Bahddou, S. et al. Changes in soil surface properties under simulated rainfall and the effect of surface roughness on runoff, infiltration and soil loss. Geoderma 431 https://doi.org/10.1016/J.GEODERMA.2023.116341 (2023).
Cordeiro, M. R. C. et al. Simulating the hydrological impacts of land use conversion from annual crop to perennial forage in the Canadian prairies using the cold regions hydrological modelling platform. Hydrol. Earth Syst. Sci. 26, 5917–5931 (2022).
Fu, J., Liu, B., Wang, W. & Xu, F. E. Evaluating main drivers of runoff changes across China from 1956 to 2000 by using different budyko-based elasticity methods. J. Environ. Manage. 329, 117070 (2023).
Guo, W. et al. Quantitative evaluation of runoff variation and its driving forces based on multi-scale separation framework. J. Hydrology: Reg. Stud. 43 https://doi.org/10.1016/J.EJRH.2022.101183 (2022).
Adam, D. World population hits eight billion—Here’s how researchers predict it will grow. Nature https://doi.org/10.1038/D41586-022-03720-6 (2022).
Li, Y. et al. Multi-model analysis of historical runoff changes in the Lancang-Mekong River Basin—Characteristics and uncertainties. J. Hydrol. 619 https://doi.org/10.1016/J.JHYDROL.2023.129297 (2023).
Zhou, S., Yu, B., Lintner, B. R., Findell, K. L. & Zhang, Y. Projected increase in global runoff dominated by land surface changes. Nat. Clim. Change. 13, 442–449. https://doi.org/10.1038/s41558-023-01659-8 (2023).
Lucila, C., Karim, T., Gonzalo, O. & Manuel, G. Climate and land use changes on streamflow and subsurface recharge in the Fluvià Basin, Spain. Water 8, 228 (2016).
Singh, D. et al. Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data. Hydrol. Earth Syst. Sci. 27, 1047–1075 (2023).
Christopher, J. K. & Shawn, P. S. Impacts of recent climate change on Wisconsin corn and soybean yield trends. Environ. Res. Lett. 3, 034003 (2008).
Zhang, G. et al. Quantifying the impacts of agricultural management practices on the water use efficiency for sustainable production in the Loess Plateau region: a meta-analysis. Field Crops Res. 291 https://doi.org/10.1016/J.FCR.2022.108787 (2023).
Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change. 5, 143–147 (2015).
Batke, S. P., Yiotis, C., Elliott-Kingston, C., Holohan, A. & McElwain, J. Plant responses to decadal scale increments in atmospheric CO2 concentration: Comparing two stomatal conductance sampling methods. Planta: Int. J. Plant. Biology. 251, 52 (2020).
Leon, H. A., Vijaya, G. K., Joseph, C. V. V. & Kenneth, J. B. Elevated CO2 increases water use efficiency by sustaining photosynthesis of water-limited maize and sorghum. J. Plant Physiol. 168, 1909–1918 (2011).
Bunce, J. A. Effects of pulses of elevated carbon dioxide concentration on stomatal conductance and photosynthesis in wheat and rice. Physiol. Plant. 149, 214–221 (2013).
Shrestha, S., Bhatta, B., Shrestha, M. & Shrestha, P. K. Integrated assessment of the climate and landuse change impact on hydrology and water quality in the Songkhram River Basin, Thailand. Sci. Total Environ. 643, 1610–1622. https://doi.org/10.1016/j.scitotenv.2018.06.306 (2018).
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).
Siebert, S. et al. Groundwater use for irrigation—A global inventory. Hydrol. Earth Syst. Sci. 14, 1863–1880. https://doi.org/10.5194/hess-14-1863-2010 (2010).
Tian, X. et al. Climate change impacts on regional agricultural irrigation water use in semi-arid environments. Agric. Water Manage. 281 https://doi.org/10.1016/j.agwat.2023.108239 (2023).
