Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic

Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small... J. Hydrol. Hydromech., 69, 2021, 1, 1–12 ©2021. This is an open access article distributed DOI: 10.2478/johh-2020-0038 under the Creative Commons Attribution ISSN 1338-4333 NonCommercial-NoDerivatives 4.0 License Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic 1* 1 2 2 Vesna Đukić , Ranka Erić , Miroslav Dumbrovsky , Veronika Sobotkova University of Belgrade, Faculty of Forestry, Department of Ecological Engineering for Soil and Water Resources Protection, Kneza Višeslava, 1, 11000 Belgrade, Serbia. Brno University of Technology, Faculty of Civil Engineering, Institute of Landscape Water Management, Antonínská 548/1, 601 90 Brno, Czech Republic. Corresponding author. E-mail: vesna.djukic@sfb.bg.ac.rs Abstract: The knowledge of spatio-temporal dynamics of soil moisture within the catchment is very important for rain- fall–runoff modelling in flood forecasting. In this study the comparison between remotely sensed soil moisture and soil moisture estimated from the SHETRAN hydrological model was performed for small and flashy Jičinka River catchment (75.9 km ) in the Czech Republic. Due to a relatively coarse spatial resolution of satellite data, the satellite soil moisture data were downscaled, by applying the method developed by Qu et al. (2015). The sub-grid variability of soil moisture was estimated on the basis of the mean soil moisture for the grid cell and the known hydraulic soil properties. The SHETRAN model was calibrated and verified to the observed streamflow hydrographs at the catchment outlet. The good correlation between the two different soil moisture information was obtained according to the majority of applied criteria. The results of the evaluation criteria indicate that the downscaled remotely sensed soil moisture data can be used as additional criteria for the calibration and validation of hydrological models for small catchments and can contribute to a better estimation of parameters, to reduce uncertainties of hydrological models and improve runoff simulations. Keywords: SHETRAN hydrological model; Downscaled remotely sensed soil moisture; Runoff and soil moisture validation; Spatio-temporal variability of soil moisture; Flash floods; Small catchment. 1 INTRODUCTION makes them unsuitable for hydrological analyses at the basin level (e.g. Brocca et al., 2010; Srivastava et al., 2013; Wang The large floods that have occurred in recent years in many and Qu, 2009). regions of the world made local, national and international A useful way to reduce the uncertainty of the model and im- authorities increasingly aware of flood and inundation hazard prove the model performance is to incorporate remotely sensed and the necessity of a better understanding of floods and flood soil moisture information. Remotely sensed soil moisture pro- protection management improvement (IPCC, 2012). Several vides information about spatial and temporal dynamics of soil flash floods occurred in the territory of the Czech Republic moisture, which can facilitate calibration and validation of during the last decade of June and at the beginning of July hydrological models of large scale (see e.g. Albergel et al., 2009, and in May and June 2010 (Danhelka et al., 2014). The 2010; Brocca et al., 2011; Jackson et al., 2010; Parajka et al., hilly and flashy Jičinka River basin (75.9 km ) in the Moravian 2006; Rötzer et al., 2014). However, due to relatively coarse - Silesian region in the Czech Republic was particularly spatial resolution of approximately several tens of kilometers, affected by serious flooding due to steep slopes of the terrain, satellite soil moisture observations cannot be effectively ap- and a high percentage of soil types with low-intensity plied to hydrological studies in small catchments. Due to varia- infiltration (Pavlik and Dumbrovský, 2014). tions in climate, soil, vegetation, topography and other factors, Rainfall–runoff models can be very useful for flash flood soil moisture is heterogeneously distributed within catchments. forecasting. The runoff generating mechanisms are highly Over the past decades, various downscaling methods of sat- dependant on soil water content. For hydrological modelling ellite soil moisture products have been studied for the im- and flood forecasting, it is important to understand the spatial- provement of their spatial resolution. The variability of soil temporal variability of soil moisture at the basin level (Cor- moisture within a grid cell has often been described by taking radini, 2014; Koster et al., 2010; Manfreda et al., 2007; Ve- into account soil texture (Crow et al., 2012; Gwak and Kim, reecken et al., 2014). Physically based and distributed models 2017; Teuling and Troch, 2005), vegetation (Western et al., can provide a lot of insight into how soil moisture changes in 1999), topography and other important physical characteristics space and time as a function of terrain, soil and vegetation of the basin which effect the soil moisture (Hupet and Van- characteristics of the basin. However, physically based distrib- clooster, 2002; Koster et al., 2016; Rosenbaum et al., 2012). uted models usually need a large number of parameters (soil The estimation of sub- grid variabilty of soil moisture on the surface, soil properties and land use), which can increase the basis of soil texture data is facilitated due to the availability of model uncertainty and decrease the performance of the model high resolution data on soil properties for the entire globe (Dai (Beven, 2006). et al., 2019; Hengl et al., 2017; Shangguan et al., 2014; The direct ground-based measurements of soil moisture are Stoorvogel et al., 2017). sufficiently accurate, but are difficult, time-consuming and A comprehensive review on the downscaling methods for limited to discrete measurements at particular locations, which satellite remote sensing based soil moisture is provided in Peng 1 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova et al. (2017). They analysed the advantages and limitations 2.2 Calibration and validation of the SHETRAN model associated with each method based on published validation studies. Currently, there are still no effective ways for evaluat- The hydrological model was obtained by the calibration and ing either the original remotely sensed soil moisture or the validation of the SHETRAN model on the basis of the downscaled soil moisture outputs. Usually, the remotely sensed measured streamflow hydrographs at the catchment outlet. The soil moisture products are validated against ground–based soil SHETRAN model was calibrated for the storm event which moisture observations. In general, good agreement was found happened in September 2007 and validated for the storm events between downscaled soil moisture and in situ measurements happened in June 2009, May 2010 and June 2010. In rainfall– (Lievens et al., 2015; Peng et al., 2017; Verhoest et al., 2015). runoff modelling, the streamflow is of crucial importance It was also concluded that the accuracy of the downscaled soil because it reflects the hydrological response of the whole moisture highly depends on the accuracy of the original soil catchment. It was assumed that the good agreement between the moisture and that it surpasses the original coarse soil moisture modeled and observed runoff hydrographs implies that the for many studies (Peng et al., 2017). other components of hydrological cycle, in this case soil However, the consistency and the level of agreement be- moisture, are appropriately determined. tween downscaled soil moisture and soil moisture simulated by The area of catchment was discretized into grid cells. The distributed hydrologic models, particualrly in small catchments adopted grid cell size in this study is 500 m × 500 m. Although are still not well understood. The objective of this study is to it can be expected that by adopting a coarser grid resolution a evaluate the consistency and the agreement between lot of spatially important data may have been lost, the use of a downscaled remotely sensed soil moisture, and soil moisture coarser grid resolution can be justified when simulating events estimated from the physically based and distributed SHETRAN of high intensity and/or hydrographs with a short concentration hydrological model in a small catchment. The evaluation is time (Molnar and Julien, 2000), as it is the case in the analysed performed for the small Jičinka River catchment (75.9 km ) in Jičinka River catchment. In the study of Molnar and Julien the Czech Republic during flash floods. The evaluation of (2000), it was concluded that a coarser grid resolution can be modeled soil moisture patterns and their consistency with satel- used in hydrological models as long as parameters are appro- lite data during runoff events provides additional information priately calibrated. about model reliability and accuracy which is an essential step Through numerous simulations it was concluded that the for the development of soil moisture assimilation strategies in Strickler′s coefficients for overland flow and for river flow, the research or operational hydrologic applications. vertical saturated hydraulic conductivity of the subsurface soil and the saturated water content had an important effect on the 2 METHOD size of overland flow. It was also concluded that the values of 2.1 SHETRAN model base flow were dependant on the horizontal saturated hydraulic conductivity in the saturated zone (Đukić and Radić, 2014; SHETRAN is a 3D coupled surface/subsurface physically Đukić and Radić, 2016). The preliminary values of model based and spatially distributed river basin model. SHETRAN parameters used in model calibration were determined from the (version V4.4.5) water flow component was used in this study. literature (Table 3) and are presented in the Table 1. The values The water flow component consists of 4 modules: evapotranspi- of all other model parameters were fixed and adopted their ration/interception; overland/channel; variably saturated subsur- average values from the literature (Table 3 and Table 1). The face and snowmelt (Ewen et al., 2000). The components of adopted values of all parameters are presented in Table 1. interception and evapotranspiration were neglected in this The agreement between the modelled and observed runoff study, because their influence is negligible in rain event mod- was evaluated using following objective functions. The first els. Both the overland and channel flows are described by the objective function is based on the formulation proposed by diffusive wave approximation of the full St. Venant equations Nash and Sutcliffe (1970) and is given by: (Saint-Venant, 1871). The continuity equation is as follows: () QQ − obs,, i sim i ∂∂ QA i =1 += q (1) CR1 = 1– (3) ∂∂ xt () QQ − obs,i obs i =1 The momentum conservation is as follows: where: Q is the observed streamflow on day i, Q is the simu- obs,i sim,i ∂∂QQ (( α /A)) ∂h gQQ ++ gA+ = 0 (2) lated streamflow, Q is the average of the observed streamflow obs ∂∂ tx ∂x CAR over the calibration (or verification) periods of n days. Due to changeable variance of model errors, the Nash – Sut- where: t is time, x is the distance measured along the channel 3 –1 2 cliffe coefficient of efficiency tends to emphasize the large (m), Q is discharge (m s ), A is the hydraulic area (m ), q is 3 –1 errors. For comparison, the function of Chiew and McMahon the tributary outflow (m s ), h is the channel depth (m), C is 0.5 –1 (1994) was used as the second objective function in which the the Chezy coefficient (m s ), R is the hydraulic radius (m) square root of the considered values were related using the α is the correction factor (–). and following equation: The soil water movement in the unsaturated zone is de- scribed using the Richards equation (Richards, 1931). The ArcGIS software ArcView 10.2 was used to prepare the QQ − () obs,, i sim i input data related to the physical characteristics of the basin and i =1 CR2=− 1 (4) n 2 for the displaying and visualisation of the spatially distributed QQ − ( obs,i obs ) soil moisture values across the basin. i =1 2 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Table 1. The ranges of model parameters used in calibration of the SHETRAN model, its optimal values (in parenthesis) and the adopted values of uncalibrated parameters for the Jičinka River basin uncalibrated parameters for the Jičinka River basin. Land use/ Overland flow/channel Soil Type Soil/rock parameters vegetation parameters depth