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The Upper Indus Basin (UIB), which covers a wide range of climatic and topographic settings, provides an ideal venue to explore the relationship between climate and topography. While the distribution of snow and glaciers is spatially and temporally heterogeneous, there exist regions with similar elevation-snow relationships. In this work, we construct elevation-binned snow-cover statistics to analyze 3415 watersheds and 7357 glaciers in the UIB region. We group both glaciers and watersheds using a hierarchical clustering approach and find that (1) watershed clusters mirror large-scale moisture transport patterns and (2) are highly dependent on median watershed elevation. (3) Glacier clusters are spatially heterogeneous and are less strongly controlled by elevation, but rather by local topographic parameters that modify solar insolation. Our clustering approach allows us to clearly define self-similar snow-topographic regions. Eastern watersheds in the UIB show a steep snow cover-elevation relationship whereas watersheds in the central and western UIB have moderately sloped relationships, but cluster in distinct groups. We highlight this snow-cover-topographic transition zone and argue that these watersheds have different hydrologic responses than other regions. Our hierarchical clustering approach provides a potential new framework to use in defining climatic zones in the cyrosphere based on empirical data.
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
(2019)
Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect.
Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies.
Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.
High Mountain Asia (HMA) – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002–2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.
High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.
Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia
(2015)
Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity.
In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10% of glacier areas, as compared to a similar to 750 glacier control data set, and can reliably classify a given Landsat scene in 3-5 min.
The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data.
Insolation differences play a primary role in controlling microclimate and vegetation cover, which together influence the development of topography. Topographic asymmetry (TA), or slope differences between terrain aspects, has been well documented in small-scale, field-based, and modeling studies. Here we combine a suite of environmental (e.g., vegetation, temperature, solar insolation) and topographic (e.g., elevation, drainage network) data to explore the driving mechanisms and markers of TA on a global scale. Using a novel empirical TA analysis method, we find that (1) steeper terrain has higher TA magnitudes, (2) globally, pole-facing terrain is on average steeper than equator-facing terrain, especially in mid-latitude, tectonically quiescent, and vegetated landscapes, and (3) high-elevation and low-temperature regions tend to have terrain steepened toward the equator. We further show that there are distinct differences in climate and vegetation cover across terrain aspects, and that TA is reflected in the size and form of fluvial drainage networks. Our work supports the argument that insolation asymmetries engender differences in local microclimates and vegetation on opposing terrain aspects, which broadly encourage the development of asymmetric topography across a range of lithologic, tectonic, geomorphic, and climatic settings.
High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.
Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite - sensors the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987-2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E, 2002-2011), AMSR2 (2012-2015), and the Global Precipitation Measurement (GPM, 2014-2015) - in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multiyear snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low (<5%), maximal snow depth dominates the uncertainty signal. In our regression across HMA, we find that forest fraction is the strongest control on SWE variability (75.8%), followed by maximal multi-year snow depth (7.82%), 90th percentile 10-m wind speed of a 10-year December-January-February (DJF) time series (5.64%), 25th percentile DJF 10-m wind speed (5.44%), and hillslope angle (5.24%). Elevation, relief, and terrain aspect show very low influence on SWE variability (<1%). We find that the GPM sensor provides the most robust regression results, and can be reliably used to estimate SWE in our study region. While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas. (C) 2016 Elsevier Inc. All rights reserved.
Snow meltwaters account for most of the yearly water budgets of many catchments in High Mountain Asia (HMA). We examine trends in snow water equivalent (SWE) using passive microwave data (1987 to 2009). We find an overall decrease in SWE in HMA, despite regions of increased SWE in the Pamir, Kunlun Shan, Eastern Himalaya, and Eastern Tien Shan. Although the average decline in annual SWE across HMA (contributing area, 2641 x 10(3) km(2)) is low (average, -0.3%), annual SWE losses conceal distinct seasonal and spatial heterogeneities across the study region. For example, the Tien Shan has seen both strong increases in winter SWE and sharp declines in spring and summer SWE. In the majority of catchments, the most negative SWE trends are found in mid-elevation zones, which often correspond to the regions of highest snow-water storage and are somewhat distinct from glaciated areas. Negative changes in SWE storage in these mid-elevation zones have strong implications for downstream water availability.
