Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress

Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an...

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Autores:
Hernández-López, Jorge Armando
Andrade, Hernán J.
Barrios, Miguel
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad de Ibagué
Repositorio:
Repositorio Universidad de Ibagué
Idioma:
eng
OAI Identifier:
oai:repositorio.unibague.edu.co:20.500.12313/5838
Acceso en línea:
https://hdl.handle.net/20.500.12313/5838
http://sciencedirect.unibague.elogim.com/science/article/pii/S004896972401283X
Palabra clave:
Tolima - Sequía agrícola
Tolima - Balance hídrico
Tolima - Estres hídrico
Agricultural drought
Drought index
Evapotranspiration
Remote sensing
Soil moisture
Rights
openAccess
License
© 2024 The Authors
id UNIBAGUE2_d16cfd1c4b9f096d7b2f57adaa6b5bd9
oai_identifier_str oai:repositorio.unibague.edu.co:20.500.12313/5838
network_acronym_str UNIBAGUE2
network_name_str Repositorio Universidad de Ibagué
repository_id_str
dc.title.eng.fl_str_mv Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
title Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
spellingShingle Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
Tolima - Sequía agrícola
Tolima - Balance hídrico
Tolima - Estres hídrico
Agricultural drought
Drought index
Evapotranspiration
Remote sensing
Soil moisture
title_short Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
title_full Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
title_fullStr Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
title_full_unstemmed Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
title_sort Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress
dc.creator.fl_str_mv Hernández-López, Jorge Armando
Andrade, Hernán J.
Barrios, Miguel
dc.contributor.author.none.fl_str_mv Hernández-López, Jorge Armando
Andrade, Hernán J.
Barrios, Miguel
dc.subject.armarc.none.fl_str_mv Tolima - Sequía agrícola
Tolima - Balance hídrico
Tolima - Estres hídrico
topic Tolima - Sequía agrícola
Tolima - Balance hídrico
Tolima - Estres hídrico
Agricultural drought
Drought index
Evapotranspiration
Remote sensing
Soil moisture
dc.subject.proposal.eng.fl_str_mv Agricultural drought
Drought index
Evapotranspiration
Remote sensing
Soil moisture
description Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an agricultural drought index (Standardized Soil Moisture Deficit Index - SMODI) conceptualized with a water balance model considering the vegetation stress caused by soil moisture deficit. This contribution was based on meteorological information, soil moisture from satellite images, hydrophysical properties of the soil and crop evapotranspiration. Information from 61 weather stations located in the dry zone of Tolima was used for estimating the water balance. SMODI was compared with the most common drought indexes: Standardized Precipitation - Evapotranspiration Index (SPEI), the Palmer Self-Calibrated Drought Index (scPDSI), and other eleven macroclimatic indexes. Pearson's correlation coefficients (r), Tukey's test, and analysis of variance were applied to analyze the degree of association between SMODI and the contrasting indexes on a quarterly basis. SMODI considers factors influencing soil moisture distribution and retention and the water stress thresholds that plants have evolved to withstand during drought periods. Consequently, this integrated approach enhances the assessment of agricultural drought by relying on pertinent physical processes. SMODI identified extremely dry, severe, moderate and normal drought 5 %, 3 %, 20 % and 72 % respectively conditions in areas characterized by Entisols, Inceptisols, and Andisols, where rice and fruit crops and pasturelands are cultivated. The SMODI has a good correlation with macroclimatic indexes (0.70 < r < 0.74).
publishDate 2024
dc.date.issued.none.fl_str_mv 2024-04-15
dc.date.accessioned.none.fl_str_mv 2025-10-28T20:24:15Z
dc.date.available.none.fl_str_mv 2025-10-28T20:24:15Z
dc.type.none.fl_str_mv Artículo de revista
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.none.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.content.none.fl_str_mv Text
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv Hernández-López, J., Andrade, H. y Barrios, M. (2024). Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress. Science of the Total Environment, 921. DOI: 10.1016/j.scitotenv.2024.171144
dc.identifier.doi.none.fl_str_mv 10.1016/j.scitotenv.2024.171144
dc.identifier.eissn.none.fl_str_mv 18791026
dc.identifier.issn.none.fl_str_mv 00489697
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/5838
dc.identifier.url.none.fl_str_mv http://sciencedirect.unibague.elogim.com/science/article/pii/S004896972401283X
identifier_str_mv Hernández-López, J., Andrade, H. y Barrios, M. (2024). Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress. Science of the Total Environment, 921. DOI: 10.1016/j.scitotenv.2024.171144
10.1016/j.scitotenv.2024.171144
18791026
00489697
url https://hdl.handle.net/20.500.12313/5838
http://sciencedirect.unibague.elogim.com/science/article/pii/S004896972401283X
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationvolume.none.fl_str_mv 921
dc.relation.ispartofjournal.none.fl_str_mv Science of the Total Environment
dc.relation.references.none.fl_str_mv Abdennour, M.A., Douaoui, A., Bradai, A., Bennacer, A., Pulido Fernandez, ´ M., 2019a. Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish. J. Soil Sci. 9 (2) https://doi.org/ 10.3232/SJSS.2019.V9.N
Abdennour, M.A., Douaoui, A., Bradai, A., Bennacer, A., Pulido Fernandez, ´ M., 2019b. Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish. J. Soil Sci. 9 (2) https://doi.org/ 10.3232/SJSS.2019.V9.N2.0
Abdikan, S., Sekertekin, A., Madenoglu, S., Ozcan, H., Peker, M., Pinar, M.O., Koc, A., Akgul, S., Secmen, H., Kececi, M., Tuncay, T., Balik Sanli, F., 2023. Surface soil moisture estimation from multi-frequency SAR images using ANN and experimental data on a semi-arid environment region in Konya. Turkey. Soil Tillage Res. 228, 105646 https://doi.org/10.1016/J.STILL.2023.105646
Abdourahamane, Z.S., Garba, I., Gambo Boukary, A., Mirzabaev, A., 2022. Spatiotemporal characterization of agricultural drought in the Sahel region using a composite drought index. J. Arid Environ. 204, 104789 https://doi.org/10.1016/J. JARIDENV.2022.104789
Adeola, A.M., Kruger, A., Makgoale, T.E., Botai, J.O., 2022. Observed trends and projections of temperature and precipitation in the Olifants River catchment in South Africa. PLoS One 17 (8), e0271974. https://doi.org/10.1371/JOURNAL. PONE.0271974
AghaKouchak, A., 2015. A multivariate approach for persistence-based drought prediction: application to the 2010-2011 East Africa drought. J. Hydrol. 526, 127–135. https://doi.org/10.1016/j.jhydrol.2014.09.063.
Aguilar Arias, H., Mora Zamora, R., Vargas Bolanos, ˜ C., 2015. Atmospheric correction methodology for aster, rapideye, spot 2 and landsat 8 images with envi flaash module software. Rev. Geogr. Am. Central. https://doi.org/10.15359/rgac.2-53.2
Al Adaileh, H., Al Qinna, M., Barta, K., Al-Karablieh, E., Rakonczai, J., Alobeiaat, A., 2019. A drought adaptation management system for groundwater resources based on combined drought index and vulnerability analysis. Earth Syst. Environ. 3 (3), 445–461. https://doi.org/10.1007/S41748-019-00118-9
Ali, S., Basit, A., Makanda, T.A., Khan, F.U., Sajid, M., Riaz, T., Sohail, A., 2023. Improving drought mitigation strategies and disaster risk reduction through MODIS and TRMM-based data in relation to climate change over Pakistan. Environ. Sci. Pollut. Res. 1, 1–13. https://doi.org/10.1007/S11356-023-25138-X/TABLES/4.
Almendares-Rivera, K., García-Santos, S., Pena-Paz, ˜ T., Chirinos-Escobar, M., 2022. Survey and evaluation of the water resource cadastre in three micro-basins of the Sampile River. Bionatura 7 (3). https://doi.org/10.21931/RB/2022.07.03.32
Andrades, J., Cuesta, L., Camargo, C., Lopez, ´ J., Torres, H., Osorio, A., 2020. Propuesta metodologica ´ para la construccion ´ y seleccion ´ de modelos digitales de elevacion ´ de alta precision. ´ Colombia Forestal 23 (2), 34–46. https://doi.org/10.14483/ 2256201X.1515
Arango, C., Dorado, J., Guzm´ an, D., Ruíz, J.F., 2012. Variabilidad climatica ´ de la precipitacion ´ en Colombia asociada al ciclo El Nino, ˜ La Nina- ˜ Oscilacion ´ del Sur (ENSO). Institute of Hydrology, Meteorology a
Aryalekshmi, B.N., Biradar, R.C., Chandrasekar, K., Mohammed Ahamed, J., 2021. Analysis of various surface energy balance models for evapotranspiration estimation using satellite data. Egypt. J. Remote Sens. Space Sci. 24 (3), 1119–1126. https:// doi.org/10.1016/J.EJRS.2021.11.007.
Azman, R.M.N.R., Noor, N.A.M., Abdullah, S., Ideris, M.M., 2022. Analysis of drought index in sub-urban area using standard precipitation evapotranspiration index (SPEI). Int. J. Integr. Eng. 14 (9), 157–163. https://doi.org/10.30880/ IJIE.2022.14.09.0
Batista, P.D., D´ amasco, R., 2016. Algunas consideraciones sobre el comportamiento de la sequía agrícola en la agricultura de Cuba y el uso de im´ agenes por sat´elites en su evaluacion. ´ Cult. Trop. 37 (3), 22–41. https://doi.org/10.13140/RG.2.1.4591.3843.
Baudoux, L., Inglada, J., Mallet, C., 2021. Toward a yearly country-scale CORINE landcover map without using images: A map translation approach. Remote Sens. 13 (6) https://doi.org/10.3390/rs13061060
Bayissa, Y., Maskey, S., Tadesse, T., van Andel, S., Moges, S., van Griensven, A., Solomatine, D., 2018. Comparison of the performance of six drought indices in characterizing historical drought for the upper Blue Nile Basin. Ethiopia. Geosciences 8 (3), 81. https://doi.org/10.3390/geosciences8030081
Bedoya-Soto, J.M., Poveda, G., Trenberth, K.E., V´elez-Upegui, J.J., 2019. Interannual hydroclimatic variability and the 2009–2011 extreme ENSO phases in Colombia: from Andean glaciers to Caribbean lowlands. Theor. Appl. Climatol. 135 (3–4), 1531–1544. https://doi.org/10.1007/S00704-018-2452-2/FIGURES/9.
Bentchakal, M., Medjerab, A., Chibane, B., Rahmani, S.E.A., 2022. Meteorological drought and remote sensing data: an approach to assess fire risks in the Algerian forest. Model. Earth Syst. Environ. 8 (3), 3847–3858. https://doi.org/10.1007/ S40808-021-01323-0/FIGURES
Bielecka, E., Jenerowicz, A., 2019. Intellectual structure of CORINE land cover research applications in web of science: A Europe-wide review. Remote Sens. 11 (17) https:// doi.org/10.3390/rs111720
Borfecchia, F., Crino, ` P., Correnti, A., Farneti, A., De Cecco, L., Masci, D., Blasi, L., Iantosca, D., Pignatelli, V., Micheli, C., 2020. Assessing the impact of water salinization stress on biomass yield of cardoon bio-energetic crops through remote sensing techniques. Resources 9 (10), 124. https://doi.org/10.3390/ RESOURCES9100124.
