Assessing the accuracy and applied use of satellite-derived precipitation estimates over Nepal

Applied Geography 34 (2012) 626e638

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Assessing the accuracy and applied use of satellite-derived precipitation estimates over Nepal John M.A. Duncan*, Eloise M. Biggs 1 Geography and Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom

a b s t r a c t Keywords: Precipitation Extremes Nepal TRMM 3B42 APHRODITE Satellite Gauge Water resources

Accurate observational precipitation data supplied at a fine spatial resolution is vital for informing sustainable water resources management in Nepal. Livelihoods in Nepal are acutely impacted by precipitation. The amount of monsoon precipitation determines water available for drinking, hydropower and irrigation. Extreme precipitation events often result in landslides, flash flooding and crop damages. Freely available satellite-derived precipitation data products have the potential to substantially inform water policy. Such products could advocate sustainable use of water resources and enhance the adaptive capacity of rural populations in Nepal to future precipitation changes. In this research, statistical measures were used to assess the accuracy of Tropical Rainfall Measuring Mission satellite-derived precipitation estimates (TRMM 3B42) relative to ground-based precipitation data (APHRODITE), seasonally from 2001 to 2007. In all seasons the majority of satellite precipitation estimates were significantly correlated with ground-based precipitation. However, satellite precipitation estimates consistently overestimated the amount of precipitation, with error greatest in the monsoon season. The satellite precipitation product inaccurately detected extreme precipitation events, ’rainy days’ and precipitation intensity in the monsoon season. Results suggest that precipitation estimates derived from this satellite product have limited use in agricultural planning, water resource management and developing mitigation measures to the impacts of extreme events in Nepal. Currently, ground-based precipitation measurements still provide the most accurate information for use in water resources management. Maintaining and developing precipitation gauge networks in Nepal, particularly in regions of high relief, is extremely important for increasing the accuracy of both ground-based and satellite-derived precipitation products. Ó 2012 Elsevier Ltd. All rights reserved.

Introduction

Revadekar & Preethi, 2010). If changes in climatic and/or environmental conditions initiate a shortfall in agricultural production, resource-poor farmers lack the resilience and adaptive capacity in their food systems to mitigate against such losses (Brown & Funk, 2008; Ericksen, 2008). Analysis of precipitation time series data by Kansakar, Hannah, Gerrard, and Rees (2004) and Ichiyanagi, Yamanaka, Muraji, and Vaidya (2007) indicated large spatial and seasonal variations in precipitation across Nepal. High frequency, short-term (daily) precipitation extremes and long-term precipitation intensity can trigger slope failures and landslides in many hazardous regions due to unconsolidated geology and steep mountainous terrain (Dahal & Hasegawa, 2008; Gabet, Burbank, Putkonen, Pratt-Sitaula, & Ojha, 2004). Damage from landslides in the Himalayan region costs in excess of one billion US dollars annually, causing significant loss of life and damage to infrastructure, property and agricultural land (Dahal & Hasegawa, 2008; Dahal, Hasegawa, Masuda, & Yamanaka, 2006; Gabet et al., 2004). Revadekar and Preethi (2010) found that

Precipitation is vital for sustaining livelihoods in Nepal; an essential resource for enabling rain-fed agriculture, recharging potable water supplies and replenishing river levels for sustaining irrigation systems and hydropower generation. The majority of Nepal’s population resides in rural locations, with 80 percent of rural livelihoods dependent on subsistence agriculture (Pariyar, 2003). Subsistence farmers, who are dependent upon rain-fed agriculture for their livelihoods to produce food and generate income, are acutely impacted by fluctuations in water resources (e.g. Agrawala et al., 2003; Biggs, Watmough, & Hutton, 2011), fluctuations which are generally correlated with trends in precipitation (Easterling et al., 2007) and climatic extremes (Ludi, 2009; * Corresponding author. Tel.: þ44 23 8059 9655; fax: þ44 23 8059 3131. E-mail address: [email protected] (J.M.A. Duncan). 1 Tel.: þ44 23 8059 9655; fax: þ44 23 8059 3131. 0143-6228/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2012.04.001

