Impacts of Climate Change on Rainfall Erosivity in the Huai Luang Watershed, Thailand

Plangoen, Pheerawat; Udmale, Parmeshwar

doi:10.3390/atmos8080143

Cited by 24 publications

(9 citation statements)

References 28 publications

(39 reference statements)

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“…Several issues may arise due to accelerated soil losses on achieving of the Sustainable Development Goals of the United Nations [8], as these goals are dependent on a healthy biophysical environment in which the soil is the base [9]. In order to predict these soil erosion future changes it is necessary precipitation [37,38], monthly [39,40] and daily rainfall indices [41,42]. A different approach estimated projected R changes, using a weather generator with spatial and temporal downscaled precipitation values coming from various GCMs [43].…”

Section: Introductionmentioning

confidence: 99%

“…A number of papers in Europe examined the potential increase of rainfall erosivity using temporal trends of high resolution precipitation data Water 2020, 12, 687 3 of 20 in Western Germany [34], Belgium [35] and in the Czech Republic [36]. Other studies in various parts of the world used GCMs in conjunction with empirical equations that predict R using annual precipitation [37,38], monthly [39,40] and daily rainfall indices [41,42]. A different approach estimated projected R changes, using a weather generator with spatial and temporal downscaled precipitation values coming from various GCMs [43].Random Forests [44] is a data-driven algorithm in the area of supervised learning which tries to fit a model using a set of paired input variables and their associated output response and can be used in classification and regression problems.…”

mentioning

confidence: 99%

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Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Vantas

Sidiropoulos

Λουκάς

2020

Water

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A future variation of precipitation characteristics, due to climate change, will affect the ability of rainfall to precipitate soil loss. In this paper, the monthly and annual values of rainfall erosivity (R) in Greece are calculated, for the historical period 1971–2000, using precipitation records that suffer from a significant volume of missing values. In order to overcome the data limitations, an intermediate step is applied using the calculation of monthly erosivity density, which is more robust to the presence of missing values. Spatial Quantile Regression Forests, a data driven algorithm that imitates kriging without the need of strict statistical assumptions, was utilized and validated, in order to create maps of R and its uncertainty using error propagation. The monthly average precipitation for the historical period 1971–2000 estimated by five (5) Global Circulation Models-Regional Climatic Models were validated against observed values and the one with the best performance was used to estimate projected changes of R in Greece for the future time period 2011–2100 and two different greenhouse gases concentration scenarios. The main findings of this study are: (a) the mean annual R in Greece is 1039 MJ·mm/ha/h/y, with a range between 405.1 and 3160.2 MJ·mm/ha/h/y. The highest values are calculated at the mountain range of Pindos and the lowest at central Greece; (b) the monthly R maps adhere to the spatiotemporal characteristics of precipitation depth and intensities over the country; (c) the projected R values, as an average over Greece, follow the projected changes of precipitation of climatic models, but not in a spatially homogenous way.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Vantas

Sidiropoulos

Λουκάς

2020

Water

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“…Since the statistical characteristics of the raw historical GCM data differ from the reanalysis data, bias correction of GCM outputs is essential. Multiplicative factors, change factors, delta change approach and quantile mapping are a few examples of simple approaches often used to bias correct the raw GCM output and also as downscaling add on [29,[33][34][35][36]. These approaches have some limitations as well, for example, adopting observed distribution and capturing average statistics of historical time series without focusing on the extremes [37][38][39].…”

