Evaluation of gridded multi-satellite precipitation (TRMM -TMPA) estimates for performance in the Upper Indus Basin (UIB) Asim J Khan Advisor: Prof. Dr. Rer. nat. Manfred Koch Department of Geo-hydraulics and Engineering Hydrology, University of Kassel, Germany

Presentation Outline  Introduction  Study Area  Upper Indus basin  Indus River Basin  Datasets  TMPA Data (TRMM 3B42 V7)  Gauge Rainfall Data  Methodology  Qualitative Statistical Analysis  Categorical Statistics  Visual Comparison  Results  Conclusions

Introduction  Daily climatic data with acceptable gridded resolution critical for hydrological modelling.  Mountainous regions-Two major issues:  sparsity of the data sampling points (gauge stations), and  the discontinuities in the data series or the quality of the temporal records.  Currently an increasing number of gridded climatic products such as satellite based gridded data are readily available  These satellite products though they have high spatial resolution, may show considerable errors and biases.  The present study aims to evaluate the capability of the “Tropical Rainfall Measurement Mission” (TRMM) “Multi-satellite Precipitation Analysis” (TMPA) in estimating appropriate precipitation rates in the Upper Indus basin (UIB)

Study area – Upper Indus Basin (UIB) Area: about 170,000 km 2 Length (UIB): about 1125 km long Location: 31º - 37º N 72º - 82º E Features Feed Largest irrigation system of the world UIB contains the greatest area of perennial glacial ice cover ( km 2 ) outside the polar regions of the earth The altitude within the UIB ranges from as low as 455 m to height of 8611 m (Tahir et al., 2011). Most of the annual precipitation originates in the west and falls in winter and spring whereas occasional rains are brought by the monsoonal incursions to trans-Himalayan areas (Wake, 1987).

Indus River basin  Area: about 912,000 km 2  Length (Indus): largest rivers in Asia about 2880 km  Location :  24º - 37º  70º - 82º E

Datasets Satellite Rainfall Product- TMPA Data (TRMM 3B42 V7)  The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) provides 0.25x0.25° 3-hourly estimates of precipitation  The TMPA depends on input from two different types of satellite sensors, namely microwave and IR.  Current study used TMPA Data (TRMM 3B42 V7) aggregated to daily time step (1 st January 1998 to 31 st December 2008)

Gauge Rainfall data  The daily time series of fourteen (14) meteorological stations (1 st January 1998 to 31 st December 2008) were used for the point-to-point validation of the TRMM data. StationLatitude (N)Longitude (E)Elevation (m.a.s.l) Khunjrab Burzil Zani pass Shandur Ziarat Kot Pass Yasin Hushey Deosai Rama Ushkore Naltar Ratu Shigar

Quantitative Statistics  Quantitative comparison through widely used statistical indicators:  correlation coefficient (R),  mean relative bias error (BIAS),  the mean error (MAE), and  root mean square error (RMSE)

Categorical statistics A 2x2 Contingency Table was used to list rain events, no events, misses by TRMM and false-alarms by the TRMM, over the Indus river basin. Based on these, categorical statistics were derived including, Accuracy (Ac), bias score or frequency bias index (FBI), probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and true skill statistics (TSS) (Wilks 1995, 2006). (2x2 Contingency Table) OBSERVED VALUES (GAUGE DATA) TOTAL YES NO ESTIMATED VALUES (TRMM-ESTIMATES) YES -a- Hits -b- False Alarms Total-Yes Estimated NO -c- Misses -d- Correct negative Total-No Estimated TOTAL Total-Yes Observed Total-No Observed TOTAL

Results  The results are presented under three groups: i.Quantitative statistics  Monthly and Annual  Summer and Winter Season ii.Categorical Statistics iii.Visual comparison  TRMM Estimates and Gauge data for mean monthly rainfall for all station  Monthly, Annual and Seasonal Time series for rainfall totals at Khunjrab Station  Monthly, Annual and Seasonal Time series for rainfall totals at Yasin Station

