Contribution of Remote Sensing and GIS to Identify the Potential Area for Artificial Recharge in Fractured Area in the Talmakent Region, Western High Atlas, Morocco

2022 ◽  
Vol 3 (1) ◽  
pp. 24-36
Author(s):  
Hayat Ait inoh ◽  
Mohamed Tayebi ◽  
Rajji Abdelatif
Keyword(s):  
Remote Sensing ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Artificial Recharge ◽  
Drainage System ◽  
High Atlas ◽  
Recharge Potential ◽  
Fractured Medium ◽  
Potential Area

In view of the progressive retreat of groundwater due to rarity, continuous depletion and overexploitation of water, especially in mountainous areas, which are a major source of water, there is a need for artificial recharge for better management of these resources to ensure their long-term sustainability. The approach used is a contribution of new geomatic technologies; Remote Sensing coupled with Geographic Information Systems, for the mapping of potential areas of artificial recharge in the fractured medium of the Talmakent region, which is located in the western high atlas and is characterized by the presence of impermeable rocks. This study requires the consideration of different factors influencing the recharge potential, which are the characteristics of the land surface such as geology lineaments, geomorphology and drainage system. All these criteria are grouped in a GIS prototype in which a multi-criteria overlay analysis has been done for the cartographic restitution of the potential areas for artificial groundwater recharge. The existing basins in the area revealed that only 6% of the total area was identified as having a high potential for groundwater recharge, hence suitable for the implementation of new artificial recharge structures. While 94% of the area has a low to moderate recharge potential, hence unsuitable for groundwater recharge processes.

Application of remote sensing and GIS for delineating groundwater recharge potential zones of Kovilpatti Municipality, Tamil Nadu using IF technique

2015 ◽  
Vol 9 (2) ◽  
pp. 137-150 ◽  
Author(s):  
S. Selvam ◽  
Farooq A. Dar ◽  
N. S. Magesh ◽  
C. Singaraja ◽  
S. Venkatramanan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Groundwater Recharge ◽  
Tamil Nadu ◽  
Remote Sensing And Gis ◽  
Recharge Potential ◽  
If Technique

A LONG TERM FIELD EXPERIMENT FOR RADIATIVE TRANSFER MODEL DEVELOPMENT AND LAND SURFACE PROCESSES REMOTE SENSING

2008 ◽  
Vol 52 ◽  
pp. 13-18
Author(s):  
Hui LU ◽  
Toshio KOIKE ◽  
Hiroyuki TSUTSUI ◽  
David Ndegwa KURIA ◽  
Tobias GRAF ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Field Experiment ◽  
Land Surface ◽  
Model Development ◽  
Surface Processes ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Term Field

SMOSREX: A long term field campaign experiment for soil moisture and land surface processes remote sensing

2006 ◽  
Vol 102 (3-4) ◽  
pp. 377-389 ◽  
Author(s):  
Patricia de Rosnay ◽  
Jean-Christophe Calvet ◽  
Yann Kerr ◽  
Jean-Pierre Wigneron ◽  
François Lemaître ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Processes ◽  
Land Surface Processes ◽  
Field Campaign ◽  
Term Field

scholarly journals Changes in Mesopotamian Wetlands: Investigations Using Diverse Remote Sensing Datasets

2021 ◽  
Author(s):  
Ali K. M. Al-Nasrawi ◽  
Ignacio Fuentes ◽  
Dhahi Al-Shammari
Keyword(s):  
Remote Sensing ◽  
Drainage System ◽  
Meteorological Drought ◽  
Significant Loss ◽  
Google Earth ◽  
Social Communities ◽  
Term Change ◽  
Supported Living ◽  
Mesopotamian Marshes