Song, J., Yang, Z., Xia, J. & Cheng, D. The impact of mining-related human activities on runoff in northern Shaanxi, China. J. Hydrol. 598 https://doi.org/10.1016/J.JHYDROL.2021.126235 (2021).
Yu, Y. et al. Natural revegetation has dominated annual runoff reduction since the grain for green program began in the Jing River Basin, Northwest China. J. Hydrol. 625 https://doi.org/10.1016/j.jhydrol.2023.129978 (2023).
Alonso, J., Silveira, L. & Vervoort, R. W. Assessing effects of afforestation on streamflow in Uruguay: From small to large basins. Hydrol. Process. 38 https://doi.org/10.1002/hyp.15272 (2024).
Zuo, Y., Chen, J., Lin, S. & He, K. The runoff changes are controlled by combined effects of multiple regional environmental factors in the alpine hilly region of Northwest China. Sci. Total Environ. 862, 160835. https://doi.org/10.1016/J.SCITOTENV.2022.160835 (2023).
Fan, M. et al. Temperature contributes more than precipitation to runoff in the high mountains of Northwest China. Remote Sens. 14, 4015 (2022).
Wang, M., Zhang, Y., Lu, Y., Gao, L. & Wang, L. Attribution analysis of streamflow changes based on large-scale hydrological modeling with uncertainties. Water Resour. Manage. 37, 713–730 (2022).
Chen, H. et al. Quantitative assessment of impact of climate change and human activities on streamflow changes using an improved three-parameter monthly water balance model. Remote Sens. 14, 4411 (2022).
Sofi, M. S. et al. Modeling the hydrological response of a snow-fed river in the Kashmir Himalayas through SWAT and Artificial neural network. Int. J. Environ. Sci. Technol. 21, 3115–3128. https://doi.org/10.1007/s13762-023-05170-7 (2024).
Rautela, K. S., Kumar, D., Gandhi, B. G. R., Kumar, A. & Dubey, A. K. Long-term hydrological simulation for the estimation of snowmelt contribution of Alaknanda River Basin, Uttarakhand using SWAT. J. Water Supply: Res. Technology-Aqua. 72, 139–159. https://doi.org/10.2166/aqua.2023.176 (2023).
Rautela, K. S., Kuniyal, J. C., Goyal, M. K., Kanwar, N. & Bhoj, A. S. Assessment and modelling of hydro-sedimentological flows of the eastern river Dhauliganga, north-western Himalaya, India. Nat. Hazards. 120, 5385–5409. https://doi.org/10.1007/s11069-024-06413-7 (2024).
Rautela, K. S., Gupta, V., Devi, J. P., Majeed, L. R. & Kuniyal, J. C. Modeling stage-discharge and sediment-discharge relationships in data-scarce Himalayan River Basin Dhauliganga, Central Himalaya, using neural networks. CLEAN-SOIL AIR WATER. https://doi.org/10.1002/clen.202300388 (2024).
Ni, X. et al. Simple additive simulation overestimates real influence: Altered nitrogen and rainfall modulate the effect of warming on soil carbon fluxes. Glob. Change Biol. 23, 3371–3381 (2017).
Li, J. et al. Hydrological and erosion responses of steep spoil heaps to taproot and fibrous root grasses under simulated rainfalls. J. Hydrol. 618 https://doi.org/10.1016/J.JHYDROL.2023.129169 (2023).
Feng, Z. et al. Responses of soil greenhouse gas emissions to land use conversion and reversion-A global meta-analysis. Glob. Change Biol. 28, 6665–6678 (2022).
Li, D., Zhu, L., Xu, W. & Ye, C. Quantifying the impact of climate change and human activities on runoff at a tropical watershed in South China. Front. Environ. Sci. https://doi.org/10.3389/FENVS.2022.1023188 (2022).
Huang, D. Q., Zhu, J., Zhang, Y. C. & Huang, A. N. Uncertainties on the simulated summer precipitation over Eastern China from the CMIP5 models. J. Geophys. Research: Atmos. 118, 9035–9047. https://doi.org/10.1002/jgrd.50695 (2013).
