An Introduction to MODISI and SCMOD Methods for Correction of the MODIS Snow Assessment Algorithm

Detection, monitoring and precise assessment of the snow covered regions is an important issue. Snow cover area and consequently the amount of runoff generated from snowmelt have a significant effect on water supply management. To precisely detect and monitor the snow covered area we need satellite images with suitable spatial and temporal resolutions where we usually lose one for the other. In this study, products of two sensors MODIS and ASTER both on board of TERRA platform having low and high spatial resolution respectively were used. The objective of the study was to modify the snow products of MODIS by using simultaneous images of ASTER. For this, MODIS snow index image with high temporal resolution were compared with that of ASTER, using regression and correlation analysis. To improve NDSI index two methods were developed. The first method generated from direct comparison of ASTER averaged NDSI with those of MODIS (MODISI). The second method generated by dividing MODIS NDSI index into 10 codes according to their percentage of surface cover and then compared the results with the difference between ASTER averaged and MODIS snow indices (SCMOD). Both methods were tested against some 16 MODIS pixels. It is found that the precision of the MODISI method was more than 96%. This for SCMOD was about 98%. The RMSE of both methods were as good as 0.02.     

The Influence of Streambed Heterogeneity on Hyporheic Flow in Gravelly Rivers

Deposits of open‐framework gravel occurring in gravelly streambeds can exert a significant influence on hyporheic flow. The influence was quantified using a numerical model of the hyporheic zone. The model included open‐framework gravel stratasets represented with commonly observed characteristics including a volume fraction of about one‐third of the streambed sediment, a hydraulic conductivity two orders of magnitude greater than other strata present, and a spatial connectivity forming preferential‐flow pathways. The influence of open‐framework gravel stratasets on hyporheic flow was much greater than the influence of the channel morphology including meanders, point bars, dunes, and ripples. Seventy percent of the total hyporheic exchange occurred across 30% of the channel boundary at locations of open‐framework gravel stratasets. The maximum local interfacial flux rates occurred at these locations, and were orders of magnitude greater than those at other locations. The local flux rates varied by six orders of magnitude over the channel boundary. The composite flow rate through the model with open‐framework gravel stratsets was an order of magnitude greater than that through an equivalent but homogeneous model.     

Sediment settling in the Latian Dam in Iran

Among the difficulties that influence future dam operations,reservoir sedimentation is the most problematic for engineers.This study predicted the amount and pattern of sedimentation for use in estimation of the useful lifespan of reservoirs and identification of optimal locations for outlets and intakes at the initial stages of dam design.Hydrographic surveys of different dams can provide better insight into this phenomenon.Latian Dam in Iran has conducted hydrographic surveys during 7 time periods.The amount and process of sedimentation in this reservoir were determined,and predictions of distribution of sediments were validated by well-known,common methods.The formation of a delta in the reservoir was investigated for different time periods after operation.Future problems due to the impacts of sedimentation on dam operation and the useful lifespan of the reservoir were predicted.In addition,the study results may be used for developing empirical methods to predict sedimentation patterns in other reservoirs.     

Earthquake relocation in the Central Alborz region of Iran using a non-linear probabilistic method

In this study, we calculate accurate absolute locations for nearly 3,000 shallow earthquakes (≤20 km depth) that occurred from 1996 to 2010 in the Central Alborz region of northern Iran using a non-linear probabilistic relocation algorithm on a local scale. We aim to produce a consistent dataset with a realistic assessment of location errors using probabilistic hypocenter probability density functions. Our results indicate significant improvement in hypocenter locations and far less scattering than in the routine earthquake catalog. According to our results, 816 earthquakes have horizontal uncertainties in the 0.5–3.0 km range, and 981 earthquakes are relocated with focal-depth errors less than 3.0 km, even with a suboptimal network geometry. Earthquake relocated are tightly clustered in the eastern Tehran region and are mainly associated with active faults in the study area (the Mosha and Garmsar faults). Strong historical earthquakes have occurred along the Mosha and Garmsar faults, and the relocated earthquakes along these faults show clear north-dipping structures and align along east–west lineations, consistent with the predominant trend of faults within the study region. After event relocation, all seismicity lies in the upper 20 km of the crust, and no deep seismicity (>20 km depth) has been observed. In many circumstances, the seismicity at depth does not correlate with surface faulting, suggesting that the faulting at depth does not directly offset overlying sediments.     