Boudad, B., Sahbi, H., Manssouri, I., 2018. Characteraize rainfall and drought. 9 (1), 219–227.
Cai, S., Zuo, D., Wang, H., Xu, Z., Wang, G.Q., Yang, H., 2023. Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China. Agric. Water Manag. 278, 108142 https://doi.org/10.1016/J. AGWAT.2023.108142
Campozano, L., C´elleri, R., Trachte, K., Bendix, J., Samaniego, E., 2016. Rainfall and cloud dynamics in the Andes: A southern Ecuador case study. Adv. Meteorol. 2016 https://doi.org/10.1155/2016/3192765.
Cao, M., Chen, M., Liu, J., Liu, Y., 2022. Assessing the performance of satellite soil moisture on agricultural drought monitoring in the North China plain. Agric. Water Manag. 263, 107450 https://doi.org/10.1016/J.AGWAT.2021.107450
Cappello, C., De Iaco, S., Maggio, S., Posa, D., 2022. Modeling spatio-temporal complex covariance functions for vectorial data. Spatial Statistics 47, 100562. https://doi. org/10.1016/J.SPASTA.2021.100562
Ceron, ´ W.L., Kayano, M.T., Andreoli, R.V., Canchala, T., Carvajal-Escobar, Y., AlfonsoMorales, W., 2021. Rainfall variability in southwestern Colombia: changes in ENSOrelated features. Pure Appl. Geophys. 178 (3), 1087–1103. https://doi.org/10.1007/ S00024-021-02673-7/FIGURES/1
Chatterjee, S., Desai, A.R., Zhu, J., Townsend, P.A., Huang, J., 2022. Soil moisture as an essential component for delineating and forecasting agricultural rather than meteorological drought. Remote Sens. Environ. 269, 112833 https://doi.org/ 10.1016/J.RSE.2021.112833
Chen, D., Gao, G., Xu, C.Y., Guo, J., Ren, G., 2005. Comparison of the Thornthwaite method and pan data with the standard penman-Monteith estimates of reference evapotranspiration in China. Clim. Res. 28 (2) https://doi.org/10.3354/cr028123
Chen, Y., Yang, J., Xu, Y., Zhang, W., Wang, Y., Wei, J., Cheng, W., 2022. Remote-sensing drought monitoring in Sichuan Province from 2001 to 2020 based on MODIS data. Atmosphere 13 (12), 1970. https://doi.org/10.3390/ATMOS13121970
Cooper, E., Blyth, E., Cooper, H., Ellis, R., Pinnington, E., Dadson, S.J., 2021. Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations. Hydrol. Earth Syst. Sci. 25 (5), 2445–2458. https://doi.org/10.5194/ HESS-25-2445-2021.
Corporacion ´ Autonoma ´ Regional del Tolima (CORTOLIMA), Corporacion ´ Colombiana de Investigacion ´ Agropecuaria (CORPOICA), 2009. Proyecto zonificacion ´ y ordenacion ´ de 250.000 hectareas ´ de zonas secas como acciones de lucha contra la desertificacion ´ y la sequía en el departamento del Tolima
Corporacion ´ Autonoma ´ Regional del Tolima (CORTOLIMA), Universidad del Tolima, 2022. Evaluacion ´ Regional del Agua del departamento del Tolima Fase 2. Technical Report.
Cruz-Roa, A.F., Barrios, M.I., 2018. Estimacion ´ de datos faltantes de lluvia mensual a trav´es de la asimilacion ´ de informacion ´ satelital y pluviom´etrica en una cuenca andina tropical Estimation of monthly rainfall missing data through rain gauge and satellite data assimilation in a tropical. Idesia 36. https://doi.org/10.4067/S0718- 342920180050016
Cuesta, A., Montoro, A., Jochum, A.M., Lopez, ´ P., Calera, A., 2005. Metodología operativa para la obtencion ´ del coeficiente de cultivo desde imagenes ´ de sat´elite. Inform. T´ecnica Econ. Agrar. 101–3, 212–224. https://acortar.link/19QYbF
D’Andrea, G., Simˇ ůnek, V., Castellaneta, M., Vacek, Z., Vacek, S., Pericolo, O., Zito, R.G., Ripullone, F., 2022. Mismatch between annual tree-ring width growth and NDVI index in Norway spruce stands of Central Europe. Forests 13 (9), 1417. https://doi. org/10.3390/F13091417.
Danylenko, I., Bohaienko, V., 2020. Monitoring of soil moisture in the south of Ukraine using active and passive remote sensing data. Int. J. Plant Prod. 11528, 30–38. https://doi.org/10.1117/12.2571049.
Debaeke, P., Amigues, J.P., 2008. Face a ` la s´echeresse et a ` la p´enurie d’eau, quelles mesures pour ajuster la demande agricole a ` l’offre de ressource en eau ?, 3, pp. 74–81. https://doi.org/10.1051/LHB:2008031.
Dendir, Z., Simane, B., 2021. Farmers’ perceptions about changes in climate variables: perceived risks and household responses in different agro-ecological communities. Southern Ethiopia. Clim. Serv. 22, 100236 https://doi.org/10.1016/J. CLISER.2021.100236
DNP. Efectos econ´ omicos de futuras sequías en Colombia: Estimaci´ on a partir del Fen´ omeno El Nino ˜ 2015. Departamento Nacional de Planeacion. ´ Bogota. https://colaboracion. dnp.gov.co/CDT/Estud
Doostmohammadi, M., Jafari, A., Asghari, O., 2015. Geostatistical modeling of uniaxial compressive strength along the axis of the Behesht-Abad tunnel in Central Iran. Bull. Eng. Geol. Environ. 74 (3), 789–802. https://doi.org/10.1007/S10064-014-0663-Z/ FIGURES/1
Duan, L., Fan, K., Li, W., Liu, T., 2019. Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China. Theor. Appl. Climatol. 135, 45–59. https://doi.org/10.1007/s00704-017- 2347-7
Eklund, L., Seaquist, J., 2015. Meteorological, agricultural and socioeconomic drought in the Duhok governorate. Iraqi Kurdistan. Natur. Hazard. 76 (1), 421–441. https:// doi.org/10.1007/s11069
Espinosa, L.A., Portela, M.M., Matos, J.P., Gharbia, S., 2022. Climate change trends in a European coastal metropolitan area: rainfall, temperature, and extreme events (1864-2021). Atmosphere 13 (12), 1995. https://doi.org/10.3390/ ATMOS131219
Faiz, M.A., Zhang, Y., Tian, X., Tian, J., Zhang, X., Ma, N., Aryal, S., 2022. Drought index revisited to assess its response to vegetation in different agro-climatic zones. J. Hydrol. 614 (PA), 128543 https://doi.org/10.1016/j.jhydrol.2022.128543.
43 Fan, K., Zhang, Q., Gu, X., Singh, V.P., Xu, C.-Y., Shen, Z., Wang, G., 2022. Global soil moisture drought identification and responses to natural and anthropogenic forcings. J. Hydrol. 610, 127993.
Fathi-Taperasht, A., Shafizadeh-Moghadam, H., Minaei, M., Xu, T., 2022. Influence of drought duration and severity on drought recovery period for different land cover types: evaluation using MODIS-based indices. Ecol. Indic. 141, 109146 https://doi. org/10.1016/J.ECOLIND.2022.109146
Federacion ´ Nacional de Arroceros (FEDEARROZ), 2017. IV Censo nacional arrocero 2016. In FEDEARROZ. https://cutt.ly/QX3LeQM
Franc´es, F., V´elez, J.J., Munera, J.C., Medici, C., Busi, G., 2014. Descripcion ´ del modelo conceptual distribuido de simulacion ´ hidrologica ´ TETIS v. 8. In Universidad Polit´ecnica de Valencia Departamento de Ingeniería Hidr´ aulica y Medio Ambiente Grupo de Investigacion ´ de Modelacion ´ Hidrologica ´ y Ambiental (G
Gonzalez, ´ G.P., Vall-Llossera, M., María, F., Guillem, P., 2015. Comparacion ´ entre los productos de humedad del suelo smos y ecv ´Indice general (p. 68). https ://upcommons.upc.edu/bitstream/handle/2117/117785/Nuevos+Productos+Hu medad+de+Nivel+L4+de+SMOS.pdf?sequen
Guarín Giraldo, G.W., Poveda, G., 2013. Variabilidad espacial y temporal del almacenamiento de agua en el suelo en Colombia. Revista de La Academia Colombiana de Ciencias Exactas, Físicas y Naturales 37, 89–113
Hao, Z., AghaKouchak, A., 2013. Multivariate standardized drought index: A parametric multi-index model. Adv. Water Resour. 57, 12–18. https://doi.org/10.1016/J. ADVWATR
Hao, Z., Aghakouchak, A., 2014. A nonparametric multivariate multi-index drought monitoring framework. J. Hydrometeorol. 15 (1), 89–101. https://doi.org/10.1175/ JHM-D-12-0160.1.
Hao, Z., Singh, V.P., 2015. Drought characterization from a multivariate perspective: A review. J. Hydrol. 527, 668–678.
Huang, J., Zhuo, W., Li, Y., Huang, R., Sedano, F., Su, W., Dong, J., Tian, L., Huang, Y., Zhu, D., Zhang, X., 2020. Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. Int. J. Digit. Earth 13 (4), 504–526. https://doi.org/10.1080/17538947.2018.1542040
IGAC, 2004. Aspectos practicos ´ de la adopcion ´ de marco geoc´entrico nacional de referencia Magna-Sirgas como datum oficial de Colombia. Instituto Geografico ´ Agustin Codazzi, Bogota.
Ikegwuoha, D.C., Dinka, M.O., 2020. Drought prediction in the Lepelle River basin, South Africa under general circulation model simulations. J. Water Land Develop. 45, 42–53. https://doi.org/10.24425/jwld.2020.133044.
Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), I. de H. M. y E. A, 2011. Clasificaciones clim´ aticas Colombia. La Adaptacion ´ de Colombia, II Congreso Nacional Del Clima. https://acortar.link/kBaqb3
Meteorology and Environmental Studies (IDEAM), I. de H. M. y E. A, 2013. Zonificacion ´ y codificacion ´ de unidades hidrogr´ aficas e hidrogeologicas ´ de Colombia. Publicacion ´ Aprobada Por El Comit´e de Comunicaciones y Publicaciones Del IDEAM.
