Assessments of various precipitation product performances and disaster monitoring utilities over the Tibetan Plateau

Abstract

The Tibetan Plateau, often referred to as Asia's water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0-4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0-2.1) than rainfall (main range of RMSE: 2.0-3.8) and sleet (main range of RMSE: 1.5-3.8). This study's findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau.

Keywords: Extreme precipitation; Meteorological drought; Precipitation; Remote sensing; Snow; Tibetan Plateau.

Fig. 1

The location and topography of the Tibetan Plateau. The elevation data of the study area was provided by NASA (https://search.earthdata.nasa.gov/search).

Fig. 2

Comparing 1961–2014 (long time series) and 2000–2014 (used in this study) precipitation characteristics in month precipitation (a), annual precipitation (b), daily average station precipitation of correlation (c) and relative bias (d).

Fig. 3

Spatial distribution of daily average station precipitation from 2000 to 2014 for the gauge observations (a), precipitation products of CMAP (b), GPCP (c), IMERG (d), G-CLSM (e), G-Noah (f) and G-VIC (g) over the Tibetan Plateau.

Fig. 4

Spatial distribution of station precipitation frequency from 2000 to 2014 for the gauge observations (a), precipitation products of CMAP (b), GPCP (c), IMERG (d), G-CLSM (e), G-Noah (f) and G-VIC (g) over the Tibetan Plateau.

Fig. 5

Basic statistical metrics at the daily time scale for different precipitation products compared to gauge observations: correlation coefficient (a), RMSE (b), relative bias (c), POD (d), FAR (e), and CSI (f).

Fig. 6

Taylor diagram estimating the monthly precipitation performance of different precipitation products based on gauge observations (a). Spatial distribution of the station precipitation relative bias for CMAP (b).

Fig. 7

The RMSE of correct detections (a) and false alarms (b) between the gauge precipitation observations and precipitation products on a daily basis.

Fig. 8

Relative bias of extreme precipitation indexes of different precipitation products during the 2000 to 2014 base period at station (a). The station values of the extreme precipitation indexes (R95p and R99p shown in Fig. 8b, R×1day and R×5day shown in Fig. 8c, as well as R10 and R20 shown in Fig. 8d) for the various precipitation products from 2000 to 2014.

Fig. 9

RMSE (a) and correlation coefficient (b) of SPI-n between gauge observations and different precipitation products at stations. Number (c) and frequency (d) of extreme drought of SPI-n among different precipitation products.

Fig. 10

The time series average value of snowfall of snowfall for G-CLSM (a), G-Noah (b), and G-VIC (c). The proportion of rainfall to total precipitation was determined by G-CLSM (d), G-Noah (e), and G-VIC (f). The station average value of daily snowfall was shown for the period from 2000 to 2014 (h).

Fig. 11

The Spatial distribution of rainfall RMSE for (a) G-CLSM, (b) G-Noah, and (c) G-VIC. The spatial distribution of snowfall RMSE for (d) G-CLSM, (e) G-Noah, and (f) G-VIC. The spatial distribution of sleet RMSE for (g) G-CLSM, (h) G-Noah, and (i) G-VIC. The Box diagram compares the RMSE of (j) rainfall, (k) snowfall, and (l) sleet for GLDAS series products.