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Gridded precipitation products on the Hindu Kush-Himalaya: Performance and accuracy of seven precipitation products [1]

['Bhogendra Mishra', 'Policy Research Institute', 'Kathmandu', 'Science Hub', 'Saroj Panthi', 'Ministry Of Forest', 'Environment', 'Land Conservation', 'Pokhara', 'Gandaki Province']

Date: 2023-08

Abstract Climate change is expected to change precipitation and temperature patterns, which will impact the hydrological regime in Asia. Most river systems in the region originate from the Hindu Kush-Himalayas, and the altered precipitation patterns pose a threat to their sustainability, making it a major concern for planners and stakeholders. Obtaining accurate data on precipitation distribution is crucial for water accounting, which poses challenge. To address this, gridded precipitation products developed from satellite imagery and modeling techniques have become a viable alternative or addition to observed rainfall. However, the accuracy of these products in the region is uncertain. In this study, we aim to evaluate and compare the seven most commonly used precipitation products for the regions to address this gap. The study evaluated seven rainfall products, namely APHRODITE, TRMM, CHIRPS, PERSIANN-CDR, CMORPH, WFDEI, and GPCC by comparing daily, dekadal, and monthly rainfall data to 168 stations data in six countries and 11 river basins in the HKH region. The analysis used four continuous statistical indicators (Pearson correlation coefficient, Bias, Root Mean Square Error, and Nash–Sutcliffe Efficiency coefficient) and two categorical indicators (Probability of Detection and False Alarm Ratio). APHRODITE consistently performed well in several basins with high r values and low RMSE values, but had positive or negative bias values in different basins. CMORPH had the lowest positive bias value in the Ganga_Brahmaputra basin, while GPCC showed the largest r value and lowest RMSE value in the Sindha basin. CHIRPS performed well in Afghanistan, but had positive bias values. GPCC performed well in Myanmar and Pakistan, but had negative or positive bias values. APHRODITE performed consistently well in Nepal, but had negative bias values. Overall, the performance of different gridded precipitation products varies depending on the country and type of evaluation.

Citation: Mishra B, Panthi S, Ghimire BR, Poudel S, Maharjan B, Mishra Y (2023) Gridded precipitation products on the Hindu Kush-Himalaya: Performance and accuracy of seven precipitation products. PLOS Water 2(8): e0000145. https://doi.org/10.1371/journal.pwat.0000145 Editor: Sher Muhammad, ICIMOD: International Centre for Integrated Mountain Development, NEPAL Received: January 5, 2023; Accepted: June 11, 2023; Published: August 8, 2023 Copyright: © 2023 Mishra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All data were acquired from the public repositories and incorporated all sources in the manuscripts. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist.