Texture k k n S S vs hs θs θr α t tR –1 –1 1/3 –1 1/3 –1 (m) (m day ) (m day ) (–) (–) (–) (–) (m s ) (m s ) Sandy clay 0.223–0.5814 0.223–0.5814 0.419–0.695 4–8 0–0.7 0.047 0.014 1.317 Forest Dystric loam (0.300) (0.300) (0.480) (7) Cambisol Sandy clay 0.223–0.5814 0.223–0.5814 0.419–0.695 Arable 8–20 0.7–1.2 0.047 0.014 1.317 loam (0.300) (0.300) (0.480) land (18) Geological (sandstone, 0.01–5 Natural 7–18 15–40 1.2–4 4 0.6 0.1 0.001 1.1 substrate schists) (4) grasslands (16) (30) 0.217–0.4105 0.217–0.4105 0.437–0.442 Scarce 30–50 Rendzina 0–0.5 Clay loam 0.075 0.013 1.415 (0.270) (0.270) (0.440) vegetation (42) Geological (sandstone, 0.01–5 1.2–4 4 substrate schists) (4) 0.217–0.4105 0.217–0.4105 0.426–0.469 0.075 0–0.5 Clay loam 0.013 1.415 Eutric (0.270) (0.270) (0.44) cambisol 0.128–0.192 0.426–0.469 0.5–1.05 Loam 0.15 0.078 0.036 1.56 (0.15) (0.430) Geological (sandstone, 0.01–5 1.2–4 4 substrate schists) (4) 0.217–0.4105 0.217–0.4105 0.426–0.469 0–0.40 Clay loam 0.075 0.013 1.415 (0.255) (0.255) (0.430) 0.130–0.196 Fluvisol 0.40–1.0 Silty loam 0.163 0.452 0.093 0.005 1.68 (0.163) Sandy clay 0.223–0.5814 0.223–0.5814 0.419–0.695 1.0–1.25 0.047 0.014 1.317 loam (0.300) (0.300) (0.480) Geologic (sandstone, 0.01–5 1.25-4 4 0.6 0.1 0.001 1.1 substrate schists) (4) The third introduced criterion is potentially useful in the () QQ−−(Q Q) obs,, i obs sim i sim context of prediction, for example, where simulations must be i =1 R = (9) as close as possible to the observed values at each time step nn (Ye et al., 1997). It is defined by: () QQ−−(QQ)  obs,, i obs sim i sim ii == 11 QQ − obs,, i sim i n i =1 () QQ − CR3=− 1 (5)  obs sim i =1 dd =−1,0≤ ≤1 (10) QQ − obs,i obs i =1 () QQ−+Q −Q sim obs obs obs i =1 The fourth criterion (Pereira and Pruitt, 2004) quantifies the ability of the model to accurately reproduce streamflow vol- 2.2 Downscaling approach for sub-grid variability umes over the periods of observation. Criterion CR4 differs estimation from the other three criteria (CR1–CR3), because it does not measure deviation from the observed values at each step of The satellite soil moisture data were downscaled by applying simulation. Therefore, CR4 cannot be used alone as a criterion the method developed by Qu et al. (2015) in this study. The for calibration. This criterion is defined by: downscaling approach (Qu et al., 2015) applied in this study is based on the Mualem-van Genuchten (MvG) model, in which nn  the unsaturated soil hydraulic properties are described using the QQ   sim,, i obs i model of van Genuchten (1980) for the water retention function ii == 11  CR4=− 1 − (6) nn in combination with the hydraulic conductivity function  QQ  obs i sim i  ,, introduced by Mualem (1976). The soil water retention ii == 11  equation, θ(h), is given by: In addition to the already mentioned four criteria (CR1– S (θθ − ) es r CR4) the following statistical measures were also used: the θθ ()hh =+ ≤ 0 (11) root-mean-square error (RMSE), the mean absolute error  1 + α h  (MAE), the coefficient of correlation (R) and the index of agreement (d). They are expressed by the following equations: 3 –3 where θ is the volumetric water content (cm cm ) at pressure head h (cm); θ and θ are the residual and saturated water con- s r 3 –3 –1 MAE = QQ − (7)  tent (cm cm ), respectively; α (cm ), n (–), and m (–) (m = obs,, i sim i i =1 1 – 1/n) are shape parameters. The hydraulic conductivity func- tion, K(h), is given by: Lm 1/m () QQ − obs,, i sim i  KS()=− K S 1 (1− S ) , h≤0 (12) ese e  i =1 RMSE = (8) –1 where Ks is the saturated hydraulic conductivity (cm d ) and K 3 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova –1 (cm d ) is the hydraulic conductivity and L is the pore connec- were used for the evaluation of the hydrologic model perfor- tivity parameter (L = 0.5); S is effective saturation given by: mance. However, in this case, instead of the simulated (Q ) e sim and the observed streamflow values (Q ), the values of SM obs HM θθ () h − and SM are used in Equations (3) – (10). scatt S = (13) θθ − sr 3 DATA 3.1 Study area For each grid of coarse scale satellite data product, the corre- lation between the standard deviation and soil moisture σθ() The Jičinka River basin up to the ″Novy Jičin″ water level is expressed as a function of the mean and the standard devia- monitoring station (Fig. 1) is situated in the Moravian – Silesi- tion of the soil hydraulic parameters (K , θ , θ , α, n,) (Montzka s s r an Region, in the eastern part of the Czech Republic. The Jičin- et al., 2018) using the following equation: ka River is a tributary of the Moravice River, which belongs to the basin of the Opava River and to the basin of the Baltic Sea. σ = θ Altitudes in the Jičinka River basin vary from 270 meters above 22 sea level in the lower part of the basin to 1000 meters in the   σρ aa σρ σ ρ 22 2 ff 13 αα nn bb σ++ +   source areas of the basin. The steep slopes of the terrain in the 12 α (14) 11 ++ aaρρ() a a ()1+aρ a ()   22fn 2 α 2 2 2   Jičinka River basin with the average slope of 9.1% significantly 0  affect the characteristics of runoff.  a σρ  aσρ 3 n 22 22 1 αα n ++ bbσσ +22 bb − + bb −   34 n θ 12 23  11 ++ aaρρ  22 α n  f is the log-transformed saturated hydraulic conductivity (ln Ks); ρ is the vertical correlation length of the respective parameters. The coefficients a –a and b –b are related to the mean of the 1 3 0 4 soil hydraulic parameters: θ , θ , h, α and n. They are calculated s r using the equations which are described in papers (Montzka et al., 2018; Qu et al., 2015). The sub-grid surface soil moisture values can be estimated based on the known average value of surface soil moisture within a grid cell and using the estimated value of the σθ() function and proxy information. It is supposed that the spatial variability of soil moisture within each coarse scale satellite pixel is related to the known spatial variability of the proxy data (Montzka et al., 2018). By multiplication with the provided soil moisture standard deviation at the given mean surface soil moisture, the sub-grid surface soil moisture values can be cal- culated using the following equation: PP − ij , θθ =+σ θ (15) () ij , θ where: θ is the predicted soil moisture at this fine scale ij , location; P , is the proxy data at the fine scale sub-grid i j y-location i and x-location j, P is the mean of the proxy, and σP is the standard deviation of the proxy. In this paper the sur- face soil moisture data were downscaled from its original reso- lution of 25 km to 1 km resolution. This was done by using the saturated hydraulic conductivity as a proxy for soil moisture heterogeneity. After that, the obtained values of soil moisture Fig. 1. The Jičinka River basin with the river system and the rain were resampled at a 500 m resolution. gauging and hydrological stations in the basin. 2.4 Comparison of surface soil moisture estimates Four pedological soil types identified in the studied basin in- After the calibration and validation of the SHETRAN model, clude the following: fluvisol (10.5%), eutric cambisol (46.2%), simulations of soil moisture in unsaturated zone were per- dystric cambisol (41.3%) and rendzina (2%) (Fig. 2). The hy- formed using the sets of parameters optimized for the calibra- drogeological behaviour of the whole basin is defined by the tion and the validation rain events. In that way, soil moisture dominant presence of tertiary and quaternary rocks (sandstones, estimates are one of the results obtained by applying the schists, loess, sand and gravel) which occupy about 75% of the SHETRAN hydrological model. basin (Fig. 2). Four different vegetation types identified in the The consistency between the surface soil moisture simulated Jičinka River basin are forest (34%), natural grasslands (48%), by the hydrologic model (SMHM) and the surface soil moisture arable land (8.5%) and orchards (0.03%) (Fig. 2). Urban areas downscaled from the satellite retrieved soil moisture product occupy about 9.4% of the basin area. (SM ) was spatially analyzed using the same criteria which scatt 4 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Fig. 2. The map of soil types, vegetation types, geological types and the slope map of the Jičinka River basin up to the "Novy Jičin" water level monitoring station. Table 2. The total amounts of precipitation (P ) fallen on the ground, the volume of precipitation (V ), the maximum runoff value (Q ) tot p max and the runoff volume (Vr). Number Simulation period Ptot Vp Qmax Vr 3 3 3 3 3 of rain event (mm) (10 m ) (m /s) (10 m ) 1 5.09.2007. (10:00) 17.09.2007. (10:00) 190.13 14429.9 98 6387.5 2 22.06.2009. (05:00 – 29.06.2009. (7:00) 150.2 11403 264 5805.5 3 11.05.2010. (13:00 – 29.05.2010. (3:00) 232.8 17670.4 75.6 15666.4 4 30.05.2010. (11:00 – 2.06.2010. (14:00) 46.2 3502.2 43.1 4429.9 3.2 Input data for the SHETRAN model logical station. The characteristics of precipitation and runoff are presented in Table 2. The average hourly heights of rainfall in the basin during the The soil, geological, vegetation and slope maps of the Jičin- analyzed rain events were calculated by applying the method of ka basin were obtained in the form of vector polygon data at the Thiessen polygons (Thiessen, 1911) based on the hourly precip- corresponding digitized maps (Fig. 2). All input data and the itation levels measured at the climatological stations in the corresponding input parameters used in SHETRAN are summa- basin (Hodslavice, Verovice and Novy Jičin) (Fig. 1). The rized in Table 3. hourly values of runoff are measured at the ″Novy Jičin″ hydro- 5 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova Table 3. Input data and parameters used in the SHETRAN model. Type of input Input parameters Source of input data data Meteorological Hourly precipitation Czech Hydrometeorological Institute Hydrological Registered streamflow hydrographs Topographic Digital elevation model (DEM) of the resolution: 10 m × 10 m T.G. Masaryk Water Research Institute Land use/ Strickler’s coefficient for overland flow (St) and for channel Corine Land Cover Databases vegetation flow (StR) (Engman,1986) https://land.copernicus.eu/pan-european/corine- distribution land-cover Soil types Hydraulic soil/rock properties (porosity and specific storage, http://globalchange.bnu.edu.cn/research/soil5.jsp. residual water content (θ ), saturated water content (θ ), verti- Geological type r s ″Czech Geological Survey″-ArcGIS online cal saturated hydraulic conductivity (kvs), horizontal saturated conductivity (k ), van Genuchten - α, van Genuchten - n hs 3.3 Satellite soil moisture data of: 0 – 0.05 m, 0.05 – 0.15 m, 0.15 – 0.30 m, 0.30 – 0.60 m, 0.60 – 1.00 m, and 1.00 – 2.00 m. Grid maps of hydraulic soil The satellite soil moisture data used in this study were taken parameters for the Jičinka River catchment, which were derived from the European Space Agency (ESA) Climate Change Ini- from the GSDE database, are presented in Fig 3. tiative (CCI) Soil Moisture (SM) project (http://www.esasoilmoisture-cci.org). The ESA CCI SM v04.7 4 RESULTS AND DISCUSSION product consists of three surface soil moisture data sets: the 4.1 Results of the hydrologic model performance evaluation “ACTIVE Product”, the “PASSIVE Product” and the “COM- BINED Product” (Dorigo et al., 2017). The ESA CCI SM The comparative overview of the results of the calibration product was obtained by combining the soil moisture retrievals and validation of the SHETRAN model are presented in Fig. 4. from seven passive (SMMR, SSM/I, TMI, AMSR-E, WindSat, The figure shows a close relationship between the modeled and AMSR2 and SMOS) and two active (ERS AMI and ASCAT) the observed runoff hydrographs. The results of the assessment microwave sensors into a global data set spanning the period of the quality of model simulations by applying CR1 – CR4 1979 – 2010. The homogenized and merged products present criteria in Eqs. (3) – (6), and by applying the statistical surface soil moisture with a global coverage and a coarse spa- measures in Eqs. (7) – (9) are presented in Table 4. tial resolution of approximately 25 km and a high temporal On the basis of Table 4, it can be concluded that the good resolution of 1 day (Gruber et al., 2019). agreement was found between the observed and the modeled In this study the ACTIVE product was used. The “ACTIVE streamflow hydrographs for both the calibration and the valida- Product” was created by the University of Vienna (TU Wien) tion rain events. It should be noted that the best performance based on observations from C-band scatterometers. The sensors measures were observed for the calibration rain event in Sep- ERS AMI and ASCAT operate at similar frequencies (5.3 GHz tember 2007 according to the majority of the applied criteria. A C-band) and share a similar design. Different algorithms are small drop in the determined values of the applied criteria from used for the retrieval of soil moisture from satellite measure- calibration to verification indicates that the model performance ments (Gruber et al., 2017). decreases only slightly. It should also be noted that there was an The ACTIVE soil moisture data are provided in terms of improvement of the model performance for the verification rain saturation degree [%], ranging between 0 (dry) and 100 (satu- event in June in 2010 compared to the calibration rain event in rated). In this study soil moisture values were used in volumet- September 2007, according to the CR4 criteria, and according 3 3 ric units (m /m ). Soil moisture values in volumetric units were to the obtained values of the Mean Absolute Error (MAE) and calculated by multiplying the degree of saturation by soil poros- the Root Mean Square Error (RMSE). 