References

1. 1. Бугаец А.Н., Пшеничникова Н.Ф., Терешкина А.А., Краснопеев С.М., Гарцман Б.И., Голодная О.М., Ознобихин В.И. Цифровая почвенная карта бассейна р. Уссури // Почвоведение. 2017. № 8. С. 936–945.
2. 2. Комплексные стационарные исследования лесов Приморья. Л.: Наука, 1967. 187 с.
3. 3. Мировая реферативная база почвенных ресурсов 2014. Международная система почвенной классификации для диагностики почв и создания легенд почвенных карт. Исправленная и дополненная версия 2015. М.: ФАО, МГУ, 2017. 216 с.
4. 4. Мотовилов Ю.Г. Моделирование полей характеристик речного стока// Избранные тр. ИВП РАН. 1967–2017. М.: КУРС, 2017. Т. 2. С. 47–70.
5. 5. Мотовилов Ю.Г., Бугаец А.Н., Гончуков Л.В. ECOMAG-AMUR – Гидроэкологическая модель для оперативной противопаводковой информационно-моделирующей системы в бассейне реки Амур // Свид. регистрации программы для ЭВМ 2022664831. 05.08.2022. Заявка № 2022663910 от 26.07.2022.
6. 6. Мотовилов Ю.Г., Гельфан А.Н. Модели формирования стока в задачах гидрологии речных бассейнов. М.: РАН, 2018, 300 с.
7. 7. Роде А.А. Основы учения о почвенной влаге. Т. II. Л.: Гидрометеоиздат, 1969. 286 с.
8. 8. Степанов И.Н. Пространство и время в науке о почвах: Недокучаев. Почвоведение. М.: Наука, 2003. ISBN 5-02-002812-6
9. 9. Теория и методы физики почв: Коллективная монография / Под ред. Е.В. Шеина, Л.О. Карпачевского. М.: Гриф и К, 2007. 616 с.
10. 10. Beven K. Rainfall-runoff modelling. The Primer. Chichester: Ltd. John Wiley & Sons, 2001. 356 p. doi: 10.1002/9781119951001
11. 11. Beven K.J., Kirkby M.J., Free, J.E., Lamb R. A history of TOPMODEL // Hydrol. Earth Syst. Sci. 2021. V. 25. P. 527–549. https://doi.org/10.5194/hess-25-527-2021, 2021
12. 12. Bloschl G., Grayson R. Spatial observations and interpolation // Spatial patterns in catchment hydrology: observations and modelling. Cambridge / Eds R. Grayson, G. Bloschl. Cambridge, Univ. Press, 2000. P. 17–50.
13. 13. Bloschl G., Sivapalan M. Scale issues in hydrological modelling: a review // Hydrol. Processes. 1995. V. 9. P. 251–290.
14. 14. Boehner J., Selige T. Spatial prediction of soil attributes using terrain analysis and climate regionalisation / Eds J. Boehner, K.R. McCloy, J. Strobl // SAGA – Analysis and Modelling. Goettingen: Goettinger Geographische Abhandlungen, 2006. P. 13–28.
15. 15. Bugaets A., Gartsman B., Gelfan A., Motovilov Y., Gonchukov L., Kalugin A., Moreido V., Suchilina Z., Fingert E., Sokolov O. The integrated system of hydrological forecasting in the Ussuri river basin based on the ECOMAG model // Geosci. (Switzerland). 2018. Т. 8. № 1. С. 5.
16. 16. Coleman M.L., Niemann J.D. Controls on topographic dependence and temporal instability in catchment-scale soil moisture patterns // Water Resour. Res. 2013. V. 49 (3). P. 1625–1642. http://dx.doi.org/10.1002/wrcr.20159
17. 17. Fang B., Lakshmi V. Soil moisture at watershed scale: remote sensing techniques // J. Hydrol. 2014. V. 516. P. 258–272. http://dx.doi.org/10.1016/j.jhydrol.2013.12.008
18. 18. Flores A.N., Entekhabi D., Bras R.L. Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment // J. Terramech. 2014. V. 51. P. 53–66. http://dx.doi.org/10.1016/j.jterra.2013.11.004
19. 19. Gerrard A.J. Soils and landforms: An integration of geomorphology and pedology. London: George Allen & Unwin Publ., 1981. 218 p.
