak and Jensen-Haise were assessed and the Hargreaves-Samani method performed better than others. An attempt was made to calibrate the Hargreaves-Samani equation; however, its overall performance did not improved and the two newly proposed equations are suggested to be used in Australia.

Beschreibung: Balancing human demands for water with environmental requirements to maintain functioning ecosystems requires the quantification of ecological water requirements. In arid regions, high spatial variability of vegetation cover and different water consumption of plant species make it different to estimate reasonable ecological water requirements. We developed a simple and practical approach that estimates the vegetation water requirements (VWRs) of desert riparian ecosystems. This model is species-specific and spatially-explicit; it considers the water consumption characteristics required by different species and highlights the impacts that high vegetation cover spatial variability has in arid regions on evapotranspiration. The model was parameterized based on the observation of the water consumption of two typical desert riparian species, Populus euphratica and Tamarix spp., in the lower basin of the Tarim River in northwestern China. Comparisons between the modeling results and measured data for two mature Populus and Tamarix stands indicate that the model is reasonable predictive. A case study in the lower basin of the Tarim River demonstrated the model’s practicality and transferability. This model could run based on near real-time or forest weather data and spatial vegetation patterns, and provides a continuous estimation of the temporal and spatial variations of the VWR. Particularly, this model forecasts VWRs under different vegetation spatial distribution and coverage scenarios, and evaluates the impacts and consequences of different management actions. This model can serve as a useful tool for management agencies interested in improving their decisions to allocate river water between human activities and natural ecosystems in arid regions.

Beschreibung: This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system.

Beschreibung: For most land and ice surfaces, the measured radar backscatter at the spatial resolution of a wind scatterometer is insensitive to the azimuth angle. However, for regions of East Antarctica, the backscatter strongly depends on the azimuth angle. This relationship between backscatter and azimuth angle is often modeled with a Fourier series. Although previous work has separately examined the data from QuikSCAT, a Ku-band scatterometer, and the Advanced Scatterometer (ASCAT), a C-band scatterometer, this letter compares the two on the same high-resolution grid. We find that, although QuikSCAT has superior azimuth angle coverage (due to its measurement geometry) compared to ASCAT, both are suitable to estimate the radar backscatter azimuth angle modulation of East Antarctica. The ASCAT data exhibit a much larger azimuth modulation than the QuikSCAT data. This is attributed to the different wavelengths of the microwave signal: At C-band (5.7 cm), East Antarctica has features that show radar backscatter to be more dependent on azimuth angle than at Ku-band (2.2 cm). This letter also examines the ASCAT and QuikSCAT azimuth modulation over previously identified regions of wind glaze. Although azimuth modulation is expected to be minimal over wind glaze, we find the wind-glaze regions to contain more structure than previously suggested.

Beschreibung: A novel polarimetric calibration scheme on the basis of lexicographic matrix decomposition is proposed, and a new polarimetric active radar calibrator (PARC) with two independently rotatable antennas is designed to obtain the lexicographic matrices. Thus, the proposed method is realizable and can operate with the lexicographic target vectors instead of the polarization scattering matrix (PSM). The base elements of the lexicographic target vectors are the vectors corresponding to the new PARC with the antennas at different orientations. Moreover, the elements of the vector are just the elements of the PSM. Hence, the error coefficients corresponding to a polarimetric measurement system are directly achieved, and the polarimetric calibration is simple and accurate. Experimental results confirm the superiority of the presented strategy. A 35-dB improvement in the system cross-polarization isolation is obtained.

Beschreibung: Target detection plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel matched subspace detector based on low-rank regularized least squares (LRLS-MSD) is proposed for hyperspectral target detection. As pixels in an HSI have global correlation and can be represented in subspace, the low-rank regularization is introduced in the least squares model. An effective algorithm is presented to solve the problem. Then, the detection results are generated according to the generalized likelihood ratio test with statistical hypotheses. The experimental results suggest an advantage of the low-rank regularization over other classical target detection methods.

Beschreibung: An improved facet model for the predication of the normalized radar cross section (NRCS) of electrically large rough sea surface was proposed based on the first-order small slope approximation (SSA-1) method, the Bragg scattering mechanism, and the specular scattering mechanism. The proposed method is able to evaluate both the complex reflective function and NRCS of electrically large sea surfaces from as low as ultrahigh frequency band to as high as Ka-band. The main idea is that a tilt sea facet can be regarded as the superposition of a planner facet and the microscopic profile; the latter is assumed to be a set of sinusoidal ripple patches. Thus, the integration kernel in SSA-1 over several small facets can be replaced by a large facet with a short wave modification. The efficiency increases because of the much larger mesh size than SSA. Then, both the backscattering and bistatic scattering NRCS results calculated by the proposed method were compared with those predicated by SSA-1, and all of the results show that the proposed method has the merits of high calculation efficiency as well as calculation accuracy.