Search

An effective planning and management to deal with potential impacts of drought requires accurate estimation and analysis of this natural complex phenomenon. Application of new fusion approaches using high-resolution satellite-based products, unlike ground-based observations, can provide accurate drought analysis. This study examines three advanced fusion-based methodologies including Ordered Weighted Averaged (OWA) approach based on ORNESS weighting method (ORNESS-OWA) and ORLIKE weighting method (ORLIKE-OWA) as well as K-nearest neighbors algorithm (KNN) to fuse estimations by five individual estimator models using different remotely sensed data products. The precipitation data from Global Precipitation Climatology Project (GPCP), CPC Merged Analysis of Precipitation (CMAP), CICS High-Resolution Optimal Interpolation Microwave Precipitation from Satellites (CHOMPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM), The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) and Global Land Data Assimilation System Version-2 (GLDAS-2) products is utilized in estimating nonparametric-SPI as a meteorological drought index versus ground-based observations analysis. To achieve more accurate drought estimation, ground-based observations are classified in different clusters based on K-means clustering algorithm. Five individual Artificial Intelligence (AI) models including Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), M5P model tree, Group Method of Data Handling (GMDH) and Support Vector Regression (SVR) are developed for each cluster and their best results are used in fusion process. In addition, the Genetic Algorithm (GA) optimization model is utilized to determine optimal weights in weighting methods. Estimation performance of all models are evaluated using statistical error indices of Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R 2 ). Application of proposed methodology is verified over Fars province in Iran and the results are compared. Results showed that ORNESS-OWA method with lowest estimation error (MARE of 2.51% and R 2 of 95%) had the superb performance in comparison with all other individual AI and fusion-based models. Also, the proposed framework based on remotely sensed precipitation data and fusion-based models demonstrated an effective proficiency in drought estimation. [ABSTRACT FROM AUTHOR]

A rib waveguide structure with a core of As2Se3, upper cladding of air and lower cladding of MgF2 is proposed for generation of a very broad and flat supercontinuum spectrum. Generation of a flat and broad supercontinuum requires high pumping power, but increasing the power leads to material failure in the waveguide. Here we have designed a structure with a high nonlinear coefficient, which can generate a very broad and flat supercontinuum with a width of more than 11 μm by using a pumping power of only 100 W. To the extent that we have investigated the literature, there is no report so far on such a supercontinuum with only an input power of 100 W in waveguides. The appropriate dispersion profile of the structure has resulted in zero dispersion wavelengths (ZDW) at two wavelengths of 2.1 μm and 2.8 μm. Therefore, this makes the laser pump suitable for the designed structure to be available commercially. The high refractive index difference between the core and the cladding, as well as the decrease of the pumping wavelength have led to the obtained small effective mode area. As a result, the high nonlinear coefficient of 65.4 W −1 m −1 is obtained at the central wavelength of 2.4 μm. Our numerical simulations and analyses indicate that by applying a laser pump with a pulse duration of 85 fs and an input power of 100W to the proposed waveguide having only a length of 1 cm, the output supercontinuum is achieved covering the wavelength range of 1.5 μm to more than12 μm, (at the −30 dB level from the peak). [ABSTRACT FROM AUTHOR]

Paddy drying is an energy-intensive process which affects final product quality. Energy consumption and rice milling quality upon drying paddy with an industrial horizontal rotary dryer (IHRD) developed in northern Iran were investigated and compared with those of the conventional industrial batch-type bed dryer (IBBD) at five paddy final moisture levels (8.0, 9.0, 10.0, 11.0, and 12.0%, w.b.). The results indicated that the IHRD consumed specific electrical energy between 5.5 and 17.41 MJ kg −1 water evaporated compared to 2.64–7.48 MJ kg -1 water evaporated in IBBD and specific thermal energy between 11.5 and 36.44 MJ kg −1 water evaporated in IHRD compared to 7.78–22.09 MJ kg −1 water evaporated in IBBD with a decrease in paddy final moisture content in the range of 12.0%–8.0% (w.b.). Thermal energy use efficiency was estimated to be as 38.8% and 26.3% for IBBD and IHRD, respectively, at moisture drop range of 14.5%–12%, w. b. Major milling quality attributes of the final product of paddy dried with IHRD were significantly superior to those of IBBD. It was concluded that the drying time of IHRD can be cut down about 26% without any significant changes in final milled rice quality; resulting to reduce the IBBD's total specific energy use around 10.43 MJ kg −1 water evaporated. Completely discharging the drying air and kick turning the drying drum of IHRD with a suitable time interval were proposed as a subject of investigation to improve the energy efficiency of the tested dryer. [ABSTRACT FROM AUTHOR]

