Yawar Hussain

Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

Vulnerability and impact assessment of extreme climatic event: A case study of southern Punjab, Pakistan

Understanding of frequency, severity, damages and adaptation costs of climate extremes is crucial to manage their aftermath. Evaluation of PRECIS RCM modelled data under IPCC scenarios in Southern Punjab reveals that monthly mean temperature is 30°C under A2 scenario, 2.4°C higher than A1B which is 27.6°C in defined time lapses. Monthly mean precipitation under A2 scenario ranges from 12 to 15mm and for A1B scenario it ranges from 15 to 19mm. Frequency modelling of floods and droughts via poisson distribution shows increasing trend in upcoming decades posing serious impacts on agriculture and livestock, food security, water resources, public health and economic status. Cumulative loss projected for frequent floods without adaptation will be in the range of USD 66.8-79.3 billion in time lapse of 40years from 2010 base case. Drought damage function @ 18% for A2 scenario and @ 13.5% for A1B scenario was calculated; drought losses on agriculture and livestock sectors were modelled. Cumulative loss projected for frequent droughts without adaptation under A2 scenario will be in the range of USD 7.5-8.5 billion while under A1B scenario it will be in the range of USD 3.5-4.2 billion for time lapse of 60years from base case 1998-2002. Severity analysis of extreme events shows that situation get worse if adaptations are not only included in the policy but also in the integrated development framework with required allocation of funds. This evaluation also highlights the result of cost benefit analysis, benefits of the adaptation options (mean & worst case) for floods and droughts in Southern Punjab. Additionally the research highlights the role of integrated extreme events impact assessment methodology in performing the vulnerability assessments and to support the adaptation decisions. This paper is an effort to highlight importance of bottom up approaches to deal with climate change.

Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1°, these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25°) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.

Modelling the vulnerability of groundwater to contamination in an unconfined alluvial aquifer in Pakistan

Abstract The area of Thal Doab is located in the Indus Basin and is underlain by a thick alluvial aquifer called the Thal Doab aquifer (TDA). The TDA is undergone intense hydrological stress owing to rapid population growth and excessive groundwater use for livestock and irrigated agricultural land uses. The potential impact of these land uses on groundwater quality was assessed using a DRASTIC model in a Geographic Information System environment. Seven DRASTIC thematic maps were developed at fixed scale and then combined into a groundwater vulnerability map. The resultant vulnerability index values were grouped into four zones as low, moderate, high and very high. The study has established that 76% of the land area that is underlain by the TDA has a high to very high vulnerability to groundwater contamination mainly because of a thin soil profile, a shallow water table and the presence of soils and sediments with high hydraulic conductivity values. In addition, only 2 and 22% of the total area lie in low and moderate vulnerability zones, respectively. The outcomes of this study can be used to improve the sustainability of the groundwater resource through proper land-use management.

Integrated climate change risk assessment and evaluation of adaptation perspective in southern Punjab, Pakistan

Climate change is posing stresses on water resources, food security, population, environment and economy of the southern Punjab. Integrated climate change risk assessment is carried out using assessed likelihood approach for defined mean, hot & dry, central, warm & wet climate models over selected time slices and adaptation plans. Climate models are based on the 5th, 50th & 95th percentiles of PRECIS RCM projections of temperature & precipitation under IPCC A2 & A1B scenarios. Four time slices 2015, 2035, 2065 and 2085 are selected to assess the temporal climate change risk and to evaluate the performance of selected adaptations to reduce climate threats over considered assets. Results are presented in terms of risk indices and risk reduction units (RRUs). In first half of the 21st century, climate change risk will continue to increase from current level and is high (>10) in most of the selected time slices. Maximum ensembles of climate models, time slices and adaptation plans observe moderate (37-40 RRUs) and high (40-55 RRUs) risk class. Cumulative risk has been calculated through integration of sectoral sensitivity e.g. population density, land use, food security and multidimensional poverty to climate change risk class using AHP and overlaying in GIS environment. About 90% and 83% area of southern Punjab is falling in high cumulative risk. About 13% area, comprising Muzaffargarh and Rajanpur district is under very high cumulative risk. Water induced adaptations like development of water resources, dam & flood control protection, temporary flood barriers and water resource acquisition are the preferred and suitable adaptations as these observed >100 RRUs for most of the ensembles. Assessing baseline vulnerability and sectoral sensitivity to climate stimuli are the hot spots requiring priority attention and firm decision making by disaster management authorities and communities residing in southern Punjab.

Benefits of the Successive GPM Based Satellite Precipitation Estimates IMERG–V03, –V04, –V05 and GSMaP–V06, –V07 Over Diverse Geomorphic and Meteorological Regions of Pakistan

Launched in 2014, the Global Precipitation Measurement (GPM) mission aimed at ensuring the continuity with the Tropical Rainfall Measuring Mission (TRMM) launched in 1997 that has provided unprecedented accuracy in Satellite Precipitation Estimates (SPEs) on the near-global scale. Since then, various SPE versions have been successively made available from the GPM mission. The present study assesses the potential benefits of the successive GPM based SPEs product versions that include the Integrated Multi–Satellite Retrievals for GPM (IMERG) version 3 to 5 (–v03, –v04, –v05) and the Global Satellite Mapping of Precipitation (GSMaP) version 6 to 7 (–v06, –v07). Additionally, the most effective TRMM based SPEs products are also considered to provide a first insight into the GPM effectiveness in ensuring TRMM continuity. The analysis is conducted over different geomorphic and meteorological regions of Pakistan while using 88 precipitations gauges as the reference. Results show a clear enhancement in precipitation estimates that were derived from the very last IMERG–v05 in comparison to its two previous versions IMERG–v03 and –v04. Interestingly, based on the considered statistical metrics, IMERG–v03 provides more consistent precipitation estimate than IMERG–v04, which should be considered as a transition IMERG version. As expected, GSMaP–v07 precipitation estimates are more accurate than the previous GSMaP–v06. However, the enhancement from the old to the new version is very low. More generally, the transition from TRMM to GPM is successful with an overall better performance of GPM based SPEs than TRMM ones. Finally, all of the considered SPEs have presented a strong spatial variability in terms of accuracy with none of them outperforming the others, for all of the gauges locations over the considered regions.