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precipitation intensity and frequency of ‘rainy days’ and extreme events are significantly correlated with crop yields in monsoon climates. Changes in runoff in upstream river reaches of this region can impact several hundred million people throughout the Gangetic Plain whose livelihoods are dependent on this vast freshwater resource (Singh, Bassignana-Khadka, Karky, & Sharma, 2011). Well-informed policy can encourage sustainable use of water resources through reducing the vulnerability of rural communities to environmental change by adopting suitable adaptation and mitigation strategies to ensure agricultural production is optimised (Ludi, 2009). Remote sensing offers a valuable tool to assess largescale climate variability in the Himalayan region at fine spatial and temporal resolutions. Several remote sensing products, such as the Tropical Rainfall Measuring Mission (TRMM), provide precipitation estimates at relatively fine spatiotemporal resolution up to near realtime. Precipitation variability remains poorly understood in Nepal as research investigating precipitation patterns is limited (Ichiyanagi et al., 2007). Satellite sensor data could hold great potential for analysing variability and monitoring water resources in Nepal, especially given the vulnerability of rural livelihoods to rapidly changing climatic and environmental conditions. Moreover, TRMM is freely available, making the product appealing for use in developing water resources policy in countries such as Nepal, where there are limited economic resources to administer adequate quantities of in situ data (Shrestha, Bajracharya, & Mool, 2008), and the quality and instant availability of existing ground-based precipitation data are limited (Anders et al., 2006; Singh et al., 2011). Nonetheless, gauge data are routinely taken as ‘ground-truth’, as without these data, assessing the accuracy of satellite products in these areas becomes increasingly difficult. It also remains questionable as to whether remote sensing data products can capture precipitation variability as accurately as ground-based observations in Nepal. This research explores the use of remote sensing to detect changes in precipitation and investigates whether satellite precipitation estimates can adequately capture spatial and temporal variability when compared to ground reference data across Nepal.

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Satellite-derived precipitation observations only estimate precipitation and are not direct measures of quantity. It is therefore important to establish the accuracy of satellite precipitation through validating estimates against ground measurements from rain gauges or radar observations (Shrestha et al., 2008). Research validating TRMM precipitation over Nepal is limited. Barros, Joshi, Putkonen, and Burbank (2000) compared TRMM precipitation radar (PR) and microwave radiometer (TMI) precipitation estimates with gauge data over a limited spatial extent (the Marsyandi river basin in central Nepal). They found satellite-derived precipitation estimates to have low probability of detection and skill scores relative to precipitation events at gauge stations; TMI at low (510 me1500 m) and high (2100 me4400 m) elevation and PR at high elevation. Anders et al. (2006) found TRMM river basin-wide estimates of average precipitation to be well correlated with river basin-wide runoff in the Himalayas; although no attempt to model runoff processes was made, they noted TRMM consistently underestimated basin-wide runoff and sampling errors of 15e50% occurred for TRMM annual precipitation. This paper provides (i) a statistical comparison of satellite- and gauge-derived precipitation products; and (ii) an assessment into the capacity of satellite-derived precipitation estimates to detect frequent and intense extreme precipitation events relative to ground-based observations. Precipitation in Nepal Nepal lies between the Himalayan Mountains and the Gangetic Plain. A northesouth physiographic gradient reflects substantial diversity in climatic and environmental characteristics (Kansakar et al., 2004), ranging from High Mountain in the north, through Mid-Mountain and Hills, to Lowland in the south (Fig. 1). Nepal is a country characterised by steep, complex topography which makes meteorological observations challenging (Ichiyanagi et al., 2007). Four distinct climatic seasons reflect changing characteristics in precipitation patterns; pre-monsoon (MarcheMay), monsoon

Fig. 1. The three principle physiographic zones of Nepal. These zones have been adapted from the five standard physiographic zones as published by ICIMOD (2011); Lowland consists of the Terai and Siwaliks, Mid-Mountain and Hills consists of the Middle and High Mountain zones and High Mountains constitutes the High Himalayas.

