As aforementioned, there exist many error sources in satellitesoilmoisturemeasurements. However in real life, it is extremely difficult to investigate these error sources separately and discover their interdependencies. The statistical errordistribution model of satellitesoilmoisture established with the assistance of hydrological model could be a practical and efficient solution. The errordistribution model may change with time (i.e., nonstationarity), but it is more challenging to build such a model from the start. In this study, the model will be built by exploring the significant characteristics of satellitesoilmoisture errors from the long-time historical records without considering the nonstationarity. One of the simplest empirical based errordistributionmodelling is via the systematic error analysis. It collects the information of differences or ratios between satellite estimated and hydrological modelled soilmoisture pairs. This methodology has been proven to be effective and time efficient in numerous studies (Anagnostou et al., 1998; Borga and Tonelli, 2000; Seo et al., 1999; Smith and Krajewski, 1991) and provides a fundamental framework for more complex errordistribution models (Dai et al., 2014b).
Proper identification of satellitesoilmoisture uncertainty in runoff modelling is relevant for flow ensemble studies (e.g., error propagation). For example, if the observed flow falls outside the forecasted ensembles, then further revisions are required in the formulation of the hydrological model, its states or inputs. However if the chosen errordistribution model is wrong (i.e., flow uncertainty bands become too wide or too narrow), it can lead to false conclusions regarding the adequacy of the input datasets, the hydrological model and its parameters. Furthermore understanding the uncertainty features of remote sensed soilmoisture is also useful in controlling and correcting the soilmoisture status in a hydrological model after dry periods, so that error accumulation impact can be reduced. Therefore errordistributionmodelling of satellitesoilmoisturemeasurements is vital to the data application in the hydrological community. This paper demonstrates the first attempt in modellingsatellitesoilmoistureerrordistribution in hydrologicalapplications, therefore, there are many rooms for improvements.
As aforementioned, there exist many error sources in satellitesoilmoisturemeasurements. However in real life, it is extremely difficult to investigate these error sources separately and discover their interdependencies. The statistical errordistribution model of satellitesoilmoisture established with the assistance of hydrological model could be a practical and efficient solution. The errordistribution model may change with time (i.e., nonstationarity), but it is more challenging to build such a model from the start. In this study, the model will be built by exploring the significant characteristics of satellitesoilmoisture errors from the long-time historical records without considering the nonstationarity. One of the simplest empirical based errordistributionmodelling is via the systematic error analysis. It collects the information of differences or ratios between satellite estimated and hydrological modelled soilmoisture pairs. This methodology has been proven to be effective and time efficient in numerous studies ( Anagnostou et al., 1998 ; Borga and Tonelli, 2000 ; Seo et al., 1999 ; Smith and Krajewski, 1991 )
To assess the soil water status in the root zone, evapotranspiration (ET) and precipitation are two important boundary conditions that need to be accurately assessed at the upper boundary of the soil profile (Brutsaert, 2005; Li et al., 2012 ; Nosetto et al., 2012). There are a number of studies that have examined to estimate and evaluate evapotranspiration using remote sensing (Winsemiuse et al., 2008; Lazzara and Rana, 2010) and hydrologicalmodelling (Li et al., 2012; Nosetto et al., 2012). Root uptake of water and nutrients is considered an important process controlling water ﬂow (recharge) and nutrient transport (leaching) to the groundwater in numerical models simulating water content and ﬂuxes in the subsurface and thus also exerting a major inﬂuence on predictions of climate change impacts (Feddes and Raats, 2004). The common approach for estimating root water uptake through hydrological modeling is to relate the root length and mass distribution of roots to water uptake patterns. Numerical methods are increasingly established and adopted (calibrated, evaluated and validated) for application to water resources planning and management using hydrological models. They can be applied to solve realistic field and laboratory situation problems as opposed to analytical models (Šimůnek and van Genuchten, 2008). The Hydrus-1D model (Šimůnek et al., 2008a) that is used in this study has been used in a wide range of applications in research and irrigation management (e.g., Hanson et al., 2008; Forkutsa et al., 2009; Roberts et al., 2008, 2009), water harvesting (Verbist et al., 2009), and also to simulate the fate of nutrients by evaluating and comparing fertilization strategies for different crops and contaminants in soils (e.g., Seuntjens et al., 2001, 2002a,b; Cote et al., 2003; Gärdenäs et al., 2005; Ajdary et al., 2007; Crevoisier et al., 2008).
