Incorporating Advanced Scatterometer Surface and Root Zone Soil Moisture Products into the Calibration of a Conceptual Semi-Distributed Hydrological Model

The role of soil moisture is widely accepted as a significant factor in the mass and energy balance of catchments as a controller in surface and subsurface runoff generation. The paper examines the potential of a new dataset based on advanced scatterometer satellite remote sensing of soil moisture (ASCAT) for multiple objective calibrations of a dual-layer, conceptual, semi-distributed hydrological model. The surface and root zone soil moisture indexes based on ASCAT data were implemented into calibration of the hydrological model. Improvements not only in the instrument specifications, i.e., better temporal and spatial sampling, but also in the higher radiometric accuracy and retrieval algorithm, were applied. The analysis was performed in 209 catchments situated in different physiographic and climate zones of Austria for the period 2007–2018. We validated the model for two validation periods. The results show that multiple objective calibrations have a substantial positive effect on constraining the model parameters. The combined use of soil moisture and discharges in the calibration improved the soil moisture simulation in more than 73% of the catchments, except for the catchments with higher forest cover percentages. Improvements also occurred in the runoff model efficiency, in more than 27% of the catchments, mostly in the watersheds with a lower mean elevation and a higher proportion of farming land use, as well as in the Alpine catchments where the runoff is not significantly influenced by snowmelt and glacier runoff.

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Diagnostic calibration of a hydrological model in an alpine area

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-1253-2014 ◽

2014 ◽

Vol 11 (1) ◽

pp. 1253-1300 ◽

Cited By ~ 4

Author(s):

Z. He ◽

F. Tian ◽

H. C. Hu ◽

H. V. Gupta ◽

H. P. Hu

Keyword(s):

Hydrological Modeling ◽

Hydrological Model ◽

Generation Mechanism ◽

Spatiotemporal Variability ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Runoff Generation ◽

Alpine Area ◽

Long Time ◽

Tailan River Basin

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

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Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-1103-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 1103-1141 ◽

Cited By ~ 2

Author(s):

X. Fang ◽

J. W. Pomeroy ◽

C. J. Westbrook ◽

X. Guo ◽

A. G. Minke ◽

...

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Snow Accumulation ◽

Model Parameters ◽

Field Observations ◽

Runoff Generation ◽

Stream Channels ◽

Physically Based ◽

Set Up ◽

Spot 5

Abstract. The eastern Canadian Prairies are dominated by cropland, pasture, woodland and wetland areas. The region is characterized by many poor and internal drainage systems and large amounts of surface water storage. Consequently, basins here have proven challenging to hydrological model predictions which assume good drainage to stream channels. The Cold Regions Hydrological Modelling platform (CRHM) is an assembly system that can be used to set up physically based, flexible, object oriented models. CRHM was used to create a prairie hydrological model for the externally drained Smith Creek Research Basin (~400 km2), east-central Saskatchewan. Physically based modules were sequentially linked in CRHM to simulate snow processes, frozen soils, variable contributing area and wetland storage and runoff generation. Five "representative basins" (RBs) were used and each was divided into seven hydrological response units (HRUs): fallow, stubble, grassland, river channel, open water, woodland, and wetland as derived from a supervised classification of SPOT 5 imagery. Two types of modelling approaches calibrated and uncalibrated, were set up for 2007/08 and 2008/09 simulation periods. For the calibrated modelling, only the surface depression capacity of upland area was calibrated in the 2007/08 simulation period by comparing simulated and observed hydrographs; while other model parameters and all parameters in the uncalibrated modelling were estimated from field observations of soils and vegetation cover, SPOT 5 imagery, and analysis of drainage network and wetland GIS datasets as well as topographic map based and LiDAR DEMs. All the parameters except for the initial soil properties and antecedent wetland storage were kept the same in the 2008/09 simulation period. The model performance in predicting snowpack, soil moisture and streamflow was evaluated and comparisons were made between the calibrated and uncalibrated modelling for both simulation periods. Calibrated and uncalibrated predictions of snow accumulation were very similar and compared fairly well with the distributed field observations for the 2007/08 period with slightly poorer results for the 2008/09 period. Soil moisture content at a point during the early spring was adequately simulated and very comparable between calibrated and uncalibrated results for both simulation periods. The calibrated modelling had somewhat better performance in simulating spring streamflow in both simulation periods, whereas the uncalibrated modelling was still able to capture the streamflow hydrographs with good accuracy. This suggests that prediction of prairie basins without calibration is possible if sufficient data on meteorology, basin landcover and physiography are available.