Coherence among climate signals, precipitation, and groundwater

Climate signals may affect groundwater level at different time scales in different geographical regions, and those patterns or time scales can be estimated using coherence analysis. This study shows that the synthesis effort required to search for patterns at the physical geography scale is possible, and this approach should be applicable in other regions of the world. The relations between climate signals, Southern Oscillation Index, Pacific Decadal Oscillation, North Atlantic Oscillation, North Pacific Pattern (SOI, PDO, NAO, and NP), precipitation, and groundwater level in three geographical areas of Wisconsin are examined using a three-tiered coherence analysis. In the high frequency band (<4(-1) cycles/year), there is a significant coherence between four climate signals and groundwater level in all three areas. In the low frequency band (>8(-1) to ≤23(-1) cycles/year), we found significant coherence between the SOI and NP signals and groundwater level in the forested area, characterized by shallow wells constructed in sand and gravel aquifers. In the high frequency band, there is significant coherence between the four climate signals and precipitation in all three areas. In the low frequency band, the four climate signals have effect on precipitation in the agricultural area, and SOI and NP have effect on precipitation in the forested and driftless areas. Precipitation affects groundwater level in all three areas, and in high, low and intermediate frequency bands. In the agricultural area, deeper aquifers and a more complex hydrostratigraphy and land use dilute the effect of precipitation on groundwater level for interdecadal frequencies.     

A new boundary element solution to evaluate the geometric effects of the canyon site on the displacement response spectrum

This paper presents a step-by-step procedure using the three-dimensional boundary element approach to study the behavior of semi-circular canyons under seismic shear waves. The boundary element code TDASC allows utilization for various canyon geometries, evaluation of concurrent seismic waves and calculation of the ground motions on canyons due to an excitation at any arbitrary point of the incident field. Considering the widening ratio of the canyon(including prismatic, semi-prismatic and non-prismatic canyons), wave characteristics(wavelength, dimensionless period, direction) and maximum amplification pattern, the solution was applied to carry out a series of parametric studies. It was shown that canyon form can significantly affect the displacement amplification, especially at the points located on its edges. By increasing the wave dimensionless frequency(η 1), the amplification pattern becomes more complex. On the basis of the results from a variety of considered cases, a new expression has been presented for the limiting wavelength beyond which the widening of the canyon will not have a major effect on the displacement amplification. To verify the reliability of the proposed approach, the obtained results, expressed in terms of displacement amplitude, were compared with those from the available published literature and a reasonably good agreement was observed.     

Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model

The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error.     

Spatial correlation for horizontal and vertical components of acceleration from northern Iran seismic events

The evaluation of seismic risk of spatially distributed systems requires the spatial correlation model for ground motion intensity measures. This study investigates the spatial correlation of four earthquakes recorded in northern Iran. The intra-event spatial correlation for both horizontal and vertical components of spectral acceleration at eight periods in the range of 0.0–3.0 s is estimated using geostatistical tools. An exponential form is chosen to fit experimental semivariograms, and the correlation ranges of spectral accelerations as a function of period are derived. The results show similar trend of correlation ranges for both components. It should be mentioned that the ranges for the vertical component, in general, are higher than those observed for the horizontal one. For both components, the correlation ranges as a function of period are divided into three segments. The first and the third one are increasing while the second one is decreasing with increasing period.     

Consistency of precipitation products over the Arabian Peninsula and interactions with soil moisture and water storage

The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R² = 0.84) was achieved with TRMM forcing, which indicates its potential to monitor changes in TWS over the arid peninsular region.