Manuscript prepared for Hydrol. Earth Syst. Sci. Discuss.with version 2.0 of the LATEX class copernicusdiscussions.cls.Date: 3 February 2013

Satellite products for precipitation estimationin mountain regions: A case study in NepalN. Y. Krakauer1, S. M. Pradhanang2, T. Lakhankar1, and A. Jha3

1Department of Civil Engineering and NOAA-CREST, The City College of New York, NewYork, USA2Institute for Sustainable Cities, City University of New York, New York, New York, USA3Department of Agricultural and Resource Economics, Colorado State University, Fort Collins,Colorado, USA

Correspondence to: N. Y. Krakauer(nkrakauer@ccny.cuny.edu)

Abstract

Precipitation in mountain regions is often highly variable and poorly observed, limiting abilitiesto manage water resource challenges. Here, we evaluate the TRMM 3B-43monthly precipi-tation product over Nepal against weather station data. We find that the TRMM precipitationproduct exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Skill5

can be increased substantially by adjusting for spatial and seasonal biases using the griddedstation precipitation product APHRODITE and by incorporating information from the finer res-olution satellite product PERSIANN, with the overall Nash-Sutcliffe efficiency increasing from0.71 (unadjusted TRMM) to 0.88 (adjusted TRMM). The adjusted TRMM product is promisingfor use in water resources applications.10

1

1 Introduction

Mountain regions are critical to regional water resources, with often heavy precipitation supply-ing river flow to extensive downstream reaches, and are also vulnerable to hydrological hazardssuch as flooding (Viviroli et al., 2007, 2011). Since precipitation in mountain regions may varystrongly in space, accurate spatially distributed data are critical to assessing mountain water re-5

sources, yet in many regions few weather station measurements are available in near real time.Remote sensing precipitation products offer potentially high spatial and temporal resolutionand low latency, but discrepancies between available products put their accuracy for mountainregions in question (Tian and Peters-Lidard, 2010).

Here, we evaluate the accuracy of remotely sensed precipitation for Nepal, located between10

latitudes 26◦-31◦N and longitudes 80◦-89◦E on the southern side of the Himalayas (Figure 1).Nepal’s low and middle foothills receive large amounts of rain from about July to Septemberas part of the South Asian monsoon, but populations there may experiencewater shortagesduring the rest of the year (Merz et al., 2003; Manandhar et al., 2012). Winter snow at highelevations may be an important contributor to spring and summer streamflow (Hannah et al.,15

2005), while the Himalayan peaks and the Tibetan plateau behind them are in a relatively dry“rain shadow” (Winiger et al., 2005). Precipitation amount and timing is a key contributor tohazards such as flooding and landslides (Chalise and Khanal, 2002; Agrawala et al., 2003; Merzet al., 2006). However, few precipitation measurements from Nepal are publicly available, andthe national rain gauge network is inadequate to capture precipitation variability in mountain20

regions (Sharma, 2009).Several studies have previously applied satellite precipitation products forthe Indian subcon-

tinent. (The products mentioned will be discussed more fully in the Methods section below.)Bhatt and Nakamura (2005) and Bookhagen and Burbank (2006) used the TRMM satellite-borne precipitation radar to study the seasonality and diurnal variation of precipitation around25

the Himalayas, but without validating the satellite precipitation estimates against other obser-vations. Brown (2006) compared available daily station data from India andSri Lanka to twosatellite precipitation products, using as a criterion for accuracy the discrepancy between the

2

gauge and satellite yearly accumulations. Islam and Uyeda (2007) compared precipitation sea-sonal cycles and number of rain days in two versions of the TRMM precipitation product tostation measurements over Bangladesh, showing that a low bias in TRMM monsoon rainfallwas mitigated in a more recent version. Focusing specifically on Nepal, Islamet al. (2010)found that Version 6 of the TRMM product generally underestimated daily precipitation com-5

pared to available station data and then used linear regression to calibrate theTRMM product,although without considering geographic variation in the calibration factor.Duncan and Biggs(2012), on the other hand, found that TRMM generally overestimated precipitation over Nepalcompared to the gridded station measurement product APHRODITE, concluding that TRMMtherefore is of limited use for water resource management or hazard planning in Nepal.10

In this work, our goals are (a) systematically quantify the performance of satellite precipita-tion products over Nepal compared to station observations; (b) assess how to calibrate satelliteproducts for good performance in representing both absolute amounts ofprecipitation and pat-terns of spatial and temporal variability. We focus on the monthly timescale, which is relevantfor water resources applications such as agriculture and hydropower.15

