scholarly journals Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

2015 ◽  
Vol 19 (4) ◽  
pp. 1659-1676 ◽  
Author(s):  
C. Alvarez-Garreton ◽  
D. Ryu ◽  
A. W. Western ◽  
C.-H. Su ◽  
W. T. Crow ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Distributed Model ◽  
Ensemble Prediction ◽  
Generation Process ◽  
Streamflow Prediction ◽  
Flood Prediction ◽  
Lumped Model ◽  
Model Soil ◽  
Semi Arid

Abstract. 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 km2) 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 generation process, and for seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided a more accurate streamflow prediction (Nash–Sutcliffe efficiency, NSE = 0.77) than the lumped model (NSE = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments (two of them with NSE below 0.3). After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24%, respectively; the false alarm ratio was reduced by 9% in both cases; the peak volume error was reduced by 58 and 1%, respectively; the ensemble skill was improved (evidenced by 12 and 13% reductions in the continuous ranked probability scores, respectively); and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed satellite SM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving some characteristics of the streamflow ensemble prediction; however, the updated prediction is still poor since SM-DA does not address the systematic errors found in the model prior to assimilation.

scholarly journals Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

2014 ◽  
Vol 11 (9) ◽  
pp. 10635-10681 ◽  
Author(s):  
C. Alvarez-Garreton ◽  
D. Ryu ◽  
A. W. Western ◽  
C.-H. Su ◽  
W. T. Crow ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Distributed Model ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Flood Prediction ◽  
Lumped Model ◽  
Model Soil ◽  
Main Driver ◽  
Semi Arid

Abstract. 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 active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40 000 km2). 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 and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash–Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM–DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively; the NS of the ensemble mean increased by 7 and 38%, respectively; the false alarm ratio was reduced by 15 and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM–DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM–DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM–DA does not address systematic errors in the model.

scholarly journals Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signatures of snow and complex terrain

2008 ◽  
Vol 5 (4) ◽  
pp. 1927-1966 ◽  
Author(s):  
C. J. Williams ◽  
J. P. McNamara ◽  
D. G. Chandler
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Spatial Variability ◽  
Complex Terrain ◽  
Soil Depth ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Spatial Correlations ◽  
Static Properties ◽  
Semi Arid

Abstract. The controls on the spatial distribution of soil moisture include static and dynamic variables. The superposition of static and dynamic controls can lead to different soil moisture patterns for a given catchment during wetting, draining, and drying periods. These relationships can be further complicated in snow-dominated mountain regions where soil water input by precipitation is largely dictated by the spatial variability of snow accumulation and melt. In this study, we assess controls on spatial and temporal soil moisture variability in a small (0.02 km2), snow-dominated, semi-arid catchment by evaluating spatial correlations between soil moisture and site characteristics through different hydrologic seasons. We assess the relative importance of snow with respect to other catchment properties on the spatial variability of soil moisture and track the temporal persistence of those controls. Spatial distribution of snow, distance from divide, soil texture, and soil depth exerted significant control on the spatial variability of moisture content throughout most of the hydrologic year. These relationships were strongest during the wettest period and degraded during the dry period. As the catchment cycled through wet and dry periods, the relative spatial variability of soil moisture tended to remain unchanged. We suggest that the static properties in complex terrain (slope, aspect, soils) impose first order controls on the spatial variability of snow and consequent soil moisture, and that the interaction of dynamic (timing of water input) and static properties propagate that relative constant spatial variability through the hydrologic year. The results demonstrate snow exerts significant influence on how water is retained within mid-elevation semi-arid catchments throughout the year and infer that reductions in annual snowpacks associated with changing climate regimes may strongly influence spatial and temporal soil moisture patterns and catchment physical and biological processes.

scholarly journals Hydrological model assessment for flood early warning in a tropical high mountain basin

2016 ◽  
Author(s):  
María Carolina Rogelis ◽  
Micha Werner ◽  
Nelson Obregón ◽  
Nigel Wright
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Model Performance ◽  
Distributed Model ◽  
High Mountain ◽  
Model Assessment ◽  
Lumped Model ◽  
Water Storage Capacity ◽  
Soil Moisture Accounting ◽  
The Impact

