تقيم دقة بيانات ناسا باور في تقدير متغيرات الطقس (هطول الامطار ودرجة الحرارة) لمستجمعات مياه نهر خاصة جاي
محتوى المقالة الرئيسي
الملخص
تهدف هذه الدراسة الىتقييم دقة وموثوقية بيانات الاقمار الصناعية من مشروع ناسا باور في التقاط المتغيرات المناخية المهمة , وهي هطول الامطار ودرجة حرارة الهواء, مقارنة بالبيانات التي تم جمعها من محطات الارصاد الجوية الارضية التابعة الى المكز الوطني لبيانات المناخ (NCDC) الموجودة في حوض نهر خاصة جاي . يتميز الحوض بتدرج طوبوغرافي يبدأ من المرتفعات الشمالية الشرقية وينحدر بأتجاه الجنوب الغربي. تشهد اجهزة حوض نهر خاصة جاي مناخأ شبه جاف, مع صيف حار وجاف وشتاءا بارد ورطب نسبيأ .تم جمع سجلات هطول الامطار ودرجات الحرارة اليومية (2010-2024) من اربع محطات ارصاد جوية,بالضافة الاى سجلات من تسجيل ناسا باور في اقرب نقاط الشبكة لتقييم موثوقية مجموعة البيانات من ناسا باور, استخدمنا العديد من المؤشراتالاحصائية ذات الصلة ( معامل , R2ومعامل الارتباط CC , ومعامل ناش NSE, ومتوسط الخطأ MBE, وجذر متوسط مربع الخطأ RMBSE ) على مقاييس زمنية يومية وشهرية وسنوية. اشار تحليل هطول الامطار الى وجود تطابق ممتاز بين بيانات قمر ناسا باور الصناعي والبيانات في الموقع. للمقارنات الشهرية , كانR2=0.89 ,CC=0.94 بينما للمقارنات للمقارنات السنوية كان R2=0.81, CC=0.88 . بشكل عام, تحسن التطابق مع مقاييس زمنية اطول, مما يشير الى قدرة بيانات القمر الصناعي على التقاط اتجاهات هطول الامطار بدقة بمرور الوقت .كما يعزز نطاق قيم NSE من 0.72 الى 0.87 قدرة البيانات على اعادة انتاج تغيرات هطول الامطار بمرور الوقت.
##plugins.themes.bootstrap3.displayStats.downloads##
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Abidalla, W.A., and Abed, B.S., 2025. Predicting Future Surface Runoff Delivered to the Euphrates River Using LARSWG and SWAT Models:Sahiliya Valley in the Iraqi Western Desert as a Case Study. Journal of Engineering, 31(2), pp. 156-176. https://doi.org/10.31026/j.eng.2025.02.10
Aboelkhair, H., Morsy, M., and El Afandi, G., 2019. Assessment of agroclimatology NASA POWER reanalysis datasets for temperature types and relative humidity at 2 m against ground observations over Egypt. Advances in Space Research, 64(1), pp. 129-142. https://doi.org/10.1016/j.asr.2019.03.032
Akkem, Y., Biswas, S.K., and Varanasi, A., 2024. A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Engineering Applications of Artificial Intelligence, 131, P. 107881. https://doi.org/10.1016/j.engappai.2024.107881
AL Thamiry, H.A., and Azzubaidi, R.Z., 2020. Survey and discharge measurements of the Iraqi Border crossing rivers. J. Eng. Sci. Technol, 15(6), pp. 4288-4302
Ali, A.A., Al-Thamiry, H.A., and Alazawi, S.Q., 2011. Estimation of runoff for Goizha-Dabashan Watershed with aid of remote sensing techniques. Journal of Engineering, 17(02), pp. 306-320. https://doi.org/10.31026/j.eng.2011.02.08
Ali, S., Abed, B.S., and Rashid, M., 2023. Generation of IDF equation case study Al-Shuwaija watersheds/(IRAQ-IRAN). Wasit Journal of Engineering Sciences, 11(3), pp. 14-26. https://doi.org/10.31185/ejuow.Vol11.Iss3.448
Al-Juhaishi, M.R., Oleiwi, A.S., and Aed, B.S., 2024. Modeling surface runoff in Al-Mohammadi Valley: Influence of climate and soil parameters. International Journal of Design and Nature and Ecodynamics, 19(3), pp. 1043-1049. https://.doi.org/10.18280/ijdne.190333
Al-Kahachi, S.A., Al-Tawash, B.S., and Al-Tamimi, O.S., 2022. Distribution and enrichments of abundant and trace elements in Al-Khassa Sub Basin Soil, Kirkuk, Northeastern of Iraq. Iraqi Journal of Science, pp. 5338-5352. https://doi.org/10.24996/ijs.2022.63.12.22
Al-Khafaji, M.S., Ibrahim, H.M., and Abdullah, H.S., 2017. Assessment of water clarity within Dokan lake using remote sensing techniques. Journal of Engineering, 23(8), pp. 13-28. https://doi.org/10.31026/j.eng.2017.08.02
Al-Kilani, M.R., Rahbeh, M., Al-Bakri, J., Tadesse, T., and Knutson, C., 2021. Evaluation of remotely sensed precipitation estimates from the NASA POWER project for drought detection over Jordan. Earth Systems and Environment, 5(3), pp. 561-573. https://doi.org/10.1007/s41748-021-00245-2
Al-Qurnawi, W.S., 2014. Groundwater vulnerability assessment and well head protection zones of Alton Kopry Basin, Kirkuk Governorate Northeast of Iraq. Unpublished Ph. D. Thesis, University of Basrah, Iraq.
