Spatial and seasonal patterns of rainfall erosivity in the Lake Kivu region: Insights from a meteorological observatory network

2021 ◽  
pp. 030913332110017
Author(s):  
R.M. Bagalwa ◽  
C. Chartin ◽  
S. Baumgartner ◽  
S. Mercier ◽  
M. Syauswa ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Prediction Models ◽  
Temporal Dynamics ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Lake Kivu ◽  
Meteorological Observatory ◽  
Resolution Data

In the Lake Kivu region, water erosion is the main driver for soil degradation, but observational data to quantify the extent and to assess the spatial-temporal dynamics of the controlling factors are hardly available. In particular, high spatial and temporal resolution rainfall data are essential as precipitation is the driving force of soil erosion. In this study, we evaluated to what extent high temporal resolution data from the TAHMO network (with poor spatial and long-term coverage) can be combined with low temporal resolution data (with a high spatial density covering long periods of time) to improve rainfall erosivity assessments. To this end, 5 minute rainfall data from TAHMO stations in the Lake Kivu region, representing ca. 37 observation-years, were analyzed. The analysis of the TAHMO data showed that rainfall erosivity was mainly controlled by rainfall amount and elevation and that this relation was different for the dry and wet season. By combining high and low temporal resolution databases and a set of spatial covariates, an environmental regression approach (GAM) was used to assess the spatiotemporal patterns of rainfall erosivity for the whole region. A validation procedure showed relatively good predictions for most months (R2 between 0.50 and 0.80), while the model was less performant for the wettest (April) and two driest months (July and August) (R2 between 0.24 and 0.38). The predicted annual erosivity was highly variable with a range between 2000 and 9000 MJ mm ha−1 h−1 yr−1 and showed a pronounced east–west gradient which is strongly influenced by local topography. This study showed that the combination of high and low temporal resolution rainfall data and spatial prediction models can be used to improve the assessments of monthly and annual rainfall erosivity patterns that are grounded in locally calibrated and validated data.

scholarly journals Comparison of Different Rainfall Erosion Estimation Methods for the Island of Crete

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 67 ◽  
Author(s):  
Dimitrios D. Alexakis ◽  
Manolis Grillakis
Keyword(s):  
Temporal Resolution ◽  
Total Duration ◽  
Power Estimation ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Daily Data

Interactions between soil and rainfall plays a vital role in ecological, hydrological and biogeochemical cycles of land. Among those interactions, the phenomenon of rainfall induced soil erosion is crucial to the soil functions, as it affects the soil structure and organic matter content that subsequently affects soil ability to hold moisture and nutrients. The erosive power of a specific rainfall event is regulated by its intensity and total duration. Various methodologies have been developed and tested to estimate the rainfall erosivity in different hydroclimatic regions and using different rainfall measuring timescales. Studies have shown that high temporal resolution measurements provide a more robust erosivity estimation. Nonetheless the sparsity and scarcity of such high temporal resolution data make the accurate estimation of rainfall erosivity difficult. Here, we compare different erosion power estimation methods based on different rainfall timescales for the island of Crete. Sub-daily (30-min) rainfall data based estimation is used as the basis for the assessment of a daily data based estimation methodology and two different methods that use monthly rainfall data. Modified Fournier Index (MFI) is incorporated in the study through different literature approaches and a regression equation is developed between rainfall erosivity power and MFI index for Crete. Results indicate that the use of daily data in the rainfall erosive power estimation is a good approximation of the sub-daily estimation, while formulas based on monthly rainfall data tend to exhibit larger deviations.

scholarly journals Exploring the possible role of satellite-based rainfall data to estimate inter‐ and intra‐annual global rainfall erosivity

2021 ◽  
Author(s):  
Nejc Bezak ◽  
Pasquale Borrelli ◽  
Panos Panagos
Keyword(s):  
Trend Analysis ◽  
Temporal Resolution ◽  
Rainfall Data ◽  
Winter Period ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Main Challenge ◽  
Precipitation Estimates ◽  
Recent Developments ◽  
Substantial Progress

