Hydrological real-time modeling using remote sensing data (original) (raw)
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Hydrology and Earth System Sciences, 2011
Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.
Hydrology
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satelli...
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.
Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting
2008
The aim of this paper is to examine the benefits of updating soil moisture of a distributed rainfall runoff model in forecasting large floods. The updating method uses Ensemble Kalman Filter concepts and involves an iterative similarity approach that avoids calculation of the Jacobian that relates the states and the observations. The soil moisture is updated based on observed runoff in a real-time mode, and is then used as an initial condition for the flood forecasts. The case study is set in the 622 km 2 Kamp catchment, Austria. The results indicate that the updating procedure indeed improves the forecasts substantially. The mean absolute normalised error of the peak flows of six large floods decreases from 25% to 12% (3 h lead time), and from 25% to 19% (48 h lead time). The Nash-Sutcliffe efficiency of forecasting runoff for these flood events increases from 0.79 to 0.92 (3 h lead time), and from 0.79 to 0.88 (48 h lead time). The flood forecasting system has been in operational use since early 2006. ª
The aim of this paper is to examine the benefits of updating soil moisture of a distributed rainfall runoff model in forecasting large floods. The updating method uses Ensemble Kalman Filter concepts and involves an iterative similarity approach that avoids calculation of the Jacobian that relates the states and the observations. The soil moisture is updated based on observed runoff in a real-time mode, and is then used as an initial condition for the flood forecasts. The case study is set in the 622 km 2 Kamp catchment, Austria. The results indicate that the updating procedure indeed improves the forecasts substantially. The mean absolute normalised error of the peak flows of six large floods decreases from 25% to 12% (3 h lead time), and from 25% to 19% (48 h lead time). The Nash-Sutcliffe efficiency of forecasting runoff for these flood events increases from 0.79 to 0.92 (3 h lead time), and from 0.79 to 0.88 (48 h lead time). The flood forecasting system has been in operational use since early 2006. ª
Assimilating Remotely Sensed Surface Soil Moisture into SWAT Using Ensemble Kalman Filter
Watershed Management Conference 2010: Innovations in Watershed Management under Land Use and Climate Change, 2010
Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the submonthly scale accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain gauge observations. In order to adapt to the irregular retrieval frequency of heritage soil moisture products, a new variable correction window method is developed that allows for better efficiency in leveraging temporally sparse satellite soil moisture retrievals. Results confirm the advantage of using this variable window method relative to an existing fixed-window version of SMART over a range of one-to 30-day accumulation periods. Using this modified version of SMART and heritage satellite surface soil moisture products, a 1. 0-deg, 20-year (1979 to 1998) global rainfall dataset over land is corrected and validated. Relative to the original precipitation product, the corrected dataset demonstrates improved correlation with a global gauge-based daily rainfall product, lower root-mean-square-error (−13%) on a 10-day scale and provides a higher probability of detection (þ5%) and lower false alarm rates (−3.4%) for five-day rainfall accumulation estimates. This corrected rainfall dataset is expected to provide improved rainfall forcing data for the land surface modeling community.
Hydrology and Earth System Sciences, 2015
Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large (> 40 000 km<sup>2</sup>) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation by explicitly correcting bias in soil moisture and streamflow in the ensemble ge...
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.
Geoscience and Remote Sensing IEEE International Symposium, 2010
This paper describes the development of information systems and techniques for improving hydrological forecasting by applying satellite observations, weather radars, and in situ measurements from automatic monitoring stations. In the methodology developed and demonstrated, the observation data are accompanied with a detailed soil and land cover information. The information system is concerned with the following physical characteristics relevant to river