Ansari, A., Pranesti, A., Telaumbanua, M., Alam, T., Wulandari, R.A., and Nugroho, B.D.A., 2023. Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach. Heliyon, 9(9). https://doi.org/10.1016/j.heliyon.2023.e19639
Bai, W., Wang, G., Huang, F., Sun, Y., Du, Q., Xia, J., Wang, X., Meng, X., Hu, P., Yin, C., and Tan, G., 2025. Review of assimilating spaceborne global navigation satellite system remote sensing data for tropical cyclone forecasting. Remote Sensing, 17(1), P. 118. https://doi.org/10.3390/rs17010118
Barron-Lugo, J.A., Lopez-Arevalo, I., Gonzalez-Compean, J.L., Alvarado-Barrientos, M.S., Carretero, J., Sosa-Sosa, V.J., and Montella, R., 2024. A GIS-big data model for improving the coverage and analysis processes of territory observation, and integrating ground-based observations with retrospective meteorological data. International Journal
of Applied Earth Observation and Geoinformation, 128, P. 103736. https://doi.org/10.1016/j.jag.2024.103736
Baumann, P., Mazzetti, P., Ungar, J., Barbera, R., Barboni, D., Beccati, A., Bigagli, L., Boldrini, E., Bruno, R., Calanducci, A., and Campalani, P., 2016. Big data analytics for earth sciences: the EarthServer approach. International Journal of Digital Earth, 9(1), pp. 3-29. https://doi.org/10.1080/17538947.2014.1003106
Bhandari, M., Shrestha, S., and New, J., 2012. Evaluation of weather datasets for building energy simulation. Energy and Buildings, 49, pp. 109-118. https://doi.org/10.1016/j.enbuild.2012.01.033
Budamala, V., and Mahindrakar, A.B., 2022. Flexible user interface for machine learning techniques to enhance the complex geospatial hydro-climatic models with future perspective. Geocarto International, 37(12), pp. 3469-3488. https://doi.org/10.1080/10106049.2020.1864027
Dawood, A.H., 2024. Flood risk analysis and vulnerability assessment for Erbil area, Doctoral dissertation, Salahaddin University-Erbil.
Faybishenko, B., Versteeg, R., Pastorello, G., Dwivedi, D., Varadharajan, C., and Agarwal, D., 2022. Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data. Stochastic Environmental Research and Risk Assessment, 36(4), pp. 1049-1062. https://doi.org/10.1007/s00477-021-02106-w
Fu, Y., Zhu, Z., Liu, L., Zhan, W., He, T., Shen, H., Zhao, J., Liu, Y., Zhang, H., Liu, Z., and Xue, Y., 2024. Remote sensing time series analysis: A review of data and applications. Journal of Remote Sensing, 4, P. 0285. https://doi.org/10.34133/remotesensing.0285
Gu, L., Yin, J., Wang, S., Chen, J., Qin, H., Yan, X., He, S., and Zhao, T., 2023. How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling. Journal of Hydrology, 617, P. 128920. https://doi.org/10.1016/j.jhydrol.2022.128920
Hazra, A., Maggioni, V., Houser, P., Antil, H., and Noonan, M., 2019. A Monte Carlo-based multi-objective optimization approach to merge different precipitation estimates for land surface modeling. Journal of Hydrology, 570, pp. 454-462. https://doi.org/10.1016/j.jhydrol.2018.12.039
Hegyi, B., Stackhouse, P.W., Taylor, P., and Patadia, F., 2024, January. Nasa POWER: providing present and future climate services based on NASA data for the energy, agricultural, and sustainable buildings communities. In 104th American Meteorological Society (AMS) Annual Meeting.