Chen, L. & Frauenfeld, O. W. A comprehensive evaluation of precipitation simulations over China based on CMIP5 multimodel ensemble projections. J. Geophys. Research: Atmos. 119, 5767–5786. https://doi.org/10.1002/2013jd021190 (2014).
Vandana, K., Islam, A., Sarthi, P. P., Sikka, A. K. & Kapil, H. Assessment of potential impact of climate change on streamflow: A case study of the Brahmani River basin, India. J. WATER Clim. CHANGE. 10, 624–641. https://doi.org/10.2166/wcc.2018.129 (2019).
Abeysingha, N. S., Islam, A. & Singh, M. Assessment of climate change impact on flow regimes over the Gomti River basin under IPCC AR5 climate change scenarios. J. WATER Clim. CHANGE. 11, 303–326. https://doi.org/10.2166/wcc.2018.039 (2020).
Abbas, A. et al. Evaluation and projection of precipitation in Pakistan using the coupled model intercomparison project phase 6 model simulations. Int. J. Climatol. 42, 6665–6684. https://doi.org/10.1002/joc.7602 (2022).
Huang, W. R., Chang, Y. H., Deng, L. & Liu, P. Y. Simulation and projection of summer convective afternoon rainfall activities over Southeast Asia in CMIP6 models. J. Clim. 34, 5001–5016. https://doi.org/10.1175/JCLI-D-20-0788.1 (2021).
Kushwaha, P., Pandey, V. K., Kumar, P. & Sardana, D. CMIP6 model evaluation for mean and extreme precipitation over India. Pure. appl. Geophys. 181, 655–678. https://doi.org/10.1007/s00024-023-03409-5 (2024).
Reddy, N. M. & Saravanan, S. Extreme precipitation indices over India using CMIP6: A special emphasis on the SSP585 scenario. Environ. Sci. Pollut. Res. 30, 47119–47143. https://doi.org/10.1007/s11356-023-25649-7 (2023).
Wyser, K., Kjellstrom, E., Koenigk, T., Martins, H. & Doscher, R. Warmer climate projections in EC-Earth3-Veg: The role of changes in the greenhouse gas concentrations from CMIP5 to CMIP6. Environ. Res. Lett. 15 https://doi.org/10.1088/1748-9326/ab81c2 (2020).
Mostafa, E., Li, X. & Sadek, M. Urbanization trends Analysis using hybrid modeling of fuzzy analytical hierarchical process-cellular Automata-Markov Chain and investigating its impact on land surface temperature over Gharbia City, Egypt. Remote Sens. 15, 843 (2023).
Hou, G., Zhang, H., Liu, Z., Chen, Z. & Cao, Y. Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model. Front. Ecol. Evol. https://doi.org/10.3389/FEVO.2022.1031678 (2022).
Hao, L., He, S., Zhou, J., Zhao, Q. & Lu, X. Prediction of the landscape pattern of the Yancheng Coastal Wetland, China, based on XGBoost and the MCE-CA-Markov model. Ecol. Ind. 145 https://doi.org/10.1016/J.ECOLIND.2022.109735 (2022).
Zhang, Z. et al. Research on the optimal allocation of agricultural water and soil resources in the Heihe River Basin based on SWAT and intelligent optimization. Agric. Water Manage. 279 https://doi.org/10.1016/J.AGWAT.2023.108177 (2023).
Wang, Z. et al. A generalized reservoir module for SWAT applications in watersheds regulated by reservoirs. J. Hydrol. 616 https://doi.org/10.1016/J.JHYDROL.2022.128770 (2023).
Dash, S. S., Sahoo, B. & Raghuwanshi, N. S. SWAT model calibration approaches in an integrated paddy-dominated catchment-command. Agric. Water Manage. 278 https://doi.org/10.1016/J.AGWAT.2023.108138 (2023).
Sun, Q., Miao, C. & Duan, Q. Extreme climate events and agricultural climate indices in China: CMIP5 model evaluation and projections. Int. J. Climatol. 36, 43–61. https://doi.org/10.1002/joc.4328 (2016).
