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

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Study Area

^{2}. It is located east of the Iberian Peninsula, mostly in the Valencian Community (Figure 1). The altitude ranges from 38 to 1751 m above sea level. From a geological point of view, there is a dominance of permeable limestone formations, with 73% of the drainage basin occupied by karst landscapes [43,44]. Therefore, the deep percolation capacity and permeabilities of the aquifers are very high, as well as transmission losses in the river network being significant.

#### 2.2. Satellite Datasets

#### 2.3. Eco-Hydrological Model: TETIS

#### 2.4. SM-Based Efficiency Indices

**M**is a spatio-temporal dataset with dimensions (s, t), where each of the rows s represents a map for a given time, and each column t is the temporal series for each pixel of the spatial division.

**N**(s, t) (Equation (1)), by subtraction of the spatial mean, denominated

**B**(1, t), from

**M**, in each temporal series corresponding to the matrix

**M**.

**N**=

**M**−

**B**,

**C**(Equation (2)) is then calculated and the equation of eigenvalues is solved. In Equation (3),

**Λ**is a diagonal matrix containing the eigenvalues λ

_{s}of the correlation matrix

**C**, with the columns of

**E**being their corresponding eigenvectors.

**C**=

**N**×

^{T}**N**,

**E**’s eigenvectors can be thought of as a map describing spatial patterns (EOFs). In general, the n eigenvalues and their corresponding eigenvectors are selected, which explain most of the variance, and the dataset is reconstructed as shown in Equation (4), where a

_{i}represents the temporal evolution of EOF

_{i}, which will be referred to hereafter as loadings. Generally, the number of eigenvalues selected to rebuild the dataset (n) is less than the number of time series(s)

_{i}is the explained variance of the EOF methodology for the main component i; pc is the number of main components that explain at least 95% of the variance; load_obs

_{i,j}is the observed SM loading series obtained by the EOF methodology; load_sim

_{i,j}is the simulated SM loading series obtained by the EOF methodology.

_{3}.

#### 2.5. Optimisation Algorithm and Goodness-of-Fit Indexes

## 3. Results and Discussion

#### 3.1. Selection of SM-Based OF

_{2}was selected, as it was the most robust in the transition from calibration to the validation period. In addition, it showed the best value and the highest robustness in the NSE index for flow-rates, in addition to performing well in LAI validation for the calibration period, which, despite being a negative value, is the best among all proposals. The behavior of the LAI is quite striking, especially within the calibration period, because it has very negative values, except in option 2, but has a noticeable uptick in the validation period. The negative NSE in the LAI can be explained by a low temporal variability (see for example Figure 3) and a possible initial condition problem in the calibration period.

_{2}, a comparison was made between the streamflow-based configuration (NSE index) and the SM-based alternative selected in order to analyze the main advantages and disadvantages of using only the SM as the only variable in the calibration step of the distributed ecohydrological model.

#### 3.2. Calibration Period

#### 3.3. Validation Period

^{3}/s). For performance indices, the NSE index between observed and simulated flows for both configurations was 0.47 and 0.45, respectively. The STE

_{2}values were 0.03 for the streamflow-based configuration and 0.58 for the SM-based approach.

#### 3.4. Value of Satellite Information

_{2}(SM) = 0.01). During the model validation period, it showed a significant reduction in the time index (NSE(Q) = 0.47) and was unable to obtain a good representation of the SM-based index by presenting a low value (STE

_{2}(SM) = 0.03), even slightly higher than in the calibration period.

_{2}itself, it was also possible to see good stability of the index in the transition from the calibration period to the validation period, also presenting a slight reduction (STE

_{2}(SM) = 0.05).

