Effect of sulfur and iron fertilizers on yield, yield components and nutrient uptake in sesame (Sesamum indicum L.) under water stress

To evaluate the effects of sulfur and iron fertilizers on yield, yield components and nutrient uptake in sesame (Sesamum indicum L.) under water stress, a field experiment was conducted as split factorial design with three replication at Dezful, Khuzestan, Iran. Two irrigation regimes were used (well-watered and water-limited) as the main plots and subplot consisted of three levels of sulfur (B1 = 0, B2 = 100 and B3 = 200 kg.ha-1) and three foliar application of iron (C1 = 0, C2 = 3 and C3 = 6*1000 concentrations). The results showed that water stress significantly reduced biological yield (10.26%) and number of capsule per plant. Interaction between water stress and combination of iron and sulfur fertilizers had significant effect on grain yield. The highest grain yield was obtained by well water treatment and b2c2 fertilizers treatment. Interaction between water stress and combination of iron and sulfur fertilizers had significant effect on nitrogen and iron content in the seeds. The highest iron content of seeds was obtained at water stress treatment and b1c2, and highest nitrogen content was at b1c2 and b1c3 fertilizers treatments.Key words: Sesame, water stress, sulfur, iron, yield, nutrients content

Assessing the contribution of different uncertainty sources in streamflow projections

Hydrological models are commonly used to quantify the hydrological impacts of climate change using general circulation model (GCM) simulations as input. However, application of the model results with respect to future changes in streamflow scenarios remains limited by the large uncertainties stemming from various sources. Therefore, this study aimed to explore uncertainties involved in climate change impact assessment in Hulu Langat Basin, Malaysia, and define the contribution of uncertainty sources to the final uncertainty level. Hydrological model parameters, GCMs, and emission scenario uncertainties were considered the main uncertainty contributors in local-scale impact studies. The equidistant quantile matching method is used to bias-correct simulations of 19 GCMs under two emission scenarios of RCP4.5 and RCP8.5. The Soil and Water Assessment Tool (SWAT) hydrological model is next run by the bias-corrected GCM data to generate a wide spectrum of future streamflow scenarios. Projected monthly streamflow pattern under RCP8.5 showed a different temporal pattern from the observed one. Hydrological model parameter uncertainty was proven to be a larger uncertainty contributor than emission scenario during baseline climate. GCM and emission scenario uncertainties escalated as progressed in time and GCM uncertainty showed larger increments. The monthly pattern of effect of each uncertainty source varied when comparing the two periods of 2030s and 2080s. Therefore, for a superior management of water resources, a study of climate change impacts and uncertainty sources on a smaller scale than the decadal or annual scales can be more informative to the decision makers

Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting

Assessment of Moisture Status and Crop Production in Different Climate of Iran

Drought varies with regard to the time of occurrence, duration, intensity, and extent of the area affected from year to year. The objective of this study was therefore to gather and analyze standardized information on Role of Early Warning Systems by FAO56 and UNESCO models for cereals (wheat, barley, corn and rice), leguminous (bean, chickpea, lentil and alfalfa) and industrial crops (soybean, sunflower, canola, sugare beat, potato and cotton) in Iran environmental zones. To gather information on perceived risks and foreseen impacts of climatic factors on crops production, we designed a set of qualitative and quantitative data from agrometeorological and agriculture organizations in 44 stations in Iran (1961-2010). Annual average rainfall (mm.y-1) and ETo (mm.y-1) in stations with very dry climate are 76.56 and 3001.03, respectively, these rates for stations with dry climate are 195.41 mm.y-1 and 2249.44 mm.y-1, for stations with semi dry climate is 343.9 mm.y-1 and 1351.62 mm.y-1, for stations with semi humid climate is 583.8 mm.y-1 and 1153.4 mm.y-1 and for stations with humid climate is 1272.16 mm.y-1 and 949.91 mm.y-1. The maximum and minimum of Annual average rainfall happened in Rasht (1337.5 mm.y-1) and Zabol (57.7 mm.y-1) stations, and the maximum and minimum for Annual average ETo happened in Chabahar (3909.15 mm.y-1) and Anzali harbor (890.6 mm.y-1), respectively. Therefore, 13.63 percent of stations have suitable conditions for crop productions and 86.37 percent are in critical and unsustainable conditions

Trend analysis of major hydroclimatic variables in the Langat River basin, Malaysia