For effective real-time forecast of discharge the modeled states of the system need to be updated using observed dis- charges. This so-called data assimilation problem can be solved in different ways. In real-time applications a new assimilation problem is formulated at every time step. To solve this problem efficiently sequential assimilation tech- niques are considered superior (McLaughlin, 2002). Sequen- tial assimilation algorithms, also known as filtering algo- rithms, are divided into two steps: first a propagation step, where the system states are propagated through time using a model and forcing data; second an update step, where the modeled states of the system are updated based on the dif- ference between the model output and the real-world obser- vation. To solve nonlinear filtering problems the Ensem- ble Kalman Filter (EnKF) has proven to be both efficient and robust (Evensen, 1994). EnKF has, along with standard batch calibration, the advantage of being able to incorpo- rate a wide range of uncertainties. The uncertainties of forc- ing data, model parameters and model output are considered separately but can be incorporated in the same mathematical scheme (Thiemann et al., 2001).
Precipitation is perhaps the most important variable in con- trolling energy and mass fluxes that dominate climate and particularly the terrestrial hydrological and ecological sys- tems. Precipitation estimates, together with hydrologic mod- els, provide the foundation for understanding the global en- ergy and water cycles (Sorooshian, 2004; Ebert et al., 2007). However, obtaining accurate measurements of precipitation at regional to global scales has always been challenging due to its small-scale, space–time variability, and the sparse networks in many regions. Such limitations impede precise modeling of the hydrologic responses to precipitation. There is a clear need for improved, spatially distributed precipita- tion estimates to support hydrological modeling applications. In recent years, remotely sensed satellite precipitation has become a critical data source for a variety of hydrologi- cal applications, especially in poorly monitored regions such as sub-Saharan Africa due to its large spatial coverage. To date, a number of fine-scale, satellite-based precipitation es- timates are now in operational production. One of the most frequently used is the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) product (Huff- man et al., 2007). Over the 17-year lifetime since the launch of the Tropical Rainfall Measuring Mission (TRMM) in 1997, a series of high-resolution (0.25 ◦ and 3-hourly), quasi-
the hydrological model generated soil water information, and to better link the shallow satellitesoil layer with the deeper and variable soil layers in the hydrological model.
One may argue that the XAJ model already works well in operational flood forecasting with its existing inputs, and therefore there is no need to use the satellite data. However, there are many cases in which it does not work well enough. For example, if there is missing data concurrent with an imminent storm, soilmoisture observations such as those from the satellites will be very useful for quickly initialising the model’s soilmoisture state (i.e., to reduce the need for warming up/ spinning up the model), so that the model is ready for flood forecasting. Another case is when there is an accumulated error with the model’s evapotranspiration and precipitation estimation which can cause time-drift of the model’s soilmoisture state. Soilmoisture observations in this case can help to adjust this time-drift by data assimilation techniques, and in this way the over- and under-estimation of the flow peaks could be minimised. A successful hydrological model should be able to make full use of the available data, and satellitesoilmoisture observations do provide extra information to compliment the conventional hydrologicalmeasurements. It is important to recognise the weaknesses in existing models, and encourage the community to explore the ways to improve those models’ compatibility with the new measurements.
Accurate soilmoisture information is very important for real-time lood forecasting. Although satellitesoilmoisture observations are useful information, their validations are generally hindered by the large spatial diference with the point-based measurements, and hence they cannot be directly applied in hydrologicalmodelling. his study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with diferent climate types and vegetation covers. he results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). he MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very efective in dividing soilmoisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). he overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soilmoisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.
