Paprika’s hydrological stations. BasinArea (km²)% glac. Phakding 120828.5 Pheriche 14638.8 Dingboche 13637.9 Khote 14835,3.

Slides:

Advertisements

Similar presentations

Center for Environmental Systems Research - University of Kassel Possible impacts of climate change on water availability – sensitivity anlysis and response.

Advertisements

P ROBLEMS IN DETECTING TREND IN HYDROMETEOROLOGICAL SERIES FOR CLIMATE CHANGE STUDIES Jasna Plavšić 1 and Zoran Obušković 2 1 University of Belgrade –

Climate and Hydrological and Extremes in Lake Victoria Basin An Assessment of Vulnerability and Adaptation to Climate Variability and Change Impacts on.

Andy Chan Geo 387H Physical Climatology Fall 2007.

Characteristics of High-Resolution Satellite Precipitation Products in Spring and Summer over China Yan Shen 1, A.-Y. Xiong 1 Pingping Xie 2 1. National.

Climate Research in Nepal Himalayas Saraju K. Baidya (Department of Hydrology & Meteorology) “Mountains, witnesses of global changes. Research in the Himalaya.

Estimation and Prediction of Fresh Water Runoff Based on Atmospheric Data: Preliminary results Cody D. S. Sipkema 4 th Year Environmental Engineering Co-op.

Lucinda Mileham, Dr Richard Taylor, Dr Martin Todd

Third Pole Environment Workshop, 26-28th October, Kathmandu

International Centre for Integrated Mountain Development Kathmandu, Nepal Mandira Singh Shrestha Satellite rainfall application in the Narayani basin CORDEX.

Elsa Nickl and Cort Willmott Department of Geography

Task: (ECSK06) Regional downscaling Regional modelling with HadGEM3-RA driven by HadGEM2-AO projections National Institute of Meteorological Research (NIMR)/KMA.

Mechanistic crop modelling and climate reanalysis Tom Osborne Crops and Climate Group Depts. of Meteorology & Agriculture University of Reading.

Consistency of recently observed trends over the Baltic Sea basin with climate change projections 7th Study Conference on BALTEX June 2013, Sweden.

Application and Evaluation of a Snowmelt Runoff Model in the Tamor River Basin, Eastern Himalaya using a Markov Chain Monte Carlo Data Assimilation Approach.

Last time… Key questions 1.Why does air move? 2.Are movements of winds random across Earth’s surface, or do they follow regular patterns? 3.Implications.

Past and future changes in temperature extremes in Australia: a global context Workshop on metrics and methodologies of estimation of extreme climate events,

Arctic Land Surface Hydrology: Moving Towards a Synthesis Global Datasets.

Alan F. Hamlet Marketa McGuire Elsner Ingrid Tohver Kristian Mickelson JISAO/CSES Climate Impacts Group Dept. of Civil and Environmental Engineering University.

CARPE DIEM Centre for Water Resources Research NUID-UCD Contribution to Area-3 Dusseldorf meeting 26th to 28th May 2003.

Regional Climate Modeling in the Source Region of Yellow River with complex topography using the RegCM3: Model validation Pinhong Hui, Jianping Tang School.

Changes in Freeze-Thaw and Permafrost Dynamics and Their Hydrological Implication over the Russian Arctic Drainage Basin Tingjun Zhang and R. G. Barry.

Identiﬁcation of Temporal Trends of Extreme Discharge in Arctic Region Muhammad Ajmal · Jiyae Shin · Tae-Woong Kim IntroductionIntroduction Motivation.

Atmospheric Processes Associated with Snow Cover Ablation Events and their Effect on the Flood Hydroclimatology of the Chesapeake Bay Gina Henderson and.

Spatial Interpolation of monthly precipitation by Kriging method

Helgi Björnsson, Institute of Earth Sciences, University of Iceland, Reykjavik, Iceland Contribution of Icelandic ice caps to sea level rise: trends and.

Solid and liquid precipitation in major river catchments originating in the European Alps Klaus Haslinger, Barbara Chimani, Reinhard Böhm Zentralanstalt.

NARCCAP Users Meeting April 2011 Results from NCEP-driven RCMs Overview Based on Mearns et al. (BAMS, 2011) Results from NCEP-driven RCMs Overview Based.

Assessment of Hydrology of Bhutan What would be the impacts of changes in agriculture (including irrigation) and forestry practices on local and regional.

Lake and Stream Hydrology 2009 UJ,UH, &TPU Timo Huttula JY/BYTL& SYKE/VTO

Land Cover Change and Climate Change Effects on Streamflow in Puget Sound Basin, Washington Lan Cuo 1, Dennis Lettenmaier 1, Marina Alberti 2, Jeffrey.

Summary of observed changes in precipitation and temperature extremes (D9)

Climate-related changes on New England lakes and rivers during the last two centuries Glenn Hodgkins Rob Dudley Tom Huntington USGS Maine Water Science.