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Bai, P.; Liu, X.; Yang, T.; Li, F.; Liang, K.; Hu, S.; Liu, C. Assessment of the Influences of Different Potential Evapotranspiration Inputs on the Performance of Monthly Hydrological Models under Different Climatic Conditions. J. Hydrometeorol.
**2016**, 17, 2259–2274. [Google Scholar] [CrossRef] - Beven, K.; Freer, J. Equifinality, Data Assimilation, and Uncertainty Estimation in Mechanistic Modelling of Complex Environmental Systems Using the GLUE Methodology. J. Hydrol.
**2001**, 249, 11–29. [Google Scholar] [CrossRef] - Bitew, M.M.; Gebremichael, M. Evaluation of Satellite Rainfall Products through Hydrologic Simulation in a Fully Distributed Hydrologic Model. Water Resour. Res.
**2011**, 47, 1–11. [Google Scholar] [CrossRef] - Wu, Q.; Liu, S.; Cai, Y.; Li, X.; Jiang, Y. Improvement of Hydrological Model Calibration by Selecting Multiple Parameter Ranges. Hydrol. Earth Syst. Sci.
**2017**, 21, 393–407. [Google Scholar] [CrossRef] [Green Version] - Parajka, J.; Blöschl, G. The Value of MODIS Snow Cover Data in Validating and Calibrating Conceptual Hydrologic Models. J. Hydrol.
**2008**, 358, 240–258. [Google Scholar] [CrossRef] - Winsemius, H.C.; Schaefli, B.; Montanari, A.; Savenije, H.H.G. On the Calibration of Hydrological Models in Ungauged Basins: A Framework for Integrating Hard and Soft Hydrological Information. Water Resour. Res.
**2009**, 45. [Google Scholar] [CrossRef] [Green Version] - Rakovec, O.; Kumar, R.; Mai, J.; Cuntz, M.; Thober, S.; Zink, M.; Attinger, S.; Schäfer, D.; Schrön, M.; Samaniego, L. Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins. J. Hydrometeorol.
**2016**, 17, 287–307. [Google Scholar] [CrossRef] - Akbar, R.; Das, N.; Entekhabi, D.; Moghaddam, M. Active and Passive Microwave Remote Sensing Synergy for Soil Moisture Estimation. In Satellite Soil Moisture Retrieval; Elsevier Inc.: Amsterdam, The Netherlands, 2016; pp. 187–207. [Google Scholar] [CrossRef]
- Brocca, L.; Ciabatta, L.; Massari, C.; Camici, S.; Tarpanelli, A. Soil Moisture for Hydrological Applications: Open Questions and New Opportunities. Water (Switzerland)
**2017**, 9, 140. [Google Scholar] [CrossRef] - Domeneghetti, A.; Tarpanelli, A.; Brocca, L.; Barbetta, S.; Moramarco, T.; Castellarin, A.; Brath, A. The Use of Remote Sensing-Derived Water Surface Data for Hydraulic Model Calibration. Remote Sens. Environ.
**2014**, 149, 130–141. [Google Scholar] [CrossRef] - Gupta, H.V.; Kling, H.; Yilmaz, K.K.; Martinez, G.F. Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling. J. Hydrol.
**2009**, 37, 80–91. [Google Scholar] [CrossRef] [Green Version] - Li, H.T.; Brunner, P.; Kinzelbach, W.; Li, W.P.; Dong, X.G. Calibration of a Groundwater Model Using Pattern Information from Remote Sensing Data. J. Hydrol.
**2009**, 377, 120–130. [Google Scholar] [CrossRef] - Petropoulos, G.P.; Ireland, G.; Barrett, B. Surface Soil Moisture Retrievals from Remote Sensing: Current Status, Products & Future Trends. Phys. Chem. Earth
**2015**, 83–84, 36–56. [Google Scholar] [CrossRef] - Ruiz-Pérez, G.; González-Sanchis, M.; Del Campo, A.D.; Francés, F. Can a Parsimonious Model Implemented with Satellite Data Be Used for Modelling the Vegetation Dynamics and Water Cycle in Water-Controlled Environments? Ecol. Modell.
