Comparison and Correction of High-Mountain Precipitation Data Based on Glacio-Hydrological Modeling in the Tarim River Headwaters (High Asia)

Abstract

Mountain precipitation is often strongly underestimated as observations are scarce, biased toward lower-lying locations and prone to wind-induced undercatch, while topographical heterogeneity is large. This presents serious challenges to hydrological modeling for water resource management and climate change impact assessments in mountainous regions of the world, where a large population depends on water supply from the mountains. The headwaters of the Tarim River, covering four remote and highly glacierized Asian mountain ranges, are vital water suppliers to large agricultural communities along the Taklamakan Desert, northwest China. Assessments of future changes to these water towers have been hampered because of the large precipitation uncertainties. In this study, six existing precipitation datasets (observation-based reanalysis datasets, satellite observation datasets, and the output of high-resolution regional climate models) were compared over five headwaters of the Tarim River. The dataset incorporating the highest observation density (APHRODITE) is then corrected by calibrating the glacio-hydrological model Soil and Water Integrated Model–Glacier Dynamics (SWIM-G) to observed discharge, glacier hypsometry, and modeled glacier mass balance. Results show that this form of inverse modeling is able to inform the precipitation correction in such data-scarce conditions. Substantial disagreement of annual mean precipitation between the analyzed datasets, with coefficients of variation in catchment mean precipitation of 68% on average, was found. The model-based precipitation estimates are on average 1.5–4.3 times higher than the APHRODITE data, but fall between satellite-based and regional climate model results.

Altitudinal Distribution of Meltwater and Its Effects on Glacio‐Hydrology in Glacierized Catchments, Central Asia

“…In most of the catchments, precipitation increased up to a certain elevation and then decreased. A similar altitudinal distribution of precipitation has been described in glacierized regions by Immerzeel et al () and Wortmann et al (). Glacier area, meltwater, and total runoff have their maxima around the middle altitudes.…”

“…Five datasets were compared and then selected APHRODITE precipitation and PGMFD temperature data to force the model. It is argued that APHRODITE precipitation is underestimated at higher elevations and needs to be corrected (Immerzeel et al ; Wortmann et al ). In this study, precipitation dataset was used without correction; however, precipitation lapse rates were derived using mass balance data at higher elevations to overcome the uncertainties in gridded precipitation.…”

Deriving Bias and Uncertainty in MERRA-2 Snowfall Precipitation Over High Mountain Asia

“…APHRODITE-2 shows the minimum snowfall across all products, and the differences between APHRODITE-2 and other products are most apparent in the two southern tiles (27 snowfall biases shown in Figure 7. Our results suggest that snowfall at these high-altitude tiles is generally underestimated in the gauge-based products over HMA, which is consistent with Immerzeel et al (2015) and Wortmann et al (2018), where high-altitude precipitation was found to be underestimated in many existing products. Since the gauge-based products derive their estimates primarily by interpolating rain-gauges located at lower elevations, it is possible that those gauges undercatch precipitation and do not capture orographic effects, which consequently underestimates snowfall.…”

“…Immerzeel et al (2015) showed, by inversely inferring precipitation from glacier mass balance, that high-elevation precipitation in the upper Indus basin is underestimated in APHRODITE, ERA-Interim, and TRMM, where ERA-Interim is a global atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF; Dee et al, 2011). Similarly, through evaluating runoff from glacio-hydrological modeling against observations, Wortmann et al (2018) showed APHRODITE underestimates precipitation by a factor of 1.5-4.4 in Tarim headwater catchments. The information gleaned from these previous studies generally provides bulk bias estimates through inferring precipitation from spatially integrated variables like streamflow or glacier mass balance.…”

A Tri-Approach for Diagnosing Gridded Precipitation Datasets for Watershed Glacio-Hydrological Simulation in Mountain Regions

“…gridded datasets over elevational profile may have been caused by the dynamic climatic system (Pang et al, 2014), precipitation dependency on altitude (Immerzeel et al, 2015;Wortmann et al, 2018) and the approaches used to generate these datasets (Harris et al, 2014;Huffman et al, 2010;Saha et al, 2010). The reason for the better representation of OBS climatology by the APHRO dataset is the use of observed data in its generation; however, the precipitation at ungauged elevation ranges is not extrapolated in APHRO dataset (Ji et al, 2020;Yatagai et al, 2012), which would affect its application in mountain glacierized catchments.…”