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(JuneeSeptember), post-monsoon (OctobereNovember) and winter (DecembereFebruary) (Ichiyanagi et al., 2007; Kansakar et al., 2004). Monsoon precipitation is driven by high winds from the Bay of Bengal bringing moist air currents over Nepal. The Asian summer monsoon exhibits large temporal and spatial variations with precipitation patterns governed by both monsoon circulation dynamics and orography; the latter having the most influence in the Himalayan region (Malik, Marwan, & Kurths, 2010). TRMM precipitation estimates have been investigated relative to topographic variation in this region, and the complex ridge and valley topography of south-facing Himalayan slopes has been found to influence the diurnal cycle of precipitation (Barros et al., 2000; Barros & Lang, 2003; Bhatt & Nakamura, 2005). Precipitation peaks between midnight and early morning in valley areas, with a secondary afternoon peak over ridges (Barros et al., 2000; Barros & Lang, 2003). Anders et al. (2006) observe greater amounts of precipitation correspond to large river valleys (e.g. the Tsangpo/Brahmaputra River) and bands of precipitation associated with rises in topography approaching the Himalayas. Monsoon precipitation is greatest in central and eastern Nepal (Ichiyanagi et al., 2007) and this eastewest gradient in monsoon precipitation across the Himalayas is detectable using TRMM precipitation estimates (Anders et al., 2006). Western Nepal receives less pre-monsoon and post-monsoon precipitation however it receives greater winter precipitation associated with westerly weather systems (Ichiyanagi et al., 2007; Kansakar et al., 2004). These extra-tropical winter weather systems originate over the Caspian Sea and precipitation generally falls as snow in the High Mountain, snow and rain in the Mid-Mountain and Hills, and light to moderate rain over the Lowland (Singh & Kumar, 1997). Observations from TRMM precipitation estimates show consistent inter-annual spatial patterns of precipitation across Nepal and the Himalayas though relative amounts of annual precipitation received can vary (Anders et al., 2006; Bhatt & Nakamura, 2005). Precipitation data products Satellite-derived TRMM 3B42 V6 The Tropical Rainfall Measuring Mission (TRMM) is a satellite venture monitoring tropical and subtropical precipitation. The TRMM 3B42 satellite-derived precipitation data product (Version 6; V6) was used in this research. This product is produced by merging passive microwave data from several low orbit satellites with infrared (IR) data collected by the international constellation of geosynchronous Earth orbit satellites (GEO-IR) and PR active microwave data (Andermann, Bonnet, & Gloaguen, 2011; Huffman et al., 2007). TRMM 3B42 generally produces a smoother precipitation field than gauge data due to the temperature structure of clouds (Krishnamurti, Mishra, Simon, & Yatagai, 2009), and in areas of increased gauge density, TRMM 3B42 is of comparatively coarser spatial resolution. A near real-time estimate of precipitation from satellite measurements is available with a nine-hour time lag at a three-hourly interval. Additional estimates calibrated with ground measurements are subsequently produced within 10e15 days of the end of the month (Huffman et al., 2007).2 This study used TRMM 3B42 data at a daily temporal resolution accumulated from the three-hourly precipitation product. Ground-based APHRODITE The only ground-based precipitation observations available in Nepal are derived from a network of approximately 200 gauges located

