Fine-Scale Variations of Near-Surface-Temperature Lapse Rates in the High Drakensberg Escarpment, South Africa: Environmental Implications

Grab, Stefan

doi:10.1657/1938-4246-45.4.500

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…The temperature lapse rate derived from the remote sensing based Ta ranges from −4 to −7 K/km, which is generally consistent with the reported observation values from independent station observations [61]. Due to the influence of terrain on air flow and surface radiation, the lapse rate varies with elevation and terrain aspect [64,65]. The lapse rates at different elevation bands and aspects derived from the remote sensing based daily Ta are presented in Tables 2 and 3.…”

Section: Lapse Rate Derived From the Station Observed Cldas And Remosupporting

confidence: 84%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

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Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

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Section: Lapse Rate Derived From the Station Observed Cldas And Remosupporting

confidence: 84%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

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“…Similar to the earlier literatures (e.g. De Scally, ; Rolland, ; Harlow et al ., ; Marshall et al ., ; Blandford et al ., ; Gardner et al ., ; Grab, ; Kattel et al ., , , ), in this study we use lapse rate (or gradient) to refer to the decrease of air temperature with slope or topographic elevation (hereafter referred to as near‐surface temperature lapse rate or TLR).…”

Section: Introductionmentioning

confidence: 99%

Temperature–topographic elevation relationship for high mountain terrain: an example from the southeastern Tibetan Plateau

Kattel

2018

Intl Journal of Climatology

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This study quantifies the spatial–temporal distribution of the near‐surface temperature lapse rate (TLR) on the southeastern Tibetan Plateau (SETP) on the northern slopes of the eastern Himalayas, using the jackknife regression model. A 20‐year (1985–2004) climatic data set of 16 stations between the elevation range of 3553–4801 m asl was used for this investigation. Controls for the spatial–temporal variation on TLRs and causes of higher mean square error (MSE) from the jackknife regressions were also analysed. Dry convection due to increasing sensible heating at lower elevations associated with clear skies, the effect of cold air surges, cloud/fog, as well as pronounced long‐wave radiation loss due to snow cover at higher elevation, results in the super‐dry adiabatic TLR in the dry winter. In response to the effects of high rainfall and humidity, intense latent heating at higher elevation due to moist adiabatic cooling causes TLRs to decrease significantly in summer. The variation in net radiation due to differences in moisture, rainfall, cloud cover and air mass between higher (fewer) and lower (higher) elevations further contributes to reducing TLRs values in this season. Observed steeper values of TLRs at higher elevations and more shallow values at lower elevations are due to the thermal contrast between snow‐free ground and snow‐covered mountain terrain, as well as the effect of variations in air mass and moisture. Based on derived values, this article also estimates near‐surface temperature at higher elevations and analyses the precision of estimated values. Measurement of the lowest discrepancy (bias) between observed and estimated values from this study suggests that the estimation skills of the model are good enough. More precisely, including the smallest MSE from the jackknife regression and biases of the estimated values can be made for summer, perhaps due to the moisture controlled throughout the SETP, i.e. associated with the Indian monsoon. Higher MSE and biases are observed during winter and are due to the substantial effects of westerlies, as well as the station's geographical coordinate, local topography and microclimate. The MSE and the estimated biases are lowest at high‐elevation stations and are associated with the lower contaminating effects of the surface. This estimation model, using derived values from this investigation, could be useful for glacier‐hydro‐climatic and ecological modelling in this region.

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“…Other work has shown evidence for high lapse rates of the Drakensberg, where temperature can decrease at an average of -8.4°C 100 m -1 (Grab 2013). This may explain why the four Helichrysum species exhibited niche conservatism and show no correlation between shape and climatic variables; yet have very different leaf shapes among three of the species.…”

Section: Discussionmentioning

confidence: 94%

“…One possibility for this is that there are subtle climatic, likely temperature, differences at the different elevations for each species (Grab 2013), but the broader scale of the climate data overlooked these potential differences. For instance, a recent study showed that the coarseness of climate data significantly affected the amount of spatial overlap between current and predicted distribution of plant species in the California Floristic Province (Franklin et al 2013).…”

Section: Discussionmentioning

confidence: 99%

“…The potential information inferred from elevation data separate from climate data is particularly important in mountainous regions such as the Eastern Mountain Region (EMR) of South Africa (which includes the DAC) where plant diversity is high and plant morphology varies tremendously as a result of the range of ecological habitats available (Körner 2003;Linder 2005;Carbutt and Edwards 2006). Therefore, we tested if elevation differed significantly among these species that occur in the Drakensberg and surrounding mountain regions, as we could thus investigate if elevation reflected potential differences in microhabitat temperature as altitude increases (Grab 2013) that climate variables at this scale could not identify. Further, given that elevation is often cited as a primary means to distinguish between taxa in the field (Hilliard 1977;Pooley 1998), it is important to elucidate the relationship of leaf shape and elevation.…”

Section: Climatic Data Compilationmentioning

confidence: 99%

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Climate and leaf shape relationships in four Helichrysum species from the Eastern Mountain Region of South Africa

Glennon

Cron

2015

Evol Ecol

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Morphological variation in plants can be affected by climate, which influences how species are identified as well as hypotheses of species divergence. We tested whether climatic niches were correlated with the observed morphological (leaf) variation of four geographically overlapping and reproductively similar Helichrysum species (Hilliard's Group 4 in the southern African flora). We found that two species, H. gymnocomum and H. odoratissimum, showed clear evidence for climatic niche conservatism and that across all species leaf shape was not significantly correlated with climatic niche. Interestingly, there did appear to be a pattern, albeit statistically non-significant, that linked cold, dry climates to leaf shape, particularly for the variable and widespread H. odoratissimum. For example, smaller, narrower leaf shapes were generally found in cooler, drier regions, while large, broad leaves (particularly those of H. odoratissimum) are found in warmer, more humid regions. In addition, the data also showed that two species, H. odoratissimum and H. gymnocomum, comprised a single variable group. The climate-leaf shape patterns could potentially reflect the morphological variation between these two species. Collectively, these findings suggest further work is necessary to determine the role of climate and leaf shape variation in species divergence in the Eastern Mountain Region.

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Fine-Scale Variations of Near-Surface-Temperature Lapse Rates in the High Drakensberg Escarpment, South Africa: Environmental Implications

Cited by 12 publications

References 38 publications

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

Temperature–topographic elevation relationship for high mountain terrain: an example from the southeastern Tibetan Plateau

Climate and leaf shape relationships in four Helichrysum species from the Eastern Mountain Region of South Africa

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