Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

Assessing a Multivariate Approach Based on Scalogram Analysis for Agricultural Drought Monitoring

Due to the complexity of agricultural drought, univariate indices may not be suitable for assessing its impacts comprehensively. The main objective of this study was to develop a new multivariate drought index using the Scalogram concepts, in which the input data weights and their cluster separation were performed based on the entropy theory and fuzzy k-means algorithm, respectively. The newly developed index, named as SCI index, integrates the four weighted individual quantitative indicators such as the difference between precipitation and potential evapotranspiration (D_i), the moisture departure (d_i), the Soil Moisture index (SMI), and the Vegetation Condition Index (VCI) to quantify agricultural drought in monthly and annual timescales in the various climate conditions of Golestan province, Iran. Next, the Composite Drought Index (CDI) was calculated for the selected stations by the same variables in the SCI index as an input. According to the results a good agreement and a high behavioral similarity for the identifying moisture conditions was found between SCI index and CDI index and even other well-known drought indices such as SPEI and SPDI. But the intensity with extremes of wet and dry conditions in the CDI significantly were more than the SCI index and other ones. Comparing results obtained by the Standardized Yield Index (SYI) for rainfed wheat with the SCI index showed that at most stations when a severe drought as happened in 2000–2001 and 2007–2008, severe crops losses also occurred. The flexible structure of SCI index provides a comprehensive approach to quantify agricultural drought and can be adapted to characterize other types of drought on a practical basis.     

Kinetic study of slaughterhouse wastewater treatment by electrocoagulation using Fe electrodes

In this study, treatment of slaughterhouse wastewater by electrocoagulation was investigated in batch system using Fe electrodes. The effect of various variables such as electrode number, current density and operating time was tested. Pollutant removal efficiency increased with increasing electrode number and operating time. The biochemical oxygen demand (BOD(5))(,) chemical oxygen demand (COD), total suspended solid (TSS), and total nitrogen (TN) removal efficiencies using eight electrodes at a contact time of 50 min and a current density of 10 A/m(2) were 66, 62, 60, and 56%, respectively. Higher electrode numbers will allow shorter operating times to achieve certain removal efficiencies. Also, removal efficiencies increased by increasing the current density; the highest removal efficiencies of BOD(5,) COD, TSS, and TN at a contact time of 50 min and a current density of 25 A/m(2) were 97, 93, 81, and 84%, respectively. The results also show that the reactor pH varies directly with the current density; at 25 A/m(2), the reactor pH increased from an initial value of 7.1 to 7.7 after 50 min. The experimental results showed that the kinetics of BOD(5), COD, TSS and TN removal could be fitted adequately using a first order kinetic model (higher R(2)).     

Monetary policy effectiveness and stock market cycles in ASEAN-5

This article examines the asymmetric effects of monetary policy on real output in bull and bear phases of stock market in five ASEAN economies (Malaysia, Singapore, Indonesia, the Philippines and Thailand) using the recently developed pooled mean group (PMG) technique. Stock market cycles are identified by employing Markov switching models and the rule-based nonparametric approach. Estimating the models using monthly data from 1991:1 to 2011:12, the results show that monetary policy (measured by short-term interest rate) has a negative and statistically significant long-run effect on real output in bull and bear market periods while the effects are stronger in bear periods than bulls. In the short run, there is no statistically significant relationship between monetary policy and real output. These results are consistent with finance constraints (capital market imperfection) models that predict that monetary policy is more effective during bear periods than bulls.     

Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non-linear Regression and Computational Intelligence Methods

The evaluation of water resources given by snowfall is very important in the mountainous basins. In this study, the snow depth (SD) and snow water equivalent (SWE) were investigated to quantify the water resources stored in the snow. Multivariate non-linear regression (MNLR) method, four types of artificial neural network (ANN) and neural network-genetic algorithm (NNGA) model were initially evaluated to predict SWE in the Samsami basin of Iran. Afterwards, ordinary kriging (OK) technique was applied to interpolate the SWE values estimated by the best-performed model. For this regard, seven different MNLR, ANN and NNGA models comprising various combinations of climatic and topographic parameters including elevation (El.), slope (S), north–south (N-S) and east–west (E-W) aspects, maximum upwind slope (Sx), longitude (X) and latitude (Y) were developed to evaluate degree of effect of each of these parameters on SWE. The different experiment results showed that the NNGA5 model characterized by Delta-Bar-Delta learning algorithm and Sigmoid activation function with inputs of El., Sx, N-S aspects, S and X performed best in estimating SWE. In general, the results indicated that the NNGA technique was the most suitable method for estimation of SWE in the study area. The ANN and MNLR models were identified as the next categories, respectively. The sensitivity analysis revealed that El. and Sx were more important parameters influencing SWE than the other input parameters.