2 See TRMM website http://trmm.gsfc.nasa.gov/data_dir/data.htmlfor further information.

across the country, with the majority located in the Lowland and MidMountain and Hills (Kansakar et al., 2004). Data are gathered and maintained by the Department of Hydrology and Meteorology. Precipitation data recorded by these gauges have been used in conjunction with other precompiled precipitation datasets data to generate the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) of Water Resources project. APHRODITE can be considered as a ‘ground-truth’ precipitation data product for monsoon Asia (Yatagai et al., 2009). The APHRODITE data product used in this research (APHRO_MA_V1003R1) has a 0.25  0.25 gridded spatial resolution and a daily temporal resolution, equalling that of TRMM 3B42, making it an ideal validation product. APHRO_MA_V1003R1 was produced using a modified version of the distance-weighting interpolation method (Shepard, 1968; Willmott, Rowe, & Philpot, 1985) to interpolate rain-gauge observations obtained from meteorological stations throughout the Monsoon Asia region onto a 0.25  0.25 grid. The method used to develop the APHRO_MA_V1003R1 product used a similar algorithm to that presented by Yatagai et al. (2009). Details of this algorithm for the V1003R1 data are yet to be published but an improved product has been generated using a weighting function which considers local topography when calculating interpolated precipitation.3 An increased density of precipitation gauges increases the accuracy of gridded precipitation products (Xie et al., 2007); as APHRODITE has a spatial extent for Monsoon Asia, data used for interpolating precipitation over Nepal extended beyond those gauge stations purely located within the country’s administrative boundary. A dense network of gauges over Nepal was used to compute APHRODITE (Yatagai et al., 2009; Supplementary Material 3) and the use of daily observation data over Nepal resulted in the generation of one of the most reliable daily precipitation products over Nepal and the Himalayan region (Yatagai et al., 2009). Representation of orography was incorporated into the calculation of APHRODITE at a fine spatial resolution (0.05  0.05 ); this is important to represent the topographic influence on the spatial distribution of precipitation especially given the complex terrain of the Nepal Himalayas (Xie et al., 2007; Yatagai et al., 2009). The reliability of APHRODITE daily precipitation data over Nepal was quantitatively assessed through comparison with gauge data by Andermann et al. (2011). They found APHRODITE had minimal bulk error and r2 values ranging from 0.83 to 0.98 when compared to monthly precipitation gauge measurements and 0.89 when compared to daily precipitation rates in the Jhiku Khola catchment, Nepal (Andermann et al., 2011). Comparisons made in India indicate that APHRODITE is well correlated (r > 0.6) with the Indian Meteorological Department’s (IMD) widely used and well validated 1  1 daily precipitation product for most corresponding grid cells; for the majority of locations across India the difference between the products is less than 3 mm/day (Rajeevan & Bhate, 2008). This demonstrated accuracy of the APHRODITE product and its robust method of generation indicate that it is a reliable product to use as ‘ground-truth’ for validation of satellitederived precipitation estimates. The developers of APHRODITE had this use in mind when developing the product (Yatagai et al., 2009) and have used previous versions of the product to validate satellitederived precipitation estimates (Javanmard, Yatagai, Nodzu, BodaghJamali, & Kawamoto, 2010; Xie et al., 2007).

Statistical comparison of precipitation data APHRODITE and TRMM 3B42 precipitation data were quantitatively compared on a cell-by-cell basis to avoid detecting misleading

3

See APHRO_V1003R1 readme document online at www.chikyu.ac.jp/precip.

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trends and losing fine detail which can occur when precipitation data are aggregated and assessed over large regions (Ghosh, Luniya, & Gupta, 2009). Comparisons were made over a 7-year period between 2001 and 2007 for (i) annual; (ii) pre-monsoon (MarcheMay); (iii) monsoon (JuneeSeptember); (iv) post-monsoon (OctobereNovember); and (v) winter (DecembereFebruary) daily precipitation time series. Both error and correlation statistics were calculated to assess the accuracy and agreement between datasets. The root-mean square error (RMSE) and the root-mean square factor (RMSF) were used to quantify the error in the TRMM 3B42 precipitation estimates relative to the APRHODITE precipitation data. RMSE was used to measure the quantitative agreement between APHRODITE and TRMM 3B42 time series, which was calculated as

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u1 X RMSE ¼ t ðxi  yi Þ2 n

(1)

i¼1

where n is the number of observations in the time series, x is the APHRODITE precipitation data and y is the TRMM 3B42 precipitation estimate for grid cell i. Lewis and Harrison (2007) stated that RMSE is highly correlated with the magnitude of surface rain-rate such that poorly performing satellites in certain cloud structure conditions could appear more accurate in estimating precipitation than ground reference data. RMSE overemphasises the large differences which may result from erroneous data (Gjertsen, Salek, & Michelson, 2004) and for precipitation amounts the RMSF has been found to provide more information than the RMSE (De Bruijn & Brandsma, 2000). Therefore, in addition to RMSE, the RMSF comparing the two datasets was also calculated. RMSE is interpreted as giving scale to the additive error whereas RMSF is interpreted as giving scale to the multiplicative error. The RMSF was calculated as

) ( n   2 1=2 1X xi ln RMSF ¼ exp n yi

(2)

i¼1

where terms are defined as in Equation (1). The closer the RMSF value was to 1, the more accurate the satellite estimate relative to the APHRODITE observation (De Bruijn & Brandsma, 2000). The Pearson productemoment correlation coefficient r was calculated to measure the cofluctuation between the two precipitation datasets. Correlation was determined using

r¼

  n  1 X xi  x yi  y sx sy n  1 i¼1

(3)

where x and y are the sample means and sx and sy are the sample standard deviations. A correlation of 1 would indicate a perfect positive linear relationship between precipitation variables and a correlation of 1 a perfect negative linear relationship. Only significant (p < 0.01) values of r were discussed in this study. Annual Correlation coefficients indicated that the association between TRMM 3B42 and APRHODITE were statistically significant (p < 0.01) across Nepal (Fig. 2a). Correlations were greatest in the south-east with a maximum correlation coefficient of 0.6, and lowest in the north-west at 0.1. Annual comparison of TRMM 3B42 and APHRODITE indicated large RMSE in south-central Nepal. Error values were greatest furthest south in the Lowland region, diminishing north towards the High Mountain (Fig. 2b). Large RMSE was associated with high correlation coefficients (Fig. 2a and b); this is

