Avaliação da seca meteorológica e seus impactos na cobertura vegetal e nas águas superficiais na nascente da bacia do rio Little Zab

Autores

DOI:

https://doi.org/10.5965/223811712342024739%20

Palavras-chave:

RDI, MSAVI2, NDWI, Kurdistan, Iraq, Iran

Resumo

A integração de informações sobre incidentes de seca nos processos de planeamento e análise pode ajudar os gestores da terra, da água e urbanos a prepararem-se de forma mais eficaz para os perigos relacionados com a água. Esta pesquisa tem como objetivo avaliar espaço-temporalmente as características da seca a montante da Bacia do Rio Little Zab de 2004 a 2018, combinando vários índices meteorológicos e derivados de satélite para superar as limitações de medição. O Coeficiente de Variação (CV) foi utilizado principalmente para investigar a inconsistência da precipitação em uma escala de tempo anual. O Índice de Seca de Reconhecimento (RDI), o segundo Índice de Vegetação Ajustado ao Solo Modificado (MSAVI2) e o Índice de Água por Diferença Normalizada (NDWI) foram adotados como índices de seca meteorológica, agrícola e hidrológica, respectivamente. Por fim, foi aplicado o teste estatístico Coeficiente de Correlação de Pearson (PCC) para compreender a relação entre as variáveis ​​implementadas. Os resultados exibiram valores de CV moderados (22,4% –28,5%) nos dados anuais de precipitação. Com base nos resultados do RDIst, foi reconhecido um evento substancial de seca extrema a grave no ano hidrológico de 2007-2008 e continuou até 2008-2009 com intensidades inferiores na maioria dos observatórios. Os valores do NDWI mostraram que a área superficial do reservatório de Dukan atingiu as suas extensões mínimas de 133 km2 e 123 km2 em 2008 e 2009, respectivamente. Embora os valores médios do MSAVI2 tenham detectado com competência os incidentes de seca de 2008 e 2009, essas deficiências de precipitação prejudicaram posteriormente a cobertura vegetal em 2010. Houve uma correlação positiva significativa entre precipitação, RDIst, NDWI e valores médios de MSAVI2. Conclui-se que a seca meteorológica na região de pesquisa leva instantaneamente à seca hidrológica, resultando em seca agrícola com um atraso de um ano.

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Referências

ABUBAKAR HB et al. 2020. Drought characterization and trend detection using the reconnaissance drought index for setsoto municipality of the free state province of south africa and the impact on maize yield. Water 12: 1–16.

AL-KAKEY O et al. 2023a. Assessing CFSR climate data for rainfall-runoff modeling over an ungauged basin between Iraq and Iran. Kuwait Journal of Science 50: 405–414.

AL-KAKEY O et al. 2023b. Proposing Optimal Locations for Runoff Harvesting and Water Management Structures in the Hami Qeshan Watershed, Iraq. ISPRS International Journal of Geo-Information 12: 312.

AL-QURAISHI AMF et al. 2021. Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index. Journal of Arid Land 13: 413–430.

AL-SAADY Y et al. 2015. Land use and land cover (LULC) mapping and change detection in the Little Zab River Basin (LZRB), Kurdistan Region, NE Iraq and NW Iran. Freiberg Online Geoscience 43: 1–32.

ALBARAKAT R et al. 2022. Assessment of drought conditions over Iraqi transboundary rivers using FLDAS and satellite datasets. Journal of Hydrology: Regional Studies 41: 101075.

ASADI ZARCH MA et al. 2011. Drought Monitoring by Reconnaissance Drought Index (RDI) in Iran. Water Resources Management 25: 3485–3504.

ASFAW A et al. 2018. Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin. Weather and Climate Extremes 19: 29–41.

AZADI S et al. 2022. The Gavkhouni Wetland Dryness and Its Impact on Air Temperature Variability in the Eastern Part of the Zayandeh-Rud River Basin, Iran. Water 14: 172.