The development of methods for improving the assessment of resilience of socio-ecological systems has become increasingly important as the severity and variability of global climate patterns continue to compound and intensify. The present study was conducted with the aim of using a dynamically-coupled bio-physical and socioeconomic model to test the efficacy of stakeholder-defined policy measures in conferring resilience to two agroecological variables, farm income and water-table depth, experiencing both socio-ecological shocks and climatic stress in the Rechna Doab basin of northeastern Pakistan. The objective was accomplished by using a dynamically-coupled physical and group-built system dynamics modelling framework for scenario testing and output generation, including three NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-DGGP), two relevant socio-ecological shock scenarios (market inflation and canal water supply variability), and three stakeholder-defined policy measures. Resilience was assessed using the following system functionality metrics: 1) The degree of return for each variable after a perturbation, i.e., the extent to which the observed variable returns to baseline functioning; 2) The return time of the variable to baseline functioning; 3) The rate of variable return to baseline; 4) Overall perturbation of the system post-disturbance; and 5) The corrective impact of the shock on system functionality. Differences in the resilience metrics were subsequently compared based on the behavioral change(s) of the study variables in response to the application of the three selected policy scenarios. The results presented here indicate that rainwater harvesting is the most effective stakeholder-defined policy measure for improving or maintaining resilience of the tested study variables in the Rechna Doab agricultural basin; this holds true for every climate and shock scenario with the exception of water-table depth in the upper and mid-watershed regions under canal supply shock conditions, for which canal lining is the most effective policy measure. The irrigation improvement and canal lining policies were more effective in improving the resilience metrics of water-table depth than those of the farm income variable, as the water-table depth variable is not bound so tightly by economic constraints; although there were regional differences in policy efficacy, these trends hold true, on average, for the entire watershed. The results presented herein align well with previous studies conducted in the region which note similar patterns in spatial resilience of agroecosystem variables (i.e., upper to lower watershed) in the Recha Doab basin as well as regional differences in the efficacy of the stakeholder-defined policy measures. This unique approach for assessing socio-environmental policies can help improve decision-making processes with respect to agro-infrastructure and climate change mitigation strategies in vulnerable agroecosystems. [Display omitted] • Improving the resilience of socio-ecological systems has become crucial with the intensification of global climate patterns • Quantify resilience of climate-stressed socio-agroecological variables via application of stakeholder-defined policies • Rainwater harvesting is the most effective stakeholder-defined policy measure for achieving variable-level resilience • Methods can be utilized in single-system analyses as well as comparative studies across spatial and temporal boundaries • The methods used herein can help improve socio-ecological policy development and climate change mitigation strategy [ABSTRACT FROM AUTHOR]

• This paper introduces an interactive analysis toolbox named Nexus of Food, Energy, and Water (NeFEW). • NeFEW synthesizes global water-food-energy data at the country-level. • NeFEW enables modeling interdependencies of water, food and energy resources for user-specified applications. Energy, water, and food resources are highly interdependent. Agricultural irrigation accounts for 84% of global consumptive freshwater use, the food supply chain demands up to 30% of global primary energy use, and roughly 80% of global electricity generation depends on water for cooling (an average of nearly 100 L of water withdrawn per kWh). Improving understanding of the complex interactions of this resource nexus is, therefore, a top priority for human well-being, sustainable development, and policymaking. Here, we present an interactive analysis toolbox, Ne xus of F ood, E nergy, and W ater (NeFEW), that synthesizes available global data to enable modeling and analysis of these resources and their interdependencies at the country-level and for user-specified categories and quantities. Sample analyses also presented here include country-specific estimates of water resources required to produce different types of food and energy, energy required per quantity of water or agricultural product supplied, and C O 2 -equivalent emissions associated with water and energy provision. [ABSTRACT FROM AUTHOR]

In this research, a fuzzy inference system (FIS) coupled with image processing technique was developed as a decision-support system for qualitative grading of milled rice. Two quality indices, namely degree of milling (DOM) and percentage of broken kernels (PBK) were first graded by rice processing experts into five classes. Then, images of the same samples were captured using a machine vision system. The information obtained from the sample image processing was transferred to FIS. The FIS classifier consisted of two input linguistic variables, namely, DOM and PBK, and one output variable (Quality), all in the form of triangle membership functions. Altogether, 25 rules were considered in the FIS rule base using the AND operator and Mamdani inference system. In order to evaluate the developed system, statistical performance of the FIS classifier was compared with the experts’ judgments. Results of analysis showed a 89.8% agreement between the grading results obtained from the developed system and those determined by the experts. [ABSTRACT FROM AUTHOR]