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likely resultant of south and east Nepal receiving the greatest quantities of precipitation, so despite a high correlation coefficient a large RMSE also ensued. At an annual temporal resolution there were no obvious spatial patterns in the RMSF (Fig. 2c). RMSF values indicated TRMM 3B42 overestimated precipitation across much of Nepal, with greatest accuracy predictions (RMSF z 1) clustered in areas of north-west Nepal (Fig. 2c). Pre-monsoon The largest correlation coefficients between TRMM 3B42 and APHRODITE were observed in the south Nepal in the Lowland (Fig. 3a). Correlation coefficients between TRMM 3B42 and APHRODITE were not statistically significant (p > 0.01) the majority of the High Mountain, particularly in the north-west region (Fig. 3a). A maximum RMSE of 11.0 mm was calculated during the pre-monsoon season (Fig. 3b). Error was greatest in the eastern half of the country, with large error values predominantly clustered in the centre and far-east of Nepal. High RMSF values were calculated for central and north-east Nepal indicating that the TRMM 3B42 data overestimated precipitation by up to a factor of 8.7 relative to APHRODITE (Fig. 3c). Cells where TRMM 3B42 precipitation has a predicted value close to that of APHRODITE were located in the north-west of Nepal. TRMM 3B42 overestimated precipitation for the majority of Nepal for the pre-monsoon season. Monsoon Correlation coefficients were statistically significant (p < 0.01) across much of Nepal. Correlations were greatest further south with a maximum correlation coefficient of 0.6 (Fig. 4a). RMSE values were greatest across the Lowland with a cluster of large errors in the south-central region of Nepal (Fig. 4b). The RMSE in this region exceeded 25 mm during the monsoon season (Fig. 4b). The RMSF followed similar spatial patterns to that observed during the premonsoon season (Fig. 4c). RMSF values peaked at a factor of 8.0 in the south-east Lowland, and ground-based and satellite-derived precipitation were greatest matched in the north-central and north-west regions over the High Mountain. Post-monsoon For the post-monsoon season correlation coefficients were greatest across central and south-east Nepal. A maximum correlation coefficient of 0.89 was calculated in the central High Mountain with a corresponding RMSF of 0.9 (Fig. 5a and c). Correlations were generally low for western Nepal with many coefficients not statistically significant (p > 0.01) (Fig. 5a). Greatest error was observed in a central northesouth strip and in the far south-east of the Lowland, with RMSE exceeding 9 mm in some regions. RMSE was lowest in the west and north-east of Nepal (Fig. 5b). There were some clusters of high RMSF values in central and eastern regions across the Mid-Mountain and Hills (Fig. 5c). Overall, RMSF values were lower than that observed in the pre-monsoon and monsoon seasons, with TRMM 3B42 and APHRODITE best-matched in the north-west of Nepal (Fig. 5c). Winter Correlation coefficients dictating the level of agreement between TRMM 3B42 and APHRODITE were statistically significant (p < 0.01) at nearly all spatial locations for the winter season (Fig. 6a). Greatest correlations were observed across central Nepal, although local variation in coefficients was noticeable across the entire country (Fig. 6a). The spatial pattern of errors for the winter

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Fig. 2. Statistical comparison of TRMM 3B42 and APHRODITE annual daily time series between 2001 and 2007 (a) correlation coefficient (p < 0.01), (b) RMSE, (c) RMSF.

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Fig. 3. Statistical comparison of TRMM 3B42 and APHRODITE pre-monsoon (MarcheMay) daily time series between 2001 and 2007 (a) correlation coefficient (p < 0.01), (b) RMSE, (c) RMSF.