Quantitative Statistics Monthly and Annual MONTHLYANNUAL STATIONSRBIASMAERMSERBIASMAERMSE Shigar Ushkor Yasin Zani Pass Zyarat Kot Pass Naltar Rama Ratu Shandur Burzil deosai Hushey Khunjrab Average Maximum Minimum  For monthly data, the mean relative BIAS was huge and ranged from a negative 0.53 to a positive 9.68, with an average monthly BIAS of 1.44 for all the stations.  There was no indication of a similar trend or sequence in the over or under estimation in the monthly data,  The monthly relative MAE and RMSE also showed great variation ranging from 0.56 to and 0.36 to 3.28 times the original amounts as well as all stations averages of 2.17and 0.80, respectively.  The annual indices followed the same pattern, but with considerably better matches.  The relative BIAS ranged from a negative value of 0.66 to a positive value of 2.65, while MAE and RMSE ranged from 0.22 to 2.65 and 0.27 to 2.77 respectively.  The average MAE and RMSE for all stations were 0.64 and 0.78 respectively.

 Summer season mostly showing a positive BIAS while winter predominantly a negative BIAS.  R ranged from a negative 0.47 to a positive 0.68 for the summer season while to 0.66 for winter season, with average values of 0.22 and 0.20 respectively.  The data for only 29% of the stations had somewhat reasonable R value of above  relative BIAS- from-0.64 to 3.6 and to 1.93 times of the original, with average values of 0.26 and for summer and winter season respectively.  RMSE- minimum of 0.3 to a maximum of 3.44 for summer, averaging 0.94, while 0.43 to 2.5 for winter season, averaging SUMMER SEASONWINTER SEASON STATIONSRBIASMAERMSERBIASMAERMSE Shigar Ushkor Yasin Zani Pass Zyarat Kot Pass Naltar Rama Ratu Shandur Burzil deosai Hushey Khunjrab Average Maximum Minimum Quantitative Statistics Summer and Winter

Categorical statistics for daily TRMM estimate and gauge rain data  Accuracy looked pretty good as it values for all the stations were above 0.50 and an average of  Frequency bias index (FBI) varied on both sides with nine stations showing an overestimation while five showed underestimation. The average of FBI for all stations together was 1.05, showing a slight overestimation.  For most of the stations, the value of POD was below 0.50 with only four stations having values above it.  The value of False Alarm Ratio (FAR) for all the station except one, were too high and with an average of  In the same way, the CSI and TSS values were also not very promising as only three station had values above 0.30 in case of former and only one station having value about 0.20 in case of the later.  Overall, the categorical statistics indicates that TRMM estimates do not have a very good match with the gauge data and therefore it can only be used after some corrections and adjustment made thereof AccuracyFBIPODFARCSITSS Shigar Ushkor Yasin Zani Pass Zyarat Kot Pass Naltar Rama Ratu Shandur Burzil deosai Hushey Khunjrab Average Maximum Minimum

Visual comparison Comparison of TRMM Estimates and Gauge data for mean monthly rainfall for all stations with seasonal demarcation

Visual comparison Time series of TRMM Estimates and Gauge data for rainfall totals at Khunjrab Station; a. Monthly, b. Annual, and c. Seasonal (S=Summer, W=Winter)

Visual comparison Time series of TRMM Estimates and Gauge data for rainfall totals at Yasin Station; a. Monthly, b. Annual, and c. Seasonal (S=Summer, W=Winter)

Conclusion  considerable errors  No uniform trend detected  Summer-Over estimated Winter-Under estimated  TRMM 3B42 V7 may only be used for hydrological modeling in UIB after improvements and local calibration The results indicated that the TMPA product showed considerable errors in estimating rain amounts at the gauge stations, throughout the study area as well as throughout the time period studied. There was no uniform trend of under or overestimation found for the region as a whole, as some stations TMPA product tends to overestimate while at others the opposite. The seasonal values though showed a specific pattern, with the summer rain slightly overestimated while the winter predominantly underestimated at almost all locations and time scales. This product had an overall poor agreement with rain gauge data in the study area, at all temporal scales and unreliable for most months and years with RMSE exceeding Therefore the TRMM 3B42 V7 may only be regarded suitable for further applications in the study region, if some improvements and local calibration are carried out first to the data.