Papers by Prajjwal Panday
gest.umbc.edu
The recent IPCC (2007) report has confirmed, through observations and model-based studies, substa... more The recent IPCC (2007) report has confirmed, through observations and model-based studies, substantial hydrological changes in mountain watersheds where hydrology is dominated by cryospheric processes. The response of cryospheric processes to a warming climate in mountainous areas can be analyzed by examining the responses in the seasonal and annual hydrologic regimes of rivers where snow and ice contribute significantly to the runoff. This study utilizes a snowmelt runoff model in the Tamor River Basin in the eastern Nepalese Himalaya which is driven by remotely sensed snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS). The Snowmelt Runoff Model (SRM) is calibrated using daily streamflow from 2002 to 2005 and the streamflow can be predicted with a high degree of accuracy. Although snowmelt hydrology has been examined at a large watershed scale in the Hindu Kush-Himalaya region, changes in hydrologic processes with climate change need to be examined and compared across finer basin scales for assessing water availability and vulnerability. Three climate change scenarios were used to drive the model in order to understand the impact of changing conditions. A scenario of a 4°C temperature increase and 20% precipitation increase results in a significantly increased runoff volume by ~23%, with streamflow exceeding present conditions in all months. A second scenario of a 4°C temperature shifts the snowmelt runoff but does not alter flow volume significantly. An increase in precipitation by 20% with temperatures unchanged results in a 15% increase in runoff volume, mainly during the summer months. These results show the importance of improved monitoring and modeling in the region to better understand the impact of climate change on hydrology.
Geospatial Techniques for Managing Environmental Resources, 2011
Deforestation reduced forest cover in Brazil’s Xingu River Basin (XB; area: 510,000 km2) from 90%... more Deforestation reduced forest cover in Brazil’s Xingu River Basin (XB; area: 510,000 km2) from 90% of the basin in the 1970s to 75% in the 2000s. Such large-scale land cover changes can substantially alter regional water budgets, but their influence can be difficult to isolate from that of natural climate variability. In this study, we estimate changes to the XB water balance from the 1970s to the 2000s due to climate variations and deforestation, using a combination of long-term observations of rainfall and discharge; satellite-based estimates of evapotranspiration (MODIS) and surface water storage (GRACE); and numerical modeling estimates (IBIS) of water budget components (evapotranspiration, soil moisture, and discharge). Model simulations over this period suggest that climate variations alone accounted for a −82 mm decrease (mean per unit area) in annual discharge (−14%, from 8190 m3 s−1 to 7806 m3 s−1), due to a −2% decrease in precipitation and +3% increase in evapotranspiration. Deforestation alone caused a +34 mm increase in annual discharge (+6%), as a result of a −3% decrease in evapotranspiration and +1% increase in soil moisture across the XB. Climate variability and land cover change thus had opposite effects on the XB water balance, with climate effects masking deforestation-induced changes to the water budget. Protected areas, which cover 55% of the basin, have helped to mitigate the effects of past deforestation on water recycling in the Xingu. However, our results suggest that continued deforestation outside protected areas could trigger changes of sufficient magnitude to offset climate variability.
The Hindu Kush-Himalayan (HKH) region epitomizes a geographic region where cryospheric processes ... more The Hindu Kush-Himalayan (HKH) region epitomizes a geographic region where cryospheric processes coupled with hydrological regimes are under threat owing to a warming climate and shifts in climate extremes. In this study, we analyse global climate models in the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) archives to investigate the qualitative aspects of change and trends in temperature and precipitation indices. Specifically, we examine and evaluate multi-model, multi-scenario climate change projections and seven extreme temperature and precipitation indices over the eastern Himalaya (EH) and western Himalaya-Karakoram (WH) regions for the 21st century. Density distribution plots of observed climate indices for meteorological stations and gridded indices are also analysed, which indicate significant negative trends in the annual number of frost days and significant increasing trends in warm nights in the EH region over the 1960–2000 period. Multi-model average (MMA) projections additionally indicate continued trends towards more extreme conditions consistent with a warmer, wetter climate. Precipitation projections indicate increased mean precipitation with more frequent extreme rainfall during monsoon season in the EH region, and a wetter cold season in the WH region. Time series of all MMA precipitation indices exhibit significant increasing trends over the 1901–2099 period. By comparison, time series of temperature indices show decreases in the intra-annual extreme temperature range and total number of frost days, as well as increases in warm nights. In general, these future projections point towards increases in summertime temperatures and modifications in precipitation across both regions.