Reducing Canada's vulnerability to climate change - ESS J28 Earth Science for National Action on Climate Change Canada Water Accounts AET estimates for.

Glacier shrinkage in the Himalayas under the monsoon climate with the abundance of debris and dirt NAKAWO, Masayoshi National Institutes for the Humanities.

Gridded Rainfall Estimation for Distributed Modeling in Western Mountainous Areas 1. Introduction Estimation of precipitation in mountainous areas continues.

RESULTS OF RESEARCH RELATED TO CHARIS IN KAZAKHSTAN I. Severskiy, L. Kogutenko.

IUFRO_20051 Variations of land water storage over the last half century K. Laval, T. Ngo-duc, J. Polcher University PM Curie Paris/Lab Meteor Dyn /IPSL.

MODSCAG fractional snow covered area (fSCA )for central and southern Sierra Nevada Spatial distribution of snow water equivalent across the central and.

The climate and climate variability of the wind power resource in the Great Lakes region of the United States Sharon Zhong 1 *, Xiuping Li 1, Xindi Bian.

Printed by Introduction: The nature of surface-atmosphere interactions are affected by the land surface conditions. Lakes (open water.

Spatial distribution of snow water equivalent across the central and southern Sierra Nevada Roger Bales, Robert Rice, Xiande Meng Sierra Nevada Research.

Adjustment of Global Gridded Precipitation for Orographic Effects Jennifer C. Adam 1 Elizabeth A. Clark 1 Dennis P. Lettenmaier 1 Eric F. Wood 2 1.Dept.

Feng Zhang and Aris Georgakakos School of Civil and Environmental Engineering, Georgia Institute of Technology Sample of Chart Subheading Goes Here Comparing.

Indo-UK Programme on Climate Change Impacts in India : Delhi Workshop, Sep. 5-6, 2002 Impacts of Climate Change on Water Resources G.B. Pant INDIAN INSTITUTE.

Assessment of CCI Glacier and CCI Land cover data for hydrological modeling of the Arctic ocean drainage basin David Gustafsson, Kristina Isberg, Jörgen.

Based on the Mezentsev-Choudhury-Yang equation (with n representing catchments characteristics): and water balance equation R = P ─ E, Yang et al. [2011]

Evapotranspiration Estimates over Canada based on Observed, GR2 and NARR forcings Korolevich, V., Fernandes, R., Wang, S., Simic, A., Gong, F. Natural.

 coldest month greater than 18°C  rainfall every month greater than 60 mm.

Arctic terrestrial water storage changes from GRACE satellite estimates and a land surface hydrology model Fengge Su a Douglas E. Alsdorf b, C.K. Shum.

Evaluation of gridded multi-satellite precipitation (TRMM -TMPA) estimates for performance in the Upper Indus Basin (UIB) Asim J Khan Advisor: Prof. Dr.

ARC GIS IN THE SANTA RIVER BASIN IN PERU GIS in Water Resources Fall 2006 Professor: Dr. David Maidment Presented by Presented by Eusebio Ingol.

Results: Model Skill Cool season RegCM2 precipitation exhibits negative (positive) bias in the south central U. S. (northwestern U.S.). Warm season precipitation.

UBC/UW 2011 Hydrology and Water Resources Symposium Friday, September 30, 2011 DIAGNOSIS OF CHANGING COOL SEASON PRECIPITATION STATISTICS IN THE WESTERN.

Reducing Canada's vulnerability to climate change - ESS Towards a water budget for Canada: are reanalyses suitable for the task?

Data collection and 1 st preprocessing Action : Large scale Hydrological cycle Valerio Lucarini (1,2) Ida Kriegerová (1) Róbert Danihlík (1) (1)CINFAI.

(Srm) model application: SRM was developed by Martinec (1975) in small European basins. With the progress of satellite remote sensing of snow cover, SRM.

HYDROLOGICAL BALANCE OF THE DANUBE RIVER BASIN: AN INTERCOMPARISON STUDY Lucarini, V. (1,2), Kriegerova, I. (2), Danihlik, R. (2), Speranza, A. (1,2) (1)

Discharge-Area Relations from Selected Drainages

Estimating Changes in Flood Risk due to 20th Century Warming and Climate Variability in the Western U.S. Alan F. Hamlet Dennis P. Lettenmaier.

5th International Conference on Earth Science & Climate Change

Analysis of Hydro-climatology of Malawi

Hydrologic implications of 20th century warming in the western U.S.

Yinsheng Zhang，Jie Ding，F Suhaib

Flow Prediction on the Yampa River

150 years of land cover and climate change impacts on streamflow in the Puget Sound Basin, Washington Dennis P. Lettenmaier Lan Cuo Nathalie Voisin University.

On HRM3 (a.k.a. HadRM3P, a.k.a. PRECIS) North American simulations

Focus of analysis – reason for using PRECIS RCM

EC Workshop on European Water Scenarios Brussels 30 June 2003

Precip, Tmax, Tmin scaled to match CRU monthly data.