Abstract. Despite recent developments in modelling global soil erosion by water, to date no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainstorms rainfall erosivity. As this data scarcity is likely to characterize the upcoming years, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored. For this purpose, the high spatial and temporal resolution global precipitation estimates obtained with the NOAA CDR Climate Prediction Center MORPHing technique (CMORPH) were used. Alternatively, the erosivity density (ED) concept was used to estimate global rainfall erosivity as well. The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. At continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity map was observed in Europe. Worse agreement was detected for Africa and South America. Further analyses conducted at monthly scale for Europe revealed seasonal misalignments, with the occurrence of underestimation of the CMORPH estimates in the summer period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was found for autumn, especially in Central and Eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions, while in high erosivity regions and seasons (> 1,000–2,000 MJ mm ha−1 h−1 yr−1), the agreement with estimates obtained from pluviograph data such as GloREDa is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH. The application of a simple-linear function correction of the CMORPH data was applied to provide better fit to the GloREDa and correct systematic underestimation. This correction improved the performance of the CMORPH but in areas with the highest rainfall erosivity rates the underestimation was still observed. A preliminary trend analysis of the CMORPH rainfall erosivity estimates was also performed for the 1998–2019 period. According to this trend analysis, increasing and statistically significant trend was more frequently observed than decreasing trend.

A Rainfall Erosivity database for Brazil from MSWEP rainfall dataset

2021 ◽  
Author(s):  
Ingrid Petry ◽  
Fernando Mainardi Fan
Keyword(s):  
Temporal Resolution ◽  
Soil Loss ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Regression Equations ◽  
R Factor ◽  
Thiessen Polygons ◽  
Erosivity Index ◽  
Factor Map

<p>In erosion studies the behavior of rainfall is primordial, since rain is responsible for the first stage of the erosion process: the detachment of soil particles. The erosive potential of rainfall, erosivity, is considered in the universal soil loss equations (R)USLE family through the parameter R, or R factor. This factor is calculated from the rainfall erosivity index, which is the product of kinetic energy of the rain by the maximum intensity of the rain of 30 minutes of duration. As sub-hour rainfall data is not always available, there are in the literature a series of equations obtained from regression, which use monthly and annual rainfall and present a good estimate of erosivity for your study site. In Brazil, in addition to limitations regarding the temporal resolution of rainfall data, there are also spatial limitations. Monitoring stations are concentrated mostly in urbanized areas, usually near the coast. The other regions, such as agricultural and forest areas, are poorly monitored, and these areas are of great interest for monitoring erosion, not only because they are periodically exposed soil areas, but also because of the high rainfall rates that humid forests like Amazon have. MSWEP is a rainfall database that combines observed, satellite and reanalysis data. It has global coverage, temporal resolution of 3 hours, spatial 0.1º and data from 1979 to 2016. Databases like this have great potential to be used in areas such as Brazil, due to its spatial and temporal resolution. In this context, considering the relevance that the soil loss equations still present today, this work developed a rainfall erosivity database entitled REDB-BR (Rainfall Erosivity Database for Brazil). It provides the R factor in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset. The R factor was calculated trough 73 erosivity index regression equations, which mostly uses the Modified Fournier Index (MFI), a relation between monthly precipitation and annual precipitation. Thiessen polygons were used in order to spatialize and define the areas of each equation. Over the Brazilian territory, the R factor ranges from 1.200 to 20.000 MJ mm ha-1 h-1 year-1, with the higher values in the North region, and the lowest values in the Northeast. The spatial patterns of erosivity are very similar to the climatic zones of Brazil. The R factor map takes advantage of MSWEP dataset and presents a spatial resolution very detailed to a country with continental scale such as Brazil. The database includes the equations shapefile and table, Thiessen Polygons shapefile and the R factor map in raster format, which allows more possibilities of application. The database can be accessed at <https://zenodo.org/record/4428308#.X_hxsOhKiUk>. We identified sudden changes in behavior between the delimited areas, which suggests a need for more regression equations in order to better represent the behavior of the erosivity in the Brazilian territory.</p>

scholarly journals Mapping rainfall erosivity at a regional scale: a comparison of interpolation methods in the Ebro Basin (NE Spain)

2009 ◽  
Vol 13 (10) ◽  
pp. 1907-1920 ◽  
Author(s):  
M. Angulo-Martínez ◽  
M. López-Vicente ◽  
S. M. Vicente-Serrano ◽  
S. Beguería
Keyword(s):  
Soil Erosion ◽  
Prediction Models ◽  
Regional Scale ◽  
Geostatistical Simulation ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Ebro Basin ◽  
Spatial Coverage ◽  
Interpolation Techniques ◽  
Ne Spain

