Hydrological modelling for post-monsoon agricultural drought assessment and implications for the agro-economy

References

• Aadhar, S. and Mishra, V., 2021. On the occurrence of the worst drought in South Asia in the observed and future climate. Environmental Research Letters, 16 (2), 024050. doi:10.1088/1748-9326/abd6a6.
   Web of Science ®Google Scholar
• Agrawal, S. and Chakraborty, A., 2020. Evaluation of ESACCI satellite soil moisture product using in-situ CTCZ observations over India. Journal of Earth System Science, 129 (1), 129. doi:10.1007/s12040-020-01384-2.
   Web of Science ®Google Scholar
• Asoka, A., et al. 2017. Relative contribution of monsoon precipitation and pumping to changes in groundwater storage in India. Nature Geoscience, 10 (2), 109–117. doi:10.1038/ngeo2869.
   Web of Science ®Google Scholar
• Bates, B., Kundzewicz, Z., and Wu, S., 2008. Climate change and water.
   Google Scholar
• Belgiu, M. and Drăguţ, L., 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
   Web of Science ®Google Scholar
• Bolten, J.D., et al. 2010. Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3 (1), 57–66. doi:10.1109/JSTARS.2009.2037163.
   Web of Science ®Google Scholar
• Cornish, P.S., et al. 2015. Improving crop production for food security and improved livelihoods on the East India Plateau. I. Rainfall-related risks with rice and opportunities for improved cropping systems. Agricultural Systems, 137, 166–179. doi:10.1016/j.agsy.2015.01.008.
   Web of Science ®Google Scholar
• Cosby, B.J., et al. 1984. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research, 20 (6), 682–690. doi:10.1029/WR020i006p00682.
   Web of Science ®Google Scholar
• Danodia, A., Kushwaha, A., and Patel, N.R., 2021. Remote sensing-derived combined index for agricultural drought assessment of rabi pulse crops in Bundelkhand region, India. Environment, Development and Sustainability, 23 (10), 15432–15449. doi:10.1007/s10668-021-01305-3.
   Web of Science ®Google Scholar
• Das, H.P., 2005. Agricultural drought mitigation and management of sustained agricultural development in India. In: V.K. Mannava, P. Raymond, P. Motha, and H.P. Das, eds. Natural disasters and extreme events in agriculture. Berlin/Heidelberg: Springer-Verlag, 277–303. doi:10.1007/3-540-28307-2_16.
   Google Scholar
• Das, P., et al. 2018. Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985–2005 using variable infiltration capacity approach. Journal of Earth System Science, 127 (2), 19. doi:10.1007/s12040-018-0921-8.
   Web of Science ®Google Scholar
• Das, P. and Pandey, V., 2019. Use of logistic regression in land-cover classification with moderate-resolution multispectral data. Journal of the Indian Society of Remote Sensing, 47 (8), 1443–1454. doi:10.1007/s12524-019-00986-8.
   Web of Science ®Google Scholar
• Datta, S., et al. 2021. Estimation of surface moisture content using sentinel-1 C-band SAR data through machine learning models. Journal of the Indian Society of Remote Sensing, 49 (4), 887–896. doi:10.1007/s12524-020-01261-x.
   Web of Science ®Google Scholar
• Davis, K.F., et al. 2018. Alternative cereals can improve water use and nutrient supply in India. Science Advances, 4 (7). doi:10.1126/sciadv.aao1108.
   Web of Science ®Google Scholar
• DeFries, R., et al. 2023. Climate resilience of dry season cereals in India. Scientific Reports, 13 (1), 9960. doi:10.1038/s41598-023-37109-w.
   PubMed Web of Science ®Google Scholar
• Dorigo, W., et al. 2017. ESA CCI soil moisture for improved earth system understanding: state-of-the art and future directions. Remote Sensing of Environment, 203, 185–215. doi:10.1016/j.rse.2017.07.001.
