Dynamic modeling of glaciated watershed processes: Retrospective analysis and future
predictions in the headwaters of the ...
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Dynamic modeling of glaciated watershed processes


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Dynamic modeling of glaciated watershed processes

  1. 1. Dynamic modeling of glaciated watershed processes: Retrospective analysis and future predictions in the headwaters of the Zongo River, Bolivia Chris Frans1, Erkan Istanbulluoglu1, Bibi Naz1*, Dennis Lettenmaier1, Thomas Condom2 1Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA, 2IRD/UJF-Grenoble 1/CNRS/Grenoble-INP, Laboratorie d’etude des Transferts en Hydrologie et Environnement (LTHE) *Now at Oak Ridge National Laboratory, Oak Ridge, TN, USA 1. Summary In high-altitude regions the contribution of glacier melt to runoff is often critical to sustain water supply throughout the year. The tropical glaciers of the South American Andes, whose melt contributes to water supply and energy production, have retreated at unprecedented rates since the 1970’s. Loss of these ice reservoirs will have critical implications to downstream densely populated areas. In this study we use a recently developed glacio-hydrological model to evaluate the contribution of glacier melt to streamflow and track this contribution in time with changing glacier area. A glacier model based on the shallow ice approximation (SIA), solving time- evolving and spatially-distributed balance equations for glacier mass and momentum, is integrated within the Distributed Hydrology Soil Vegetation Model (DHSVM). The model is used to simulate the glacio-hydrologic behavior of the headwaters of the Zongo River during the historical period of 1985-2010. Model performance is evaluated by comparing model predictions with observed glacier extent, mass balance, discharge, and terms of the surface energy balance. Further, the modeling approach is used to predict this transitioning contribution of glacier melt into the future using a stochastic statistical downscaling technique where multiple realizations of future climate are produced using predictions from general circulation models (GCMs) and a weather generator (AWE-GEN). The results of this study demonstrate the applicability of dynamic modeling, of both glacier and watershed processes, for prediction of trends and uncertainties of streamflow in vulnerable high altitude areas that rely on glacier melt. Resumen El agua de glaciares es un recurso importante para el suminstro de agua para consumo humano a lo largo del año. Los glaciares tropicales de los Andes Sudamericanos han retrocedido a velocidades imprecedentes después los setentas. La pérdida de estas reservas de agua tendrán consecuencias críticas en localidades altamente pobladas localizadas aguas abajo. En este estudio usamos un modelo hidrológico-glacial recientemente desarrollado para evaluar la contribución del glacial en la descarga del Río Zongo y para evaluar si dicha contribución cambia con el retroceso del glaciar en la cabecera del río. Un modelo de procesos glaciales basado en la aproximación de hielo superficial (SIA por sus siglas en Inglés), que resuelve las ecuaciones de balance de masa que evolucionan a lo largo del tiempo, y que es espacialemente distribuida, fue integrado al modelo hidrolócigo DSHVM (Distributed Hydrology Soil Vegetation Model). El modelo resultante fue evaluado contra observaciones de la extensión del glaciar, balance de masa, descarga del río, y elementos del balance de energía. Más tarde fue usado para simular los procesos hidrológicos-glaciares de las cabeceras del Río Zongo durante el periodo histórico de 1985 a 2010. Este futuro también fue investigado a través de este modelo, usando ensambles de datos generados por modelos climáticos globales (GCM por sus siglas en Inglés) y por el modelo generador de variables meteorológicas AWE-GEN (Advanced Weather GENerator). Los resultados de este estudio demuestran el uso del modelado dinámico de los procesos glaciares y los procesos hidrológicos en una cuenca en conjunto, para predecir tendencias e incertidumbre en la descarga de ríos en zonas altas vulnerables que dependen de agua de glaciares. 