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

2020 ◽  
Vol 12 (4) ◽  
pp. 709 ◽  
Author(s):  
Abhishek Banerjee ◽  
Ruishan Chen ◽  
Michael E. Meadows ◽  
R.B. Singh ◽  
Suraj Mal ◽  
...  
Keyword(s):  
River Basin ◽  
Temporal Trends ◽  
Google Earth ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
Google Earth Engine ◽  
Spatio Temporal ◽  
Uttarakhand Himalaya ◽  
Rainy Days

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.

Less rain and rainy days - lessons from 45 years of rainfall data (1971 to 2015) in the Kathmandu Valley, Nepal

2021 ◽  
Author(s):  
Rajaram Prajapati ◽  
Rocky Talchabhadel ◽  
Priya Silwal ◽  
Surabhi Upadhyay ◽  
Brandon Ertis ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Temporal Trends ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Kathmandu Valley ◽  
Light Rain ◽  
Mk Test ◽  
Spatio Temporal ◽  
Rainy Days

Abstract Understanding spatio-temporal variability in rainfall patterns is crucial for evaluating water balances needed for water resources planning and management. This paper investigates spatio-temporal variability in rainfall and assesses the frequency of daily rainfall observations from seven stations in the Kathmandu Valley, Nepal, from 1971–2015. Daily rainfall totals were classified into five classes, namely, A (light rain, daily rainfall < 10 mm in a day), B (between 10–50 mm), C (between 50–100 mm), D (between 100–150 mm) and E (> 150 mm). The relationship between daily rainfall and rainfall frequency of various rainfall rate classes were analysed. Kriging method was used for interpolation in interpreting seasonal and annual rainfall data and spatial maps were generated using QGIS. The Mann-Kendall (MK) test was performed to determine the temporal trends and Theil-Sen’s (TS) slope estimator was used in quantifying the magnitude of trends. Mountain stations showed a decreasing trend in rainfall for all seasons, ranging from − 8.4 mm/year at Sankhu to -21.8 mm/year at Thankot, whereas, a mixed pattern was found on the Valley floor. Mean annual rainfall in the Valley was 1610 mm. Both annual rainfall and the number of rainy days decreased in the Kathmandu Valley over the study period. The study indicated a significant reduction in rainfall after 2000. Since springs and shallow groundwater are the primary sources of water supply for residents in the Kathmandu Valley, it is apparent that decreasing rainfall will have (and is already having) an adverse impact on domestic, industrial, and agricultural water supplies, and the livelihoods of people.

scholarly journals Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems

2018 ◽  
Vol 10 (9) ◽  
pp. 1488 ◽  
Author(s):  
Roberta Ravanelli ◽  
Andrea Nascetti ◽  
Raffaella Cirigliano ◽  
Clarissa Di Rico ◽  
Giovanni Leuzzi ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Urban Heat Island ◽  
Landsat Imagery ◽  
Google Earth ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Google Earth Engine ◽  
Spatio Temporal

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992–2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

scholarly journals Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine

2020 ◽  
Vol 12 (1) ◽  
pp. 131
Author(s):  
Sofia Hakdaoui ◽  
Anas Emran ◽  
Biswajeet Pradhan ◽  
Abdeljebbar Qninba ◽  
Taoufik El Balla ◽  
...  
Keyword(s):  
Salt Water ◽  
Radar Data ◽  
Google Earth ◽  
Machine Learning Classification ◽  
Server Side ◽  
Radar Images ◽  
Hypersaline Water ◽  
Google Earth Engine ◽  
Spatio Temporal

Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.

How to assess the vulnerability and the risk of flooding of the most important catchment in the Republic of Djibouti?

