Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing

Immerzeel, Walter W.; Droogers, P.; Jong, S.M. de; Bierkens, Marc F. P.

doi:10.1016/j.rse.2008.08.010

Cited by 616 publications

(451 citation statements)

References 33 publications

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“…Recent estimates (C. Mayer et al 2012, unpublished data) from high-altitude accumulation pits (above 5500 m MSL) indicate from 6 to 8 m of snow depth [;3 m snow water equivalent (SWE)] during 2009-11 in the study area. Recent studies show that precipitation is at maximum around 5000 m MSL and decreases rapidly there above (Immerzeel et al 2012a). …”

Section: Case Study Areamentioning

confidence: 99%

“…For western Karakoram they found maximum precipitation near 5000 m MSL, and then it decreased to very low values near 7000 m MSL. Recently, Immerzeel et al (2012a) applied an inverse approach to estimate the spatial distribution of precipitation within the Hunza catchment, north of the Shigar River, starting with the glaciers' mass balance and applying a simplified hydrological model. They used a linearly increasing vertical lapse rate until 5500 m MSL and then used a decreasing precipitation gradient, with acceptable results.…”

Section: A Weather Datamentioning

confidence: 99%

“…According to recent estimates, more than 50% of the water flowing in the upper Indus basin, in northern Pakistan, is due to snow and ice melt (Immerzeel et al 2010). Relying on agriculture, the economy of the Himalayan regions is highly dependent on water availability and irrigation systems (e.g., Akhtar et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Over longer time scales, however, our projections indicate a substantial decrease of snow and ice cover, leading to a large loss of snow/ice mass in a period of approximately 50 years and consequent decrease in stream flows. Among others, Immerzeel et al (2009) investigated the effects of snow and ice melt contribution to the discharge of the Indus River, concluding that stream flows can be predicted using a snowmelt runoff model (SRM) driven by remotely sensed precipitation and snow cover data and calibrated using daily discharge data. They investigated the future (2071-2100) effects of retreating glaciers on the discharge of the upper Indus River.…”

mentioning

confidence: 99%

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Future Hydrological Regimes in the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment

Soncini

Bocchiola

Confortola

et al. 2015

Journal of Hydrometeorology

100

124

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The mountain regions of the Hindu Kush, Karakoram, and Himalayas (HKH) are considered Earth's ''third pole,'' and water from there plays an essential role for downstream populations. The dynamics of glaciers in Karakoram are complex, and in recent decades the area has experienced unchanged ice cover, despite rapid decline elsewhere in the world (the Karakoram anomaly). Assessment of future water resources and hydrological variability under climate change in this area is greatly needed, but the hydrology of these high-altitude catchments is still poorly studied and little understood. This study focuses on a particular watershed, the Shigar River with the control section at Shigar (about 7000 km 2 ), nested within the upper Indus basin and fed by seasonal melt from two major glaciers (Baltoro and Biafo). Hydrological, meteorological, and glaciological data gathered during 3 years of field campaigns (2011-13) are used to set up a hydrological model, providing a depiction of instream flows, snowmelt, and ice cover thickness. The model is used to assess changes of the hydrological cycle until 2100, via climate projections provided by three state-of-the-art global climate models used in the recent IPCC Fifth Assessment Report under the representative concentration pathway (RCP) emission scenarios RCP2.6, RCP4.5, and RCP8.5. Under all RCPs, future flows are predicted to increase until midcentury and then to decrease, but remaining mostly higher than control run values. Snowmelt is projected to occur earlier, while the ice melt component is expected to increase, with ice thinning considerably and even disappearing below 4000 m MSL until 2100.