   Web of Science ®Google Scholar
• Du, E., Di Vittorio, A., and Collins, W.D., 2016. Evaluation of hydrologic components of community land model 4 and bias identification. The International Journal of Applied Earth Observation and Geoinformation, 48, 5–16. doi:10.1016/j.jag.2015.03.013.
   Web of Science ®Google Scholar
• Dwivedi, S., et al. (2023) India’s climate research agenda : 2030 and beyond events. Available from https://dst.gov.in/sites/default/files/India%27sClimateResearchAgenda2030andbeyond.pdf [Accessed 25 June 2022].
   Google Scholar
• Economic Survey, 2021. Economic survey 2020-21. Department of Economic Affairs. Ministry of Finance, Government of India. Available from: https://www.indiabudget.gov.in/economicsurvey/ [Accessed 20 February 2023].
   Google Scholar
• Gao, H., et al. 2010. Water budget record from variable infiltration capacity (VIC) model. Algorithm Theoretical Basis Document for Terrestrial Water Cycle Data Records (Vic), 120–173. Available from: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Water+Budget+Record+from+Variable+Infiltration+Capacity+(+VIC+)+Model#2 [Accessed 23 June 2022].
   Google Scholar
• Gruber, A., et al. 2019. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology. Earth System Science Data, 11 (2), 717–739. doi:10.5194/essd-11-717-2019.
   Web of Science ®Google Scholar
• Gupta, A.K., et al. 2014. Bundelkhand drought: retrospective analysis and way ahead. National Institute of Disaster Management, 148.
   Google Scholar
• Gupta, H. and Chakrapani, G.J., 2005. Temporal and spatial variations in water flow and sediment load in Narmada River Basin, India: natural and man-made factors. Environmental Geology, 48 (4–5), 579–589. doi:10.1007/s00254-005-1314-2.
   Web of Science ®Google Scholar
• Haddeland, I., Lettenmaier, D.P., and Skaugen, T., 2006. Effects of irrigation on the water and energy balances of the Colorado and Mekong river basins. Journal of Hydrology, 324 (1–4), 210–223. doi:10.1016/j.jhydrol.2005.09.028.
   Web of Science ®Google Scholar
• Haddeland, I., Skaugen, T., and Lettenmaier, D.P., 2007. Hydrologic effects of land and water management in North America and Asia: 1700– 1992. Hydrology and Earth System Sciences, 11, 1035–1045. doi:10.5194/hess-11-1035-2007
   Web of Science ®Google Scholar
• Hengade, N., Eldho, T.I., and Ghosh, S., 2018. Climate change impact assessment of a river basin using CMIP5 climate models and the VIC hydrological model. Hydrological Sciences Journal, 63 (4), 596–614. doi:10.1080/02626667.2018.1441531.
   Web of Science ®Google Scholar
• Iizumi, T. and Wagai, R., 2019. Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration. Scientific Reports, 9 (1), 1–8. doi:10.1038/s41598-019-55835-y.
   PubMedGoogle Scholar
• Ikonen, J., et al. 2016. The Sodankylä in situ soil moisture observation network: an example application of ESA CCI soil moisture product evaluation. Geoscientific Instrumentation, Methods and Data Systems, 5 (1), 95–108. doi:10.5194/gi-5-95-2016.
   Web of Science ®Google Scholar
• Joseph, J., et al. 2018. Hydrologic impacts of climate change: comparisons between hydrological parameter uncertainty and climate model uncertainty. Journal of Hydrology, 566, 1–22. doi:10.1016/j.jhydrol.2018.08.080.
   Web of Science ®Google Scholar
• Joseph, J. and Ghosh, S., 2023. Representing Indian agricultural practices and paddy cultivation in the variable infiltration capacity model. Water Resources Research, 59 (1). doi:10.1029/2022WR033612.
   Web of Science ®Google Scholar
• Joshi, P., Birthal, P., and Bourai, V., 2002. Socioeconomic constraints and opportunities in rainfed rabi cropping in rice fallow areas of India. The International Crops Research Institute for the Semi-Arid Tropics, (June). Available from: http://r4d.dfid.gov.uk/PDF/outputs/RLPSRImpactAss16.pdf [Accessed 24 June 2022].
