Emerging Technologies and Synergies for Airborne and Space-Based Measurements of Water Vapor Profiles

A deeper understanding of how clouds will respond to a warming climate is one of the outstanding challenges in climate science. Uncertainties in the response of clouds, and particularly shallow clouds, have been identified as the dominant source of the discrepancy in model estimates of equilibrium climate sensitivity. As the community gains a deeper understanding of the many processes involved, there is a growing appreciation of the critical role played by fluctuations in water vapor and the coupling of water vapor and atmospheric circulations. Reduction of uncertainties in cloud-climate feedbacks and convection initiation as well as improved understanding of processes governing these effects will result from profiling of water vapor in the lower troposphere with improved accuracy and vertical resolution compared to existing airborne and space-based measurements. This paper highlights new technologies and improved measurement approaches for measuring lower tropospheric water vapor and their expected added value to current observations. Those include differential absorption lidar and radar, microwave occultation between low-Earth orbiters, and hyperspectral microwave remote sensing. Each methodology is briefly explained, and measurement capabilities as well as the current technological readiness for aircraft and satellite implementation are specified. Potential synergies between the technologies are discussed, actual examples hereof are given, and future perspectives are explored. Based on technical maturity and the foreseen near-mid-term development path of the various discussed measurement approaches, we find that improved measurements of water vapor throughout the troposphere would greatly benefit from the combination of differential absorption lidar focusing on the lower troposphere with passive remote sensors constraining the upper-tropospheric humidity.     

Improving sustainability of urban drainage systems for climate change adaptation using best management practices: a case study of Tehran,Iran

Although the effectiveness of best management practices (BMPs) in reducing urban flooding is widely recognized, the improved sustainability achieved by implementing BMPs in upstream suburban areas, reducing downstream urban floods, is still debated. This study introduces a new definition of urban drainage system (UDS) sustainability, focusing on BMP usage to enhance system performance after adaptation to climate change. Three types of hydraulic reliability index (HRI) plus robustness and improvability indices were used to quantify the potential enhanced sustainability of the system in a changing climate, together with a climate change adaptability index (CCAI). The sustainability of UDS for the safe conveyance of storm-water runoff was investigated under different land-use scenarios: No BMP, BMP in urban areas, and BMP inside and upstream of urban areas, considering climate change impacts. Rainfall–runoff simulation alongside drainage network modelling was conducted using a storm-water management model (US EPA SWMM) to determine the inundation areas for both base-line and future climatic conditions. A new method for disaggregating daily rainfall to hourly, proposed to provide a finer resolution of input rainfall to SWMM, was applied to a semi-urbanized catchment whose upstream runoff from mountainous areas may contribute to the storm-water runoff in downstream urban parts. Our findings confirm an increase in the number of inundation points and reduction in sustainability indices of UDS due to climate change. The results present an increase in UDS reliability from 4% to 16% and improvements in other sustainability indicators using BMPs in upstream suburban areas compared to implementing them in urban areas.     

System dynamic modeling to assess the effect of subsurface drain spacing and depth on minimizing the environmental impacts

The excessive loss of soil nitrogen through drainage losses causes different environmental problems. The depth and spacing drain of drains play an important role in the quality and quantity of discharged drainage into the environment. In this paper, a simple but comprehensive model using system dynamic approach for water cycle and nitrogen dynamics was used to simulate the effect of drain depth and spacing on nitrate and ammonium losses in a sugarcane agro-industrial company. Twenty-four scenarios were modeled including the combination of four different drain depths and six drain spacing to compare the effect of drain depth and spacing on the nitrogen uptake by plant, denitrification, net mineralization, the amount of ammonium losses through runoff, nitrate and ammonium losses through drainage water, the sum of excessive water, the stress day index and the relative yield. The results indicated that optimal drainage system density is obtained in the depth of 1.1 m and spacing of 80 m, in a way that the total drainage losses would be reduced up to an acceptable level. The optimum designing of the drainage systems according to environmental criteria can control nitrogen pollution load at farm level and can therefore have appropriate results both in terms of economic and environmental considerations.     

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.     

Designing infill directional drilling in mineral exploration by using particle swarm optimization algorithm

Geostatistical optimization in designing infill boreholes is an important cost-effective approach in increasing the accuracy of the tonnage and grade of an ore deposit. In this research, a new approach is proposed to design the optimum infill directional boreholes. In the proposed approach, the Kriging estimation variance is considered as the objective function and the number and properties of the optimum boreholes are estimated to minimize the objective function. The optimization procedure is implemented by Particle Swarm Optimization (PSO) algorithm. Range of the spatial and directional properties of new boreholes is determined by considering the primary information of the mineralization and administrative constraint of drilling. Then, the PSO algorithm is iteratively applied, and in each iteration, the variation of the estimated Kriging variance after drilling the new boreholes is determined and properties of the new boreholes are updated. The iterative procedure of the algorithm is continued until minimum Kriging variance is satisfied. The approach was applied to the Dalli Cu-Au porphyry deposit in Iran and three new infill directional boreholes were designed by considering six earlier boreholes from the preliminary exploration stage. New optimum boreholes were located where less information from the preliminary exploration stage exists and the highest variance is considered. Two new boreholes are near to vertical (78°) and the third is an inclined with 55° dip. By drilling these three new boreholes, the estimated grade model could be upgraded by 20%. For simplicity, quickness and the ability to search for the required numbers and specifications of a group of directional boreholes in a 3D environment are the most advantages aspects of the proposed approach.     

Establishing a predictive performance indicator for real‐time hybrid simulation

Real‐time hybrid simulation (RTHS) is increasingly being recognized as a powerful cyber‐physical technique that offers the opportunity for system evaluation of civil structures subject to extreme dynamic loading. Advances in this field are enabling researchers to evaluate new structural components/systems in cost‐effective and efficient ways, under more realistic conditions. For RTHS, performance metric clearly needs to be developed to predict and evaluate the accuracy of various partitioning choices while incorporating the dynamics of the transfer system, and computational/communication delays. In addition, because of the dynamics of the transfer system, communication delays, and computation delays, the RTHS equilibrium force at the interface between numerical and physical substructures is subject to phase discrepancy. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In this paper, a new performance indicator, predictive performance indicator (PPI), is proposed to assess the sensitivity of an RTHS configuration to any phase discrepancy resulting from transfer system dynamics and computational/communication delays. The predictive performance indicator provides a structural engineer with two sets of information as follows: (i) in the absence of a reference response, what is the level of fidelity of the RTHS response? and (ii) if needed, what partitioning adjustments can be made to effectively enhance the fidelity of the response? Moreover, along with the RTHS stability switch criterion, this performance metric may be used as an acceptance criteria for conducting single‐degree‐of‐freedom RTHS. Copyright © 2014 John Wiley & Sons, Ltd.