Exploiting Copernicus Climate Change Service (C3S) to assess ongoing and future soil erosion over Italy

2020 ◽  
Author(s):  
Guido Rianna ◽  
Monia Santini ◽  
Marco Mancini ◽  
Roberta Padulano ◽  
Sergio Noce
Keyword(s):  
Soil Erosion ◽  
Web Application ◽  
Soil Loss ◽  
Climate Data ◽  
Time Step ◽  
R Factor ◽  
Data Store ◽  
Daily Time Step ◽  
Soil Losses ◽  
Daily Time

<p>Soil erosion by water greatly affects Italy impacted by 24% of total soil loss of Europe, 33% of agricultural lands exposed, and costs, e.g. for crop production, up to about 600Meuro. Furthermore, expected increases in severity and magnitude of extreme precipitation events could exacerbate such an issue.</p><p>In this regard, rainfall information at very fine spatial and temporal resolution represents a key point; unfortunately, weather stations are not spread uniformly across regions and they uncommonly provide free data at sub-daily scale. Moreover, the reliable projections of how rainfall will change in the coming decades are hard to store and manage for non-experts.</p><p>In trying to overcome such a gap, Copernicus Climate Change Service (C3S) provides several tools. The C3S is part of the Copernicus Earth Observation Programme and is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. In particular, Climate Data Store (CDS) hosts rainfall time series for the historical period and most recent decades from observational (E-OBS) and reanalysis (ERA5, ERA5-Land, UERRA) datasets, at (sub) daily time step and with horizontal resolution ranging from 31 km to 5.5 km. For the future, the simulations’ ensemble within EURO-CORDEX (resolution ~12 km, daily time step) are available for robust evaluations, i.e. to consider the uncertainty due to alternative greenhouse gas concentration scenarios and model chain used.</p><p>In this context, in the last months, C3S funded the Demo Case SOIL EROSION implemented by the CMCC Foundation and aimed at assessing ongoing and future soil loss by water erosion over Italy. The Demo Case is expected to develop further specific datasets and a web-application by exploiting products and tools also provided by Climate Data Store (CDS) infrastructure.</p><p>To assess soil losses, the largely adopted Revised Universal Soil Loss Equation (RUSLE) is selected. Such an empirical equation combines rainfall erosivity (R-factor), evaluated in this case by exploiting datasets in CDS, to soil susceptibility to erosion due to soil intrinsic properties but also to land cover, land management, and topography. Gridded datasets related to R-factor and soil losses will be then made available within the CDS catalog. Moreover, the web application will permit visualizing and retrieving trends and results for specific areas (e.g. NUTS) in the way of maps and graphs. In addition to the "Basic" mode, the Application is expected to support "what-if" analysis ("Advanced" mode) permitting to understand how variations in land use (C-factor) or management practice (P-factor) can influence soil losses at large scale under current and future conditions.</p>

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

scholarly journals Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

2014 ◽  
Vol 49 (3) ◽  
pp. 215-224 ◽  
Author(s):  
Daniel Fonseca de Carvalho ◽  
Valdemir Lucio Durigon ◽  
Mauro Antonio Homem Antunes ◽  
Wilk Sampaio de Almeida ◽  
Paulo Tarso Sanches de Oliveira
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Central Area ◽  
Remote Sensing Data ◽  
High Water ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
R Factor ◽  
Factor C ◽  
Soil Losses

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha-1 , but the central area, with a loss of nearly 300.0 Mg ha-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

scholarly journals Determination of erosion rainfall criteria based on natural rainfall measurement and its impact on spatial distribution of rainfall erosivity in the Czech Republic

2019 ◽  
Vol 14 (No. 3) ◽  
pp. 153-162 ◽  
Author(s):  
Jiří Brychta ◽  
Miloslav Janeček
Keyword(s):  
Spatial Distribution ◽  
Temporal Resolution ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Control Measures ◽  
Rainfall Erosivity ◽  
Time Step ◽  
R Factor ◽  
Soil Losses ◽  
Runoff Plots