   Google Scholar
• Kalma, J.D. and Boulet, G., 1998. Measurement and prediction of soil moisture in a medium-sized catchment. Hydrological Sciences Journal, 43 (4), 597–610. doi:10.1080/02626669809492156.
   Web of Science ®Google Scholar
• Kulkarni, S.S., et al. 2020. Developing a remote sensing-based combined drought indicator approach for agricultural drought monitoring over Marathwada, India. Remote Sensing, 12 (13), 2091. doi:10.3390/rs12132091.
   Web of Science ®Google Scholar
• Liang, X., et al. 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research: Atmospheres, 99 (D7), 14415–14428. doi:10.1029/94JD00483.
   Web of Science ®Google Scholar
• Liaw, A. and Wiener, M., 2002. Classification and regression by randomForest. R News, 2 (3), 18–22. Available from: https://cran.r-project.org/doc/Rnews/ [Accessed 24 June 2022].
   Google Scholar
• Likith, M., Harod, R., and Eswar, R., 2022. Exploring the use of satellite observations of soil moisture, solar-induced chlorophyll fluorescence and vegetation optical depth to monitor droughts across India. Journal of Earth System Science, 131 (2), 94. doi:10.1007/s12040-022-01848-7.
   Web of Science ®Google Scholar
• Lohmann, D., et al. 1998. Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrological Sciences Journal, 43 (1), 131–141. doi:10.1080/02626669809492107.
   Web of Science ®Google Scholar
• Lohmann, D.A.G., Nolte-Holube, R., and Raschke, E., 1996. A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus A: Dynamic Meteorology and Oceanography, 48 (5), 708–721. doi:10.1034/j.1600-0870.1996.t01-3-00009.x.
   Web of Science ®Google Scholar
• Mahto, S.S. and Mishra, V., 2020. Dominance of summer monsoon flash droughts in India. Environmental Research Letters, 15 (10), 104061. doi:10.1088/1748-9326/abaf1d.
   Web of Science ®Google Scholar
• Mao, Y., et al. 2017. Spatio-temporal analysis of drought in a typical plain region based on the soil moisture anomaly percentage index. Science of the Total Environment, 576, 752–765. doi:10.1016/j.scitotenv.2016.10.116.
   PubMed Web of Science ®Google Scholar
• Martínez-Fernández, J., et al. 2015. A soil water based index as a suitable agricultural drought indicator. Journal of Hydrology, 522, 265–273. doi:10.1016/j.jhydrol.2014.12.051.
   Web of Science ®Google Scholar
• Martínez-Fernández, J., et al. 2016. Satellite soil moisture for agricultural drought monitoring: assessment of the SMOS derived soil water deficit index. Remote Sensing of Environment, 177, 277–286. doi:10.1016/j.rse.2016.02.064.
   Web of Science ®Google Scholar
• Meng, S., et al. 2020. The relative contribution of vegetation greening to the hydrological cycle in the Three-North region of China: a modelling analysis. Journal of Hydrology, 591, 125689. doi:10.1016/j.jhydrol.2020.125689.
   Web of Science ®Google Scholar
• Mishra, V., et al. 2018. Reconstruction of droughts in India using multiple land-surface models (1951–2015). Hydrology and Earth System Sciences, 22 (4), 2269–2284. doi:10.5194/hess-22-2269-2018.
   Web of Science ®Google Scholar
• Mishra, V., Shah, R., and Thrasher, B., 2014. Soil moisture droughts under the retrospective and projected climate in India*. Journal of Hydrometeorology, 15 (6), 2267–2292. doi:10.1175/JHM-D-13-0177.1.
   Web of Science ®Google Scholar
• Mladenova, I.E. et al., 2020. Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Frontiers in Big Data, 3. doi:10.3389/fdata.2020.00010.
   PubMedGoogle Scholar
• Mostovoy, G.V. and Anantharaj, V.G., 2008. Observed and simulated soil moisture variability over the Lower Mississippi Delta region. Journal of Hydrometeorology, 9 (6), 1125–1150. doi:10.1175/2008JHM999.1.