Jackson, T.J., Bindlish, R., Cosh, M.H., Zhao, T., Starks, P.J., Bosch, D.D., Seyfried, M., Moran, M.S., Goodrich, D.C., Kerr, Y.H., Leroux, D., 2012. Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the U.S. IEEE Trans. Geosci. Remote Sens. 50 (5 PART 1) https://doi.org/10.1109/ TGRS.2011.2168
Javed, T., Zhang, J., Bhattarai, N., Sha, Z., Rashid, S., Yun, B., Ahmad, S., Henchiri, M., Kamran, M., 2021. Drought characterization across agricultural regions of China using standardized precipitation and vegetation water supply indices. J. Clean. Prod. 313, 127866 https://doi.org/10.1016/J.JCLEPRO.2021.127866
Jim´enez-Donaire, M.D.P., Giraldez, ´ J.V., Vanwalleghem, T., 2020. Impact of climate change on agricultural droughts in Spain. Water 12 (11), 3214. https://doi.org/ 10.3390/W1
Kao, S.C., Govindaraju, R.S., 2010. A copula-based joint deficit index for droughts. J. Hydrol. 380 (1–2), 121–134. https://doi.org/10.1016/J.JHYDROL.2009.10.029.
Karishma, C.G., Kannan, B., Nagarajan, K., Panneerselvam, S., Pazhanivelan, S., 2022. Spatial and temporal estimation of actual evapotranspiration of lower Bhavani basin, Tamil Nadu using surface energy balance algorithm for land model. J. Appl. Natur. Sci. 14 (2), 566–574. https://doi.org/10.31018/JANS.V14I2.3412.
Keyantash, J.A., Dracup, J.A., 2004. An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour. Res. 40 (9), 9304. https://doi.org/10.1029/2003WR002610
Kimm, H., Guan, K., Gentine, P., Wu, J., Bernacchi, C.J., Sulman, B.N., Griffis, T.J., Lin, C., 2020. Redefining droughts for the U.S. Corn Belt: the dominant role of atmospheric vapor pressure deficit over soil moisture in regulating stomatal behavior of maize and soybean. Agric. For. Meteorol. 287, 107930. https://doi.org/ 10.1016/J.AGRFORMET.2020.107930
Li, J., Huimin, L., 2022. Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China plain. Environ. Res. Commun. 4 (7), 075014 https://doi.org/10.1088/2515-7620/AC814C
Loaiza Ceron, ´ W., Carvajal-Escobar, Y., Andreoli de Souza, R.V., Toshie Kayano, M., Gonzalez ´ Lopez, ´ N., 2020. Spatio-temporal analysis of the droughts in Cali, Colombia and their primary relationships with the El Nino-southern ˜ oscillation (ENSO) between 1971 and 2011. Atmosfera ´ 33 (1), 51–69. https://doi.org/10.20937/ ATM.52639
Ma, N., Szilagyi, J., Zhang, Y., 2021. Calibration-free complementary relationship estimates terrestrial evapotranspiration globally. Water Resour. Res. 57 (9), e2021WR029691 https://doi.org/10.1029/2021WR029691.
Mabrouk, E.H., Moursy, F.I., Morsy, M., 2022. Assessment of climate characteristics and long-term trends of rainfall and drought in the Congo River basin. J. Water Clim. Change 13 (11), 3906–3933. https://doi.org/10.2166/WCC.2022.241
Mammoliti, E., Fronzi, D., Mancini, A., Valigi, D., Tazioli, A., 2021. Waterbalance, a webapp for thornthwaite–Mather water balance computation: comparison of applications in two European watersheds. Hydrology 8 (1). https://doi.org/ 10.3390/hydrology8010034.
Maroufpoor, S., Fakheri-Fard, A., Shiri, J., 2019. Study of the spatial distribution of groundwater quality using soft computing and geostatistical models. ISH. J. Hydraul. Eng. 25 (2) https://doi.org/10.1080/09715010.2017.1408036
Martínez-Fern´ andez, J., Gonzalez-Zamora, ´ A., Sanchez, ´ N., Gumuzzio, A., 2015. A soil water based index as a suitable agricultural drought indicator. J. Hydrol. 522, 265–273. https://doi.org/10.1016/J.JHYDROL.2014.12.051
Martínez-Fern´ andez, J., Gonzalez-Zamora, ´ A., Sanchez, ´ N., Gumuzzio, A., HerreroJim´enez, C.M., 2016. Satellite soil moisture for agricultural drought monitoring: assessment of the SMOS derived soil water deficit index. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2016.02.064
Mayorga, R., Hurtado, G., 2006. La Sequía En Colombia. Nota T´ecnica del IDEAM. (Institute of Hydrology, Meteorol ogy and environmental studies), 66. http://www. ideam.gov.co/documents/21021/21147/NotaT´ecnicaSequia.pdf/d9ba4965-f7c d-4a2f-a875-2a38b1d6a9
McMullan, K.D., Martín-Neira, M., Hahne, A., Borges, A., 2009. SMOS-Earth’s water monitoring mission. In: Space Technologies for the Benefit of Human Society and Earth. Springer, Netherlands, pp. 3–35. https://doi.org/10.1007/978-1-4020-9573- 3_1.
Mengyang, M., Wang, Q., Zhao, Y., Liu, R., Li, E., 2023. The counteracting effects of large-scale vegetation restoration and increased precipitation on drought in the Huang-Huai-Hai-Yangtze River basin. J. Hydrol. 622, 129733 https://doi.org/ 10.1016/J.JHYDROL.2023.129733.
Minea, I., Iosub, M., Boicu, D., 2021. Multi-scale approach for different type of drought in temperate climatic conditions. Nat. Hazards 2021, 1–25. https://doi.org/10.1007/ S11069
Mishra, A., & Singh, V. P. (2010). A review of drought concepts. In J. Hydrol. (Vol. 391, Issues 1–2, pp. 202–216). doi:https://doi.org/10.1016/j.jhydrol.2010.07.012
Mishra, A., Ines, A.V., Das, N.D., Khedun, C.P., Singh, V.P., Sivakumar, B., Hansen, J.W., 2015. Anatomy of a local-scale drought: application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2014.10.038
Montealegre Bocanegra, J., 2014. Actualizacion ´ del componente Meteorologico ´ del modelo institucional del IDEAM (Institute of Hydrology, Meteorology and Environmental Studies) sobre el efecto clim´ atico de los fenomenos ´ El Nino ˜ y La Nina ˜ en Colombia, como insumo para el Atlas Climatologico. ´ In IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales). http://www.ideam.gov.co/docu ments/21021/440517/Actualizacion+Modelo+Institucional+El+Nino˜ +-+La+Nina ˜ .pdf/02f5e53b-0349-41f1-87e0-5513286d1d1d.
Moran-Tejeda, ´ E., Bazo, J., Lopez-Moreno, ´ J.I., Aguilar, E., Azorín-Molina, C., SanchezLorenzo, A., Martínez, R., Nieto, J.J., Mejía, R., Martín-Hern´ andez, N., VicenteSerrano, S.M., 2016. Climate trends and variability in Ecuador (1966–2011). Int. J. Climatol. 36 (11), 3839–3855. https://doi.org/10.1002/JOC.4597
Mukherjee, S., Mishra, A., Trenberth, K.E., 2018. Climate change and drought: a perspective on drought indices. Curr. Clim. Chang. Rep. 4 (2), 145–163. https://doi. org/10.1007/s40641-018-0098-x.
Nicolai-Shaw, N., Gudmundsson, L., Hirschi, M., Seneviratne, S.I., 2016. Long-term predictability of soil moisture dynamics at the global scale: persistence versus largescale drivers. Geophys. Res. Lett. 43 (16), 8554–8562. https://doi.org/10.1002/ 2016GL069
Ojeda-Flechas, C.D., Burbano-Rodríguez, J.A., Carvajal-Escobar, Y., Hern´ andez-Torres, F. L., 2020. Characterization of comprehensive drought events associated with the enso warm phase through satellite images in the Valle del Cauca, Colombia [Caracterizacion ´ de eventos de sequía integral, asociados a la fase calida ´ de enos, mediante imagenes ´ satelitale]. DYNA (Colombia) 87 (214), 204–214. https://www. scopus.com/inward/record.uri?eid=2-s2.0-85091362594&doi=10.15446%2FD YNA.V87N214.83221&partnerID=40&md5=bd5b8fa9035f39ca494945e1057a1 b13
Osroosh, Y., Troy Peters, R., Campbell, C.S., Zhang, Q., 2015. Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold. Comput. Electron. Agric. 118 https://doi.org/10.1016/j. compag.2015.09.006
Parsons, D.J., Rey, D., Tanguy, M., Holman, I.P., 2019. Regional variations in the link between drought indices and reported agricultural impacts of drought. Agric. Syst. 173 https://doi.org/10.1016/j.agsy.2019.02.015.
Pena, ˜ L.E., Barrios, M., Franc´es, F., 2016. Flood quantiles scaling with upper soil hydraulic properties for different land uses at catchment scale. J. Hydrol. 541, 1258–1272. https://doi.org/10.1016/J.JHYDROL.2016.08.031
Pohl, F., Rakovec, O., Rebmann, C., Hildebrandt, A., Boeing, F., Hermanns, F., Attinger, S., Samaniego, L., Kumar, R., 2023. Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites. Sci. Data 10 (1), 1–12. https://doi.org/10.1038/s41597-023-02192-1.
Poveda, G., Alvarez, ´ D.M., Rueda, O.A., ´ 2011. Hydro-climatic variability over the Andes of Colombia associated with ENSO: A review of climatic processes and their impact on one of the Earth’s most important biodiversity hotspots. Clim. Dyn. 36 (11− 12), 2233–2249. https://doi.org/10.1007/s00382-010-0931-y
Pradawet, C., Khongdee, N., Pansak, W., Spreer, W., Hilger, T., Cadisch, G., 2023. Thermal imaging for assessment of maize water stress and yield prediction under drought conditions. J. Agron. Crop Sci. 209 (1), 56–70. https://doi.org/10.1111/ JAC.12582
Ramos Castro, J.E., 2016. Aplicacion ´ de sistemas de informacion ´ geografica ´ a la georeferenciacion ´ de sitios arqueologicos, ´ como aporte al desarrollo de un Atlas Arqueoastronomico ´ de Honduras. Ciencias Espaciales. https://doi.org/10.5377/ce. v6i1.2556
Rhee, J., Im, J., Carbone, G.J., 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens. Environ. https://doi. org/10.1016/j.rse.2010.07.005.
Rodrigues, M.S., Castrignano, ` A., Belmonte, A., da Silva, K.A., da Trindade Lessa, B.F., 2020. Geostatistics and its potential in agriculture 4.0. Rev. Ciˆenc. Agron. 51 (5) https://doi.org/10.5935/1806-6690.20200095.
Rokhzadi, A., Mohammadian, A., Charron, M., 2018. An optimally stable and accurate second-order SSP Runge-Kutta IMEX scheme for atmospheric applications. J. Adv. Model. Earth Syst. 10 (1), 18–42. https://doi.org/10.1002/2017MS001065.
Rueda Calier, F., Penaranda ˜ Mallungo, L.A., Vel´ asquez Vargas, W.L., Díaz Baez, ´ S.A., 2016. Aplicacion ´ de una metodología de analisis ´ de datos obtenidos por percepcion ´ remota orientados a la estimacion ´ de la productividad de cana ˜ para panela al cuantificar el NDVI (índice de vegetacion ´ de diferencia normalizada). Corpoica Ciencia y Tecnología Agropecuaria. https://doi.org/10.21930/rcta.vol16_num1_art: 377.