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Soil Moisture Initialization Input Scale Effect on Parameter Value Identification of a Physically Based Distributed Hydrologic Modelling

10.5194/egusphere-egu2020-22194 ◽

2020 ◽

Author(s):

Nawa Raj Pradhan ◽

Steven Brown ◽

Ian Floyd

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Hydrological Model ◽

Root Zone ◽

Soil Column ◽

Estimation Method ◽

Model Parameters ◽

Analysis Model ◽

Soil Moisture Data ◽

Moisture Estimation

<p>Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture, and parameter value identification are critical for a physics based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide relatively accurate estimates of soil moisture, but such techniques are limited due to the need for a variety of measurement accessories, which are difficult to obtain to cover a large area sufficiently. Available satellite-based digital soil moisture data is at 9 kilometers to 50 kilometers in resolution which completely filters the soil moisture details at the hill slope scale. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column that informs nothing about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed root zone soil moisture at 30 meter resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 meter resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 meter resolution SERVES soil moisture data was resampled to 4500 meter and 9000 meter resolutions and was separately employed in the calibrated hydrological model to determine the effect soil moisture resolution &#160;has on the simulated outputs and the model parameters. It was found that the simulated discharge significantly decreased as the initial soil moisture resolution was coarsened. To compensate for this underestimated simulated discharge, the soil hydraulic conductivity value decreased logarithmically with respect to the decreased resolutions. This study will reduce parameter value identification uncertainty especially in flood and soil erosion modelling at multi scale watershed in a changing climate.</p>

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Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy

Remote Sensing ◽

10.3390/rs12101556 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1556

Author(s):

Feng Ju ◽

Ru An ◽

Zhen Yang ◽

Lijun Huang ◽

Yaxing Sun

Keyword(s):

Monte Carlo ◽

Soil Moisture ◽

Brightness Temperature ◽

Hydrological Model ◽

Root Zone ◽

Evolution Strategy ◽

Microwave Emission ◽

Model Parameters ◽

State Variables

Hydrological models play an essential role in data assimilation (DA) systems. However, it is a challenging task to acquire the distributed hydrological model parameters that affect the accuracy of the simulations at a grid scale. Remote sensing data provide an ideal observation for DA to estimate parameters and state variables. In this study, a special assimilation scheme was proposed to jointly estimate parameters and soil moisture (SM) by assimilating brightness temperature (TB) from the Soil Moisture and Ocean Salinity (SMOS) mission. Variable infiltration capacity (VIC) hydrological model and L-band microwave emission of the biosphere model (L-MEB) are coupled as model and observation operators, respectively. The scheme combines two stages of estimators, one for the static model parameters and the other for the dynamic state variables. The estimators approximate the posterior probability distribution of an unknown target through sequential Monte Carlo (SMC) sampling. Markov chain Monte Carlo (MCMC) and immune evolution strategy are embedded in both stages to solve particle impoverishment problems. To evaluate the effectiveness of the scheme, the estimated SM sets are compared with in-situ observations and SMOS products in Maqu on the Tibetan Plateau. Specifically, the root mean square error decreased from 0.126 to 0.087 m3m−3 for surface SM, with a slight impact on the root zone. The temporal correlation between DA results and in-situ measurements increased to 0.808 and 0.755 for surface SM (+0.057) and root zone SM (+0.040), respectively. The results demonstrate that assimilating TB has tremendous potential as an approach to improve the estimation of distributed model parameters and SMs of surface and root zone at a grid scale, and the immune evolution strategy is effective for increasing the accuracy of approximation in sampling.

