Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe? (original) (raw)
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2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
The assimilation of satellite soil moisture data into rainfallrunoff modelling represents an important issue not only for research purposes but also for hydrological application addressing flood forecasting. Notwithstanding the large effort made in the last three decades, only few studies demonstrated a benefit deriving from the use of satellite soil moisture data in hydrology. This matter can be ascribed to the differences in the quality of the assimilated data, in the climatic conditions and in the data assimilation techniques that have been adopted. Based on that, this study compares different satellite soil moisture products in different catchments worldwide to shed light about the more suitable products and climatic conditions that should be employed for improving runoff prediction. The results reveal that the employed soil moisture products can be conveniently used to improve runoff prediction. However, reliability differs according to the climatic region and the accuracy of satellite retrievals.
Assimilation of observed soil moisture data in storm rainfall-runoff modeling
Journal of Hydrologic Engineering, 2009
Estimation of antecedent wetness conditions is one of the most important aspects of storm rainfall-runoff modeling. This study investigated the use of observations of near-surface soil moisture carried out in a small experimental plot to estimate wetness conditions of five nested catchments, from 13 to 137 km 2 in area, in Central Italy, including the plot itself. In particular, the relationship between the observed degree of saturation, e , and the potential maximum retention parameter, S, of the soil conservation service-curve number ͑SCS-CN͒ method for abstraction was investigated using 15 rainfall-runoff events ͑ten for calibration and five for verification͒ that occurred in the period 2002-2005. Two antecedent precipitation indices ͑API͒ and one base flow index ͑BFI͒ were also considered for the estimation of wetness conditions. When interpreting S as the mean soil water deficit of the catchment, an inverse linear relationship with e was found with the coefficient of determination decreasing with catchment area, but still significant for the largest catchment. On the contrary, the reliability of regression increased with catchment area when BFI was employed. Both API indices led to poor results for all investigated catchments. The accuracy of the modified SCS-CN method, i.e., incorporating e for the estimation of S, coupled with a geomorphological unit hydrograph transfer function, was tested in simulating the catchment response. Assimilating the observed soil moisture in the rainfall-runoff model, both the runoff volume and the peak discharge were well predicted with average Nash-Sutcliffe efficiency greater than 90% in the verification phase.
In the Ensemble Kalman Filter (EnKF)-based data assimilation, the background prediction of a model is updated using observations and relative weights based on the model prediction and observation uncertainties. In practice, both model and observation uncertainties are difficult to quantify thus have been often assumed to be spatially and temporally independent Gaussian random variables. Nevertheless, it has been shown that incorrect assumptions regarding the structure of these errors can degrade the performance of the stochastic data assimilation. This work investigates the autocorrelation structure of the microwave satellite soil moisture retrievals and explores how assumed observation error structure affects streamflow prediction skill when assimilating these observations into a rainfall-runoff model. An AMSR-E soil moisture product and the Probability Distribution Model (PDM) are used for this purpose. Satellite soil moisture data is transformed with an exponential filter to make it comparable to the root zone soil moisture state of the model. The exponential filter formulation explicitly incorporates an autocorrelation component in the rescaled observation, however, the error structure of this operator has been treated until now as an independent Gaussian process. In this work, the variance of the rescaled observation error is estimated based on the residuals from the rescaled satellite soil moisture and the calibrated model soil moisture state. Next, the observation error structure is treated as a Gaussian independent process with time-variant variance; a weakly autocorrelated random process (with autocorrelation coefficient of 0.2) and a strongly autocorrelated random process (with autocorrelation coefficient of 0.8). These experiments are compared with a control case which corresponds to the commonly used assumption of Gaussian independent observation error with time-fixed variance. Model error is represented by perturbing rainfall forcing data and soil moisture state. These perturbations are assumed to represent all forcing and model structural/parameter errors. Error parameters are calibrated by applying two discharge ensemble verification criteria. Assimilation results are compared and the impacts of the observation error structure assumptions are assessed. The study area is the semi-arid 42,870 km 2 Warrego at Wyandra River catchment, located in Queensland, Australia. This catchment is chosen for its flooding history, along with having geographical and climatological conditions that enable soil moisture satellite retrievals to have higher accuracy than in other areas. These conditions include large area, semi-arid climate and low vegetation cover. Moreover, the catchment is poorly instrumented, thus satellite data provides valuable information. Results show a consistent improvement of the model forecast accuracy of the control case and in all experiments. However, given that a stochastic assimilation is designed to correct stochastic errors, the systematic errors in model prediction (probably due to the inaccurate forcing data within the catchment) are not addressed by these experiments. The assumed observation error structures tested in the different experiments do not exhibit significant effect in the assimilation results. This case study provides useful insight into the assimilation of satellite soil moisture retrievals in poorly instrumented semi-arid catchments.
Hydrology and Earth System Sciences Discussions, 2015
The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tuned river models. A possible solution to the problem may be to drive the coarse resolution models with locally available high spatial resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee river basin in Australia. <br><br> To this end, a 0.0...
INTEGRATION OF SATELLITE SOIL MOISTURE AND RAINFALL OBSERVATIONS OVER THE ITALIAN TERRITORY
Journal of Hydrometeorology, 2015
State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered: H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2R ASC , considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010-13. In the validation period 2012-13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2R ASC is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42-RT, respectively. The integration of the products is found to improve the threat score for medium-high rainfall accumulations. Since SM2R ASC , H05, and 3B42-RT datasets are provided in near-real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems.
Hydrology and Earth System Sciences Discussions, 2017
A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins, and among those, the use of earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase the knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCR-GLOBWB using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum Er Rbia basin. Daily simulations at a spatial resolution of 5 arcmin × 5 arcmin are performed with varying parameters values for the 32-year period…
Water Resources Research, 2016
This paper explores the use of active and passive microwave satellite soil moisture products for improving streamflow prediction within four large (>5000km 2) semiarid catchments in Australia. We use the probability distributed model (PDM) under a data-scarce scenario and aim at correcting two key controlling factors in the streamflow generation: the rainfall forcing data and the catchment wetness condition. The soil moisture analysis rainfall tool (SMART) is used to correct a near real-time satellite rainfall product (forcing correction scheme) and an ensemble Kalman filter is used to correct the PDM soil moisture state (state correction scheme). These two schemes are combined in a dual correction scheme and we assess the relative improvements of each. Our results demonstrate that the quality of the satellite rainfall product is improved by SMART during moderate-to-high daily rainfall events, which in turn leads to improved streamflow prediction during high flows. When employed individually, the soil moisture state correction scheme generally outperforms the rainfall correction scheme, especially for low flows. Overall, the combined dual correction scheme further improves the streamflow predictions (reduction in root mean square error and false alarm ratio, and increase in correlation coefficient and Nash-Sutcliffe efficiency). Our results provide new evidence of the value of satellite soil moisture observations within data-scarce regions. We also identify a number of challenges and limitations within the schemes.
Although the complexity of physically based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e. HBV. This has been rarely done for conceptual models as satellite data are often used in spatial calibration of the distributed models. Three different soil moisture products from ESA CCI SM v04.4, AMSR-E and SMAP, and total water storage anomalies from GRACE are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture are used to analyse...
Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins
2015
This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255 to 6967 km2) and two different periods (May-Aug & Sep-Nov) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity and error ratios between precipitation and runoff are presented. Overall, we show strong over/under-estimation associated with the near-real-time 3B42/CMORPH products. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation off low simulations yields8higher degree of consistency for the moderate to large basin scales and May-Aug period. Gauge-adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall-runoff transformation.