3 3 ity (expressed in m /m ). Soil porosity data used in the conver- sion to volumetric soil moisture measurements have been taken 4.2 Comparison of soil moisture estimates from the GLDAS-Noah dataset (Rodell et al., 2004). For each of the analyzed rain events grid maps of daily val- 3.4 Soil hydraulic data ues of soil moisture at a 500 m resolution were created for the two different soil moisture sources. The spatial patterns of the The soil hydraulic properties are described using the maximum surface soil moisture estimated for the days of the peak runoff occurring during the analysed rain events are pre- following parameters: the saturated water content (θs), the residual residual water content (θr), the saturated hydraulic sented in Fig. 5. conductivity (K ), and the empirical parameters - vanGenuchten On the basis of Fig 5. it can be seen that there is an agree- - α and van Genuchten - n. These parameters are used in the ment between the soil moisture values simulated by application SHETRAN hydrological model, and they are also used in the the SHETRAN model and the values estimated by downscaling applied procedure of soil moisture downscaling from satellite from the scatterometer soil moisture data. As it can be ex- data. pected, the estimated spatially distributed values of soil mois- The high resolution soil hydraulic data were taken from the ture for the days of the peak runoff occurring during the ana- Global Soil Dataset for use in Earth System Models (GSDE) lysed rain events correspond to the values of the saturated water (Shangguan et al., 2014) located at content in majority of cases for the analysed rain events. Only http://globalchange.bnu.edu.cn/research/soil5.jsp. This database in the case of the rain event in June 2010, the average daily provides the global values of soil hydraulic and thermal param- values of estimated soil moisture are lower in regard to the eters at the spatial resolution of 30" and for vertical resolutions values of the saturated water content. 6 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Fig. 3. Grid maps of hydraulic soil parameters for the Jičinka River catchment derived from the GSDE database: saturated water content 3 –3 –1 3 –3 –1 (cm cm ), saturated hydraulic conductivity (cm day ) residual water content (cm cm ), and empirical parameters α (cm ) and n (–). 120 0 50 0 P(mm) 100 2 Q (m3/s) P(mm) 0.7 September 2007 80 Q (m3/s ) 4 1.4 30 June 2010 60 6 P P 2.1 Qobs 8 40 20 Qobs 2.8 Qsim 20 10 Qsim Date 10 3.5 0 12 0 4.2 300 0 Fig. 4. Observed (Qobs) and the SHETRAN model simulated (Qmod) Q (m /s ) P(mm) 250 streamflow hydrographs at the Jičinka River monitoring station (″Novy Jičin″ water level monitoring station) for the calibration 200 June 2009 (September 2007) and validation (June 2009, May 2010 and June P(mm) 30 2010) rain events. Qsim Qobs Date Table 4. The results of the application of criteria CR1–CR4 and 0 50 statistical measures (MAE, RMSE, R and d) for the assessment of the SHETRAN model simulations. Criterion Calibration rain event Validation rain events 90 0 September 2007 June 2009 May 2010 June 2010 Q (m3/s P(mm) 70 CR1 0.940 0.905 0.810 0.872 60 May 2010 2 CR2 0.895 0.747 0.803 0.759 Psr (mm) CR3 0.720 0.533 0.602 0.577 30 Qobs 4 CR4 0.897 0.786 0.908 0.926 Qsim Date MAE 2.082 3.550 3.412 1.348 0 6 RMSE 3.598 7.381 6.036 2.0349 R 0.975 0.961 0.908 0.965 d 0.986 0.978 0.951 0.973 11-May- 10 22-Jun-09 5-Sep-2007 22-Jun-09 12-May- 10 23-Jun-09 6-Sep-2007 14-May- 10 23-Jun-09 7-Sep-2007 15-May- 10 24-Jun-09 8-Sep-2007 17-May- 10 24-Jun-09 9-Sep-2007 25-Jun-09 18-May- 10 25-Jun-09 10-Sep-2007 20-May- 10 26-Jun-09 11-Sep-2007 21-May- 10 26-Jun-09 12-Sep-2007 27-Jun-09 23-May- 10 13-Sep-2007 27-Jun-09 24-May- 10 28-Jun-09 14-Sep-2007 26-May- 10 28-Jun-09 15-Sep-2007 29-Jun-09 27-May- 10 16-Sep-2007 29-Jun-09 29-May- 10 17-Sep-2007 30-Jun-09 29-May-10 30-May-10 31-May-10 1-Jun-10 2-Jun-10 3-Jun-10 4-Jun-10 5-Jun-10 6-Jun-10 7-Jun-10 8-Jun-10 9-Jun-10 10-Jun-10 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova Fig. 5. The spatial patterns of the maximum surface soil moisture simulated by the SHETRAN hydrologic model (left) and estimated by downscaling from the scatterometer (right) for the days of the peak runoff occurring: 7 September 2007; 24 June 2009; 17 May 2010 and 2 June 2010. Table 5. The assessment of the agreement between the two spatially distributed soil moisture estimates for the Jičinka River catchment according to criterion functions: CR3, CR4, MAE, R, RMSE and d (average/ minimum /maximum values). Criterion Calibration rain event Validation rain events September 2007 June 2009 May 2010 June 2010 CR3 –0.28/–4.35/0.30 –0.495/–1.387/0 –1.45/–5.1/–0.02 –2.5/–7/–0.34 CR4 0.89/0.62/0.99 0.81/0.71/0.999 0.76/0.56/0.99 0.72/0.58/0.999 MAE 0.07/0.04/0.30.0 0.09/0.05/0.37 0.11/0.05/0.16 0.11/0.04/0.25 R 0.8/0.06/0.995 0.63/0.004/0.99 0.68/0.12/0.99 0.71/0.196/0.99 RMSE 0.096/0.08/0.196 0.11/0.06/0.15 0.12/0.05/0.18 0.13/0.05/0.17 d 0.55/0.18/0.66 0.51/0.15/0.91 0.49/0.24/0.68 0.41/0.15/0.53 8 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Fig. 6. The spatial distribution of the correlation coefficient, the mean absolute error and the index of agreement across the Jičinka River catchment for the analyzed rain events in September 2007, June 2009, May 2010 and June 2010. The consistency between the two sources of soil moisture in- maximum values of the applied evaluation criteria at the level of formation was spatially analyzed in terms of the same evaluation the Jičinka River catchment are also shown in Table 5. criteria which were used for the evaluation of the hydrological It should be noted that negative values of criteria CR1 – CR3 model performance. The spatial distribution of the correlation were obtained when soil moisture estimates from the two dif- coefficient, the mean absolute error and the index of agreement ferent sources were compared. The values of criteria CR3, are shown in Fig. 6. The determined average, minimum and which ranges from –7 to 0.3, are shown in Table 5. However, 9 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova the obtained negative values do not indicate the lack of agree- On the basis of the results of the evaluation criteria it can be ment between the two types of soil moisture estimates. This concluded that a good correlation between spatially and tempo- result was obtained because spatial and temporal differences in rally distributed soil moisture values estimated from the soil moisture are small in very wet conditions which prevail SHETRAN model and by downscaling from the satellite soil during the analyzed intensive rain events. Although the differ- moisture data was obtained. The results of the evaluation crite- ences between the soil moisture values from the hydrological ria indicate that soil moisture values obtained by downscaling model and derived from satellite data are not significant, in satellite data can be used as additional criteria for the calibra- cases of all the analyzed rain events, the temporal differences tion and validation of hydrological models in small catchments. between the satellite derived soil moisture values for a grid cell In that way, downscaled satellite soil moisture data can facili- and its average value are smaller. In that way, negative values tate and improve model parameterization and runoff simula- of the evaluation criteria CR1 to CR3 were reached. On the tions by reducing uncertainties in calibration and validation of other hand, the better values of the other evaluation criteria hydrological models for small catchments. Although both (Table 5 and Fig. 6) signify that there is a correlation between sources of soil moisture information have certain limitations the two types of soil moisture estimates. The average values of and uncertainties, their integrated use could improve hydrologi- the coefficient of correlation at the catchment level are in the cal simulations. range between 0.625 to the 0.797, while the average values of the index of agreement are in the range between 0.413 to 0.717. Acknowledgements. This paper is a result of scientific collabo- The obtained results are satisfactory if we take into account that ration between the Department of Ecological Engineering for there is a considerable amount of uncertainty in soil moisture Soil and Water Resources Protection at the Faculty of Forestry, estimated by both the hydrological model and by downscaling University of Belgrade and the Institute of Landscape Water from microwave remote sensing. Management at the Faculty of Civil Engineering, Brno Univer- A comparison between the simulated soil moisture of large- sity of Technology. This paper was supported by the Czech scale hydrological models and satellite derived soil moisture National Agency for Agricultural Research (Project No. was performed for many areas across the world (Alvarez- QK1720303), the Technology Agency of the Czech Republic Gorreton et al., 2016; Badou et al., 2018; Brocca et al., 2011; (Project No. TH04030363) and by the Ministry of Education, Laiolo et et al., 2014; Lopez et al., 2017; Parajka et al., 2009; Science and Technological Development, Republic of Serbia Wanders et al.,2013; Xiong et al., 2018). Good agreement was (Grant No. TR43007). often obtained between these two sources of soil moisture esti- mation. On the basis of the results obtained in these studies, it REFERENCES was concluded that the use of remotely sensed soil moisture Albergel, C., Calvet, J.C., Mahfouf, J.F., Rüdiger, C., Barbu, data in calibration and parameter identification of large-scale A.L., Lafont, S., Roujean, J.L., Walker, J.P., Crapeau, M., hydrological models contributed to a better simulation of soil Wigneron, J.P., 2010. Monitoring of water and carbon fluxes moisture content over the catchment and better discharge simu- using a land data assimilation system: A case study for lations. southwestern France. Hydrology and Earth System Sciences, This study has shown that downscaled soil moisture data 14, 1109–1124. DOI: 10.5194/hess-14-1109-2010 may also be used as additional criteria for the calibration and Alvarez-Garreton, C., Ryu, D., Western, A.W., Crow, W.T., validation of distributed hydrological models in small catch- Su, C.-H., Robertson, D.R., 2016. Dual assimilation of satel- ments, since good correlation was obtained between the two lite soil moisture to improve streamflow prediction in data types of soil moisture estimates in this study. This type of anal- scarce catchments. Water Resour. Res., 52, 5357–5375. ysis was not performed in previous studies. In that way, the use DOI: 10.1002/2015WR018429 of satellite derived soil moisture data can reduce parameter Badou, D.F., Diekkruger, B., Montzka, C., 2018. Validation of uncertainty and improve rainfall-runoff modeling and the accu- satellite soil moisture in the absence of in situ soil moisture: racy of flood predictions for small catchments. Similarly to the the ecase of the Tropical Yankin Basin. South African Jour- previously obtained results for large basins, it can be concluded nal of Geomatics, 7, 3. http://dx.doi.org/10.4314/sajg.v7i3.3 that downscaled remote sensed soil moisture can be potentially Beven, K., 2006. A manifesto for the equifinality thesis. Journal used to improve the understanding of hydrological processes in of Hydrology, 320, 18–30. small cathments. Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., Hasenauer, S., 2010. Improving runoff pre- 5 CONCLUSION diction through the assimilation of the ASCAT soil moisture product. Hydrology and Earth System Sciences, 14, 1881– Accurate soil moisture information is very important for 1893. DOI:10.5194/hess-14-1881-2010 flood forecasting. Due to variation in climate, soil, vegetation, Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, topography and other factors, soil moisture content is highly T., Wagner, W., Dorigo, W., Matgen, P., Martinez- variable in time and space. The knowledge of spatio-temporal Fernandez, J., Llorens, P., et al., 2011. Soil moisture estima- dynamics of soil moisture within the catchment can improve tion through ascat and amsr-e sensors: An intercomparison the estimation of runoff and contribute to a better understanding and validation study across europe. Remote Sens. Environ., of hydrological processes in catchments. 115, 3390–3408. In this study the comparison of spatial and temporal dynam- Chiew, F., McMahon, T., 1994. Application of the daily rain- ics of soil moisture, estimated from the physically based and fall–runoff model MODHYDROLOG to 28 Australian distributed SHETRAN hydrological model and by downscaling catchments. Journal of Hydrology, 153, 383–416. from satellite soil moisture data was performed on the example Corradini, C., 2014. Soil moisture in the development of hydro- of the small Jičinka River catchment (75.9 km ) in the Czech logical processes and its determination at different spatial Republic. The SHETRAN model was calibrated and verified on scales. J. Hydrol., 516, 1–5. the basis of observed streamflow hydrographs at the catchment Crow, W.T., Berg, A.A., Cosh, M.H., Loew, A., Mohanty, outlet. 10 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic B.P., Panciera, R., de Rosnay, P., Ryu, D., Walker, J.P., 2010. Validation of advanced microwave scanning radiome- 2012. Upscaling sparse ground-based soil moisture observa- ter soil moisture products. IEEE Transactions on Geoscience tions for the validation of coarse-resolution satellite soil and Remote Sensing, 48, 12, 4256–4272. DOI: moisture products. Rev. Geophys., 50, 2. 10.1109/TGRS.2010.2051035 https://doi.org/10.1029/2011RG000372 Koster, R.D., Mahanama, S.P.P., Livneh, B., Lettenmaier, D.P., Dai, Y., Xin, Q.,Wei, N., Zhang, Y., Shangguan,W.,Zuan, H., Reichle, R.H., 2010. Skill in streamflow forecasts derived Zhang, Z., Liu,S., Lu, X., 2019. A global high resolution data from large - scale estimates of soil moisture and snow. Na- set of soil hydraulic and thermal properties for land surface ture Geosciences, 3, 613–616. DOI: 10.1038/ngeo944 modelling. Journal of Advances in Modelling Earth Systems, Koster, R.D., Brocca, L., Crow, W.T., Burgin, M.S., De Lan- 11, 9, 2996–3023. https://doi.org/10.1029/2019MS001784 noy, G.J.M., 2016. Precipitation estimation using l-band and Danhelka, J., Kubat J., Šercl P., Čekal, R. (Eds.), 2014. Floods c-band soil moisture retrievals. Water Resour. Res., 52, 7213–7225. in the Czech Republic in June 2013. Czech Hydrometeoro- Laiolo, P., Gabellani, S., Pulvirenti, L., Boni, G., Rudari, R., et. logical Institute, Prague, Czech Republic. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, al., 2014. Validation of remote sensing soil moisture prod- G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., ucts with a distributed continuous hydrological model. In: Haas, E., Hamer, D.P., Hirschi, M., Ikonen, J., De Jeu, R., IEEE Geoscience and Remote Sensing Symposium. Quebec Kidd, R., Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P., City, pp. 3319–3322. DOI: 10.1109/IGARSS.2014.6947190. 2017. ESA CCI Soil Moisture for improved Earth system Lievens, H., S.K., Tomer, A., Al Bitar, G., De Lannoy, M., understanding: State-of-the art and future directions. Remote Drusch, G., Dumedah, H.-J. H., Franssen, Y., Kerr, B., Mar- Sensing of Environment, 203, 185–215. tens, Pan, M., 2015. SMOS soil moisture assimilation for https://doi.org/10.1016/j.rse.2017.07.001 improved hydrologic simulation in the Murray Darling Ba- Đukić, V., Radić, Z., 2014. GIS based estimation of sediment sin, Australia. Remote Sens. Environ., 168, 146–162. discharge and areas of soil erosion and deposition for the López López, P., Sutanudjaja, E.H., Schellekens, J., Sterk, G., torrential Lukovska River Catchment in Serbia. Water Re- and Bierkens, M.F.P., 2017. Calibration of a large-scale hy- drological model using satellite-based soil moisture and sources Management, 28, 13, 4567–4581. evapotranspiration products. Hydrol. Earth Syst. Sci., 21, https://link.springer.com/article/10.1007/s11269-014-0751-7 3125–3144. https://doi.org/10.5194/hess-21-3125-2017. Đukić, V., Radić, Z., 2016. Sensitivity analysis of a physically based distributed model. Water Resources Management, 3, Manfreda, S., McCabe, M.F., Fiorentino, M., Rodriguez-Iturbe, 1669–1684. https://link.springer.com/article/10.1007/s11269- I., Wood, E.F., 2007. Scaling characteristics of spatial pat- 016-1243-8 terns of soil moisture from distributed modelling. Adv. Wa- Ewen, J., Parkin, G., O'Connell, P.E., 2000. SHETRAN: Dis- ter Resour., 30, 2145–2150. tributed river basin flow and transport modelling system. Molnar, D.K., Julien, P.Y., 2000. Grid-size effects on surface ASCE J. Hydrologic Eng., 5, 250–258. Available at: runoff modelling. Journal of Hydrologic Engineering, 5, 1. https://research.ncl.ac.uk/shetran/SHETRAN_ASCE_paper. Montzka, C., Rötzer, K., Bogena, H.R., Sanchez, N., Vereeck- pdf en, H., 2018. A new soil moisture downscaling approach for Gruber, A., Dorigo, W.A., Crow, W., Wagner, W., 2017. Triple SMAP, SMOS, and ASCAT by predicting sub-grid variabil- collocation-based merging of satellite soil moisture retriev- ity. Remote Sens., 10, 427. Mualem, Y., 1976. A new model predicting the hydraulic con- als. IEEE Transactions on Geoscience and Remote Sensing, ductivitynof unsaturated porous media. Water Resour. Res., 55, 12, 1–13. https://doi.org/10.1109/TGRS.2017.2734070 12, 513–522. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dori- go, W., 2019. Evolution of the ESA CCI Soil Moisture Cli- Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through mate Data Records and their underlying merging methodol- conceptual models: Part I. A discussion of principles. Jour- ogy. Earth System Science Data, 11, 717–739. nal of Hydrology, 27, 3, 282–290. https://doi.org/10.5194/essd-11-717-2019 Pavlik, F., Dumbrovský, M., 2014. Influence of landscape Gwak, Y., Kim, S., 2017. Factors affecting soil moisture spatial retention capacity upon flood processes in Jičínka River ba- variability for a humid forest hillslope. Hydrol. Processes, sin. Acta Universitatis Agriculturae et Silviculturae Mende- 31, 431–445. lianae Brunensis, 62, 1, 191–199. DOI: Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez 10.11118/actaun201462010191 Gonzalez, M., Kilibarda, M., Blagoti´c, A., Shangguan, W., Parajka, J., Naeimi, V., Blöschl, G., Wagner, W., Merz, R., Wright, M.N., Geng, X., Bauer-Marschallinger, B., et al., Scipal, K., 2006. Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. 2017. Soilgrids 250 m: Global gridded soil information Hydrol. Earth Syst. Sci., 10, 353–368. based on machine learning. PLoS ONE, 12, e0169748. Parajka, J., Naeimi, V., Blöschl, G., Komma, J., 2009. Match- Hupet, F., Vanclooster, M., 2002. Intraseasonal dynamics of soil moisture variability within a small agricultural maize ing ERS scatterometer based soil moisture patterns with cropped field. J. Hydrol., 261, 86–101. simulations of a conceptual dual layer hydrologic model IPCC, 2012. Managing the risks of extreme events and disasters over Austria. Hydrol. Earth Syst. Sci., 13, 259–271, to advance climate change adaptation. A Special Report of https://doi.org/10.5194/hess-13-259-2009 Working Groups I and II of the Intergovernmental Panel on Peng, J., Loew, A., Zhang, S., Wang, J., Niesel, J., 2017. Spa- Climate Change [Field, C.B., V. Barros, T.F., Stocker, D., tial downscaling of satellite soil moisture data using a Vege- Qin, D.J., Dokken, K.L., Ebi, M.D., Mastrandrea, K.J., tation Temperature Condition Index. IEEE Trans. Geosci. Mach, G.-K., Plattner, S.K., Allen, M.T., Midgley, P.M. Remote Sens., 54, 1, 558–566. (eds.)]. Cambridge University Press, Cambridge, UK, and Pereira, A.R., Pruitt, W.O., 2004. Adaptation of the New York, NY, USA, 582 p. Thornthwaite scheme for estimating daily reference evapo- Jackson, T.J., Cosh, M.H., Bindlish, R., Starks, P.J., Bosch, transpiration. Agricultural Water Management, 66, 3, 251–257. D.D., Seyfried, M., Goodrich, D.C., Moran, M.S., Du, J., 11 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova Qu, W., Bogena, H.R., Huisman, J.A., Vanderborght, J., Schuh, Thiessen, A.H., 1911. Precipitation averages for large areas. M., Priesack, E., Vereecken, H., 2015. Predicting subgrid Monthly Weather Review, 39, 7, 1082–1084. variability of soil water content from basic soil information. http://dx.doi.org/10.1175/1520-0493(1911)39<1082b: PAF- Geophys. Res. Lett., 42, 789–796. LA>2.0.CO;2 Richards, L.A., 1931. Capillary conduction of liquids through van Genuchten, M.T., 1980. A closed-form equation for pre- porous mediums. Physics, 1, 5, 318–333. DOI: dicting the hydraulic conductivity of unsaturated soils. Soil 10.1063/1.1745010 Sci. Soc. Am. J., 44, 892–898. Rodell, M., Houser, P., Jambor, U., Gottschalck, J., Mitchell, Vereecken, H., Huisman, J.A., Pachepsky, Y., Montzka, C., van K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Mar- J., Bosilovich, M., 2004. The global land data assimilation tinez, G., Vanderborght, J., 2014. On the spatio-temporal system. Bull. Am. Meteorol. Soc., 85, 381–394. dynamics of soil moisture at the field scale. J. Hydrol., 516, https://doi.org/10.1175/BAMS-85-3-381 76–96. Rosenbaum, U., Bogena, H.R., Herbst, M., Huisman, J.A., Pe- Verhoest, N.E.C., et al., 2015. Copula-based downscaling of terson, T.J., Weuthen, A., Western, A.W., Vereecken, H., coarse-scale soil moisture observations with implicit bias cor- 2012. Seasonal and event dynamics of spatial soil moisture rection, IEEE Trans.Geosci. Remote Sens., 53, 6, 3507–3521. patterns at the small catchment scale. Water Resour. Res., 48, Wanders, N., Bierkens, M.F.P., Jong, S.M., Roo, A., Karssen- 10. https://doi.org/10.1029/2011WR011518 berg, D., 2013. The benefits of using remotely sensed soil Rötzer, K., Montzka, C., Bogena, H., Wagner, W., Kerr, Y.H., moisture in parameter identification of large scale hydrolog- Kidd, R., Vereecken, H., 2014. Catchment scale validation ical models. Water Resour. Res., 50, 6874–6891. DOI: of smos and ascat soil moisture products using hydrological 10.1002/2013WR014639 modeling and temporal stability analysis. J. Hydrol., 519, Wang, L., Qu, J.J., 2009. Satellite remote sensing applications 934–946. for surface soil moisture monitoring: a review. Frontiers of Saint-Venant, A.J.C.B., 1871. Théorie du mouvement non Earth Science in China, 3, 2, 237–247. permanent des eaux, avec application aux crues des rivières Western, A.W., Grayson, R.B., Bloschl, G., Willgoose, G.R., et a l’introduction de marées dans leurs lits. Comptes rendus McMahon, T.A., 1999. Observed spatial organization of soil hebdomadaires des séances de l'Académie des sciences, 73, moisture and its relation to terrain indices. Water Resour. 147–154, 237–240. Res., 35, 797–810. Stoorvogel, J.J., Bakkenes, M., Temme, A.J.A.M., Batjes, Xiong, L., Yang, H., Zeng, L., Xu, C.-Y., 2018. Evaluating N.H., ten Brink, B.J.E., 2017. S-world: A global soil map for Consistency between the Remotely Sensed Soil Moisture environmental modelling. Land Degrad. Dev., 28, 22–33. and the Hydrological Model-Simulated Soil Moisture in the Shangguan, W., Dai, Y.J., Duan, Q.Y., Liu, B.Y., Yuan, H., Qujiang Catchment of China. Water, 10, 3, 291. 2014. A global soil data set for earth system modeling. J. https://doi.org/10.3390/w10030291 Adv. Model. Earth Syst., 6, 249–263. Ye, W., Bates, B.C., Viney, N.R., Silvapan, M., Jakeman, A.J., Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Islam, T., 2013. 1997. Performance of conceptual rainfall–runoff models in Appraisal of SMOS soil moisture at a catchment scale in a low-yielding ephemeral catchments. Water Resources Re- temperate maritime climate. Journal of Hydrology, 498, search, 33, 1, 153–166. 292–304. Teuling, A.J., Troch, P.A., 2005. Improved understanding of Received 27 September 2019 soil moisture variability dynamics. Geophys. Res. Lett., 32. Accepted 23 September 2020 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrology and Hydromechanics de Gruyter

Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic

Download PDF
12 pages

Loading next page...
