Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal

Krakauer, Nir Y.; Lakhankar, Tarendra; Anadón, José D.

doi:10.3390/rs9100986

Cited by 34 publications

(23 citation statements)

References 67 publications

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“…In Nepal, NDVI is positive and significantly correlated with the temperature but negatively correlated with the precipitation from 1982–2015 [25]. Furthermore, positive NDVI trends were also attributed to CO 2 in Nepal [83]. In this study, VCI was also positive and significantly correlated with temperature.…”

Section: Discussionsupporting

confidence: 50%

Spatial and Temporal Variation of Drought Based on Satellite Derived Vegetation Condition Index in Nepal from 1982–2015

Baniya

Tang

et al. 2019

Sensors

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Identification of drought is essential for many environmental and agricultural applications. To further understand drought, this study presented spatial and temporal variations of drought based on satellite derived Vegetation Condition Index (VCI) on annual (Jan–Dec), seasonal monsoon (Jun–Nov) and pre-monsoon (Mar–May) scales from 1982–2015 in Nepal. The Vegetation Condition Index (VCI) obtained from NOAA, AVHRR (National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer) and climate data from meteorological stations were used. VCI was used to grade the drought, and the Mann–Kendall test and linear trend analysis were conducted to examine drought trends and the Pearson correlation between VCI and climatic factors (i.e., temperature and precipitation) was also acquired. The results identified that severe drought was identified in 1982, 1984, 1985 and 2000 on all time scales. However, VCI has increased at the rate of 1.14 yr−1 (p = 0.04), 1.31 yr−1 (p = 0.03) and 0.77 yr−1 (p = 0.77) on the annual, seasonal monsoon and pre-monsoon scales, respectively. These increased VCIs indicated decreases in drought. However, spatially, increased trends of drought were also found in some regions in Nepal. For instance, northern areas mainly in the Trans-Himalayan regions identified severe drought. The foothills and the lowlands of Terai (southern Nepal) experienced normal VCI, i.e., no drought. Similarly, the Anomaly Vegetation Condition Index (AVCI) was mostly negative before 2000 which indicated deficient soil moisture. The exceedance probability analysis results on the annual time scale showed that there was a 20% chance of occurring severe drought (VCI ≤ 35%) and a 35% chance of occurring normal drought (35% ≤ VCI ≤ 50%) in Nepal. Drought was also linked with climates in which temperature on the annual and seasonal monsoon scales was significant and positively correlated with VCI. Drought occurrence and trends in Nepal need to be further studied for comprehensive information and understanding.

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Section: Discussionsupporting

confidence: 50%

Spatial and Temporal Variation of Drought Based on Satellite Derived Vegetation Condition Index in Nepal from 1982–2015

Baniya

Tang

et al. 2019

Sensors

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“…Based on satellite observations, most of South Asia, including the Indus basin, showed a greening trend over 1982-2014. This greening trend is attributable to higher soil moisture particularly over drier parts of the region, and reflects an increase in precipitation over drier areas even while overall South Asia summer monsoon strength declined [100], although, for Nepal, which is just east of the Indus basin (but has more precipitation), greening was associated primarily with increasing atmospheric CO 2 and not with precipitation change [101,102]. To better understand the implications of complex climate changes for water supply and disaster risks in the region, the impact of global and regional forcings and dynamics on climate, surface hydrology, and vegetation need to be modeled on regional and basin scales, constrained by observed trends in precipitation (such as those assessed here) and in other water flows and stocks.…”

Section: Discussionmentioning

confidence: 95%

Precipitation Trends over the Indus Basin

Krakauer

Lakhankar

Dars

2019

Climate

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A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

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“…The R 2 value is given as 0.4886. This indicates that carbon emission contributes about 48% to NDVI depletion in the study area, provided all other factors remain constant as noted by Krakauer et al, 2017. The rate of depletion of NDVI is given as 301.9 and at 242.85 ppm, NDVI would totally collapse which means it will become zero (0).…”

Section: Analysesmentioning

confidence: 75%