Abstract. A distributed model (TETIS), a semi-distributed model (TOPMODEL) and a lumped model (HEC HMS soil moisture accounting) were used to simulate the discharge response of a tropical high mountain basin characterized by soils with high water storage capacity and high conductivity. The models were calibrated with the Shuffle Complex Evolution algorithm, using the Kling and Gupta efficiency as objective function. Performance analysis and diagnostics were carried out using the signatures of the flow duration curve and through analysis of the model fluxes in order to identify the most appropriate model for the study area for flood early warning. The impact of varying grid sizes was assessed in the TETIS model and the TOPMODEL in order to chose a model with balanced model performance and computational efficiency. The sensitivity of the models to variation in the precipitation input was analysed by forcing the models with a rainfall ensemble obtained from Gaussian simulation. The resulting discharge ensembles of each model were compared in order to identify differences among models structures. The results show that TOPMODEL is the most realistic model of the three tested, albeit showing the largest discharge ensemble spread. The main differences among models occur between HEC HMS soil moisture accounting and TETIS, and HEC HMS soil moisture accounting and TOPMODEL, with HEC HMS soil moisture accounting producing ensembles in a range lower than the other two models. The ensembles of TETIS and TOPMODEL are more similar.

scholarly journals Downscaling of seasonal soil moisture forecasts using satellite data

2014 ◽  
Vol 18 (8) ◽  
pp. 2899-2905 ◽  
Author(s):  
S. Schneider ◽  
A. Jann ◽  
T. Schellander-Gorgas
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Spatial Resolution ◽  
Ensemble Prediction ◽  
Seasonal Forecasts ◽  
Envisat Asar ◽  
Model Soil ◽  
Additional Information ◽  
Satellite Sensors ◽  
Forecasting System

Abstract. A new approach to downscaling soil moisture forecasts from the seasonal ensemble prediction forecasting system of the ECMWF (European Centre for Medium-Range Weather Forecasts) is presented in this study. Soil moisture forecasts from this system are rarely used nowadays, although 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 problems 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 downscaling is adding skill to the seasonal forecasts.

scholarly journals Benchmarking ensemble streamflow prediction skill in the UK

2018 ◽  
Vol 22 (3) ◽  
pp. 2023-2039 ◽  
Author(s):  
Shaun Harrigan ◽  
Christel Prudhomme ◽  
Simon Parry ◽  
Katie Smith ◽  
Maliko Tanguy
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Base Flow ◽  
Ensemble Prediction ◽  
Flow Index ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Skill Scores ◽  
Ensemble Streamflow Prediction ◽  
The Uk

Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient =0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.

scholarly journals A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system

2010 ◽  
Vol 7 (2) ◽  
pp. 2413-2453 ◽  
Author(s):  
G. Thirel ◽  
E. Martin ◽  
J.-F. Mahfouf ◽  
S. Massart ◽  
S. Ricci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydraulic Conductivity ◽  
State Variables ◽  
Streamflow Prediction ◽  
Initial State ◽  
Model Soil ◽  
Streamflow Forecasts ◽  
Set Up ◽  
The Impact ◽  
Assimilation System

Abstract. Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using observed streamflows in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.

scholarly journals Calibration and downscaling of seasonal soil moisture forecasts using satellite data

2013 ◽  
Vol 10 (12) ◽  
pp. 14783-14799
Author(s):  
S. Schneider ◽  
A. Jann ◽  
T. Gorgas
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Ensemble Prediction ◽  
Satellite Measurement ◽  
Seasonal Forecasts ◽  
Measurement Systems ◽  
Envisat Asar ◽  
Model Soil ◽  
Additional Information ◽  
Forecasting System