   Web of Science ®Google Scholar
• Nair, A.S. and Indu, J., 2019. Improvement of land surface model simulations over India via data assimilation of satellite-based soil moisture products. Journal of Hydrology, 573, 406–421. doi:10.1016/j.jhydrol.2019.03.088.
   Web of Science ®Google Scholar
• Narasimhan, B. and Srinivasan, R., 2005. Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agricultural and Forest Meteorology, 133 (1–4), 69–88. doi:10.1016/j.agrformet.2005.07.012.
   Web of Science ®Google Scholar
• Padhee, S.K., et al. 2017. Using satellite-based soil moisture to detect and monitor spatiotemporal traces of agricultural drought over Bundelkhand region of India. GIScience & Remote Sensing, 54 (2), 144–166. doi:10.1080/15481603.2017.1286725.
   Web of Science ®Google Scholar
• Palanisamy, B., et al. 2023. Development and propagation of hydrologic drought from meteorological and agricultural drought in the Mekong River Basin. Hydrological Processes, 37 (7). doi:10.1002/hyp.14935.
   PubMed Web of Science ®Google Scholar
• Palmer, W.C., 1968. Keeping track of crop moisture conditions, nationwide: the new crop moisture index. Weatherwise, 21 (4), 156–161. doi:10.1080/00431672.1968.9932814.
   Google Scholar
• Pandey, V., et al. 2020. Multi-satellite precipitation products for meteorological drought assessment and forecasting in Central India. Geocarto International, 1–20. doi:10.1080/10106049.2020.1801862.
   Web of Science ®Google Scholar
• Pandey, V., et al. 2021a. Irrigation water demand estimation in Bundelkhand region using the variable infiltration capacity model. In: P.K. Srivastava, M. Gupta, G. Tsakiris, and N.W. Quinn eds. Agricultural water management. Elsevier, 331–347. doi:10.1016/B978-0-12-812362-1.00016-3.
   Google Scholar
• Pandey, V., et al. 2021b. Drought identification and trend analysis using long-term CHIRPS satellite precipitation product in Bundelkhand, India. Sustainability, 13 (3), 1042. doi:10.3390/su13031042.
   Web of Science ®Google Scholar
• Pandey, V. and Srivastava, P., 2018. Integration of satellite, global reanalysis data and macroscale hydrological model for drought assessment in sub-tropical region of India. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 1347–1351.
   Google Scholar
• Panu, U.S. and Sharma, T.C., 2002. Challenges in drought research: some perspectives and future directions. Hydrological Sciences Journal, 47 (sup1), S19–S30. doi:10.1080/02626660209493019.
   Web of Science ®Google Scholar
• Patel, N.R. and Yadav, K. 2015. Monitoring spatio-temporal pattern of drought stress using integrated drought index over Bundelkhand region, India. National Hazards, 77 (2), 663–677.
   Web of Science ®Google Scholar
• Rajasekaran, E., et al. 2018. SMAP soil moisture change as an indicator of drought conditions. Remote Sensing, 10 (5), 788. doi:10.3390/rs10050788.
   Web of Science ®Google Scholar
• Reichle, R.H., et al. 2017. Assessment of the SMAP level-4 surface and root-zone soil moisture product using in situ measurements. Journal of Hydrometeorology, 18 (10), 2621–2645. doi:10.1175/JHM-D-17-0063.1.
   Web of Science ®Google Scholar
• Reichle, R.H., et al. 2019. Version 4 of the SMAP level‐4 soil moisture algorithm and data product. Journal of Advances in Modeling Earth Systems, 11 (10), 3106–3130. doi:10.1029/2019MS001729.
   Web of Science ®Google Scholar
• Rondinelli, W.J., et al. 2015. Different rates of soil drying after rainfall are observed by the SMOS satellite and the South Fork in situ soil moisture network. Journal of Hydrometeorology, 16 (2), 889–903. doi:10.1175/JHM-D-14-0137.1.