**2016**, 324, 45–53. [Google Scholar] [CrossRef] [Green Version] - Beck, P.S.A.; Atzberger, C.; Arild, K.; Johansen, B.; Skidmore, A.K. Improved Monitoring of Vegetation Dynamics at Very High Latitudes: A New Method Using MODIS NDVI. Remote Sens. Environ.
**2006**, 100, 321–334. [Google Scholar] [CrossRef] - Shu, Y.; Stisen, S.; Jensen, K.H.; Sandholt, I. Estimation of Regional Evapotranspiration over the North China Plain Using Geostationary Satellite Data. Int. J. Appl. Earth Obs. Geoinf.
**2011**, 13, 192–206. [Google Scholar] [CrossRef] - Stisen, S.; Jensen, K.H.; Sandholt, I.; Grimes, D.I.F. A Remote Sensing Driven Distributed Hydrological Model of the Senegal River Basin. J. Hydrol.
**2008**, 354, 131–148. [Google Scholar] [CrossRef] - Lettenmaier, D.P.; Alsdorf, D.; Dozier, J.; Huffman, G.J.; Pan, M.; Wood, E.F. Inroads of Remote Sensing into Hydrologic Science during the WRR Era. Water Resour. Res.
**2015**, 51, 7309–7342. [Google Scholar] [CrossRef] - Demirel, M.C.; Mai, J.; Mendiguren, G.; Koch, J.; Samaniego, L.; Stisen, S. Combining Satellite Data and Appropriate Objective Functions for Improved Spatial Pattern Performance of a Distributed Hydrologic Model. Hydrol. Earth Syst. Sci.
**2018**, 22, 1299–1315. [Google Scholar] [CrossRef] [Green Version] - Herman, M.R.; Nejadhashemi, A.P.; Abouali, M.; Hernandez-Suarez, J.S.; Daneshvar, F.; Zhang, Z.; Anderson, M.C.; Sadeghi, A.M.; Hain, C.R.; Sharifi, A. Evaluating the Role of Evapotranspiration Remote Sensing Data in Improving Hydrological Modeling Predictability. J. Hydrol.
**2018**, 556, 39–49. [Google Scholar] [CrossRef] - Immerzeel, W.W.; Droogers, P. Calibration of a Distributed Hydrological Model Based on Satellite Evapotranspiration. J. Hydrol.
**2008**, 349, 411–424. [Google Scholar] [CrossRef] - Rajib, A.; Evenson, G.R.; Golden, H.E.; Lane, C.R. Hydrologic Model Predictability Improves with Spatially Explicit Calibration Using Remotely Sensed Evapotranspiration and Biophysical Parameters. J. Hydrol.
**2018**, 567, 668–683. [Google Scholar] [CrossRef] - Silvestro, F.; Gabellani, S.; Delogu, F.; Rudari, R.; Boni, G. Exploiting Remote Sensing Land Surface Temperature in Distributed Hydrological Modelling: The Example of the Continuum Model. Hydrol. Earth Syst. Sci.
**2013**, 17, 39–62. [Google Scholar] [CrossRef] [Green Version] - Zink, M.; Mai, J.; Cuntz, M.; Samaniego, L. Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature. Water Resour. Res.
**2018**, 54, 2976–2998. [Google Scholar] [CrossRef] - Contreras, S.; Jobbágy, E.G.; Villagra, P.E.; Nosetto, M.D.; Puigdefábregas, J. Remote Sensing Estimates of Supplementary Water Consumption by Arid Ecosystems of Central Argentina. J. Hydrol.
**2011**, 397, 10–22. [Google Scholar] [CrossRef] - Ramón-Reinozo, M.; Ballari, D.; Cabrera, J.J.; Crespo, P.; Carrillo-Rojas, G. 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. Environ. Earth Sci.
**2019**, 78, 340. [Google Scholar] [CrossRef] - Ruiz-Pérez, G.; Koch, J.; Manfreda, S.; Caylor, K.; Francés, F. Calibration of a Parsimonious Distributed Ecohydrological Daily Model in a Data-Scarce Basin by Exclusively Using the Spatio-Temporal Variation of NDVI. Hydrol. Earth Syst. Sci.