Abstract. A new approach to calibrate and downscale soil moisture forecasts from the seasonal ensemble prediction forecasting system of ECMWF is presented in this study. Soil moisture forecasts from this system are rarely used nowadays though they could provide valuable information. Weaknesses of the model soil scheme in forecasting soil water content are the main reason why soil water information is not used so far. The basic idea to overcome some of the modelling problems is the application of additional information provided by two satellite measurement systems (ASCAT and ENVISAT ASAR) to improve the forecast quality. Seasonal forecasts from 2011 and 2012 have been compared to in-situ measurements sites in Kenya to test the approach. Results confirm that both the calibration and the downscaling can add skill to the forecasts.

scholarly journals Flood Discharge Prediction Based on Remote-Sensed Spatiotemporal Features Fusion and Graph Attention

2021 ◽  
Vol 13 (24) ◽  
pp. 5023
Author(s):  
Chen Chen ◽  
Dingbin Luan ◽  
Song Zhao ◽  
Zhan Liao ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Flood Control ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Distributed Model ◽  
Human Beings ◽  
Flood Prediction ◽  
Lumped Model

Floods have brought a great threat to the life and property of human beings. Under the premise of strengthening flood control engineering measures and following the strategic thinking of sustainable development, many achievements have been made in flood forecasting recently. However, due to the complexity of the traditional lumped model and distributed model, the hydrologic parameter calibration process is full of difficulties, leading to a long development cycle of a reasonable hydrologic prediction model. Even for modern data-driven models, the spatial distribution characteristics of the rainfall data are also not fully mined. Based on this situation, this paper abstracts the rainfall data into the graph structure data, uses remote sensing images to extract the elevation information, introduces the graph attention mechanism to extract the spatial characteristics of rainfall, and employs long-term and short-term memory (LSTM) network to fuse the spatial and temporal characteristics for flood prediction. Through well-designed experiments, the forecasting effect of flood peak value and flood arrival time is verified. Furthermore, compared with the LSTM model and BIGRU model without spatial feature extraction, the advantages of spatiotemporal feature fusion are highlighted. The specific performance is that the RMSE (the root means square error) and R2(coefficient of determination) of the GA-RNN model have been significantly improved. Finally, we conduct experiments on the observed ten rainfall events in the history of the target watershed. According to the hydrological prediction specifications, the model can be evaluated as a Class B flood forecasting model.

scholarly journals Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signature of snow and complex terrain

2009 ◽  
Vol 13 (7) ◽  
pp. 1325-1336 ◽  
Author(s):  
C. J. Williams ◽  
J. P. McNamara ◽  
D. G. Chandler
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Spatial Variability ◽  
Complex Terrain ◽  
Soil Depth ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Spatial Correlations ◽  
Static Properties ◽  
Semi Arid

Abstract. The controls on the spatial distribution of soil moisture include static and dynamic variables. The superposition of static and dynamic controls can lead to different soil moisture patterns for a given catchment during wetting, draining, and drying periods. These relationships can be further complicated in snow-dominated mountain regions where soil water input by precipitation is largely dictated by the spatial variability of snow accumulation and melt. In this study, we assess controls on spatial and temporal soil moisture variability in a small (0.02 km2), snow-dominated, semi-arid catchment by evaluating spatial correlations between soil moisture and site characteristics through different hydrologic seasons. We assess the relative importance of snow with respect to other catchment properties on the spatial variability of soil moisture and track the temporal persistence of those controls. Spatial distribution of snow, distance from divide, soil texture, and soil depth exerted significant control on the spatial variability of moisture content throughout most of the hydrologic year. These relationships were strongest during the wettest period and degraded during the dry period. As the catchment cycled through wet and dry periods, the relative spatial variability of soil moisture tended to remain unchanged. We suggest that the static properties in complex terrain (slope, aspect, soils) impose first order controls on the spatial variability of snow and resulting soil moisture patterns, and that the interaction of dynamic (timing of water input) and static influences propagate that relative constant spatial variability through most of the hydrologic year. The results demonstrate that snow exerts significant influence on how water is retained within mid-elevation semi-arid catchments and suggest that reductions in annual snowpacks associated with changing climate regimes may strongly influence spatial and temporal soil moisture patterns and catchment physical and biological processes.

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