   Web of Science ®Google Scholar
• Roy, P.S., et al. 2015. Development of decadal (1985-1995-2005) land use and land cover database for India. Remote Sensing, 7 (3), 2401–2430. doi:10.3390/rs70302401.
   Web of Science ®Google Scholar
• Shah, D. and Mishra, V., 2020a. Integrated drought index (IDI) for Drought monitoring and assessment in India. Water Resources Research, 56 (2), 0–2. doi:10.1029/2019WR026284.
   Web of Science ®Google Scholar
• Shah, D. and Mishra, V., 2020b. Drought onset and termination in India. Journal of Geophysical Research: Atmospheres, 125 (15). doi:10.1029/2020JD032871.
   Web of Science ®Google Scholar
• Sheffield, J., 2004. A simulated soil moisture based drought analysis for the United States. Journal of Geophysical Research: Atmospheres, 109 (D24), D24108. doi:10.1029/2004JD005182.
   Web of Science ®Google Scholar
• Sheffield, J. and Wood, E.F., 2007. Characteristics of global and regional drought, 1950–2000: analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. Journal of Geophysical Research: Atmospheres, 112 (D17), D17115. doi:10.1029/2006JD008288.
   Web of Science ®Google Scholar
• Sheffield, J. and Wood, E.F., 2008. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Climate Dynamics, 31 (1), 79–105. doi:10.1007/s00382-007-0340-z.
   Web of Science ®Google Scholar
• Shellito, P.J., et al. 2016. SMAP soil moisture drying more rapid than observed in situ following rainfall events. Geophysical Research Letters, 43 (15), 8068–8075. doi:10.1002/2016GL069946.
   Web of Science ®Google Scholar
• Shrestha, A., Nair, A.S., and Indu, J., 2020. Role of precipitation forcing on the uncertainty of land surface model simulated soil moisture estimates. Journal of Hydrology, 580, 124264. doi:10.1016/j.jhydrol.2019.124264.
   Web of Science ®Google Scholar
• Singh, R.P., Roy, S., and Kogan, F., 2003. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing, 24 (22), 4393–4402. doi:10.1080/0143116031000084323.
   Web of Science ®Google Scholar
• Soulis, K.X., et al. 2020. A new model-based approach for the evaluation of the net contribution of the European Union rural development program to the reduction of water abstractions in agriculture. Sustainability, 12 (17), 7137. doi:10.3390/su12177137.
   Web of Science ®Google Scholar
• Spennemann, P.C., et al. 2015. A comparison of GLDAS soil moisture anomalies against standardized precipitation index and multisatellite estimations over South America. Journal of Hydrometeorology, 16 (1), 158–171. doi:10.1175/JHM-D-13-0190.1.
   Web of Science ®Google Scholar
• Sridhar, V., et al. 2008. Development of the soil moisture index to quantify agricultural drought and its “user friendliness” in severity-area-duration assessment. Journal of Hydrometeorology, 9 (4), 660–676. doi:10.1175/2007JHM892.1.
   Web of Science ®Google Scholar
• Srivastava, P.K., et al. 2013. Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology, 498, 292–304. doi:10.1016/j.jhydrol.2013.06.021
   Web of Science ®Google Scholar
• Srivastava, P.K., et al. 2014. Assessment of SMOS soil moisture retrieval parameters using tau–omega algorithms for soil moisture deficit estimation. Journal of Hydrology, 519, 574–587. doi:10.1016/j.jhydrol.2014.07.056
   Web of Science ®Google Scholar
• Srivastava, P.K., et al. 2016. Available data sets and satellites for terrestrial soil moisture estimation. In: P.K. Srivastava, G.P. Petropoulos, and Y.H. Kerr, eds. Satellite soil moisture retrieval. Elsevier, 29–44.
   Google Scholar
• Suman, S., et al. 2020. Appraisal of SMAP operational soil moisture product from a global perspective. Remote Sensing, 12 (12), 1977. doi:10.3390/rs12121977.