 
/lp/de-gruyter/spatio-temporal-analysis-of-remotely-sensed-and-hydrological-model-BgTyVc1bFk

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
de Gruyter
Copyright
© 2021 Vesna Đukić et al., published by Sciendo
ISSN
0042-790X
eISSN
0042-790X
DOI
10.2478/johh-2020-0038
Publisher site
See Article on Publisher Site

Abstract

J. Hydrol. Hydromech., 69, 2021, 1, 1–12 ©2021. This is an open access article distributed DOI: 10.2478/johh-2020-0038 under the Creative Commons Attribution ISSN 1338-4333 NonCommercial-NoDerivatives 4.0 License Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic 1* 1 2 2 Vesna Đukić , Ranka Erić , Miroslav Dumbrovsky , Veronika Sobotkova University of Belgrade, Faculty of Forestry, Department of Ecological Engineering for Soil and Water Resources Protection, Kneza Višeslava, 1, 11000 Belgrade, Serbia. Brno University of Technology, Faculty of Civil Engineering, Institute of Landscape Water Management, Antonínská 548/1, 601 90 Brno, Czech Republic. Corresponding author. E-mail: vesna.djukic@sfb.bg.ac.rs Abstract: The knowledge of spatio-temporal dynamics of soil moisture within the catchment is very important for rain- fall–runoff modelling in flood forecasting. In this study the comparison between remotely sensed soil moisture and soil moisture estimated from the SHETRAN hydrological model was performed for small and flashy Jičinka River catchment (75.9 km ) in the Czech Republic. Due to a relatively coarse spatial resolution of satellite data, the satellite soil moisture data were downscaled, by applying the method developed by Qu et al. (2015). The sub-grid variability of soil moisture was estimated on the basis of the mean soil moisture for the grid cell and the known hydraulic soil properties. The SHETRAN model was calibrated and verified to the observed streamflow hydrographs at the catchment outlet. The good correlation between the two different soil moisture information was obtained according to the majority of applied criteria. The results of the evaluation criteria indicate that the downscaled remotely sensed soil moisture data can be used as additional criteria for the calibration and validation of hydrological models for small catchments and can contribute to a better estimation of parameters, to reduce uncertainties of hydrological models and improve runoff simulations. Keywords: SHETRAN hydrological model; Downscaled remotely sensed soil moisture; Runoff and soil moisture validation; Spatio-temporal variability of soil moisture; Flash floods; Small catchment. 1 INTRODUCTION makes them unsuitable for hydrological analyses at the basin level (e.g. Brocca et al., 2010; Srivastava et al., 2013; Wang The large floods that have occurred in recent years in many and Qu, 2009). regions of the world made local, national and international A useful way to reduce the uncertainty of the model and im- authorities increasingly aware of flood and inundation hazard prove the model performance is to incorporate remotely sensed and the necessity of a better understanding of floods and flood soil moisture information. Remotely sensed soil moisture pro- protection management improvement (IPCC, 2012). Several vides information about spatial and temporal dynamics of soil flash floods occurred in the territory of the Czech Republic moisture, which can facilitate calibration and validation of during the last decade of June and at the beginning of July hydrological models of large scale (see e.g. Albergel et al., 2009, and in May and June 2010 (Danhelka et al., 2014). The 2010; Brocca et al., 2011; Jackson et al., 2010; Parajka et al., hilly and flashy Jičinka River basin (75.9 km ) in the Moravian 2006; Rötzer et al., 2014). However, due to relatively coarse - Silesian region in the Czech Republic was particularly spatial resolution of approximately several tens of kilometers, affected by serious flooding due to steep slopes of the terrain, satellite soil moisture observations cannot be effectively ap- and a high percentage of soil types with low-intensity plied to hydrological studies in small catchments. Due to varia- infiltration (Pavlik and Dumbrovský, 2014). tions in climate, soil, vegetation, topography and other factors, Rainfall–runoff models can be very useful for flash flood soil moisture is heterogeneously distributed within catchments. forecasting. The runoff generating mechanisms are highly Over the past decades, various downscaling methods of sat- dependant on soil water content. For hydrological modelling ellite soil moisture products have been studied for the im- and flood forecasting, it is important to understand the spatial- provement of their spatial resolution. The variability of soil temporal variability of soil moisture at the basin level (Cor- moisture within a grid cell has often been described by taking radini, 2014; Koster et al., 2010; Manfreda et al., 2007; Ve- into account soil texture (Crow et al., 2012; Gwak and Kim, reecken et al., 2014). Physically based and distributed models 2017; Teuling and Troch, 2005), vegetation (Western et al., can provide a lot of insight into how soil moisture changes in 1999), topography and other important physical characteristics space and time as a function of terrain, soil and vegetation of the basin which effect the soil moisture (Hupet and Van- characteristics of the basin. However, physically based distrib- clooster, 2002; Koster et al., 2016; Rosenbaum et al., 2012). uted models usually need a large number of parameters (soil The estimation of sub- grid variabilty of soil moisture on the surface, soil properties and land use), which can increase the basis of soil texture data is facilitated due to the availability of model uncertainty and decrease the performance of the model high resolution data on soil properties for the entire globe (Dai (Beven, 2006). et al., 2019; Hengl et al., 2017; Shangguan et al., 2014; The direct ground-based measurements of soil moisture are Stoorvogel et al., 2017). sufficiently accurate, but are difficult, time-consuming and A comprehensive review on the downscaling methods for limited to discrete measurements at particular locations, which satellite remote sensing based soil moisture is provided in Peng 1 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova et al. (2017). They analysed the advantages and limitations 2.2 Calibration and validation of the SHETRAN model associated with each method based on published validation studies. Currently, there are still no effective ways for evaluat- The hydrological model was obtained by the calibration and ing either the original remotely sensed soil moisture or the validation of the SHETRAN model on the basis of the downscaled soil moisture outputs. Usually, the remotely sensed measured streamflow hydrographs at the catchment outlet. The soil moisture products are validated against ground–based soil SHETRAN model was calibrated for the storm event which moisture observations. In general, good agreement was found happened in September 2007 and validated for the storm events between downscaled soil moisture and in situ measurements happened in June 2009, May 2010 and June 2010. In rainfall– (Lievens et al., 2015; Peng et al., 2017; Verhoest et al., 2015). runoff modelling, the streamflow is of crucial importance It was also concluded that the accuracy of the downscaled soil because it reflects the hydrological response of the whole moisture highly depends on the accuracy of the original soil catchment. It was assumed that the good agreement between the moisture and that it surpasses the original coarse soil moisture modeled and observed runoff hydrographs implies that the for many studies (Peng et al., 2017). other components of hydrological cycle, in this case soil However, the consistency and the level of agreement be- moisture, are appropriately determined. tween downscaled soil moisture and soil moisture simulated by The area of catchment was discretized into grid cells. The distributed hydrologic models, particualrly in small catchments adopted grid cell size in this study is 500 m × 500 m. Although are still not well understood. The objective of this study is to it can be expected that by adopting a coarser grid resolution a evaluate the consistency and the agreement between lot of spatially important data may have been lost, the use of a downscaled remotely sensed soil moisture, and soil moisture coarser grid resolution can be justified when simulating events estimated from the physically based and distributed SHETRAN of high intensity and/or hydrographs with a short concentration hydrological model in a small catchment. The evaluation is time (Molnar and Julien, 2000), as it is the case in the analysed performed for the small Jičinka River catchment (75.9 km ) in Jičinka River catchment. In the study of Molnar and Julien the Czech Republic during flash floods. The evaluation of (2000), it was concluded that a coarser grid resolution can be modeled soil moisture patterns and their consistency with satel- used in hydrological models as long as parameters are appro- lite data during runoff events provides additional information priately calibrated. about model reliability and accuracy which is an essential step Through numerous simulations it was concluded that the for the development of soil moisture assimilation strategies in Strickler′s coefficients for overland flow and for river flow, the research or operational hydrologic applications. vertical saturated hydraulic conductivity of the subsurface soil and the saturated water content had an important effect on the 2 METHOD size of overland flow. It was also concluded that the values of 2.1 SHETRAN model base flow were dependant on the horizontal saturated hydraulic conductivity in the saturated zone (Đukić and Radić, 2014; SHETRAN is a 3D coupled surface/subsurface physically Đukić and Radić, 2016). The preliminary values of model based and spatially distributed river basin model. SHETRAN parameters used in model calibration were determined from the (version V4.4.5) water flow component was used in this study. literature (Table 3) and are presented in the Table 1. The values The water flow component consists of 4 modules: evapotranspi- of all other model parameters were fixed and adopted their ration/interception; overland/channel; variably saturated subsur- average values from the literature (Table 3 and Table 1). The face and snowmelt (Ewen et al., 2000). The components of adopted values of all parameters are presented in Table 1. interception and evapotranspiration were neglected in this The agreement between the modelled and observed runoff study, because their influence is negligible in rain event mod- was evaluated using following objective functions. The first els. Both the overland and channel flows are described by the objective function is based on the formulation proposed by diffusive wave approximation of the full St. Venant equations Nash and Sutcliffe (1970) and is given by: (Saint-Venant, 1871). The continuity equation is as follows: () QQ − obs,, i sim i ∂∂ QA i =1 += q (1) CR1 = 1– (3) ∂∂ xt () QQ − obs,i obs i =1 The momentum conservation is as follows: where: Q is the observed streamflow on day i, Q is the simu- obs,i sim,i ∂∂QQ (( α /A)) ∂h gQQ ++ gA+ = 0 (2) lated streamflow, Q is the average of the observed streamflow obs ∂∂ tx ∂x CAR over the calibration (or verification) periods of n days. Due to changeable variance of model errors, the Nash – Sut- where: t is time, x is the distance measured along the channel 3 –1 2 cliffe coefficient of efficiency tends to emphasize the large (m), Q is discharge (m s ), A is the hydraulic area (m ), q is 3 –1 errors. For comparison, the function of Chiew and McMahon the tributary outflow (m s ), h is the channel depth (m), C is 0.5 –1 (1994) was used as the second objective function in which the the Chezy coefficient (m s ), R is the hydraulic radius (m) square root of the considered values were related using the α is the correction factor (–). and following equation: The soil water movement in the unsaturated zone is de- scribed using the Richards equation (Richards, 1931). The ArcGIS software ArcView 10.2 was used to prepare the QQ − () obs,, i sim i input data related to the physical characteristics of the basin and i =1 CR2=− 1 (4) n 2 for the displaying and visualisation of the spatially distributed QQ − ( obs,i obs ) soil moisture values across the basin. i =1 2 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Table 1. The ranges of model parameters used in calibration of the SHETRAN model, its optimal values (in parenthesis) and the adopted values of uncalibrated parameters for the Jičinka River basin uncalibrated parameters