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Fig. 4. Statistical comparison of TRMM 3B42 and APHRODITE monsoon (JuneeSeptember) daily time series between 2001 and 2007 (a) correlation coefficient (p < 0.01), (b) RMSE, (c) RMSF.

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Fig. 5. Statistical comparison of TRMM 3B42 and APHRODITE post-monsoon (OctobereNovember) daily time series between 2001 and 2007 (a) correlation coefficient (p < 0.01), (b) RMSE, (c) RMSF.

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Fig. 6. Statistical comparison of TRMM 3B42 and APHRODITE winter (DecembereFebruary) daily time series between 2001 and 2007 (a) correlation coefficient (p < 0.01), (b) RMSE, (c) RMSF.

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season did not follow that apparent in all other seasons. Greatest RMSE values were concentrated in the south-west of Nepal, with error diminishing further east (Fig. 6b). Winter had a maximum RMSE of 6.4 mm, the smallest of all seasons, which is likely to reflect low observed precipitation rates during the winter season (Fig. 6b). As with the RMSE, the RMSF indicated a differing spatial pattern to that of other seasons. Highest RMSF values were located in the north-west (Fig. 6c). However, in the north-west region the highest and lowest RMSF values were spatially adjacent. This indicated that there was great disparity between datasets with the TRMM 3B42 failing to obtain an accurate characterisation of winter precipitation in this vicinity. RMSF values were lowest overall for the winter season compared to other seasons (Fig. 6c). Extremes analysis The accuracy of TRMM 3B42 to estimate precipitation extremes was assessed by investigating extreme precipitation frequency and intensity characteristics. The number of extreme precipitation events, number of ‘rainy days’ and precipitation intensity were analysed across the three physiographic zones (Fig. 1). Approximately 80e90 percent of Nepal’s precipitation falls within the monsoon season (Kansakar et al., 2004) therefore extremes were only annually analysed for the monsoon season. Extreme events constituted analysing the frequency of events which occurred above the 90th and 95th percentile. Percentile thresholds were determined

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using the APHRODITE 2001e2007 monsoon daily precipitation time series for each 0.25  0.25 grid cell. Calculating percentile thresholds per grid cell ensured that spatial variation in precipitation remained accounted for (Revadekar & Preethi, 2010). The number of extreme events per year was calculated in each grid cell for both TRMM 3B42 and APHRODITE using thresholds derived from the long-term APHRODITE (2001e2007) daily precipitation time series. A ‘rainy day’ equated to a day where precipitation was equal to or greater than 1 mm; rainy days determined using this threshold have been significantly correlated with crop yield in India (Revadekar & Preethi, 2010). The number of ‘rainy days’ per year was calculated for each grid cell for both TRMM 3B42 and APHRODITE datasets. Annual precipitation intensity was calculated separately for both datasets by dividing the grid cell annual total monsoon precipitation by the grid cell annual number of rainy days. All variables (90th percentile, 95th percentile, ‘rainy days’ and precipitation intensity) were averaged across the physiographic zones. Precipitation frequency Frequency of extreme events detected by TRMM 3B42 relative to APHRODITE varied annually and across physiographic zones. Interannual variation in both 90th and 95th percentile exceedance detected by TRMM 3B42 was less than that observed in APHRODITE (Fig. 7a and b). TRMM 3B42 overestimated the frequency of extreme events in the High Mountain (Fig. 7a and b), with some

Fig. 7. Comparison of TRMM 3B42 and APHRODITE monsoon (JuneeSeptember) precipitation frequency and intensity across the three principle physiographic zones from 2001 to 2007 for number of (a) 90th percentile exceedance events, (b) 95th percentile exceedance events, (c) rainy days, and (d) precipitation intensity.