2021 ◽  
Author(s):  
Golab Moussa Omar ◽  
Jean-Emmanuel Paturel ◽  
Christian Salles ◽  
Gil Mahe ◽  
Mohamed Jalludin
Keyword(s):  
Temporal Variability ◽  
Groundwater Resources ◽  
Annual Rainfall ◽  
Series Data ◽  
Boreal Summer ◽  
Precipitation Regime ◽  
Daily Data ◽  
Spatio Temporal ◽  
The Republic ◽  
Rainy Days

&lt;p&gt;&lt;span&gt;This study focus on the catchment of Ambouli wadi which is one of the country&amp;#8217;s largest watersheds covering 794 km&amp;#178; (3.5 % of the total area of the Republic of Djibouti). Because of its groundwater resources, this exoreic watershed is of major importance. Indeed, the aquifer is the main source of drinking water supply for the city of Djibouti-city. In addition, this wadi is also responsible for floods causing human suffering and severe economic damages. Despite the importance of the catchment for the development of Djibouti-city, Ambouli wadi has been the subject of few scientific studies. This partly explains the scarcity of rainfall stations and therefore data in this area. Analysis of the spatio-temporal variability of rainfall is required to assess the risk of flooding. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In an arid country like the Republic of Djibouti flash floods are an important concern for the management of water resources systems and risk prevention and protection. The desertic climate of the country is characterized by high levels of temperature and evaporation, and also by very weak and irregular annual rainfall, distributed in two major seasons : a cooler season (from October to March) with high relative humidity and low temperatures comprised between 22&amp;#176;C and 30&amp;#176;C, and a hot and dry season (from June to September). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Rain data were collected from a network of 9 raingauge stations at different time scales, from monthly to hourly. These data are provided by the national meteorological agency (4 stations) and the early warning system of CERD National Research Center (5 stations).&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;span&gt;The spatio-temporal variability of rainfall, is characterized using the Standardized Precipitation Index (SPI) and the analysis of rainfall normals over 30 years (1951-1980 and 1961-1990). Long time series data were available from 4 of the 9 stations: (Djibouti-serpent, Djibouti-aeorodrome, Oueah and Arta). At annual scale, the variability is clearly described by a succession of dry and humid years. Also, the monthly rainfall clearly demonstrates the well-known bimodal precipitation regime of east Africa. It shows, two peaks corresponding to the &amp;#171;&amp;#160;long rain&amp;#160;&amp;#187; and the &amp;#171;&amp;#160;short rain&amp;#160;&amp;#187; rainy seasons, which correspond to the period of March-April-May and of October-November-December, respectively. On the other hand, we also observe a dry period which is characterized by a rainfall deficit (negative rainfall index for almost all the stations) corresponding to the boreal summer (June to September). &lt;/span&gt;&lt;span&gt;Daily data is currently collecting from the Djibouti-aerodrome station (1981-2017) for a better understanding of the precipitation regime. Rainy days are computed from daily data (rainfall &gt; 1 mm) and we find an annual average of 11 wet days with a minimum in 1988 (1 rainy day) and a maximum in 1993 (23 rainy days). &lt;/span&gt;&lt;/p&gt;

scholarly journals Forest ecological monitoring of the Shiyang River basin based on Google Earth Engine

2021 ◽  
Vol 932 (1) ◽  
pp. 012011
Author(s):  
Y Wang
Keyword(s):  
River Basin ◽  
Terrestrial Ecosystems ◽  
Google Earth ◽  
Ecological Environment ◽  
Ecological Quality ◽  
Natural Forests ◽  
Shiyang River Basin ◽  
Natural Factors ◽  
Google Earth Engine

Abstract The Shiyang River basin is a typical inland arid region and one of the most fragile and sensitive areas of terrestrial ecosystems in China, and it is important to understand its ecological changes in a timely and accurate manner. This article selects the Shiyang River basin forest as the research area and uses Google Earth Engine (GEE) to evaluate and monitor the ecological environment quality of the Shiyang River basin from 1990 to 2020. The geographical detector model (GDM) was also used to analyse the sensitivity of the forest ecological environment to three natural factors: elevation, temperature and altitude. The results showed that the ecological quality of the natural forest is significantly better than that of the man-made forest area, and the ecological quality grade is higher. The forest change area RSEI has a large annual variation in ecological quality and is vulnerable to external factors. Among the influencing natural factors, the sensitive factors of precipitation and altitude are both greater than 84%. The temperature sensitivity of natural forests is stronger than that of man-made forests, ranging from 66% to 92% overall.

