Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

[EN] The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (Delta NSE = 0.1) and greater sensitivity to the spatio-temporal variables' spatial representation.The study was supported by the Spanish projects TETIS-MED (CGL2014-58127-C3-3-R) and TETIS-CHANGE (RTI2018-093717-B-100), the European project iAqueduct and the Paraguayan Funding Program BECAL.Echeverria, C.; Ruiz Perez, G.; Puertes-Castellano, C.; Samaniego. L.; Barrett, B.; Francés, F. (2019). Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation. Water. 11(12):1-19. https://doi.org/10.3390/w111226131191112Bai, P., Liu, X., Yang, T., Li, F., Liang, K., Hu, S., & Liu, C. (2016). Assessment of the Influences of Different Potential Evapotranspiration Inputs on the Performance of Monthly Hydrological Models under Different Climatic Conditions. Journal of Hydrometeorology, 17(8), 2259-2274. doi:10.1175/jhm-d-15-0202.1Beven, K., & Freer, J. (2001). Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology, 249(1-4), 11-29. doi:10.1016/s0022-1694(01)00421-8Bitew, M. M., & Gebremichael, M. (2011). Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model. Water Resources Research, 47(6). doi:10.1029/2010wr009917Wu, Q., Liu, S., Cai, Y., Li, X., & Jiang, Y. (2017). Improvement of hydrological model calibration by selecting multiple parameter ranges. Hydrology and Earth System Sciences, 21(1), 393-407. doi:10.5194/hess-21-393-2017Parajka, J., & Blöschl, G. (2008). The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. Journal of Hydrology, 358(3-4), 240-258. doi:10.1016/j.jhydrol.2008.06.006Winsemius, H. C., Schaefli, B., Montanari, A., & Savenije, H. H. G. (2009). On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information. Water Resources Research, 45(12). doi:10.1029/2009wr007706Rakovec, O., Kumar, R., Mai, J., Cuntz, M., Thober, S., Zink, M., … Samaniego, L. (2015). Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins. Journal of Hydrometeorology, 17(1), 287-307. doi:10.1175/jhm-d-15-0054.1Akbar, R., Das, N., Entekhabi, D., & Moghaddam, M. (2016). Active and Passive Microwave Remote Sensing Synergy for Soil Moisture Estimation. Satellite Soil Moisture Retrieval, 187-207. doi:10.1016/b978-0-12-803388-3.00010-3Li, H. T., Brunner, P., Kinzelbach, W., Li, W. P., & Dong, X. G. (2009). Calibration of a groundwater model using pattern information from remote sensing data. Journal of Hydrology, 377(1-2), 120-130. doi:10.1016/j.jhydrol.2009.08.012Beck, P. S. A., Atzberger, C., Høgda, K. A., Johansen, B., & Skidmore, A. K. (2006). Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 100(3), 321-334. doi:10.1016/j.rse.2005.10.021Shu, Y., Stisen, S., Jensen, K. H., & Sandholt, I. (2011). Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data. International Journal of Applied Earth Observation and Geoinformation, 13(2), 192-206. doi:10.1016/j.jag.2010.11.002Stisen, S., Jensen, K. H., Sandholt, I., & Grimes, D. I. F. (2008). A remote sensing driven distributed hydrological model of the Senegal River basin. Journal of Hydrology, 354(1-4), 131-148. doi:10.1016/j.jhydrol.2008.03.006Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., & Wood, E. F. (2015). Inroads of remote sensing into hydrologic science during the WRR era. Water Resources Research, 51(9), 7309-7342. doi:10.1002/2015wr017616Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., & Stisen, S. (2018). Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. Hydrology and Earth System Sciences, 22(2), 1299-1315. doi:10.5194/hess-22-1299-2018Silvestro, F., Gabellani, S., Delogu, F., Rudari, R., & Boni, G. (2013). Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model. Hydrology and Earth System Sciences, 17(1), 39-62. doi:10.5194/hess-17-39-2013Zink, M., Mai, J., Cuntz, M., & Samaniego, L. (2018). Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature. Water Resources Research, 54(4), 2976-2998. doi:10.1002/2017wr021346Contreras, S., Jobbágy, E. G., Villagra, P. E., Nosetto, M. D., & Puigdefábregas, J. (2011). Remote sensing estimates of supplementary water consumption by arid ecosystems of central Argentina. Journal of Hydrology, 397(1-2), 10-22. doi:10.1016/j.jhydrol.2010.11.014Ramón-Reinozo, M., Ballari, D., Cabrera, J. J., Crespo, P., & Carrillo-Rojas, G. (2019). Altitudinal and temporal evapotranspiration dynamics via remote sensing and vegetation index-based modelling over a scarce-monitored, high-altitudinal Andean páramo ecosystem of Southern Ecuador. Environmental Earth Sciences, 78(11). doi:10.1007/s12665-019-8337-6Ruiz-Pérez, G., Koch, J., Manfreda, S., Caylor, K., & Francés, F. (2017). Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI. Hydrology and Earth System Sciences, 21(12), 6235-6251. doi:10.5194/hess-21-6235-2017Ahmad, S., Kalra, A., & Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69-80. doi:10.1016/j.advwatres.2009.10.008Kornelsen, K. C., & Coulibaly, P. (2015). Reducing multiplicative bias of satellite soil moisture retrievals. Remote Sensing of Environment, 165, 109-122. doi:10.1016/j.rse.2015.04.031Li, Y., Grimaldi, S., Pauwels, V. R. N., & Walker, J. P. (2018). Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations. Journal of Hydrology, 557, 897-909. doi:10.1016/j.jhydrol.2018.01.013Schlerf, M., & Atzberger, C. (2006). Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sensing of Environment, 100(3), 281-294. doi:10.1016/j.rse.2005.10.006Yang, H., Xiong, L., Ma, Q., Xia, J., Chen, J., & Xu, C.-Y. (2019). Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework. Remote Sensing, 11(11), 1335. doi:10.3390/rs11111335Yassin, F., Razavi, S., Wong, J. S., Pietroniro, A., & Wheater, H. (2019). Hydrologic-Land Surface Modelling of a Complex System under Precipitation Uncertainty: A Case Study of the Saskatchewan River Basin, Canada. doi:10.5194/hess-2019-207Barrett, B., Pratola, C., Gruber, A., & Dwyer, E. (2016). Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites. Satellite Soil Moisture Retrieval, 209-228. doi:10.1016/b978-0-12-803388-3.00011-5Legates, D. R., Mahmood, R., Levia, D. F., DeLiberty, T. L., Quiring, S. M., Houser, C., & Nelson, F. E. (2010). Soil moisture: A central and unifying theme in physical geography. Progress in Physical Geography: Earth and Environment, 35(1), 65-86. doi:10.1177/0309133310386514Perry, M. A., & Niemann, J. D. (2007). Analysis and estimation of soil moisture at the catchment scale using EOFs. Journal of Hydrology, 334(3-4), 388-404. doi:10.1016/j.jhydrol.2006.10.014Western, A. W., & Blöschl, G. (1999). On the spatial scaling of soil moisture. Journal of Hydrology, 217(3-4), 203-224. doi:10.1016/s0022-1694(98)00232-7Vereecken, H., Huisman, J. A., Bogena, H., Vanderborght, J., Vrugt, J. A., & Hopmans, J. W. (2008). On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resources Research, 44(4). doi:10.1029/2008wr006829Zink, M., Kumar, R., Cuntz, M., & Samaniego, L. (2017). A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty. Hydrology and Earth System Sciences, 21(3), 1769-1790. doi:10.5194/hess-21-1769-2017Barrett, B., Whelan, P., & Dwyer, E. (2013). Detecting changes in surface soil moisture content using differential SAR interferometry. International Journal of Remote Sensing, 34(20), 7091-7112. doi:10.1080/01431161.2013.813654Kerr, Y. H., Wigneron, J.