**2017**, 21, 6235–6251. [Google Scholar] [CrossRef] [Green Version] - Ahmad, S.; Kalra, A.; Stephen, H. Estimating Soil Moisture Using Remote Sensing Data: A Machine Learning Approach. Adv. Water Resour.
**2010**, 33, 69–80. [Google Scholar] [CrossRef] - Kornelsen, K.C.; Coulibaly, P. Reducing Multiplicative Bias of Satellite Soil Moisture Retrievals. Remote Sens. Environ.
**2015**, 165, 109–122. [Google Scholar] [CrossRef] - Li, Y.; Grimaldi, S.; Pauwels, V.R.N.; Walker, J.P. Hydrologic Model Calibration Using Remotely Sensed Soil Moisture and Discharge Measurements: The Impact on Predictions at Gauged and Ungauged Locations. J. Hydrol.
**2018**, 557, 897–909. [Google Scholar] [CrossRef] - Schlerf, M.; Atzberger, C. Inversion of a Forest Reflectance Model to Estimate Structural Canopy Variables from Hyperspectral Remote Sensing Data. Remote Sens. Environ.
**2006**, 100, 281–294. [Google Scholar] [CrossRef] - Yang, H.; Xiong, L.; Ma, Q.; Xia, J.; Chen, J.; Xu, C.-Y. Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework. Remote Sens.
**2019**, 11, 1335. [Google Scholar] [CrossRef] [Green Version] - Yassin, F.; Razavi, S.; Wong, J.S.; Pietroniro, A.; Wheater, H. Hydrologic-Land Surface Modelling of a Complex System under Precipitation Uncertainty: A Case Study of the Saskatchewan River Basin, Canada. Hydrol. Earth Syst. Sci. Discuss.
**2019**, 2019, 1–40. [Google Scholar] [CrossRef] - Barrett, B.; Pratola, C.; Gruber, A.; Dwyer, E. Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites; Elsevier Inc.: Amsterdam, The Netherlands, 2016. [Google Scholar] [CrossRef] [Green Version]
- Legates, D.R.; Mahmood, R.; Levia, D.F.; DeLiberty, T.L.; Quiring, S.M.; Houser, C.; Nelson, F.E. Soil Moisture: A Central and Unifying Theme in Physical Geography. Prog. Phys. Geogr.
**2011**, 35, 65–86. [Google Scholar] [CrossRef] - Perry, M.A.; Niemann, J.D. Analysis and Estimation of Soil Moisture at the Catchment Scale Using EOFs. J. Hydrol.
**2007**, 334, 388–404. [Google Scholar] [CrossRef] - Western, A.W.; Blöschl, G. On the Spatial Scaling of Soil Moisture. J. Hydrol.
**1999**, 217, 203–224. [Google Scholar] [CrossRef] - Vereecken, H.; Huisman, J.A.; Bogena, H.; Vanderborght, J.; Vrugt, J.A.; Hopmans, J.W. On the Value of Soil Moisture Measurements in Vadose Zone Hydrology: A Review. Water Resour. Res.
**2008**, 44. [Google Scholar] [CrossRef] [Green Version] - Zink, M.; Kumar, R.; Cuntz, M.; Samaniego, L. A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty. Hydrol. Earth Syst. Sci.
**2017**, 21, 1769–1790. [Google Scholar] [CrossRef] [Green Version] - Barrett, B.; Helan, P.W.; Wyer, E.D. Detecting Changes in Surface Soil Moisture Content Using Differential SAR Interferometry (DInSAR). Int. J. Remote Sens.
**2009**, 34, 7091–7112. [Google Scholar] [CrossRef] [Green Version] - Kerr, Y.H.; Wigneron, J.P.; Al Bitar, A.; Mialon, A.; Srivastava, P.K. Soil Moisture from Space: Techniques and Limitations; Elsevier Inc.: Amsterdam, The Netherlands, 2016. [Google Scholar] [CrossRef]
- Wigneron, J.; Chanzy, A.; Waldteufel, P.; Kerr, Y.; Huet, S. Retrieving Surface Soil Moisture over a Wheat Field: Comparison of Different Methods. Remote Sens. Environ.