Section: Bias Correctionmentioning

confidence: 99%

An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

Cheevaprasert

Mehrotra²,

Thianpopirug

et al. 2020

Hydrology

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This study presents an exhaustive evaluation of the performance of three statistical downscaling techniques for generating daily rainfall occurrences at 22 rainfall stations in the upper Ping river basin (UPRB), Thailand. The three downscaling techniques considered are the modified Markov model (MMM), a stochastic model, and two variants of regression models, statistical models, one with single relationship for all days of the year (RegressionYrly) and the other with individual relationships for each of the 366 days (Regression366). A stepwise regression is applied to identify the significant atmospheric (ATM) variables to be used as predictors in the downscaling models. Aggregated wetness state indicators (WIs), representing the recent past wetness state for the previous 30, 90 or 365 days, are also considered as additional potential predictors since they have been effectively used to represent the low-frequency variability in the downscaled sequences. Grouping of ATM and all possible combinations of WI is used to form eight predictor sets comprising ATM, ATM-WI30, ATM-WI90, ATM-WI365, ATM-WI30&90, ATM-WI30&365, ATM-WI90&365 and ATM-WI30&90&365. These eight predictor sets were used to run the three downscaling techniques to create 24 combination cases. These cases were first applied at each station individually (single site simulation) and thereafter collectively at all sites (multisite simulations) following multisite downscaling models leading to 48 combination cases in total that were run and evaluated. The downscaling models were calibrated using atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis database and validated using representative General Circulation Models (GCM) data. Identification of meaningful predictors to be used in downscaling, calibration and setting up of downscaling models, running all 48 possible predictor combinations and a thorough evaluation of results required considerable efforts and knowledge of the research area. The validation results show that the use of WIs remarkably improves the accuracy of downscaling models in terms of simulation of standard deviations of annual, monthly and seasonal wet days. By comparing the overall performance of the three downscaling techniques keeping common sets of predictors, MMM provides the best results of the simulated wet and dry spells as well as the standard deviation of monthly, seasonal and annual wet days. These findings are consistent across both single site and multisite simulations. Overall, the MMM multisite model with ATM and wetness indicators provides the best results. Upon evaluating the combinations of ATM and sets of wetness indicators, ATM-WI30&90 and ATM-WI30&365 were found to perform well during calibration in reproducing the overall rainfall occurrence statistics while ATM-WI30&365 was found to significantly improve the accuracy of monthly wet spells over the region. However, these models perform poorly during validation at annual time scale. The use of multi-dimension bias correction approaches is recommended for future research.

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“…The LS factor was computed using parameters such as flow accumulation, cell size and slope. It is observed that, the steeper and longer slope more the risk of higher erosion in many existing studies [33][34][35]. In this study, high resolution (5 m) digital elevation model has been applied to determine the extent area coverage of the hills to make classification based on slope gradients.…”

Section: E Topography (Ls-factor)mentioning

confidence: 99%

“…In this study, eight classes of vegetation covers were identified as Forest, water body, scrub, flower, mixed horticulture, urban settlement, bare land and tea farm. The C values for each vegetation class were excerpted from Tenaga National Berhad Research [34] and the reports of investigations established by previous studies [35,36].…”

Section: F Vegetation Cover (C-factor)mentioning

confidence: 99%

Susceptibility to Soil Erosion and Risk Assessment at Hilly Farms Using Geospatial Techniques

Nasidi

Wayayok

Abdullah

et al. 2020

JETAP

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Soil erosion is a serious environmental challenge which persistently diminishes available land resources globally.The impact of soil erosion is more severeat hilly regions where various techniques are deployed to evaluateits risk levels. However, the traditional approach of estimating the magnitude of erosion is tedious, costly, and considerably time consuming. This study was intended to assess the risk associated with soil erosion at hilly areas of Cameron Highlands through geospatial means. The Digital Elevation Model (DEM) with 5m resolution from Interferometric Synthetic Aperture Radar (IfSAR) was utilized to generate the slope inthe highlands. Soil erosion rates was estimated using Universal Soil Loss Equation (USLE), while information about land use and cover were sourced from relevant government agencies. Inversed Distance Weighted (IDW) method of spatial interpolation was applied to predict the values of unknown pixels. The analysis shows that, there is 217.5 km2of the highlands which is greater than 45-degreeaccounted for about 30.5% of the total land area. Moreover, erosion risk assessment indicated that 66.3%, 11.4%, 11.7% and 10.8% are respectively classified as very low, law, moderate and high susceptible to soil erosion. In general, the risk of soil erosion is relatively low and could be attributed to den sevegetation coverage within the study watershed despite the steep slopes where it was found to be at very high risk to soil erosion susceptibility. However, there is need to deploy best management practices to reduce the effect of soil disturbances at hilly areas and prevent excessive soil loss in future.

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Impacts of Climate Change on Rainfall Erosivity in the Huai Luang Watershed, Thailand

Cited by 24 publications

References 28 publications

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

Susceptibility to Soil Erosion and Risk Assessment at Hilly Farms Using Geospatial Techniques

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