Fluvial fill terraces preserve sedimentary archives of landscape responses to climate change, typically over millennial timescales. In the Humahuaca Basin of NW Argentina (Eastern Cordillera, southern Central Andes), our 29 new optically stimulated luminescence ages of late Pleistocene fill terrace sediments demonstrate that the timing of past river aggradation occurred over different intervals on the western and eastern sides of the valley, despite their similar bedrock lithology, mean slopes, and precipitation. In the west, aggradation coincided with periods of increasing precipitation, while in the east, aggradation coincided with decreasing precipitation or more variable conditions. Erosion rates and grain size dependencies in our cosmogenic Be-10 analyses of modern and fill terrace sediments reveal an increased importance of landsliding compared to today on the west side during aggradation, but of similar importance during aggradation on the east side. Differences in the timing of aggradation and the Be-10 data likely result from differences in valley geometry, which causes sediment to be temporarily stored in perched basins on the east side. It appears as if periods of increasing precipitation triggered landslides throughout the region, which induced aggradation in the west, but blockage of the narrow bedrock gorges downstream from the perched basins in the east. As such, basin geometry and fluvial connectivity appear to strongly influence the timing of sediment movement through the system. For larger basins that integrate subbasins with differing geometries or degrees of connectivity (like Humahuaca), sedimentary responses to climate forcing are likely attenuated.
Uplifted Neogene marine sediments and Quaternary fluvial terraces in the Mut Basin, southern Turkey, reveal a detailed history of surface uplift along the southern margin of the Central Anatolian plateau from the Late Miocene to the present. New surface exposure ages (Be-10, Al-26, and Ne-21) of gravels capping fluvial strath terraces located between 28 and 135 m above the Goksu River in the Mut Basin yield ages ranging from ca. 25 to 130 ka, corresponding to an average incision rate of 0.52 to 0.67 mm/yr. Published biostratigraphic data combined with new interpretations of the fossil assemblages from uplifted marine sediments reveal average uplift rates of 0.25 to 0.37 mm/yr since Late Miocene time (starting between 8 and 5.45 Ma), and 0.72 to 0.74 mm/yr after 1.66 to 1.62 Ma. Together with the terrace abandonment ages, the data imply 0.6 to 0.7 mm/yr uplift rates from 1.6 Ma to the present. The different post-Late Miocene and post-1.6 Ma uplift rates can imply increasing uplift rates through time, or multi-phased uplift with slow uplift or subsidence in between. Longitudinal profiles of rivers in the upper catchment of the Mut and Ermenek basins show no apparent lithologic or fault control on some knickpoints that occur at 1.2 to 1.5 km elevation, implying a transient response to a change in uplift rates. Projections of graded upper relict channel segments to the modern outlet, together with constraints from uplifted marine sediments, show that a slower incision/uplift rate of 0.1 to 0.2 mm/yr preceded the 0.7 mm/yr uplift rate. The river morphology and profile projections therefore reflect multi-phased uplift of the plateau margin, rather than steadily increasing uplift rates. Multi-phased uplift can be explained by lithospheric slab break-off and possibly also the arrival of the Eratosthenes Seamount at the collision zone south of Cyprus.
