Simple Method for Using Precomputed Hydrologic Models in Flood Forecasting with Uniform Rainfall and Soil Moisture Pattern

2015 ◽  
Vol 20 (12) ◽  
pp. 04015039 ◽  
Author(s):  
Herman Guillermo Dolder ◽  
Norman L. Jones ◽  
E. James Nelson
Keyword(s):  
Soil Moisture ◽  
Flood Forecasting ◽  
Hydrologic Models ◽  
Simple Method ◽  
Moisture Pattern

PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

2019 ◽  
Vol 5 (1) ◽  
pp. 97-106
Author(s):  
Rudi Budi Agung ◽  
Muhammad Nur ◽  
Didi Sukayadi
Keyword(s):  
Soil Moisture ◽  
Growth And Development ◽  
Large Scale ◽  
Simple Method ◽  
Moisture Sensor ◽  
Sensor Development ◽  
Soil Moisture Sensor ◽  
Application Systems ◽  
Harvesting Systems ◽  
Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

scholarly journals A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

2020 ◽  
Author(s):  
Toby N. Carlson ◽  
George Petropoulos
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Fluxes ◽  
Surface Model ◽  
Landsat 8 ◽  
Simple Method ◽  
Practical Applications ◽  
Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

scholarly journals Using globally available soil moisture indicators for flood modelling in Mediterranean catchments

2013 ◽  
Vol 10 (8) ◽  
pp. 10997-11033 ◽  
Author(s):  
C. Massari ◽  
L. Brocca ◽  
S. Barbetta ◽  
C. Papathanasiou ◽  
M. Mimikou ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Weather Forecasting ◽  
Early Warning Systems ◽  
Flood Forecasting ◽  
Rainfall Runoff ◽  
Small Catchment ◽  
Continuous Models ◽  
Available Soil Moisture ◽  
Risk Event ◽  
Mediterranean Regions

Abstract. Floods are one of the most dangerous natural hazards in Mediterranean regions. Flood forecasting tools and early warning systems can be very beneficial to reduce flood risk. Event-based rainfall runoff models are frequently employed for operational flood forecasting purposes because of their simplicity and the reduced number of parameters involved with respect to continuous models. However, the advantages that are related with the reduced parameterization face against the need for a correct initialization of the model, especially in areas affected by strong climate seasonality. On the other hand, the use of continuous models may be very problematic in poorly gauged areas. This paper introduces a simplified continuous rainfall-runoff model, which uses globally available soil moisture retrievals to identify the initial wetness condition of the catchment, and, only event rainfall data to simulate discharge hydrographs. The model calibration involves only 3 parameters. For soil moisture, beside in situ and modelled data, satellite products from the Advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer for Earth observation (AMSR-E) sensors are employed. Additionally, the ERA-LAND reanalysis soil moisture product of the European Centre for Medium Range Weather Forecasting (ECMWF) is used. The model was tested in the small catchment of Rafina, 109 km2 located in the Eastern Attica region, Greece. Specifically, fifteen rainfall-runoff events were modelled by considering different configurations for the initial soil moisture conditions. Comparing the performance of the different soil moisture products, it was found that all global indicators allow reproducing fairly well the selected flood events providing much better results than the situation where a constant initial condition is provided. ERA-LAND slightly outperforms the satellite soil moisture products and in general, all the indicators give the same performance obtained by ground and continuously simulated soil moisture data. Due to the wide diffusion of globally available soil moisture retrievals and the small amount of parameters used, the proposed modelling approach is very suitable for runoff prediction in poorly gauged areas.

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2014 ◽  
Vol 18 (6) ◽  
pp. 2343-2357 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Lead Times ◽  
Validation Data ◽  
Data Set ◽  
Discharge Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

Tracking the storage and dissipation of atmospheric river storm water in the Russian River watershed using GPS elastic displacements

2021 ◽  
Author(s):  
Ellen Knappe ◽  
Adrian Borsa ◽  
Hilary Martens ◽  
Donald Argus ◽  
Zachary Hoylman ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Storage ◽  
Heavy Precipitation ◽  
Storm Water ◽  
Hydrologic Models ◽  
Effective Technique ◽  
Atmospheric Rivers ◽  
River Watershed ◽  
Russian River ◽  
Flooding Events

