Quantifying ice storage in upper Indus river basin using ground-penetrating radar measurements and glacier bed topography model version 2

Ice reserve estimates is a fundamental prerequisite for water resources management. We selected the UIB (upper Indus river basin) as study area because it contains the most abundant mid-latitude glaciers outside the polar region, however, the ice reserve estimates remain unclear due to the harsh topography. In this study, we validated the parameters of the GlabTop2 model (Glacier Bed Topography version 2) using the ice thickness measured by GPR (ground-penetrating radar) and compared the “GPR-measured ice thickness and ice bed elevation” versus “the estimated results obtained from GlabTop2.” Integrated with IDW (inverse distance weighted) interpolated results of glaciers of various sizes, a reasonable parametric scheme (τ = 120 kPa and f = 0.8) of GlabTop2 was applied on vast amounts of glaciers in the UIB region. The GlabTop2 estimates indicated that the ice thickness of the UIB varied from 0 to 736.0 ± 110.0 m, with an average value of 74.5 ± 11.2 m. A significant spatial heterogeneity exists in the sub-basins. The Shyok, Shigar and Hunza that contain the most abundant ice reserve. Lesser quantities are stored in the Western Himalaya and Hindu Kush ranges, which account for 11.3 % and 6.7 % of the total ice reserve in the UIB, respectively. A total volume of 1162.4 ± 175.1 km³ of glacier can be converted to 1046.2 ± 157.6 Gt ice reserve; this is 13.6 times the annual average discharge obtained from the outlet of the Besham hydrological station. We aim to present estimates that can provide the baseline information for glaciology study of the Indus river.     

Adaptive state estimation of groundwater contaminant boundary input flux in a 2-dimensional aquifer

In many circumstances involving heat and mass transfer issues, it is considered impractical to measure the input flux and the resulting state distribution in the domain. Therefore, the need to develop techniques to provide solutions for such problems and estimate the inverse mass flux becomes imperative. Adaptive state estimator (ASE) is increasingly becoming a popular inverse estimation technique which resolves inverse problems by incorporating the semi-Markovian concept into a Bayesian estimation technique, thereby developing an inverse input and state estimator consisting of a bank of parallel adaptively weighted Kalman filters. The ASE is particularly designed for a system that encompasses independent unknowns and /or random switching of input and measurement biases. The present study describes the scheme to estimate the groundwater input contaminant flux and its transient distribution in a conjectural two-dimensional aquifer by means of ASE, which in particular is because of its unique ability to efficiently handle the process noise giving an estimation of keeping the relative error range within 10% in 2-dimensional problems. Numerical simulation results show that the proposed estimator presents decent estimation performance for both smoothly and abruptly varying input flux scenarios. Results also show that ASE enjoys a better estimation performance than its competitor, Recursive Least Square Estimator (RLSE) due to its larger error tolerance in greater process noise regimes. ASE’s inherent deficiency of being slower than the RLSE, resulting from the complexity of algorithm, was also noticed. The chosen input scenarios are tested to calculate the effect of input area and both estimators show improved results with an increase in input flux area especially as sensors are moved closer to the assumed input location.     

Integrated coastal zone management towards Indonesia global ocean observing system (INA-GOOS): Review and recommendation

Indonesia as an archipelagos country consisting of more than 17,508 islands has a challenging problem in terms of island management and the provisional ocean services to the coastal community. The population growth (especially in coastal areas) followed by declining natural resources caused by overfishing and overexploitation is one of the biggest concerns both for the central and the local governments in Indonesia. Integrated Coastal Zone Management (ICZM) is one of the attempted solutions to overcome these difficulties, however, it is known to be unsatisfactory even after several programs under ICZM have been developed and implemented. Therefore, further facilitation of ICZM programs is necessary in order to benefit to Indonesian governmental management of their islands, coastal areas and oceans. In doing so, ICZM will also make a significant contribution towards the implementation of the Indonesia Global Ocean Observing System (INA-GOOS). This paper aims to review ICZM activities in Indonesia to obtain a better understanding of, and the need for ICZM implementations in support of their policy, sustainability and decision-making process in the direction of INA-GOOS. It is expected that the ICZM implementations will have a high impact on INA-GOOS programs.     

