Evaluation of the suitability of six drought indices in naturally growing, transitional vegetation zones in Inner Mongolia (China)

Wang, Yuqing; Zhang, Chengfu; Meng, Fan‐Rui; Bourque, Charles P.‐A.; Zhang, Cunhou

doi:10.1371/journal.pone.0233525

Cited by 22 publications

(29 citation statements)

References 43 publications

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“…We selected four grassland types in the Inner Mongolia (112°21'-118°22′E, 43°25′-47°33′N), an autonomous region of China, which includes meadow steppe, typical steppe, steppe desert, and desert steppe. Recent studies have demonstrated that these grasslands are of great threat of climatic variability (John et al, 2018;Wang et al, 2020) (John et al, 2016(John et al, , 2018Wang et al, 2020). Vegetation in meadow steppe and typical steppe are herbaceous in nature and shallow rooted species that are less tolerant to increasing climatic variability (John et al, 2018), while vegetation in steppe desert and desert steppe are deep rooted species that can absorb shocks resulted from disturbance (e.g., low precipitation in growing-season) (Wang et al, 2020).…”

Section: Description Of Study Areamentioning

confidence: 99%

“…Recent studies have demonstrated that these grasslands are of great threat of climatic variability (John et al, 2018;Wang et al, 2020) (John et al, 2016(John et al, , 2018Wang et al, 2020). Vegetation in meadow steppe and typical steppe are herbaceous in nature and shallow rooted species that are less tolerant to increasing climatic variability (John et al, 2018), while vegetation in steppe desert and desert steppe are deep rooted species that can absorb shocks resulted from disturbance (e.g., low precipitation in growing-season) (Wang et al, 2020). Mean annual temperature is −2.2°C in meadow steppe, 3.0°C in typical steppe, 5.1°C in steppe desert, and 7.2°C in desert steppe (Wang et al, 2020).…”

Section: Description Of Study Areamentioning

confidence: 99%

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Response of grassland net primary productivity to dry and wet climatic events in four grassland types in Inner Mongolia

Hossain

Kabir

Nila

et al. 2021

Plant Enviro Interactions

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Increasing frequency and intensity of climate extremes have profound impacts on grassland biodiversity functioning and stability. Using Moderate Resolution Imaging Spectroradiometer (MODIS) net primary productivity (NPP) data and standardized precipitation evapotranspiration index, we assessed the response of NPP to growingseason and annual climate extremes and time-lag of climatic conditions across four grassland types (meadow steppe, typical steppe, steppe desert, and desert steppe) in Inner Mongolia, China from the period 2000 to 2019. Results showed that annual NPP varied significantly across four grassland types, with the highest NPP in meadow steppe and the lowest in desert steppe. Annual NPP of all grassland types increased over the past 20 years, but NPP in meadow steppe and typical steppe decreased for the period 2012-2019. Irrespective of grassland type, the 1-and 2-month timelag of climatic conditions showed significant effects on annual NPP. Growing-season climate was found the better predictor of annual NPP in all grassland types than the annual climate. Compared with growing-season normal climates, annual NPP was lowest in extreme dry events in all grasslands, while highest in extreme wet events in meadow steppe and typical steppe, and in moderate wet events in steppe desert and desert steppe. Typical steppe and steppe desert are highly vulnerable to the increasing intensity of climate extremes, as we found that the losses of NPP in these grasslands in extreme dry were almost double than that of moderate dry events. Surprisingly, for meadow steppe and desert steppe, the losses of NPP for both moderate and extreme dry events were almost the same, which highlights that a lowintensity drought may have profound impacts on the annual NPP of these grasslands.The study provides the key insight in scientific basis to improve our understanding of the effects of climate extremes on grassland NPP, which is critical to sustainable management of grassland and maintain ecosystem stability.

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Section: Description Of Study Areamentioning

confidence: 99%

Section: Description Of Study Areamentioning

confidence: 99%

Response of grassland net primary productivity to dry and wet climatic events in four grassland types in Inner Mongolia

Hossain

Kabir

Nila

et al. 2021

Plant Enviro Interactions

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show abstract

“…Studying the change in aridity at the regional scale helps to understand the magnitude, identify the extreme hot spot areas and spatial pattern of the changes as well as the shift of lands from one class of aridity to another arid class. The transition of aridity is considered as a foremost and assertive impact of global climate change [27,43,44] . The study examines the sensitivity of gridded precipitation data sets for identification of semi-arid regions over India.…”

Section: Discussionmentioning

confidence: 99%

“…The Moderate Resolution Imaging Spectro-radiometer (MODIS) provides these vegetation indices fortnightly that are useful in anomaly studies. The NDVI anomaly can be studied along with meteorological indices (AIA and SPI) to characterize drought related impacts on growing vegetation at timescales from month to year [24][25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

Long Term Spatio-temporal Variations of Seasonal and Decadal Aridity in India

Pinjarla

Joshi

et al. 2021

j. of atmos. sci. res.

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A comprehensive analysis of climate data (1958-2018) is carried out at the national scale in India to assess spatiotemporal variation in aridity. The aridity is analyzed using UNEP (United Nations Environment Programme) Aridity Index (AI), which is the ratio between Precipitation (P) and Potential Evapotranspiration (PET). Freely available Terra-Climate database, P and PET variables, offered an unprecedented opportunity for monitoring variations in AI and aridity index anomalies (AIA) at interseasonal and inter-decadal basis. The study also assesses longer term patterns of P and AI anomalies with vegetation anomalies. The results indicate that significant clustered areas with maximum dryness are located at west-central part of India, the state of Maharashtra. Overall, there is a gradual increase in the extent of arid zone during 60-year period and spatially maximum extent of percentage change in aridity area is observed. The change patterns of AI in India are largely driven by the changing patterns of precipitation. The maximum impact of decline in precipitation on AIA was observed during Kharif season frequently, for every 4-5 years during 1972-1992. The pattern repeated in the last few recent years (2013- 2018), the decline in precipitation resulted increased aridity. The study also reveals that the availability and usage of irrigation sources have increased from 2014 to 2018. Thus, despite of less precipitation positive vegetation has been resulted in this period. The findings are important to understand the impacts of climate change on land use pattern, and land and water resource management.

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“…The SPI method is one of the 150 methods developed as drought indices. Apart from the SPI, the other common methods used in drought studies are the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), and the Precipitation Anomaly Percentage ( Wang et al, 2020 ). The SPEI is the index used to take into account the drought damage based on the climate balance between precipitation and evapotranspiration.…”

Section: Introductionmentioning

confidence: 99%

Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia

Prasetyo

Hartomo

Paseleng

2021

PeerJ Computer Science

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This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

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Evaluation of the suitability of six drought indices in naturally growing, transitional vegetation zones in Inner Mongolia (China)

Cited by 22 publications

References 43 publications

Response of grassland net primary productivity to dry and wet climatic events in four grassland types in Inner Mongolia

Response of grassland net primary productivity to dry and wet climatic events in four grassland types in Inner Mongolia

Long Term Spatio-temporal Variations of Seasonal and Decadal Aridity in India

Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia

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