<p>GPS is emerging as an effective technique to estimate changes in total water storage at Earth's surface.  In California's mountains, GPS indicates that more subsurface storage is lost during drought and gained during years of heavy precipitation than predicted by hydrology models [Argus et al. 2017].  Atmospheric rivers provide a majority of the annual precipitation in coastal environments across North America. The Russian River watershed is often affected by these large storms, which can produce extensive flooding events. In this study, we estimate changes in water storage for the 2017 water year (October 2016 – September 2017), a historically wet year in California, in which more than 20 atmospheric rivers impacted the Russian River watershed. Using GPS displacements, we quantify the water gained during higher intensity atmospheric rivers. We further resolve the time it takes for the storm water to dissipate: that is, we distinguish between water that runs off into rivers and water that is stored in the ground as soil moisture. Finally, we investigate the empirical relationships between GPS displacement and precipitation, evapotranspiration, and soil moisture estimates with the aim of improving constraints to hydrologic models.</p>

A Soil Moisture Sensorweb for Use in Flood Forecasting Applications

2003 ◽  
Author(s):  
P M Teillet ◽  
R P Gauthier ◽  
T J Pultz ◽  
A Deschamps ◽  
G Fedosejevs ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Flood Forecasting

scholarly journals On the scaling characteristics of observed and simulated spatial soil moisture fields

2009 ◽  
Vol 16 (1) ◽  
pp. 141-150 ◽  
Author(s):  
M. Gebremichael ◽  
R. Rigon ◽  
G. Bertoldi ◽  
T. M. Over
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Spatial Correlation ◽  
Hydrologic Model ◽  
Invariance Property ◽  
Spatial Average ◽  
Hydrologic Models ◽  
Model Simulations

Abstract. By providing continuous high-resolution simulations of soil moisture fields, distributed hydrologic models could be powerful tools to advance the scientific community's understanding of the space-time variability and scaling characteristics of soil moisture fields. However, in order to use the soil moisture simulations from hydrologic models with confidence, it is important to understand whether the models are able to represent in a reliable way the processes regulating soil moisture variability. In this study, a comparison of the scaling characteristics of spatial soil moisture fields derived from a set of microwave radiometer observations from the Southern Great Plains 1997 experiment and corresponding simulations using the distributed hydrologic model GEOtop is performed through the use of generalized variograms. Microwave observations and model simulations are in agreement with respect to suggesting the existence of a scale-invariance property in the variograms of spatial soil moisture fields, and indicating that the scaling characteristics vary with changes in the spatial average soil water content. However, observations and simulations give contradictory results regarding the relationship between the scaling parameters (i.e. spatial organization) and average soil water content. The drying process increased the spatial correlation of the microwave observations at both short and long separation distances while increasing the rate of decay of correlation with distance. The effect of drying on the spatial correlation of the model simulations was more complex, depending on the storm and the simulation examined, but for the largest storm in the simulation most similar to the observations, drying increased the long-range correlation but decreased the short-range. This is an indication that model simulations, while reproducing correctly the total streamflow at the outlet of the watershed, may not accurately reproduce the runoff production mechanisms. Consideration of the scaling characteristics of spatial soil moisture fields can therefore serve as a more intensive means for validating distributed hydrologic models, compared to the traditional approach of only comparing the streamflow hydrographs.

Assimilation of soil moisture into hydrological models for flood forecasting: a variational approach

2003 ◽  
Vol 29 (6) ◽  
pp. 679-686 ◽  
Author(s):  
L. Oudin ◽  
A. Weisse ◽  
C. Loumagne ◽  
S. Le Hégarat-Mascle
Keyword(s):  
Soil Moisture ◽  
Variational Approach ◽  
Flood Forecasting ◽  
Hydrological Models

scholarly journals Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale

2006 ◽  
Vol 10 (3) ◽  
pp. 353-368 ◽  
Author(s):  
J. Parajka ◽  
V. Naeimi ◽  
G. Blöschl ◽  
W. Wagner ◽  
R. Merz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Ungauged Catchments ◽  
Soil Moisture Dynamics ◽  
Model Efficiency ◽  
The Relationship ◽  
Runoff Model ◽  
Moisture Dynamics

Abstract. This paper examines the potential of scatterometer data from ERS satellites for improving hydrological simulations in both gauged and ungauged catchments. We compare the soil moisture dynamics simulated by a semidistributed hydrologic model in 320 Austrian catchments with the soil moisture dynamics inferred from the satellite data. The most apparent differences occur in the Alpine areas. Assimilating the scatterometer data into the hydrologic model during the calibration phase improves the relationship between the two soil moisture estimates without any significant decrease in runoff model efficiency. For the case of ungauged catchments, assimilating scatterometer data does not improve the daily runoff simulations but does provide more consistent soil moisture estimates. If the main interest is in obtaining estimates of catchment soil moisture, reconciling the two sources of soil moisture information seems to be of value because of the different error structures.

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