Sabzipour, B., Asghari, O., Sarang, A., 2019. Evaluation and optimal redesigning of river water-quality monitoring networks (RWQMN) using geostatistics approach (case study: Karun, Iran). Sustainable. Water Resour. Manag. 5 (2) https://doi.org/ 10.1007/s40899-017-0176-6
Satg´e, F., Espinoza, R., Zol´ a, R.P., Roig, H., Timouk, F., Molina, J., Garnier, J., Calmant, S., Seyler, F., Bonnet, M.P., 2017. Role of climate variability and human activity on Poopo ´ Lake droughts between 1990 and 2015 assessed using remote sensing data. Remote Sens. 9 (3), 218. https://doi.org/10.3390/RS9030218
Saxton, K.E., Rawls, W.J., 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70 (5), 1569–1578. https://doi.org/10.2136/sssaj2005.0117.
Scaini, A., Sanchez, ´ N., Vicente-Serrano, S.M., Martínez-Fern´ andez, J., 2015. SMOSderived soil moisture anomalies and drought indices: A comparative analysis using in situ measurements. Hydrol. Process. https://doi.org/10.1002/hyp.10150.
Sedano, K., Carvajal, Y., 2013. Variabilidad clim´ atica, cambio clim´ atico y gestion ´ integrada del riesgo de inundaciones en Colombia. Rev. Semillas. 46-47, 47–53.
Sepulcre-Canto, G., Horion, S., Singleton, A., Carrao, H., Vogt, J., 2012. Development of a combined drought Indicator to detect agricultural drought in Europe. Nat. Hazards Earth Syst. Sci. 12 (11), 3519–3531. https://doi.org/10.5194/NHESS-12-3519- 2012
SGC, S.G.C., UNAL, U.N. de C., 2015. Memoria explicativa de zonificacion ´ de la susceptibilidad y la amenaza relativa por movimientos en masa, escala 1:100.000 plancha 280 - Palmira. https://nanourl.org/jF9
Silva, D. F. Da, Neto, P. F. de S., Silva, S. D. da, Lima, M. J. da S., Cavalcante, I. B. da S., Oliveira, S. T. de, Mendonça, H. da S., Batista, B. A., Rocha, L. H. dos S., Almeida, H. R. R. de C., Pereira, M. P. S., & Araújo, L. E. de. (2021). Identificaç˜ ao das causas clim´ aticas dos eventos extremos e dos impactos dos ENOS Canonico ˆ e Modoki nas macrorregioes ˜ de Alagoas. Revista Brasileira de Geografia Física, 14(4), 1880–1897. 10.26848/RBGF.V14.4.P1880-1897
Sousa, P.M., Trigo, R.M., Aizpurua, P., Nieto, R., Gimeno, L., Garcia-Herrera, R., 2011. Trends and extremes of drought indices throughout the 20th century in the Mediterranean. Nat. Hazards Earth Syst. Sci. 11 (1) https://doi.org/10.5194/nhess11-33-2011.
Souza, R.S., Delgado, R.C., de Andrade, C.F., dos Santos, G.L., Magistrali, I.C., de Oliveira, C.M.M., Teodoro, P.E., Capristo Silva, G.F., da Silva Junior, C.A., d´e Avila Rodrigues, R., 2021. Vegetation degradation in ENSO events: Drought assessment, soil use and vegetation evapotranspiration in the Western Brazilian Amazon. https:// doi.org/10.1016/j.rsase
Sungmin, O., Park, S.K., 2023. Flash drought drives rapid vegetation stress in arid regions in Europe. Environ. Res. Lett. 18 (1), 014028 https://doi.org/10.1088/1748-9326/ A
Tan, L., Zheng, K., Zhao, Q., Wu, Y., Liu, J., Zhang, Y., Ning, T., 2021. Evapotranspiration estimation using remote sensing technology based on a SEBAL model in the upper reaches of the Huaihe River basin. Atmosphere 12 (12), 1599. https://doi.org/ 10.3390/ATMOS1212159
Tian, L., Yuan, S., Quiring, S.M., 2018. Evaluation of six indices for monitoring agricultural drought in the south-Central United States. Agric. For. Meteorol. 249 (June 2017), 107–119. https://doi.org/10.1016/j.agrformet.2017.11.024
Tramblay, Y., Quintana Seguí, P., 2022. Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme. Nat. Hazards Earth Syst. Sci. 22 (4), 1325–1334. https://doi.org/10.5194/NHESS-22- 1325-202
Tsakiris, G., Vangelis, H., 2005. Establishing a drought index incorporating evapotanspiration. Eur. Water 9, 3–11.
Umar, N., Gray, A., 2023. Comparing single and multiple imputation approaches for missing values in univariate and multivariate water level data. Water (Switzerland) 15 (8), 1519. https://doi.org/10.3390/W15081519/S1
United Nations (UN), National unit for disaster risk management (UNGRD), Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), Ministry of the environment and sustainable development (MINAMBIENTE), 2018. Estrategia nacional para la gestion ´ integral de la sequía en colombia. https://knowledge.unccd. int/sites/default/files/country_profile_documents/ENGIS%2520para%252 0publicaci%25C3%25B3n_Colombia.pdf.
Valiente, O.M., ´ 2015. Sequía: definiciones, tipologías y m´etodos de cuantificacion. ´ Invest. Geogr. https://doi.org/10.14198/INGEO2001.26.06
Veysi, S., Naseri, A.A., Hamzeh, S., Bartholomeus, H., 2017. A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agric. Water Manag. 189 https://doi.org/10.1016/j.agwat.2017.04.016.
Vicente-Serrano, S.M., Beguería, S., Lopez-Moreno, ´ J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23 (7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1.
Vicente-Serrano, S.M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J.J., Lopez-Moreno, ´ J. I., Azorín-Molina, C., Revuelto, J., Moran-Tejeda, ´ E., S´ anchez-Lorenzo, A., 2012. An´ alisis comparativo de diferentes índices de sequía para aplicaciones ecologicas, ´ agrícolas e hidrologicas. ´ Octavo Congreso Internacional de La Asociacion ´ Espanola ˜ de Climatología: Cambio Climatico, ´ Extremos e Impactos 679–689. https://repositor io.aemet.es/bitstream/20.500.11765/8332/1/0066_VIII-2012-SM_VICE
Vicente-Serrano, S.M., Aguilar, E., Martínez, R., Martín-Hernandez, ´ N., AzorinMolina, C., Sanchez-Lorenzo, A., El Kenawy, A., Tom´ as-Burguera, M., MoranTejeda, E., Lopez-Moreno, ´ J.I., Revuelto, J., Beguería, S., Nieto, J.J., Drumond, A., Gimeno, L., Nieto, R., 2017. The complex influence of ENSO on droughts in Ecuador. Clim. Dyn. 48 (1–2), 405–427. https://doi.org/10.1007/S00382-016-3082-Y/ FIGURES/8
Virnodkar, S.S., Pachghare, V.K., Patil, V.C., Jha, S.K., 2020. Remote sensing and machine learning for crop water stress determination in various crops: A critical review. In: Precision Agriculture, Vol. 21, Issue 5. Springer US. https://doi.org/ 10.1007/s11119-020-09711-9
Wang, W., Ertsen, M.W., Svoboda, M.D., Hafeez, M., 2016. Propagation of drought: from meteorological drought to agricultural and hydrological drought. Adv. Meteorol. 2016 https://doi.org/10.1155/2016/6547209
Wei, W., Zhang, H., Ma, L., Wang, X., Guo, Z., Xie, B., Zhou, J., Wang, J., 2022. Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model. J. Environ. Manag. 323, 116208 https://doi.org/10.1016/J. JENVMAN.2022
WMO. (2018). Guide to meteorological instruments and methods of observation; WMONo. 8: Measurement of Meteorological Variables. In World Meteorological Organization: Vol. I (Issue 8).
Wong, C.Y.S., Bambach, N.E., Alsina, M.M., McElrone, A.J., Jones, T., Buckley, T.N., Kustas, W.P., Magney, T.S., 2022. Detecting short-term stress and recovery events in a vineyard using tower-based remote sensing of photochemical reflectance index (PRI). Irrig. Sci. 40 (4–5), 683–696. https://doi.org/10.1007/S00271-022-00777-Z/ FIGURES/7
Wu, D., Li, Z., Zhu, Y., Li, X., Wu, Y., Fang, S., 2021. A new agricultural drought index for monitoring the water stress of winter wheat. Agric. Water Manag. 244, 106599 https://doi.org/10.1016/J.AGWAT.2020.106599
Wu, X.P., Liu, S., Luan, J., Wang, Y., Gao, X., Chen, C., 2023. Nitrogen addition alleviates drought effects on water status and growth of Moso bamboo (Phllostachys edulis). For. Ecol. Manag. 530, 120768 https://doi.org/10.1016/J.FORECO.2023.120768
Xing, Z., Ma, M., Wei, Y., Zhang, X., Yu, Z., Yi, P., 2020. A new agricultural drought index considering the irrigation water demand and water supply availability. Nat. Hazards 104 (3), 2409–2429. https://doi.org/10.1007/S11069-020-04278-0/FIGURES/11.
Xu, Z., Wu, Z., Shao, Q., He, H., Guo, X., 2023. From meteorological to agricultural drought: propagation time and probabilistic linkages. J. Hydrol. Region. Stud. 46, 101329 https://doi.org/10.1016/J.EJRH.2023.101329.
Yalçın, S., Es¸it, M., Çoban, O., ¨ 2023. A new deep learning method for meteorological drought estimation based-on standard precipitation evapotranspiration index. Eng. Appl. Artif. Intell. 124, 106550 https://doi.org/10.1016/J.ENGAPPAI.2023.106550.
Yang, C., Liu, C., Gu, Y., Wang, Y., Xing, X., Ma, X., 2023. A novel comprehensive agricultural drought index accounting for precipitation, evapotranspiration, and soil moisture. Ecol. Indic. 154, 110593 https://doi.org/10.1016/j.ecolind.2023.110593.
Yang, Y., Li, H.G., Wang, J., Wang, H.L., He, F., Su, Y., Zhang, Y., Feng, C.H., Niu, M., Li, Z., Liu, C., Yin, W., Xia, X., 2020. ABF3 enhances drought tolerance via promoting ABA-induced stomatal closure by directly regulating ADF5 in Populus euphratica. J. Exp. Bot. 71 (22), 7270–7285. https://doi.org/10.1093/JXB/ERAA383.
Yao, P., Qian, L., Wang, Z., Meng, H., Ju, X., 2022. Assessing drought, flood, and high temperature disasters during sugarcane growth stages in southern China. Agriculture 12 (12), 2117. https://doi.org/10.3390/AGRICULTURE12122117
Yildirim, G., Rahman, A., Singh, V.P., 2022. Meteorological and hydrological drought hazard, frequency and propagation analysis: A case study in Southeast Australia. J. Hydrol. Region. Stud. 44, 101229 https://doi.org/10.1016/J.EJRH.2022.101229
Yu, M., Zhang, J., Wei, L., Wang, G., Dong, W., Liu, X., 2023. Impact of soil textures on agricultural drought evolution and field capacity estimation in humid regions. J. Hydrol. 626, 130257 https://doi.org/10.1016/J.JHYDROL.2023.130257.
