Empirical assessment of rockfall and debris flow risk along the Karakoram Highway,Pakistan

Natural Hazards - The Karakoram Highway links north Pakistan with southwest China. It passes through unique geomorphological, geological and tectonic setting. This study focused 200-km-long section...     

Wetting Induced Deformation of Soils Triggering Landslides in Pakistan

Geotechnical and Geological Engineering - Shallow and deep seated landslides in natural slopes are often induced by rainfall. The cause of the failure is usually considered to be due to the...     

Prediction criteria for groundwater potential zones in Kemuning District,Indonesia using the integration of geoelectrical and physical parameters

The presence of groundwater is strongly related to its geological and geohydrological conditions.It is,however,important to study the groundwater potential in an area before it is utilized to provide clean water.Werner-Schlumberger’s method was used to analyze the groundwater potential while hydraulic properties such as soil porosity and hydraulic conductivity were used to determine the quality and ability of the soil to allow water’s movement in the aquifer.The results show that the aquifer in the Sekara and Kemuning Muda is at a depth of more than 6 meters below the ground level with moderate groundwater potential.It is also found that the aquifer at depths of over 60 m have high groundwater potential.Moreover,soil porosity in Kemuning is found to be average while the ability to conduct water was moderate.This makes it possible for some surface water to seep into the soil while the remaining flows to the rivers and ditches.     

Statistically downscaled probabilistic multi‐model ensemble projections of precipitation change in a watershed

This paper presents the development of a probabilistic multi‐model ensemble of statistically downscaled future projections of precipitation of a watershed in New Zealand. Climate change research based on the point estimates of a single model is considered less reliable for decision making, and multiple realizations of a single model or outputs from multiple models are often preferred for such purposes. Similarly, a probabilistic approach is preferable over deterministic point estimates. In the area of statistical downscaling, no single technique is considered a universal solution. This is due to the fact that each of these techniques has some weaknesses, owing to its basic working principles. Moreover, watershed scale precipitation downscaling is quite challenging and is more prone to uncertainty issues than downscaling of other climatological variables. So, multi‐model statistical downscaling studies based on a probabilistic approach are required. In the current paper, results from the three well‐reputed statistical downscaling methods are used to develop a Bayesian weighted multi‐model ensemble. The three members of the downscaling ensemble of this study belong to the following three broad categories of statistical downscaling methods: (1) multiple linear regression, (2) multiple non‐linear regression, and (3) stochastic weather generator. The results obtained in this study show that the new strategy adopted here is promising because of many advantages it offers, e.g. it combines the outputs of multiple statistical downscaling methods, provides probabilistic downscaled climate change projections and enables the quantification of uncertainty in these projections. This will encourage any future attempts for combining the results of multiple statistical downscaling methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows

ABSTRACT

In this study, the distributed catchment-scale model, DiCaSM, was applied on five catchments across the UK. Given its importance, river flow was selected to study the uncertainty in streamflow prediction using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology at different timescales (daily, monthly, seasonal and annual). The uncertainty analysis showed that the observed river flows were within the predicted bounds/envelope of 5% and 95% percentiles. These predicted river flow bounds contained most of the observed river flows, as expressed by the high containment ratio, CR. In addition to CR, other uncertainty indices – bandwidth B, relative bandwidth RB, degrees of asymmetry S and T, deviation amplitude D, relative deviation amplitude RD and the R factor – also indicated that the predicted river flows have acceptable uncertainty levels. The results show lower uncertainty in predicted river flows when increasing the timescale from daily to monthly to seasonal, with the lowest uncertainty associated with annual flows.     

Toward an Ecological Explanation of Agricultural Unemployment in Bangladesh

Cultural ecology theoretical framework was found suitable to explain unemployment in agriculture in three villages in Bangladesh. Path analysis and multiple regression statistics were used to examine the direct, indirect, and total effects of eight independent human, social, environmental, and technology variables as well as their aggregate contribution on agricultural unemployment. The multiple regression model explains 86.2% of the total variation in unemployment in agriculture; they were followed by environmental constraints and labor saving technology variables.     