.  and Merlin et al.  also evaluated the downscaled soilmoisture at different spatial resolutions, with the statistics results shown in Table 2. Figure 13 shows that the spatial variability of soilmoisture is in agreement with the landscape heterogeneity, especially at the 1 km resolution compared to 5 km and 10 km. However, the higher resolution soilmoisture does not always pro- vide better accuracy [van der Velde et al., 2015], which might be due to the surface heterogeneity and the remaining scale mismatch between downscaled and in situ soilmoisture. Although the spatial resolution of the downscaled soilmoisture is highly improved, the grid size of the downscaled soilmoisture is still much bigger compared to point-scale measurements. Based on over 3600 in situ measurements collected during the SGP97, SGP99, SMEX02, and SMEX03 ﬁeld campaigns, Famiglietti et al.  generalized the spatial variability of soilmoisture within spatial scales ranging from 2.5 m to 50 km. They found that the mean soilmoisture variability increased from 0.036 cm 3 /cm 3 within 2.5 m scale to 0.071 cm 3 /cm 3 within 50 km scale. Due to the large spatial variability of soilmoisture, the average value within 1 km 2 cannot certainly show bet- ter agreement with point measurements, compared to that of 5 km 2 or even larger extent. Nevertheless, the amiability of downscaled soilmoisture at various spatial resolutions will facilitate different applications such as numerical weather prediction and hydrological modeling. Particularly, the precision agriculture will bene ﬁt
A review of the literature demonstrated that satellite-based SM products have huge potential for improving monitoring and forecasting in India and the UK. NWP modelling is one of the most mature satellite SM applications (Wagner et al., 2013a) and in the UK satellite SM observations are used in two operational NWP models. Furthermore, an India-UK collaboration is now exploring the use of satellite SM for improving predictions of the Indian monsoon. Satellite SM data has great potential for monitoring of water-related hazard, such as regional droughts, particularly in India, and flood forecasting. Potential agricultural applications include assessing crop condition, predicting yields and monitoring irrigation. There are opportunities to develop these products into commercial agricultural services or for use in supporting government decision-making. The user survey carried out in this study identified SM data as being highly important across many different disciplines. The application areas identified as having the most potential to benefit from satellite SM data were: drought monitoring and forecasting; hydrologicalmodelling; water management; agricultural management; land surface modelling; and flood monitoring and forecasting. A wide range of other potential applications were also mentioned including climate change studies, land management, crop productivity, vegetation stress, geohazards, land-atmosphere interactions and NWP.
The SWAT model is a semi-distributed hydrological model that operates on a daily time-step and was developed in order to assess the impact of water flow, agricultural management practices, sediment and nutrient transport simulation in large complex river basins under different hydrologic, geologic and climatic conditions . SWAT is one of the most widely used hydrological model and has found countless applications all across the world, wherefore more recently the focus has been the simulation of regional hydrological impact of climate change [48-50]. In the SWAT model a catchment or basin is divided into a number of sub-basins (=198 in the present basin), which are then, based on the topography (DEM), soil type, land-use and, optionally, slope characteristics, further subdivided into so-called hydrologic response units (HRUs) with identical characteristic of some of these properties. For the large and complex KRB, the total number of HRU’s is more than 11000 (see , for details).
A possible solution to the problem may be to drive these original coarse-resolution models with high-resolution me- teorological data. Several meteorological forcing data sets at a global scale are available, including the European Cen- tre for Medium-Range Weather Forecasts – ECMWF ERA- Interim – global atmospheric reanalysis data (Dee et al., 2011), the Climatic Research Unit Time Series – CRU TS – (Mitchell and Jones, 2005), the NASA reanalysis Modern- Era Retrospective Analysis for Research and Applications – MERRA – (Rienecker et al., 2011) and the WATCH Forcing Data methodology applied to ERA-Interim reanalysis data – WFDEI – (Weedon et al., 2014). They are the result of integrating Bayesian merging of the available earth obser- vations, in situ data sets and models to construct consistent large-scale meteorological time series. Some recent scientific efforts are conducted to improve the quality and availability of these data sets, for example increasing their spatial and temporal resolution (Cannon, 2011; Ebtehaj and Foufoula- Georgoiu, 2013; Atkinson, 2013). The use of high-spatial- resolution meteorological data would indirectly improve the resolution of the large-scale model, producing higher accu- racy discharge estimates. However, when models that are de- signed for coarse spatial resolution are used at smaller spa- tial scale, issues may arise with the representation of field- scale processes. One of the major issues in this respect is the neglect of lateral flow, misleading the representation of the complex interactions between river water and ground- water (surface runoff, subsurface runoff, soilmoisture state, etc.). At the moment, more research is required to under- stand the gain that can be obtained using these higher-spatial- resolution forcing data for uncalibrated global hydrological models at finer spatial resolutions.
