Spatial Interpolation of Rainfall Based on DEM

Huang, Bing; Hu, Tiesong

doi:10.1007/978-3-540-89465-0_15

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…We applied Eq. (4) to derive the R-factor from a rainfall surface map generated using rainfall data of over 200 meteorological stations within Ethiopia in combination with the 30 m DEM following the approaches suggested by (Huang and Tiesong, 2009). Despite the fact that our study area is relatively small, we preferred to use a 'spatially distributed' rainfall surface map in order to pick up on potential variations across the 1500 m elevation gradient of the watershed.…”

Section: Rainfall Erosivity (R) Factormentioning

confidence: 99%

Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia

Tamene¹,

Adimassu

Ellison

et al. 2017

Geomorphology

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Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications.

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Section: Rainfall Erosivity (R) Factormentioning

confidence: 99%

Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia

Tamene¹,

Adimassu

Ellison

et al. 2017

Geomorphology

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“…As candidate factors, we used the height (source: EU-DEM v.1.1, resolution: 25m) which is considered an important factor (Modallaldoust et al, 2008;Huang and Hu, 2009), the coordinates, and the aspect. From the mentioned 4 predictors only the x coordinate was considered by the model not influential for the given region and for the annual average precipitation value.…”

Section: Fig 4 Annual Average Rainfall Map Interpolated Using Backwmentioning

confidence: 99%

Precipitation Used as Key Factor in Tapered Line Based River Representation

Magyari-Sáska¹

2020

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GIS and cartography represent two different study areas but both of them share the map concept. Nowadays, most GIS software incorporates advanced representation and labeling possibilities, and thanks to the database, which is an essential component of any geoinformation system, there are multiple symbolization possibilities, using the stored data. Taking into account this symbiosis between the two components of modern GIS: analysis and advanced representation this study wants push further the cartographic possibilities of them taking advantage of their analysis and modelling part. Through this study we wanted to develop and test a method for river symbolization in which the river's line width to be proportional with the average precipitation amount at every part of the river. The idea of this study came by working with hand drawn historical maps in which this type of representation is common and very suggestive. Although there are some interesting approaches which want to symbolize river courses based on data, this type of approach presented in this study has not been found. The result obtained from a situation where inaccuracies are inevitable due to very different data sources: a recent DEM with 25m resolution and a map from the beginning of the 20th century with scale of 1: 300000, has shown that this method based on modeling, analysis and GIS operations can be used successfully to have a more accurate, more real and eye catching symbolization of watercourses.

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“…Numerous studies have established different climatic geospatial datasets such as precipitation and surface temperature, etc., using interpolation algorithms based on a certain number of recorded locations directed by the other auxiliary variables (i.e. : DEM) to improve the results (Ahmed et al, 2014;Huang and Hu, 2009;Sanabria et al, 2013). In this sense, the present study developed a hybrid interpolation method to estimate the Rc in ungauged basins of Africa using the Inverse Distance Weighting (IDW) interpolation algorithm directed by major runoff controlling factors (Potential runoff coefficient, surface temperature and precipitation).…”

Section: Runoff Coefficient Estimation In Ungauged Basinsmentioning

confidence: 99%

Spatial Relationship between Precipitation and Runoff in Africa

Karamage

Fan

Justine

et al. 2018

Preprint

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Abstract. Lack of sufficient and reliable hydrological information is a key hindrance to water resource planning and management in Africa. Hence, the objective of this research is to examine the relationship between precipitation and runoff at three spatial scales, including the whole continent, 25 major basins and 55 countries. For this purpose, the long-term monthly runoff coefficient (Rc) was estimated using the long-term monthly runoff data (R) calculated from the Global Runoff Data Centre (GRDC) streamflow records and Global Precipitation Climatology Centre (GPCC) precipitation datasets for the period of time spanning from 1901 to 2017. Subsequently, the observed Rc data were interpolated in order to estimate Rc over the ungauged basins under guidance of key runoff controlling factors, including the land-surface temperature (T), precipitation (P) and potential runoff coefficient (Co) inferred from the land use and land cover, slope and soil texture information. The results show that 16 % of the annual mean precipitation (672.52 mm) becomes runoff (105.72 mm), with a runoff coefficient of 0.16, and the remaining 84 % (566.80 mm) evapotranspirates over the continent during 1901–2017. Spatial analysis reveals that the precipitation–runoff relationship varies significantly among different basins and countries, mainly dependent on climatic conditions and its inter-annual variability. Generally, high runoff depths and runoff coefficients are observed over humid tropical basins and countries with high precipitation intensity compared to those located in subtropical and temperate drylands.

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Spatial Interpolation of Rainfall Based on DEM

Cited by 3 publications

References 7 publications

Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia

Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia

Precipitation Used as Key Factor in Tapered Line Based River Representation

Spatial Relationship between Precipitation and Runoff in Africa

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