Moriasi, D. N. et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE. 50, 885–900 (2007).
Kapil, H., Sikka, A. K., Sarthi, P. P., Islam, A. & Vandana, K. Assessment of potential impact of climate change on streamflow: A case study of the Brahmani River basin, India. J. Water Clim. Change. 10, 624–641. https://doi.org/10.2166/wcc.2018.129 (2019).
Desai, S., Singh, D. K., Islam, A. & Sarangi, A. Multi-site calibration of hydrological model and assessment of water balance in a semi-arid river basin of India. Quatern. Int. 571, 136–149. https://doi.org/10.1016/j.quaint.2020.11.032 (2021).
Bedewi, S. A. & Arigaw, A. K. Multi-site calibration of hydrological model and the response of water balance components to land use land cover change in a rift valley Lake Basin in Ethiopia. Sci. Afr. 15 (2022).
Abbaspour, K. C. et al. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. J. Hydrol. 524, 733–752 (2015).
Zhou et al. Projection of China’s future runoff based on the CMIP6 mid-high warming scenarios. Scientia Sinica(Terrae). 53, 505–524 (2023).
Zhang, Y. et al. Future global streamflow declines are probably more severe than previously estimated. Nat. Water. 1, 261–271. https://doi.org/10.1038/s44221-023-00030-7 (2023).
Do, H. X. et al. Historical and future changes in global flood magnitude—evidence from a model–observation investigation. Hydrol. Earth Syst. Sci. 24, 1543–1564 (2020).
Cook, B. I. et al. Twenty-First Century Drought projections in the CMIP6 forcing scenarios. Earth’s Future. 8 https://doi.org/10.1029/2019ef001461 (2020).
Betts, R. A. et al. Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature 448, 1037–1041 (2007).
Mehdi, B., Ludwig, R. & Lehner, B. Evaluating the impacts of climate change and crop land use change on streamflow, nitrates and phosphorus: A modeling study in Bavaria. J. Hydrology: Reg. Stud. 4, 60–90 (2015).
Li, X., Tian, Y., Sun, J., Wei, Y. & Li, F. Evolutionary effect separation of watershed characteristics for the multi-source contributions to runoff changes in the Yellow River, China. Ecol. Ind. 143 https://doi.org/10.1016/J.ECOLIND.2022.109398 (2022).
Mavimbela, S. S. W., Dlamini, P. & Rensburg, L. D. Infiltration-excess runoff properties of dryland floodplain soil types under simulated rainfall conditions. Arid Land. Res. Manage. 33, 235–254 (2019).
Huang, P., Li, Z., Yao, C., Li, Q. & Yan, M. Spatial combination modeling framework of saturation-excess and infiltration-excess runoff for semihumid watersheds. Advances in Meteorology 1–15 (2016).
Savin, C. et al. Climate processes and drivers in the Pacific and global warming: A review for informing Pacific planning agencies. Clim. Change. 176 https://doi.org/10.1007/S10584-022-03467-Z (2023).
Nico, W. et al. Global warming overshoots increase risks of climate tipping cascades in a network model. Nat. Clim. Change. 13, 75–82 (2022).
Feng, Y., Romps, I. N. R., Chambers, J. Q. & D. M. & Amazon windthrow disturbances are likely to increase with storm frequency under global warming. Nat. Commun. 14, 101 (2023).
Alamdari, N., Claggett, P., Sample, D. J., Easton, Z. M. & Nayeb, Y. M. Evaluating the joint effects of climate and land use change on runoff and pollutant loading in a rapidly developing watershed. J. Clean. Prod. 330 https://doi.org/10.1016/J.JCLEPRO.2021.129953 (2022).
Huang, X., Liu, J., Peng, S. & Huang, B. The impact of multi-scenario land use change on the water conservation in central Yunnan urban agglomeration, China. Ecol. Ind. 147 https://doi.org/10.1016/J.ECOLIND.2023.109922 (2023).
