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 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 Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime

2021 ◽  
Author(s):  
Stefano Materia ◽  
Constantin Ardilouze ◽  
Chloé Prodhomme ◽  
Markus G. Donat ◽  
Marianna Benassi ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Heat Waves ◽  
Climate Regime ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Surface Temperatures ◽  
The Mediterranean

AbstractLand surface and atmosphere are interlocked by the hydrological and energy cycles and the effects of soil water-air coupling can modulate near-surface temperatures. In this work, three paired experiments were designed to evaluate impacts of different soil moisture initial and boundary conditions on summer temperatures in the Mediterranean transitional climate regime region. In this area, evapotranspiration is not limited by solar radiation, rather by soil moisture, which therefore controls the boundary layer variability. Extremely dry, extremely wet and averagely humid ground conditions are imposed to two global climate models at the beginning of the warm and dry season. Then, sensitivity experiments, where atmosphere is alternatively interactive with and forced by land surface, are launched. The initial soil state largely affects summer near-surface temperatures: dry soils contribute to warm the lower atmosphere and exacerbate heat extremes, while wet terrains suppress thermal peaks, and both effects last for several months. Land-atmosphere coupling proves to be a fundamental ingredient to modulate the boundary layer state, through the partition between latent and sensible heat fluxes. In the coupled runs, early season heat waves are sustained by interactive dry soils, which respond to hot weather conditions with increased evaporative demand, resulting in longer-lasting extreme temperatures. On the other hand, when wet conditions are prescribed across the season, the occurrence of hot days is suppressed. The land surface prescribed by climatological precipitation forcing causes a temperature drop throughout the months, due to sustained evaporation of surface soil water. Results have implications for seasonal forecasts on both rain-fed and irrigated continental regions in transitional climate zones.

ESTIMATED SOIL WATER RESERVES APPLICABLE TO A WHEAT-FALLOW ROTATION FOR GENERALIZED SOIL AREAS MAPPED IN SOUTHERN SASKATCHEWAN

1984 ◽  
Vol 64 (4) ◽  
pp. 667-680 ◽  
Author(s):  
R. DE JONG ◽  
J. A. SHIELDS ◽  
W. K. SLY
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Holding Capacity ◽  
Climatic Data ◽  
Moisture Budget ◽  
Model Soil ◽  
Available Water ◽  
Soil Zone ◽  
Water Holding ◽  
Soil Moisture Budget

Long-term mean soil water reserves for a spring wheat-fallow rotation in the southern half of Saskatchewan were calculated using the Versatile Soil Moisture Budget. Four different available water-holding capacity classes and climatic data from 53 stations were used as input to the model. Soil water reserve data for the following times, seeding on 1 May in the crop year, at heading on 30 June, and on 1 May in the fallow year, were mapped. These were then combined with an available water-holding capacity map to portray in a single map the combined droughtiness due to climatic and soil attributes. Estimated soil water reserves compared well with measured data from one location in the Brown soil zone. The temporal and spatial changes in water reserves are discussed and related to summerfallowing. The maps provide information for use in making potential grain yield estimates. Key words: Soil water, wheat-fallow rotation, generalized soil areas, Saskatchewan, Versatile soil moisture budget

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 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 Development and Evaluation of a Pan-European Multimodel Seasonal Hydrological Forecasting System

2019 ◽  
Vol 20 (1) ◽  
pp. 99-115 ◽  
Author(s):  
Niko Wanders ◽  
Stephan Thober ◽  
Rohini Kumar ◽  
Ming Pan ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
General Circulation ◽  
Hydrological Model ◽  
General Circulation Models ◽  
Hydrologic Model ◽  
Historical Period ◽  
Seasonal Forecasts ◽  
Forecasting System ◽  
Temporal And Spatial