2 Methods

2.1 Data used

The primary satellite precipitation product considered here over Nepal is TRMM Product 3B-43, released by NASA’s Goddard Space Flight Center. TRMM stands for the Tropical RainfallMeasuring Mission, a satellite launched in late 1997 that includes several instruments for mea-20

suring precipitation rates from space using microwave, infrared, and visible wavelengths (Kum-merow et al., 1998). Product 3B-43 aggregates the 3-hour precipitationproduct, 3B-42, whichincorporates microwave and infrared observations from multiple satellites including TRMM, tocalendar months. 3B-43 uses rain gauge data in order to remove any detected biases from theestimated precipitation field (Huffman et al., 2010). Advantages of TRMM 3B-43 compared to25

other satellite products include relatively high spatial resolution (0.25 degree), extensive vali-

3

dation and bias adjustment based on ground measurements, and the availabilityof a version ofthe 3-hour product processed in near real time, so that it can be operationally used for waterresources applications in Nepal. We used the latest research version (7A) of TRMM 3B-43,which was available from 1998 through September 2012.

Given that Nepal’s topographic contrasts may induce large precipitation differences over5

short distances, a precipitation product with higher spatial resolution is desirable. We there-fore also considered Precipitation Estimation from Remotely Sensed Information using Arti-ficial Neural Networks-Cloud Classification System (PERSIANN-CCS) (Hong et al., 2004).PERSIANN-CCS uses geosynchronous satellite infrared cloud imagery toachieve high spatialand temporal resolutions, trained against satellite microwave data and ground-based gauges and10

radar. PERSIANN-CCS was available at 0.04 degree, 3 hour resolutionfor 2006-2010, whichwe aggregated in the time domain to monthly. There is no published validation of PERSIANN-CCS over Nepal, though Brown (2006) evaluated a PERSIANN-CCS product with coarser spa-tial resolution (0.25 degree) over India and Sri Lanka.

The Asian Precipitation–Highly Resolved Observational Data Integration Towards Evalua-15

tion of Water Resources (APHRODITE) project has produced a daily precipitation dataset overAsia based solely on rain gauge measurements (Yatagai et al., 2012). Thisincludes station dataobtained by negotiation with national meteorological agencies, including Nepal’s, that are notpublicly available in their original form. The station data were interpolated usingan algorithmthat takes into account topography and using climatology to estimate missing values. We used20

the APHRODITE V1101 monsoon Asia product at the best available resolution of 0.25 degree,available for 1951-2007, aggregating the daily values to monthly.

Almost complete daily precipitation series for 11 stations,covering 1988-2007, were obtainedfrom the Nepal Department of Hydrology and Meteorology (DHM). As well, incomplete dailyprecipitation series for 2007-2012 were obtained from the DHM website for a superset of 1825

stations (Figure 1). Considering only months with data for all days, this gavea total of 2984observations (station-months).

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2.2 Calibration of the TRMM precipitation product

We considered modifying the TRMM 3B-43 precipitation productPTRMM in several ways toimprove its ability to capture precipitation variations in Nepal. One modified product involvesmultiplyingPTRMM by a scale factorSA that depends on season (month,m) and grid cell (x, y)5

based on the disagreement between TRMM and Aphrodite during their period of overlap:

SA(m,x, y) =〈PAPHRODITE(m,x, y)〉/〈PTRMM(m,x, y)〉

〈PAPHRODITE〉/〈PTRMM〉, (1)

where 〈·〉 denotes averaging across years. The denominator refers to the averageAPHRODITE precipitation across all stations and months divided by the corresponding TRMMamount. Dividing by this quantity corrects for the tendency we found for APHRODITE to un-10

derestimate precipitation compared to TRMM and the ground stations, even whilemaintainingaccurate spatial and seasonal patterns. The resulting scaled precipitation product isPTRMM-A .

Another modification tested involved using the PERSIANN product to give a multiplier SP

that accounts for heterogeneity in precipitation amount within the TRMM grid. To deriveSP ,we first degraded the PERSIANN productPPERSIANN to TRMM’s 0.25 degree resolution to give15

PPERSIANN-25. Then

SP (x, y) = 〈PPERSIANN(x, y)〉/〈PPERSIANN-25(x, y)〉. (2)

Note that unlike with the computation ofSA, this averages across seasonal variability. (Wefound that letting this scalar vary by season did not improve the fit to station data.) The resultingscaled precipitation product isPTRMM-P.20

A final TRMM modification isPTRMM-AP. This multipliesPTRMM by bothSA andSP .