Zhang, Z., Xu, W., Shi, Z., Qin, Q., 2021. Establishment of a comprehensive drought monitoring index based on multisource remote sensing data and agricultural drought monitoring. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 2113–2126. https://doi.org/10.1109/JSTARS.2021.3052194
Zhang, Z., Ma, J., Wang, T., Song, W., Hao, L., 2023. Identify the relationship of meteorological drought and ecohydrological drought in Xilin Gol grassland. China. Natur. Hazard. 1–16 https://doi.org/10.1007/S11069-022-05778-X/TABLES/1.
Zhao, C., Brissette, F., Chen, J., 2023a. Projection of future extreme meteorological droughts using two large multi-member climate model ensembles. J. Hydrol. 618, 129155 https://doi.org/10.1016/J.JHYDROL.2023.129155.
Zhao, H., Huang, Y., Wang, X., Li, X., Lei, T., 2023b. The performance of SPEI integrated remote sensing data for monitoring agricultural drought in the North China plain. Field Crop Res. 302, 109041 https://doi.org/10.1016/J.FCR.2023.109041.
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spelling Hernández-López, Jorge Armando06281105-b65d-4bbf-81fa-535b78086e42-1Andrade, Hernán J.a44912b7-65dc-47e2-a8ed-6c799a6dd5d6-1Barrios, Miguel4c4cfb17-1bb8-4541-8328-196e92f67fb9-12025-10-28T20:24:15Z2025-10-28T20:24:15Z2024-04-15Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an agricultural drought index (Standardized Soil Moisture Deficit Index - SMODI) conceptualized with a water balance model considering the vegetation stress caused by soil moisture deficit. This contribution was based on meteorological information, soil moisture from satellite images, hydrophysical properties of the soil and crop evapotranspiration. Information from 61 weather stations located in the dry zone of Tolima was used for estimating the water balance. SMODI was compared with the most common drought indexes: Standardized Precipitation - Evapotranspiration Index (SPEI), the Palmer Self-Calibrated Drought Index (scPDSI), and other eleven macroclimatic indexes. Pearson's correlation coefficients (r), Tukey's test, and analysis of variance were applied to analyze the degree of association between SMODI and the contrasting indexes on a quarterly basis. SMODI considers factors influencing soil moisture distribution and retention and the water stress thresholds that plants have evolved to withstand during drought periods. Consequently, this integrated approach enhances the assessment of agricultural drought by relying on pertinent physical processes. SMODI identified extremely dry, severe, moderate and normal drought 5 %, 3 %, 20 % and 72 % respectively conditions in areas characterized by Entisols, Inceptisols, and Andisols, where rice and fruit crops and pasturelands are cultivated. The SMODI has a good correlation with macroclimatic indexes (0.70 < r < 0.74).application/pdfHernández-López, J., Andrade, H. y Barrios, M. (2024). Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress. Science of the Total Environment, 921. DOI: 10.1016/j.scitotenv.2024.17114410.1016/j.scitotenv.2024.1711441879102600489697https://hdl.handle.net/20.500.12313/5838http://sciencedirect.unibague.elogim.com/science/article/pii/S004896972401283XengElsevier B.V.Países bajos921Science of the Total EnvironmentAbdennour, M.A., Douaoui, A., Bradai, A., Bennacer, A., Pulido Fernandez, ´ M., 2019a. Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish. J. Soil Sci. 9 (2) https://doi.org/ 10.3232/SJSS.2019.V9.NAbdennour, M.A., Douaoui, A., Bradai, A., Bennacer, A., Pulido Fernandez, ´ M., 2019b. Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish. J. Soil Sci. 9 (2) https://doi.org/ 10.3232/SJSS.2019.V9.N2.0Abdikan, S., Sekertekin, A., Madenoglu, S., Ozcan, H., Peker, M., Pinar, M.O., Koc, A., Akgul, S., Secmen, H., Kececi, M., Tuncay, T., Balik Sanli, F., 2023. Surface soil moisture estimation from multi-frequency SAR images using ANN and experimental data on a semi-arid environment region in Konya. Turkey. Soil Tillage Res. 228, 105646 https://doi.org/10.1016/J.STILL.2023.105646Abdourahamane, Z.S., Garba, I., Gambo Boukary, A., Mirzabaev, A., 2022. Spatiotemporal characterization of agricultural drought in the Sahel region using a composite drought index. J. Arid Environ. 204, 104789 https://doi.org/10.1016/J. JARIDENV.2022.104789Adeola, A.M., Kruger, A., Makgoale, T.E., Botai, J.O., 2022. Observed trends and projections of temperature and precipitation in the Olifants River catchment in South Africa. PLoS One 17 (8), e0271974. https://doi.org/10.1371/JOURNAL. PONE.0271974AghaKouchak, A., 2015. A multivariate approach for persistence-based drought prediction: application to the 2010-2011 East Africa drought. J. Hydrol. 526, 127–135. https://doi.org/10.1016/j.jhydrol.2014.09.063.Aguilar Arias, H., Mora Zamora, R., Vargas Bolanos, ˜ C., 2015. Atmospheric correction methodology for aster, rapideye, spot 2 and landsat 8 images with envi flaash module software. Rev. Geogr. Am. Central. https://doi.org/10.15359/rgac.2-53.2Al Adaileh, H., Al Qinna, M., Barta, K., Al-Karablieh, E., Rakonczai, J., Alobeiaat, A., 2019. A drought adaptation management system for groundwater resources based on combined drought index and vulnerability analysis. Earth Syst. Environ. 3 (3), 445–461. https://doi.org/10.1007/S41748-019-00118-9Ali, S., Basit, A., Makanda, T.A., Khan, F.U., Sajid, M., Riaz, T., Sohail, A., 2023. Improving drought mitigation strategies and disaster risk reduction through MODIS and TRMM-based data in relation to climate change over Pakistan. Environ. Sci. Pollut. Res. 1, 1–13. https://doi.org/10.1007/S11356-023-25138-X/TABLES/4.Almendares-Rivera, K., García-Santos, S., Pena-Paz, ˜ T., Chirinos-Escobar, M., 2022. Survey and evaluation of the water resource cadastre in three micro-basins of the Sampile River. Bionatura 7 (3). https://doi.org/10.21931/RB/2022.07.03.32Andrades, J., Cuesta, L., Camargo, C., Lopez, ´ J., Torres, H., Osorio, A., 2020. Propuesta metodologica ´ para la construccion ´ y seleccion ´ de modelos digitales de elevacion ´ de alta precision. ´ Colombia Forestal 23 (2), 34–46. https://doi.org/10.14483/ 2256201X.1515Arango, C., Dorado, J., Guzm´ an, D., Ruíz, J.F., 2012. Variabilidad climatica ´ de la precipitacion ´ en Colombia asociada al ciclo El Nino, ˜ La Nina- ˜ Oscilacion ´ del Sur (ENSO). Institute of Hydrology, Meteorology aAryalekshmi, B.N., Biradar, R.C., Chandrasekar, K., Mohammed Ahamed, J., 2021. Analysis of various surface energy balance models for evapotranspiration estimation using satellite data. Egypt. J. Remote Sens. Space Sci. 24 (3), 1119–1126. https:// doi.org/10.1016/J.EJRS.2021.11.007.Azman, R.M.N.R., Noor, N.A.M., Abdullah, S., Ideris, M.M., 2022. Analysis of drought index in sub-urban area using standard precipitation evapotranspiration index (SPEI). Int. J. Integr. Eng. 14 (9), 157–163. https://doi.org/10.30880/ IJIE.2022.14.09.0Batista, P.D., D´ amasco, R., 2016. Algunas consideraciones sobre el comportamiento de la sequía agrícola en la agricultura de Cuba y el uso de im´ agenes por sat´elites en su evaluacion. ´ Cult. Trop. 37 (3), 22–41. https://doi.org/10.13140/RG.2.1.4591.3843.Baudoux, L., Inglada, J., Mallet, C., 2021. Toward a yearly country-scale CORINE landcover map without using images: A map translation approach. Remote Sens. 13 (6) https://doi.org/10.3390/rs13061060Bayissa, Y., Maskey, S., Tadesse, T., van Andel, S., Moges, S., van Griensven, A., Solomatine, D., 2018. Comparison of the performance of six drought indices in characterizing historical drought for the upper Blue Nile Basin. Ethiopia. Geosciences 8 (3), 81. https://doi.org/10.3390/geosciences8030081Bedoya-Soto, J.M., Poveda, G., Trenberth, K.E., V´elez-Upegui, J.J., 2019. Interannual hydroclimatic variability and the 2009–2011 extreme ENSO phases in Colombia: from Andean glaciers to Caribbean lowlands. Theor. Appl. Climatol. 135 (3–4), 1531–1544. https://doi.org/10.1007/S00704-018-2452-2/FIGURES/9.Bentchakal, M., Medjerab, A., Chibane, B., Rahmani, S.E.A., 2022. Meteorological drought and remote sensing data: an approach to assess fire risks in the Algerian forest. Model. Earth Syst. Environ. 8 (3), 3847–3858. https://doi.org/10.1007/ S40808-021-01323-0/FIGURESBielecka, E., Jenerowicz, A., 2019. Intellectual structure of CORINE land cover research applications in web of science: A Europe-wide review. Remote Sens. 11 (17) https:// doi.org/10.3390/rs111720Borfecchia, F., Crino, ` P., Correnti, A., Farneti, A., De Cecco, L., Masci, D., Blasi, L., Iantosca, D., Pignatelli, V., Micheli, C., 2020. Assessing the impact of water salinization stress on biomass yield of cardoon bio-energetic crops through remote sensing techniques. Resources 9 (10), 124. https://doi.org/10.3390/ RESOURCES9100124.Boudad, B., Sahbi, H., Manssouri, I., 2018. Characteraize rainfall and drought. 9 (1), 219–227.Cai, S., Zuo, D., Wang, H., Xu, Z., Wang, G.Q., Yang, H., 2023. Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China. Agric. Water Manag. 278, 108142 https://doi.org/10.1016/J. AGWAT.2023.108142Campozano, L., C´elleri, R., Trachte, K., Bendix, J., Samaniego, E., 2016. Rainfall and cloud dynamics in the Andes: A southern Ecuador case study. Adv. Meteorol. 2016 https://doi.org/10.1155/2016/3192765.Cao, M., Chen, M., Liu, J., Liu, Y., 2022. Assessing the performance of satellite soil moisture on agricultural drought monitoring in the North China plain. Agric. Water Manag. 