Abstract. Rainfall erosivity is a major causal factor of soil erosion, and it is included in many prediction models. Maps of rainfall erosivity indices are required for assessing soil erosion at the regional scale. In this study a comparison is made between several techniques for mapping the rainfall erosivity indices: i) the RUSLE R factor and ii) the average EI30 index of the erosive events over the Ebro basin (NE Spain). A spatially dense precipitation data base with a high temporal resolution (15 min) was used. Global, local and geostatistical interpolation techniques were employed to produce maps of the rainfall erosivity indices, as well as mixed methods. To determine the reliability of the maps several goodness-of-fit and error statistics were computed, using a cross-validation scheme, as well as the uncertainty of the predictions, modeled by Gaussian geostatistical simulation. All methods were able to capture the general spatial pattern of both erosivity indices. The semivariogram analysis revealed that spatial autocorrelation only affected at distances of ~15 km around the observatories. Therefore, local interpolation techniques tended to be better overall considering the validation statistics. All models showed high uncertainty, caused by the high variability of rainfall erosivity indices both in time and space, what stresses the importance of having long data series with a dense spatial coverage.

scholarly journals Evaluation of discrepancies in spatial distribution of rainfall erosivity in the Czech Republic caused by different approaches using GIS and geostatistical tools

2017 ◽  
Vol 12 (No. 2) ◽  
pp. 117-127 ◽  
Author(s):  
J. Brychta ◽  
M. Janeček
Keyword(s):  
Spatial Distribution ◽  
Czech Republic ◽  
Temporal Resolution ◽  
Agricultural Land ◽  
Interpolation Method ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Yearly Variation ◽  
The Czech Republic ◽  
R Factor

The study presents all approaches of rainfall erosivity factor (R) computation and estimation used in the Czech Republic (CR). A lot of distortions stem from the difference in erosive rainfall criteria, time period, tipping rain gauges errors, low temporal resolution of rainfall data, the type of interpolation method, and inappropriate covariates. Differences in resulting R values and their spatial distribution caused by the described approaches were analyzed using the geostatistical method of Empirical Bayesian Kriging and the tools of the geographic information system (GIS). Similarity with the highest temporal resolution approach using 1-min rainfall data was analyzed. Different types of covariates were tested for incorporation to the cokriging method. Only longitude exhibits high correlation with R and can be recommended for the CR conditions. By incorporating covariates such as elevation, with no or weak correlation with R, the results can be distorted even by 81%. Because of significant yearly variation of R factor values and not clearly confirmed methodology of R values calculation and their estimation at unmeasured places we recommend the R factor for agricultural land in the Czech Republic R = 40 MJ/ha·cm/h +/– 10% depends on geographic location.

A Strategy for Scaling ERA5 Sea Winds Using Climate Model Data

2020 ◽  
Author(s):  
Silvio Davison ◽  
Francesco Barbariol ◽  
Alvise Benetazzo ◽  
Luigi Cavaleri ◽  
Paola Mercogliano
Keyword(s):  
Temporal Resolution ◽  
Climate Model ◽  
Grid Point ◽  
High Temporal Resolution ◽  
Widespread Application ◽  
Northern Adriatic ◽  
Wind Speeds ◽  
The Past ◽  
Statistical Strategy ◽  
Resolution Data

<p>Over the past decade, reanalysis data products have found widespread application in many areas of research and have often been used for the assessment of the past and present climate. They produce reliable atmospheric fields at high temporal resolution, albeit at low-to-mid spatial resolution. On the other hand, climatological analyses, quite often down-scaled to represent conditions also in enclosed basins, lack the historical sequence of stormy events and are often provided at poor temporal resolution.</p><p>In this context, we investigated the possibility of using the ERA5 reanalysis 10-m wind (25-km and 1-hour resolution data) to assess the Mediterranean Sea wind climate (past and scenario). We propose a statistical strategy to relate ERA5 wind speeds over the sea to the past and future wind speeds produced by the COSMO-CLM (8-km and 6-hour resolution data) climatological model. In particular, the probability density function of the ERA5 wind speed at each grid point is adjusted to match that of COSMO-CLM. In this way, past ERA5 winds are corrected to account for the COSMO-CLM energy, while ERA5 scaled wind sequence can be projected in the future with COSMO-CLM scenario energy. Comparison with past observations confirms the validity of the adopted method.</p><p>In the Venezia2021 project, we have applied this strategy for the assessment of the changing wind and, after WAVEWATCH III model runs, also the wave climate in the Northern Adriatic Sea, especially in front of Venice and the MOSE barriers, under two IPCC (RCP 4.5 and 8.5) scenarios.</p><p>In general, this strategy may be applied to produce a scaled wind dataset in enclosed basins and improve past wave modeling applications based on any reanalysis wind data.</p>

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