Introduction The Himalayas have been called the ‘water tower’ of Asia as more than 3 billion people are estimated to depend on the combined flows of the major rivers originating in the region [1]. The pressure on the area’s water resources is immense [2] and is expected to increase significantly in the future due to a combination of climate change and socioeconomic developments [1, 3]. The combination of steep topography, rapidly draining soils and a strongly seasonal climate delivering 80% of the rainfall during the summer monsoon in the greater part of the Himalayas, renders the natural ecosystems of the region particularly fragile and thus vulnerable to disturbance [4]. Studies in the Lesser Himalayas (3,700 to 4,500 m) by [4] suggested a significant increase in rainfall quantity and intensity during the monsoon and decreased in dry season rainfall. Such changes in precipitation patterns are a big threat to the sustainability of the river systems in the region. Owing to the socioeconomic and ecological importance of these river systems, the effects of climate change are of extreme concern among the planners and stakeholders in the region. Accurate and detailed information on spatial variation in precipitation is critical in understanding the effects of climate change on Himalayan River systems. A robust climate change analysis and adaptation planning will not be possible without long-term hydro-meteorological monitoring. However, a lack of reliable and consistent data on precipitations severely limits our scientific understanding of the climate change impacts on these rivers [5]. Moreover, long-term observed data are rare in the higher altitude area of the Himalayan region, largely due to the remoteness. On top of that, systematic bias is also present through the urbanization effect on meteorological observations, and wind effect observed precipitations [6]. Therefore, a better understanding of the spatial and temporal extents of Himalayan precipitation is instrumental in water resources planning in the region. Gridded precipitation products developed from satellite imagery sources have well served these gaps and stood as a strong alternative to the in-situ stations. These products come with varying spatial and temporal resolution along with individual strengths and weaknesses [7]. The use of gridded precipitation data has been common for a long time for various applications such as hydrological, disaster management, and agricultural studies. With the increasing importance of these data for applications such as hydrological modeling, assessing climate change impacts, extreme climate induced hazards, and vegetation studies, it is important assess their quality as different gridded datasets may perform differently. Li et al. (2018) found that the distribution of simulated daily discharge values agreed well with observations while using in the WRF-Hydro (v3.5.1) modeling system in the Beas Basin [8]. In another study, Immerzeel et. al. (2009) found that the discharge obtained from the TRMM 3B43 matched with the observed discharge in the Himalayan river basins [9]. However it is noted that the snowmelt constitutes up to 50% of the total annual discharge in the Indus catchments, (far western) and ~25% in the Tsangpo (far eastern) catchment [10], therefore the performance depends on various factors such as other input data sources like temperature, soil moisture, land cover etc. as well as the model structures and parameters. The accumulated precipitation was found in overestimation in Southern China [11] while GPM-IERG products perform satisfactorily to catch the flash flood in Yunnan China even though some products over-estimation the precipitation [12]. Therefore, such products perform differently in a different region of the Himalayas and therefore separate evaluation is important. Those products are validated for the different locations of the world however, their general consistencies are not ensured all over the world as the accuracy varies from place to place, and product by product. Same product does not perform equally all over the world [13–17]. A number of studies have already been conducted to compare the gridded precipitation estimations with the ground measurement (Dembélé and Zwart, 2016; Serrat-Capdevila et al., 2016; Bhardwaj et al., 2017; Ghulami, Hussain et al., 2017; Nawaz et al., 2017; Shukla et al., 2019) [18]. Results from such studies have depicted huge variances in performances, in different location topography, climate season, etc. [8, 17, 19–24]. The pattern is determined by a number of factors such as Global geography topography distance from the ocean [25]. A large part of the HKH region still remains unstudied. Therefore, in this study we aim to evaluate the most common precipitation products in the HKH regions. This study considered the seven most commonly used precipitation products based on the literature review. The product includes Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) 1801_R1 [26, 27], A Climate Hazard Group InfraRed Precipitation with Station Data (CHIRPS) [27, 28], The climate prediction center morphing method (CMORPH) [29], The Precipitation Estimation from Remotely Sensed Information using the Artificial Neural Networks Climate Data Record (PERSIANN-CDR) [27, 30]. The WFDEI meteorological forcing data set has been produced using the WATCH Forcing Data (WFD) [31, 32], Tropical Rainfall Measuring Mission (TRMM 3B42) [29, 33, 34] and Global Precipitation Climatology Centre (GPCC) [30, 35, 36]. These products were identified as the most commonly used products in the region. We compared the identified gridded precipitation with the ground measurement obtained from the 168 stations scattered throughout the HKH region. Six statistical measures, with four continuous and two categorical statics, were considered to compare with three different time accumulation, daily, 10-day, and 30-day. This study distinguishes itself through its notable features, including a broader spatial coverage, a comprehensive evaluation of multiple precipitation products, an analysis considering three-time accumulation durations, and localized assessments at the national and basin levels. These contributions play a significant role in advancing our understanding of gridded precipitation patterns and their reliability within the HKH region. Study area HKH is the largest continuous mountainous range extending over 3500km from Afghanistan in the west to Myanmar in the east and hosts all 14 mountains above 8000m heigh (Fig 1). Ten major river systems of Asia–Amu Darya, Tarim, Indus, Ganges, Brahmaputra, Irrawaddy, Yangtze, Yellow, Mekong, and Salween are originated from the higher mountainous region of HKH. The total population of HKH is more than 240 million covering more than 4.2 million km2 [37]. PPT PowerPoint slide

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TIFF original image Download: Fig 1. Map of the Hindu Kush Himalayan region, displaying the major river network and locations of precipitation stations that were taken into account for analysis. Source base layer: [42]. https://doi.org/10.1371/journal.pwat.0000145.g001 Monsoon precipitation contributes higher in the Siwalik and Pir-Panjal range of lower Himalayas and continuously decreases towards the northwards into the High Himalaya. The western part of the Karakoram Himalaya is likely to have heavy snowfall during winter due to the western disturbances that emanate from upper tropospheric westerlies [38, 39]. Monsoon contributes above 80% of annual precipitation in the central and eastern Himalayas. Precipitation is highly variable across the Himalayans river basins with annual mean precipitation is estimated 435, 1094 and 2143mm in the Indus, the Ganges, and the Brahmaputra river basins respectively [40, 41].

