The Climate Near the Ground

. Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

Deriving global flood hazard maps of fluvial floods through a physical model cascade

The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin's water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members.

Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes

The issues related to the appropriate planning of the territory are particularly pronounced in highly inhabited areas (urban areas), where in addition to protecting the environment, it is important to consider an anthropogenic (urban) development placed in the context of sustainable growth. This work aims at mathematically simulating the changes in the land use, by implementing an artificial neural network (ANN) model. More specifically, it will analyze how the increase of urban areas will develop and whether this development would impact on areas with particular socioeconomic and environmental value, defined as multifunctional areas. The simulation is applied to the Chianti Area, located in the province of Florence, in Italy. Chianti is an area with a unique landscape, and its territorial planning requires a careful examination of the territory in which it is inserted.

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Artificial neural network for multifunctional areas

Winter westerly disturbances (WWD) are the primary climatic influence within High Mountain Asia during the boreal winter. Here we investigate variations and changes in WWD over the period 1979–2010 and relationships with heavy (85th percentile) precipitation in the Karakoram/western Himalaya (KH) and central Himalaya (CH) using multiple datasets. We show that heavy precipitation events occurring in the KH and CH are often spatiotemporally independent, suggesting differing behavior of WWD affecting each region. The wavelet power spectrum of 200 hPa geopotential height anomalies is used to characterize the frequency and magnitude of individual disturbances and to distinguish synoptic scale variability through time. Our analysis exhibits an enhancement in the strength and frequency of WWD in the KH and indicates an increase in local heavy precipitation events. In contrast, the CH is observed to experience weakening influence of these disturbances and consequently, a decrease in heavy precipitation. Furthermore, we investigate multi-annual variability of WWD and teleconnections with some known modes of climate variability affecting central Asia, including the Arctic Oscillation, Eurasian/Polar Pattern, the El Nino Southern Oscillation, and the Siberian High. Although there is clear evidence that these modes affect circulation and precipitation in High Mountain Asia, their competing influences on WWD are complex and non-linear. These results suggest that a thorough understanding of WWD and their spatiotemporal variations are crucial to improve our knowledge of the hydrologic cycle within High Mountain Asia as well as our ability to project the future status of Asia’s water resources.

Multi-Annual Variations in Winter Westerly Disturbance Activity Affecting the Himalaya

Over the past 30 years, Land development and consumption have been out of control and expanding out of order, especially to marginal areas of some metropolises in Pakistan. In this research, the dynamic change of urban sprawl and its spatial and temporal characteristics was analyzed for Karachi division, Pakistan. To achieve the goal different image processing techniques were applied i.e., (i) Supervised, (ii) Unsupervised image classification and (iii) Normalized Difference Built-Up Index on Landsat imageries for the years 1991, 2000 and 2013. Unsupervised classification technique was proved to be more effective with an overall agreement of 81%. The study concluded that from 1991-2000, the urbanization in Karachi was increased from 486-729.2 km 2 , whereas from 2000-2013 the urbanization was almost double i.e., 1582.5 km 2 . Apart from the derived results, this study also proved the potentials of remote sensing data and effectiveness of demonstrated/proposed techniques in urban geographic studies.

https://docsdrive.com/pdfs/std/std/2015/179-188.pdf

Remote Sensing and GIS Applications for Assessment of Urban Sprawl in Karachi, Pakistan

The Upper Indus basin (UIB) is characterized by contrasting hydrometeorological behaviors; therefore, it has become pertinent to understand hydrometeorological trends at the sub-watershed level. Many studies have investigated the snow cover and hydrometeorological modeling at basin level but none have reported the spatial variability of trends and their magnitude at a sub-basin level. This study was conducted to analyze the trends in the contrasting hydrological regimes of the snow and glacier-fed river catchments of the Hunza and Astore sub-basins of the UIB. Mann-Kendall and Sen’s slope methods were used to study the main trends and their magnitude using MODIS snow cover information (2001–2015) and hydrometeorological data. The results showed that in the Hunza basin, the river discharge and temperature were significantly (p ≤ 0.05) decreased with a Sen’s slope value of −2.541 m3·s−1·year−1 and −0.034 °C·year−1, respectively, while precipitation data showed a non-significant (p ≥ 0.05) increasing trend with a Sen’s slope value of 0.023 mm·year−1. In the Astore basin, the river discharge and precipitation are increasing significantly (p ≤ 0.05) with a Sen’s slope value of 1.039 m3·s−1·year−1 and 0.192 mm·year−1, respectively. The snow cover analysis results suggest that the Western Himalayas (the Astore basin) had a stable trend with a Sen’s slope of 0.07% year−1 and the Central Karakoram region (the Hunza River basin) shows a slightly increasing trend with a Sen’s slope of 0.394% year−1. Based on the results of this study it can be concluded that since both sub-basins are influenced by different climatological systems (monsoon and westerly), the results of those studies that treat the Upper Indus basin as one unit in hydrometeorological modeling should be used with caution. Furthermore, it is suggested that similar studies at the sub-basin level of the UIB will help in a better understanding of the Karakoram anomaly.