263, 107450 https://doi.org/10.1016/J.AGWAT.2021.107450Cappello, C., De Iaco, S., Maggio, S., Posa, D., 2022. Modeling spatio-temporal complex covariance functions for vectorial data. Spatial Statistics 47, 100562. https://doi. org/10.1016/J.SPASTA.2021.100562Ceron, ´ W.L., Kayano, M.T., Andreoli, R.V., Canchala, T., Carvajal-Escobar, Y., AlfonsoMorales, W., 2021. Rainfall variability in southwestern Colombia: changes in ENSOrelated features. Pure Appl. Geophys. 178 (3), 1087–1103. https://doi.org/10.1007/ S00024-021-02673-7/FIGURES/1Chatterjee, S., Desai, A.R., Zhu, J., Townsend, P.A., Huang, J., 2022. Soil moisture as an essential component for delineating and forecasting agricultural rather than meteorological drought. Remote Sens. Environ. 269, 112833 https://doi.org/ 10.1016/J.RSE.2021.112833Chen, D., Gao, G., Xu, C.Y., Guo, J., Ren, G., 2005. Comparison of the Thornthwaite method and pan data with the standard penman-Monteith estimates of reference evapotranspiration in China. Clim. Res. 28 (2) https://doi.org/10.3354/cr028123Chen, Y., Yang, J., Xu, Y., Zhang, W., Wang, Y., Wei, J., Cheng, W., 2022. Remote-sensing drought monitoring in Sichuan Province from 2001 to 2020 based on MODIS data. Atmosphere 13 (12), 1970. https://doi.org/10.3390/ATMOS13121970Cooper, E., Blyth, E., Cooper, H., Ellis, R., Pinnington, E., Dadson, S.J., 2021. Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations. Hydrol. Earth Syst. Sci. 25 (5), 2445–2458. https://doi.org/10.5194/ HESS-25-2445-2021.Corporacion ´ Autonoma ´ Regional del Tolima (CORTOLIMA), Corporacion ´ Colombiana de Investigacion ´ Agropecuaria (CORPOICA), 2009. Proyecto zonificacion ´ y ordenacion ´ de 250.000 hectareas ´ de zonas secas como acciones de lucha contra la desertificacion ´ y la sequía en el departamento del TolimaCorporacion ´ Autonoma ´ Regional del Tolima (CORTOLIMA), Universidad del Tolima, 2022. Evaluacion ´ Regional del Agua del departamento del Tolima Fase 2. Technical Report.Cruz-Roa, A.F., Barrios, M.I., 2018. Estimacion ´ de datos faltantes de lluvia mensual a trav´es de la asimilacion ´ de informacion ´ satelital y pluviom´etrica en una cuenca andina tropical Estimation of monthly rainfall missing data through rain gauge and satellite data assimilation in a tropical. Idesia 36. https://doi.org/10.4067/S0718- 342920180050016Cuesta, A., Montoro, A., Jochum, A.M., Lopez, ´ P., Calera, A., 2005. Metodología operativa para la obtencion ´ del coeficiente de cultivo desde imagenes ´ de sat´elite. Inform. T´ecnica Econ. Agrar. 101–3, 212–224. https://acortar.link/19QYbFD’Andrea, G., Simˇ ůnek, V., Castellaneta, M., Vacek, Z., Vacek, S., Pericolo, O., Zito, R.G., Ripullone, F., 2022. Mismatch between annual tree-ring width growth and NDVI index in Norway spruce stands of Central Europe. Forests 13 (9), 1417. https://doi. org/10.3390/F13091417.Danylenko, I., Bohaienko, V., 2020. Monitoring of soil moisture in the south of Ukraine using active and passive remote sensing data. Int. J. Plant Prod. 11528, 30–38. https://doi.org/10.1117/12.2571049.Debaeke, P., Amigues, J.P., 2008. Face a ` la s´echeresse et a ` la p´enurie d’eau, quelles mesures pour ajuster la demande agricole a ` l’offre de ressource en eau ?, 3, pp. 74–81. https://doi.org/10.1051/LHB:2008031.Dendir, Z., Simane, B., 2021. Farmers’ perceptions about changes in climate variables: perceived risks and household responses in different agro-ecological communities. Southern Ethiopia. Clim. Serv. 22, 100236 https://doi.org/10.1016/J. CLISER.2021.100236DNP. Efectos econ´ omicos de futuras sequías en Colombia: Estimaci´ on a partir del Fen´ omeno El Nino ˜ 2015. Departamento Nacional de Planeacion. ´ Bogota. https://colaboracion. dnp.gov.co/CDT/EstudDoostmohammadi, M., Jafari, A., Asghari, O., 2015. Geostatistical modeling of uniaxial compressive strength along the axis of the Behesht-Abad tunnel in Central Iran. Bull. Eng. Geol. Environ. 74 (3), 789–802. https://doi.org/10.1007/S10064-014-0663-Z/ FIGURES/1Duan, L., Fan, K., Li, W., Liu, T., 2019. Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China. Theor. Appl. Climatol. 135, 45–59. https://doi.org/10.1007/s00704-017- 2347-7Eklund, L., Seaquist, J., 2015. Meteorological, agricultural and socioeconomic drought in the Duhok governorate. Iraqi Kurdistan. Natur. Hazard. 76 (1), 421–441. https:// doi.org/10.1007/s11069Espinosa, L.A., Portela, M.M., Matos, J.P., Gharbia, S., 2022. Climate change trends in a European coastal metropolitan area: rainfall, temperature, and extreme events (1864-2021). Atmosphere 13 (12), 1995. https://doi.org/10.3390/ ATMOS131219Faiz, M.A., Zhang, Y., Tian, X., Tian, J., Zhang, X., Ma, N., Aryal, S., 2022. Drought index revisited to assess its response to vegetation in different agro-climatic zones. J. Hydrol. 614 (PA), 128543 https://doi.org/10.1016/j.jhydrol.2022.128543.43 Fan, K., Zhang, Q., Gu, X., Singh, V.P., Xu, C.-Y., Shen, Z., Wang, G., 2022. Global soil moisture drought identification and responses to natural and anthropogenic forcings. J. Hydrol. 610, 127993.Fathi-Taperasht, A., Shafizadeh-Moghadam, H., Minaei, M., Xu, T., 2022. Influence of drought duration and severity on drought recovery period for different land cover types: evaluation using MODIS-based indices. Ecol. Indic. 141, 109146 https://doi. org/10.1016/J.ECOLIND.2022.109146Federacion ´ Nacional de Arroceros (FEDEARROZ), 2017. IV Censo nacional arrocero 2016. In FEDEARROZ. https://cutt.ly/QX3LeQMFranc´es, F., V´elez, J.J., Munera, J.C., Medici, C., Busi, G., 2014. Descripcion ´ del modelo conceptual distribuido de simulacion ´ hidrologica ´ TETIS v. 8. In Universidad Polit´ecnica de Valencia Departamento de Ingeniería Hidr´ aulica y Medio Ambiente Grupo de Investigacion ´ de Modelacion ´ Hidrologica ´ y Ambiental (GGonzalez, ´ G.P., Vall-Llossera, M., María, F., Guillem, P., 2015. Comparacion ´ entre los productos de humedad del suelo smos y ecv ´Indice general (p. 68). https ://upcommons.upc.edu/bitstream/handle/2117/117785/Nuevos+Productos+Hu medad+de+Nivel+L4+de+SMOS.pdf?sequenGuarín Giraldo, G.W., Poveda, G., 2013. Variabilidad espacial y temporal del almacenamiento de agua en el suelo en Colombia. Revista de La Academia Colombiana de Ciencias Exactas, Físicas y Naturales 37, 89–113Hao, Z., AghaKouchak, A., 2013. Multivariate standardized drought index: A parametric multi-index model. Adv. Water Resour. 57, 12–18. https://doi.org/10.1016/J. ADVWATRHao, Z., Aghakouchak, A., 2014. A nonparametric multivariate multi-index drought monitoring framework. J. Hydrometeorol. 15 (1), 89–101. https://doi.org/10.1175/ JHM-D-12-0160.1.Hao, Z., Singh, V.P., 2015. Drought characterization from a multivariate perspective: A review. J. Hydrol. 527, 668–678.Huang, J., Zhuo, W., Li, Y., Huang, R., Sedano, F., Su, W., Dong, J., Tian, L., Huang, Y., Zhu, D., Zhang, X., 2020. Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. Int. J. Digit. Earth 13 (4), 504–526. https://doi.org/10.1080/17538947.2018.1542040IGAC, 2004. Aspectos practicos ´ de la adopcion ´ de marco geoc´entrico nacional de referencia Magna-Sirgas como datum oficial de Colombia. Instituto Geografico ´ Agustin Codazzi, Bogota.Ikegwuoha, D.C., Dinka, M.O., 2020. Drought prediction in the Lepelle River basin, South Africa under general circulation model simulations. J. Water Land Develop. 45, 42–53. https://doi.org/10.24425/jwld.2020.133044.Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), I. de H. M. y E. A, 2011. Clasificaciones clim´ aticas Colombia. La Adaptacion ´ de Colombia, II Congreso Nacional Del Clima. https://acortar.link/kBaqb3Meteorology and Environmental Studies (IDEAM), I. de H. M. y E. A, 2013. Zonificacion ´ y codificacion ´ de unidades hidrogr´ aficas e hidrogeologicas ´ de Colombia. Publicacion ´ Aprobada Por El Comit´e de Comunicaciones y Publicaciones Del IDEAM.Jackson, T.J., Bindlish, R., Cosh, M.H., Zhao, T., Starks, P.J., Bosch, D.D., Seyfried, M., Moran, M.S., Goodrich, D.C., Kerr, Y.H., Leroux, D., 2012. Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the U.S. IEEE Trans. Geosci. Remote Sens. 50 (5 PART 1) https://doi.org/10.1109/ TGRS.2011.2168Javed, T., Zhang, J., Bhattarai, N., Sha, Z., Rashid, S., Yun, B., Ahmad, S., Henchiri, M., Kamran, M., 2021. Drought characterization across agricultural regions of China using standardized precipitation and vegetation water supply indices. J. Clean. Prod. 313, 127866 https://doi.org/10.1016/J.JCLEPRO.2021.127866Jim´enez-Donaire, M.D.P., Giraldez, ´ J.V., Vanwalleghem, T., 2020. Impact of climate change on agricultural droughts in Spain. Water 12 (11), 3214. https://doi.org/ 10.3390/W1Kao, S.C., Govindaraju, R.S., 2010. A copula-based joint deficit index for droughts. J. Hydrol. 380 (1–2), 121–134. https://doi.org/10.1016/J.JHYDROL.2009.10.029.Karishma, C.G., Kannan, B., Nagarajan, K., Panneerselvam, S., Pazhanivelan, S., 2022. Spatial and temporal estimation of actual evapotranspiration of lower Bhavani basin, Tamil Nadu using surface energy balance algorithm for land model. J. Appl. Natur. Sci. 14 (2), 566–574. https://doi.org/10.31018/JANS.V14I2.3412.Keyantash, J.A., Dracup, J.A., 2004. An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour. Res. 40 (9), 9304. https://doi.org/10.1029/2003WR002610Kimm, H., Guan, K., Gentine, P., Wu, J., Bernacchi, C.J., Sulman, B.N., Griffis, T.J., Lin, C., 2020. Redefining droughts for the U.S. Corn Belt: the dominant role of atmospheric vapor pressure deficit over soil moisture in regulating stomatal behavior of maize and soybean. Agric. For. Meteorol. 287, 107930. https://doi.org/ 10.1016/J.AGRFORMET.2020.107930Li, J., Huimin, L., 2022. Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China plain. Environ. Res. Commun. 