Spatial analysis of temperature time series over the Upper Indus Basin (UIB) Pakistan

Latif, Yasir; Ma, Yaoming; Yaseen, Muhammad; Sher, Muhammad; Wazir, Muhammad Atif

doi:10.1007/s00704-019-02993-8

Cited by 39 publications

(25 citation statements)

References 69 publications

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“…3.3 in any of the Terra or Aqua data. This step significantly improved the snow product, mainly reducing the overestimation in the images captured from offnadir view (Li et al, 2016) and edge-pixels replication due to the broad swath of MODIS (Zeng et al, 2011;Zhang et al, 2017). The cloud cover removed in all the images during the study period by the methodology described from Sect.…”

Section: Merging Terra-and Aqua-filtered Snow Productsmentioning

confidence: 99%

“…Also, obscuration of old snow and glacier ice due to their low albedo is challenging for MODIS to capture and is the contributing factor in underestimation of snow and ice cover extent. In contrast, the larger sensor zenith angle (SZA; Li et al, 2016) and low spatial resolution (Hou et al, 2019;Huang et al, 2017) mainly cause overestimation of snow. The overestimation is also significantly influenced by the broad swath of MODIS that amplifies the edge pixels by more than 4 times compared to the pixels at the image centre (Zeng et al, 2011;Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…A long-term (2002-2018) meticulous estimate of the combined Terra and Aqua 8 d composite snow cover for HMA ( Fig. 1) will facilitate climate (Latif et al, 2020) and glacio-hydrological modelling and understanding of the present dynamics of the cryosphere in the region (Brun et al, 2017;Muhammad et al, 2019a). The product will also lead to improving and developing associated products, e.g.…”

Section: Introductionmentioning

confidence: 99%

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An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Sher

Thapa

2020

Earth Syst. Sci. Data

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Abstract. Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

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Section: Merging Terra-and Aqua-filtered Snow Productsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Sher

Thapa

2020

Earth Syst. Sci. Data

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“…This finding could be explained by enhanced melting from the observed warming during winter [53,54]. The Khunjerab climate station in Hunza, which is located at 4710 m a.s.l., also shows an increasing trend in both annual and seasonal temperatures [21,55]. On the other hand, earlier studies have shown an increasing trend in the SCA [18,24,33].…”

Section: Discussionmentioning

confidence: 69%

Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018

Thapa

Sher

2020

Water

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Snowmelt significantly contributes to meltwater in most parts of High Mountain Asia. The Karakoram region is one of these densely glacierized and snow-covered regions. Several studies have reported that glaciers in the Karakoram region remained stable or experience slight mass loss. This trend has called for further investigation to understand changes in other components of the cryosphere. This study estimates the comparative snow cover area (SCA) and snowline altitude (SLA) changes between 2003 and 2018 in the Karakoram region and its subbasins, including Hunza, Shigar, and Shyok. We used three different 8-day composite snow products of the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study including (1) Original Aqua (MYD10A2), (2) Original Terra (MOD10A2), and (3) Improved Terra-Aqua (MOYDGL06*) snow products from 2003 to 2018. We used Mann–Kendall and Sen Slope methods to assess trends in the SCA and SLA. Our results show that the original snow products are significantly biased when investigating seasonal and annual trends. However, discarding a cloud cover of >20% in the original products improves the results and makes them more comparable to our improved snow product. The original products (without cloud removal) overestimate the SCA during summer and underestimate the SCA during winter and year-round throughout the Karakoram region. The bias in the mean annual SCA between improved and Aqua and Terra cloud threshold products for the Karakoram region is found to be −1.67% and 1.1%, respectively. The improved (MOYDGL06*) product reveals a statistically insignificant decreasing trend of the SCA on the annual scale between 2003 and 2018 in the Karakoram region and all three subbasins. The annual trends decreased at −0.13%, −0.1%, −0.08%, and −0.05% in the Karakoram, Hunza, Shigar, and Shyok, respectively. The monthly trends were slightly positive overall in December. The annual maximum SLA shows a statistically significant upward trend of 13 m above sea level (m a.s.l.) per year for the entire Karakoram region. This finding suggests a significant uncertainty in water resource planning based on the original snow data, and this study recommends the use of the improved snow product for a better understanding.

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“…The major level of confidence associated with these extreme events are related to extreme heat and cold events [ 6 ]. Therefore, the study of temperature variables is a widespread research field [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

Gómez-Gómez

Carmona-Cabezas

Sánchez‐López

et al. 2021

Entropy

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The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.

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Spatial analysis of temperature time series over the Upper Indus Basin (UIB) Pakistan

Cited by 39 publications

References 69 publications

An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018

Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

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