New paper: climate change impacts on upper Indus basin hydrology

The Indus is one of the most meltwater-dependent rivers on Earth, and hosts a large, rapidly growing population and the world’s largest irrigation scheme. Understanding the hydrology of the upper Indus basin is challenging. The Hindu Kush, Karakoram and Himalayan mountain ranges are difficult to access, hampering field measurements of meteorological, glaciological and hydrological processes. These processes are therefore still poorly understood. To make it more complex, climate change projections for the Indus basin show a very large spread. In our recent (open access) paper published in PLoS ONE we present hydrological projections for the 21st century in the upper Indus basin, based on a cryospheric-hydrological model forced with an ensemble of downscaled GCM outputs.

passu

The Hunza river in front of the Passu cones (upper Indus basin).

 

Three methodological advances are introduced:

  • A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used.
  • The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance.
  • An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes.

sources

Our projections indicate decreases in glacier melt contribution in favor of snow melt and rainfall-runoff contribution to stream flow in the upper Indus basin, at the end of the 21st century.

 

The focus of the analysis in our study is not only on changes in sources of runoff and water availability but also on changes in seasonality and hydrological extremes, which are still large unknowns in the upper Indus basin. We conclude that the upper Indus basin faces a very uncertain future in terms of water availability towards the end of the 21st century. Despite the large uncertainties in future climate and water availability, basin-wide patterns and trends of intra-annual shifts in water availability are consistent across climate change scenarios. For the near future these trends mainly consist of minor increases in summer flows combined with increased flows during other seasons. For the far future the trends show decreases in summer flows combined with stronger increasing flows during the other seasons. Furthermore, increases in intensity and frequency of extreme discharges are found for most of the upper Indus basin and for most scenarios and models considered, implying increases in flooding events during the 21st century.

The study is presented at the AGU Fall meeting in San Francisco on Monday, 12 December 09:15 – 09:30, Moscone West – 3005

 

extremes

Analysis of future changes indicates increases in the frequency and magnitude of extreme flows for most of the UIB and most of the climate change scenarios.

New ICIMOD video on our Himalayan research

We know very little about glaciers in the high mountains. We know they’re shrinking and temperatures are rising faster at higher altitudes than anywhere else on the planet. But, due to extreme conditions and inaccessibility, we have much to learn. Detailed field measurements are being made on just twelve out of some 54,000 glaciers in the Himalayas. More measurements are needed because these glaciers feed the rivers people living down below rely on.

 


Directed and produced by Susan Hale Thomas

New paper: investigations on debris-covered glaciers

Glaciers covered by debris – rocks, dirt, silt, and sand – are common in the Himalayas. Depending on who’s counting (and where you are looking), debris covers nearly 25% of the total glacierized area in the region.  Experiments and previous studies have shown that really thin debris enhances melt, but that anything over 2 cm thick insulates the ice melt.  But what is the net effect of debris cover on glacier melt rates? Our recently published (open access) paper in the Cryosphere tries to answer this question.

 

ChangriNup-L820160324

Khumbu Glacier (center) is debris covered. So is the bottom 2/3 of Changri Nup Glacier, located to the west. Everest is at the far right of this Landsat scene.

 

Unfortunately, the answer is not so easy to obtain. Traditional mass balance stake measurements are (a) difficult to install and maintain on debris-covered glaciers, and (b) impossibly biased towards locations where it is possible to drill. You could look at surface elevation changes over part of the glacier with either photogrammetry, UAV, or satellite (we use all three), but if you do this you also need to consider the emergence velocity (or increase in elevation) of the glacier as it flows downhill. On any given point in the ablation zone, the total surface elevation change is a function of both emergence and melt. And to estimate the mean emergence velocity, you need to measure the ice flux through a cross-section of the glacier.