SMOS makes both ascending and descending overpasses, however the performance of those retrievals remains unclear [42, 46-48]. Based on the literature review, previous studies mainly focused on the downscaling, assimilation, and evaluation of the SMOS ascending overpass in order to minimise the observation error caused by the daytime soil drying effect and the impact of vertical soil-vegetation temperature gradients [31, 37, 38, 42, 45]. It is expected that satellitesoilmoisturemeasurements are more accurate in the hours near dawn when the soil profile has the most time to return to an equilibrium state from the previous day’s fluxes . Hence, based on this hypothesis, it is more likely to be true that ascending soilmoisturemeasurements would have better performance than their descending counterparts . In addition, based on evaporation demand, it is expected that soil would be wetter at night and drier during the day; in other words, the ascending pass should hold higher soilmoisture values than the descending pass if there is no rainfall during the day . However it is found by  the SMOS descending orbit shows a stronger potential for improved hydrological predictions in a medium-size cropland catchment. This outcome contradicts the previous hypothesis from other studies that ascending soilmoisturemeasurements should have better performance than their descending counterparts. Additionally in , it is explored that SMOS retrievals from the descending overpass are consistently wetter (about 11.7% by volume) than the ascending retrievals, which is again not expected. Based on the mixed results from the published literature, it is encouraged to carry out more research on this topic, with extended spectrum of catchment types and satellite products, so that a look-up table could be established.
Even though initial funding for the establishment of the ISMN, as provided by ESA, mainly focuses on the SMOS mission, other satellite-based soilmoisture products from the existing and future missions such as AMSR-E, ASCAT, and SMAP can profit from the established network (for refer- ences see the individual networks in Sect. 4). However, the intention of the ISMN is to go beyond the role of satellite val- idation resource and to serve other scientific and application- oriented communities as well, such as hydrological model- ing, numerical weather forecasting, and water management. To fulfill this task, the ISMN stores not only surface soilmoisture but also soil water content of the deeper layers and relevant hydrometeorological variables such as precipitation and temperature of the air and soil. In addition, to better serve time critical applications (ranging from several hours to a few days), the ISMN has been structured in a way that enables processing incoming soilmoisturemeasurements on a fully automated basis. The automated daily update of data from the FMI network shows that a NRT assimilation and redistribution of data is possible. Nevertheless, prerequisite for the added value of such a mode is the presence of opera- tional measurement and processing chains on the part of the data providers in order to guarantee timely data delivery to the ISMN.
In hydrology, the discrepancy between simulated and observed streamflow (Q) data can be used to update a model’s state variables, which has applications in basin-wide estimations and hydrological forecasting [1–4]. Data assimilation (DA) procedures, which allow for accurate modeling of hydrological variables, can be used to provide the necessary ground conditions (e.g., soilmoisture (θ), snowpack and snow water equivalent) for mathematical models. These state variables, along with other past and present model states (e.g., the contents of upper and lower boxes in a response routine in the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model), can also be used in the model’s updating procedures. Given the high level of uncertainty and the inherent difficulties in hands-on measurements of soilmoisture, snowpack and its meltwater content, satellite-based data can be used as input data for hydrological models. The spatial and temporal distribution of satellite data can, however, differ substantially from that of data obtained through simulations generated by mathematical models. A properly configured hydrological simulation model should assimilate these observations, rather than use them directly as inputs for the deterministic model. This can be achieved through the application of the DA procedure, coupled with the use of model state variables.