The response of surface processes to climatic forcing is fundamental for understanding the impacts of climate change on landscape evolution. In the Himalaya, most large rivers feature prominent fill terraces that record an imbalance between sediment supply and transport capacity, presumably due to past fluctuations in monsoon precipitation and/or effects of glaciation at high elevation. Here, we present volume estimates, chronological constraints, and Be-10-derived paleo-erosion rates from a prominent valley fill in the Yamuna catchment, Garhwal Himalaya, to elucidate the coupled response of rivers and hillslopes to Pleistocene climate change. Although precise age control is complicated due to methodological problems, the new data support formation of the valley fill during the late Pleistocene and its incision during the Holocene. We interpret this timing to indicate that changes in discharge and river-transport capacity were major controls. Compared to the present day, late Pleistocene hillslope erosion rates were higher by a factor of similar to 2-4, but appear to have decreased during valley aggradation. The higher late Pleistocene erosion rates are largely unrelated to glacial erosion and could be explained by enhanced sediment production on steep hillslopes due to increased periglacial activity that declined as temperatures increased. Alternatively, erosion rates that decrease during valley aggradation are also consistent with reduced landsliding from threshold hillslopes as a result of rising base levels. In that case, the similarity of paleo-erosion rates near the end of the aggradation period with modern erosion rates might imply that channels and hillslopes are not yet fully coupled everywhere and that present-day hillslope erosion rates may underrepresent long-term incision rates. (C) 2015 Elsevier B.V. All rights reserved.
High Asian glacial landscapes have large variations in topographic relief and the size and steepness of snow accumulation areas. Associated differences in glacial cover and dynamics allow a first-order determination of the dominant processes shaping these landscapes. Here we provide a regional synthesis of the topography and flow characteristics of 287 glaciers across High Asia using digital elevation analysis and remotely sensed glacier surface velocities. Glaciers situated in low-relief areas on the Tibetan Plateau are mainly nourished by direct snowfall, have little or no debris cover, and have a relatively symmetrical distribution of velocities along their length. In contrast, avalanche-fed glaciers with steep accumulation areas, which occur at the deeply incised edges of the Tibetan Plateau, are heavily covered with supraglacial debris, and flow velocities are highest along short segments near their headwalls but greatly reduced along their debris-mantled lower parts. The downstream distribution of flow velocities suggests that the glacial erosion potential is progressively shifted upstream as accumulation areas get steeper and hillslope debris fluxes increase. Our data suggest that the coupling of hillslopes and glacial dynamics increases with topographic steepness and debris cover. The melt-lowering effect of thick debris cover allows the existence of glaciers even when they are located entirely below the snow line. However, slow velocities limit the erosion potential of such glaciers, and their main landscape-shaping contribution may simply be the evacuation of debris from the base of glacial headwalls, which inhibits the formation of scree slopes and thereby allows ongoing headwall retreat by periglacial hillslope processes. We propose a conceptual model in which glacially influenced plateau margins evolve from low-relief to high-relief landscapes with distinctive contributions of hillslope processes and glaciers to relief production and decay.
Controversy about the current state and future evolution of Himalayan glaciers has been stirred up by erroneous statements in the fourth report by the Intergovernmental Panel on Climate Change(1,2). Variable retreat rates(3-6) and a paucity of glacial mass-balance data(7,8) make it difficult to develop a coherent picture of regional climate-change impacts in the region. Here, we report remotely-sensed frontal changes and surface velocities from glaciers in the greater Himalaya between 2000 and 2008 that provide evidence for strong spatial variations in glacier behaviour which are linked to topography and climate. More than 65% of the monsoon-influenced glaciers that we observed are retreating, but heavily debris-covered glaciers with stagnant low-gradient terminus regions typically have stable fronts. Debris-covered glaciers are common in the rugged central Himalaya, but they are almost absent in subdued landscapes on the Tibetan Plateau, where retreat rates are higher. In contrast, more than 50% of observed glaciers in the westerlies-influenced Karakoram region in the northwestern Himalaya are advancing or stable. Our study shows that there is no uniform response of Himalayan glaciers to climate change and highlights the importance of debris cover for understanding glacier retreat, an effect that has so far been neglected in predictions of future water availability(9,10) or global sea level(11).
