Document Type : Research Paper
Authors
Department of Environmental Engineering, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran.
10.22126/arww.2022.7033.1228
Abstract
The goal of this study was to check the climatological, hydrological, hydrogeological, topographical, and also vegetation cover situation of the wetland by using the google earth engine cloud system and calculation of current and future hydrological water balance of the wetland. For this purpose, data from TRMM, MODIS, Terra, LANDSAT, GRACE, and ALOS satellites were used. The results showed that GEE has a lot of potential and application for preparing time series and monitoring areas where little information is available about its past situation. According to the rainfall of 1.1333 mm3, surface runoff of 12.20 mm3, and evapotranspiration of 13.875 mm3 in the wetland area, the water balance of the wetland is -0.452 mm3. This amount indicates the volume of water that the wetland has based on climatic and hydrological relations. This amount will be equal to 1.4 mm3 in 2040, which shows that the wetland condition will improve in the future.
Keywords
Abatzoglou J.T., Dobrowski S.Z., Parks S.A., Hegewisch K.C., TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Scientific Data 5 (2018) 1-12.
Adeli S., Salehi B., Mahdianpari M., Quackenbush L.J., Brisco B., Tamiminia H. Shaw S. Wetland monitoring using SAR data: A meta-analysis and comprehensive review, Remote Sensing 12 (2020) 1-28.
Agutu N.O., Awange J.L., Ndehedehe C., Kirimi F., Kuhn M., GRACE-derived groundwater changes over Greater Horn of Africa: Temporal variability and the potential for irrigated agriculture, Science of The Total Environment 693 (2019) 1-56.
Ahmadi J., Kahforoushan D., Fatehifar E., Zoroufchi Benis K., Nadjafi M., Forecasting Surface Area Fluctuations of Urmia Lake by Image Processing Technique, Journal of Applied Research in Water and Wastewater 2 (2015) 183-187.
Akhter L.P., Gabriel H.F., Haider S., Akib S. Hydraulic performance of constructed wetland at NUST H-12 campus, Journal of Applied Research in Water and Wastewater 8 (2021) 169-173.
Alonso A., Muñoz-Carpena R., Kennedy R.E., Murcia C., Wetland landscape spatio-temporal degradation dynamics using the new Google Earth Engine cloud-based platform: Opportunities for non-specialists in remote sensing, Transactions of the ASABE 59 (2016) 1331-1342.
Amani M., Ghorbanian A., Ahmadi S.A., Kakooei M., Moghimi A., Mirmazloumi S. M., Brisco B., Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020) 5326 – 5350.
Aralova D.B., Razikova I.R., Sulaymonova N.N., Toderich K.N., Analysis of evapotranspiration ratings in the amudarya river basins of central Asian areas with application google earth engine codes, Me′ Morchilik Va Qurilish Muammolari (2019) 119-122.
Bassi N., Kumar M.D., Sharma A., Pardha-Saradhi P., Status of wetlands in India: A review of extent, ecosystem benefits, threats and management strategies, Journal of Hydrology: Regional Studies 2 (2014) 1-19.
Bayarjargal Y., Karnieli A., Bayasgalan M., Khudulmur S., Gandush C., Tucker C.J., A comparative study of NOAA–AVHRR derived drought indices using change vector analysis, Remote Sensing of Environment 105 (2006) 9-22.
Bi L., Fu B.L., Lou P.Q., Tang T.Y., Delineation Water of Pearl River Basin Using Landsat Images from Google Earth Engine, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 42 (2020) 5-10.
Bonsor H.C., Shamsudduha M., Marchant B.P., Macdonald A.M., Taylor R.G., Seasonal and decadal groundwater changes in African sedimentary aquifers estimated using GRACE products and LSMs, Remote Sensing 10 (2018) 1-20.
Carol E., Braga F., Da Lio C., Kruse E., Tosi L., Environmental isotopes applied to the evaluation and quantification of evaporation processes in wetlands: a case study in the Ajó Coastal Plain wetland, Argentina, Environmental Earth Sciences 74 (2015) 5839-5847.
Castellazzi P., Martel R., Galloway D.L., Longuevergne L., Rivera A., Assessing groundwater depletion and dynamics using GRACE and InSAR: potential and limitations, Groundwater 54 (2016) 768-780.
Chambers D.P., Converting release‐04 gravity coefficients into maps of equivalent water thickness, report, 9 pp. Cent. for Space Res., Univ. of Texas at Austin, Austin; Texas; (2008).
Dahri Z.H., Ludwig F., Moors E., Ahmad B., Khan A., Kabat P., An appraisal of precipitation distribution in the high-altitude catchments of the Indus basin, Science of the Total Environment 548 (2016) 289-306.