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The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites

10.5194/hess-2021-189 ◽

2021 ◽

Author(s):

Rui Tong ◽

Juraj Parajka ◽

Borbála Széles ◽

Isabella Pfeil ◽

Mariette Vreugdenhil ◽

...

Keyword(s):

Soil Moisture ◽

Snow Cover ◽

Multiple Objective ◽

Model Parameters ◽

Local Similarity ◽

Ungauged Catchments ◽

Ungauged Sites ◽

Runoff Prediction ◽

Alpine Catchments ◽

Global And Local

Abstract. The recent advances in remote sensing provide opportunities for more reliably estimating the parameters of conceptual hydrologic models. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (ASCAT), and snow cover (MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of model transfer methods based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple objective calibrations.

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Ability to Forecast Regional Soil Moisture with a Distributed Hydrological Model Using ECMWF Rainfall Forecasts

Journal of Hydrometeorology ◽

10.1175/2008jhm1035.1 ◽

2009 ◽

Vol 10 (2) ◽

pp. 544-554

Author(s):

J. M. Schuurmans ◽

M. F. P. Bierkens

Keyword(s):

Soil Moisture ◽

Spatial Pattern ◽

Lead Time ◽

Hydrological Model ◽

Root Zone ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Lead Times ◽

Spatial Average ◽

Distributed Hydrological Model

Abstract This study mimics an online forecast system to provide nine day-ahead forecasts of regional soil moisture. It uses modified ensemble rainfall forecasts from the numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF), which is provided by the Royal Netherlands Meteorological Office (KNMI). Both the individual ensembles as well as the mean of the ensembles are used as input for a hydrological model of a 70-km2 study area during March–November 2006. The outcomes are compared to the model run with high-resolution rainfall fields (based on 14 rain gauges within the study area and meteorological radar) as input. It is shown that the total spatial mean rainfall is forecasted very well for all lead times. The measured rainfall (spatial mean) shows a distribution with peaks at 0–1 and >10 mm day−1. These peaks are underestimated by the ensemble mean of the forecasts and this underestimation increases with lead time. This is not the case when ensemble members are used. Besides, the modeled uncertainty in rainfall by ECMWF underestimates the true uncertainty for all lead times and the number of rainfall events (thresholds 0.1, 0.5, and 1.0 mm) is overestimated. Absolute temporal mean bias values in root zone storage—that is, soil moisture—larger than 1 mm start to show for lead times longer than 3 days. The lower and upper bounds of bias for a lead time of 9 days are approximately −4 and 7 mm, respectively (negative values mean the forecasted soil moisture is underestimated). The bias in root zone storage shows a spatial pattern that represents the spatial pattern of total rainfall: areas with less rainfall than spatial average show a negative bias and vice versa. Local differences within this spatial pattern are due to land use and soil type. The results suggest that ensemble forecasts of soil moisture using ensemble rainfall forecasts from ECMWF are of practical use for water management, even at regional scales.

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Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2015.06.002 ◽

2016 ◽

Vol 48 ◽

pp. 131-145 ◽

Cited By ~ 40

Author(s):

P. Laiolo ◽

S. Gabellani ◽

L. Campo ◽

F. Silvestro ◽

F. Delogu ◽

...

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Distributed Hydrological Model

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Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

Hydrology and Earth System Sciences ◽

10.5194/hess-18-3923-2014 ◽

2014 ◽

Vol 18 (10) ◽

pp. 3923-3936 ◽

Cited By ~ 20

Author(s):

X. Xie ◽

S. Meng ◽

S. Liang ◽

Y. Yao

Keyword(s):

Real Time ◽

Land Surface ◽

Hydrological Model ◽

Similarity Measures ◽

State Parameter ◽

Model Parameters ◽

Prediction Errors ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Short Period

Abstract. The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations.

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