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years substantially overestimated; e.g. for the 90th percentile exceedance in 2005, 22 extreme events were observed in APHRODITE and 34 estimated by TRMM 3B42 (Fig. 7a). Greater agreement occurred between the two datasets in the Lowland and MidMountain and Hills (Fig. 7a and b). For the 90th percentile, the greatest difference between extreme event frequencies for these two physiographic zones occurred in 2003. The difference between the annual frequency of 90th percentile extreme events detected between the two datasets in 2003 was 12 for the Mid-Mountain and Hills and 10 for the Lowland. This compared to differences of 7 and 7 in 2002 and 8 and 2 in 2001 respectively. TRMM 3B42 precipitation data generally indicated underestimation of rainy days per monsoon season in all three physiographic zones (Fig. 7c). Underestimations were greatest in the MidMountain and Hills and Lowland (Fig. 7c). These physiographic zones were also where inter-annual variation between precipitation datasets was most correlated; TRMM 3B42 generally consistently underestimated the frequency of rainy days for the monsoon season by approximately 20 days per year. TRMM 3B42 detected the frequency of rainy days with the greatest accuracy for the High Mountain. Concurrent disparities between data products were particularly evident in 2003. Precipitation intensity TRMM 3B42 generally detected greater precipitation intensities relative to APHRODITE for all three physiographic zones (Fig. 7d). Discrepancies in precipitation intensity were often large; e.g. in 2005, a difference in precipitation intensity of 6 mm rainy day1 was observed in the Lowland (Fig. 7d). Differences in precipitation intensity between the two datasets can be partially explained by TRMM 3B42 underestimating the number of rainy days (Fig. 7c). Discussion Satellite or ground-based precipitation? The TRMM 3B42 data product estimates precipitation using several microwave sensors which have limited temporal sampling recurrence which can result in missed short duration precipitation events (Cheema & Bastiaanssen, 2012; Huffman et al., 2007). Nepal experiences short duration convective precipitation events associated with diurnal precipitation variability caused by complex topography (Barros et al., 2000; Barros & Lang, 2003). TRMM 3B42 underestimation of the number of rainy days relative to APHRODITE in the Lowland and Mid-Mountain and Hill regions could be explained by these missed events (Fig. 7c). Discrepancies between data products as to the frequency of extreme events observed in the High Mountain may be attributable to both TRMM 3B42 and APHRODITE performing poorly at high elevations. Due to the inaccessible location of the Himalayas a sparse gauge network limits the performance of APHRODITE data in this region (Andermann et al., 2011). Consequently, satellite-derived precipitation estimates, such as TRMM 3B42, are also limited in these areas as orographic precipitation has a greater influence on remote sensing estimates and minimal ground reference data are available to calibrate products (Andermann et al., 2011; Viviroli et al., 2011). Noticeable variation in the frequency of extreme events recorded between the two products in the Lowland and Mid-Mountain and Hills (Fig. 7a and b), where APHRODITE is based on a denser gauge network, suggests TRMM 3B42 is limited in accurately detecting extreme events. While correlation coefficients between TRMM 3B42 and APHRODITE were mostly significant (Figs. 2ae6a), TRMM 3B42 consistently overestimated precipitation in all seasons (Figs. 2e6). The error quantified from RMSE and RMSF statistics

varies spatially but is substantial in some locations for certain seasons (Figs. 2be6b and 2ce6c). The satellite-derived estimates also inaccurately detected extreme precipitation events and ‘rainy days’ (Fig. 7) with the greatest error exhibited during the monsoon season when Nepal receives the majority of its precipitation (Fig. 4b and c). The mismatch between extreme event detection by data products suggests that the current TRMM 3B42 product is of limited use for agricultural planning. A reduced capacity to accurately estimate precipitation extremes, particularly during the monsoon season (Figs. 4b, 4c and 7), does not provide a valuable product for agricultural management whereby precipitation extremes are known to significantly impact crop yields across agricultural regions (Revadekar & Preethi, 2010). The riceewheat cropping system in the Lowland contributes 75 percent to Nepal’s total food supply (Regmi et al., 2009). Such agricultural regions require precise climate information due to intensive cropping calendars and intensive use of natural resources. Climate change is expected to increase the number of extreme events in South Asia, as such increasing the risk from water-related disasters (Cruz et al., 2007; Kundzewicz & Schellnhuber, 2004). This therefore necessitates accurate observational data at a suitable spatiotemporal resolution to develop mitigation strategies to extreme events. This is particularly evident at the community-level, where extreme event frequencies can substantially impact livelihoods, especially those of marginalised communities (Easterling et al., 2007; Ludi, 2009). The poor performance of TRMM 3B42 in detecting extreme events suggests its use is limited for informing climate change adaptation strategies and sustainable water resource management policy in Nepal. Applied use of TRMM 3B42 TRMM products were developed by NASA to improve predictions of the 30e60 day oscillations (the active monsoon phases) of tropical precipitation and improve climate models through furthering understanding of global energy and water cycles in the tropics (Goswami, 2005; Kummerow et al., 2000). Monsoon precipitation occurs in pulses throughout the monsoon season in association with active monsoon phases which develop over the Indian Ocean (Goswami, 2005). The spatial coverage and near realtime estimates of TRMM 3B42 mean that the development of the active phases of monsoon precipitation can be observed and tracked by TRMM 3B42 prior to the onset of precipitation in Nepal. Therefore, TRMM 3B42 has great value for informing the onset times of active monsoon precipitation phases and estimating largescale precipitation amounts. This research has found that satellite precipitation estimates cannot currently be used for sub-national applications in Nepal due to limitations in product accuracy, as indicated by the accuracy assessments performed. Extremes were inadequately detected by TRMM B342 and for now, a more holistic approach to utilising TRMM in Nepal needs to be retained e.g. using the product to track the onset of the monsoon. However, this does not dismiss the future potential of satellite-derived precipitation products. Dissemination of accurate water resource information to water managers and policy developers is vital. Near real-time accurate information regarding precipitation frequency and intensity could contribute to sustainable water resources management aimed at improving low agricultural productivity and minimising the yield gap between actual and potential yield in the region (Ladha et al., 2003; Regmi et al., 2009). Gauges provide invaluable localised information but data dissemination still remains largely limited in Nepal (Anders et al., 2006). The government has recently taken steps to improve access to data, for example, the Department of

