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

Long-term evidence of differential resistance and resilience of grassland ecosystems to extreme climate events

Grassland ecosystems are affected by the increasing frequency and intensity of extreme climate events (e.g., droughts). Understanding how grassland ecosystems maintain their functioning, resistance, and resilience under climatic perturbations is a topic of current concern. Resistance is the capacity of an ecosystem to withstand change against extreme climate, while resilience is the ability of an ecosystem to return to its original state after a perturbation. Using the growing season Normalized Difference Vegetation Index (NDVI_gs, an index of vegetation growth) and the Standardized Precipitation Evapotranspiration Index (a drought index), we evaluated the response, resistance, and resilience of vegetation to climatic conditions for alpine grassland, grass-dominated steppe, hay meadow, arid steppe, and semi-arid steppe in northern China for the period 1982-2012. The results show that NDVI_gs varied significantly across these grasslands, with the highest (lowest) NDVI_gs values in alpine grassland (semi-arid steppe). We found increasing trends of greenness in alpine grassland, grass-dominated steppe, and hay meadow, while there were no detectable changes of NDVI_gs in arid and semi-arid steppes. NDVI_gs decreased with increasing dryness from extreme wet to extreme dry. Alpine and steppe grasslands exhibited higher resistance to and lower resilience after extreme wet, while lower resistance to and higher resilience after extreme dry conditions. No significant differences in resistance and resilience of hay meadow under climatic conditions suggest the stability of this grassland under climatic perturbations. This study concludes that highly resistant grasslands under conditions of water surplus are low resilient, but low resistant ecosystems under conditions of water shortage are highly resilient.

Spatiotemporal Change of Net Primary Productivity and Its Response to Climate Change in Temperate Grasslands of China

The temperate grasslands in China play a vital part in regulating regional carbon cycle and climate change. Net primary productivity (NPP) is a crucial index that reflects ecological function of plants and the carbon sequestration capacity of grassland ecosystem. Climate change can affect NPP by changing vegetation growth, but the effects of climate change on the NPP of China's temperate grasslands remain unclear. Based on MODIS data and monthly climate data during 2000-2020, this study explored the spatiotemporal changes in grassland NPP and its response to climate change in temperate grasslands of China. We found that the annual NPP over the entire China's temperate grasslands increased significantly by 4.0 gC/m2/year from 2000 to 2020. The annual NPP showed increasing trends for all the different grassland vegetation types, with the smallest increase for temperate desert steppe (2.2 gC/m2/year) and the largest increase for temperate meadow (5.4 gC/m2/year). The correlation results showed that increased annual precipitation had a positive relationship with the NPP of temperate grasslands. Increased summer and autumn precipitation could increase grassland NPP, particularly for the temperate meadow. With regard to the effects of temperatures, increased temperature, particularly the summer maximum temperature, could decrease annual NPP. However, increased spring minimum temperature could increase the NPP of temperate desert steppe. In addition, this study found, for the first time, an asymmetric relationship between summer nighttime and daytime warming and the NPP of temperate meadow. Specifically, nighttime warming can increase NPP, while daytime warming can reduce NPP in temperate meadow. Our results highlight the importance of including seasonal climate conditions in assessing the vegetation productivity for different grassland types of temperate grasslands and predicting the influences of future climate change on temperate grassland ecosystems.

Detection of Drought Events in Setúbal District: Comparison between Drought Indices

Due to the lack of a general drought definition, water users and managers have developed and used different indices. Many studies using drought indices have been made so as to detect drought events or just to compare their results and assess their advantages and disadvantages. In Portugal, these studies have been done for common drought indices; however, an integrated evaluation and comparison using recent data is needed. Therefore, this study is intended to give an updated overview of the behaviour of the proposed indices. This study proposes the usage of PDSI, scPDSI, SPI and SPEI. With the exception of the PDSI, all indices have been calculated through R packages. The results for the studied regions in mainland Portugal suggest that the drought situations are, in general, most significant and frequent than the wet periods. From our results, we can conclude that the SPI model is more sensitive to extreme drought events and can detect them earlier. The PDSI, scPDSI and SPEI are more reliable for drought monitorization at medium and long spells, which might represent the environmental interactions more closely to the reality. Also, the scPDSI tends to reduce the importance of short period recovering. It is then advisable that impact and scientifical studies consider all of these indices or at least some of them to have a broader and complete understanding of the drought situations to be studied.

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

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 growing-season 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 time-lag 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 low-intensity 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.

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

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.