Error distribution modelling of satellite soil moisture measurements for hydrological applications

Zhuo, Lu; Dai, Qiang; Islam, Tanvir; Han, Dawei

doi:10.1002/hyp.10789

Cited by 14 publications

(10 citation statements)

References 64 publications

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“…In this study, a long-term WRF soil moisture estimation with 1-year spin-up time is used which could to some extent produce a more stable result. But since "all models are wrong" (by George E. P. Box), an uncertainty model (Zhuo et al, 2016) could be proposed to be integrated with the network design scheme. For example, we can generate a large number of probable "true soil moisture" datasets based on the proposed uncertainty model so that a set of possible soil moisture networks can be produced.…”

Section: Discussionmentioning

confidence: 99%

Soil Moisture Sensor Network Design for Hydrological Applications

Zhuo¹,

Dai²,

Zhao³

et al. 2020

Preprint

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Abstract. Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration and runoff, hence a vital state variable in the hydrological modelling. However, due to the heterogeneity of soil moisture in space most existing in-situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on the Principal Component Analysis (PCA) and Elbow curve for the determination of the optimal number of soil moisture sensors; and K-means Cluster Analysis (CA) and a selection of statistical criteria for the identification of the sensor placements. Furthermore, the long-term (10-year) soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale soil moisture (i.e., with NSE and r at 0.995 and 0.999, respectively for the areal mean estimation; and 0.973 and 0.990, respectively for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22,124 km2 catchment is needed, which in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme which are also discussed in the paper.

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Section: Discussionmentioning

confidence: 99%

Soil Moisture Sensor Network Design for Hydrological Applications

Zhuo¹,

Dai²,

Zhao³

et al. 2020

Preprint

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“…The SMOS Product. The SMOS retrieves data of emitted microwave radiation at the frequency of 1.4 GHz (L-band) with a spatial resolution of 35-50 km [20,27,36]. SMOS offers a global coverage at the equator crossing the times of 6 am (local solar time (LST), ascending) and 6 pm (LST, descending) [37].…”

Section: Methodsmentioning

confidence: 99%

Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

Zhuo

Han

2017

Advances in Meteorology

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Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This 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 different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture 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.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

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“…The set-up of the hydrological models used in most cases is based on dynamic forcing, land cover classifications and parametrizations of vegetation dynamics that are partially (or even entirely) derived from some remote sensing. Satellite data are used to varying degrees in model calibration [14][15][16], validation [9,10,17] and data assimilation [3,4,12,13,18].…”

Section: Introductionmentioning

confidence: 99%

Impact of satellite and in situ data assimilation on hydrological predictions

Musuuza¹,

Crochemore²,

Gustafsson³

et al. 2020

Preprint

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<p>The assimilation of different satellite and in-situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we use the European-scale Hydrological Predictions for the Environment hydrological model in the Ume&#228;lven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimises model performance. We further assess the effect of assimilating different satellite products namely snow water equivalent, fractional snow cover, and actual and potential evapotranspiration; as well as in situ measurements of river discharge and local reservoir inflows. We finally investigate the combinations of those products that improve model predictions of the target variables and how the model performance varies through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without ET products improves the model performance.</p>

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Error distribution modelling of satellite soil moisture measurements for hydrological applications

Cited by 14 publications

References 64 publications

Soil Moisture Sensor Network Design for Hydrological Applications

Soil Moisture Sensor Network Design for Hydrological Applications

Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

Impact of satellite and in situ data assimilation on hydrological predictions

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