for the Jičinka River basin. Land use/ Overland flow/channel Soil Type Soil/rock parameters vegetation parameters depth Texture k k n S S vs hs θs θr α t tR –1 –1 1/3 –1 1/3 –1 (m) (m day ) (m day ) (–) (–) (–) (–) (m s ) (m s ) Sandy clay 0.223–0.5814 0.223–0.5814 0.419–0.695 4–8 0–0.7 0.047 0.014 1.317 Forest Dystric loam (0.300) (0.300) (0.480) (7) Cambisol Sandy clay 0.223–0.5814 0.223–0.5814 0.419–0.695 Arable 8–20 0.7–1.2 0.047 0.014 1.317 loam (0.300) (0.300) (0.480) land (18) Geological (sandstone, 0.01–5 Natural 7–18 15–40 1.2–4 4 0.6 0.1 0.001 1.1 substrate schists) (4) grasslands (16) (30) 0.217–0.4105 0.217–0.4105 0.437–0.442 Scarce 30–50 Rendzina 0–0.5 Clay loam 0.075 0.013 1.415 (0.270) (0.270) (0.440) vegetation (42) Geological (sandstone, 0.01–5 1.2–4 4 substrate schists) (4) 0.217–0.4105 0.217–0.4105 0.426–0.469 0.075 0–0.5 Clay loam 0.013 1.415 Eutric (0.270) (0.270) (0.44) cambisol 0.128–0.192 0.426–0.469 0.5–1.05 Loam 0.15 0.078 0.036 1.56 (0.15) (0.430) Geological (sandstone, 0.01–5 1.2–4 4 substrate schists) (4) 0.217–0.4105 0.217–0.4105 0.426–0.469 0–0.40 Clay loam 0.075 0.013 1.415 (0.255) (0.255) (0.430) 0.130–0.196 Fluvisol 0.40–1.0 Silty loam 0.163 0.452 0.093 0.005 1.68 (0.163) Sandy clay 0.223–0.5814 0.223–0.5814 0.419–0.695 1.0–1.25 0.047 0.014 1.317 loam (0.300) (0.300) (0.480) Geologic (sandstone, 0.01–5 1.25-4 4 0.6 0.1 0.001 1.1 substrate schists) (4) The third introduced criterion is potentially useful in the () QQ−−(Q Q) obs,, i obs sim i sim context of prediction, for example, where simulations must be i =1 R = (9) as close as possible to the observed values at each time step nn (Ye et al., 1997). It is defined by: () QQ−−(QQ)  obs,, i obs sim i sim ii == 11 QQ − obs,, i sim i n i =1 () QQ − CR3=− 1 (5)  obs sim i =1 dd =−1,0≤ ≤1 (10) QQ − obs,i obs i =1 () QQ−+Q −Q sim obs obs obs i =1 The fourth criterion (Pereira and Pruitt, 2004) quantifies the ability of the model to accurately reproduce streamflow vol- 2.2 Downscaling approach for sub-grid variability umes over the periods of observation. Criterion CR4 differs estimation from the other three criteria (CR1–CR3), because it does not measure deviation from the observed values at each step of The satellite soil moisture data were downscaled by applying simulation. Therefore, CR4 cannot be used alone as a criterion the method developed by Qu et al. (2015) in this study. The for calibration. This criterion is defined by: downscaling approach (Qu et al., 2015) applied in this study is based on the Mualem-van Genuchten (MvG) model, in which nn  the unsaturated soil hydraulic properties are described using the QQ   sim,, i obs i model of van Genuchten (1980) for the water retention function ii == 11  CR4=− 1 − (6) nn in combination with the hydraulic conductivity function  QQ  obs i sim i  ,, introduced by Mualem (1976). The soil water retention ii == 11  equation, θ(h), is given by: In addition to the already mentioned four criteria (CR1– S (θθ − ) es r CR4) the following statistical measures were also used: the θθ ()hh =+ ≤ 0 (11) root-mean-square error (RMSE), the mean absolute error  1 + α h  (MAE), the coefficient of correlation (R) and the index of agreement (d). They are expressed by the following equations: 3 –3 where θ is the volumetric water content (cm cm ) at pressure head h (cm); θ and θ are the residual and saturated water con- s r 3 –3 –1 MAE = QQ − (7)  tent (cm cm ), respectively; α (cm ), n (–), and m (–) (m = obs,, i sim i i =1 1 – 1/n) are shape parameters. The hydraulic conductivity func- tion, K(h), is given by: Lm 1/m () QQ − obs,, i sim i  KS()=− K S 1 (1− S ) , h≤0 (12) ese e  i =1 RMSE = (8) –1 where Ks is the saturated hydraulic conductivity (cm d ) and K 3 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova –1 (cm d ) is the hydraulic conductivity and L is the pore connec- were used for the evaluation of the hydrologic model perfor- tivity parameter (L = 0.5); S is effective saturation given by: mance. However, in this case, instead of the simulated (Q ) e sim and the observed streamflow values (Q ), the values of SM obs HM θθ () h − and SM are used in Equations (3) – (10). scatt S = (13) θθ − sr 3 DATA 3.1 Study area For each grid of coarse scale satellite data product, the corre- lation between the standard deviation and soil moisture σθ() The Jičinka River basin up to the ″Novy Jičin″ water level is expressed as a function of the mean and the standard devia- monitoring station (Fig. 1) is situated in the Moravian – Silesi- tion of the soil hydraulic parameters (K , θ , θ , α, n,) (Montzka s s r an Region, in the eastern part of the Czech Republic. The Jičin- et al., 2018) using the following equation: ka River is a tributary of the Moravice River, which belongs to the basin of the Opava River and to the basin of the Baltic Sea. σ = θ Altitudes in the Jičinka River basin vary from 270 meters above 22 sea level in the lower part of the basin to 1000 meters in the   σρ aa σρ σ ρ 22 2 ff 13 αα nn bb σ++ +   source areas of the basin. The steep slopes of the terrain in the 12 α (14) 11 ++ aaρρ() a a ()1+aρ a ()   22fn 2 α 2 2 2   Jičinka River basin with the average slope of 9.1% significantly 0  affect the characteristics of runoff.  a σρ  aσρ 3 n 22 22 1 αα n ++ bbσσ +22 bb − + bb −   34 n θ 12 23  11 ++ aaρρ  22 α n  f is the log-transformed saturated hydraulic conductivity (ln Ks); ρ is the vertical correlation length of the respective parameters. The coefficients a –a and b –b are related to the mean of the 1 3 0 4 soil hydraulic parameters: θ , θ , h, α and n. They are calculated s r using the equations which are described in papers (Montzka et al., 2018; Qu et al., 2015). The sub-grid surface soil moisture values can be estimated based on the known average value of surface soil moisture within a grid cell and using the estimated value of the σθ() function and proxy information. It is supposed that the spatial variability of soil moisture within each coarse scale satellite pixel is related to the known spatial variability of the proxy data (Montzka et al., 2018). By multiplication with the provided soil moisture standard deviation at the given mean surface soil moisture, the sub-grid surface soil moisture values can be cal- culated using the following equation: PP − ij , θθ =+σ θ (15) () ij , θ where: θ is the predicted soil moisture at this fine scale ij , location; P , is the proxy data at the fine scale sub-grid i j y-location i and x-location j, P is the mean of the proxy, and σP is the standard deviation of the proxy. In this paper the sur- face soil moisture data were downscaled from its original reso- lution of 25 km to 1 km resolution. This was done by using the saturated hydraulic conductivity as a proxy for soil moisture heterogeneity. After that, the obtained values of soil moisture Fig. 1. The Jičinka River basin with the river system and the rain were resampled at a 500 m resolution. gauging and hydrological stations in the basin. 2.4 Comparison of surface soil moisture estimates Four pedological soil types identified in the studied basin in- After the calibration and validation of the SHETRAN model, clude the following: fluvisol (10.5%), eutric cambisol (46.2%), simulations of soil moisture in unsaturated zone were per- dystric cambisol (41.3%) and rendzina (2%) (Fig. 2). The hy- formed using the sets of parameters optimized for the calibra- drogeological behaviour of the whole basin is defined by the tion and the validation rain events. In that way, soil moisture dominant presence of tertiary and quaternary rocks (sandstones, estimates are one of the results obtained by applying the schists, loess, sand and gravel) which occupy about 75% of the SHETRAN hydrological model. basin (Fig. 2). Four different vegetation types identified in the The consistency between the surface soil moisture simulated Jičinka River basin are forest (34%), natural grasslands (48%), by the hydrologic model (SMHM) and the surface soil moisture arable land (8.5%) and orchards (0.03%) (Fig. 2). Urban areas downscaled from the satellite retrieved soil moisture product occupy about 9.4% of the basin area. (SM ) was spatially analyzed using the same criteria which scatt 4 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Fig. 2. The map of soil types, vegetation types, geological types and the slope map of the Jičinka River basin up to the "Novy Jičin" water level monitoring station. Table 2. The total amounts of precipitation (P ) fallen on the ground, the volume of precipitation (V ), the maximum runoff value (Q ) tot p max and the runoff volume (Vr). Number Simulation period Ptot Vp Qmax Vr 3 3 3 3 3 of rain event (mm) (10 m ) (m /s) (10 m ) 1 5.09.2007. (10:00) 17.09.2007. (10:00) 190.13 14429.9 98 6387.5 2 22.06.2009. (05:00 – 29.06.2009. (7:00) 150.2 11403 264 5805.5 3 11.05.2010. (13:00 – 29.05.2010. (3:00) 232.8 17670.4 75.6 15666.4 4 30.05.2010. (11:00 – 2.06.2010. (14:00) 46.2 3502.2 43.1 4429.9 3.2 Input data for the SHETRAN model logical station. The characteristics of precipitation and runoff are presented in Table 2. The average hourly heights of rainfall in the basin during the The soil, geological, vegetation and slope maps of the Jičin- analyzed rain events were calculated by applying the method of ka basin were obtained in the form of vector polygon data at the Thiessen polygons (Thiessen, 1911) based on the hourly precip- corresponding digitized maps (Fig. 2). All input data and the itation levels measured at the climatological stations in the corresponding input parameters used in SHETRAN are summa- basin (Hodslavice, Verovice and Novy Jičin) (Fig. 1). The rized in Table 3. hourly values of runoff are measured at the ″Novy Jičin″ hydro- 5 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova Table 3. Input data and parameters used in the SHETRAN model. Type of input Input parameters Source of input data data Meteorological Hourly precipitation Czech Hydrometeorological Institute Hydrological Registered streamflow hydrographs Topographic Digital elevation model (DEM) of the resolution: 10 m × 10 m T.G. Masaryk Water Research Institute Land use/ Strickler’s coefficient for overland flow (St) and for channel Corine Land Cover Databases vegetation flow (StR) (Engman,1986) https://land.copernicus.eu/pan-european/corine- distribution land-cover Soil types Hydraulic soil/rock properties (porosity and specific storage, http://globalchange.bnu.edu.cn/research/soil5.jsp. residual water content (θ ), saturated water content (θ ), verti- Geological type r s ″Czech Geological Survey″-ArcGIS online cal saturated hydraulic conductivity (kvs), horizontal saturated conductivity (k ), van Genuchten - α, van Genuchten - n hs 3.3 Satellite soil moisture data of: 0 – 0.05 m, 0.05 – 0.15 m, 0.15 – 0.30 m, 0.30 – 0.60 m, 0.60 – 1.00 m, and 1.00 – 2.00 m. Grid maps of hydraulic soil The satellite soil moisture data used in this study were taken parameters for the Jičinka River catchment, which were derived from the European Space Agency (ESA) Climate Change Ini- from the GSDE database, are presented in Fig 3. tiative (CCI) Soil Moisture (SM) project (http://www.esasoilmoisture-cci.org). The ESA CCI SM v04.7 4 RESULTS AND DISCUSSION product consists of three surface soil moisture data sets: the 4.1 Results of the hydrologic model performance evaluation “ACTIVE Product”, the “PASSIVE Product” and the “COM- BINED Product” (Dorigo et al., 2017). The ESA CCI SM The comparative overview of the results of the calibration product was obtained by combining the soil moisture retrievals and validation of the SHETRAN model are presented in Fig. 4. from seven passive (SMMR, SSM/I, TMI, AMSR-E, WindSat, The figure shows a close relationship between the modeled and AMSR2 and SMOS) and two active (ERS AMI and ASCAT) the observed runoff hydrographs. The results of the assessment microwave sensors into a global data set spanning the period of the quality of model simulations by applying CR1 – CR4 1979 – 2010. The homogenized and merged products present criteria in Eqs. (3) – (6), and by applying the statistical surface soil moisture with a global coverage and a coarse spa- measures in Eqs. (7) – (9) are presented in Table 4. tial resolution of approximately 25 km and a high temporal On the basis of Table 4, it can be concluded that the good resolution of 1 day (Gruber et al., 2019). agreement was found between the observed and the modeled In this study the ACTIVE product was used. The “ACTIVE streamflow hydrographs for both the calibration and the valida- Product” was created by the University of Vienna (TU Wien) tion rain events. It should be noted that the best performance based on observations from C-band scatterometers. The sensors measures were observed for the calibration rain event in Sep- ERS AMI and ASCAT operate at similar frequencies (5.3 GHz tember 2007 according to the majority of the applied criteria. A C-band) and share a similar design. Different algorithms are small drop in the determined values of the applied criteria from used for the retrieval of soil moisture from satellite measure- calibration to verification indicates that the model performance ments (Gruber et al., 2017). decreases only slightly. It should also be noted that there was an The ACTIVE soil moisture data are provided in terms of improvement of the model performance for the verification rain saturation degree [%], ranging between 0 (dry) and 100 (satu- event in June in 2010 compared to the calibration rain event in rated). In this study soil moisture values were used in volumet- September 2007, according to the CR4 criteria, and according 3 3 ric units (m /m ). Soil moisture values in volumetric units were to the obtained values of the Mean Absolute Error (MAE) and calculated by multiplying the degree of saturation by soil poros- the Root Mean Square Error (RMSE). 