GAZNAYEE HAA et al. 2022a. A Geospatial Approach for Analysis of Drought Impacts on Vegetation Cover and Land Surface Temperature in the Kurdistan Region of Iraq. Water 14: 927.

GAZNAYEE HAA et al. 2022b. Drought Severity and Frequency Analysis Aided by Spectral and Meteorological Indices in the Kurdistan Region of Iraq. Water 14: 3024.

GAZNAYEE HAA & AL-QURAISHI AMF 2019. Analysis of Agricultural Drought, Rainfall, and Crop Yield Relationships in Erbil Province, the Kurdistan Region of Iraq based on Landsat Time-Series MSAVI2. Journal of Advanced Research in Dynamical and Control Systems 11: 536–545.

HAILE GG et al. 2020. Projected Impacts of Climate Change on Drought Patterns Over East Africa. Earth’s Future 8: 1–23.

HANADÉ HOUMMA I et al. 2023. A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems. Geomatics, Natural Hazards and Risk 14: 2223384.

HASHIM BM et al. 2022. Assessment of Future Meteorological Drought Under Representative Concentration Pathways (RCP8.5) Scenario: Case Study of Iraq. Knowledge-based Engineering and Sciences 3: 64–82.

JUMAAH HJ et al. 2022. Monitoring and evaluation Al-Razzaza lake changes in Iraq using GIS and remote sensing technology. The Egyptian Journal of Remote Sensing and Space Sciences 25: 313–321.

LEMENKOVA P & DEBEIR O. 2023. Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria. Journal of Marine Science and Engineering 11: 871.

LI Z et al. 2023. Diurnal Variation Characteristics of Summer Precipitation and Related Statistical Analysis in the Ili Region, Xinjiang, Northwest China. Remote Sensing 15: 3954.

LONGUEVERGNE L et al. 2013. GRACE water storage estimates for the Middle East and other regions with significant reservoir and lake storage. Hydrology and Earth System Sciences 17: 4817–4830.

MARZIALETTI F et al. 2020. Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity. Remote Sensing 12: 2315.

MATHBOUT S et al. 2021. Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019. Atmosphere 12: 941.

MCFEETERS SK. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17: 1425–1432.

MCKEE TB et al. 1993. The Relationship of Drought Frequency and Duration to Time Scales. Proceedings of the Eighth Conference on Applied Climatology 1–6.

MOHAMMED R & SCHOLZ M. 2018. Flow–duration curve integration into digital filtering algorithms for simulating climate variability based on river baseflow. Hydrological Sciences Journal 63: 1558–1573.

MUÑOZ-SABATER J et al. 2021. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13: 4349–4383.

MUSTAFA ALEE M et al. 2023. Drought Assessment across Erbil Using Satellite Products. Sustainability 15: 6687.

ÖZELKAN E. 2020. Water Body Detection Analysis Using NDWI Indices Derived from Landsat-8 OLI. Polish Journal of Environmental Studies 29: 1759–1769.

PAWAR U et al. 2023. Spatiotemporal Rainfall Variability and Trends over the Mahi Basin, India. Climate 11: 163.

PEARSON K. 1895. VII. Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London 58: 240–242.

PETRAKIS R et al. 2016. Vegetative response to water availability on the San Carlos Apache Reservation. Forest Ecology and Management 378: 14–23.

QI J et al. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment 48: 119–126.

RANI A et al. 2022. Spatio-temporal assessment of agro climatic indices and the monsoon pattern in the Banas River Basin, India. Environmental Challenges 7: 100483.

RATNER B. 2009. The correlation coefficient: Its values range between +1/−1, or do they?. Journal of Targeting, Measurement and Analysis for Marketing 17: 139–142.

TESFAMARIAM BG et al. 2019. Characterizing the spatiotemporal distribution of meteorological drought as a response to climate variability: The case of rift valley lakes basin of Ethiopia. Weather and Climate Extremes 26: 100237.