Abstract Early civilizations have inhabited stable-water-resourced areas that supported living needs and activities, including agriculture. The Mesopotamian marshes, recognised as the most ancient human-inhabited area (~6000 years ago) and refuge of rich biodiversity, have experienced dramatic changes during the past five decades, starting to fail in providing adequate environmental functioning and support of social communities as they used to for thousands of years. The aim of this study is to observe, analyse and report the extent of changes in these marshes from 1972 to 2020. Data from various remote sensing sources were acquired through Google Earth Engine (GEE) including climate variables, land cover, surface reflectance, and surface water occurrence collections. Results show a clear wetlands dynamism over time and a significant loss in marshlands extent, even though no significant long-term change was observed in lumped rainfall from 1982, and even during periods where no meteorological drought had been recorded. Human interventions have disturbed the ecosystems, which is evident when studying water occurrence changes. These show that the diversion of rivers and the building of a new drainage system caused the migration and spatiotemporal changes of marshlands. Nonetheless, restoration plans (after 2003) and strong wet conditions (period 2018 - 2020) have helped to recover the ecosystems, these have not led the marshlands to regain their former extent. Further studies should pay more attention to the drainage network within the study area as well as the neighboring regions and their impact on the streamflow that feeds the study area.

scholarly journals Mean annual groundwater recharge depth in transboundary aquifers

2017 ◽  
Author(s):  
Chloé Meyer
Keyword(s):  
Groundwater Recharge ◽  
Surface Area ◽  
Artificial Recharge ◽  
Transboundary Water ◽  
Assessment Programme ◽  
Water Assessment ◽  
Transboundary Aquifers

Mean annual groundwater recharge depth is calculating by dividing the long-term mean groundwater recharge, including man-made components (returnflows, induced recharge, artificial recharge), by surface area of the whole aquifer. Indicator is expressed as mm/yr. For more information, visit the Transboundary Water Assessment Programme Portal on groundwater: https://ggis.un-igrac.org/ggis-viewer/viewer/twap/public/default Groundwater Recharge Transboundary

scholarly journals Climatology and Forest Phenology during 1984-2013 around Western Lake Superior, USA.

2020 ◽  
Author(s):  
Matthew Garcia
Keyword(s):  
Remote Sensing ◽  
Interannual Variability ◽  
Land Surface ◽  
Vegetation Index ◽  
Lake Superior ◽  
Growing Season ◽  
Western Lake ◽  
Phenological Cycle ◽  
The Mean

Landsat has a history of use in the diagnosis of land surface phenology, vegetation disturbance, and their impacts on numerous forest biological processes. Studies have connected remote sensing-based phenology to surface climatological patterns, often using average temperatures and derived growing degree day accumulations. I present a detailed examination of remotely sensed forest phenology in the region of western Lake Superior, USA, based on a comprehensive climatological assessment and 1984-2013 Landsat imagery. I use this climatology to explain both the mean annual land surface phenological cycle and its interannual variability in temperate mixed forests. I assess long-term climatological means, trends, and interannual variability for the study period using available weather station data, focusing on numerous basic and derived climate indicators: seasonal and annual temperature and precipitation, the traditionally defined frost-free growing season, and a newly defined metric of the climatological growing season: the warm-season plateau in accumulated chilling days. Results indicate +0.56°C regional warming during the 30-year study period, with cooler springs (–1.26°C) and significant autumn warming (+1.54°C). The duration of the climatological growing season has increased +0.27 days/y, extending primarily into autumn. Summer precipitation in my study area declined by an average –0.34 cm/y, potentially leading to moisture stress that can impair vegetation carbon uptake rates and can render the forest more vulnerable to disturbance. Many changes in temperature, precipitation, and climatological growing season are most prominent in locations where Lake Superior exerts a strong hydroclimatological influence, especially the Minnesota shoreline and in forest areas downwind (southeast) of the lake. I then develop and demonstrate a novel phenoclimatological modeling method, applied over five Landsat footprints across my study area, that combines (1) diagnosis of the mean phenological cycle from remote sensing observations with (2) a partial-least-squares regression (PLSR) approach to modeling vegetation index residuals using these numerous meteorological and climatological observations. While the mean phenology often used to inform land surface models in meteorological and climate modeling systems may explain 50-70% of the observed phenological variability, this mixed modeling approach can explain more than 90% of the variability in phenological observations based on long-term Landsat records for this region.

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