Trend analyses of monthly, seasonal and annual rainfall, air temperature, and streamflow were performed using Mann‐Kendall test within the Langat River basin to identify gradual trends and abrupt shifts for 1980 − 2010. Annual rainfall showed an increasing trend in upstream flow, a combination of decreasing and increasing trends in middle stream flow, and a decreasing trend in downstream flow. Monthly rainfall in most months displayed an insignificant increasing trend upstream. Stations with significant increasing trends showed larger trends in summer than those of other seasons. However, they were similar to the trends observed in annual rainfall. Annual minimum air temperature showed a significant decreasing trend upstream and significant increasing trends in the middle stream and downstream areas. Annual maximum air temperature portrayed increasing trends in both upstream and middle stream areas, and a decreasing trend for the downstream area. Both monthly and seasonal maximum air temperatures exhibited an increasing trend midstream, whereas they demonstrated trends of both decreasing and/or increasing temperatures at upstream and downstream areas. Annual streamflow in upper, middle and lower catchment areas exhibited significant increasing trend at the rates of 0.036, 0.023 and 0.001 × 103 m3/y at α = 0.01, respectively. Seasonal streamflow in the upstream, midstream and downstream areas displayed an increasing trend for spring (0.55, 0.33 and 0.013 m3/y respectively) and summer (0.51, 0.37, 0.018 m3/y respectively). The greatest magnitude of increased streamflow occurred in the spring (0.54 m3/y). Significant increasing trends of monthly streamflow were noticed in January and August, but insignificant trends were found in May, September and November at all stations. Annual streamflow records at the outlet of the basin were positively correlated with the annual rainfall variable. This study concludes that the climate of the Langat River basin has been getting wetter and warmer during 1980‐2010

A Nonlocal Elasto-Plastic Model for Structured Soils at Large Strains for the Particle Finite Element Method

This work presents a robust and mesh-independent implementation of an elasto-plastic constitutive model at large strains, appropriate for structured soils, into a Particle Finite Element code specially developed for geotechnical simulations. The constitutive response of structured soils is characterized by softening and, thus, leading to strain localization. Strain localization poses two numerical challenges: mesh dependence of the solution and computability of the solution. The former is mitigated by employing a non-local integral type regularization whereas an Implicit-Explicit integration scheme is used to enhance the computability. The good performance of these techniques is highlighted in the simulation of the cone penetration test in undrained conditions.Peer ReviewedPostprint (published version

Klang River Level Forecasting Using ARIMA and ANFIS Models

Selection of the right modeling technique is always a challenging issue because every model can produce only an approximation of the reality it is attempting to illustrate. As a result, model performance in a specific situation is the only criterion that confirms the model's applicability in that particular situation. This study investigated the applicability of the adaptive neuro-fuzzy inference system (ANFIS) and the autoregressive integrated moving average (ARIMA) models in water-level modeling. Results showed a definite preference for the ANFIS model against the simple-ARIMA model, but an updated-ARIMA model outperformed ANFIS. A mean absolute error of < 1% in each model confirmed the applicability of these models in predicting the water level in the Klang River in Malaysia. On the basis of the obtained prediction accuracy level, the updated-ARIMA and ANFIS models are introduced as reliable and accurate models for prompt decision-making, planning, and urgent managing of water resources in crisis

Uncertainty analysis of Langat river flow projections using impact-based multi-model ensemble approaches

One of the major manifestations of the climate change impacts in the 21st century in a water catchment is the precipitation—frequency and intensity—pattern alteration that may result in water scarcity. It is important therefore to define the basin-scale hydrologic features under changing/variable climate for sustainable management of water resources. Spatial changes of precipitation frequency and intensity because of climate change may influence the streamflows frequency and magnitude causing intensified floods and droughts and the associated substantial local and regional impacts on the economy. Assessment of climate change hydrological impacts deals with uncertainties resulting from the application of General Circulation Models (GCM), Greenhouse Gasses Emission Scenarios (ES), downscaling methods, and hydrological models, each with their inherent uncertainty. Uncertainty assessment of the climate change impacts on streamflow of the Hulu Langat Basin is the main objective of this study. To this end, the Soil and Water Assessment Tool (SWAT) is used to model the hydrological system of the catchment. It is calibrated based on the historical streamflow data of the catchment. An ensemble of 19 GCMs under two emission scenarios (ES) is used to provide a wide range of possible future climate scenarios. Next, bias-corrected GCM’s precipitation and temperature data were used to run the SWAT model for both the current and future climate. Uncertainty in obtained streamflow scenarios was analyzed with focus on hydrological model parameters, emission scenarios,and GCM uncertainties. This research has modified the existing uncertainty model of Reliability Ensemble Averaging (REA) to be applicable at impact level of climate studies; and a probabilistic ensemble approach that is referred to as Bootstrapped Ensemble Uncertainty Modeling (BEUM) was proposed for uncertainty modeling. In the baseline climate simulations, hydrologic model parameters uncertainty was found to be larger than the emission scenario uncertainty, while GCMs were the largest source of uncertainty. However,parameter uncertainty was the smallest source in future climate periods, while GCMs and emission scenarios were the larger sources with projections of 130% and 51% relative change in annual streamflow, respectively. The projected temporal pattern of monthly streamflow for 2070-2099 under emission scenario of RCP8.5 was found to be different from observed pattern, where the usual first peak flow of the year in April is changed to May and the lowest flow rate happens in February instead of July and August. The temporal change in uncertainty sources may have to be taken into cognizance when implementing water resources projects in the future. Based on the REA method, an approximately 3.5 and 2.9 m3/s increase in mean monthly streamflow during the 2016-2045 period respectively under the emission scenarios of RCP4.5 and RCP8.5, are anticipated. The modification applied to the REA method accommodated the inclusion of hydrological model parameter uncertainty into the total uncertainty assessment. The modified REA method was able to embrace a more reliable prediction interval compared to the original REA. In addition, a full coverage of prediction intervals was possible in the proposed BEUM method, although it proved to be computationally expensive in comparison with the REA method