Presentation transcript:

Paprika’s hydrological stations

BasinArea (km²)% glac. Phakding Pheriche Dingboche Khote 14835,3

More Paprika’s hydrology

Pangboche m Kharikola m Two small slope basins could be studied in a detailed hydrological field approach. PhD project submitted for funding (feb. 2011) at UM2 CoP

Spatialized climate data sets

Available climate datasets précipitation TypeNameSpatial resol. Time resol. PeriodDerived productsFormat Precip.Aphrodite °daily monthly seasonal yearly netcdf matlab Precip.CRUTS °monthly seasonal, yearlynetcdf Precip.TRMM 3B420.25°daily dailymatlab Air temp. CRUTS °monthly seasonal, yearlynetcdf Air temp. Ncep/Ncar reanalysis 17 levels 2.5°daily regrided on 0.25° (*) daily, monthly, seasonal, yearly netcdf Air temp. Ncep/Ncar reanalysis 2m (gauss grid) 1.8°daily regrided on 0.25° (*) daily, monthly, seasonal, yearly netcdf (*) bilinear interpolation

Comparison of seasonal (monthly) precipitation CRU versus Aphrodite on period over Koshi basin

Spatial comparison of monthly precipitation CRU versus Aphrodite on period over Koshi basin (resol = 0.25°) monthly bias (CRU-Aphro) monthly correlation between CRU and Aphro (636 values for each cell) Bias: higher CRU values over Tibet plateau (~1000 mm/year) better agreement in the southern basin Correlation: good correlation CRU-Aphrodite on southern part of the basin

Spatial comparison of monthly precipitation TRMM versus Aphrodite on period over Koshi basin (resol = 0.25°) monthly bias (Aphro-TRMM) monthly correlation between TRMM and Aphro (120 values for each cell) Bias: higher TRMM values over Tibet plateau and in south of Nepal (up to 350 mm/year)  probably reflect a bad aphrodite precipitation interpolation (lack of gages) over Tibet plateau higher Aphrodite values along the montains (up to 1000 mm/year)  TRMM :only rain, Aprhodite : rain+ snow better agreement over a small area in the southern basin Correlation: low correlation TRMM-Aphrodite on north-west part of the basin

DHM’s hydrological balance

Awa Gaon Mulghat Simle Khurkot Rabuwa bazar Comparison of annual/monthly discharge and precipitation - 5 sub catchments of Koshi river at Chatara (93% of Koshi catchment area at Chatara) : Awa Gaon km² 606 : Simle km² 690 : Mulghat km² 652 : Khurkot km² 670 : Rabuwabazar km² 695 : Chatara km² Aims :  Analysing the reliability of discharge measurements  Basic anlayses of the catchment hydrology Data : -Discharge : DHM data base -Precipitation : spatial mean precipitation calculated for each catchment with aphrodite data Chatara

BasinArea (km²) % glac. Chatara ,4 Simle ,9 Awa Goan ,5 Rabuwabazar ,9 Muhlghat 58809,1 Khurkot ,8 Simle-AwaG 38000,9

The main problems : 1. For all the catchment the runoff coefficients C (annual and seasonal time steps) happen to be higher than one Exemple of the Dud Koshi catchment at the annual scale : All catchments All seasons C >>1 in 2005

2. This pattern can not be explain by snow or ice melting as it is observed in all season an mainly in Winter and October-November it does not exhibit a seasonal cycle Exemple of Dud Koshi winter (=DJF) runoff coefficient  May be it can be explain by the uncertainty of low flows measurement ? by the catchment precipitation estimations (Aphrodite)

3.Inconsistency for Arun river : Decreasing discharges at Awa Gaon and Simle No decreasing trend observed for the precipitation Arun – Awa Gaon – km² Arun – Simle – km² Decreasing trend in annual discharge Break in the annual discharge time serie (near ) No significant trend for annual precipitation (green curves)

4. Inconsistency in the sub catchments contributions to the total Koshi catchment discharge We have computed the ratio Qsubcatchment/Qkoshi (annual and seasonal scale)  The sum of the 5 ratio is greater than one ! Annual contribution of the 5 subcatchments to the Koshi annual discharge (black curve)  This pattern also appears at the seasonal scale  not systematicaly the same year than for annual discharges  the years with runoff coefficient and contribution inconsistencies are not simultaneous !

Comparison of monthly temperature, discharge and precipitation (standardized values)  Show consistent pattern : maximum discharge, temperature and precipitation simultanously happen during summer (Monsoon)

Data bases

HYDRACCESS DHM hydro & climato EvK2 climato Paprika hydro &climato Contact : Pierre Chevallier

Spatial infos Aphrodite CRU TRMM NCEP Etc. Contacts: François Delclaux Luc Neppel

ArcGis Projection WGS 84 UTM 45N (Nepal) UTM 43N (Pakistan) DEM Snow Cover Hydrology Etc. Contacts: Pierre Chevallier