 

elevation-change_three-panel_v2

Rates of surface elevation change at Changri Nup Glacier for different periods and data sources: (A) 2011 – 2014 (photogrammetry); (B) 2011 – 2015 (photogrammetry and UAV); (C) 2009 – 2014 (satellite and photogrammetry)

 

Christian Vincent and Patrick Wagnon, French glaciologists from Laboratoire de Glaciologie et Geophysique (LGGE) and Institut de Recherche pour le Development (IRD), have collected multiple datasets over 4 years to estimate the mass gain and loss over the debris-covered Changri Nup Glacier. I’d remind you that debris-covered glaciers at 5400 m of elevation are not among the easiest places to work.

But together with a team of co-authors they have measured surface velocities and surface melt rates with ablation stakes; developed digital elevation models from photogrammetry in 2011 and 2014, from unmanned aerial vehicle surveys in 2015, and from high-resolution satellite data in 2009; measured ice depths with ground-penetrating radar, and mapped ground control points and elevation profiles with differential GPS.

 

DSC05018

The lead author C. Vincent uses a differential GPS to measure a ground control point for UAV flights over the clean Changri Nup.

 

And the overall result: melt rates on the debris-covered glacier are about 60% less than what they would be if the glacier was free of debris. Ice cliffs and ponds enhanced melt locally, but not enough to offset the overall reduction in melt caused by the debris. The surface mass balance (in m of water equivalent, or m w.e.) over the debris-covered tongue, inferred from average surface lowering of -0.81 m w.e./yr and an average emergence velocity of +0.37 m w.e./yr, is -1.21 m w.e./yr. If the glacier were debris-free, we would expect to see an average mass balance rate of -3.00 m w.e./yr.

This field-based study provides strong evidence that the ‘debris-cover anomaly’ (where satellite data show that debris-covered glaciers appear to be lowering at the same rate as clean-ice glaciers) is an artifact. It also shows that, in this location at least, the effects of ponds and ice cliffs are minimal.

Why is this important? If debris-covered ice (low-angle and thick) occupies 25% of the total glacierized area, it probably contains an even greater percentage of the total ice volume. Better estimates of the net insulating effect of debris will help us improve simulations of future ice loss, and its impacts on water resources downstream.

 

This is a re-post of a recent blog by Joseph M. Shea.

Martian crater officially named Langtang

To honor Langtang, the village that was tragically destroyed by an avalanche triggered by the Nepal earthquake of April 2015, we proposed to name a Martian crater after Langtang. The idea was initiated by colleague Tjalling de Haas, who investigates debris flows and land-forms on Mars. The request was officially approved by the International Astronomical Union and Langtang will now forever be remembered, even on Mars. The crater has a diameter of 12 km and interesting enough contains glacial land-forms and the moraines of the Last Mars Glacial Maximum and the debris fans formed after the glaciers melted are clearly visible.

 

The Langtang Crater

The Langtang Crater

 

 

 

 

 

 

 

 

 

 

 

Debris flows in the Langtang Crater

Debris flows in the Langtang Crater

 

 

 

 

AWS on Ice

[Joe is a Senior Glacier Hydrologist at the International Centre for Integrated Mountain Development in Kathmandu, Nepal]

True fact: there have been not one but two workshops dedicated specifically to the installation of automatic weather stations (AWS) on glaciers.

Jiten_160508_MG_2806

The newly-installed AWS at Yala Glacier. We didn’t get these views when we did the work. (Photo credit: Jitendra Bajracharya)

One of the biggest unknowns in how glaciers will respond to climate change are the meteorological conditions and melt rates at the glacier surface, and how these conditions relate to data from standard observation networks and/or climate reanalysis products. But setting up precise sensors on a surface that can move, melt, and be buried by snow – sometimes all of these in the same day – is a big challenge. Unfortunately, for all challenges (including drinking milk upside down through a straw) you either learn by experience (AKA “mistakes”), or you learn from the experiences of others. For some reason I’ve tended to go with the former.

Our recent AWS installation at Yala Glacier is another attempt to obtain a year-long record of meteorological conditions at 5350 m in the Himalayas. At this altitude, temperatures are rarely above zero and the melt of snow or ice is basically controlled by the radiation balance at the surface (see below for a more technical discussion). So our station will record radiation received and emitted or reflected by the surface, air temperature and relative humidity, wind speed and direction, and surface height changes from melt and snowfall.

