Application and Evaluation of a Snowmelt Runoff Model in the Tamor River Basin, Eastern Himalaya Using a Markov Chain Monte Carlo (MCMC) Data Assimilation Approach

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 approx. 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 4000-5500m 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

Changing temperature and precipitation extremes in the Hindu Kush-Himalayan region: An analysis of CMIP3 and CMIP5 simulations and projections

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

Detection of the timing and duration of snowmelt in the Hindu Kush-Himalaya using QuikSCAT, 2000-2008

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. © 2011 IOP Publishing Ltd

Supraglacial Lake Classification in the Everest Region of Nepal Himalaya

Climate change has been linked to widespread retreats and recent disappearances of small glaciers in mountainous regions such as the Patagonia, Andes, Alps and Himalaya (Dyurgerov and Meier, 2000; Fujita et al., 2006; Kargel et al., 2005; Oerlemans, 2005; Paul, Kaab et al., 2004; Racoviteanu et al., 2008; Thakur, 2010). Alpine glaciers are particularly sensitive to changes in climate due to their proximity to melting conditions, wide altitude range, and variability in debris cover (Haeberli et al., 2007; Racoviteanu et al., 2008). Alpine glacial retreat in response to climate change and recent warming trends may have severe hydroecological consequences to surrounding communities (Kehrwald et al., 2008; Milner et al., 2009). The recession of glaciers has been further linked to increases in glacier-related hazards in high mountainous areas such as avalanches, glacial lake outburst floods and debris flows which can increasingly threaten human lives, settlements, and infrastructure (Huggel et al., 2002; Quincey et al., 2005). This has necessitated the assessment and monitoring of potentially hazardous glacial lakes through the use of remote sensing, especially in high mountainous areas due to inaccessibility and lack of field surveys (Quincey et al., 2005)

Application and evaluation of a snowmelt runoff model in the Tamor River basin, Eastern Himalaya using a Markov Chain Monte Carlo (MCMC) data assimilation approach

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\u3c3%). 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 4000-5500m 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

Climate change and deforestation increase the vulnerability of Amazonian forests to post‐fire grass invasion

Aim We aimed to evaluate the vulnerability of the Amazon forest to post-fire grass invasion under present and future climate scenarios. Location Amazon Basin. Time period 1981–2017 and 2070–2099. Major taxa studied Plants. Methods We combined a fire–ecosystem model with remote sensing data and empirically-derived equations to evaluate the effects of a high-intensity fire (i.e., during an extreme drought) and logging in forest edges on tree canopy, and exotic grass cover under present and unmitigated climate change scenarios. We also contrasted simulated vegetation recovery time (as a function of climate variability) and current fire return intervals to identify areas in which fire–grass feedbacks could lock the system in a grass-dominated state. Results Under current climatic conditions, 14% of the Amazon was found to be vulnerable to post-fire grass invasion, with the south-eastern Amazon at the highest risk of invasion. We found that under unmitigated climate change, by the end of the century, 21% of the Amazon would be vulnerable to post-fire grass invasion. In 3% of the Amazon, fire return intervals are already shorter than the time required for grass exclusion by canopy recovery, implying a high risk of irreversible shifts to a fire-maintained degraded forest grassy state. The south-eastern region of the Amazon is currently at highest risk of irreversible degradation. Main conclusions Although resilience is evident in areas with low fire activity, increased fire frequency and intensity could push large Amazon forest areas towards a tipping point, causing transitions to states with low tree and high grass cover

Climate change and deforestation increase the vulnerability of Amazonian forests to post‐fire grass invasion

Aim We aimed to evaluate the vulnerability of the Amazon forest to post-fire grass invasion under present and future climate scenarios. Location Amazon Basin. Time period 1981–2017 and 2070–2099. Major taxa studied Plants. Methods We combined a fire–ecosystem model with remote sensing data and empirically-derived equations to evaluate the effects of a high-intensity fire (i.e., during an extreme drought) and logging in forest edges on tree canopy, and exotic grass cover under present and unmitigated climate change scenarios. We also contrasted simulated vegetation recovery time (as a function of climate variability) and current fire return intervals to identify areas in which fire–grass feedbacks could lock the system in a grass-dominated state. Results Under current climatic conditions, 14% of the Amazon was found to be vulnerable to post-fire grass invasion, with the south-eastern Amazon at the highest risk of invasion. We found that under unmitigated climate change, by the end of the century, 21% of the Amazon would be vulnerable to post-fire grass invasion. In 3% of the Amazon, fire return intervals are already shorter than the time required for grass exclusion by canopy recovery, implying a high risk of irreversible shifts to a fire-maintained degraded forest grassy state. The south-eastern region of the Amazon is currently at highest risk of irreversible degradation. Main conclusions Although resilience is evident in areas with low fire activity, increased fire frequency and intensity could push large Amazon forest areas towards a tipping point, causing transitions to states with low tree and high grass cover