4 (7), 075014 https://doi.org/10.1088/2515-7620/AC814CLoaiza Ceron, ´ W., Carvajal-Escobar, Y., Andreoli de Souza, R.V., Toshie Kayano, M., Gonzalez ´ Lopez, ´ N., 2020. Spatio-temporal analysis of the droughts in Cali, Colombia and their primary relationships with the El Nino-southern ˜ oscillation (ENSO) between 1971 and 2011. Atmosfera ´ 33 (1), 51–69. https://doi.org/10.20937/ ATM.52639Ma, N., Szilagyi, J., Zhang, Y., 2021. Calibration-free complementary relationship estimates terrestrial evapotranspiration globally. Water Resour. Res. 57 (9), e2021WR029691 https://doi.org/10.1029/2021WR029691.Mabrouk, E.H., Moursy, F.I., Morsy, M., 2022. Assessment of climate characteristics and long-term trends of rainfall and drought in the Congo River basin. J. Water Clim. Change 13 (11), 3906–3933. https://doi.org/10.2166/WCC.2022.241Mammoliti, E., Fronzi, D., Mancini, A., Valigi, D., Tazioli, A., 2021. Waterbalance, a webapp for thornthwaite–Mather water balance computation: comparison of applications in two European watersheds. Hydrology 8 (1). https://doi.org/ 10.3390/hydrology8010034.Maroufpoor, S., Fakheri-Fard, A., Shiri, J., 2019. Study of the spatial distribution of groundwater quality using soft computing and geostatistical models. ISH. J. Hydraul. Eng. 25 (2) https://doi.org/10.1080/09715010.2017.1408036Martínez-Fern´ andez, J., Gonzalez-Zamora, ´ A., Sanchez, ´ N., Gumuzzio, A., 2015. A soil water based index as a suitable agricultural drought indicator. J. Hydrol. 522, 265–273. https://doi.org/10.1016/J.JHYDROL.2014.12.051Martínez-Fern´ andez, J., Gonzalez-Zamora, ´ A., Sanchez, ´ N., Gumuzzio, A., HerreroJim´enez, C.M., 2016. Satellite soil moisture for agricultural drought monitoring: assessment of the SMOS derived soil water deficit index. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2016.02.064Mayorga, R., Hurtado, G., 2006. La Sequía En Colombia. Nota T´ecnica del IDEAM. (Institute of Hydrology, Meteorol ogy and environmental studies), 66. http://www. ideam.gov.co/documents/21021/21147/NotaT´ecnicaSequia.pdf/d9ba4965-f7c d-4a2f-a875-2a38b1d6a9McMullan, K.D., Martín-Neira, M., Hahne, A., Borges, A., 2009. SMOS-Earth’s water monitoring mission. In: Space Technologies for the Benefit of Human Society and Earth. Springer, Netherlands, pp. 3–35. https://doi.org/10.1007/978-1-4020-9573- 3_1.Mengyang, M., Wang, Q., Zhao, Y., Liu, R., Li, E., 2023. The counteracting effects of large-scale vegetation restoration and increased precipitation on drought in the Huang-Huai-Hai-Yangtze River basin. J. Hydrol. 622, 129733 https://doi.org/ 10.1016/J.JHYDROL.2023.129733.Minea, I., Iosub, M., Boicu, D., 2021. Multi-scale approach for different type of drought in temperate climatic conditions. Nat. Hazards 2021, 1–25. https://doi.org/10.1007/ S11069Mishra, A., & Singh, V. P. (2010). A review of drought concepts. In J. Hydrol. (Vol. 391, Issues 1–2, pp. 202–216). doi:https://doi.org/10.1016/j.jhydrol.2010.07.012Mishra, A., Ines, A.V., Das, N.D., Khedun, C.P., Singh, V.P., Sivakumar, B., Hansen, J.W., 2015. Anatomy of a local-scale drought: application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2014.10.038Montealegre Bocanegra, J., 2014. Actualizacion ´ del componente Meteorologico ´ del modelo institucional del IDEAM (Institute of Hydrology, Meteorology and Environmental Studies) sobre el efecto clim´ atico de los fenomenos ´ El Nino ˜ y La Nina ˜ en Colombia, como insumo para el Atlas Climatologico. ´ In IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales). http://www.ideam.gov.co/docu ments/21021/440517/Actualizacion+Modelo+Institucional+El+Nino˜ +-+La+Nina ˜ .pdf/02f5e53b-0349-41f1-87e0-5513286d1d1d.Moran-Tejeda, ´ E., Bazo, J., Lopez-Moreno, ´ J.I., Aguilar, E., Azorín-Molina, C., SanchezLorenzo, A., Martínez, R., Nieto, J.J., Mejía, R., Martín-Hern´ andez, N., VicenteSerrano, S.M., 2016. Climate trends and variability in Ecuador (1966–2011). Int. J. Climatol. 36 (11), 3839–3855. https://doi.org/10.1002/JOC.4597Mukherjee, S., Mishra, A., Trenberth, K.E., 2018. Climate change and drought: a perspective on drought indices. Curr. Clim. Chang. Rep. 4 (2), 145–163. https://doi. org/10.1007/s40641-018-0098-x.Nicolai-Shaw, N., Gudmundsson, L., Hirschi, M., Seneviratne, S.I., 2016. Long-term predictability of soil moisture dynamics at the global scale: persistence versus largescale drivers. Geophys. Res. Lett. 43 (16), 8554–8562. https://doi.org/10.1002/ 2016GL069Ojeda-Flechas, C.D., Burbano-Rodríguez, J.A., Carvajal-Escobar, Y., Hern´ andez-Torres, F. L., 2020. Characterization of comprehensive drought events associated with the enso warm phase through satellite images in the Valle del Cauca, Colombia [Caracterizacion ´ de eventos de sequía integral, asociados a la fase calida ´ de enos, mediante imagenes ´ satelitale]. DYNA (Colombia) 87 (214), 204–214. https://www. scopus.com/inward/record.uri?eid=2-s2.0-85091362594&doi=10.15446%2FD YNA.V87N214.83221&partnerID=40&md5=bd5b8fa9035f39ca494945e1057a1 b13Osroosh, Y., Troy Peters, R., Campbell, C.S., Zhang, Q., 2015. Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold. Comput. Electron. Agric. 118 https://doi.org/10.1016/j. compag.2015.09.006Parsons, D.J., Rey, D., Tanguy, M., Holman, I.P., 2019. Regional variations in the link between drought indices and reported agricultural impacts of drought. Agric. Syst. 173 https://doi.org/10.1016/j.agsy.2019.02.015.Pena, ˜ L.E., Barrios, M., Franc´es, F., 2016. Flood quantiles scaling with upper soil hydraulic properties for different land uses at catchment scale. J. Hydrol. 541, 1258–1272. https://doi.org/10.1016/J.JHYDROL.2016.08.031Pohl, F., Rakovec, O., Rebmann, C., Hildebrandt, A., Boeing, F., Hermanns, F., Attinger, S., Samaniego, L., Kumar, R., 2023. Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites. Sci. Data 10 (1), 1–12. https://doi.org/10.1038/s41597-023-02192-1.Poveda, G., Alvarez, ´ D.M., Rueda, O.A., ´ 2011. Hydro-climatic variability over the Andes of Colombia associated with ENSO: A review of climatic processes and their impact on one of the Earth’s most important biodiversity hotspots. Clim. Dyn. 36 (11− 12), 2233–2249. https://doi.org/10.1007/s00382-010-0931-yPradawet, C., Khongdee, N., Pansak, W., Spreer, W., Hilger, T., Cadisch, G., 2023. Thermal imaging for assessment of maize water stress and yield prediction under drought conditions. J. Agron. Crop Sci. 209 (1), 56–70. https://doi.org/10.1111/ JAC.12582Ramos Castro, J.E., 2016. Aplicacion ´ de sistemas de informacion ´ geografica ´ a la georeferenciacion ´ de sitios arqueologicos, ´ como aporte al desarrollo de un Atlas Arqueoastronomico ´ de Honduras. Ciencias Espaciales. https://doi.org/10.5377/ce. v6i1.2556Rhee, J., Im, J., Carbone, G.J., 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens. Environ. https://doi. org/10.1016/j.rse.2010.07.005.Rodrigues, M.S., Castrignano, ` A., Belmonte, A., da Silva, K.A., da Trindade Lessa, B.F., 2020. Geostatistics and its potential in agriculture 4.0. Rev. Ciˆenc. Agron. 51 (5) https://doi.org/10.5935/1806-6690.20200095.Rokhzadi, A., Mohammadian, A., Charron, M., 2018. An optimally stable and accurate second-order SSP Runge-Kutta IMEX scheme for atmospheric applications. J. Adv. Model. Earth Syst. 10 (1), 18–42. https://doi.org/10.1002/2017MS001065.Rueda Calier, F., Penaranda ˜ Mallungo, L.A., Vel´ asquez Vargas, W.L., Díaz Baez, ´ S.A., 2016. Aplicacion ´ de una metodología de analisis ´ de datos obtenidos por percepcion ´ remota orientados a la estimacion ´ de la productividad de cana ˜ para panela al cuantificar el NDVI (índice de vegetacion ´ de diferencia normalizada). Corpoica Ciencia y Tecnología Agropecuaria. https://doi.org/10.21930/rcta.vol16_num1_art: 377.Sabzipour, B., Asghari, O., Sarang, A., 2019. Evaluation and optimal redesigning of river water-quality monitoring networks (RWQMN) using geostatistics approach (case study: Karun, Iran). Sustainable. Water Resour. Manag. 5 (2) https://doi.org/ 10.1007/s40899-017-0176-6Satg´e, F., Espinoza, R., Zol´ a, R.P., Roig, H., Timouk, F., Molina, J., Garnier, J., Calmant, S., Seyler, F., Bonnet, M.P., 2017. Role of climate variability and human activity on Poopo ´ Lake droughts between 1990 and 2015 assessed using remote sensing data. Remote Sens. 9 (3), 218. https://doi.org/10.3390/RS9030218Saxton, K.E., Rawls, W.J., 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70 (5), 1569–1578. https://doi.org/10.2136/sssaj2005.0117.Scaini, A., Sanchez, ´ N., Vicente-Serrano, S.M., Martínez-Fern´ andez, J., 2015. SMOSderived soil moisture anomalies and drought indices: A comparative analysis using in situ measurements. Hydrol. Process. https://doi.org/10.1002/hyp.10150.Sedano, K., Carvajal, Y., 2013. Variabilidad clim´ atica, cambio clim´ atico y gestion ´ integrada del riesgo de inundaciones en Colombia. Rev. Semillas. 46-47, 47–53.Sepulcre-Canto, G., Horion, S., Singleton, A., Carrao, H., Vogt, J., 2012. Development of a combined drought Indicator to detect agricultural drought in Europe. Nat. Hazards Earth Syst. Sci. 12 (11), 3519–3531. https://doi.org/10.5194/NHESS-12-3519- 2012SGC, S.G.C., UNAL, U.N. de C., 2015. Memoria explicativa de zonificacion ´ de la susceptibilidad y la amenaza relativa por movimientos en masa, escala 1:100.000 plancha 280 - Palmira. https://nanourl.org/jF9Silva, D. F. Da, Neto, P. F. de S., Silva, S. D. da, Lima, M. J. da S., Cavalcante, I. B. da S., Oliveira, S. T. de, Mendonça, H. da S., Batista, B. A., Rocha, L. H. dos S., Almeida, H. R. R. de C., Pereira, M. P. S., & Araújo, L. E. de. (2021). Identificaç˜ ao das causas clim´ aticas dos eventos extremos e dos impactos dos ENOS Canonico ˆ e Modoki nas macrorregioes ˜ de Alagoas. Revista Brasileira de Geografia Física, 14(4), 1880–1897. 