Previous studies have drawn attention to substantial hydrological changes taking place in mountai... more Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree-day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash-Sutcliffe metric~0.84, annual volume bias < 3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002-2006 period is estimated to be 29.7 ± 2.9% (which includes 4.2 ± 0.9% from snowfall that promptly melts), whereas 70.3 ± 2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000-5500 m range contributes the most to basin runoff, averaging 56.9 ± 3.6% of all snowmelt input and 28.9 ± 1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree-day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds. Figure 4. Snow cover depletion curves from the Moderate Resolution Imaging Spectroradiometer 8-day maximum snow cover 500-m resolution product in the Tamor River basin from 2002 to 2006 in (a) zone 2, elevation range from 2500-4000 m, (b) zone 3, elevation range from 4000-5500 m and (c) zone 4, elevation range greater than 5500 m.
The Hindu Kush-Himalayan (HKH) region holds the largest mass of ice in Central Asia and is highly... more The Hindu Kush-Himalayan (HKH) region holds the largest mass of ice in Central Asia and is highly vulnerable to global climate change, experiencing significant warming (0.21 ± 0.08 °C/decade) over the past few decades. Accurate monitoring of the timing and duration of snowmelt across the HKH region is important, as this region is expected to experience further warming in response to increased greenhouse gas forcing. Despite the many advantages and applications of satellite-derived radar scatterometer data shown for capturing ice and snow melt dynamics at high latitudes, similar comprehensive freeze/thaw detection studies at lower latitudes (including the HKH region) are still absent from the scientific literature. A comprehensive freeze/thaw detection study is utilized on perennial snow/ice and seasonal snow cover for the first time in the Himalayan and Karakoram regions. A dynamic threshold-based method is applied to enhanced QuikSCAT Ku-band backscatter observations from 2000 to 2008 that (a) provides spatial maps of the timing of melt, freeze, and melt season duration, and (b) emphasizes regional variability in freeze/thaw dynamics. The resulting average melt durations for 2000–2008 are 161 ± 11 days (early May–mid-October) for the eastern Himalayas, 130 ± 16 days (late May–early October) for the central Himalayas, 124 ± 13 days (mid-May–mid-September) for the western Himalayas, and 124 ± 12 days (late May–late September) for the Karakoram region. The eastern Himalayan region has on average an earlier melt onset, a later freeze-up, and therefore a longer melt season (~5 weeks) relative to the central and western Himalayan and the Karakoram regions. Snowmelt dynamics exhibit regional and interannual variability with clear connections to terrain features, in particular elevation and aspect. With respect to ongoing controversies surrounding melt in the Himalayan region, this study provides an overall perspective of regional differences in melt onset, freeze-up, and melt duration that have important implications for glaciological and hydrological processes across the HKH region.
The Hindu Kush–Himalayan (HKH) region with its surrounding mountains in central Asia is a region ... more The Hindu Kush–Himalayan (HKH) region with its surrounding mountains in central Asia is a region that has been warming at an alarming rate and is sensitive to climate change due to its heterogeneous terrain and high altitude. In a region where research is limited due to the paucity of field-based biophysical observations, analysis of remotely sensed data such as the normalized difference vegetation index (NDVI) can provide invaluable information on spatio-temporal patterns in linkages among land use, climate and vegetative phenological cycles, and trends in vegetative cover. In this study, NDVI data with 8 km spatial resolution for each 15 day composite period from 1982 to 2006 were analysed using a seasonal trend analysis technique, where the first step determines the annual mean and seasonal NDVI patterns across the HKH region. The second step analyses the non-parametric trends in magnitude and timing of the annual mean and seasonal NDVI cycle. The seasonal vegetation cycles were compared for the first and last ten years of the time series and were also analysed across areas undergoing significant change. Results indicated an overall greening trend in NDVI magnitude in most areas, particularly over open shrubland, grassland and cropland. Trends in the annual seasonal timing of NDVI indicated an earlier green-up for most parts of this region. Results also confirmed deforestation trends observed in a few states in northeastern India and Myanmar (Shan state) within the HKH region.
The iterative and convergent nature of ensemble learning algorithms provides potential for improv... more The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests)andanon-ensemblelearningalgorithm(classificationtrees)usingmultiple criteria related to algorithm and training data characteristics. The ensemble learning algorithms had comparably high accuracy (Kappa range: 0.76-0.78), which was 11% higherthanthatofclassificationtrees.Ensemblelearningtechniqueswerenotinfluencedbycalibrationdatavariability,wererobusttoone-fifthcalibrationdatanoise, and insensitive to a 50% reduction in calibration data size.
