Monthly stream flow forecasting via dynamic spatio-temporal models

In this research, a dynamic linear spatio-temporal model (DLSTM) was developed and evaluated for monthly streamflow forecasting. For parameter estimation, coupled expectation–maximization (EM) algorithm and Kalman filter was adopted. This combination enables the model to estimate the state vector and parameters concurrently. Different forecast scenarios including various combinations of upstream stations were considered for downstream station streamflow forecasting. Several statistical criteria, nonparametric and visual tests were used for model evaluation. Results indicated that the spatio-temporal model performed acceptably in almost all scenarios. The dynamic model was able to capitalize on coupled spatial and temporal information provided that there is spatial connectivity in the studied hydrometric stations network. Moreover, threshold level method was used for model evaluation in drought and wet periods. Results indicated that, in validation phase, the model was able to forecast the drought duration and volume deficit/over threshold, although volume deficit/over threshold could not be accurately simulated.     

The discovery of a giant debris arc in the Coma cluster

We present the discovery of a giant low-surface-brightness arc, of length ∼80 kpc, in the Coma cluster. The arc consists of a diffuse luminous matrix with surface brightness μ_B<26.5 mag arcsec⁻² and a number of embedded condensations. It is not associated with any giant galaxy in Coma in particular; neither does it have the properties of a gravitational arc. We argue that a fast interaction between the nearby barred S0 galaxy IC 4026 and either IC 4041 or RB 110 is the most natural explanation for the origin of the arc.     

Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems.Recently,the Copernicus Climate Change Service(C3S)database has been releasing monthly forecasts for lead times of up to three months for public use.This study evaluated the ensemble forecasts of three C3S models over the period 1993-2017 in Iran’s eight classified precipitation clusters for one-to three-month lead times.Probabilistic and non-probabilistic criteria were used for evaluation.Furthermore,the skill of selected models was analyzed in dry and wet periods in different precipitation clusters.The results indicated that the models performed best in western precipitation clusters,while in the northern humid cluster the models had negative skill scores.All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons.Moreover,with increasing lead time,the forecast skill of the models worsened.In terms of forecasting in dry and wet years,the forecasts of the models were generally close to observations,albeit they underestimated several severe dry periods and overestimated a few wet periods.Moreover,the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models.In general,the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran.For the clusters considered in Iran and for the long-range system versions considered,the Météo France model had lower skill than the other models.     

Application of the WEPP model to determine sources of run‐off and sediment in a forested watershed

This study investigates critical run‐off and sediment production sources in a forested Kasilian watershed located in northern Iran. The Water Erosion Prediction Project (WEPP) watershed model was set up to simulate the run‐off and sediment yields. WEPP was calibrated and validated against measured rainfall–run‐off–sediment data. Results showed that simulated run‐off and sediment yields of the watershed were in agreement with the measured data for the calibration and validation periods. While low and medium values of run‐off and sediment yields were adequately simulated by the WEPP model, high run‐off and sediment yield values were underestimated. Performance of the model was evaluated as very good and satisfactory during the calibration and validation stages, respectively. Total soil erosion and sediment load of the study watershed during the study period were determined to be 10 108 t yr^?1 and 8735 t yr^?1, respectively. The northern areas of the watershed with dry farming were identified as the critical erosion prone zones. To prioritize the subwatersheds based on their contribution to the run‐off and sediment production at the watershed's main outlet, unit response approach (URA) was applied. In this regard, subwatersheds close to the main outlet were found to have the highest contribution to sediment yield of the whole watershed. Results indicated that depending on the objective of land and water conservation practices, particularly, for controlling sediment yield at the main outlet, critical areas for implementing the best management practices may be identified through conjunctive application of WEPP and URA. Copyright © 2014 John Wiley & Sons, Ltd.     

Recognition of shallow karst water resources and cave potentials using thermal infrared image and terrain characteristics in semi-arid regions of Iran

Shallow karst water resources and caves may influence land surface temperatures due to cold transfer property of rocks and evaporation from buried karst. The objective of this research was to develop a method for recognition of karst areas based on evaluating the surface characteristics that manifest itself by low land surface temperature in the satellite images. Investigation of thermal ETM⁺ image of the study region in Iran showed that parts of carbonate rocks that bear karst water are relatively cooler compared to areas with similar terrain conditions. Relational modeling provided useful information on spatial distribution of areas that have the potential to hold karst water resources and/or caves. Further inspection of ASTER images, along with geotechnical, geophysical and geological field surveys verified the approach. Significant correlation was found between electrical resistivity and thermal band values. The method may be used as a primary exploratory tool for shallow karst water explorations in similar areas.     

Application of Fuzzy Set Theory to Rock Engineering Classification Systems: An Illustration of the Rock Mass Excavability Index

The characterization of rock masses is one of the integral aspects of rock engineering. Over the years, many classification systems have been developed for characterization and design purposes in mining and civil engineering practices. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This results in subjective uncertainties, leading to the misuse of such classifications in practical applications. Fuzzy set theory is an effective tool to overcome such uncertainties by using membership functions and an inference system. This study illustrates the potential application of fuzzy set theory in assisting engineers in the rock engineering decision processes for which subjectivity plays an important role. So, the basic principles of fuzzy set theory are described and then it was applied to rock mass excavability (RME) classification to verify the applicability of fuzzy rock engineering classifications. It was concluded that fuzzy set theory has an acceptable reliability to be employed for all rock engineering classification systems.     

Efficiency of standardized image processing in the fragmentation prediction in the case of Sungun open-pit mine

The paper proposes a standardized image-processing procedure with the use of sieve analysis results for calibration which is utilized to measure the size distribution of fragmentation at Sungun mine. Through this procedure, a number of 19 bench blasting in various levels have been initially selected as the target of the study for each, multiple photos were taken immediately after blast from suitable perspectives and locations of the muckpiles surfaces. The number of image sampling was chosen adequately high to achieve further reliability of the whole photography procedure. Then fragments of each muckpile were separately mixed by a loader, where another image sampling from these new muckpiles, bucket of loaders, and haulage trucks was performed. For the purpose of sieve analysis, seven sieves with the mesh sizes between 1.27 cm (0.5 in) and 25.4 cm (10 in) were designed, manufactured, and then installed at Sungun semi-industrial laboratory. Additionally, three mass samples of the mixed fragments were randomly chosen among the 19 muckpiles for sieving. During image analysis stage, “sieve shift” and “mass power” factors, required to obtain standardized size distribution, were precisely assigned when the results obtained by the image analysis software was in accordance with the sieving results. In order to validate the reliability of the image processing, a comparative analysis of the achieved results was made with the results of the original Kuz–Ram model [Cunningham (1983) The Kuz–Ram model for prediction of fragmentation from blasting. In: Proceedings of the first international symposium on rock fragmentation by blasting, Lulea, Sweden, pp 439–454]. Finally, the image-processing procedure was found to be more efficient, with results close-matched to the real results of the sieve analysis.     

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index（SPI） was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation（AMO）,Atlantic Meridional Mode（AMM）,the Bivariate ENSO Time series（BEST）,the East Central Tropical Pacific Surface Temperature（NINO 3.4）,the Central Tropical Pacific Surface Temperature（NINO 4）,the North Tropical Atlantic Index（NTA）,the Southern Oscillation Index（SOI）,and the Tropical Northern Atlantic Index（TNA）.These indices accounted for 81% of the variance in the Principal Components Analysis（PCA） method.The Atlantic surface temperature（SST：Atlantic） had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.