Quantifying climate internal variability using an hourly ensemble generator over South Korea

Identifying climate internal variability (CIV) generated by non-linear interactions and feedbacks among many components of the climate system is essential and challenging because of its irreducible and unpredictable characteristics. A range of studies have addressed this issue; however, these studies focused on the first order moments of few representative climate variables at relatively larger spatial and temporal scales. To investigate the magnitude and the spatial pattern of CIV relevant at finer spatial (point) and temporal (hourly) scales, CIV is assessed over a 30-year period in South Korea by analyzing 100-member ensemble generated using an hourly weather generator and a bootstrapping approach. Statistics addressing the first and second order moments, occurrences, and extremes are successfully verified at various temporal scales. The CIV is then estimated by the ‘detrended’ and ‘differenced’ methods for the four metrics proposed at different scales that signify rainfall volume, maxima, and occurrence. Consequently, the implications of this study are the following: (1) the estimation of CIV using bootstrapped ensembles often fails to represent the proper uncertainty range, resulting in high chances of underestimating extreme statistics, such as the maximum rainfall depth; (2) regardless of which of the two methods is used, no significant difference in the CIV estimation is observed; and (3) a temporal scale-dependency is observed for the proposed metrics used to identify the magnitude and the seasonal pattern of the CIV—the utility of an hourly time series and its associated extreme properties deserves significant attention. Ultimately, the spatial mapping and grouping of CIV will provide valuable information to identify which regions have high variability compared to climatological norms and thus are more vulnerable to extremes, and will serve as a guide for planning adaptation and mitigation measures against future extreme events.     

Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine

With recent advances in downscaling methodologies, soil moisture (SM) estimation using microwave remote sensing has become feasible for local application. However, disaggregation of SM under all sky conditions remains challenging. This study suggests a new downscaling approach under all sky conditions based on support vector regression (SVR) using microwave and optical/infrared data and geolocation information. Optically derived estimates of land surface temperature and normalized difference vegetation index from MODerate Resolution Imaging Spectroradiometer land and atmosphere products were utilized to obtain a continuous spatio-temporal input datasets to disaggregate SM observation from Advanced SCATterometer in South Korea during 2015 growing season. SVR model was compared to synergistic downscaling approach (SDA), which is based on physical relationship between SM and hydrometeorological factors. Evaluation against in situ observations showed that the SVR model under all sky conditions (R: 0.57 to 0.81, ubRMSE: 0.0292 m³ m^?3 to 0.0398 m³ m^?3) outperformed coarse ASCAT SM (R: 0.55 to 0.77, ubRMSE: 0.0300 m³ m^?3 to 0.0408 m³ m^?3) and SDA model (mean R: 0.56 to 0.78, ubRMSE: 0.0324 m³ m^?3 to 0.0436 m³ m^?3) in terms of statistical results as well as sensitivity with precipitation. This study suggests that the spatial downscaling technique based on remote sensing has the potential to derive high resolution SM regardless of weather conditions without relying on data from other sources. It offers an insight for analyzing hydrological, climate, and agricultural conditions at regional to local scale.     

Traffic ‐and Industry‐Related Air Pollution Exposure Assessment in an Asian Megacity

Like other Asian countries, Pakistan is facing the issue of air pollution due to rapid urbanization, enormous transportation increases, and other related human activities. Moreover, continuously increasing emission sources have not only raised pollutant concentrations but also their types, thus damaging both human health and the environment. Faisalabad is the third largest megacity of Pakistan and its state of air quality is getting worse due to factors such as industrialization, high traffic volumes, and extensive fossil‐fuel‐burning activities. This review article aims to highlight the present status of air pollution in this city with special reference to particulate matter, elemental profiles, gaseous pollutants, organic–inorganic particulate contents as well as their sources. The concentration levels of these entities were also compared with other national and international cities, and related environmental standards. It is found that current levels of these pollutants are beyond safety limits as specified by various environment protection agencies and organizations. Several weak aspects and gaps are also identified along with suggestions for improvements of the present situation and directions for future research.     

Look-ahead vertical seismic profiling inversion approach for density and compressional wave velocity in Bayesian framework

To reduce drilling uncertainties, zero-offset vertical seismic profiles can be inverted to quantify acoustic properties ahead of the bit. In this work, we propose an approach to invert vertical seismic profile corridor stacks in Bayesian framework for look-ahead prediction. The implemented approach helps to successfully predict density and compressional wave velocity using prior knowledge from drilled interval. Hence, this information can be used to monitor reservoir depth as well as quantifying high-pressure zones, which enables taking the correct decision during drilling. The inversion algorithm uses Gauss–Newton as an optimization tool, which requires the calculation of the sensitivity matrix of trace samples with respect to model parameters. Gauss–Newton has quadratic rate of convergence, which can speed up the inversion process. Moreover, geo-statistical analysis has been used to efficiently utilize prior information supplied to the inversion process. The algorithm has been tested on synthetic and field cases. For the field case, a zero-offset vertical seismic profile data taken from an offshore well were used as input to the inversion algorithm. Well logs acquired after drilling the prediction section was used to validate the inversion results. The results from the synthetic case applications were encouraging to accurately predict compressional wave velocity and density from just a constant prior model. The field case application shows the strength of our proposed approach in inverting vertical seismic profile data to obtain density and compressional wave velocity ahead of a bit with reasonable accuracy. Unlike the commonly used vertical seismic profile inversion approach for acoustic impedance using simple error to represent the prior covariance matrix, this work shows the importance of inverting for both density and compressional wave velocity using geo-statistical knowledge of density and compressional wave velocity from the drilled section to quantify the prior covariance matrix required during Bayesian inversion.     