Article history: Received 5 May 2017; Accepted 6 July 2017; Available online 18 July 2017
The United Nations recognize the critical role of soils in sustainable development, given that soils contribute to ecosystem services related to several of the United Stations Sustainable Development Goals (e.g. food security in developing countries, health, water security/resources, biodiversity)  . Soilmoisture is an essential component of the soil-vegetation-atmosphere system determining physical processes (e.g. water cycle and energy balance, land-atmosphere interactions) and the functioning of plants and other soil biota  . The interdisciplinary study of soilmoisture is crucial to understand links between soils and climate and to improve climate models and agricultural production, given the impact on crop yield and food security [2,3] . It can show a high spatial variability due to diverse factors like topography, ground water level, soil type or vegetation cover [4,5] and these variations produce signi ﬁcant changes in regional runoff, crop productivity or groundwater recharge, among others. In [3,4] we showed the spatial impact of soil water de ﬁcit and excess on the main crops of Pampean Region of Argentina, one of the major grain producers of the world.
Abstract. A new approach to downscaling soilmoisture forecasts from the seasonal ensemble prediction forecasting system of the ECMWF (European Centre for Medium-Range Weather Forecasts) is presented in this study. Soilmoisture forecasts from this system are rarely used nowadays, al- though they could provide valuable information. Weaknesses of the model soil scheme in forecasting soil water content and the low spatial resolution of the seasonal forecasts are the main reason why soil water information has hardly been used so far. The basic idea to overcome some of these prob- lems is the application of additional information provided by two satellite sensors (ASCAT and Envisat ASAR) to improve the forecast quality, mainly to reduce model bias and increase the spatial resolution. Seasonal forecasts from 2011 and 2012 have been compared to in situ measurement sites in Kenya to test this two-step approach. Results confirm that this down- scaling is adding skill to the seasonal forecasts.
Despite these issues, the development of an operational processing chain that allows daily soilmoisture updates is particularly promising with regard to applications that aim at the confirmation of satellite-based rainfall estimates (Brocca et al., 2013) or at closing the gap between rainfall estimates and the response of vegetation (Enenkel et al., 2014). In this regard, the integration of the latest generation of soil mois- ture sensors, such as Sentinel-1 of the ESA and the Euro- pean Commission (EC) or NASA’s SMAP (SoilMoisture Active/Passive), whose L-band radiometer is still active af- ter the failure of the radar, could lead to further improve- ments. These new sensors are able to retrieve soilmoisture at a far higher resolution than ASCAT or AMSR2: in case of Sentinel-1, around 1 km for operational products and be- low 100 m for research products. Of course the higher spatial resolution has a drawback, which is a decrease in temporal resolution. While ASCAT on MetOp-A alone covers more than 80 % of the globe every day, the two Sentinel-1 satel- lites will take 6–12 days to scan the total global land mass in the default interferometric wide swath (IWS) mode (World Meteorological Organization, 2013). Despite the differences in spatial resolution, it is possible to increase the temporal resolution of the CCI NRT dataset to fit various applications.
Microwave radiometry has a long legacy of providing estimates of remotely sensed near surface soilmoisturemeasurements over continental and global scales. A consistent assessment of the errors and uncertainties associated with these retrievals is important for their effective utilization in modeling, data assimilation and end-use application environments. This article presents an eval- uation of soilmoisture retrieval products from AMSR-E, ASCAT, SMOS, AMSR2 and SMAP instruments using information theory-based metrics. These metrics rely on time series analysis of soilmoisture retrievals for estimating the measurement error, level of randomness (entropy) and regularity (complexity) of the data. The results of the study indicate that the measurement errors in the remote sensing retrievals are significantly larger than that of the ground soilmoisture measure- ments. The SMAP retrievals, on the other hand, were found to have reduced errors (comparable to