20. 20. Grayson R.B., Bloschl G., Western A.W., McMahon T.A. Advances in the use of observed spatial patterns of catchment hydrological response // Adv Water Resour. 2002. V. 25. P. 1313–1334. https://doi.org/10.1016/S0309-1708 (02)00060-X
21. 21. Hoehn D.C., Niemann J.D., Green T.R., Jones A.S., Grazaitis P.J. Downscaling soil moisture over regions that include multiple coarse-resolution grid cells // Remote Sensing Environ. 2017. V. 199. P. 187–200. DOI: 10.1016/j.rse.2017.07.021
22. 22. Kaheil Y.H., Gill M.K., Mckee M., Bastidas L.A., Rosero E. Downscaling and assimilation of surface soil moisture using ground truth measurements // IEEE Trans. Geosci. Remote Sens. 2008. V. 46 (5). P. 1375–1384. http://dx.doi.org/10.1109/Tgrs.2008.916086
23. 23. Kim G., Barros A.P. Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data // Remote Sens. Environ. 2002. 83 (3). P. 400–413. http://dx.doi.org/10.1016/S0034-4257 (02)00044-5
24. 24. Merlin O., Escorihuela M.J., Mayoral M.A., Hagolle O., Al Bitar A., Kerr Y. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: an evaluation study at 3 km and 100 m resolution in Catalunya, Spain // Remote Sens. Environ. 2013. V. 130. P. 25–38. http://dx.doi.org/10.1016/j.rse.2012.11.008
25. 25. Motovilov Yu.G., Bugaets A.N., Gartsman B.I., Gonchukov L.V., Kalugin A.S., Moreido V.M., Suchilina Z.A., Fingert E.A. Assessing the sensitivity of a model of runoff formation in the Ussuri river basin // Water Resour. 2018. Т. 45. № S1. С. S128–S134.
26. 26. Motovilov Yu.G., Gottschalk L., Engeland K., Rodhe A. Validation of a distributed hydrological model against spatial observation // Agricultural Forest Meteorol. 1999. V. 98–99. P. 257–277.
27. 27. Ranney K.J., Niemann J.D., Lehman B.M., Green T.R., Jones A.S. A method to downscale soil moisture to fine resolutions using topographic, vegetation, and soil data // Adv. Water Resour. 2015. V. 76. P. 81–96. http://dx.doi.org/10.1016/j.advwatres.2014.12.003
28. 28. Rase W.-D. Volume-preserving interpolation of a smooth surface from polygon-related data // J. Geogr. Systems. 2001. V. 3. P. 199–213. doi:10.1007/pl00011475
29. 29. Sahoo A.K., De Lannoy G.J.M., Reichle R.H., Houser P.R. Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA // Adv. Water Resour. 2013. V. 52. P. 19–33. http://dx.doi.org/10.1016/j.advwatres.2012.08.007
30. 30. Song C.Y., Jia L., Menenti M. Retrieving high-resolution surface soil moisture by downscaling AMSR-E brightness temperature using MODIS LST and NDVI data // IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014. V. 7 (3) P. 935–942. http://dx.doi.org/10.1109/Jstars.2013.2272053
31. 31. Soulsby C., Tetzlaff D., Dunn S.M., Waldron S. Scaling up and out in runoff process understanding–Insights from nested experimental catchment studies // Hydrol. Processes. 2006. V. 20. P. 2461–2465.
32. 32. Waldo R., Tobler W.R. Smooth pycnophylactic interpolation for geographical regions // J. Am. Statistical Association. 1979. V. 74 (367). P. 519–530.
33. 33. Wilson J.P., Gallant J.C. Terrain analysis: principles and applications. New York: Wiley, 2000. 512 p.
34. 34. Wilson D.J., Western A.W., Grayson R.B. A terrain and data-basedmethod for generating the spatial distribution of soil moisture // Adv. Water Resour. 2005. V. 28 (1). P. 43–54. http://dx.doi.org/10.1016/j.advwatres.2004.09.007