Geostatistical Estimation of Daily Monsoon Precipitation at Fine Spatial Scale: Koshi River Basin

Gonga-Saholiariliva, Nahossio; Neppel, Luc; Chevallier, Pierre; Dagognet, François; Savéan, Marie

doi:10.1061/(asce)he.1943-5584.0001388

Cited by 9 publications

(9 citation statements)

References 55 publications

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“…In mountainous areas, temperature and altitude generally correlate well linearly, considering a large timescale (Valéry et al, 2010;Gottardi et al, 2012). In the majority of studies based on field observations, air temperature values are extrapolated using the inverse distance weighting method (IDW) (Andermann et al, 2012;Immerzeel et al, 2012;Duethmann et al, 2013;Nepal et al, 2014).…”

Section: Temperaturementioning

confidence: 99%

“…The spatial and temporal variability in the precipitation is then not represented in these studies. Moreover, the geostatistical co-kriging method has been applied by Gonga-Saholiariliva et al (2016) for monsoon precipitation interpolation over the Kosi catchment. However, the provided precipitation fields overall underestimate the observations, and this method is shown not to be adequate for the interpolation of solid precipitation.…”

Section: Model Of Orographic Precipitationmentioning

confidence: 99%

“…Temperature Precipitation Temperature Precipitation used method to spatialize precipitation in mountainous areas (Valéry et al, 2010;Gottardi et al, 2012;Duethmann et al, 2013;Nepal et al, 2014). In the French Alps, Valéry et al (2010) combine the IDW method with a multiplicative altitudinal factor.…”

Section: -2015 2015-2016mentioning

confidence: 99%

“…Products for precipitation estimation currently available in this area, e.g. the APHRODITE interpolation product (Yatagai et al, 2012) and the Tropical Rainfall Measuring Mission (TRMM) remote product (Bookhagen and Burbank, 2006), do not represent spatial and temporal variability in orographic effects at a resolution smaller than 10 km (Gonga-Saholiariliva et al, 2016). Consequently, substantial uncertainty remains in water budgets simulated for this region, as highlighted by Savéan et al (2015).…”

Section: Introductionmentioning

confidence: 99%

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Response to review 1

Eeckman¹

2017

Preprint

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This paper provides a new representation of the effect of altitude on precipitation that represents spatial and temporal variability in precipitation in the Everest region. Exclusive observation data are used to infer a piecewise linear function for the relation between altitude and precipitation and significant seasonal variations are highlighted. An original ensemble approach is applied to provide non-deterministic water budgets for middle and high-mountain catchments. Physical processes at the soilatmosphere interface are represented through the Interactions Soil-Biosphere-Atmosphere (ISBA) surface scheme. Uncertainties associated with the model parametrization are limited by the integration of in situ measurements of soils and vegetation properties. Uncertainties associated with the representation of the orographic effect are shown to account for up to 16 % of annual total precipitation. Annual evapotranspiration is shown to represent 26 % ± 1 % of annual total precipitation for the mid-altitude catchment and 34% ± 3 % for the high-altitude catchment. Snowfall contribution is shown to be neglectable for the mid-altitude catchment, and it represents up to 44 % ± 8 % of total precipitation for the highaltitude catchment. These simulations on the local scale enhance current knowledge of the spatial variability in hydroclimatic processes in high-and mid-altitude mountain environments.

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Section: Temperaturementioning

confidence: 99%

Section: Model Of Orographic Precipitationmentioning

confidence: 99%

Section: -2015 2015-2016mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Response to review 1

Eeckman¹

2017

Preprint

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“…Some other methods having fewer advantages over traditional methods have been reported by (Goovaerts 2000;Drogue, Humbert, Deraisme, Mahr, & Freslon, 2002;Buytaert, Celleri, Willems, De Bievre, & Wyseure, 2006). However, geostatistical methods are used generally for monthly and annual data (Diodato, 2005;Mair & Fares, 2010;Gonga-Saholiariliva, Neppel, Chevallier, Delclaux, & Savéan, 2016) because these methods are not easy to apply on daily estimation of precipitation in a complex topography (Ly, Charles, & Degre, 2011;Castro, Gironás, & Fernández, 2014). Furthermore, the accuracy of different methods varies from region to region (Hwang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spatial Estimation of Average Daily Precipitation Using Multiple Linear Regression by Using Topographic and Wind Speed Variables in Tropical Climate

Anees

Abdullah

Nawawi

et al. 2018

Journal of Environmental Engineering and Landscape Management

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Complex topography and wind characteristics play important roles in rising air masses and in daily spatial distribution of the precipitations in complex region. As a result, its spatial discontinuity and behaviour in complex areas can affect the spatial distribution of precipitation. In this work, a two-fold concept was used to consider both spatial discontinuity and topographic and wind speed in average daily spatial precipitation estimation using Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR) in tropical climates. First, wet and dry days were identified by the two methods. Then the two models based on MLR (Model 1 and Model 2) were applied on wet days to estimate the precipitation using selected predictor variables. The models were applied for month wise, season wise and year wise daily averages separately during the study period. The study reveals that, Model 1 has been found to be the best in terms of categorical statistics, R2 values, bias and special distribution patterns. However, it was found that sets of different predictor variables dominates in different months, seasons and years. Furthermore, necessities of other data for further enhancement of the results were suggested.

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Identifying meaningful covariates that can improve the interpolation of monsoon rainfall in a low‐lying tropical region

Das

Wahiduzzaman

2021

Intl Journal of Climatology

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The assessment of spatio‐temporal distribution of rainfall over the entire monsoon season is critical for the water resources management in a tropical low‐lying region like Bangladesh. The primary objective is to find suitable interpolation methods for each monsoon month as well as the accumulated rainfall for the entire season in Bangladesh. As the unique position of Bangladesh and monsoon system both contribute the variation in rainfall, the identification of associated suitable covariates can offer reliable estimates when applied with the appropriate interpolation model. Several potential covariates are identified diligently including the remotely sensed annul average monsoon rainfall (RAAMR), distance to Bay‐of‐Bengal and distance to Hindu‐Kush Himalayan Region, among others. This study mainly considers the multivariate approach: kriging with external drift (KED) which is able to take external information to positive impact. The method is then compared with the more traditional univariate, ordinary kriging (OK) and the inverse distance weighting (IDW) method using the cross‐validation technique. Daily rainfall data from 1970 to 2016 at 34 stations were used for the assessment. Result shows that the KED model is identified suitable for the considered variants of June, August, September and annual average monsoon rainfall whereas the OK is appropriate for July. Overall, the KED is found superior model with the incorporation of RAAMR data. The successful inclusion of remotely sensed data in the kriging system paves a new dimension in the climatological research for countries where climate‐measuring resources are limited.

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Geostatistical Estimation of Daily Monsoon Precipitation at Fine Spatial Scale: Koshi River Basin

Cited by 9 publications

References 55 publications

Response to review 1

Response to review 1

Spatial Estimation of Average Daily Precipitation Using Multiple Linear Regression by Using Topographic and Wind Speed Variables in Tropical Climate

Identifying meaningful covariates that can improve the interpolation of monsoon rainfall in a low‐lying tropical region

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