Quantitative and qualitative simulation of groundwater by mathematical models in Rafsanjan aquifer using MODFLOW and MT3DMS

Agriculture sector by using 80% of freshwater is the greatest water consumer in Iran. Excessive use of agricultural fertilizers in last decade, caused accumulation of enormous amounts of salts and subsequence declined the physical properties of soil. In desert and dry regions such as Rafsanjan plain, use of the groundwater resources is more than the surface water resources. Therefore, information about the quality of these resources remains a necessary task for optimum management, protection of water resources, and stopping the future damages. In this study, the groundwater quantity and quality of Rafsanjan plain was investigated by MODFLOW and MT3DMS. The presented quantitative model for this aquifer was compared by observed data and calibrated. This model was used to predict a 10-year period. Results show that water elevation decreases approximately 15 m for 10 years to come in this plain. Qualitative model results show that most quality parameters will increase. Electrical conductivity will increase more than other parameters. As values of this parameter will reach 16,000 µs/l for next 5 years. Therefore, we suggest that exploitation of water from these resources should be reduced and discharge from some of agricultural wells stop; also we suggested that recharge to groundwater resources should be increased and agricultural activities should be limited or improved using of modern irrigation systems in this plain.     

Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)

Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought forecasting in southeastern part of East Azerbaijan province, Iran. The Wavelet-ANN and ANFIS models were first trained using the observed data recorded from 1952 to 1992 and then used to predict meteorological drought over the test period extending from 1992 to 2011. The performances of the different models were evaluated by comparing the corresponding values of root mean squared error coefficient of determination (R ²) and Nash–Sutcliffe model efficiency coefficient. In this study, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead. It was demonstrated that wavelet transform can improve meteorological drought modeling. It was also shown that ANFIS models provided more accurate predictions than ANN models. This study confirmed that the optimum number of neurons in the hidden layer could not be always determined using specific formulas; hence, it should be determined using a trial-and-error method. Also, decomposition level in wavelet transform should be delineated according to the periodicity and seasonality of data series. The order of models with regard to their accuracy is as following: Wavelet-ANFIS, Wavelet-ANN, ANFIS, and ANN, respectively. To the best of our knowledge, no research has been published that explores coupling wavelet analysis with ANFIS for meteorological drought and no research has tested the efficiency of these models to forecast the meteorological drought in different time scales as of yet.     

Comparison of IWG and SDSM weather generators for climate change impact assessment

Theoretical and Applied Climatology - Weather generators (WGs) typically have unique advantages to the climate change impacts assessment. However, each of them has the weaknesses, strengths, and...     

Reduction of Nitrate Loadings to Ground Water

Non-point source pollution of ground water systems has become a national concern in recent years. Researchers and regulatory agencies are investigating the source and processes of the contamination. Agricultural best management practices (BMPs) traditionally developed to reduce non-point source pollution of surface water resources are being investigated for their impact on ground water quality. This study used the CREAMS model to simulate the long-term effects of seven different BMPs on nitrate nitrogen (NO₃-N) loadings to a shallow, unconfined ground water system. Two representative watersheds, 5.8 and 8.9 hectares (14.3 and 22 acres) in area, in the Coastal Plain physiographic region of Maryland were selected for study. Soils in these watersheds belong to the Matapeake silt loam series and have moderate infiltration capacity. Results from this study indicated that BMPs used in conjunction with winter cover (barley) reduced NO₃-N leaching to the ground water system. It was also found that turfgrass reduced surface losses of water and nitrogen, but increased leaching losses of water and NO₃-N significantly. All of the BMPs simulated in this study resulted in leachate NO₃-N concentrations exceeding 10 ppm, the U.S. EPA health standard for public drinking water, indicating a need for alternate practices for reducing nitrate leaching.     

Developing food,water and energy nexus workflows

ABSTRACT

There is a growing recognition of the interdependencies among the supply systems that rely upon food, water and energy. Billions of people lack safe and sufficient access to these systems, coupled with a rapidly growing global demand and increasing resource constraints. Modeling frameworks are considered one of the few means available to understand the complex interrelationships among the sectors, however development of nexus related frameworks has been limited. We describe three open-source models well known in their respective domains (i.e. TerrSysMP, WOFOST and SWAT) where components of each if combined could help decision-makers address the nexus issue. We propose as a first step the development of simple workflows utilizing essential variables and addressing components of the above-mentioned models which can act as building-blocks to be used ultimately in a comprehensive nexus model framework. The outputs of the workflows and the model framework are designed to address the SDGs.     

Modeling Management Practice Effects on Pesticide Movement to Ground Water

The assessment of agricultural impacts on water quality are now being redirected to include both ground water and surface water. Mathematical models have enhanced the ability of scientists'to evaluate these impacts. A variety of public domain models are available that can aid in evaluating the effects of managerial activities on pesticide movement to ground water. However, the ideal non-point source (NPS) pollution management model does not exist. Current models fail to adequately describe the transport of chemicals to ground water and, simultaneously, the effect of managerial practices on transport mechanisms. Much more work is necessary to develop a model that can describe water quality impacts of agricultural practices in a holistic framework that includes ground water and surface water concerns.     

Low-frequency transfer of seismic energy by superficial soil deposits and soft rocks

Recordings of recent strong earthquakes obtained on alluvial sites show that the maximum horizontal accelerations tend towards a limit of about 0.45 to 0.50g, associated with large displacements. by contrast, vertical accelerations do not appear to be subject to such a limit (1.7g for the 1979 Imperial Valley earthquake). Theoretical linear elasticity models, when applied to superficial layers of low strength, seem to be inadequate for the prediction of near-field ground motions in alluvial deposits. A good approximation for the horizontal component of certain Imperial Valley records was, however, obtained through a non-linear approach, using local soil properties together with a reasonable hypothesis for motion at the base of the superficial layers in question that included large values of acceleration for high-frequency shear waves.