Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province,Iran)

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

     

Application of soft-computing techniques in forecasting sediment load and concentration

ABSTRACT

This paper investigates conventional and soft-computing methods for the estimation of suspended sediment concentration (SSC) and load (SSL) in rivers. Frequently used methods of sediment rate curve (SRC) and multi-nonlinear regression, and soft-computing methods of multi-layer perceptron, multi-linear regression and adaptive neuro-fuzzy inference system are implemented using various hydrological and hydraulic parameters for the Little Kickapoo Creek Watershed, Illinois, USA. All methods performed equally well in the estimation of SSL, without any noticeable outperformance from any from the methods. However, the application of soft-computing methods decreased SSC estimation errors considerably as compared to the results of SRC. The results are significant in the way they reconcile traditionally used hydrological parameters into the soft-computing methods. Overall, soft-computing methods are recommended for the estimation of SSC in rivers because of their reasonably better performance and ease of implementation.     

Extracting of prospective groundwater potential zones using remote sensing data,GIS, and a probabilistic approach in Bojnourd basin,NE of Iran

Various groundwater potential zones for the assessment of groundwater availability in the Bojnourd basin have been investigated using remote sensing, GIS, and a probabilistic approach. Five independent groundwater factors, including topography, ground slope, stream density, geology units, lineament density, and a groundwater productivity factor, i.e., springs’ discharge, were applied. Discharge rates of 226 springs over the area were collected, and the probabilistic model was designed by the discharge rates of springs as the dependent variable. For training the probabilistic model, a ratio of 70/30% of springs’ discharge was applied and discharge rates of 151 springs were selected to randomly train the model. The frequency ratio for each factor was calculated, and the groundwater potential zones were extracted by summation of frequency ratio maps. The groundwater potential map was also classified into four classes, viz., “very good” (with a frequency ratio of >6.75), “good” (5.5FR6.75), “moderate” (4.75FR5.5), and “poor” (FR4.75). Then, the model was verified based on a success-rate curve method which resulted in obtaining an accuracy ratio of 75.77%. Finally, sensitivity analysis was applied by a factor removal method in five steps. Results reveal that topography factor has the biggest effect on the groundwater potential map and removing this factor eventuates in the lowest accuracy of the final map (AUC = 63. 73%). The groundwater potential map is fairly affected by removing the lineament density factor with an accuracy of 68.80%. Removing the lineament density factor has the lowest effect on the final map with accuracy of 68.80%.     

Performance of quasilinear elastic constitutive models in simulation of geosynthetic encased columns

Past numerical simulations of geosynthetic encased columns (GECs) using different versions of the quasilinear elastic hyperbolic model for the encased granular material have, in certain cases, yielded unrealistic results. In this paper the cause of such results is investigated by performing three-dimensional finite element analyses of GECs in soft clay, utilizing three common functional forms of the hyperbolic model for the encased granular material. Results indicate that one form of the hyperbolic model can predict an unrealistic lateral response for GECs during application of load to the column. In addition, the inability of hyperbolic models to properly account for soil behavior near failure compromises their ability to realistically capture the behavior of encased granular soil in GECs. Modeling the behavior of soil near failure is essential for properly simulating the behavior of GECs, as soil shear failure is necessary to mobilize the tensile stresses in the encasement and improve the stress–displacement response of the GEC. Although this type of hyperbolic model behavior was demonstrated for the specific case of encased soil in a GEC, the limitations of the hyperbolic model described herein apply equally to other geotechnical problems in which some portion of the soil mass is at or near failure.     

Socioeconomic impacts of environmental risks in the western Makran zone (Chabahar,Iran)

Natural Hazards - The western Makran subduction zone is capable of producing considerable tsunami run-up heights that penetrate up to 5 km inland. In this study, we show how climate change...     

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.     

Monitoring of dust storm and estimation of aerosol concentration in the Middle East using remotely sensed images

Arabian Journal of Geosciences - Dust storms are one of the major environmental disasters in the arid regions of Middle East, occurring in very high frequency. As a result, monitoring dust storms...     

Numerical simulation of groundwater flow and contamination transport in Nahavand plain aquifer,west of Iran

Numerical simulation of groundwater flow used for the estimation of hydraulic and hydrologic parameters which is an important tool for the management of aquifers. This study presents the results of a mathematical model developed for the simulation of groundwater flow in Nahavand plain aquifer in the southwest Hamadan province. For this purpose Groundwater Modeling Software (GMS) was used which supports the MODFLOW-2000 code. After gathering required data such as the hydrological, hydrogeological and topography maps, a 3D hydrogeological model of plain was constructed with borehole and surface elevation data. Then MODFLOW was used for simulation of flow. After initial simulation of the flow, the model was calibrated in steady state with trial-and-error and parameter estimation methods the observed head of groundwater table monitoring data of 1997. Results of calibration show that error between observed head and computed head is in allowable range. Also results of computed head with model show that groundwater flow is in the direction of the dominate slope (southeast to northwest). Finally MODPATH code which simulates advective transport of particles was used for estimation of flow path and source of contaminants.     

Landslide susceptibility assessment at the Wuning area,China: a comparison between multi-criteria decision making,bivariate statistical and machine learning methods

Natural Hazards - The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of...