Sun, Z. et al. A healthier water use strategy in primitive forests contributes to stronger water conservation capabilities compared with secondary forests. Sci. Total Environ. 851, 158290. https://doi.org/10.1016/J.SCITOTENV.2022.158290 (2022).
Deuschle, D., Minella, J. P. G., Hörbe, T. A. N., Londero, A. L. & Schneider, F. J. A. Erosion and hydrological response in no-tillage subjected to crop rotation intensification in southern Brazil. Geoderma 340, 157–163 (2019).
Quijano, L., Beguería, S., Gaspar, L. & Navas, A. Estimating erosion rates using 137 cs measurements and WATEM/SEDEM in a Mediterranean cultivated field. Catena 138, 38–51 (2016).
He, Y., Yang, H., Liu, Z. & Yang, W. A framework for attributing runoff changes based on a monthly water balance model: An assessment across China. J. Hydrol. 615 https://doi.org/10.1016/J.JHYDROL.2022.128606 (2022).
Das, P. et al. Historical and projected changes in Extreme High temperature events over East Africa and associated with meteorological conditions using CMIP6 models. Glob. Planet Change. 222 https://doi.org/10.1016/J.GLOPLACHA.2023.104068 (2023).
Hamed, M. M. et al. Future Köppen-Geiger climate zones over Southeast Asia using CMIP6 Multimodel Ensemble. Atmos. Res. 283 https://doi.org/10.1016/J.ATMOSRES.2022.106560 (2023).
He, J., Brogniez, H. & Picon, L. Evaluation of tropical water vapour from CMIP6 global climate models using the ESA CCI Water Vapour climate data records. Atmos. Chem. Phys. 22, 12591–12606 (2022).
Gao, X. et al. Changes in global vegetation distribution and Carbon fluxes in response to global warming: Simulated results from IAP-DGVM in CAS-ESM2. Adv. Atmos. Sci. 39, 1285–1307 (2022).
Yang, Y. & Tang, J. Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China. Clim. Change. 176 https://doi.org/10.1007/S10584-023-03481-9 (2023).
I., H. S. P. P. C. & Climate CO2 controls on global vegetation distribution at the last glacial maximum: Analysis based on palaeovegetation data, biome modelling and palaeoclimate simulations. Glob. Change Biol. 9, 983–1004 (2003).
Li, H., Renssen, H. & Roche, D. M. Global vegetation distribution driving factors in two dynamic global vegetation models of contrasting complexities. Glob. Planet Change. 180, 51–65 (2019).
Flora, G. Daily briefing: Melting Himalayan glaciers will affect more than one billion people. Nature https://doi.org/10.1038/D41586-022-03230-5 (2022).
Young, J. C. et al. A changing hydrological regime: Trends in magnitude and timing of glacier ice melt and glacier runoff in a high latitude coastal watershed. Water Resour. Res. 57 https://doi.org/10.1029/2020WR027404 (2021).
Xing, W., Wang, W., Zou, S. & Deng, C. Projection of future runoff change using climate elasticity method derived from Budyko framework in major basins across China. Glob. Planet Change. 162, 120–135 (2018).
Perraud, J. M., Wang, B., Chiew, F. H. S., Vaze, J. & Teng, J. Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J. Hydrometeorol. 13, 122–139. https://doi.org/10.1175/jhm-d-11-058.1 (2012).
Islam, A., Ahuja, L. R., Garcia, L. A., Ma, L. & Saseendran, A. S. Modeling the effect of elevated CO2 and climate change on reference evapotranspiration in the semi-arid central great plains. Trans. ASABE. 55, 2135–2146 (2012).
Chordia, J., Panikkar, U. R., Srivastav, R. & Shaik, R. U. Uncertainties in prediction of streamflows using SWAT model—role of remote sensing and precipitation sources. Remote Sens. 14, 5385 (2022).
Karlsson, I. B. et al. Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change. J. Hydrol. 535, 301–317. https://doi.org/10.1016/j.jhydrol.2016.01.069 (2016).