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Section: Case Study Areamentioning

confidence: 99%

Section: A Weather Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Future Hydrological Regimes in the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment

Soncini

Bocchiola

Confortola

et al. 2015

Journal of Hydrometeorology

100

124

View full text Add to dashboard Cite

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“…For the time period they cover, their particular strength is in global scale observations with consistent and reproducible methods. Key products derived from satellite data are glacier outlines and inventories (in combination with a digital elevation model, DEM, e.g., Andreassen et al 2012), consistent DEMs of the surface topography with global coverage (e.g., the SRTM DEM or the ASTER GDEM), elevation changes over entire glaciers from differencing DEMs from two epochs or at points from repeat altimetry (e.g., Nuth et al 2010), surface flow velocities for determination of mass fluxes (e.g., Melkonian et al 2013Melkonian et al , 2016, glacier mass changes from space-borne gravimetry observations (using the GRACE satellites, e.g., Jacob et al 2012) and glacier facies mapping (ice, firn, snow) that is used as a proxy for mass balance (e.g., Rabatel et al 2008) and an important input dataset for hydrologic models or for calibration and/or validation of distributed mass balance models (e.g., Immerzeel et al 2009;Paul et al 2009). …”

Section: Satellite Remote Sensing Of Glaciersmentioning

confidence: 99%

Observation-Based Estimates of Global Glacier Mass Change and Its Contribution to Sea-Level Change

Marzeion¹,

Champollion²,

Haeberli³

et al. 2017

Space Sciences Series of ISSI

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Glaciers have strongly contributed to sea-level rise during the past century and will continue to be an important part of the sea-level budget during the twenty-first century. Here, we review the progress in estimating global glacier mass change from in situ measurements of mass and length changes, remote sensing methods, and mass balance modeling driven by climate observations. For the period before the onset of satellite observations, different strategies to overcome the uncertainty associated with monitoring only a small sample of the world's glaciers have been developed. These methods now yield estimates generally reconcilable with each other within their respective uncertainty margins. Whereas this is also the case for the recent decades, the greatly increased number of estimates obtained from remote sensing reveals that gravimetry-based methods typically arrive at lower mass loss estimates than the other methods. We suggest that strategies for better interconnecting the different methods are needed to ensure progress and to increase the temporal and spatial detail of reliable glacier mass change estimates.

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Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data

et al. 2014

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[1] We explore the possibility of using remotely sensed soil moisture data and in situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically based and fully coupled groundwater-land surface model operating at a daily basis and having a resolution of 30 arc sec (about 1 km at the equator). As a test bed, we use the combined Rhine-Meuse basin (total area: about 200,000 km 2 ), where there are 4250 point-scale observed groundwater head time series that are used to verify the model results. Calibration is performed by simulating 3045 model runs with varying parameter values affecting groundwater head dynamics. The simulation results of all runs are evaluated against the remotely sensed soil moisture time series of SWI (Soil Water Index) and field discharge data. The former is derived from European Remote Sensing scatterometers and provides estimates of the first meter profile soil moisture content at 30 arc min resolution (50 km at the equator). From the evaluation of these runs, we then introduce a stepwise calibration approach that considers stream discharge first, then soil moisture, and finally verify the resulting simulation to groundwater head observations. Our results indicate that the remotely sensed soil moisture data can be used for the calibration of upper soil hydraulic conductivities determining simulated groundwater recharge of the model. However, discharge data should be included to obtain full calibration of the coupled model, specifically to constrain aquifer transmissivities and runoff-infiltration partitioning processes. The stepwise approach introduced in this study, using both discharge and soil moisture data, can calibrate both discharge and soil moisture, as well as predicting groundwater head dynamics with acceptable accuracy. As our approach to parameterize and calibrate the model uses globally available data sets only, it opens up the possibility to set up large-extent coupled groundwater-land surface models in other basins or even globally.Citation: Sutanudjaja, E. H., L. P. H. van Beek, S. M. de Jong, F. C. van Geer, and M. F. P. Bierkens (2014), Calibrating a largeextent high-resolution coupled groundwater-land surface model using soil moisture and discharge data, Water Resour. Res., 50,[687][688][689][690][691][692][693][694][695][696][697][698][699][700][701][702][703][704][705]

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Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing

Cited by 616 publications

References 33 publications

Future Hydrological Regimes in the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment

Future Hydrological Regimes in the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment

Observation-Based Estimates of Global Glacier Mass Change and Its Contribution to Sea-Level Change

Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data

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