2. Study Location 4. Retrospective Analysis 3. Modeling Method 5. Future Predictions 6. Conclusions • Retrospective (1987-2010) model simulations indicate that on average glacier melt represented 31% of annual discharge from the watershed. This value increases seasonally, up to 90% during dry years. • An analysis of 11 CMIP5 RCP4.5 GCM outputs reveals that high altitude air temperatures are expected to increase throughout the year, with the highest increases of 3-4 degrees during the winter months by 2100. • Initial results of future simulations indicate that glacier melt will both increase and decrease seasonally in the near future with ongoing recession, however will decline throughout the entire year in the latter half of the 21st century. • These findings demonstrate the potential of this coupled glacio- hydroloigcal physics based modeling as a powerful tool for the prediction of glaciated watershed processes in the context of water resources. • Further analysis of uncertainty is required to strengthen the confidence in model predictions DHSVM Glacier Dynamics Model Watershed Hydrologic Processes Accumulation and Ablation Surface Mass Balance Dynamic Ice Flow in Response to Surface Mass Balance Change in Glacier thickness and Area Melt Streamflow Coupling of Models Ice Snow Bedrock/ Soil 1 12 2 ( ) ( , ) ( ) 2 nn mice xy n m m xy ice xy A g S D H S H C g S H n          ( ) ice n xy xy water bS D S t          nb IWE SWE    Glen’s Flow Law Weertman Sliding Law Non-linear Ice Diffusion Momentum Diffusivity A S O N D J F M A M J J A -100 -50 0 50 100 150 Month Wattsm-2 Surface Energy Balance (Elev. 5050 m, WY 2000) SWnet LE SH LWnet Normalized Difference Snow Index (NDSI) (Landsat 4-5 TM scenes) Headwaters of the Zongo River • Located 30 km north of La Paz (-16.25, -68.1) • 14 drainage area • Runoff is intercepted and routed to reservoir for hydro-electricity production • Glacier extent decreased 54% from 1987-2010 (Huayna Potosi, left) • Distinct seasonality: wet and warm Nov. to March, dry and cold May through August. • Accumulation occurs during the wet season while ablation occurs throughout the year. 2 km Distributed Hydrology Soil Vegetation Model (DHSVM, Wigmosta et al., 1994) • Fully distributed physically based hydrology model • Sub-daily timesteps • Two layer energy balance snow/ice accumulation and melt model UBC Glacier Dynamics Model (Garry Clarke, UBC; Jarosch et al., 2013) • Based on shallow ice approximation • Solves time-evolving balance equations for glacier mass and momentum • Spatially distributed • Monthly time-step • Forced with mass balance calculated by DHSVM model The glacier dynamics model is integrated in the DHSVM hydrology model. DHSVM is run at subdaily timesteps, simulating all hydrologic processes, including snow accumulation and melt, snow densification to ice, and glacier ice ablation. Mass balance for each gridcell covered with a glacier is provided to the glacier dynamics model at a monthly time-step. Flow of ice is calculated in response to surface mass balance fluctuations. The thickness and extent of glacier ice is updated accounting for the simulated dynamic ice flow [Naz et al. (in review)]. Model Inputs • 50 meter Digital Elevation Model (DEM, ASTER GDEM) • Hourly air temperature, relative humidity, wind speed, incoming incoming shortwave and longwave radiation, precipitation -Source: Modern Era Retrospective Analysis for Research and applications (MERRA,NASA) bias corrected using hourly GLACIOCLIM observations. • Soil Depth (estimated) • Bed Topography (estimated, Clarke et al. 2013) • Land Cover (Texas A&M