An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

Banerjee, Abhishek; Chen, Ruishan; Meadows, Michael E.; Singh, R. B.; Mal, Suraj; Sengupta, Dhritiraj

doi:10.3390/rs12040709

Cited by 69 publications

(26 citation statements)

References 70 publications

(116 reference statements)

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“…Here, the precipitation dataset includes both rainfall and snow. Banerjee et al [59] analyzed the rainfall trend and variability in the Uttarakhand Himalaya from 1983 to 2008 and highlighted that CHIRPS has a greater degree of correspondence with observed rainfall than the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). Several previous studies have analyzed the CHIRPS dataset and reported satisfactory results with other model simulated, satellite, and ground-based observations [60][61][62][63].…”

Section: Meteorological Datamentioning

confidence: 99%

A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

2021

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The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH).

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Section: Meteorological Datamentioning

confidence: 99%

A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

2021

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“…Within GEE it is available as rainfall intensity (mm·hr −1 ) from 1999 to 2019. This dataset has been used in a large number of scientific studies carried out on GEE (Wang et al ., 2019; Zeng et al ., 2019; Banerjee et al ., 2020).…”

Section: Dataset and Methodsmentioning

confidence: 99%

Evaluation of precipitation datasets available on Google earth engine over India

Dubey

Gupta

Goyal

et al. 2021

Intl Journal of Climatology

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Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills and presence of systematic biases (during 2001–2018). All these products represent the climatological features reasonably well. Presence of systematic biases in these products is also observed from their evaluation. Biases across the periphery of the country are relatively on the higher side in comparison to the central regions. The magnitude of spatial variability is represented better for winter precipitation in comparison to summer precipitation. During both winter and summer, ensemble mean of various products outperforms individual products in terms of both RMSE and correlation. Performance of these products is also assessed across various Indian states, elevation bands and climate zones. The ability of these products to represent the seasonality was observed to be highest for the states with mid‐ranged peaks (10–20 mm·day−1) which tend to decrease with both increasing and decreasing peaks. Ability of the precipitation products to resemble the annual cycle does not vary with the amount of precipitation, although individual disparity among the products exists. Additionally, an alternative approach for data evaluation using Multiple Triple Collocation (MTC) was performed for the period 2001–2015 using an additional dataset obtained from soil‐moisture‐based rainfall estimates (SM2RAIN). Results from MTC convey that ERA5 performs relatively poor in comparison to the other products for central India followed by CFSR. In brief, the comprehensive evaluation of precipitation products reported herein will act a valuable reference for the researchers as well as decision makers to select the optimal product for their intended application and will inform the users about the various uncertainties in the foundations and specification of these products.

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“…The improved capacity of data science and infrastructure, e.g., cloud computing, Google Earth Engine (GEE) and big Earth data approaches, facilitates data sharing and the integration and modeling processes [87][88][89]. For example, the capacity and service from GEE open opportunities for explorations that benefit from decades of data acquisition from remote sensing [90][91][92][93][94][95][96].…”

Section: Remote Sensing Applications In Monitoring Of Protected Areasmentioning

confidence: 99%

Remote Sensing Applications in Monitoring of Protected Areas

et al. 2020

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Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue.

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An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

Cited by 69 publications

References 70 publications

A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

Evaluation of precipitation datasets available on Google earth engine over India

Remote Sensing Applications in Monitoring of Protected Areas

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