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Hydrology and Meteorology’s real-time Flood Forecasting Project4 which holds great potential for localised water resources monitoring. However, limited dissemination and spatiotemporal coverage of gauge precipitation measurements in the High Mountain could advocate that a freely available satellite-derived product may be useful for monitoring precipitation in remote locations, where estimated precipitation data is better than no data at all. Realistically, until the accuracy of satellite-derived precipitation estimates can be increased for use at finer spatial and temporal scales, reliance will remain with utilising ground-based data products in Nepal. The spatiotemporal resolution and accuracy of APHRODITE (except in the High Mountain) make it a valuable source of precipitation data for water policy development. However, the use of this data product will become increasingly limited as it becomes outdated. The latest APHRODITE product, APHRO_V1003R1 used in this study, has a temporal coverage from 1951 to 2007; no product has been released to provide data for subsequent years. Consequently, it is important that the gauge network in Nepal is maintained to ensure data currency, and is developed in regions where coverage is sparse. Increasing the spatial coverage of the gauge network in the High Mountain would enable an improved interpolated ground product, as well as improved ground calibration of satellite precipitation products. More gauges would increase the supply of data to inform water resources management and policy in the High Mountain; this is an important consideration given the vulnerability of the High Mountain to climate change (Singh et al., 2011). In addition, accurate ground data would provide improved hydrometeorological information for locations downstream of these vast glacial-fed catchments (Singh et al., 2011; Viviroli et al., 2011). This is of particular importance for marginalised communities located along watercourses throughout South Asia as their livelihoods are highly vulnerable to fluctuations in freshwater supplies, especially given the impacts indicated by future climate change projections (Agrawala et al., 2003). Summary TRMM 3B42 correlated well with APHRODITE in annual and seasonal precipitation between 2001 and 2007. TRMM 3B42 overestimated the amount of precipitation in all seasons; overestimations were greatest during the monsoon season when Nepal receives the majority of annual precipitation. TRMM 3B42 underperformed in estimating extreme precipitation events accurately in all physiological zones, particularly the High Mountain. TRMM 3B42 did not detect ‘rainy days’ well in the Lowland or MidMountain and Hills. Despite the benefits of near real-time, freely available, spatially-distributed satellite data, the discrepancies between the TRMM 3B42 and APHRODITE precipitation mean TRMM 3B42 data has limited applied use for localised water management. Nepal requires location-specific and accurate precipitation data to facilitate sustainable water resource management and policy development, livelihoods and enhance adaptive capacity to climate change induced future precipitation variability. The intricate variation and relationship between climatic, topographic and environmental variables across Nepal means that detecting spatial variation in precipitation is extremely complex. Focus should be on utilising and developing groundbased precipitation measurement products for use in subnational water resource and agricultural planning. Particular attention should be given to increasing gauge network coverage in areas of variable relief thus improving the accuracy of products such as APHRODITE and TRMM in these regions. Until the accuracy

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