-P., Al Bitar, A., Mialon, A., & Srivastava, P. K. (2016). Soil Moisture from Space. Satellite Soil Moisture Retrieval, 3-27. doi:10.1016/b978-0-12-803388-3.00001-2Pardé, M., Wigneron, J.-P., Chanzy, A., Waldteufel, P., Kerr, Y., & Huet, S. (2003). Retrieving surface soil moisture over a wheat field: Comparison of different methods. Remote Sensing of Environment, 87(2-3), 334-344. doi:10.1016/j.rse.2003.08.002Herrera, S., Fernández, J., & Gutiérrez, J. M. (2015). Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: assessing the effect of the interpolation methodology. International Journal of Climatology, 36(2), 900-908. doi:10.1002/joc.4391George H. Hargreaves, & Zohrab A. Samani. (1985). Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1(2), 96-99. doi:10.13031/2013.26773Yan, K., Park, T., Yan, G., Chen, C., Yang, B., Liu, Z., … Myneni, R. (2016). Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements. Remote Sensing, 8(5), 359. doi:10.3390/rs8050359Francés, F., Vélez, J. I., & Vélez, J. J. (2007). Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology, 332(1-2), 226-240. doi:10.1016/j.jhydrol.2006.06.032Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J., Bogena, H., … Vanderborght, J. (2014). On the spatio-temporal dynamics of soil moisture at the field scale. Journal of Hydrology, 516, 76-96. doi:10.1016/j.jhydrol.2013.11.061Ji, L., & Gallo, K. (2006). An Agreement Coefficient for Image Comparison. Photogrammetric Engineering & Remote Sensing, 72(7), 823-833. doi:10.14358/pers.72.7.823Gebler, S., Hendricks Franssen, H.-J., Kollet, S. J., Qu, W., & Vereecken, H. (2017). High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data. Journal of Hydrology, 547, 309-331. doi:10.1016/j.jhydrol.2017.01.048Koch, J., Jensen, K. H., & Stisen, S. (2015). Toward a true spatial model evaluation in distributed hydrological modeling: Kappa statistics, Fuzzy theory, and EOF-analysis benchmarked by the human perception and evaluated against a modeling case study. Water Resources Research, 51(2), 1225-1246. doi:10.1002/2014wr016607Duan, Q., Sorooshian, S., & Gupta, V. (1992). Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research, 28(4), 1015-1031. doi:10.1029/91wr02985Abdulla, F. A., & Lettenmaier, D. P. (1997). Development of regional parameter estimation equations for a macroscale hydrologic model. Journal of Hydrology, 197(1-4), 230-257. doi:10.1016/s0022-1694(96)03262-3Post, D. A., Jones, J. A., & Grant, G. E. (1998). An improved methodology for predicting the daily hydrologic response of ungauged catchments. Environmental Modelling & Software, 13(3-4), 395-403. doi:10.1016/s1364-8152(98)00044-9Wagener, T., & Montanari, A. (2011). Convergence of approaches toward reducing uncertainty in predictions in ungauged basins. Water Resources Research, 47(6). doi:10.1029/2010wr009469Wagener, T., & Wheater, H. S. (2006). Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty. Journal of Hydrology, 320(1-2), 132-154. doi:10.1016/j.jhydrol.2005.07.015Manfreda, S., Mita, L., Dal Sasso, S. F., Samela, C., & Mancusi, L. (2018). Exploiting the use of physical information for the calibration of a lumped hydrological model. Hydrological Processes, 32(10), 1420-1433. doi:10.1002/hyp.11501Milella, P., Bisantino, T., Gentile, F., Iacobellis, V., & Trisorio Liuzzi, G. (2012). Diagnostic analysis of distributed input and parameter datasets in Mediterranean basin streamflow modeling. Journal of Hydrology, 472-473, 262-276. doi:10.1016/j.jhydrol.2012.09.03