Source Sector Contributions to Aerosol Levels in Pakistan

Urban air pollution in Pakistan is a serious challenge and it causes significant damage to human health and ecosystems. This paper presents a modelling study using the Weather Research and Forecasting Model coupled with Chemistry(WRF-Chem) to simulate the spatial distributions and temporal variations of aerosol concentrations over Pakistan, focusing on contributions of domestic emission sectors(transport, industry, residential, and energy) to mass concentrations of sulfate(SO2–4), nitrate(NO–3), ammonium(NH+4), black carbon(BC), and organic carbon(OC) during the months of January, April, July, and October in 2010. Sensitivity studies indicate that, averaged over January, April, July, and October of 2010, energy and industry sectors have the largest contributions to SO2–4 concentrations, each of which contributes about 10%?20% to SO2– 4over the polluted eastern Pakistan. The contributions from residential and transport sectors to NO–3 concentrations reach 40%?50% in central Pakistan. The residential sector has the highest contribution of 50%–80% to BC and OC loading in northeastern and southern Pakistan. Examination of sector contributions to aerosol levels in Lahore, the most polluted city in Pakistan, suggests that reductions in emissions in the residential sector should be an efficient measure for improving particulate matter air quality in this region.     

A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium

To investigate climate variability in Asia during the last millennium, the spatial and temporal evolution of summer (June–July–August; JJA) temperature in eastern and south-central Asia is reconstructed using multi-proxy records and the regularized expectation maximization (RegEM) algorithm with truncated total least squares (TTLS), under a point-by-point regression (PPR) framework. The temperature index reconstructions show that the late 20th century was the warmest period in Asia over the past millennium. The temperature field reconstructions illustrate that temperatures in central, eastern, and southern China during the 11th and 13th centuries, and in western Asia during the 12th century, were significantly higher than those in other regions, and comparable to levels in the 20th century. Except for the most recent warming, all identified warm events showed distinct regional expressions and none were uniform over the entire reconstruction area. The main finding of the study is that spatial temperature patterns have, on centennial time-scales, varied greatly over the last millennium. Moreover, seven climate model simulations, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), over the same region of Asia, are all consistent with the temperature index reconstruction at the 99 % confidence level. Only spatial temperature patterns extracted as the first empirical orthogonal function (EOF) from the GISS-E2-R and MPI-ESM-P model simulations are significant and consistent with the temperature field reconstruction over the past millennium in Asia at the 90 % confidence level. This indicates that both the reconstruction and the simulations depict the temporal climate variability well over the past millennium. However, the spatial simulation or reconstruction capability of climate variability over the past millennium could be still limited. For reconstruction, some grid points do not pass validation tests and reveal the need for more proxies with high temporal resolution, accurate dating, and sensitive temperature signals, especially in central Asia and before AD 1400.     

A study of aerosol properties over Lahore (Pakistan) by using AERONET data

It is well established that aerosols affect the climate in a variety of ways. In order to understand these effects, we require an insight into the properties of aerosols. In this paper we present a study of aerosol properties such as aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol radiative forcing (ARF) over mega city of Lahore (Pakistan). The data from Aerosol Robotic Network (AERONET) have been used for the period December 2009 to October 2011. The seasonal average values of AOD, asymmetry parameter (ASY) and volume size distribution in coarse mode were observed to be highest in summer. On the other hand, the average values of Angstrom exponent (AE) and imaginary part of refractive index (RI) were found to be maximum in winter. The average value of real part of RI was found to be higher in spring than in all other seasons. The SSA exhibited an increasing trend with wavelength in the range 440 nm–1020 nm in spring, summer and fall indicating the dominance of coarse particles (usually dust). However, a decreasing trend was found in winter in the range 675 nm–1020 nm pointing towards the dominance of biomass and urban/industrial aerosols. As far as aerosol radiative forcing (ARF) is concerned, we have found that during the spring season ARF was lowest at the surface of Earth and highest at top of the atmosphere (TOA). This indicates that the atmosphere was warmer in spring than in all the remaining seasons.