Discussion The gridded precipitation datasets used in this study are typically based on observations from rain gauges, radar, and satellite remote sensing, which are interpolated onto a regular grid to create a spatially continuous dataset. APHRODITE has consistently demonstrated superior performance in most of the river basins and countries within the HKH region, when compared to the seven other products considered. Similar results were obtained in a comparative study with CHIRPS and PERSIANN-CDR in the Tibetan Plateau and its surroundings [26]. In another study, APHRODITE outperformed the other five precipitation products (CPC-RFE, GSMaP, TRMM-3B42, TRMM-2B31) in the five small river basins in the Himalayas [51]. However, all products were found to be quite reliable when evaluated on a monthly basis, as they demonstrated high correlation coefficients and low RMSE. A similar observation was made in an analysis of the Northwest Himalayan Region [36]. Overall, APHRODITE consistently outperforms other products by better tracking above-threshold precipitation events, in which other products tend to underestimate the precipitation quantity [51]. The source of error in different products, obtained from various methodology, might be different, including the spatial and temporal resolution of the dataset, the interpolation method used, and the quality and density of the input observations. In the higher altitude regions of the Himalayas, the quality and density of input observations are reported to be relatively low and mostly very sparse [35, 40], making it challenging to accurately represent precipitation patterns and amounts. Additionally, the high-altitude regions in the Himalayas are characterized by steep slopes, deep valleys, and complex atmospheric processes [52], which further complicates precipitation measurement and estimation. Furthermore, the rain shadow regions in the Himalayas, such as the regions to the north of the Great Himalayas, receive less precipitation due to the orographic effect [51], which can be difficult to accurately capture in gridded precipitation products. The higher temporal/spatial resolution datasets (hourly or few km scale) may have lower accuracy compared to datasets with lower temporal/spatial resolution (longer time of several km scales) due to the higher variability and uncertainty in precipitation measurements at this resolution. In the Himalayas, where precipitation is highly variable in space and time, accurately representing precipitation patterns and amounts can be particularly challenging. However, if gridded precipitation datasets are carefully validated and calibrated against observed streamflow data, they can still provide useful information for hydrological modeling in the Himalayas. By considering a wider geographic area, evaluating multiple products, analyzing different time scales, and providing localized insights, our study contributes greatly to enhancing knowledge in this field. Ultimately, these findings strengthen our understanding of gridded precipitation in the HKH region. However, a large number of precipitation products, including ERA5 and ERA5-Land, Multi-Source Weighted-Ensemble Precipitation (MSWEP) v2.2, Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2), and Integrated Multi-Satellite Retrievals (IMERG) for Global Precipitation Mission (GPM), near-real-time precipitation estimations based on machine learning algorithms, such as FY4QPE-MSA and PECA-FY4A etc. among others, are available for use in this region and have gained popularity in various applications. Therefore, we encourage future studies to assess the quality of these products. Furthermore, it would be worthwhile to study how different precipitation products perform in various topographical settings, as we have not taken this into account in this study.

Conclusion and recommendation The performance of gridded precipitation products varies across countries and basins. After considering statistical parameters, it was found that CHIRPS is the better product for Afghanistan, APHRODITE for China, India, and Nepal, and GPCC for Pakistan, for all daily, dekadal, and monthly analyses. However, the performance of the products is not consistent across different basins. APHRODITE performs better than other products in most basins, except for the Ganga_Bhramaputra basin where CMORPH had favorable results in daily analysis. In dekadal analysis, APHRODITE outperformed other products in all basins except Ganga_Bhramaputra (CMORPH) and Sindha (GPCC). When it comes to monthly analysis, APHRODITE performed better in most basins except for Ganga (TRMM), Ganga_Bhramaputra (CMORPH), Irrawaddy (CHIRPS), and Sindha (CDR). In general, APHRODITE and TRMM performed best in all the indicators for all stations in the region, while CHIRPS performed the worst. Due to the highly heterogeneous topography, gridded precipitation may not capture the timing and spatial variation within a short distance. As the gridded precipitation is an aggregation of a spatial extent of a single-pixel, it may moderate the results, and the timing could also have a significant impact on it. Given the highly heterogeneous topography and potential impact of timing and spatial variation on the accuracy of gridded precipitation products, it is recommended that daily estimates be used with caution in the HKH region. Nonetheless, some products’ dekadal and monthly estimates can be used with confidence. It is advisable for researchers and professionals to evaluate the products for their specific study area and objectives before incorporating these gridded products into their analysis.

Acknowledgments Thanks also to Global Historical Climatology Network (GHCN) for the collection of an integrated database of daily climate summaries from land surface stations across the globe.

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[1] Url: https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000145

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