**2003**, 87, 334–344. [Google Scholar] [CrossRef] - Sánchez García, A.; Francés García, F. Estudio Del Régimen Hídrico En La Rambla de La Viuda (Provincia de Castellón). In Análisis de La Influencia de La Información Estándar En El Modelo Hidrológico; Universitat Politécnica de València: Valencia, Spain, 2015. [Google Scholar]
- Montalvo Montenegro, C.I.; Francés García, F. Estimación de Pérdidas de Cauce, Modelización de Transporte de Sedimentos y Cambio Climático de Una Cuenca Mediterránea (Rambla de La Viuda); Universitat Politécnica de València: Valencia, Spain, 2017. [Google Scholar]
- Herrera, S.; Fernández, J.; Gutiérrez, J.M. Update of the Spain02 Gridded Observational Dataset for EURO-CORDEX Evaluation: Assessing the Effect of the Interpolation Methodology. Int. J. Climatol.
**2016**, 36, 900–908. [Google Scholar] [CrossRef] [Green Version] - Hargreaves, G.H.; Samani, Z. Reference Crop Evapotranspiration From Temperature. Appl. Eng. Agric.
**1985**, 1, 96–99. [Google Scholar] [CrossRef] - Pablos, M.; González-Haro, C.; Barcelona Expert Center Team. BEC SMOS Land Products Description; Barcelona Expert Center: Barcelona, Spain, 2019. [Google Scholar]
- Piles, M.; Pou, X.; Camps, A.; Vall-llosera, M. Quality Report: Validation of SMOS-BEC L4 High Resolution Soil Moisture Products, Version 3.0 or “All-Weather”. Tech. Rept.
**2015**. Available online: http://bec.icm.csic.es/doc/BEC-SMOS-L4SMv3-QR.pdf (accessed on 10 December 2019). - Yan, K.; Park, T.; Yan, G.; Chen, C.; Yang, B.; Liu, Z.; Nemani, R.R.; Knyazikhin, Y.; Myneni, R.B. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements. Remote Sens.
**2016**, 8, 359. [Google Scholar] [CrossRef] [Green Version] - Francés, F.; Vélez, J.I.; Vélez, J.J. Split-Parameter Structure for the Automatic Calibration of Distributed Hydrological Models. J. Hydrol.
**2007**, 332, 226–240. [Google Scholar] [CrossRef] - Francés García, F.; Benito Casado, J. La Modelación Distribuida Con Pocos Parámetros de Las Crecidas. Ing. Del Agua
**1995**, 2, 7–24. [Google Scholar] [CrossRef] [Green Version] - Pasquato, M.; Medici, C.; Friend, A.D.; Francés, F. Comparing Two Approaches for Parsimonious Vegetation Modelling in Semiarid Regions Using Satellite Data. Ecohydrology
**2015**, 1036, 1024–1036. [Google Scholar] [CrossRef] - Hannachi, A.; Jolliffe, I.T.; Stephenson, D.B. Empirical Orthogonal Functions and Related Techniques in Atmospheric Science: A Review. Int. J. Climatol.
**2007**, 27, 1119–1152. [Google Scholar] [CrossRef] - Obukhov, A.M. The Statistically Orthogonal Expansion of Empirical Functions. Bull. Acad. Sci. USSR. Geophys. Ser. (Engl. Transl.)
**1960**, 1, 288–291. [Google Scholar] - Obukhov, A.M. Statistically Homogeneous Fields on a Sphere. Usp. Mat. Nauk
**1947**, 2, 196–198. [Google Scholar] - Vereecken, H.; Huisman, J.A.; Pachepsky, Y.; Montzka, C.; van der Kruk, J.; Bogena, H.; Weihermüller, L.; Herbst, M.; Martinez, G.; Vanderborght, J. On the Spatio-Temporal Dynamics of Soil Moisture at the Field Scale. J. Hydrol.
**2012**, 516, 76–96. [Google Scholar] [CrossRef] - Koch, J.; Koch, J.; Jensen, A.K.H.; Stisen, S. Evaluating Spatial Patterns in Hydrological Modelling. Ph.D. Thesis, University of Copenhagen, Copenhagen, Denmark, December 2016. [Google Scholar] [CrossRef]
- Hargrove, W.; Hoffman, F.; Hessburg, P. Mapcurves: A Quantitative Method for Comparing Categorical Maps. J. Geogr. Syst.