Du Z., Bin L., Ling F., Li W., Tian W., Wang H., Zhang X., Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China, Journal of Applied Remote Sensing 6 (2012) 1-16.
Duan Z., Bastiaanssen W.G.M., First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure, Remote Sensing of Environment 131 (2013) 1- 13.
Gholamalifard M., Kutser T., Esmaili-Sari A., Abkar A., Naimi B., Remotely Sensed Empirical Modeling of Bathymetry in the Southeastern Caspian Sea, Journal of Remote Sensing 5 (2013) 2746-2762.
Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., Moore R., Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sensing of Environment 202 (2017) 18-27.
Groeneveld D.P., Baugh W.M., Correcting satellite data to detect vegetation signal for eco-hydrologic analyses, Journal of Hydrology 344 (2007) 135–145.
Grundling P., Clulow, A.D., Price J.S., Everson C.S., Quantifying the water balance of Mfabeni Mire (iSimangaliso Wetland Park, South Africa) to understand its importance, functioning and vulnerability, Mires Peat 16 (2015) 1-18.
Hafizt M., Manessa M.D.M., Adi N.S., Prayudha B., Benthic habitat mapping by combining lyzenga’s optical model and relative water depth model in Lintea Island, Southeast Sulawesi, IOP Conf. Ser.: Earth and Environmental Science 98 (2017) 1-10.
Hayashi M., van der Kamp G., Rosenberry D.O., Hydrology of prairie wetlands: understanding the integrated surface-water and groundwater processes, Wetlands 36 (2016) 237-254.
Heidarzadeh M., Abdi N., Varvani Farahani J., Ahmadi A., Toranjzar, H. The effect of Typha Latifolia L. on heavy metals phytoremediation at the urban and industrial wastewater entrance to the Meighan wetland, Iran, Journal of Applied Research in Water and Wastewater 7 (2020) 167-171.
Hird J.N., De Lancey E.R., McDermid G.J., Kariyeva J., Google earth engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping, Remote sensing 9 (2017) 1-27.
Huang H., Chen Y., Clinton N., Wang J., Wang X., Liu C., Zhu Z., Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine, Remote Sensing of Environment 202 (2017) 166-176.
Johnson W.C., Ecosystem Services Provided by Prairie Wetlands in Northern Rangelands, Rangelands 41 (2019) 44-48.
Lang M.W., Mc Carty G.W., Lidar intensity for improved detection of inundation below the forest canopy Wetlands 29 (2009) 1166-1178.
Laurence C.S., Satellite remote sensing of river inundation,stage and discharge: A review, Hydrological Processes 11 (1997) 1427-1439.
Mahdianpari M., Salehi B., Mohammadimanesh F., Homayouni S., Gill E., The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the google earth engine cloud computing platform, Remote Sensing 11 (2019) 1-27.
Mahdianpari M., Salehi B., Mohammadimanesh F., Homayouni S., Gill E., The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the google earth engine cloud computing platform, Remote Sensing 11 (2019) 1-27.
McFeeters S.K., The use of the normalized difference water index (NDWI) in the delineation of open water, International Journal of Remote Sensing (2007) 1425-1432.
Meroni M., Fasbender D., Rembold F., Atzberger C., Klisch A., Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options, Remote Sensing of Environment 221 (2019) 508-521.
Nguyen A., Pasquier P., Marcotte D., Influence of groundwater flow in fractured aquifers on standing column wells performance, Geothermics 58 (2015) 39-48.
Mitchell T.D. Pattern Scaling: An Examination of Accuracy of the Technique for Describing Future Climates, Climatic Change 60 (2003) 217-242.
Mohite J., Sawant S., Pappula S., Spatiotemporal surface water mapping using Sentinel-1 data for regional drought assessment, In Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI 11149 (2019) 47-53.
Montgomery J., Brisco B., Chasmer L., Devito K., Cobbaert D., Hopkinson C., SAR and LiDAR temporal data fusion approaches to boreal wetland ecosystem monitoring, Remote Sensing 11 (2019) 1-24.
Nicholls E.M., Carey S.K., Humphreys E.R., Clark M.G., Drewitt G.B., Multi‐year water balance assessment of a newly constructed wetland, Fort McMurray, Alberta, Hydrological Processes 30 (2016) 2739-2753.
Nugent E., Bishop A., Grosse R., LaGrang T., Varner D., Vrtiska M., An assessment of landscape carrying capacity for waterfowl and shorebirds in Nebraska’s Rainwater Basin, A conservation effects assessment project wildlife component assessment report, Rainwater Basin Joint Venture: Wood River; (2015).