   Web of Science ®Google Scholar
• Szczypta, C., et al. 2014. Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts. Geoscientific Model Development, 7 (3), 931–946. doi:10.5194/gmd-7-931-2014.
   Web of Science ®Google Scholar
• Tang, C. and Piechota, T.C., 2009. Spatial and temporal soil moisture and drought variability in the Upper Colorado River Basin. Journal of Hydrology, 379 (1–2), 122–135. doi:10.1016/j.jhydrol.2009.09.052.
   Web of Science ®Google Scholar
• Telwala, Y., 2023. Unlocking the potential of agroforestry as a nature-based solution for localizing sustainable development goals: a case study from a drought-prone region in rural India. Nature-Based Solutions, 3, 100045. doi:10.1016/j.nbsj.2022.100045.
   Google Scholar
• Wang, X., et al. 2007. Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of Hydrology, 340 (1–2), 12–24. doi:10.1016/j.jhydrol.2007.03.022.
   Web of Science ®Google Scholar
• Wang, A., Lettenmaier, D.P., and Sheffield, J., 2011. Soil moisture drought in China, 1950–2006. Journal of Climate, 24 (13), 3257–3271. doi:10.1175/2011JCLI3733.1.
   Web of Science ®Google Scholar
• Watson, A., et al. 2022. Using soil-moisture drought indices to evaluate key indicators of agricultural drought in semi-arid Mediterranean Southern Africa. Science of the Total Environment, 812, 152464. doi:10.1016/j.scitotenv.2021.152464.
   PubMed Web of Science ®Google Scholar
• Wens, M., et al. 2020. Simulating small-scale agricultural adaptation decisions in response to drought risk: an empirical agent-based model for semi-arid Kenya. Frontiers in Water, 2. doi:10.3389/frwa.2020.00015.
   Google Scholar
• Wens, M.L.K., et al. 2021. Complexities of drought adaptive behaviour: linking theory to data on smallholder farmer adaptation decisions. International Journal of Disaster Risk Reduction, 63, 102435. doi:10.1016/j.ijdrr.2021.102435.
   Web of Science ®Google Scholar
• Wong, G., De Lanen, H.A.J., and Torfs, P.J.J.F., 2013. Probabilistic analysis of hydrological drought characteristics using meteorological drought. Hydrological Sciences Journal, 58 (2), 253–270. doi:10.1080/02626667.2012.753147.
   Web of Science ®Google Scholar
• Wu, Z., et al. 2007. Thirty‐five year (1971–2005) simulation of daily soil moisture using the variable infiltration capacity model over China. Atmosphere-Ocean, 45 (1), 37–45. doi:10.3137/ao.v450103.
   Web of Science ®Google Scholar
• Yang, H., et al. 2017. A modified soil water deficit index (MSWDI) for agricultural drought monitoring: case study of Songnen Plain, China. Agricultural Water Management, 194, 125–138. doi:10.1016/j.agwat.2017.07.022.
   Web of Science ®Google Scholar
• Yao, N., et al. 2022. Response of wheat and maize growth-yields to meteorological and agricultural droughts based on standardized precipitation evapotranspiration indexes and soil moisture deficit indexes. Agricultural Water Management, 266, 107566. doi:10.1016/j.agwat.2022.107566.
   Web of Science ®Google Scholar
• Yuan, F., et al. 2004. An application of the VIC-3L land surface model and remote sensing data in simulating streamflow for the Hanjiang River basin. Canadian Journal of Remote Sensing, 30 (5), 680–690. doi:10.5589/m04-032.
   Web of Science ®Google Scholar
• Zhang, X., et al. 2017. Droughts in India from 1981 to 2013 and implications to wheat production. Scientific Reports, 7 (1), 44552. doi:10.1038/srep44552.
   PubMedGoogle Scholar
• Zhou, J., et al. 2019. Regional assimilation of in situ observed soil moisture into the VIC model considering spatial variability. Hydrological Sciences Journal, 64 (16), 1982–1996. doi:10.1080/02626667.2019.1662024.
   Web of Science ®Google Scholar