Rainfall erosivity is the main factor of the USLE or RUSLE equations. Its accuracy depends on recording precision and its temporal resolution, number of stations and their spatial distribution, length of recorded period, recorded period, erosion rainfall criteria, time step of rainfall intensity and interpolation method. This research focuses on erosion rainfall criteria. A network of 32 ombrographic stations, 1-min temporal resolution rainfall data, 35.6-year period and experimental runoff plots were used. We analysed 8951 rainfalls from ombrographic stations, 100 rainfalls and caused soil losses and runoffs from experimental runoff plots. Main parameter which influenced the number of erosion rainfalls was the precondition AND/OR which determines if conditions of rainfall total (H) have to be fulfilled simultaneously with rainfall intensity (I<sub>15</sub> or I<sub>30</sub>) or not. We proved that if parameters I<sub>15 </sub>&gt; 6.25 mm/15 min AND H &gt; 12.5 mm were fulfilled, then 84.2% of rainfalls caused soil loss &gt; 0.5 t/ha and 73.7% ≥ 1 t/ha. In the case of precondition OR only 44.6% of rainfalls caused soil loss &gt; 0.5 t/ha and 33.9% ≥ 1 t/ha. If the precondition AND was fulfilled, there were on average 75.5 rainfalls, average R factor for each rainfall was 21 MJ/ha·cm/h (without units below in the text, according international unit: 210 MJ/ha·mm/h) and average annual R factor was 45.4. In the case of precondition OR there were on average 279 rainfalls but average R factor for each rainfall was only 9.1 and average annual R factor was 67.4. Therefore if the precondition OR is used, R factor values are overestimated due to a high number of rainfalls with no or very low erosive potential. The resulting overestimated soil losses calculated using USLE/RUSLE subsequently cause an overestimation of financial expenses for erosion-control measures.  

scholarly journals Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia

2021 ◽  
Vol 11 (15) ◽  
pp. 6763
Author(s):  
Mongi Ben Zaied ◽  
Seifeddine Jomaa ◽  
Mohamed Ouessar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Scientific Evidence ◽  
Field Measurements ◽  
Spatiotemporal Variability ◽  
Soil Conditions ◽  
Storm Events ◽  
Erosion Processes ◽  
Study Results ◽  
Soil Losses

Soil erosion remains one of the principal environmental problems in arid regions. This study aims to assess and quantify the variability of soil erosion in the Koutine catchment using the RUSLE (Revised Universal Soil Loss Equation) model. The Koutine catchment is located in an arid area in southeastern Tunisia and is characterized by an annual mean precipitation of less than 200 mm. The model was used to examine the influence of topography, extreme rainstorm intensity and soil texture on soil loss. The data used for model validation were obtained from field measurements by monitoring deposited sediment in settlement basins of 25 cisterns (a traditional water harvesting and storage technique) over 4 years, from 2015 to 2018. Results showed that slope is the most controlling factor of soil loss. The average annual soil loss in monitoring sites varies between 0.01 and 12.5 t/ha/y. The storm events inducing the largest soil losses occurred in the upstream part of the Koutine catchment with a maximum value of 7.3 t/ha per event. Soil erosion is highly affected by initial and preceding soil conditions. The RUSLE model reasonably reproduced (R2 = 0.81) the spatiotemporal variability of measured soil losses in the study catchment during the observation period. This study revealed the importance of using the cisterns in the data-scarce dry areas as a substitute for the classic soil erosion monitoring fields. Besides, combining modeling of outputs and field measurements could improve our physical understanding of soil erosion processes and their controlling factors in an arid catchment. The study results are beneficial for decision-makers to evaluate the existing soil conservation and water management plans, which can be further adjusted using appropriate soil erosion mitigation options based on scientific evidence.

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

scholarly journals Evaluation of a MODIS-Based Potential Evapotranspiration Product at the Point Scale

2008 ◽  
Vol 9 (3) ◽  
pp. 444-460 ◽  
Author(s):  
Jongyoun Kim ◽  
Terri S. Hogue
Keyword(s):  
Potential Evapotranspiration ◽  
Simple Algorithm ◽  
Sensor Data ◽  
Net Radiation ◽  
Point Scale ◽  
Time Step ◽  
Satellite Platform ◽  
Daily Time Step ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Daily Time

Abstract This paper outlines the development of a continuous, daily time series of potential evapotranspiration (PET) using Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data from the Terra satellite platform. The approach is based on the Priestley–Taylor equation, incorporating a daily net radiation model during cloudless days. A simple algorithm using “theoretical clear-sky” net radiation (incorporating daily cloud fraction and cloud optical thickness) and PET is then used to estimate net radiation and PET under cloudy conditions. The method requires minimal ground-based observations for initial calibration of regional radiation algorithm coefficients. Point-scale comparisons are undertaken at four flux-tower sites in North America covering a range of hydroclimatic conditions and biomes. Preliminary results at the daily time step for a 4-yr period (2001–04) show good correlation (R2 = 0.89) and low bias (0.34 mm day−1) for three of the more humid sites. Results are further improved when aggregated to the monthly time scale (R2 = 0.95, bias = 0.31 mm day−1). Performance at the semiarid site is less satisfactory (R2 = 0.95, bias = 2.05 mm day−1 at the daily time step). In general, the MODIS-based daily PET estimates derived in this study are promising and show the potential for use in theoretical and operational water resource studies in both gauged and ungauged basins.

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