scholarly journals Conversion of the Time Series of Measured Soil Moisture Data to a Daily Time Step – a Case Study Utilizing the Random Forests Algorithm

2016 ◽  
Vol 4 (2) ◽  
pp. 183-192
Author(s):  
Milan Cisty ◽  
Lubomir Celar
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Random Forests ◽  
Time Step ◽  
Soil Moisture Data ◽  
Daily Time Step ◽  
Daily Time

scholarly journals Modeling bulk density and snow water equivalent using daily snow depth observations

2014 ◽  
Vol 8 (2) ◽  
pp. 521-536 ◽  
Author(s):  
J. L. McCreight ◽  
E. E. Small
Keyword(s):  
Time Series ◽  
Bulk Density ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Predictor Variable ◽  
Time Step ◽  
New Model ◽  
Snow Water ◽  
Daily Time Step ◽  
Daily Time

Abstract. Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily time step and demonstrate its improved skill over the existing models. Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long timescales) and the observed positive and negative anomalies from the smoothed time series (short timescales) as separate terms. A climatology of fit is also included as a predictor variable. Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled time series against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density root mean square error (RMSE) by 9.9 and 4.5% compared to previous models while increasing R2 from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window around peak SWE in each water year, our model reduces density RMSE by 24 and 17.4% relative to the previous models, with R2 increasing from 0.55 to 0.58 to 0.71 across models. Removing the challenge of parameter transfer over the full observational record increases R2 scores for both the existing and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over existing models when data are more frequent than once every 5 days and at least 3 stations are available for training.

scholarly journals Challenges in flood modelling over data scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco

2020 ◽  
Author(s):  
El Mahdi El Khalki ◽  
Yves Tramblay ◽  
Christian Massari ◽  
Luca Brocca ◽  
Mohamed El Mehdi Saidi
Keyword(s):  
Soil Moisture ◽  
In Situ Measurements ◽  
Time Step ◽  
Flood Modelling ◽  
Daily Time Step ◽  
Moisture Conditions ◽  
Scs Cn ◽  
Semi Arid ◽  
Daily Time

Abstract. The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated but remote sensing products could overcome the lack of in-situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (ESA-CCI, SMOS, SMOS-IC, ASCAT satellite products and ERA5 reanalysis) are compared to in-situ measurements and one continuous soil moisture accounting (SMA) model for basins located in the High-Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service Curve Number (SCS-CN). The ASCAT, SMOS-IC and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event, due to the rapid decay of soil moisture after rainfall in these semi-arid environments. Indeed, at the hourly time step, ERA5 and in-situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modelling and forecasting systems in semi-arid regions where soil moisture measurements are lacking.

scholarly journals Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco

2020 ◽  
Vol 20 (10) ◽  
pp. 2591-2607
Author(s):  
El Mahdi El Khalki ◽  
Yves Tramblay ◽  
Christian Massari ◽  
Luca Brocca ◽  
Vincent Simonneaux ◽  
...  
Keyword(s):  
Soil Moisture ◽  
In Situ Measurements ◽  
Flood Modeling ◽  
Time Step ◽  
Ocean Salinity ◽  
Daily Time Step ◽  
Moisture Conditions ◽  
Scs Cn ◽  
Daily Time

Abstract. The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking.

Alternative climate data sources for distributed hydrological modelling on a daily time step

2010 ◽  
Vol 25 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Ashraf El-Sadek ◽  
Max Bleiweiss ◽  
Manoj Shukla ◽  
Steve Guldan ◽  
Alexander Fernald
Keyword(s):  
Hydrological Modelling ◽  
Data Sources ◽  
Climate Data ◽  
Time Step ◽  
Daily Time Step ◽  
Distributed Hydrological Modelling ◽  
Daily Time

A model of expansion and senescence of individual leaves of field-grown maize (Zea mays L.)

1994 ◽  
Vol 74 (1) ◽  
pp. 37-42 ◽  
Author(s):  
D. W. Stewart ◽  
L M. Dwyer
Keyword(s):  
Leaf Area ◽  
Zea Mays L ◽  
Growth Models ◽  
Time Step ◽  
Leaf Area Expansion ◽  
Daily Time Step ◽  
Area Expansion ◽  
Area Development ◽  
Leaf Area Development ◽  
Daily Time

Estimation of leaf area is a major component of plant growth models. In this study, a model was developed to calculate field-grown maize leaf area expansion and senescence on an individual leaf basis. The model began with an equation, based on cumulative growing degree-days from emergence, to initiate leaf area development. The model required daily values of maximum and minimum air temperature, solar radiation and precipitation, had essentially a daily time step with day and night modes, and could be run on commonly accessible computers (micros to mainframes). The objective of the development of the model was to assist plant breeders in optimizing leaf number and shape for adaptation to specific environments. Key words: Leaf area and number, temperature, phenological development

Sign in / Sign up

Export Citation Format

Share Document