Erosion in the Himalaya is responsible for one of the greatest mass redistributions on Earth and has fueled models of feedback loops between climate and tectonics. Although the general trends of erosion across the Himalaya are reasonably well known, the relative importance of factors controlling erosion is less well constrained. Here we present 25 Be-10-derived catchment-averaged erosion rates from the Yamuna catchment in the Garhwal Himalaya, northern India. Tributary erosion rates range between similar to 0.1 and 0.5mmyr(-1) in the Lesser Himalaya and similar to 1 and 2mmyr(-1) in the High Himalaya, despite uniform hillslope angles. The erosion-rate data correlate with catchment-averaged values of 5 km radius relief, channel steepness indices, and specific stream power but to varying degrees of nonlinearity. Similar nonlinear relationships and coefficients of determination suggest that topographic steepness is the major control on the spatial variability of erosion and that twofold to threefold differences in annual runoff are of minor importance in this area. Instead, the spatial distribution of erosion in the study area is consistent with a tectonic model in which the rock uplift pattern is largely controlled by the shortening rate and the geometry of the Main Himalayan Thrust fault (MHT). Our data support a shallow dip of the MHT underneath the Lesser Himalaya, followed by a midcrustal ramp underneath the High Himalaya, as indicated by geophysical data. Finally, analysis of sample results from larger main stem rivers indicates significant variability of Be-10-derived erosion rates, possibly related to nonproportional sediment supply from different tributaries and incomplete mixing in main stem channels.
Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km(2)-steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D-infinity or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds.
The spatial pattern of extreme precipitation from 40 years of gauge data in the central Himalaya
(2022)
The topography of the Himalaya exerts a substantial control on the spatial distribution of monsoonal rainfall, which is a vital water source for the regional economy and population. But the occurrence of short-lived and high-intensity precipitation results in socio-economic losses. This study relies on 40 years of daily data from 204 ground stations in Nepal to derive extreme precipitation thresholds, amounts, and days at the 95th percentile. We additionally determine the precipitation magnitude-frequency relation. We observe that extreme precipitation amounts follow an almost uniform band parallel to topographic contour lines in the southern Himalaya mountains in central and eastern Nepal but not in western Nepal. The relationship of extreme precipitation indices with topographic relief shows that extreme precipitation thresholds decrease with increasing elevation, but extreme precipitation days increase in higher elevation areas. Furthermore, stations above 1 km elevation exhibit a power-law relation in the rainfall magnitude-frequency framework. Stations at higher elevations generally have lower values of power-law exponents than low elevation areas. This suggests a fundamentally different behaviour of the rainfall distribution and an increased occurrence of extreme rainfall storms in the high elevation areas of Nepal.
The interactions between atmosphere and steep topography in the eastern south–central Andes result in complex relations with inhomogenous rainfall distributions. The atmospheric conditions leading to deep convection and extreme rainfall and their spatial patterns—both at the valley and mountain-belt scales—are not well understood. In this study, we aim to identify the dominant atmospheric conditions and their spatial variability by analyzing the convective available potential energy (CAPE) and dew-point temperature (Td). We explain the crucial effect of temperature on extreme rainfall generation along the steep climatic and topographic gradients in the NW Argentine Andes stretching from the low-elevation eastern foreland to the high-elevation central Andean Plateau in the west. Our analysis relies on version 2.0 of the ECMWF’s (European Centre for Medium-RangeWeather Forecasts) Re-Analysis (ERA-interim) data and TRMM (Tropical Rainfall Measuring Mission) data. We make the following key observations: First, we observe distinctive gradients along and across strike of the Andes in dew-point temperature and CAPE that both control rainfall distributions. Second, we identify a nonlinear correlation between rainfall and a combination of dew-point temperature and CAPE through a multivariable regression analysis. The correlation changes in space along the climatic and topographic gradients and helps to explain controlling factors for extreme-rainfall generation. Third, we observe more contribution (or higher importance) of Td in the tropical low-elevation foreland and intermediate-elevation areas as compared to the high-elevation central Andean Plateau for 90th percentile rainfall. In contrast, we observe a higher contribution of CAPE in the intermediate-elevation area between low and high elevation, especially in the transition zone between the tropical and subtropical areas for the 90th percentile rainfall. Fourth, we find that the parameters of the multivariable regression using CAPE and Td can explain rainfall with higher statistical significance for the 90th percentile compared to lower rainfall percentiles. Based on our results, the spatial pattern of rainfall-extreme events during the past ∼16 years can be described by a combination of dew-point temperature and CAPE in the south–central Andes.
Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.