The global retreat of mountain glaciers since mid-19th century may have severe ecological and eco... more The global retreat of mountain glaciers since mid-19th century may have severe ecological and economical impacts affecting human lives and infrastructure. There is a general pattern of glacial retreat in the Nepal Himalayas leading to increased production of glacial melt water and development of supraglacial lakes (formed on the surface of glaciers) that may pose hazards such as glacial lake outburst floods to downstream populated areas. Glacier classification is an important initial step in any glacier-related assessment; however, limitation of multivariate classification algorithms and spectral similarity of supraglacial features have posed significant challenges. Existing methods of classification such as thresholding and manual digitization may underestimate or overestimate cover classes. This paper demonstrates the utility of a hybrid approach that integrates classification tree analysis (CTA) and shape complexity analysis to better differentiate supraglacial cover types and delineate supraglacial lakes in the Everest region. We used 2004 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible near-infrared (VNIR) and short-wave infrared (SWIR) imagery along with snow and vegetation indices, image band ratios and elevation derivatives to characterize and assess supraglacial conditions on the glaciers in the Everest region. The initial segmentation by CTA, a non-parametric, recursive partitioning method, was followed by a shape complexity analysis using the Square Pixel Metric (SqP) to further differentiate supraglacial features, mainly lakes. The results indicate that CTA is able to utilize ancillary variables to classify supraglacial cover types and to delineate supraglacial lakes. Shape complexity metric such as SqP was also effective in reducing misclassification of glacial lakes. The results of this semi-automated, hybrid approach classification of supraglacial cover types has been promising when used in the Everest region with very high overall accuracy.
The northern Bering and Chukchi Seas in the Pacific sector of the Arctic are among the most produ... more The northern Bering and Chukchi Seas in the Pacific sector of the Arctic are among the most productive marine ecosystems in the world and act as important carbon sinks, particularly during May and June when seasonal sea ice-associated phytoplankton blooms occur throughout the region. Sea ice melt and breakup during spring strongly drive this production by enhancing light availability in the system, increasing stratification and stabilization of the water column, and introducing a new source of nutrients to surface waters.
Glaciers are the largest reservoir of freshwater on Earth, supporting one third of the world's po... more Glaciers are the largest reservoir of freshwater on Earth, supporting one third of the world's population. The Himalaya possess one of the largest resources of snow and ice, which act as a freshwater reservoir for more than 1.3 billion people. This article describes a new project called HIMALA, which focuses on utilizing satellite-based products for better understanding of hydrological processes of the river basins of the region. With support from the US Agency for International Development (USAID), the International Centre for Integrated Mountain Development (ICIMOD), together with its partners and member countries, has been working on the application of satellite-based rainfall estimates for flood prediction. The US National Aeronautics and Space Administration (NASA) partners are working with ICIMOD to incorporate snowmelt and glacier melt into a widely used hydrological model. Thus, through improved modeling of the contribution of snow and ice meltwater to river flow in the region, the HIMALA project will improve the ability of ICIMOD and its partners to understand the impact of weather and climate on floods, droughts, and other water- and climate-induced natural hazards in the Himalayan region in Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan.
Polar Research, Jan 1, 2009
Despite the general understanding that forested catchments provide clean drinking water, very few... more Despite the general understanding that forested catchments provide clean drinking water, very few studies have explicitly tested the role of land use and land cover in regulating water quality in drinking water catchments. Parcelization and increased development in recent years has become a threat, not only to the sustainability of the current working landscape, but also to water quality in the Catskill/Delaware watersheds, which provide one billion gallons of drinking water to the residents of New York City every day. The principal ...
Conference Presentations by Prajjwal Panday
Abstract The northern Bering and Chukchi Seas in the Pacific Arctic Region (PAR) are among the mo... more Abstract The northern Bering and Chukchi Seas in the Pacific Arctic Region (PAR) are among the most productive marine ecosystems in the world and act as important carbon sinks, particularly during May and June when seasonal sea ice-associated phytoplankton blooms occur throughout the region. Recent dramatic shifts in seasonal sea ice cover across the PAR should have profound consequences for this seasonal phytoplankton production as well as the intimately linked higher trophic levels.
Abstract The Hindu Kush-Himalayan (HKH) region epitomizes regions where cryospheric processes cou... more Abstract The Hindu Kush-Himalayan (HKH) region epitomizes regions where cryospheric processes coupled with hydrological regimes are under threat owing to a warming climate. Information from multi-model ensembles of changes in temperature and precipitation in the HKH region can not only provide uncertainty in projections as represented by model consensus, but also regional and interannual variability of these changes to identify critical areas.
Uploads
Papers by Prajjwal Panday
Conference Presentations by Prajjwal Panday