2.3 Metrics for precipitation product quality

Our primary metric for the degree to which each precipitation product reproduces station ob-servations is the coefficient of determinationR2, also known in hydrology as the Nash-Sutcliffe

5

efficiency (Nash and Sutcliffe, 1970). This increases as the mean square difference betweenstation observations and the precipitation product decreases:

R2 =〈(P1 − P2)

2〉

〈(P1 − 〈P1〉)2〉), (3)

whereP1 refers to station observations,P2 to a precipitation product, and〈·〉 denotes an5

average across observation stations and months.R2 = 1 would mean that the product agreesexactly with the station observations, whileR2 = 0 indicates that the mean square error of theproduct is as large as just using the mean observed value. Unlike the correlation coefficientbetween a precipitation product and station observations,R2 penalizes systematic bias in theproduct’s precipitation estimates as well as irregular discrepancies.10

In order to understand how well the precipitation products represent different aspects of pre-cipitation means and variability, we consider the following variants:

– R2

all using all the monthly measurements as given;

– R2

mean using the mean annual values for each station (this quantifies how well the prod-uct represents spatial variability in mean precipitation, without saying anything about its15

timing);

– R2

seasonal for stations’ mean seasonal cycles (for each station, this includes 12 elementsthat give the fraction of the yearly mean seen in each month; this quantifies how well eachproduct represents seasonal precipitation patterns);

– R2

variability for monthly values with the climatology subtracted (this quantifies how well20

each product represents interannual variability in precipitation).

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3 Results

3.1 Mean, spatial and temporal variability

Mean precipitation from the station observations was 1725 mm/year. The TRMMproduct’smean was only 2% higher than the observations, while APHRODITE was 12% lower (Table5

1). PERSIANN had less than half as much precipitation as the station observations showed,implying that it is severely miscalibrated over Nepal.

Consistent with this, the PERSIANN product had by far the lowestR2

all, indicating thatit had large mean square differences from station observations. PERSIANN-25 had slightlylower R2

all than the original PERSIANN, suggesting that the high resolution of PERSIANN10

compared to TRMM and APHRODITE does contain some additional information on the finespatial distribution of precipitation. APHRODITE had the bestR2

all, at 0.89, while TRMM hadan intermediateR2

all of 0.71 (Table 1).Looking atR2 variants intended to measure the ability of products to reproduce various as-

pects of the observational field, we see that APHRODITE was by far the best in representing15

the spatial distribution of mean precipitation (R2

mean); TRMM did much worse, implying thatit may have large spatially varying biases in precipitation amount within Nepal even thoughthe average amount is about right, and PERSIANN was worse yet (Table1). The seasonal-ity of precipitation was well captured by all three products, with APHRODITEagain givingthe best performance (R2

seasonal in Table 1). The interannual variability of precipitation was20

APHRODITE’s weakest aspect, but again it did better than TRMM, while PERSIANN showedlittle discernible skill (R2

variability in Table 1).The nature of differences between the TRMM and APHRODITE precipitation fields is shown

in Figure 2. While the large-scale features, say at a 1 degree resolution,are quite similar, TRMMdoes not delineate topographically driven precipitation contrasts with the same sharpness as25

APHRODITE. This may be due to the 1 degree resolution of some of the precipitation fieldsused to construct the TRMM product (Huffman et al., 2010), which doesnot adequately resolvetopographic variability in this region. AdjustingPTRMM through multiplying bySA forcesTRMM’s spatial patterns to match those seen in APHRODITE.

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3.2 Effect of adjustments toPTRMM

Adjusting the TRMM product for spatially and seasonally varying bias usingAPHRODITE(giving PTRMM-A ) resulted in improved performance under all metrics.R2

mean now exceedsthat of the APHRODITE product itself (because TRMM doesn’t have APHRODITE’s low bias5

in mean precipitation), whileR2

all andR2

seasonal approach APHRODITE’s values.R2

variability

shows the least improvement, but still goes from 0.50 to 0.57 (Table 1).Adjusting the TRMM product to include fine spatial variability using PERSIANN(giving

PTRMM-P) also resulted in improved performance, although the gains were more modest com-pared to the APHRODITE-based adjustment. Including both adjustments (PTRMM-AP) gave the10

best performance under all metrics. The gains compared toPTRMM-A were small – for example,R2

all rose from 0.875 to 0.878, compared to 0.887 for APHRODITE – and most notable for themean spatial pattern (R2

mean, Table 1).The effect of the TRMM adjustments for two illustrative stations is shown in Figure 3. For

Kathmandu (27◦ 42’ N, 85◦ 22’ E, elevation 1337 m), the adjustment reduces the rainfall15

amount over the entire monsoon period (Figure 3a). For Dhangadhi, in thewestern part ofthe country and at lower elevation (28◦ 32’ N, 81◦ 7’ E, 140 m), the adjustment increases therainfall amount for the latter months of the monsoon.

Inspection of the differences between the adjusted TRMM productPTRMM-AP and stationobservations for 1998-2012 showed that the mean bias of the adjusted TRMM product was not20

significantly different from zero and showed no significant trend overtime (not shown). Thus,the TRMM product appears to be homogenous in time compared to the available station data.