**2006**, 8, 187–208. [Google Scholar] [CrossRef] - Ji, L.; Gallo, K. An Agreement Coefficient for Image Comparison. Photogramm. Eng. Remote Sens.
**2006**, 73, 823–833. [Google Scholar] [CrossRef] - Björnsson, H.; Venegas, S.A. A Manual for EOF and SVD Analyses of Climatic Data. CCGCR Rep.
**1997**, 97, 112–134. [Google Scholar] - Gebler, S.; Franssen, H.H.; Kollet, S.J.; Qu, W.; Vereecken, H. High Resolution Modelling of Soil Moisture Patterns with TerrSysMP: A Comparison with Sensor Network Data. J. Hydrol.
**2017**, 547, 309–331. [Google Scholar] [CrossRef] - Preisendorfer, R.W.; Mobley, C. Principal Component Analysis in Meteorology and Oceanography; Elsevier: Amsterdam, The Netherlands, 1988. [Google Scholar]
- Koch, J.; Jensen, K.H.; Stisen, S. 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. Int. J. Climatol.
**2015**, 51, 1225–1246. [Google Scholar] [CrossRef] - Duan, Q.; Sorooshian, S.; Gupta, V. Effective and Efficient Global Optimization for Conceptual Rainfall-runoff Models. Water Resour. Res.
**1992**, 28, 1015–1031. [Google Scholar] [CrossRef] - Abdulla, F.A.; Lettenmaier, D.P. Development of Regional Parameter Estimation Equations for a Macroscale Hydrologic Model. J. Hydrol.
**1997**, 197, 230–257. [Google Scholar] [CrossRef] - Post, D.; Jones, J.; Grant, G. An Improved Methodology for Predicting the Daily Hydrologic Response of Ungauged Catchments. Environ. Model. Softw.
**1998**, 13, 395–403. [Google Scholar] [CrossRef] - Wagener, T.; Wheater, H.; Gupta, H.V. Rainfall-Runoff Modelling in Gauged and Ungauged Catchments; World Scientific; Imperial College Press: London, UK, 2004. [Google Scholar]
- Yadav, M.; Wagener, T.; Gupta, H. Regionalization of Constraints on Expected Watershed Response for Improved Predictions in Ungauged Basins. Adv. Water Resour.
**2007**, 30, 1756–1774. [Google Scholar] [CrossRef] - Wagener, T.; Montanari, A. Convergence of Approaches toward Reducing Uncertainty in Predictions in Ungauged Basins. Water Resour. Res.
**2011**, 47, 1–8. [Google Scholar] [CrossRef] [Green Version] - Wagener, T.; Wheater, H.S. Parameter Estimation and Regionalization for Continuous Rainfall-Runoff Models Including Uncertainty. J. Hydrol.
**2006**, 320, 132–154. [Google Scholar] [CrossRef] - Kunnath-Poovakka, A.; Ryu, D.; Renzullo, L.J.; George, B. The Efficacy of Calibrating Hydrologic Model Using Remotely Sensed Evapotranspiration and Soil Moisture for Streamflow Prediction. J. Hydrol.
**2016**, 535, 509–524. [Google Scholar] [CrossRef] - Manfreda, S.; Mita, L.; Dal Sasso, S.F.; Samela, C.; Mancusi, L. Exploiting the Use of Physical Information for the Calibration of a Lumped Hydrological Model. Hydrol. Process.
**2018**, 32, 1420–1433. [Google Scholar] [CrossRef] - Boyle, D.P.; Barth, C.; Bassett, S. Towards Improved Hydrologic Model Predictions in Ungauged Snow-Dominated Watersheds Utilizing a Multi-Criteria Approach and SNODAS Estimates of SWE. Putt. Predict. Ungauged Basins Pract. Can. Water Resour. Assoc. Int. Assoc. Hydrol. Sci. Nepean
**2013**, 17, 231–242. [Google Scholar] - Milella, P.; Bisantino, T.; Gentile, F.; Iacobellis, V.; Liuzzi, G.T. Diagnostic Analysis of Distributed Input and Parameter Datasets in Mediterranean Basin Streamflow Modeling. J. Hydrol.
**2012**, 472, 262–276. [Google Scholar] [CrossRef]