Abstract Hydrological forecasts with a high temporal and spatial resolution are required to provide the level of information needed by end users. So far high-resolution multimodel seasonal hydrological forecasts have been unavailable due to 1) lack of availability of high-resolution meteorological seasonal forecasts, requiring temporal and spatial downscaling; 2) a mismatch between the provided seasonal forecast information and the user needs; and 3) lack of consistency between the hydrological model outputs to generate multimodel seasonal hydrological forecasts. As part of the End-to-End Demonstrator for Improved Decision Making in the Water Sector in Europe (EDgE) project commissioned by the Copernicus Climate Change Service (ECMWF), this study provides a unique dataset of seasonal hydrological forecasts derived from four general circulation models [CanCM4, GFDL Forecast-Oriented Low Ocean Resolution version of CM2.5 (GFDL-FLOR), ECMWF Season Forecast System 4 (ECMWF-S4), and Météo-France LFPW] in combination with four hydrological models [mesoscale hydrologic model (mHM), Noah-MP, PCRaster Global Water Balance (PCR-GLOBWB), and VIC]. The forecasts are provided at daily resolution, 6-month lead time, and 5-km spatial resolution over the historical period from 1993 to 2012. Consistency in hydrological model parameterization ensures an increased consistency in the hydrological forecasts. Results show that skillful discharge forecasts can be made throughout Europe up to 3 months in advance, with predictability up to 6 months for northern Europe resulting from the improved predictability of the spring snowmelt. The new system provides an unprecedented ensemble of seasonal hydrological forecasts with significant skill over Europe to support water management. This study highlights the potential advantages of multimodel based forecasting system in providing skillful hydrological forecasts.

A Real-time Ensemble Hydrological Forecasting System over Germany at Sub-seasonal to Seasonal Time Range

2020 ◽  
Author(s):  
Husain Najafi ◽  
Stephan Thober ◽  
Friedrich Boeing ◽  
Oldrich Rakovec ◽  
Matthias Kelbling ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Time Scale ◽  
Hydrologic Model ◽  
Ensemble Prediction ◽  
Time Range ◽  
Moisture Index ◽  
Hydrological Forecasting ◽  
Soil Moisture Index ◽  
Forecasting System

<p>Real-time hydrological forecasting provides valuable information to mitigate the impact of extreme hydrological events such as flood and drought. An ensemble hydrological forecasting system is developed to investigate the hydrological predictability at sub-seasonal to seasonal (S2S) time scale over Germany. The ensemble hydrological simulations are performed with the mesoscale hydrologic model (mHM) which benefits from a multiscale parameter regionalization module (MPR). The model is forced by the operational ensemble prediction System from the European Center for Medium-range Weather Forecast (ECMWF). 51 hydrological ensemble forecasts are generated in real-time (twice a week) for up to 45 days in advance. We used the initial condition records from the German Drought Monitor (GDM, www.ufz.de/duerremonitor) which provides daily up-to-date high resolution drought information at a spatial resolution of 4 km. The performance of the system is evaluated for three consecutive years started from 2016 for Soil Moisture Index (SMI) and real-time streamflow records (222 based in Zink et al 2017). Comparison between forecasted Soil Moisture Index (SMI) and the one derived by the GDM suggested promising results for certain areas over the study area at S2S time scale. The predictability of the ensemble forecasting system is evaluated against that generated with the Ensemble Streamflow Prediction (ESP) method. This research is one of the first attempts to investigate the hydrological forecasting skill at S2S time scale in Europe. The study is supported as a part of the Modular Observation Solutions for Earth System (MOSES) project.</p>

scholarly journals Remote Sensing of Soil Moisture

2013 ◽  
Vol 2013 ◽  
pp. 1-33 ◽  
Author(s):  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Microwave Frequency ◽  
Important Variable ◽  
Catchment Hydrology ◽  
Ancillary Data ◽  
Surface Hydrology ◽  
Satellite Sensors ◽  
Table Versus

Soil moisture is an important variable in land surface hydrology as it controls the amount of water that infiltrates into the soil and replenishes the water table versus the amount that contributes to surface runoff and to channel flow. However observations of soil moisture at a point scale are very sparse and observing networks are expensive to maintain. Satellite sensors can observe large areas but the spatial resolution of these is dependent on microwave frequency, antenna dimensions, and height above the earth’s surface. The higher the sensor, the lower the spatial resolution and at low elevations the spacecraft would use more fuel. Higher spatial resolution requires larger diameter antennas that in turn require more fuel to maintain in space. Given these competing issues most passive radiometers have spatial resolutions in 10s of kilometers that are too coarse for catchment hydrology applications. Most local applications require higher-spatial-resolution soil moisture data. Downscaling of the data requires ancillary data and model products, all of which are used here to develop high-spatial-resolution soil moisture for catchment applications in hydrology. In this paper the author will outline and explain the methodology for downscaling passive microwave estimation of soil moisture.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

Sign in / Sign up

Export Citation Format

Share Document