University) • Soil classes (estimated) • Hourly solar shading and skyview maps (estimated from DEM) • Initial ice thickness (estimated with glacier dynamics model) Step 1: Spin-up to steady state matching oldest satellite derived extent (1987) Step 2: 30 year spin-up with current climate to thin glaciers to transient state (iterate length of spin-up to match observed recession) Glaciological Simulation -8000 -7000 -6000 -5000 -4000 -3000 -2000 -1000 0 1000 2000 4900 5000 5100 5200 5300 5400 5500 5600 5700 Elevation (m) MassBalance(mmw.e.) Simulated and Observed Mass Balance: Zongo Glacier 1992-2009 Simulated Annual Simulated Mean Annual Observed (1991-2009) 1985 1990 1995 2000 2005 2010 -25 -20 -15 -10 -5 0 Year Balance(mw.e.) Cumulative Mass Balance: Zongo Glacier 1986-2010 Simulated Hydrological (Soruco et al. 2009) Adjusted Glaciological (Soruco et al. 2009) 19951990 20102005 Demonstrating the performance of the modeling approach (a,b) simulated mass balance is compared with observations (GLACIOCLIM, Soruco et al., 2009), (c) modeled mean monthly surface energy fluxes are compared with observations digitized from Communidad Andina (2007), and (d-g) comparing modeled I.W.E with glacier extent derived from Landsat satellite imagery. (a) (b) (c) (d) (g)(f) (e) Hydrologic Simulation Stochastic Downscaling of CMIP5 RCP4.5 GCM Output using an hourly weather generator (Advanced WEather GENerator AWE-GEN) [Fatichi et al. 2011] 1)EstimateAWE-GEN parameters from historical observations 2) Calculate statistical properties of GCM model outputs 3) Calculate PDFs of factors of change for each climate statistic using Bayesian approach with GCM output and observations 5) Extend precipitation statistics to finer time scales 4) Estimate mean factors of change from PDFs 6) Calculate new AWE- GEN parameters from future statistics obtained in steps 4,5 (parameters are calculated for each decade to represent transitional climate) 7) AWE-GEN construction of future climate forcing Factors of Change (170 PDFs total): Temperature FUT = Future time period Precipitation HIST= Historic time period S(h) = Precipitation statistic at aggregation hour interval h Precipitation Statistics: Mean, Variance, Skewness, Frequency of non-precipitation Coefficient of Variation (annual) , , ( )FUT OBS GCM FUT GCM HIST mon mon mon monT T T T   , , ( ) ( ) ( ) ( ) FUT GCM FUT OBS GCM HIST S h S h S h S h  Sept-90 Sept-91 Sept-92 Sept-93 Sept-94 Sept-95 Sept-96 Sept-97 Sept-98 0 200 400 600 literssec -1 DHSVM Qtotal DHSVM Qglacier ObservedALPACA CANAL 0 2 4 6 0 0.1 0.2 0.3 0.4 T [°C] Factor of Change for Air Temperature pdf T T GCMs 5 10 15 20 25 0 0.2 0.4 0.6 0.8 Pr [mm] Frequency Mean Precipitation (June) pdf HIST pdf FUT 0.4 0.6 0.8 1 1.2 1.4 0 0.1 0.2 0.3 0.4 Factor of Change Factor of Change for Mean Precipitation pdf FC FC GCMs -10 -5 0 5 10 0 0.1 0.2 0.3 0.4 Ta [°C] Frequency Monthly Air Temperature (June) pdf HIST pdf FUT Downscaling Steps: Example visualizations of downscaling steps: J F M A M J J A S O N D 0 1 2 3 4 5 6 Month T[°C] Ta500hPa Factors of Change 2080-2100 BCC-CSM1 ACCESS1 CanESM2 CCSM4 CNCRM CSIRO-Mk3.6 GFDL-CM3 INMCM4 MIROC5 MRIGCM NORESM1 J F M A M J J A S O N D 0 0.5 1 1.5 2 2.5 Precip. Factors of Change 2080-2100 Month [FUT/HIST] Hourly time series of Ta, SWin, RH, Wind, Precip., Cloud Cover (2011-2100) J F M A M J J A S O N D -4 -2 0 2 4 6 8 [°C] Monthly Air Temperature HIST 2080-2100 J F M A M J J A S O N D 0 50 100 150 200 [mm] Monthly Precipitation HIST 2080-2100 0 2 4 mm 0 500 1000 Wm-2 -5 0 5 C Ta SW in PPT Predicted Glacier Response 2011-2100 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 0 200 400 600 800 Discharge(literssec-1 ) 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 0 200 400 600 800 Water Year Simulated ObservedZongo Proglacial Stream Sept-93 Sept-94 Sept-95 Sept-96 0 100 200 300 400 500 literssec-1 Sept-03 Sept-04 Sept-05 Sept-06 Sept-07 Sept-08 Sept-09 