**Figure 1.**Study area: Rambla de la Viuda Catchment with the location of the Spain02 grid points (dots) and the Maria Cristina Dam.

**Figure 2.**TETIS conceptual scheme in a cell, adapted for an ephemeral river. The water content of the shallow layer tank represents the surface SM.

**Figure 3.**Comparison of the proposed soil moisture (SM)-based objective functions (OFs) in calibration and validation periods for different efficiency indexes.

**Figure 4.**Observed and simulated discharges during the calibration period (2011–2013) for streamflow-based configuration (

**a**,

**b**) and SM-based (

**c**,

**d**): hydrographs on the left (

**a**,

**c**) and flow duration curves on the right (

**b**,

**d**).

**Figure 5.**Observed and simulated mean area SM during the calibration period (2011–2013) for: (

**a**) streamflow-based configuration; (

**b**) SM-based configuration.

**Figure 6.**Observed and simulated mean area LAI during the calibration period (2011–2013) for: (

**a**) streamflow-based configuration; (

**b**) the SM-based configuration.

**Figure 7.**Spatial distribution of R for SM during calibration period (2011–2013) for: (

**a**) streamflow-based configuration; (

**b**) SM-based configuration.

**Figure 8.**Spatial distribution of R for Leaf Area Index (LAI) for the calibration period (2011–2013) for: (

**a**) the streamflow-based configuration; (

**b**) the SM-based configuration.

**Figure 9.**Observed and simulated discharges during the validation period (2014–2015) for streamflow-based configuration (

**a**,

**b**) and SM-based (

**c**,

**d**): hydrographs on the left (

**a**,

**c**) and flow duration curves on the right (

**b**,

**d**).

**Figure 10.**Observed and simulated mean areal SM for the validation period (2014–2015): (

**a**) for streamflow-based configuration; (

**b**) for SM-based configuration.

**Figure 11.**Observed and simulated mean areal LAI for the validation period (2014–2015): (

**a**) for streamflow-based configuration; (

**b**) for SM-based configuration.

**Figure 12.**Spatial distribution of R for SM for the validation period (2014–2015), for the streamflow-based (

**a**) and SM-based (

**b**) configurations.

**Figure 13.**Spatial distribution of R for LAI during the validation period (2014–2015), for the streamflow-based (

**a**) and SM-based (

**b**) configurations.

**Figure 14.**Variability of the Nash-Sutcliffe Efficiency (NSE) index between observed and simulated discharges in the search of the optimal result in calibration for streamflow-based and SM-based configurations.

**Table 1.**Comparison of the performance of the temporal and SM-based configuration in calibration and validation periods.

Configuration | Streamflow-Based | SM-Based | ||
---|---|---|---|---|

Index | NSE (Q) | STE (SM) | NSE (Q) | STE (SM) |

Calibration | 0.91 | 0.01 | 0.54 | 0.63 |

Validation | 0.47 | 0.03 | 0.44 | 0.58 |

ΔIndex_{(cal-val)} | 0.44 | 0.02 | 0.10 | 0.05 |

**Table 2.**SM Comparison of the performance in terms of simulated SM of the streamflow-based and SM-based configuration in calibration and validation periods.

Configuration | Streamflow-Based | SM-Based | ||||
---|---|---|---|---|---|---|

Index | NSE (SM) | R (SM) | BE (SM) | NSE (SM) | R (SM) | BE (SM) |

Calibration | 0.63 | −0.12 | −4.20 | 0.94 | 0.71 | −9.50 |

Validation | 0.39 | 0.11 | −31.00 | 0.50 | 0.70 | −21.40 |

ΔIndex_{(cal-val)} | 0.24 | 0.23 | 26.80 | 0.44 | 0.01 | 11.90 |

**Table 3.**Comparison of the performance in terms of simulated LAI of the streamflow-based and SM-based configuration in calibration and validation periods.

Configuration | Streamflow-Based | SM-Based | ||||
---|---|---|---|---|---|---|

Index | NSE (LAI) | R (LAI) | BE (LAI) | NSE (LAI) | R (LAI) | BE (LAI) |

Calibration | −99.01 | −0.35 | 3.90 | −0.15 | 0.45 | −6.30 |

Validation | −0.86 | −0.20 | −25.00 | 0.32 | 0.48 | −24.90 |

ΔIndex_{(cal-val)} | 98.95 | 0.15 | 21.10 | 0.47 | 0.03 | 18.60 |

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Echeverría, C.; Ruiz-Pérez, G.; Puertes, C.; Samaniego, L.; Barrett, B.; Francés, F.
Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation. *Water* **2019**, *11*, 2613.
https://doi.org/10.3390/w11122613

**AMA Style**

Echeverría C, Ruiz-Pérez G, Puertes C, Samaniego L, Barrett B, Francés F.
Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation. *Water*. 2019; 11(12):2613.
https://doi.org/10.3390/w11122613

**Chicago/Turabian Style**

Echeverría, Carlos, Guiomar Ruiz-Pérez, Cristina Puertes, Luis Samaniego, Brian Barrett, and Félix Francés.
2019. "Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation" *Water* 11, no. 12: 2613.
https://doi.org/10.3390/w11122613