Bayesian modelling of clusters of galaxies from multifrequency-pointed Sunyaev–Zel'dovich observations

We present a Bayesian approach to modelling galaxy clusters using multi-frequency pointed observations from telescopes that exploit the Sunyaev–Zel'dovich effect. We use the recently developed multinest technique to explore the high-dimensional parameter spaces and also to calculate the Bayesian evidence. This permits robust parameter estimation as well as model comparison. Tests on simulated Arcminute Microkelvin Imager observations of a cluster, in the presence of primary CMB signal, radio point sources (detected as well as an unresolved background) and receiver noise, show that our algorithm is able to analyse jointly the data from six frequency channels, sample the posterior space of the model and calculate the Bayesian evidence very efficiently on a single processor. We also illustrate the robustness of our detection process by applying it to a field with radio sources and primordial CMB but no cluster, and show that indeed no cluster is identified. The extension of our methodology to the detection and modelling of multiple clusters in multi-frequency SZ survey data will be described in a future work.     

Merged remotely sensed data for geomorphological investigations in deserts: examples from central Saudi Arabia

Image merging has gained acceptance in geological remote sensing, however it has rarely been applied in geomorphology. We report on the usefulness of principal components substitution (PCS) to merge IRS panchromatic data with multispectral Thematic Mapper (TM) imagery, to map commonly encountered desert geomorphological features, and in relative age dating of alluvial surfaces. The merged data were applied to the identification and mapping of geomorphological features along two geolo-gically different mountain fronts in central Saudi Arabia. Two types of geomorphological maps have been created. A morphogenetic map that distinguishes between aeolian landforms, fluvial landforms, desert pavements, and gypsum crusts. Second, a morphochronological map, which shows the relative age of four geomorphic surfaces developed on an alluvial fan. The construction of the two maps is supported by field observations and laboratory measurements. Using the optimum index factor (OIF), a TM band 1, 5 and 7 image (of 20 merged composites) was found to be the optimum colour composite image for the geomorphological features in this arid environment. We discuss our findings in the context of the spatial and spectral properties required for applied geomorphological remote sensing.     

Spatial distribution of trace elements associated with organic carbon along the Beiyun River basin,Beijing,China

Pollution resulting from terrestrial and aquatic trace elements has become a severe problem across the world. Organic carbon(OC) content has a high affinity for metallic contaminants and it acts as a significant sink for trace elements. The decomposition of OC content directly influences the bioavailability of trace elements. The decomposition of OC content and OC distribution vary spatially, these processes affect the release of trace elements and need further research. In the current study, se...     

Changes in precipitation extremes over arid to semiarid and subhumid Punjab,Pakistan

Asymmetrical monsoons during the recent past have resulted into spatially variable and devastating floods in South Asia. Analysis of historic precipitation extremes record may help in formulating mitigation strategies at local level. Eleven indices of precipitation extremes were evaluated using RClimDex and daily time series data for analysis period of 1981–2010 from five representative cities across Punjab province of Pakistan. The indices include consecutive dry days, consecutive wet days, number of days above daily average precipitation, number of days with precipitation ≥10 mm, number of days with precipitation ≥20 mm, very wet days, extremely wet days, simple daily intensity index, maximum 1-day precipitation quantity, maximum 5 consecutive day precipitation quantity, and annual total wet-day precipitation. Mann-Kendall test and Sen’s slope extremes were used to detect trends in indices. Droughts and excessive precipitation were dictated by elevation from mean sea level with prolonged dry spells in southern Punjab and vice versa confirming spatial trends for precipitation extremes. However, no temporal trend was observed for any of the indices. Summer in the region is the wettest season depicting contribution of monsoons during June through August toward devastating floods in the region.