Dryness/wetness pattern over the Three‐River Headwater Region: Variation characteristic, causes, and drought risks

Li, Sisi; Yao, Zhijun; Wang, Rui; Liu, Zhaofei

doi:10.1002/joc.6413

Cited by 13 publications

(5 citation statements)

References 78 publications

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“…The ecosystem in the TRHR is mainly composed of alpine grassland, wetland, glacier and alpine permanent snow cover. It is an important ecological barrier and an ecological regulation area with the largest influences range in the middle and lower reaches of rivers and their surrounding areas, and the variation of precipitation has a bearing on the water ecological security of China and even Asia (Li et al 2020).…”

Section: Study Areamentioning

confidence: 99%

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

et al. 2021

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The three-river headwaters region (TRHR) is the birthplace of the Yangtze River, the Yellow River and the Lantsang River in China. Based on the grid surface precipitation data released by China Meteorological Administration (CMA), this paper evaluated the accuracy and error components of four near-real-time satellite precipitation products (GSMaP-NRT, GSMaP-MVK, IMERG-Early and IMERG-Late) in the era of a GPM (Global Precipitation Measurement) in TRHR. The conclusions are as follows: (1) The precipitation in TRHR is concentrated in the east and south, and the precipitation in the west is very low. IMERG (Early and Late) has a good spatial distribution of precipitation, while GSMaP has an obvious spatial smoothing of precipitation distribution, and does not better highlight the local precipitation characteristics. (2) The inversion accuracy of the satellite products is the best in the source region of the Lantsang River, followed by the source region of the Yellow River. The satellite products all show the lower correlation coefficient and serious underestimation of precipitation in the west of the TRHR. In addition, the closer to the west of the TRHR, the lower hit rate and the higher false alarm rate of the satellite products, especially the NRT and MVK products. In the eastern margin of the Yellow River headwater region and the Lantsang River headwater Region, RMSE and overestimated precipitation were higher in NRT and MVK, and FAR was higher in spite of higher POD and CSI. (3) The errors of GSMaP in the source region of the Yellow River and the Lantsang River are mainly caused by misreporting precipitation and overestimating the precipitation level, while the errors of GSMaP in the west of the TRHR are mainly caused by missing measurements of precipitation events. The underestimated precipitation of IMERG mainly comes from the missed measurement of precipitation and the underestimate of precipitation level, and there is no large false precipitation. (4) In addition, we found that the satellite products in the lake distribution area of the TRHR have serious missed precipitation errors, indicating that the GPM satellite products have the poor detection ability of precipitation near plateau lakes. On the whole, the precipitation inversion accuracy of IMERG (Early and Late) products is higher, which can better detect the occurrence of precipitation events, but the estimation of precipitation level is still not accurate. The precision of precipitation of satellite products near inland lakes on the plateau is poor, so the algorithm improvement of new products needs to be further solved in the future.

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Section: Study Areamentioning

confidence: 99%

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

et al. 2021

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“…However, these limitations were addressed by the development of the standardized precipitation evapotranspiration index (SPEI) [22]. The following literature extensively summarizes recent SPEI based studies at a regional scale, temporal scale, types of datasets used and statistical method used [23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Using different drought indices, most of these studies revealed that SPEI is most sensitive to drought conditions due to the evaporative demand component (as demonstrated in potential evapotranspiration (PET)) indicating SPEI importance as a favorable drought index.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Drought Variability in Myanmar Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Its Impact on Crop Production

et al. 2021

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Drought research is an important aspect of drought disaster mitigation and adaptation. For this purpose, we used the Standardized Precipitation Evapotranspiration Index (SPEI) to investigate the spatial-temporal pattern of drought and its impact on crop production. Using monthly precipitation (Precip) and temperature (Temp) data from 1986–2015 for 39 weather stations, the drought index was obtained for the time scale of 3, 6, and 12 months. The Mann–Kendall test was used to determine trends and rates of change. Precip and Temp anomalies were investigated using the regression analysis and compared with the drought index. The link between drought with large-scale atmospheric circulation anomalies using the Pearson correlation coefficient (R) was explored. Results showed a non-uniform spatial pattern of dryness and wetness which varied across Myanmar agro-ecological zones and under different time scales. Generally, results showed an increasing trend for the SPEI in the three-time scales, signifying a high tendency of decreased drought from 1986–2015. The fluctuations in dryness/wetness might linked to reduction crop production between 1986–1999 and 2005, 2008, 2010, 2013 cropping years. Results show relationship between main crops production and climate (teleconnection) factors. However, the low correlation values (i.e., <0.49) indicate the extent of the relationship within the natural variability. However, readers are urged to interpret this result cautiously as reductions in crop production may also be affected by other factors. We have demonstrated droughts evolution and trends using weather stations, thus providing useful information to aid policymakers in developing spatially relevant climate change adaptation and mitigation management plans for Myanmar.

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“…According to the linear fitting results, the average increase rate of precipitation in 21 years is 45.22 mm/10 a, especially between 2016-2020, the precipitation increase trend is more evident. During the same time period, the increase in precipitation in the Three Rivers source was close to 50 mm/10 a, which was higher than the global average, areas at the same latitude, and the whole region of China [31,32]. To sum up, the study area presents a warm-humid climate trend.…”

Section: Discussionmentioning

confidence: 62%

Change in Alpine Grassland NPP in Response to Climate Variation and Human Activities in the Yellow River Source Zone from 2000 to 2020

Zhang

et al. 2022

Sustainability

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Identifying the relative contributions of climate change and human activities to alpine grassland dynamics is critical for understanding grassland degradation mechanisms. In this study, first, the actual NPP (NPPa) was obtained by MOD17A3. Second, we used the Zhou Guangsheng model to simulate the potential met net primary productivity (NPPp). Finally, the NPP generated by anthropogenic activities (NPPh) was estimated by calculating the difference between NPPp and NPPa. Then, the relative contributions of climate change and human activities to NPP changes in grasslands were quantitatively assessed by analyzing trends in NPPp and NPPa. Thereby, the drivers of NPP change in the Yellow River source grassland were identified. The results showed that the temperature and precipitation in the study area showed a warm-humid climate trend from 2000 to 2020. The NPPp and NPPa increased at a rate of 1.07 g C/m2 and 1.51 g C/m2 per year, respectively, while the NPPh decreased at a rate of 0.46 g C/m2 per year. It can be seen that human activities had a positive effect on the change of NPP in the Yellow River source grassland from the change rate. The relative contribution analysis showed that 55.90% of grassland NPP increased due to climate change, 40.16% of grassland NPP increased due to human activities, and the grassland degradation was not significant. The research results can provide a theoretical basis and technical support for the next step of the Yellow River source grassland ecological protection project.

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Dryness/wetness pattern over the Three‐River Headwater Region: Variation characteristic, causes, and drought risks

Cited by 13 publications

References 78 publications

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

Spatio-Temporal Analysis of Drought Variability in Myanmar Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Its Impact on Crop Production

Change in Alpine Grassland NPP in Response to Climate Variation and Human Activities in the Yellow River Source Zone from 2000 to 2020

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