• Soil erosion is estimated at 2.32 Mg/ha/yr across all LULC classes in the US. • Croplands are responsible for 44% of soil erosion on 17% of US land. • Cultivated and other vegetated lands contribute to 90% of soil erosion. • The highest soil erosion rates are seen in corn, soybean, and wheat lands. • Future urban and cropland expansion will increase soil erosion rates in the US. Brought on by anthropogenic actions, accelerated soil erosion inflicts extreme changes in terrestrial and aquatic ecosystems. These field-scale (30 m) changes have neither been fully surveyed in the present, nor predicted for a probable future. Water-driven soil erosion (i.e., sheet and rill erosion) rates across the contiguous United States were estimated for the present, and then predicted for the future using three alternative Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (2.6, 4.5, and 8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The G2 erosion model which is integrated with Machine Learning (ML) and Remote Sensing (RS) techniques were used to estimate soil erosion based on gauge observations of long-term precipitation, and climate and land use land cover (LULC) scenarios. The baseline model (2020) estimated soil erosion rates of 2.32 Mg ha−1 yr−1 under current conservation agriculture practices (CPs). Maintaining current CPs, future scenarios predict an 8 % to 21 % increase in soil erosion under different combinations of SSP-RCP climate and LULC change scenarios. The findings of this study can help policy makers for future conservation planning on maintaining soil fertility, mitigating environmental impacts, and promoting food security. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of nitrogen rates, tillage, and planting methods on the energy use efficiency (EUE) and greenhouse gas emissions (GHG) of rapeseed (Brassica napus L.) cultivation after rice harvesting in paddy fields. A split factorial experiment in a complete randomized block design was conducted at the Rice Research Institute of Iran during 2016–2018. The conventional tillage, minimum tillage, and no-tillage methods were considered as the main plots and two planting methods of direct seeding and transplanting, and the amounts of 0, 100, 200, and 300 kg ha−1 of pure nitrogen (N) as factorial in subplots. The results showed that the treatments of CTN3 (conventional tillage × transplanting × 300 kg of N fertilizer) with the averages of 39174.8 MJ ha−1 and 1108.36 kg CO 2 eq. ha−1, and CDN3 (conventional tillage × direct seeding × 300 kg of N fertilizer) with 38386.0 MJ ha−1 and 1138.36 kg CO 2 eq. ha−1 had the highest energy input and GHG emissions, respectively. Maximum EUE and energy productivity and low GHG were obtained using the treatment combinations of no-tillage and minimum tillage at 100 kg ha−1 of nitrogen in both planting methods, suggesting them as the promising choices for planting rapeseed in Guilan province. [ABSTRACT FROM AUTHOR]

Grazing exclusion (GE) is commonly used worldwide in the management of degraded grasslands. While the effects of GE on soil organic carbon (SOC) and soil total nitrogen (STN) are intensively studied, little consensus has emerged due to highly variable background conditions. Drawing on 4081 observations from over 200 experiments worldwide, we applied a hierarchical meta-analysis to evaluate the effect of GE on SOC and STN. Overall, GE significantly increased SOC (14.74%) and STN (28.75%) compared to traditional grazing. However, the effect was mainly dependent on GE duration, site elevation and the mean annual precipitation. The improvement in SOC and STN were greatest when the duration of GE exceeded 10 years and the mean annual temperature, precipitation and elevation ranged between 0°C and 10 °C, less than or equal to 300 mm, and 1500 m–2500 m, respectively. In temperate and desert grasslands, long-term GE (>10 years) improved SOC and STN, whereas in meadow grasslands, short-term GE (≤5 years) contributed the most to their improvement. Long-term GE (>10 years) had a significant positive effect on SOC and STN in historically lightly and heavily grazed areas, but not in historically moderately grazed areas. As the response of SOC and STN under GE duration can vary from positive to negative based on different conditions, an appropriate GE duration implementation strategy should be developed in a context-dependent manner during future restoration of degraded grasslands. [ABSTRACT FROM AUTHOR]

An agent-based-Nash modeling framework has been developed to find a sustainable solution for groundwater management in Daryan Aquifer, Fars Province, Iran. This framework also includes a MODFLOW simulation model, an Artificial Neural Network (ANN), and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization model. Groundwater state was simulated using MODFLOW and it was calibrated based on the measured data provided by Regional Water Organization (RWO) of Fars Province. In order to reduce the computational time, an ANN was trained and validated based on the input-output data of the MODFLOW model to estimate groundwater level. The validated ANN was linked to a nonhomogeneous elitist NSGA-II multi-objective optimization model to find a Pareto optimal front among the three objectives of reducing irrigation water deficit, increasing equity in water allocation, and reducing groundwater drawdown, as the objectives of the three main groundwater resource stakeholders; farmers, the government executive sector, and the environmental protection institutes. The Nash bargaining model was applied to the optimal solutions in order to find a compromise among the stakeholders. Social influential factors in the study environment, and policy mechanisms to encourage agents to cooperate with the management decisions were implemented in the agent-based model. These factors include training, incentives, penalties, and social norming (neighbors' impacts), as well as considering the executive and judicial systems. After application of the agent-based model, computed optimum solutions were modified according to social conditions. Finally, the Nash bargaining model was used again to find a compromise among modified optimal objectives of the stakeholders. Implementation of this solution led to 58.3% less water extraction and approximately 3 m water level uplift. [ABSTRACT FROM AUTHOR]