Qtotal Qglacier Observed Proglacial Stream (Monthly) 7. References Clarke, Garry K. C., Faron S. Anslow, Alexander H. Jarosch, Valentina Radić, Brian Menounos, Tobias Bolch, Etienne Berthier, 2013: Ice volume and subglacial topography for western canadian glaciers from mass balance fields, thinning rates, and a bed stress model. J. Climate, 26, 4282–4303. Fatichi, Simone, Valeriy Y. Ivanov, and Enrica Caporali. "Simulation of future climate scenarios with a weather generator." Advances in Water Resources34.4 (2011): 448-467. Comunidad Andina (2007), Is it the end of snowy heights? Glaciers and Climate change in the Andean Community. General Secretariat of the Andean Community . GLACIOCLIM database http://www-lgge.ujf-grenoble.fr/ServiceObs/ Last accessed 12/1/2012. AH, Jarosch. "Restoring mass conservation to shallow ice flow models over complex terrain." The Cryosphere 7.24 (2013): 229-240. Naz, B. S., Frans, C. D., Clarke, G. K. C., Burns, P., and Lettenmaier, D. P.: Modeling the effect of glacier recession on streamflow response using a coupled glacio-hydrological model, Hydrol. Earth Syst. Sci. Discuss., 10, 5013-5056, doi:10.5194/hessd-10-5013- 2013, 2013.Soruco Wigmosta, Mark S., Lance W. Vail, and Dennis P. Lettenmaier. "A distributed hydrology‐vegetation model for complex terrain." Water resources research 30.6 (1994): 1665-1679. We would like to thank Pat Burns for his production of the Landsat glacier extent estimates. Importance of Glacier Dynamics No Dynamics with Dynamics Comparing simulations (1985- 2010) (a) without and (b) with glacier dynamics highlights the importance of representing the dynamic redistribution of ice over longer periods of time. 4 6 8 10 12 x 10 7 [m3 ] 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 10 15 20 25 30 [%] Glacier Volume Glacier Extent Predicted Hydrologic Response 2011-2100 Glacio-Hydrologic Simulations of 8 Future Climate Realizations: 2040 2060 2080 2100 (a) (b) (c) (d) (e) (f) Modeled evolution of glacier (a) volume, (b) extent, and (c-f) spatial changes of ice water equivalent (I.W.E.). (a) (b) S O N D J F M A M J J A 0 200 400 600 800 literssec-1 S O N D J F M A M J J A S O N D J F M A M J J A Total Discharge Glacier Melt Discharge Dry 2005 Mean 1987-2010 Wet 2009 All Watershed Discharge The model was calibrated to optimize both streamflow and cumulative mass balance of Zongo glacier. Simulated daily (monthly) discharge of the proglacial stream is shown at right (below). Nash Sutcliffe efficiency values for monthly flow range from 0.4-0.7 for the different locations and periods of record in the watershed. On average glacier melt contributes 40% of total monthly watershed discharge but can represent nearly 90% of total discharge during low flows in dry years (left). 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 0 500 1000 [mm] S O N D J F M A M J J A 0 50 100 150 [mm] S O N D J F M A M J J A 2030-2050 2080-2100 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 0 200 400 600 800 1000 1200 1400 mm S O N D J F M A M J J A 0 50 100 150 200 mm S O N D J F M A M J J A Glacier Melt Total Runoff Total Runoff Glacier Melt 1987-2010 Total Runoff 1987-2010 Glacier Melt 2030-2050 2080-2100 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 0 200 400 600 800 1000 1200 1400 mm S O N D J F M A M J J A 0 50 100 150 200 mm S O N D J F M A M J J A Glacier Melt Total Runoff Total Runoff Glacier Melt 1987-2010 Total Runoff 1987-2010 Glacier Melt 2030-2050 2080-2100 S O N D J F M A M J J A -60 -40 -20 0 20 40 60 [%] S O N D J F M A M J J A -60 -40 -20 0 Total Runoff Glacier Melt 2030-2050 2080-2100 Changes in Watershed Runoff Initial predictions indicate decreased (increased) runoff in summer (winter) in the near future (2030-2100) through changes in glacier melt . In the far future (2080-2100), decreased runoff is predicted throughout the year (up to -36%), through drastic declines in glacier melt contribution.