Fan Zhang, Hongbo Zhang, Li Wang Institute of Tibetan Plateau Research, Chinese Academy of Sciences

Transcription

1 Hydroogica modeing in apine catchments on the Tibetan Pateau Fan Zhang, Hongbo Zhang, Li Wang Institute of Tibetan Pateau Research, Chinese Academy of Sciences

2 Outine 1.Effects of temperature and precipitation parameters on hydroogica modeing in a high mountain catchment 2.Temperature apse rate estimation from MODIS LST 3.Precipitation gradient based on precipitation observation 2

3 Introduction Accurate runoff simuation is meaningfu for water resources management and panning Hydroogica modeing: chaenging for apine regions with scarce gauges Meteoroogica data shortage: Temperature Lapse Rate (TLR) and Precipitation Gradient (PG) To evauate effect of precipitation parameters on snow cover area (SCA) and runoff simuation and access modeing strategies in a sparsey gauged catchment 3

4 Study area 30 m 30 m DEM ( datamirror.csdb.cn) Karuxung River catchment : 286 km 2 Fifty Gaciers: 58 km 2 (20%) Wengguo hydroogica station: 4550m as Langkazi weather station: 4432 m as 4

5 Estimation of TLR and PG Avaiabe data for estimating TLR and PG are scarce Annua mean temperature in 1975 Annua precipitation in 1975 T and P differences between Langkazi and Jiangzi weather stations have the potentia to represent the TLR and PG in the catchment 5

6 Estimation of TLR and PG Monthy TLRs and PGs derived from the Langkazi and Jiangzi weather stations: fit scientific expectations and used as mode inputs 6

7 SCA and Runoff simuation Semi-distributed atitude zone based temperatureindex mode operating on a daiy time step Daiy T and P at Langkazi are extrapoated to each eevation zone using TLR and PG Mode caibration: MODIS 8-day maximum snow cover extent and Wengguo hydroogica station runoff data during Mode verification period: 2006

8 SCA and Runoff simuation R 2 = R 2 = Snow covers the whoe catchment in eary spring Not a the snow mets during summer Simuation refects seasona SCA evoution in genera 8

9 SCA and Runoff simuation R 2 = R 2 = Good fits between runoff observation and simuation are good with R 2 reaching ~0.86 for both mode caibration and vaidation periods. 9

10 Sensitivity Anaysis Case TLR-1 Case TLR-2 PG-1 PG-2 TLR = ºC/100m (oca average) TLR = ºC/100m PG = 0.26 mm/day/100m (oca average) PG = 0.0 mm/day/100m The seasona changes of TLR and PG shoud be considered for accurate snow cover and runoff modeing. 10

11 Key Points TLR and PG determine the form and amount of precipitation and spatia distribution of temperature and precipitation in hydroogica modeing of the high mountain catchment. Seasona patterns of both parameters shoud be considered in the modeing studies when meteoroogica observations are ony avaiabe at ow eevation. 11

12 Outine 1.Effects of temperature and precipitation parameters on hydroogica modeing in a high mountain catchment 2.Temperature apse rate estimation from MODIS LST 3.Precipitation gradient based on precipitation observation 12

13 Introduction The air temperature apse rate (TLR) is commony used for interpoating air temperature in mountainous areas Ø The accurate estimation of the TLR argey reies on sufficienty dense and representative observations. Ø The sparse distribution of stations in the west TP, especiay in high-atitude regions, may ead to a arge bias 13

14 Introduction Remotey sensed and surface temperature (LST) can provide comprehensive temperature observations compared with the imited ground stations Ø The MODerate resoution Imaging Spectroradiometer (MODIS) LST data are generay used for air temperature estimation Ø The MODIS LST data aone have the potentia to directy refect the spatia variation of air temperature 14

15 Methods This study empoyed two methods in estimating the TLR based on MODIS LSTs incuding: (1)Directy computing the apse rate From MODIS LST (DFM) (2)based on air Temperature Estimation from MODIS LST (TEM) The resuts are then compared with the observed TLR derived from 86 stations across the TP, to vaidate and evauate the two proposed methods 15

16 Estimation of TLR station Estimating equation: Tair = a Z + b Based on neighboring CMA stations Ø For each CMA station, the nearest neighboring CMA stations were added sequentiay. Ø The TLR as we as the correation coefficient (R) and its significance eve were computed. Ø The process continued unti a strong negative correation was indicated at the 0.05 significance eve. 16

17 Estimation of TLR station Observed TLR station is highy variabe in space The average TLR station s of a the 86 stations show strong monthy variation that it presents a doube-humped pattern 17

