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 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 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 Measurement systems of soil water matric potential and evaluation of soil moisture under different irrigation depths

2012 ◽  
Vol 32 (3) ◽  
pp. 467-478 ◽  
Author(s):  
José M. G. Beraldo ◽  
José E. Cora ◽  
Edemo J. Fernandes
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Retention Curve ◽  
Soil Matric Potential ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Matric Potential ◽  
Measurement Systems ◽  
Neutron Attenuation ◽  
New Methodologies

The development of new methodologies and tools that enable to determine the water content in soil is of fundamental importance to the practice of irrigation. The objective of this study was to evaluate soil matric potential using mercury tensiometer and puncture digital tensiometer, and to compare the gravimetric soil moisture values obtained by tensiometric system with gravimetric soil moisture obtained by neutron attenuation technique. Four experimental plots were maintained with different soil moisture by irrigation. Three repetitions of each type of tensiometer were installed at 0.20 m depth. Based on the soil matric potential and the soil water retention curve, the corresponding gravimetric soil moisture was determined. The data was then compared to those obtained by neutron attenuation technique. The results showed that both tensiometric methods showed no difference under soil matric potential higher than -40 kPa. However, under drier soil, when the water was replaced by irrigation, the soil matric potential of the puncture digital tensiometer was less than those of the mercury tensiometer.

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.

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 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.

scholarly journals Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 708
Author(s):  
Phanthasin Khanthavong ◽  
Shin Yabuta ◽  
Hidetoshi Asai ◽  
Md. Amzad Hossain ◽  
Isao Akagi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Distribution ◽  
Water Status ◽  
Soil Layer ◽  
Shoot Biomass ◽  
Soil Conditions ◽  
Crop Adaptation ◽  
Soil Moisture Status ◽  
Root Distributions

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions.

scholarly journals The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

2007 ◽  
Vol 24 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Sabine Philipps ◽  
Christine Boone ◽  
Estelle Obligis
Keyword(s):  
Soil Moisture ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Auxiliary Data ◽  
Additional Information ◽  
Ocean Salinity

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

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