Investigating Snow Cover and Hydrometeorological Trends in Contrasting Hydrological Regimes of the Upper Indus Basin

Remote Sensing has made substantial contribution in flood monitoring and damage assessment. This study implies 1. The remotely sensed multi-temporal MODIS data for mapping and delineation of flood extents. 2. Conduct field survey in Punjab province for multi sector damage assessment and 3. Develop flood damage index map using Multi- Criterion Decision Analysis (MCDA). Supervised image classification and Modified Normalized Difference Water Index (MNDWI) techniques were utilized to delineate the flood inundated area. Flood damage was assessed by analysing 68 different social and physical parameters recorded from the field. By incorporating these parameters along with flood extents the damage index map was produced and segregated on the basis of level of severity. The results revealed that the maximum inundation of 2007.4 km2 was occurred at Jhang district because of the breach of Trimmu Barrage. As per flood damage index, total 9 out of 36 districts were severely affected by flood of Chenab and Jhelum rivers and experienced the major loss of human life, agriculture, live stocks and infrastructure. This study concludes that remote sensing and GIS is very effective and valuable approach for mapping the flood dynamics and hazard assessment.

Spatio-Temporal Mapping and Multi-Sector Damage Assessment of 2014 Flood in Pakistan using Remote Sensing and GIS

Glacier avalanches are natural hazards that could damage infrastructures and threaten lives in high-altitude mountainous terrains. On April 7, 2012, a massive ice avalanche struck a Pakistani base at Gayari sector, Saltoro Valley, and buried/killed 148 soldiers and civilians. Keeping in view the catastrophe, a study was designed with the objectives to: 1) model and simulate the Gayari sector glacier avalanche incident in terms of height, extent, velocity, pressure, and momentum; 2) generate hazard risk assessment of possible other glacier avalanches in the Saltoro Valley through modeling and simulation; and 3) suitability analysis of current camp sites and recommendation of new safe camps sites locations in the Saltoro valley. To simulate the Gayari sector glacier event and other Glacier possible avalanches, a physical process-based rapid mass movements (RAMMS) was used. The RAMMS has two main components, i.e., Voellmy–Salm (VS) model and random kinetic energy, which deals with variables such as avalanche height and the mean avalanche velocity during the course of simulation. The suitability analysis of current camp sites were achieved using weighted overlay analysis with different constraints in ArcGIS Spatial Analyst. The RAMMS model simulation of the Gayari avalanche event predicted a maximum velocity of 74 ms -1, generating a pressure of 5074 kPa and attaining a height of 45 m, whereas the predicted debris volume on the ground was 3.8145 million m 3. A good agreement was found between actual debris height and extent, as compared with the RAMMS model output. The RAMMS model simulated all the potential tributary glacier avalanches of Saltoro valley very well. The weighted overlay analyses in ArcGIS Spatial Analyst revealed that the existing camp sites are safe and were not threatened by the glacier avalanche hazard. However, it was recommended that the Gayari camp should not be constructed at the same location and should be relocated to the proposed safe camp sites identified in this research study. The proposed methodology developed in the current study could be applied in the Siachen conflict zone for avalanche hazard/risk analysis of all the camp sites located in the valley.

Modeling and Simulation of Glacier Avalanche: A Case Study of Gayari Sector Glaciers Hazards Assessment

exploration and exploitation of mineral resources to sustain their economic growth. Usually, the traditional mineral exploration techniques require enormous finances, prolonged time, and tremendous manpower, particularly in areas that are not easily reachable (Maduaka, 2014). Furthermore, mineral exploration required state-of-the-art techniques and expertise along with geological, geochemical, and geophysical datasets, which may not be easily available or may be lacking where access is problematic (Kaiser et al., 2002; Bemis et al., 2014). Modern remote sensing technology has proved to be one of the highly efficient and robust techniques used for mineral exploration. The use of remote sensing satellite images for geological mapping and mineral exploration usually involves studying the physicochemical properties of rocks and weathering soils, such as mineralogy, landforms, geochemical signatures, and the spatiall distribution of lineaments (Bhattacharya et al., 2012). A fundamental principle of mineral exploration is that it is quite possible that undiscovered deposits will be located in the close vicinity of discovered ones. For example, if mining is taking place in a particulat area, then similar minerals will be more likely found nearer to the discovered deposit, and as the distance increases, the likelihood of new discoveries will decrease. In that situation, before drilling exploratory boreholes at new locations, remote sensing can be used effectively to identify regions with higher chances of mineralization, mainly through multior hyperspectral remote sensing images (Gholami, Moradzadeh, and Yousef, 2012; Ciampalini et al., 2013). The use of reflectance spectroscopic information derived from remote sensing data allows effective localization of mineral exploration and reduces the cost and time spent on fieldwork for geological, geophysical, and geochemical studies (Short and Lowman Jr, 1973; Tedesco, 2012; Marjoribanks, 2010). Several remote sensing studies for mineral exploration and lithological mapping have been done in arid and semi-arid regions. In areas with good geological exposure, satellites in orbit are capable of acquiring spectral reflectance data directly from rock or/and soils (Sabins, 1999; Di Tommaso and Rubinstein, 2007; Zhang et al., 2007; Pour and Hashim, 2012; Mahboob, Iqbal, and Atif, 2015). Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data from Landsat

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Iqra Atif

Papers

Remote Sensing and GIS Applications for Assessment of Urban Sprawl in Karachi, Pakistan

Investigating Snow Cover and Hydrometeorological Trends in Contrasting Hydrological Regimes of the Upper Indus Basin

Spatio-Temporal Mapping and Multi-Sector Damage Assessment of 2014 Flood in Pakistan using Remote Sensing and GIS

Modeling and Simulation of Glacier Avalanche: A Case Study of Gayari Sector Glaciers Hazards Assessment

Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data from Landsat