RUSLE Model based Assessment of Soil Erosion in Parbati River Basin, Central India using Google Earth Engine and GIS

2021 ◽  
Author(s):  
Rohit Kumar ◽  
Benidhar Deshmukh ◽  
Kiran Sathunuri
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Pattern ◽  
River Basin ◽  
Soil Loss ◽  
Google Earth ◽  
Rusle Model ◽  
High Soil ◽  
Basin Scale ◽  
Google Earth Engine

&lt;p&gt;Land degradation is a global concern posing significant threat to sustainable development. One of its major aspects is soil erosion, which is recognised as one of the critical geomorphic processes controlling sediment budget and landscape evolution. Natural rate of soil erosion is exacerbated due to anthropogenic activities that may lead to soil infertility. Therefore, assessment of soil erosion at basin scale is needed to understand its spatial pattern so as to effectively plan for soil conservation. This study focuses on Parbati river basin, a major north flowing cratonic river and a tributary of river Chambal to identify erosion prone areas using RUSLE model. Soil erodibility (K), Rainfall erosivity (R), and Topographic (LS) factors were derived from National Bureau of Soil Survey and Land Use Planning, Nagpur (NBSS-LUP) soil maps, India Meteorological Department (IMD) datasets, and SRTM30m DEM, respectively in GIS environment. The crop management (C) and support practice (P) factors were calculated by assigning appropriate values to Land use /land cover (LULC) classes derived by random forest based supervised classification of Sentinel-2 level-1C satellite remote sensing data in Google Earth Engine platform. High and very high soil erosion were observed in NE and NW parts of the basin, respectively, which may be attributed to the presence of barren land, fallow areas and rugged topography. The result reveals that annual rate of soil loss for the Parbati river basin is ~319 tons/ha/yr (with the mean of 1.2 tons/ha/yr). Lowest rate of soil loss (i.e. ~36 tons/ha/yr with mean of 0.22 tons/ha/yr) has been observed in the open forest class whereas highest rate of soil loss (i.e. ~316 tons/ha/yr with mean of 32.08 tons/ha/yr) have been observed in gullied area class. The study indicates that gullied areas are contributing most to the high soil erosion rate in the basin. Further, the rate of soil loss in the gullied areas is much higher than the permissible value of 4.5&amp;#8211;11 tons/ha/yr recognized for India. The study helps in understanding spatial pattern of soil loss in the study area and is therefore useful in identifying and prioritising erosion prone areas so as to plan for their conservation.&lt;/p&gt;

scholarly journals Dynamic Changes of Soil Erosion in the Taohe River Basin Using the RUSLE Model and Google Earth Engine

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1293 ◽  
Author(s):  
Hao Wang ◽  
Hu Zhao
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Water Conservation ◽  
Yellow River ◽  
Google Earth ◽  
Large Area ◽  
Conservation Area ◽  
Conservation Practice ◽  
Rusle Model ◽  
Google Earth Engine

The Taohe River Basin is the largest tributary and an important water conservation area in the upper reaches of the Yellow River. In order to investigate the status of soil erosion in this region, we conducted a research of soil erosion. In our study, several parameters of the revised universal soil loss equation (RUSLE) model are extracted by using Google Earth Engine. The soil erosion modulus of the Taohe River Basin was calculated based on multi-source data, and the spatio-temporal variation characteristics of the soil erosion intensity were analyzed. The results showed the following: (1) the average soil erosion modulus of the Taohe River Basin in 2000, 2005, 2010, 2015 and 2018 were 1424, 1195, 1129, 1099 and 1124 t·ha−1·year−1, respectively, and the overall downward trend was obvious. (2) The ranges of soil erosion in the Taohe River Basin in 2000, 2005, 2010, 2015 and 2018 are basically the same—mainly with slight erosion—and the soil erosion in the middle and lower reaches was more serious. (3) When dealing with the vegetation cover factor and conservation practice factor in the RUSLE model, Google Earth Engine provided a new approach for soil erosion investigation and monitoring over a large area.

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