Simulating the interactions between the water and the socio-economic system in a stressed endorheic basin

ABSTRACT

The endorheic basin of Zayandehrud in Iran suffers from environmental problems, social tensions, and economic instability. Lack of understanding how the water system and the socio-economic system interact may explain these challenges. A system dynamics model, being a holistic simulation tool, was developed for the Zayandehrud basin and used to evaluate several policy scenarios. The indices of employment, gross regional product, the volume of groundwater and surface water stored, flow into the basin’s end lake, and the water flow in the river were used to evaluate the scenarios. The findings demonstrate that focusing on supply-based activities or water demand management cannot solely improve the condition of the Zayandehrud basin. It is required to reconsider the development policies of the region in a broader context. Reducing the irrigated area by 15% and developing new industries up to a certain limit may make the combined water and socio-economic system sustainable.     

Application of neural network and ANFIS model for earthquake occurrence in Iran

This study examined the spatial-temporal variations in seismicity parameters for the September 10^th, 2008 Qeshm earthquake in south Iran. To this aim, artificial neural networks and Adaptive Neural Fuzzy Inference System (ANFIS) were applied. The supervised Radial Basis Function (RBF) network and ANFIS model were implemented because they have shown the efficiency in classification and prediction problems. The eight seismicity parameters were calculated to analyze spatial and temporal seismicity pattern. The data preprocessing that included normalization and Principal Component Analysis (PCA) techniques was led before the data was fed into the RBF network and ANFIS model. Although the accuracy of RBF network and ANFIS model could be evaluated rather similar, the RBF exhibited a higher performance than the ANFIS for prediction of the epicenter area and time of occurrence of the 2008 Qeshm main shock. A proper training on the basis of RBF network and ANFIS model might adopt the physical understanding between seismic data and generate more effective results than conventional prediction approaches. The results of the present study indicated that the RBF neural networks and the ANFIS models could be suitable tools for accurate prediction of epicenteral area as well as time of occurrence of forthcoming strong earthquakes in active seismogenic areas.     

Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971–2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015–2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

     

Comparison between bivariate and trivariate flood frequency analysis using the Archimedean copula functions,a case study of the Karun River in Iran

Natural Hazards - Historically, severe floods have caused great human and financial losses. Therefore, the flood frequency analysis based on the flood multiple variables including flood peak,...     

Reduced-Order Salinity Modeling of the Urmia Lake Using MIKE3 and Proper Orthogonal Decomposition Models

Increasing salinity in Urmia Lake, located in the north-west of Iran, has turned into a critical issue, particularly because the lake is the habitat of a unique multi-cellular organism called Artemia Urmiana. During the past decades, several anthropogenic changes have taken place in the lake, which have resulted in increased salinity. This study introduces a reduced-order framework based on MIKE3 simulation model and proper orthogonal decomposition (POD) to simulate salinity patterns in Urmia Lake. Spatio-temporal variations of salinity in the lake firstly were simulated by MIKE3, and close matches were observed between salinity estimates from MIKE3 and those of the field data. Thereafter, 365 daily snapshots were taken from MIKE3 simulations, and subsequently 365 POD basis modes were computed. Due to high percentage of conserved energy of the lake system (salinity of lake) within the first ten POD basis modes, these modes were considered to develop a reduced-order salinity model (ROSM). Finally, results from MIKE3 were compared with the ROSM. It was shown that the first ten modes (among 365 modes) obtained by the POD conserved approximately more than 99.8% of the energy of the system. Moreover, using the first ten modes resulted in an error in magnitude of less than 0.01. Therefore, the ROSM could successfully capture the variations of salinity in the lake via its first ten modes.     

Wavelet-based multifractal analysis of earthquakes temporal distribution in Mammoth Mountain volcano,Mono County,Eastern California

This paper presents a wavelet-based multifractal approach to characterize the statistical properties of temporal distribution of the 1982–2012 seismic activity in Mammoth Mountain volcano. The fractal analysis of time-occurrence series of seismicity has been carried out in relation to seismic swarm in association with magmatic intrusion happening beneath the volcano on 4 May 1989. We used the wavelet transform modulus maxima based multifractal formalism to get the multifractal characteristics of seismicity before, during, and after the unrest. The results revealed that the earthquake sequences across the study area show time-scaling features. It is clearly perceived that the multifractal characteristics are not constant in different periods and there are differences among the seismicity sequences. The attributes of singularity spectrum have been utilized to determine the complexity of seismicity for each period. Findings show that the temporal distribution of earthquakes for swarm period was simpler with respect to pre- and post-swarm periods.