Pekel J.F., Cottam A., Gorelick N., Belward A.S., High-resolution mapping of global surface water and its long-term changes, Nature 540 (2016) 418-422.
Prigent C., Papa F., Aires F., Jimenez C., Rossow W., Matthews E., Changes in land surface water dynamics since the 1990s and relation to population pressure, Geophysical Research Letters 39 (2012) 1–5.
Proud S.R., Fensholt R., Rasmussen L.V., Sandholt I., Rapid response flood detection using the MSG geostationary satellite, International Journal of Applied Earth Observation and Geoinformation 13 (2011) 536–544.
Rosenberry D.O., Hayashi M., Assessing and measuring wetland hydrology, In: Anderson JT, Davis DA (eds.), Wetland Techniques (2013) 87–225.
Santillan J.R., Makinano-Santillan M., Vertical accuracy assessment of 30-m resolution alos, aster, and srtm global dems over northeastern mindanao, Philippines, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences XLI-B4 (2016) 149-156.
Sazib N., Bolten J., Mladenova I., Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine, Water 12 (2020) 1-20.
Schmidt M., Lucas R., Bunting P., Verbesselt J., Armston J., Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia, Remote Sensing of Environment 158 (2015) 156–168.
Silva C.D.O.F., de Castro Teixeira A.H., Manzione R.L., Agriwater: An R package for spatial modelling of energy balance and actual evapotranspiration using satellite images and agrometeorological data, Environmental Modelling & Software 120 (2019) 1-20.
Sekaranom A.B., Nurjani E., Hadi M.P., Marfai M.A., Comparsion of TRMM Precipitation Satellite Data over Central Java Region–Indonesia, Quaestiones Geographicae 37 (2018) 97-114.
Smith L.M., Playas of the great plains, University of Texas Press 3 (2003). Tang Z., Li Y., Gu Y., Jiang W., Xue Y., Hu Q., Li R., Assessing Nebraska playa wetland inundation status during 1985–2015 using Landsat data and Google Earth Engine, Environmental Monitoring and Assessment 188 (2016) 1-14.
Taqvaee M., Hosseinkhah H. Barname rizi-ye tose-e-ye gardeshgari mobtani bar ravesh-e ayandde pazhouhi va senario nevisi (motale-e-ye moredi: shahr-e Yasuj) [Tourism industry development planning based on futures research and scenario writing method (Case study: Yasuj city)], Tourism Planning and Development 6 (2018) 8-30.
Tiner R.W., Wetland indicators: A guide to wetland formation, identification, delineation, classification, and mapping, Crc Press; (2016).
Tiner R.W., Lang M.W., Klemas V.V., Remote Sensing of Wetlands: Applications and Advances; CRC Press: Boca Raton, FL: USA; (2015).
Vandandorj S., Gantsetseg B., Boldgiv B. Spatial and temporal variability in vegetation cover of Mongolia and its implications, Journal of Arid Land 7 (2015) 450-461.
Wang Y., Ma J., Xiao X., Wang X., Dai S., Zhao B., Long-term dynamic of Poyang Lake surface water: A mapping work based on the google earth engine cloud platform, Remote Sensing 11 (2019) 1-21.
Wendland E., Barreto C.E.A.G., Gomes L.H., Water balance in the Guarani Aquifer outcrop zone based on hydrogeologic monitoring, Journal of Hydrology 342 (2007) 261-269.
Xia H., Zhao J., Qin Y., Yang J., Cui Y., Song H., Meng Q., Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine, Remote Sensing 11 (2019) 1-18.
Xu C.H., Xu Y., The Projection of Temperature and Precipitation over China under RCP Scenarios using a CMIP5 Multi-Model Ensemble, Atmospheric and Oceanic Science Letters 5 (2012) 527−533.
Xu R.G., Qiao G., Wu Y.J., Cao Y.J., Extraction of Rivers and Lakes on Tibetan Plateau Based on Google Earth Engine. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42 (2019) 1797-1801.
Zhou P., Li G., Lu Y., Li M., Numerical modeling of the effects of beach slope on water-table fluctuation in the unconfined aquifer of Donghai Island, China, Hydrogeology Journal 22 (2014) 383-396.
Zhu Z., Woodcock C.E., Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment 118 (2012) 83-94.
Zotarelli L., Dukes M.D., Romero C.C., Migliaccio K.W., Morgan K.T., Step by step calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method), Institute of Food and Agricultural Sciences, University of Florida; (2010).
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