Addition of Phosphorus and Nitrogen Support the Invasiveness of Alternanthera Philoxeroides Under Water Stress

Coordination of plant functional traits with changes in the environment is helpful to understand the mechanisms underlying both invasiveness and adaptation of plants. Thus, to investigate the performance and functional traits in invasive Alternanthera philoxeroides (Mart.), an experiment of water stress is conducted with different nutrient concentrations. Alternanthera philoxeroides plants are grown under natural and nutrient soils and subjected to three levels of nitrogen (N) and phosphorus (P) solution: ambient P and N concentration (P?N?), P addition with an ambient N concentration (P+N?), and P addition with high N concentration (P+N+) in combination with three different irrigation water levels as 1) 100% irrigation, 2) 50% irrigation, and 3) 25% deficit irrigation. Based on results, A. philoxeroides produces significantly higher biomass in both soils under 100% irrigation with P+N? treatment and exhibits higher values of leaf area and root length. However, 25% irrigation with P?N? treatment in both soils exerts a significant negative effect on relative growth rate and root/shoot ratio of A. philoxeroides plants. Under 50% irrigation in soils with both P+N? and P+N+ treatments, high values of leaf nitrogen are recorded. Moreover, nutrient soil is more supportive to A. philoxeroides than natural soil.     

Risk assessment of extreme air pollution based on partial duration series: IDF approach

The occurrences of extreme pollution events have serious effects on human health, environmental ecosystems, and the national economy. To gain a better understanding of this issue, risk assessments on the behavior of these events must be effectively designed to anticipate the likelihood of their occurrence. In this study, we propose using the intensity–duration–frequency (IDF) technique to describe the relationship of pollution intensity (i) to its duration (d) and return period (T). As a case study, we used data from the city of Klang, Malaysia. The construction of IDF curves involves a process of determining a partial duration series of an extreme pollution event. Based on PDS data, a generalized Pareto distribution (GPD) is used to represent its probabilistic behaviors. The estimated return period and IDF curves for pollution intensities corresponding to various return periods are determined based on the fitted GPD model. The results reveal that pollution intensities in Klang tend to increase with increases in the length of time between return periods. Although the IDF curves show different magnitudes for different return periods, all the curves show similar increasing trends. In fact, longer return periods are associated with higher estimates of pollution intensity. Based on the study results, we can conclude that the IDF approach provides a good basis for decision-makers to evaluate the expected risk of future extreme pollution events.     

Flood risk assessment of River Indus of Pakistan

Annual flood peak discharges is widely used in risk assessment. Major sources of flooding in Pakistan are River Jhelum, River Chenab, River Kabul, and upper and lower parts of River Indus. These rivers are major tributaries of the River Indus System which is one of the most important systems of the world and the greatest system of Pakistan. River Indus is the longest river of Pakistan containing seven gauge stations and several barrages, and it plays a vital role in the irrigation system and power generation for the country. This paper estimates the risk of flood in River Indus using historical data of maximum peak discharges. On the basis of our analysis, we find out which dam/barrage reservoir need to be updated in capacity, and whether there are more dams/barrages needed.     

Attribution of runoff change in the alpine basin: a case study of the Heihe Upstream Basin,China

Quantifying the relative contributions of different factors to runoff change is helpful for basin management, especially in the context of climate change and anthropogenic activities. The effect of snow change on runoff is seldom evaluated. We attribute the runoff change in the Heihe Upstream Basin (HUB), an alpine basin in China, using two approaches: a snowmelt-based water balance model and the Budyko framework. Results from these approaches show good consistency. Precipitation accounts for 58% of the increasing runoff. The contribution of land-cover change seems unremarkable for the HUB as a whole, where land-cover change has a major effect on runoff in each sub-basin, but its positive effect on increasing runoff in sub-basins 1 and 3 is offset by the negative effect in sub-basin 2. Snow change plays an essential role in each sub-basin, with a contribution rate of around 30%. The impact of potential evapotranspiration is almost negligible.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR S. Huang     

Vicissitudes experienced by the oldest urban center in Bangladesh in relation to the migration of the Brahmaputra River

Bangladesh is one of the most densely populated countries in the world. There are 57 trans-frontier rivers in the country, the widest being the Brahmaputra River. The river's channels have frequently changed course, but the relationship between such river migrations and human settlement patterns has remained unstudied. In this paper, optically stimulated luminescence and radiocarbon dating techniques were applied to sedimentary and organic materials in the oldest urban center (Wari-Bateshwar) in Bangladesh. The results showed that the landscape around the urban center was dominated by floodplain and peatland facies between 7.9–7.6 and 4.7–1.6 ka, respectively. Humans occupied this area at ~3.2 ka, and manufactured delicate semi-precious gemstone beads between ~2.4 and 1.8 ka. The urban center might be the same as an important city of the Gangaridai described in historical records. Due to fluvial migration at ~1.8 ka, the area surrounding the urban center was covered by fluvial sand. Humans might have been forced to abandon the urban center, leading to the bead processing technology in Bangladesh being lost forever.