Experience tells us that ‘floating’ weather stations, such as tripods that simply sit on the top of the surface, don’t work so well on glaciers. The surface melts down unevenly, the station can be buried and damaged by heavy snowfall, and there is no way to get a record of surface lowering: the surface height sensor needs to be mounted at a fixed height in order to get information that makes any sense.

DSC01549

Our first attempt to measure conditions on Yala Glacier, found after typhoon HudHud in October 2014.

For the new station, we used a slick tower design that can be built up in the field (full credit to Alex Jarosch and Faron Anslow; tower recipe below or see P. 52-55 here). Essentially, we connect three 2.0 m aluminum pipes vertically to make a 6.0 m tall triangular structure. Horizontal supports brace the top 2.0 m of the tower, and the bottom 4.0 m of each leg is drilled in to the ice. If you’re going to try this at home, don’t forget to stick small plastic caps on the bottom of the pipes that go in the ice. Without these, the weight of the tower would be supported on a very small surface area and it would melt into the ice – probably due to heat conduction through the aluminum. If the tower sinks into the ice during the experiment, the surface height measurements are meaningless. (Thanks, experience!)

Once the base and the tower are installed and leveled, the waterproof enclosure (which contains the battery, solar charge controller, and the datalogger) and all sensors were mounted to the tower. In the time-lapse animation shown below, you can see the clouds rolling up and down over us as we mount the sensors. In response, we shed layers and then put them back on, because the thickness of the cloud layer really affects the ‘felt’ temperature at the surface (you should really read the technical explanation below). Air temperatures during the setup hovered around 0C.

 

DCIM100GOPRO

The tower from above: logger box and temperature/humidity sensor is on the left, wind sensor is top right, and it looks like three people are required to mount the net radiometer (which measures shortwave and longwave radiation – really, there is no longer an excuse to not read just a little bit more in depth below).

The full installation took only half a day, and we were back drinking tea in camp by mid-afternoon (though thankfully not upside down and with straws). But getting the equipment and the tower components up there literally took a small army. We have nothing but huge gratitude and respect for Dawa Sherpa and Ngawang Sherpa who helped haul everything up the glacier, and to all the trekking agency staff who carried everything up from the trailhead at 1600 m to the basecamp at 5000 m.

[Thanks to Maxime Litt and Desiree Treichler for their help in the field, but also for the pre-field testing and programming. This is a critical step in the recipe.]

 

Glacier Station Recipe

  • 9 x 2.0 m aluminum pipe (48.25 mm OD)
  • 9 x 0.50 m aluminum pipe (48.25 mm OD)
  • 3 internal pipe connectors
  • 3 external pipe connectors
  • 3 plastic cap ends (large corks also approved)
  • 18 x 90 degree joints (48.25 mm OD)
  • Ice auger (4-5 m)
  • Plumber’s level
  • Tools
  • AWS components and all mounting hardware (!)
  • Patience
  • Reasonably good weather
  • Preparation, preparation, preparation

Radiation Balance Details

The net radiation at the surface (Q*) can be calculated from incoming and outgoing shortwave and longwave radiation:

Q* = Sin – Sout + Lin – Lout

Shortwave radiation comes from the sun: its highest at solar noon, and zero at night. But the amount of radiation reaching the surface depends on clouds and the atmospheric conditions, and the amount of shortwave radiation absorbed at the surface depends on the reflectivity (or albedo) of the surface. Brighter surfaces reflect more radiation, and have a higher albedo, which means less energy available for melt.

Longwave radiation is a mainly function of temperature: incoming longwave radiation is emitted by the atmosphere, and the earth’s surface emits longwave radiation upwards. Temperatures near the surface will be warmer on cloudy nights because the clouds both (a) emit greater longwave radiation towards the surface than a clear sky and (b) trap some of the longwave radiation emitted by the ground. Incoming longwave radiation is also a function of water vapour in the atmosphere, which affects the temperature profile.

 

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