Ibrahim, I.A., and Al-Dabbas, M., 2021. Analysis of climate parameters as indicators of climate changes in central and eastern Iraq: Khanaqin climate conditions as a case study. Iraqi Journal of Science, pp. 4747-4757. https://doi.org/10.24996/ijs.2021.62.12.13
Jia, A., Liang, S., Wang, D., Mallick, K., Zhou, S., Hu, T., and Xu, S., 2024. Advances in methodology and generation of all-weather land surface temperature products from polar-orbiting and geostationary satellites: A comprehensive review. IEEE Geoscience and Remote Sensing Magazine. https://doi.org/10.1109/MGRS.2024.3421268
Jiang, C., Parteli, E.J., Xia, Q., and Shao, Y., 2024. Evaluation of precipitation reanalysis products for regional hydrological modelling in the Yellow River Basin. Theoretical and Applied Climatology, 155(4), pp. 2605-2626. https://doi.org/10.1007/s00704-023-04758-w
Kheyruri, Y., Sharafati, A., and Ahmadi Lavin, J., 2024. Performance assessment of NASA POWER temperature product with different time scales in Iran. Acta Geophysica, 72(2), pp. 1175-1189. https://doi.org/10.1007/s11600-023-01186-2
Langsdale, M., Verhoelst, T., Povey, A., Schutgens, N., Dowling, T., Lambert, J.C., Compernolle, S., and Kern, S., 2025. The challenges and limitations of validating satellite-derived datasets using independent measurements: lessons learned from essential climate variables. Surveys in Geophysics, pp. 1-38. https://doi.org/10.1007/s10712-025-09898-4
Li, H., Zhang, C., Chu, W., and Shen, D., 2023. A process-based deep learning hydrological model for daily rainfall-runoff simulation. Available at SSRN 4613999. http://doi.org/10.2139/ssrn.4613999
Li, Z., Jiang, X., and Wang, G., 2024. Numerical models, observing systems, and data assimilation for prediction of ocean mesoscale eddies. Ocean-Land-Atmosphere Research, 3, P. 0059. https://doi.org/10.34133/olar.0059
Liu, Z., Shie, C.L., Li, A., and Meyer, D., 2020. NASA global satellite and model data products and services for tropical meteorology and climatology. Remote Sensing, 12(17), P. 2821. https://doi.org/10.3390/rs12172821
Mahmood, K.M. and Mohammed-Ali, W.S., 2025. A hydraulic performance model of Khassa Chai River under varying flow conditions. engineering, Technology & Applied Science Research, 15(2), pp. 20934-20940. https://doi.org/10.48084/etasr.9675
Mahmoud, M.I., and Kasim, M.N., 2019. Sediment yield problems in Khassa Chai watershed using hydrologic models. Cihan University-Erbil Scientific Journal, 3(1), pp. 34-41.
Mankin, K.R., Mehan, S., Green, T.R., and Barnard, D.M., 2025. Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets. Hydrology and Earth System Sciences, 29(1), pp. 85-108. https://doi.org/10.5194/hess-29-85-2025
Marzouk, O.A., 2021. Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements.
Heliyon, 7(3). https://doi.org/10.1016/j.heliyon.2021.e06625
Merlone, A., Beges, G., Bottacin, A., Brunet, M., Gilabert, A., Groselj, D., Harper, A., Hechler, P., Ivanov, M., Musacchio, C., and Trewin, B., 2024. Climatological reference stations: Definitions and requirements. International Journal of Climatology, 44(5), pp. 1710-1724. https://doi.org/10.1002/joc.8406
Monteiro, L.A., Sentelhas, P.C., and Pedra, G.U., 2018. Assessment of NASA/POWER satellite‐based weather system for Brazilian conditions and its impact on sugarcane yield simulation. International Journal of Climatology, 38(3), pp. 1571-1581. https://doi.org/10.1002/joc.5282
Mutlu, A., 2025. Comparative evaluation of NASA, ERA5, and observational data for accuracy and reliability. Theoretical and Applied Climatology, 156(7), P. 367. https://doi.org/10.1007/s00704-025-05605-w
Nama, A.H., Alwan, I.A., and Pham, Q.B., 2024. Climate change and future challenges to the sustainable management of the Iraqi marshlands. Environmental Monitoring and Assessment, 196(1), P. 35. https://doi.org/10.1007/s10661-023-12168-8
Negm, A., Jabro, J., and Provenzano, G., 2017. Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological variables and reference evapotranspiration in Sicily, Italy. Agricultural and forest meteorology, 244, pp. 111-121.
