The objective of this study is to adopt a methodology for analysing spatial patterns of danger of forest fire at Margalla Hills, Islamabad, Pakistan. The work is concentrated on burnt areas using Landsat data and to classify forest fire severity with different parameters (climatic, vegetation, topography and human activities). In addition to these four variables, the extent of the burned areas was measured. Statistical analysis at each fire scene was used to measure the effect on the variables. To calculate the fire severity ratio correlated to each variable, logistic and stepwise regressions were used. The results showed that the burned areas have increased at a rate of 25.848 ha/day (R 2 ¼ 0.98) if the number of total days since the start of fire has increased. As a result, forest density, distance to roads, average quarterly maximum temperature and average quarterly mean wind speed were highly correlated with the fire severity. Only average quarterly maximum temperature and forest density affected the size of the burnt areas. Prediction maps indicate that 53% of forests are in the very low severity level (0.25-0.45), 25% in the low level (0.45-0.65) and 22% in high and very high levels (>0.65).

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The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.

Floods are the most frequent and destructive natural disasters causing damages to human lives and their properties every year around the world. Pakistan in general and the Peshawar Vale, in particular, is vulnerable to recurrent floods due to its unique physiography. Peshawar Vale is drained by River Kabul and its major tributaries namely, River Swat, River Jindi, River Kalpani, River Budhni and River Bara. Kabul River has a length of approximately 700 km, out of which 560 km is in Afghanistan and the rest falls in Pakistan. Looking at the physiography and prevailing flood characteristics, the development of a flood hazard model is required to provide feedback to decision-makers for the sustainability of the livelihoods of the inhabitants. Peshawar Vale is a flood-prone area, where recurrent flood events have caused damages to standing crops, agricultural land, sources of livelihood earnings and infrastructure. The objective of this study was to determine the effectiveness of the ANN algorithm in the determination of flood inundated areas. The ANN algorithm was implemented in C# for the prediction of inundated areas using nine flood causative factors, that is, drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use. For the preparation of spatial geodatabases, thematic layers of the drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use were generated in the GIS environment. A Neural Network of nine, six and one neurons for the first, second and output layers, respectively, were designed and subsequently developed. The output and the resultant product of the Neural Network approach include flood hazard mapping and zonation of the study area. Parallel to this, the performance of the model was evaluated using Root Mean Square Error (RMSE) and Correlation coefficient (R2). This study has further highlighted the applicability and capability of the ANN in flood hazard mapping and zonation. The analysis revealed that the proposed model is an effective and viable approach for flood hazard analysis and zonation.

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Snowmelt runoff is an important element of the hydrological cycle as global warming and climate change are causing the retreat of glaciers particularly in the northern region of Pakistan. These climatic variations cause significant changes in snow and the glaciated environment causing an influence on the Indus river runoff contributed by Chitral Basin. The daily discharge of the Chitral River Basin (CRB) over the Hindukush area is simulated in this study using the Snowmelt Runoff Model (SRM) model under realistic concentration pathways and climate change scenarios in the period from 2011 to 2020. The daily temperature, precipitation, and snow cover data were used as input variables to determine discharge by using SRM. The results of the simulation showed that snow cover is sensitive to climate change, particularly when there is an increase in temperature. The Coefficient of determination (R2) values indicate that SRM is good for daily runoff simulations in combination with MODIS-derived Snow-Covered Area (SCA) and can be optimized for long-term runoff simulations in the CRB. The results of the model simulation showed that the SRM composite reliability values were 0.83-0.91 (R2) in the period from 2011 to 2020 and the snow melting is increasing with respect to time and rising temperatures. The results revealed that the sensitivity of climatic changes, particularly temperature and precipitation fluctuations from north to south, is a major factor in the spatiotemporal melting of snow over the years. The model calculations technique assists effective water resource management, promoting environmental preservation as well as community economic prosperity.

Due to limited gauge network, Pakistan presents a challenge for cryosphere, hydrological, and ecological studies. Thus, before using precipitation and temperature products for hydro-climatic applications, they must be properly assessed. This study compared six satellite-based precipitation products (SBPPs), one satellite-based temperature product (SBTP), and three temperature reanalysis products (TRPs) to in-situ gauge data to assess their accuracy using ground gauge-based rainfall measurements for the period (2000–2020). The evaluation investigated point-to-pixel data on daily, monthly, seasonal (winter, spring, summer, and fall), and yearly timescales. All products were assessed using four continuous indices (RMSE, CC, bias, and rBias) and four categorical indices (false alarm ratio, probability of detection, success ratio, and crucial success index). According to the evaluation findings, CHIRPS and IMERG outperformed soil moisture family products in daily spatial-temporal capabilities. In terms of accuracy, ERA5 outperformed other temperature products. Monthly satellite-based temperature and precipitation data and temperature reanalysis products performed better than daily estimates (CC < 0.7 and rBIAS within ± 10). On a seasonal scale, IMERG precipitation estimates and ERA5 temperature estimates agreed well with in-situ gauge estimates. In areas with moderate topography, SM2Rain-GPM and ASCAT performed effectively. The evaluation of temperature reanalysis products on the ground demonstrated higher capabilities than AMSR2 LPRM. In terms of detection probability, the ground validation of IMERG beat other SBPPs, whereas ERA5 fared best among SBT and TRPs. The probability density function (PDF) showed that all satellite-based precipitation products captured light precipitation occurrences (> 2mm/day). In general, MEERA-2 and GEOS-5 FP demonstrated moderate performance in low elevated regions, whereas ERA5 and AMSR2 LPRM demonstrated performance comparable to that of gauge estimations across the entire country. The ground evaluation suggests using IMERG's daily and monthly precipitation estimates and ERA5 monthly temperature estimates for hydroclimatic applications in Pakistan's subtropical climates.