TIGKAS D 2008. Drought Characterisation and Monitoring in Regions of Greece. European Water 23: 29–39.

TIGKAS D et al. 2013. The RDI as a composite climatic index. European Water 41: 17–22.

TIGKAS D et al. 2015. DrinC: a software for drought analysis based on drought indices. Earth Science Informatics 8: 697–709.

TIGKAS D et al. 2017. An Enhanced Effective Reconnaissance Drought Index for the Characterisation of Agricultural Drought. Environmental Processes 4: 137–148.

TRAN T et al. 2019. Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta. Remote Sensing 11: 2742.

TSAKIRIS G & VANGELIS H. 2005. Establishing a drought index incorporating evapotranspiration. European Water 9: 3–11.

VANSELOW K & SAMIMI C. 2014. Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests. Remote Sensing 6: 6709–6726.

VICARIO S et al. 2019. Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sensing 12: 83.

WANG M et al. 2021. Divergent responses of ecosystem water-use efficiency to extreme seasonal droughts in Southwest China. Science of The Total Environment 760: 143427.

YOUSUF MA et al. 2018. Sustainable Water Management in Iraq (Kurdistan) as a Challenge for Governmental Responsibility. Water 10: 1651.

ZHOU Z et al. 2021. Investigating the Propagation From Meteorological to Hydrological Drought by Introducing the Nonlinear Dependence With Directed Information Transfer Index. Water Resources Research 57: 1–21.

TESFAMARIAM BG et al. 2019. Characterizing the spatiotemporal distribution of meteorological drought as a response

to climate variability: The case of rift valley lakes basin of Ethiopia. Weather and Climate Extremes 26: 100237.

TIGKAS D 2008. Drought Characterisation and Monitoring in Regions of Greece. European Water 23: 29–39.

TIGKAS D et al. 2013. The RDI as a composite climatic index. European Water 41: 17–22.

TIGKAS D et al. 2015. DrinC: a software for drought analysis based on drought indices. Earth Science Informatics 8:

–709.

TIGKAS D et al. 2017. An Enhanced Effective Reconnaissance Drought Index for the Characterisation of Agricultural

Drought. Environmental Processes 4: 137–148.

TRAN T et al. 2019. Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong

River Delta. Remote Sensing 11: 2742.

TSAKIRIS G & VANGELIS H. 2005. Establishing a drought index incorporating evapotranspiration. European Water 9:

–11.

VANSELOW K & SAMIMI C. 2014. Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem

Using Remote Sensing and Random Forests. Remote Sensing 6: 6709–6726.

VICARIO S et al. 2019. Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of

Vegetation Indexes: A Story of Clouds and Fires. Remote Sensing 12: 83.

WANG M et al. 2021. Divergent responses of ecosystem water-use efficiency to extreme seasonal droughts in

Southwest China. Science of The Total Environment 760: 143427.

YOUSUF MA et al. 2018. Sustainable Water Management in Iraq (Kurdistan) as a Challenge for Governmental

Responsibility. Water 10: 1651.

ZHOU Z et al. 2021. Investigating the Propagation From Meteorological to Hydrological Drought by Introducing the

Nonlinear Dependence With Directed Information Transfer Index. Water Resources Research 57: 1–21.

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Publicado

2024-12-18

Como Citar

AL-KAKEY, Omeed; DUNGER, Volkmar; AL-MUKHTAR, Mustafa; GAZNAYEE, Heman Abdulkhaleq. Avaliação da seca meteorológica e seus impactos na cobertura vegetal e nas águas superficiais na nascente da bacia do rio Little Zab. Revista de Ciências Agroveterinárias, Lages, v. 23, n. 4, p. 739–750, 2024. DOI: 10.5965/223811712342024739 . Disponível em: https://revistas.udesc.br/index.php/agroveterinaria/article/view/25961. Acesso em: 22 dez. 2024.

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Artigo de Pesquisa - Multiseções e Áreas Correlatas

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