https://doi.org/10.1016/j.agrformet.2017.05.022
Newman, R., and Noy, I., 2023. The global costs of extreme weather that are attributable to climate change. Nature Communications, 14(1), P. 6103. https://doi.org/10.1038/s41467-023-41888-1
Qin, Y., McVicar, T.R., Huang, J., West, S., and Steven, A.D., 2022. On the validity of using ground-based observations to validate geostationary-satellite-derived direct and diffuse surface solar irradiance: Quantifying the spatial mismatch and temporal averaging issues. Remote Sensing of Environment, 280, P. 113179. https://doi.org/10.1016/j.rse.2022.113179
Quansah, A.D., Dogbey, F., Asilevi, P.J., Boakye, P., Darkwah, L., Oduro-Kwarteng, S., Sokama-Neuyam, Y.A. and Mensah, P., 2022. Assessment of solar radiation resource from the NASA-POWER reanalysis products for tropical climates in Ghana towards clean energy application. Scientific reports, 12(1), P. 10684. https://doi.org/10.1038/s41598-022-14126-9
Rasheed, N.J., Al-Khafaji, M.S., and Alwan, I.A., 2024. Variations of streamflow and sediment yield in the Mosul-Makhool Basin, North Iraq under climate change: a pre-dam construction study. H2Open Journal, 7(1), pp. 38-60. https://doi.org/10.2166/h2oj.2023.078
Rodrigues, G.C., and Braga, R.P., 2021. Evaluation of NASA POWER reanalysis products to estimate daily weather variables in a hot summer mediterranean climate. Agronomy, 11(6), P. 1207. https://doi.org/10.3390/agronomy11061207
Rouzegari, N., Bolboli Zadeh, M., Jimenez Arellano, C., Afzali Gorooh, V., Nguyen, P., Meng, H., Ferraro, R.R., Kalluri, S., Sorooshian, S. and Hsu, K., 2025. Passive microwave imagers, their applications, and benefits: a review. Remote Sensing, 17(9), P. 1654. https://doi.org/10.3390/rs17091654
Saleh, A., Tan, M.L., Yaseen, Z.M., and Zhang, F., 2024. Integrated machine learning models for enhancing tropical rainfall prediction using NASA POWER meteorological data. Journal of Water and Climate Change, 15(12), pp. 6022-6042. https://doi.org/10.2166/wcc.2024.719
Schreiner-McGraw, A.P., and Ajami, H., 2021. Combined impacts of uncertainty in precipitation and air temperature on simulated mountain system recharge from an integrated hydrologic model. Hydrology and Earth System Sciences Discussions, 2021, pp. 1-30. https://doi.org/10.5194/hess-26-1145-2022
Schuldt, S.J., Nicholson, M.R., Adams, Y.A., and Delorit, J.D., 2021. Weather-related construction delays in a changing climate: a systematic state-of-the-art review. Sustainability, 13(5), P. 2861. https://doi.org/10.3390/su13052861
Shao, C., and Nerger, L., 2024. Assimilation of ground-based GNSS data using a local ensemble Kalman filter. Scientific Reports, 14(1), P. 21682. https://doi.org/10.1038/s41598-024-72915-w
Singh, S., Mishra, K., Chavan, R., and Tiwari, H.L., 2023, December. Advancements and challenges in hydrological modeling: a comprehensive review. In International Conference on Hydraulics, Water Resources and Coastal Engineering, pp. 423-442. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-7474-6_32
Tayyeh, H.K., and Mohammed, R., 2023. Analysis of NASA POWER reanalysis products to predict temperature and precipitation in Euphrates River basin. Journal of Hydrology, 619, P. 129327. https://doi.org/10.1016/j.jhydrol.2023.129327
Valipour, M., 2016. How much meteorological information is necessary to achieve reliable accuracy for rainfall estimations?. Agriculture, 6(4), P. 53. https://doi.org/10.3390/agriculture6040053
van Leeuwen, C., Sgubin, G., Bois, B., Ollat, N., Swingedouw, D., Zito, S., and Gambetta, G.A., 2024. Climate change impacts and adaptations of wine production. Nature Reviews Earth & Environment, 5(4), pp. 258-275. https://doi.org/10.1038/s43017-024-00521-5
Wakweya, R.B., 2023. Challenges and prospects of adopting climate-smart agricultural practices and technologies: Implications for food security. Journal of Agriculture and Food Research, 14, P. 100698. https://doi.org/10.1016/j.jafr.2023.100698
Yang, D., Yang, Y., and Xia, J., 2021. Hydrological cycle and water resources in a changing world: A review. Geography and Sustainability, 2(2), pp. 115-122. https://doi.org/10.1016/j.geosus.2021.05.003
Zheng, J., and Zhang, S., 2025. Decomposing the total uncertainty in wheat modeling: an analysis of model structure, parameters, weather data inputs, and squared bias contributions. Agricultural Systems, 224, P. 104215. https://doi.org/10.1016/j.agsy.2024.104215