4 Discussion

We presented an evaluation of the TRMM 3B-43 monthly precipitation productover Nepal.We found that, compared to available station measurements, the product was on average almost25

unbiased and had some skill (positiveR2). Adjusting for spatial and seasonal bias patterns usingAPHRODITE and PERSIANN improved the TRMM product substantially, to thepoint where

8

its skill in reproducing station precipitation, as measured byR2

all, is competitive with that ofthe gridded station product APHRODITE, where TRMM has the advantageof being availablefor recent years. While local rain gauge networks combined with ground-based radar are idealfor assessing precipitation amounts, we conclude that satellite products such as TRMM can be5

very useful where, as is usually the case in Nepal, adequate ground data are not collected oravailable.

It is interesting that APHRODITE had larger bias in mean precipitation amount than TRMMeven though it was compared to (a subset of) the same station data used to generate it. Thisbias is apparently not specific to the particular station network we used, because a similar low10

bias was found in the evaluation of APHRODITE with a larger set of station data for Nepal andnearby areas by Andermann et al. (2011). It seems likely that this regional bias is an artifact ofthe processing methods used in APHRODITE, and it should be possible to remove it in a futureversion of APHRODITE. On the other hand, we found negligible mean bias for TRMM (al-though benefits were found from location-specific bias correction usingAPHRODITE), which15

is different from previous studies (Islam et al., 2010; Andermann et al.,2011). Perhaps thisdifference reflects improved correction of bias at large spatial scales inthe most recent versionof TRMM, which we used.

Several extensions of our results suggest themselves for future work. It would be of inter-est to evaluate the TRMM adjustments against station locations not used in the construction20

of APHRODITE, such as those used by Andermann et al. (2011). It would be of particularinterest to assess the skill of TRMM at higher elevations, using precipitationdata gathered inrecent projects (Singh et al., 2007; Aihong et al., 2008; Bonasoni et al., 2008), since Nepal’smeteorological station network is concentrated at the lower elevations (<2000 m) where mostof the population lives (Sharma, 2009) (cf. station locations in Figure 1). The ability of TRMM25

to capture interannual variability may be improved by better corrections for satellite orbit andsensor drift (Fisher and Wolff, 2011; Brown et al., 2009). Finally, it will be useful to evaluatethe near real time TRMM product (original and adjusted) at daily and subdaily resolutions, tak-ing into account that correlations between satellite and gauge precipitation measurements, andeven correlations between gauges, worsen at these fine temporal resolutions (Juglea et al., 2010;

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Ouma et al., 2012).

5 Conclusions

We found that the TRMM 3B-43 precipitation product exhibits reasonable skill in giving pre-cipitation over Nepal. Skill increases substantially after adjusting for spatialand seasonal biases5

using APHRODITE and incorporating fine spatial information from PERSIANN. The adjustedproduct shows promise for use in water resources applications.

Acknowledgement. Station precipitation data were supplied by the Nepal Department of Hydrology andMeteorology. This work is part of the project “Adaptation for climate change by livestock smallholdersin Gandaki river basin”, supported by USAID LCC-CRSP under subaward 9650-32, Colorado State10

University. All statements made are the views of the authorsand not the opinions of the funders or theU.S. government.

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Table 1. Evaluation of precipitation products against station observations

Bias in mean R2

all R2

mean R2

seasonal R2

variability

PAPHRODITE −12% 0.887 0.755 0.993 0.731PPERSIANN −55% 0.322 −1.487 0.751 0.014PPERSIANN-25 −55% 0.308 −1.465 0.766 0.019PTRMM +2% 0.713 0.143 0.984 0.505PTRMM-A −0% 0.875 0.875 0.992 0.566PTRMM-P +1% 0.744 0.293 0.984 0.519PTRMM-AP −0% 0.878 0.906 0.992 0.570

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Elevation (m)

50 - 1,000

1,000 - 2,000

2,000 - 3,000

3,000 - 4,000

4,000 - 8,700

Fig. 1. Topography and district boundaries of Nepal. The green symbols show the locations of stationswith precipitation data used in this study.

15

Precipitation(mm/year)

3000

100

Precipitation(mm/year)

3000

100

Fig. 2. Mean precipitation (mm/year) from (a) APHRODITE and (b) TRMM products, both for 1998-2007.

16

0

50

100

150

200

250

300

350

400

450

2008 2008.5 2009 2009.5 2010 2010.5 2011 2011.5 2012

mm/month

Kathmandu precipitation

PTRMMPTRMM, A, P

station

0

100

200

300

400

500

600

700

800

900

2008 2008.5 2009 2009.5 2010 2010.5 2011 2011.5 2012

mm/month

Dhangadhi precipitation

PTRMMPTRMM, A, P

station

Fig. 3. Monthly precipitation time series, 2008-2011, fromPTRMM (blue) andPTRMM-AP (green), alongwith station observations (when available; red markers) for two illustrative locations: (a) Kathmandu,(b) Dhangadhi.

17