Hydrometeorological Assessment of Water Budget Components in Rawalpindi Division, Pakistan: An ERA5 Reanalysis and Machine Learning Approach
DOI:
https://doi.org/10.58932/MULG0076Keywords:
Water stress, water resource management, trend analysis, linear regression (machine learning), GIS, remote sensingAbstract
This paper provides a hydro-meteorological evaluation of the Rawalpindi Division in Pakistan, based on ERA5 reanalysis information between February 2015 and December 2025 coupled with machine learning to measure trends. Water budgeting estimated dynamics of precipitation, evapotranspiration, runoff, soil moisture and change in storage over 131 months of consecutive observations. The findings indicated that the average monthly precipitation was 72.68 mm with extensive variability (CV = 87%), which is very monsoon seasonal. The major water loss process was evapotranspiration, which used 93.8 percent of the received precipitation, which is many times higher than the Mediterranean climate limit. Monsoon (July-September) added 47.8 percent of precipitation in a year, yielded positive storage (+25.31 mm/month) and severe water stress ( -40.03 mm) was found in non-parametric Mann-Kendall trend analysis which showed significant negative trends in runoff (Kendall -0.209, p = 0.0004) and soil moisture (Kendall -0.155, p = 0.0087). Ordinary least squares optimization was used to estimate the magnitude and rate of change in temporal trends and to estimate the learning rates of the change, using the basic supervised machine learning algorithm, which is linear regression. The SCS-CN technique enabled the estimation of the runoff in terms of land cover and soil properties. The analysis of the inter-annual variability found that 2016 was an extreme drought year that had cumulative storage depletion of -155.27 mm compared with 2020 that had the highest wetness of +107.14 mm storage. Thematic maps analysis showed that the patterns of distribution were uneven throughout the study area. The findings reveal that there are gross weaknesses in the water balance in the region that require strengthening water harvesting infrastructure and sustainable management practices.
References
Ahmad, I., Tang, D., Wang, T., Wang, M., & Wagan, B. (2015). Precipitation trends over time using Mann–Kendall and Spearman's rho tests in Swat River Basin, Pakistan. Advances in Meteorology, 2015, Article 431860. https://doi.org/10.1155/2015/431860
Ali, S., Li, D., Congbin, F., & Khan, F. (2015). Twenty-first century climatic and hydrological changes over Upper Indus Basin of Himalayan region of Pakistan. Environmental Research Letters, 10(1), 014007. https://doi.org/10.1088/1748-9326/10/1/014007
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., & Betts, A. K. (2009). A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System. Journal of Hydrometeorology, 10(3), 623–643. https://doi.org/10.1175/2008JHM1068.1
Bechtold, P., Semane, N., Lopez, P., Chaboureau, J.-P., Beljaars, A., & Borrell, N. (2014). Representing equilibrium and nonequilibrium convection in large-scale models. Journal of the Atmospheric Sciences, 71(2), 734–753. https://doi.org/10.1175/JAS-D-13-0163.1
Biazar, S. M., Golmohammadi, G., Nedunuri, R. R., Shaghaghi, S., & Mohammadi, K. (2025). Artificial intelligence in hydrology: Advancements in soil, water resource management, and sustainable development. Sustainability, 17(5), 2250. https://doi.org/10.3390/su17052250
Biswas, A., Sarkar, S., Das, S., et al. (2025). Water scarcity: A global hindrance to sustainable development and agricultural production—A critical review of the impacts and adaptation strategies. Cambridge Prisms: Water, 3, e4. https://doi.org/10.1017/wat.2024.16
Copernicus Climate Change Service. (2024). ERA5 post-processed daily statistics on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.4991cf48
European Centre for Medium-Range Weather Forecasts. (2024). ERA5: Data documentation. Copernicus Knowledge Base.
Feng, H., & Zhang, M. (2015). Global land moisture trends: Drying in summer but wetting in winter. Water Resources Research, 51(5), 3618–3636. https://doi.org/10.1002/2014WR016023
Forbes, R. M., Tompkins, A. M., & Untch, A. (2011). A new prognostic bulk microphysics scheme for the IFS (ECMWF Technical Memorandum No. 649). European Centre for Medium-Range Weather Forecasts.
Gemitzi, A., & Kofidou, M. (2022). A Google Earth Engine tool to assess water budget and its individual components. Global NEST Journal, 24(2), 331–336.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
Helsel, D. R., & Hirsch, R. M. (2002). Statistical methods in water resources. U.S. Geological Survey.
Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
Hillel, D. (1998). Environmental soil physics. Academic Press.
Hussain, M., Mahmood, A., & Yar, P. (2021). Precipitation variability over Potohar Plateau and its impact on rainfed agriculture. Pakistan Journal of Meteorology, 17(34), 45–58.
Immerzeel, W. W., van Beek, L. P. H., & Bierkens, M. F. P. (2010). Climate change will affect the Asian water towers. Science, 328(5984), 1382–1385. https://doi.org/10.1126/science.1183188
Jarvis, P. G. (1976). The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society B: Biological Sciences, 273(927), 593–610.
Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin.
Khan, A. J., Koch, M., & Chinchilla, K. M. (2018). Evaluation of gridded multi-satellite precipitation estimation (TRMM-3B42-V7) performance in the Upper Indus Basin. Climate, 6(3), 76. https://doi.org/10.3390/cli6030076
Lavers, D. A., Simmons, A., Vamborg, F., & Rodwell, M. J. (2022). An evaluation of ERA5 precipitation for climate monitoring. Quarterly Journal of the Royal Meteorological Society, 148(748), 3152–3165. https://doi.org/10.1002/qj.4351
Liu, W., Fu, Z., van Vliet, M. T. H., et al. (2025). Global overlooked multidimensional water scarcity. Proceedings of the National Academy of Sciences, 122(26), e2413541122. https://doi.org/10.1073/pnas.2413541122
Liyin, H., & Rosa, L. (2023). Solutions to agricultural green water scarcity under climate change. PNAS Nexus, 2(4), pgad117. https://doi.org/10.1093/pnasnexus/pgad117
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). Wiley.
Qureshi, A. S., McCornick, P. G., Sarwar, A., & Sharma, B. R. (2010). Challenges and prospects of sustainable groundwater management in the Indus Basin, Pakistan. Water Resources Management, 24(8), 1551–1569. https://doi.org/10.1007/s11269-009-9494-4
Rabie, A. B., Elhag, M., & Subyani, A. (2025). Remote sensing, GIS, and machine learning in water resources management for arid agricultural regions: A review. Water, 17(21), 3125. https://doi.org/10.3390/w17213125
Rodell, M., Houser, P. R., Jambor, U., et al. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3), 381–394. https://doi.org/10.1175/BAMS-85-3-381
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379–1389.
Sharma, A., Wasko, C., & Lettenmaier, D. P. (2018). If precipitation extremes are increasing, why aren't floods? Water Resources Research, 54(11), 8545–8551. https://doi.org/10.1029/2018WR023188
Sharma, B. R., & Sharma, D. (2008). Impact of climate change on water resources and glacier melt and potential adaptations for Indian agriculture. International Water Management Institute.
Sheffield, J., Wood, E. F., Pan, M., et al. (2018). Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions. Water Resources Research, 54(12), 9724–9758. https://doi.org/10.1029/2017WR022388
Shemer, H., Wald, S., & Semiat, R. (2023). Challenges and solutions for global water scarcity. Membranes, 13(6), 612. https://doi.org/10.3390/membranes13060612
Somasundaram, D., Zhang, F., Ediriweera, S., et al. (2020). Spatial and temporal changes in surface water area of Sri Lanka over a 30-year period. Remote Sensing, 12(22), 3701. https://doi.org/10.3390/rs12223701
Soulis, K. X. (2021). Soil Conservation Service Curve Number method: Current applications, remaining challenges, and future perspectives. Water, 13(2), 192. https://doi.org/10.3390/w13020192
Teuling, A. J., Hirschi, M., Ohmura, A., et al. (2009). A regional perspective on trends in continental evaporation. Geophysical Research Letters, 36(2), L02404. https://doi.org/10.1029/2008GL036251
Tzanakakis, V. A., Paranychianakis, N. V., & Angelakis, A. N. (2020). Water supply and water scarcity. Water, 12(9), 2347. https://doi.org/10.3390/w12092347
Ullah, S., You, Q., Ullah, W., & Ali, A. (2018). Observed changes in precipitation in China–Pakistan Economic Corridor during 1980–2016. Atmospheric Research, 210, 1–14. https://doi.org/10.1016/j.atmosres.2018.03.001
U.S. Department of Agriculture, Natural Resources Conservation Service. (2004). National engineering handbook: Part 630 hydrology, Chapter 10—Estimation of direct runoff from storm rainfall. Author.
Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trends in hydrological series. Hydrological Processes, 16(9), 1807–1827. https://doi.org/10.1002/hyp.1090
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