18 Estimation of TLR station TLR may be infuenced by atitude, ongitude, precipitation, sunshine duration, vapor pressure and humidity in the study area Correation matrix of TLR station for Tmean and various cimatic/terrain conditions

19 Estimation of TLR from MODIS LST Accuracies of the DFM (a) and TEM (b) methods for TLR estimation for Tmean, Tmin and Tmax. in comparison to TLRstation, MODIS LST-estimated TLR is more accurate for Tmean than for Tmin and Tmax. When using MODIS nighttime LSTs, both DFM and TEM show highy acceptabe accuracies for estimating the TLR of Tmean with averaged RMSD of <0.2ºC/100m. 19

20 Outine 1.Effects of temperature and precipitation parameters on hydroogica modeing in a high mountain catchment 2.Temperature apse rate estimation from MODIS LST 3.Precipitation gradient based on precipitation observation 20

21 Introduction Precipitation is one of the most critica mode inputs for accurate hydroogica simuation (Day et a., 1994; Faurès et a., 1995; Beven, 2001). Characterization of precipitation in high atitude ocaities is a chaenging task because the hydrometeoroogica data sets are extremey imited and precipitation is highy variabe in mountainous areas (Buytaert et a., 2006; Immerzee et a., 2014). Therefore, an appropriate method is needed to interpoate point observations to gridded input for distributed modes (Ahrens, 2006; Tahir et a., 2011; Immerzee et a., 2012). 21

22 Study area Mabengnong River catchment : 21 km 2 Mabengnong hydroogica station: 3023m as Nyingchi weather station: 2992 m as 22

23 Precipitation correation anaysis Four rain gauges (RHD-14-2, China) were set up at different atitudes, which ogged the precipitation every 5 minutes during Juy to August m 3023m 3255m 3345m 3396m 2992m ** ** ** ** 3023m ** ** ** ** 3255m ** ** ** ** 3345m ** ** ** ** 3396m ** ** ** ** 1 Precipitation of the Nyingchi meteoroogica station and observed by rain gauges instaed at different atitudes within the catchment shows significant correation. 23

24 Estimation of PG The average daiy PG for trace, ight and moderate precipitation is 0.10, 0.28 and 0.26 mm/d/100m, respectivey

25 Study area (2992 m as) (3907 m as) Nyingchi meteoroogica station Annua mean T : ~9.4 Annua P : ~694 mm Snowfa: November to March Average eevation of the catchment Annua mean T : ~3.1 Annua P : ~1100 mm Snowfa: October to March 25

26 SCA, Runoff and LST simuation A distributed biosphere hydroogica mode based on water and energy budgets with improved physica process for snow (WEB-DHM-S) operating on a houry time step Gridded precipitation data derived from Nyingchi station and PG characterized for the study Mode caibration: MODIS daiy snow cover area(sca), Mabengnong hydroogica station runoff data and MODIS and surface temperature (LST) during 2013 Mode vaidation period:

27 Runoff, SCA and LST simuation Runoff SCA LST Periods NSE R 2 RMSE (m 3 NSE R /s) RMSE NSE R (%) RMSE (K) Caibra4on Vaida4on The runoff, SCA and LST simuation resuts are acceptabe for the base case using the PG derived based on observation. Sensitivity anaysis shows that simuated runoff is significanty underestimated without consideration of PG. 27

28 Key Points Gridded precipitation input obtained based on the PG and HD resuts enabes the WEB-DHM-S mode to generate accurate simuation of runoff, SCA and LST. Simuated runoff is significanty underestimated without consideration of PG. Intensive precipitation observation can improve hydroogica modeing in high mountain areas with extreme topography. 28

29 29

30 Estimation of TLR from MODIS LST Four pass times of MODIS LST Terra Night (LST TN ), Terra Day (LST TD ), Aqua Night (LST AN ), Aqua Day (LST AD ) Estimation of TLR DFM (Directy From MODIS) The TLR estimated is in fact the TLR of LST The neighboring stations/grids used were the same as those used for TLR station.

31 Estimation of TLR from MODIS LST Four pass times of MODIS LST Terra Night (LST TN ), Terra Day (LST TD ), Aqua Night (LST AN ), Aqua Day (LST AD ) Estimation of TLR TEM (Temperature Estimation from MODIS) Ø Tair estimation from MODIS LSTs in combination with auxiiary variabes where SZ is soar zenith; Lon is ongitude; Lat is atitude; JD is Juian day Ø With the estimated Tair, four sets of TLR TEM vaues were a derived using: 31 The neighboring stations/grids used were the same as those used for TLR station.