10.26848/RBGF.V14.4.P1880-1897Sousa, P.M., Trigo, R.M., Aizpurua, P., Nieto, R., Gimeno, L., Garcia-Herrera, R., 2011. Trends and extremes of drought indices throughout the 20th century in the Mediterranean. Nat. Hazards Earth Syst. Sci. 11 (1) https://doi.org/10.5194/nhess11-33-2011.Souza, R.S., Delgado, R.C., de Andrade, C.F., dos Santos, G.L., Magistrali, I.C., de Oliveira, C.M.M., Teodoro, P.E., Capristo Silva, G.F., da Silva Junior, C.A., d´e Avila Rodrigues, R., 2021. Vegetation degradation in ENSO events: Drought assessment, soil use and vegetation evapotranspiration in the Western Brazilian Amazon. https:// doi.org/10.1016/j.rsaseSungmin, O., Park, S.K., 2023. Flash drought drives rapid vegetation stress in arid regions in Europe. Environ. Res. Lett. 18 (1), 014028 https://doi.org/10.1088/1748-9326/ ATan, L., Zheng, K., Zhao, Q., Wu, Y., Liu, J., Zhang, Y., Ning, T., 2021. Evapotranspiration estimation using remote sensing technology based on a SEBAL model in the upper reaches of the Huaihe River basin. Atmosphere 12 (12), 1599. https://doi.org/ 10.3390/ATMOS1212159Tian, L., Yuan, S., Quiring, S.M., 2018. Evaluation of six indices for monitoring agricultural drought in the south-Central United States. Agric. For. Meteorol. 249 (June 2017), 107–119. https://doi.org/10.1016/j.agrformet.2017.11.024Tramblay, Y., Quintana Seguí, P., 2022. Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme. Nat. Hazards Earth Syst. Sci. 22 (4), 1325–1334. https://doi.org/10.5194/NHESS-22- 1325-202Tsakiris, G., Vangelis, H., 2005. Establishing a drought index incorporating evapotanspiration. Eur. Water 9, 3–11.Umar, N., Gray, A., 2023. Comparing single and multiple imputation approaches for missing values in univariate and multivariate water level data. Water (Switzerland) 15 (8), 1519. https://doi.org/10.3390/W15081519/S1United Nations (UN), National unit for disaster risk management (UNGRD), Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), Ministry of the environment and sustainable development (MINAMBIENTE), 2018. Estrategia nacional para la gestion ´ integral de la sequía en colombia. https://knowledge.unccd. int/sites/default/files/country_profile_documents/ENGIS%2520para%252 0publicaci%25C3%25B3n_Colombia.pdf.Valiente, O.M., ´ 2015. Sequía: definiciones, tipologías y m´etodos de cuantificacion. ´ Invest. Geogr. https://doi.org/10.14198/INGEO2001.26.06Veysi, S., Naseri, A.A., Hamzeh, S., Bartholomeus, H., 2017. A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agric. Water Manag. 189 https://doi.org/10.1016/j.agwat.2017.04.016.Vicente-Serrano, S.M., Beguería, S., Lopez-Moreno, ´ J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23 (7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1.Vicente-Serrano, S.M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J.J., Lopez-Moreno, ´ J. I., Azorín-Molina, C., Revuelto, J., Moran-Tejeda, ´ E., S´ anchez-Lorenzo, A., 2012. An´ alisis comparativo de diferentes índices de sequía para aplicaciones ecologicas, ´ agrícolas e hidrologicas. ´ Octavo Congreso Internacional de La Asociacion ´ Espanola ˜ de Climatología: Cambio Climatico, ´ Extremos e Impactos 679–689. https://repositor io.aemet.es/bitstream/20.500.11765/8332/1/0066_VIII-2012-SM_VICEVicente-Serrano, S.M., Aguilar, E., Martínez, R., Martín-Hernandez, ´ N., AzorinMolina, C., Sanchez-Lorenzo, A., El Kenawy, A., Tom´ as-Burguera, M., MoranTejeda, E., Lopez-Moreno, ´ J.I., Revuelto, J., Beguería, S., Nieto, J.J., Drumond, A., Gimeno, L., Nieto, R., 2017. The complex influence of ENSO on droughts in Ecuador. Clim. Dyn. 48 (1–2), 405–427. https://doi.org/10.1007/S00382-016-3082-Y/ FIGURES/8Virnodkar, S.S., Pachghare, V.K., Patil, V.C., Jha, S.K., 2020. Remote sensing and machine learning for crop water stress determination in various crops: A critical review. In: Precision Agriculture, Vol. 21, Issue 5. Springer US. https://doi.org/ 10.1007/s11119-020-09711-9Wang, W., Ertsen, M.W., Svoboda, M.D., Hafeez, M., 2016. Propagation of drought: from meteorological drought to agricultural and hydrological drought. Adv. Meteorol. 2016 https://doi.org/10.1155/2016/6547209Wei, W., Zhang, H., Ma, L., Wang, X., Guo, Z., Xie, B., Zhou, J., Wang, J., 2022. Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model. J. Environ. Manag. 323, 116208 https://doi.org/10.1016/J. JENVMAN.2022WMO. (2018). Guide to meteorological instruments and methods of observation; WMONo. 8: Measurement of Meteorological Variables. In World Meteorological Organization: Vol. I (Issue 8).Wong, C.Y.S., Bambach, N.E., Alsina, M.M., McElrone, A.J., Jones, T., Buckley, T.N., Kustas, W.P., Magney, T.S., 2022. Detecting short-term stress and recovery events in a vineyard using tower-based remote sensing of photochemical reflectance index (PRI). Irrig. Sci. 40 (4–5), 683–696. https://doi.org/10.1007/S00271-022-00777-Z/ FIGURES/7Wu, D., Li, Z., Zhu, Y., Li, X., Wu, Y., Fang, S., 2021. A new agricultural drought index for monitoring the water stress of winter wheat. Agric. Water Manag. 244, 106599 https://doi.org/10.1016/J.AGWAT.2020.106599Wu, X.P., Liu, S., Luan, J., Wang, Y., Gao, X., Chen, C., 2023. Nitrogen addition alleviates drought effects on water status and growth of Moso bamboo (Phllostachys edulis). For. Ecol. Manag. 530, 120768 https://doi.org/10.1016/J.FORECO.2023.120768Xing, Z., Ma, M., Wei, Y., Zhang, X., Yu, Z., Yi, P., 2020. A new agricultural drought index considering the irrigation water demand and water supply availability. Nat. Hazards 104 (3), 2409–2429. https://doi.org/10.1007/S11069-020-04278-0/FIGURES/11.Xu, Z., Wu, Z., Shao, Q., He, H., Guo, X., 2023. From meteorological to agricultural drought: propagation time and probabilistic linkages. J. Hydrol. Region. Stud. 46, 101329 https://doi.org/10.1016/J.EJRH.2023.101329.Yalçın, S., Es¸it, M., Çoban, O., ¨ 2023. A new deep learning method for meteorological drought estimation based-on standard precipitation evapotranspiration index. Eng. Appl. Artif. Intell. 124, 106550 https://doi.org/10.1016/J.ENGAPPAI.2023.106550.Yang, C., Liu, C., Gu, Y., Wang, Y., Xing, X., Ma, X., 2023. A novel comprehensive agricultural drought index accounting for precipitation, evapotranspiration, and soil moisture. Ecol. Indic. 154, 110593 https://doi.org/10.1016/j.ecolind.2023.110593.Yang, Y., Li, H.G., Wang, J., Wang, H.L., He, F., Su, Y., Zhang, Y., Feng, C.H., Niu, M., Li, Z., Liu, C., Yin, W., Xia, X., 2020. ABF3 enhances drought tolerance via promoting ABA-induced stomatal closure by directly regulating ADF5 in Populus euphratica. J. Exp. Bot. 71 (22), 7270–7285. https://doi.org/10.1093/JXB/ERAA383.Yao, P., Qian, L., Wang, Z., Meng, H., Ju, X., 2022. Assessing drought, flood, and high temperature disasters during sugarcane growth stages in southern China. Agriculture 12 (12), 2117. https://doi.org/10.3390/AGRICULTURE12122117Yildirim, G., Rahman, A., Singh, V.P., 2022. Meteorological and hydrological drought hazard, frequency and propagation analysis: A case study in Southeast Australia. J. Hydrol. Region. Stud. 44, 101229 https://doi.org/10.1016/J.EJRH.2022.101229Yu, M., Zhang, J., Wei, L., Wang, G., Dong, W., Liu, X., 2023. Impact of soil textures on agricultural drought evolution and field capacity estimation in humid regions. J. Hydrol. 626, 130257 https://doi.org/10.1016/J.JHYDROL.2023.130257.Zhang, Z., Xu, W., Shi, Z., Qin, Q., 2021. Establishment of a comprehensive drought monitoring index based on multisource remote sensing data and agricultural drought monitoring. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 2113–2126. https://doi.org/10.1109/JSTARS.2021.3052194Zhang, Z., Ma, J., Wang, T., Song, W., Hao, L., 2023. Identify the relationship of meteorological drought and ecohydrological drought in Xilin Gol grassland. China. Natur. Hazard. 1–16 https://doi.org/10.1007/S11069-022-05778-X/TABLES/1.Zhao, C., Brissette, F., Chen, J., 2023a. Projection of future extreme meteorological droughts using two large multi-member climate model ensembles. J. Hydrol. 618, 129155 https://doi.org/10.1016/J.JHYDROL.2023.129155.Zhao, H., Huang, Y., Wang, X., Li, X., Lei, T., 2023b. The performance of SPEI integrated remote sensing data for monitoring agricultural drought in the North China plain. Field Crop Res. 302, 109041 https://doi.org/10.1016/J.FCR.2023.109041.© 2024 The Authorsinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/Tolima - Sequía agrícolaTolima - Balance hídricoTolima - Estres hídricoAgricultural droughtDrought indexEvapotranspirationRemote sensingSoil moistureAgricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stressArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/8f82754d-220b-4b9a-9dab-a57b1877e865/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf817651https://repositorio.unibague.edu.co/bitstreams/78783265-46af-47aa-ab47-a8f9aa07386b/download8cd91ed553b513416520cd1de25cc6fbMD52TEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain3452https://repositorio.unibague.edu.co/bitstreams/181b255d-413e-47e6-8be4-69b61418a24c/downloadcfbef107917dedf33b7f9aea914288d7MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg27126https://repositorio.unibague.edu.co/bitstreams/6aebe0eb-404c-48d2-893d-94df08c6d66c/download0ee35abcbf00394c0b33d29e83c038a9MD5420.500.12313/5838oai:repositorio.unibague.edu.co:20.500.12313/58382025-10-29 03:01:46.882https://creativecommons.org/licenses/by-nc/4.0/© 2024 The Authorshttps://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

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