3 3 ity (expressed in m /m ). Soil porosity data used in the conver- sion to volumetric soil moisture measurements have been taken 4.2 Comparison of soil moisture estimates from the GLDAS-Noah dataset (Rodell et al., 2004). For each of the analyzed rain events grid maps of daily val- 3.4 Soil hydraulic data ues of soil moisture at a 500 m resolution were created for the two different soil moisture sources. The spatial patterns of the The soil hydraulic properties are described using the maximum surface soil moisture estimated for the days of the peak runoff occurring during the analysed rain events are pre- following parameters: the saturated water content (θs), the residual residual water content (θr), the saturated hydraulic sented in Fig. 5. conductivity (K ), and the empirical parameters - vanGenuchten On the basis of Fig 5. it can be seen that there is an agree- - α and van Genuchten - n. These parameters are used in the ment between the soil moisture values simulated by application SHETRAN hydrological model, and they are also used in the the SHETRAN model and the values estimated by downscaling applied procedure of soil moisture downscaling from satellite from the scatterometer soil moisture data. As it can be ex- data. pected, the estimated spatially distributed values of soil mois- The high resolution soil hydraulic data were taken from the ture for the days of the peak runoff occurring during the ana- Global Soil Dataset for use in Earth System Models (GSDE) lysed rain events correspond to the values of the saturated water (Shangguan et al., 2014) located at content in majority of cases for the analysed rain events. Only http://globalchange.bnu.edu.cn/research/soil5.jsp. This database in the case of the rain event in June 2010, the average daily provides the global values of soil hydraulic and thermal param- values of estimated soil moisture are lower in regard to the eters at the spatial resolution of 30" and for vertical resolutions values of the saturated water content. 6 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Fig. 3. Grid maps of hydraulic soil parameters for the Jičinka River catchment derived from the GSDE database: saturated water content 3 –3 –1 3 –3 –1 (cm cm ), saturated hydraulic conductivity (cm day ) residual water content (cm cm ), and empirical parameters α (cm ) and n (–). 120 0 50 0 P(mm) 100 2 Q (m3/s) P(mm) 0.7 September 2007 80 Q (m3/s ) 4 1.4 30 June 2010 60 6 P P 2.1 Qobs 8 40 20 Qobs 2.8 Qsim 20 10 Qsim Date 10 3.5 0 12 0 4.2 300 0 Fig. 4. Observed (Qobs) and the SHETRAN model simulated (Qmod) Q (m /s ) P(mm) 250 streamflow hydrographs at the Jičinka River monitoring station (″Novy Jičin″ water level monitoring station) for the calibration 200 June 2009 (September 2007) and validation (June 2009, May 2010 and June P(mm) 30 2010) rain events. Qsim Qobs Date Table 4. The results of the application of criteria CR1–CR4 and 0 50 statistical measures (MAE, RMSE, R and d) for the assessment of the SHETRAN model simulations. Criterion Calibration rain event Validation rain events 90 0 September 2007 June 2009 May 2010 June 2010 Q (m3/s P(mm) 70 CR1 0.940 0.905 0.810 0.872 60 May 2010 2 CR2 0.895 0.747 0.803 0.759 Psr (mm) CR3 0.720 0.533 0.602 0.577 30 Qobs 4 CR4 0.897 0.786 0.908 0.926 Qsim Date MAE 2.082 3.550 3.412 1.348 0 6 RMSE 3.598 7.381 6.036 2.0349 R 0.975 0.961 0.908 0.965 d 0.986 0.978 0.951 0.973 11-May- 10 22-Jun-09 5-Sep-2007 22-Jun-09 12-May- 10 23-Jun-09 6-Sep-2007 14-May- 10 23-Jun-09 7-Sep-2007 15-May- 10 24-Jun-09 8-Sep-2007 17-May- 10 24-Jun-09 9-Sep-2007 25-Jun-09 18-May- 10 25-Jun-09 10-Sep-2007 20-May- 10 26-Jun-09 11-Sep-2007 21-May- 10 26-Jun-09 12-Sep-2007 27-Jun-09 23-May- 10 13-Sep-2007 27-Jun-09 24-May- 10 28-Jun-09 14-Sep-2007 26-May- 10 28-Jun-09 15-Sep-2007 29-Jun-09 27-May- 10 16-Sep-2007 29-Jun-09 29-May- 10 17-Sep-2007 30-Jun-09 29-May-10 30-May-10 31-May-10 1-Jun-10 2-Jun-10 3-Jun-10 4-Jun-10 5-Jun-10 6-Jun-10 7-Jun-10 8-Jun-10 9-Jun-10 10-Jun-10 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova Fig. 5. The spatial patterns of the maximum surface soil moisture simulated by the SHETRAN hydrologic model (left) and estimated by downscaling from the scatterometer (right) for the days of the peak runoff occurring: 7 September 2007; 24 June 2009; 17 May 2010 and 2 June 2010. Table 5. The assessment of the agreement between the two spatially distributed soil moisture estimates for the Jičinka River catchment according to criterion functions: CR3, CR4, MAE, R, RMSE and d (average/ minimum /maximum values). Criterion Calibration rain event Validation rain events September 2007 June 2009 May 2010 June 2010 CR3 –0.28/–4.35/0.30 –0.495/–1.387/0 –1.45/–5.1/–0.02 –2.5/–7/–0.34 CR4 0.89/0.62/0.99 0.81/0.71/0.999 0.76/0.56/0.99 0.72/0.58/0.999 MAE 0.07/0.04/0.30.0 0.09/0.05/0.37 0.11/0.05/0.16 0.11/0.04/0.25 R 0.8/0.06/0.995 0.63/0.004/0.99 0.68/0.12/0.99 0.71/0.196/0.99 RMSE 0.096/0.08/0.196 0.11/0.06/0.15 0.12/0.05/0.18 0.13/0.05/0.17 d 0.55/0.18/0.66 0.51/0.15/0.91 0.49/0.24/0.68 0.41/0.15/0.53 8 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic Fig. 6. The spatial distribution of the correlation coefficient, the mean absolute error and the index of agreement across the Jičinka River catchment for the analyzed rain events in September 2007, June 2009, May 2010 and June 2010. The consistency between the two sources of soil moisture in- maximum values of the applied evaluation criteria at the level of formation was spatially analyzed in terms of the same evaluation the Jičinka River catchment are also shown in Table 5. criteria which were used for the evaluation of the hydrological It should be noted that negative values of criteria CR1 – CR3 model performance. The spatial distribution of the correlation were obtained when soil moisture estimates from the two dif- coefficient, the mean absolute error and the index of agreement ferent sources were compared. The values of criteria CR3, are shown in Fig. 6. The determined average, minimum and which ranges from –7 to 0.3, are shown in Table 5. However, 9 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova the obtained negative values do not indicate the lack of agree- On the basis of the results of the evaluation criteria it can be ment between the two types of soil moisture estimates. This concluded that a good correlation between spatially and tempo- result was obtained because spatial and temporal differences in rally distributed soil moisture values estimated from the soil moisture are small in very wet conditions which prevail SHETRAN model and by downscaling from the satellite soil during the analyzed intensive rain events. Although the differ- moisture data was obtained. The results of the evaluation crite- ences between the soil moisture values from the hydrological ria indicate that soil moisture values obtained by downscaling model and derived from satellite data are not significant, in satellite data can be used as additional criteria for the calibra- cases of all the analyzed rain events, the temporal differences tion and validation of hydrological models in small catchments. between the satellite derived soil moisture values for a grid cell In that way, downscaled satellite soil moisture data can facili- and its average value are smaller. In that way, negative values tate and improve model parameterization and runoff simula- of the evaluation criteria CR1 to CR3 were reached. On the tions by reducing uncertainties in calibration and validation of other hand, the better values of the other evaluation criteria hydrological models for small catchments. Although both (Table 5 and Fig. 6) signify that there is a correlation between sources of soil moisture information have certain limitations the two types of soil moisture estimates. The average values of and uncertainties, their integrated use could improve hydrologi- the coefficient of correlation at the catchment level are in the cal simulations. range between 0.625 to the 0.797, while the average values of the index of agreement are in the range between 0.413 to 0.717. Acknowledgements. This paper is a result of scientific collabo- The obtained results are satisfactory if we take into account that ration between the Department of Ecological Engineering for there is a considerable amount of uncertainty in soil moisture Soil and Water Resources Protection at the Faculty of Forestry, estimated by both the hydrological model and by downscaling University of Belgrade and the Institute of Landscape Water from microwave remote sensing. Management at the Faculty of Civil Engineering, Brno Univer- A comparison between the simulated soil moisture of large- sity of Technology. This paper was supported by the Czech scale hydrological models and satellite derived soil moisture National Agency for Agricultural Research (Project No. was performed for many areas across the world (Alvarez- QK1720303), the Technology Agency of the Czech Republic Gorreton et al., 2016; Badou et al., 2018; Brocca et al., 2011; (Project No. TH04030363) and by the Ministry of Education, Laiolo et et al., 2014; Lopez et al., 2017; Parajka et al., 2009; Science and Technological Development, Republic of Serbia Wanders et al.,2013; Xiong et al., 2018). Good agreement was (Grant No. TR43007). often obtained between these two sources of soil moisture esti- mation. On the basis of the results obtained in these studies, it REFERENCES was concluded that the use of remotely sensed soil moisture Albergel, C., Calvet, J.C., Mahfouf, J.F., Rüdiger, C., Barbu, data in calibration and parameter identification of large-scale A.L., Lafont, S., Roujean, J.L., Walker, J.P., Crapeau, M., hydrological models contributed to a better simulation of soil Wigneron, J.P., 2010. Monitoring of water and carbon fluxes moisture content over the catchment and better discharge simu- using a land data assimilation system: A case study for lations. southwestern France. Hydrology and Earth System Sciences, This study has shown that downscaled soil moisture data 14, 1109–1124. DOI: 10.5194/hess-14-1109-2010 may also be used as additional criteria for the calibration and Alvarez-Garreton, C., Ryu, D., Western, A.W., Crow, W.T., validation of distributed hydrological models in small catch- Su, C.-H., Robertson, D.R., 2016. Dual assimilation of satel- ments, since good correlation was obtained between the two lite soil moisture to improve streamflow prediction in data types of soil moisture estimates in this study. This type of anal- scarce catchments. Water Resour. Res., 52, 5357–5375. ysis was not performed in previous studies. In that way, the use DOI: 10.1002/2015WR018429 of satellite derived soil moisture data can reduce parameter Badou, D.F., Diekkruger, B., Montzka, C., 2018. Validation of uncertainty and improve rainfall-runoff modeling and the accu- satellite soil moisture in the absence of in situ soil moisture: racy of flood predictions for small catchments. Similarly to the the ecase of the Tropical Yankin Basin. South African Jour- previously obtained results for large basins, it can be concluded nal of Geomatics, 7, 3. http://dx.doi.org/10.4314/sajg.v7i3.3 that downscaled remote sensed soil moisture can be potentially Beven, K., 2006. A manifesto for the equifinality thesis. Journal used to improve the understanding of hydrological processes in of Hydrology, 320, 18–30. small cathments. Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., Hasenauer, S., 2010. Improving runoff pre- 5 CONCLUSION diction through the assimilation of the ASCAT soil moisture product. Hydrology and Earth System Sciences, 14, 1881– Accurate soil moisture information is very important for 1893. DOI:10.5194/hess-14-1881-2010 flood forecasting. Due to variation in climate, soil, vegetation, Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, topography and other factors, soil moisture content is highly T., Wagner, W., Dorigo, W., Matgen, P., Martinez- variable in time and space. The knowledge of spatio-temporal Fernandez, J., Llorens, P., et al., 2011. Soil moisture estima- dynamics of soil moisture within the catchment can improve tion through ascat and amsr-e sensors: An intercomparison the estimation of runoff and contribute to a better understanding and validation study across europe. Remote Sens. Environ., of hydrological processes in catchments. 115, 3390–3408. In this study the comparison of spatial and temporal dynam- Chiew, F., McMahon, T., 1994. Application of the daily rain- ics of soil moisture, estimated from the physically based and fall–runoff model MODHYDROLOG to 28 Australian distributed SHETRAN hydrological model and by downscaling catchments. Journal of Hydrology, 153, 383–416. from satellite soil moisture data was performed on the example Corradini, C., 2014. Soil moisture in the development of hydro- of the small Jičinka River catchment (75.9 km ) in the Czech logical processes and its determination at different spatial Republic. The SHETRAN model was calibrated and verified on scales. J. Hydrol., 516, 1–5. the basis of observed streamflow hydrographs at the catchment Crow, W.T., Berg, A.A., Cosh, M.H., Loew, A., Mohanty, outlet. 10 Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic B.P., Panciera, R., de Rosnay, P., Ryu, D., Walker, J.P., 2010. Validation of advanced microwave scanning radiome- 2012. Upscaling sparse ground-based soil moisture observa- ter soil moisture products. IEEE Transactions on Geoscience tions for the validation of coarse-resolution satellite soil and Remote Sensing, 48, 12, 4256–4272. DOI: moisture products. Rev. Geophys., 50, 2. 10.1109/TGRS.2010.2051035 https://doi.org/10.1029/2011RG000372 Koster, R.D., Mahanama, S.P.P., Livneh, B., Lettenmaier, D.P., Dai, Y., Xin, Q.,Wei, N., Zhang, Y., Shangguan,W.,Zuan, H., Reichle, R.H., 2010. Skill in streamflow forecasts derived Zhang, Z., Liu,S., Lu, X., 2019. A global high resolution data from large - scale estimates of soil moisture and snow. Na- set of soil hydraulic and thermal properties for land surface ture Geosciences, 3, 613–616. DOI: 10.1038/ngeo944 modelling. Journal of Advances in Modelling Earth Systems, Koster, R.D., Brocca, L., Crow, W.T., Burgin, M.S., De Lan- 11, 9, 2996–3023. https://doi.org/10.1029/2019MS001784 noy, G.J.M., 2016. Precipitation estimation using l-band and Danhelka, J., Kubat J., Šercl P., Čekal, R. (Eds.), 2014. Floods c-band soil moisture retrievals. Water Resour. Res., 52, 7213–7225. in the Czech Republic in June 2013. Czech Hydrometeoro- Laiolo, P., Gabellani, S., Pulvirenti, L., Boni, G., Rudari, R., et. logical Institute, Prague, Czech Republic. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, al., 2014. Validation of remote sensing soil moisture prod- G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., ucts with a distributed continuous hydrological model. In: Haas, E., Hamer, D.P., Hirschi, M., Ikonen, J., De Jeu, R., IEEE Geoscience and Remote Sensing Symposium. Quebec Kidd, R., Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P., City, pp. 3319–3322. DOI: 10.1109/IGARSS.2014.6947190. 2017. ESA CCI Soil Moisture for improved Earth system Lievens, H., S.K., Tomer, A., Al Bitar, G., De Lannoy, M., understanding: State-of-the art and future directions. Remote Drusch, G., Dumedah, H.-J. H., Franssen, Y., Kerr, B., Mar- Sensing of Environment, 203, 185–215. tens, Pan, M., 2015. SMOS soil moisture assimilation for https://doi.org/10.1016/j.rse.2017.07.001 improved hydrologic simulation in the Murray Darling Ba- Đukić, V., Radić, Z., 2014. GIS based estimation of sediment sin, Australia. Remote Sens. Environ., 168, 146–162. discharge and areas of soil erosion and deposition for the López López, P., Sutanudjaja, E.H., Schellekens, J., Sterk, G., torrential Lukovska River Catchment in Serbia. Water Re- and Bierkens, M.F.P., 2017. Calibration of a large-scale hy- drological model using satellite-based soil moisture and sources Management, 28, 13, 4567–4581. evapotranspiration products. Hydrol. Earth Syst. Sci., 21, https://link.springer.com/article/10.1007/s11269-014-0751-7 3125–3144. https://doi.org/10.5194/hess-21-3125-2017. Đukić, V., Radić, Z., 2016. Sensitivity analysis of a physically based distributed model. Water Resources Management, 3, Manfreda, S., McCabe, M.F., Fiorentino, M., Rodriguez-Iturbe, 1669–1684. https://link.springer.com/article/10.1007/s11269- I., Wood, E.F., 2007. Scaling characteristics of spatial pat- 016-1243-8 terns of soil moisture from distributed modelling. Adv. Wa- Ewen, J., Parkin, G., O'Connell, P.E., 2000. SHETRAN: Dis- ter Resour., 30, 2145–2150. tributed river basin flow and transport modelling system. Molnar, D.K., Julien, P.Y., 2000. Grid-size effects on surface ASCE J. Hydrologic Eng., 5, 250–258. Available at: runoff modelling. Journal of Hydrologic Engineering, 5, 1. https://research.ncl.ac.uk/shetran/SHETRAN_ASCE_paper. Montzka, C., Rötzer, K., Bogena, H.R., Sanchez, N., Vereeck- pdf en, H., 2018. A new soil moisture downscaling approach for Gruber, A., Dorigo, W.A., Crow, W., Wagner, W., 2017. Triple SMAP, SMOS, and ASCAT by predicting sub-grid variabil- collocation-based merging of satellite soil moisture retriev- ity. Remote Sens., 10, 427. Mualem, Y., 1976. A new model predicting the hydraulic con- als. IEEE Transactions on Geoscience and Remote Sensing, ductivitynof unsaturated porous media. Water Resour. Res., 55, 12, 1–13. https://doi.org/10.1109/TGRS.2017.2734070 12, 513–522. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dori- go, W., 2019. Evolution of the ESA CCI Soil Moisture Cli- Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through mate Data Records and their underlying merging methodol- conceptual models: Part I. A discussion of principles. Jour- ogy. Earth System Science Data, 11, 717–739. nal of Hydrology, 27, 3, 282–290. https://doi.org/10.5194/essd-11-717-2019 Pavlik, F., Dumbrovský, M., 2014. Influence of landscape Gwak, Y., Kim, S., 2017. Factors affecting soil moisture spatial retention capacity upon flood processes in Jičínka River ba- variability for a humid forest hillslope. Hydrol. Processes, sin. Acta Universitatis Agriculturae et Silviculturae Mende- 31, 431–445. lianae Brunensis, 62, 1, 191–199. DOI: Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez 10.11118/actaun201462010191 Gonzalez, M., Kilibarda, M., Blagoti´c, A., Shangguan, W., Parajka, J., Naeimi, V., Blöschl, G., Wagner, W., Merz, R., Wright, M.N., Geng, X., Bauer-Marschallinger, B., et al., Scipal, K., 2006. Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. 2017. Soilgrids 250 m: Global gridded soil information Hydrol. Earth Syst. Sci., 10, 353–368. based on machine learning. PLoS ONE, 12, e0169748. Parajka, J., Naeimi, V., Blöschl, G., Komma, J., 2009. Match- Hupet, F., Vanclooster, M., 2002. Intraseasonal dynamics of soil moisture variability within a small agricultural maize ing ERS scatterometer based soil moisture patterns with cropped field. J. Hydrol., 261, 86–101. simulations of a conceptual dual layer hydrologic model IPCC, 2012. Managing the risks of extreme events and disasters over Austria. Hydrol. Earth Syst. Sci., 13, 259–271, to advance climate change adaptation. A Special Report of https://doi.org/10.5194/hess-13-259-2009 Working Groups I and II of the Intergovernmental Panel on Peng, J., Loew, A., Zhang, S., Wang, J., Niesel, J., 2017. Spa- Climate Change [Field, C.B., V. Barros, T.F., Stocker, D., tial downscaling of satellite soil moisture data using a Vege- Qin, D.J., Dokken, K.L., Ebi, M.D., Mastrandrea, K.J., tation Temperature Condition Index. IEEE Trans. Geosci. Mach, G.-K., Plattner, S.K., Allen, M.T., Midgley, P.M. Remote Sens., 54, 1, 558–566. (eds.)]. Cambridge University Press, Cambridge, UK, and Pereira, A.R., Pruitt, W.O., 2004. Adaptation of the New York, NY, USA, 582 p. Thornthwaite scheme for estimating daily reference evapo- Jackson, T.J., Cosh, M.H., Bindlish, R., Starks, P.J., Bosch, transpiration. Agricultural Water Management, 66, 3, 251–257. D.D., Seyfried, M., Goodrich, D.C., Moran, M.S., Du, J., 11 Vesna Đukić, Ranka Erić, Miroslav Dumbrovsky, Veronika Sobotkova Qu, W., Bogena, H.R., Huisman, J.A., Vanderborght, J., Schuh, Thiessen, A.H., 1911. Precipitation averages for large areas. M., Priesack, E., Vereecken, H., 2015. Predicting subgrid Monthly Weather Review, 39, 7, 1082–1084. variability of soil water content from basic soil information. http://dx.doi.org/10.1175/1520-0493(1911)39<1082b: PAF- Geophys. Res. Lett., 42, 789–796. LA>2.0.CO;2 Richards, L.A., 1931. Capillary conduction of liquids through van Genuchten, M.T., 1980. A closed-form equation for pre- porous mediums. Physics, 1, 5, 318–333. DOI: dicting the hydraulic conductivity of unsaturated soils. Soil 10.1063/1.1745010 Sci. Soc. Am. J., 44, 892–898. Rodell, M., Houser, P., Jambor, U., Gottschalck, J., Mitchell, Vereecken, H., Huisman, J.A., Pachepsky, Y., Montzka, C., van K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Mar- J., Bosilovich, M., 2004. The global land data assimilation tinez, G., Vanderborght, J., 2014. On the spatio-temporal system. Bull. Am. Meteorol. Soc., 85, 381–394. dynamics of soil moisture at the field scale. J. Hydrol., 516, https://doi.org/10.1175/BAMS-85-3-381 76–96. Rosenbaum, U., Bogena, H.R., Herbst, M., Huisman, J.A., Pe- Verhoest, N.E.C., et al., 2015. Copula-based downscaling of terson, T.J., Weuthen, A., Western, A.W., Vereecken, H., coarse-scale soil moisture observations with implicit bias cor- 2012. Seasonal and event dynamics of spatial soil moisture rection, IEEE Trans.Geosci. Remote Sens., 53, 6, 3507–3521. patterns at the small catchment scale. Water Resour. Res., 48, Wanders, N., Bierkens, M.F.P., Jong, S.M., Roo, A., Karssen- 10. https://doi.org/10.1029/2011WR011518 berg, D., 2013. The benefits of using remotely sensed soil Rötzer, K., Montzka, C., Bogena, H., Wagner, W., Kerr, Y.H., moisture in parameter identification of large scale hydrolog- Kidd, R., Vereecken, H., 2014. Catchment scale validation ical models. Water Resour. Res., 50, 6874–6891. DOI: of smos and ascat soil moisture products using hydrological 10.1002/2013WR014639 modeling and temporal stability analysis. J. Hydrol., 519, Wang, L., Qu, J.J., 2009. Satellite remote sensing applications 934–946. for surface soil moisture monitoring: a review. Frontiers of Saint-Venant, A.J.C.B., 1871. Théorie du mouvement non Earth Science in China, 3, 2, 237–247. permanent des eaux, avec application aux crues des rivières Western, A.W., Grayson, R.B., Bloschl, G., Willgoose, G.R., et a l’introduction de marées dans leurs lits. Comptes rendus McMahon, T.A., 1999. Observed spatial organization of soil hebdomadaires des séances de l'Académie des sciences, 73, moisture and its relation to terrain indices. Water Resour. 147–154, 237–240. Res., 35, 797–810. Stoorvogel, J.J., Bakkenes, M., Temme, A.J.A.M., Batjes, Xiong, L., Yang, H., Zeng, L., Xu, C.-Y., 2018. Evaluating N.H., ten Brink, B.J.E., 2017. S-world: A global soil map for Consistency between the Remotely Sensed Soil Moisture environmental modelling. Land Degrad. Dev., 28, 22–33. and the Hydrological Model-Simulated Soil Moisture in the Shangguan, W., Dai, Y.J., Duan, Q.Y., Liu, B.Y., Yuan, H., Qujiang Catchment of China. Water, 10, 3, 291. 2014. A global soil data set for earth system modeling. J. https://doi.org/10.3390/w10030291 Adv. Model. Earth Syst., 6, 249–263. Ye, W., Bates, B.C., Viney, N.R., Silvapan, M., Jakeman, A.J., Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Islam, T., 2013. 1997. Performance of conceptual rainfall–runoff models in Appraisal of SMOS soil moisture at a catchment scale in a low-yielding ephemeral catchments. Water Resources Re- temperate maritime climate. Journal of Hydrology, 498, search, 33, 1, 153–166. 292–304. Teuling, A.J., Troch, P.A., 2005. Improved understanding of Received 27 September 2019 soil moisture variability dynamics. Geophys. Res. Lett., 32. Accepted 23 September 2020

Journal

Journal of Hydrology and Hydromechanics – de Gruyter

Published: Mar 1, 2021

Keywords: SHETRAN hydrological model; Downscaled remotely sensed soil moisture; Runoff and soil moisture validation; Spatio-temporal variability of soil moisture; Flash floods; Small catchment

There are no references for this article.