Hongkui Zhou

A new framework for evaluating the impacts of drought on net primary productivity of grassland

This paper presented a valuable framework for evaluating the impacts of droughts (single factor) on grassland ecosystems. This framework was defined as the quantitative magnitude of drought impact that unacceptable short-term and long-term effects on ecosystems may experience relative to the reference standard. Long-term effects on ecosystems may occur relative to the reference standard. Net primary productivity (NPP) was selected as the response indicator of drought to assess the quantitative impact of drought on Inner Mongolia grassland based on the Standardized Precipitation Index (SPI) and BIOME-BGC model. The framework consists of six main steps: 1) clearly defining drought scenarios, such as moderate, severe and extreme drought; 2) selecting an appropriate indicator of drought impact; 3) selecting an appropriate ecosystem model and verifying its capabilities, calibrating the bias and assessing the uncertainty; 4) assigning a level of unacceptable impact of drought on the indicator; 5) determining the response of the indicator to drought and normal weather state under global-change; and 6) investigating the unacceptable impact of drought at different spatial scales. We found NPP losses assessed using the new framework were more sensitive to drought and had higher precision than the long-term average method. Moreover, the total and average losses of NPP are different in different grassland types during the drought years from 1961-2009. NPP loss was significantly increased along a gradient of increasing drought levels. Meanwhile, NPP loss variation under the same drought level was different in different grassland types. The operational framework was particularly suited for integrative assessing the effects of different drought events and long-term droughts at multiple spatial scales, which provided essential insights for sciences and societies that must develop coping strategies for ecosystems for such events.

Drought monitoring and analysis in China based on the Integrated Surface Drought Index (ISDI)

Abstract Timely and accurate monitoring of the onset and evolution of drought in China are important to reduce losses from drought. The Integrated Surface Drought Index (ISDI) which originally established in mideast China shows a large potential for real-time regional drought monitoring. However, ISDI is still at the developmental stage, and the applicability of the index requires further examination especially for China with vast area, climatic conditions, complex topography, and land cover. Furthermore, ISDI model depends on the historical training data corresponding to the study area. ISDI application in China must be remodeled using the historical training data over China. In this paper, we remodeled ISDI over China based on previous work and evaluated its capability for near real-time drought monitoring. Using the Palmer Drought Severity Index (PDSI) as a dependent variable, ISDI integrates climate-based drought indices, satellite-based Vegetation Index (VI) and land surface temperature (LST) with other biophysical and elevation data to produce a 1-km regional drought condition map at 16-day intervals. Strong relationships were determined between the calculated ISDI and PDSI for spring, summer and autumn, and all of the correlation coefficients exceeded 0.8. The initial ISDI results demonstrated a good performance for monitoring droughts in southwestern China from 2009 to 2010, high temperatures and droughts in southern China in 2013, and floods in northeastern China in 2013. The higher spatial resolution and near real-time capability of ISDI can provide important inputs for drought management and mitigation in China.

Quantitative and detailed spatiotemporal patterns of drought in China during 2001–2013

Investigation of spatiotemporal patterns of drought is essential to understand the mechanism and influencing factors of drought occurrence and development. Due to the differences in designation of various drought indices, it remains a great challenge to obtain an accurate result in spatiotemporal patterns investigation of drought. In this study, a quantitative drought monitoring index (i.e., Integrated Surface Drought Index, ISDI) was used to identify spatiotemporal patterns of drought and the drought variation trend at the pixel level during 2001-2013 over China. Eco-geographical regionalization was used as an evaluation unit to distinguish the ecological and climatic background of drought over the whole country. The results showed that the spatial distribution of drought intensity has a strong correlation with eco-geographical regionalization in China. The severe drought areas were mainly concentrated in sub-humid regions and semi-arid regions of medium temperate zones, and humid regions of middle subtropical zones. The regions with higher drought probabilities were most distributed in the south and north of China, while the regions in central and western China exhibited lower drought probabilities. The most obvious decreasing trend of ISDI from 2001 to 2013 was located in the northeast of China and south of the Yangtze River. This decrease in ISDI over time indicates a trend for progressive aggravation of drought severity in these areas. This study shows great promise in informing the future drought prevention measures and management policies under the background of more frequent extreme climate events.

Global vulnerability to agricultural drought and its spatial characteristics

As an important part of agricultural drought risk, agricultural drought vulnerability helps effectively prevent and alleviate drought impacts by quantifying the vulnerability as well as identifying its spatial distribution characteristics. In this study, global agricultural cultivation regions were chosen as the study area; six main crops (wheat, maize, rice, barley, soybean, sorghum) were selected as the hazard-affected body of agricultural drought. Then, global vulnerability to agricultural drought was assessed at a 0.5° resolution and finally, its distribution characteristics were revealed. The results indicated that the area percentages of different grades of global vulnerability to agricultural drought from low to very high were 38.96%, 28.41%, 25.37%, and 7.26%, respectively. This means that the total area percentage of high and very high vulnerability zones exceeded 30% of the study area. Although high and very high vulnerability zones were mainly distributed in arid and semi-arid regions, approximately 40% of those above were distributed in humid and semi-humid regions. In addition, only about 15% of the population in this study was located in the high vulnerability regions. Among the vulnerability factors, water deficit during the growing season and the irrigation area ratio are the key factors affecting regional vulnerability. Therefore, the vulnerability could be reduced by adjusting crop planting dates and structures as well as by improving irrigation level and capacity.

Evaluating the utility of solar-induced chlorophyll fluorescence for drought monitoring by comparison with NDVI derived from wheat canopy

Normalized Difference Vegetation Index (NDVI) has been extensively used in continuous and long-term drought monitoring over large-scale, but with late response to drought-related changes of photosynthesis. Instead, solar-induced chlorophyll fluorescence (SIF) is more closely related to photosynthesis and thus is proposed to track the impacts of drought on vegetation growth. However, the detailed difference between SIF and NDVI in responding to drought has not been thoroughly explored. Here we present continuous ground measurements of NDVI and SIF at 760nm over four plots of wheat with different intensities of drought (well-watered treatment, moderate drought, severe drought and extreme drought). The average values of seasonal SIF were significantly lower under severe drought and extreme drought, while NDVI means only showed significant reduction in extreme drought. In the seasonal patterns, daily SIF could clearly separate the difference of drought gradient, while the difference of daily NDVI was clearer in the end of the field campaign. Daily SIF also significantly and positively correlated with soil moisture, indicating that SIF could be considered as an estimator of soil moisture to detect the information about agricultural drought. Furthermore, in extreme drought plot, the correlation of SIF and soil moisture was higher than that of NDVI and soil moisture in a shorter time lag (<15-day) but weaker in a longer time lag (longer than 30-day). The relationships of growth parameters with SIF and NDVI were further analyzed, showing a saturation of NDVI and unsaturation of SIF at high values of leaf area index and relative water content. These results suggested that SIF is better fit in early drought monitoring, especially over closure canopy, while NDVI is more feasible when drought lasted over a long time scale. Our findings in the study might provide deep insight into the utility of SIF in drought monitoring.

Enhancing the Ability of a Soil Moisture-based Index for Agricultural Drought Monitoring by Incorporating Root Distribution†

Agricultural drought differs from meteorological, hydrological, and socioeconomic drought, being closely related to soil water availability in the root zone, specifically for crop and crop growth stage. In previous studies, several soil moisture indices (e.g., the soil moisture index, soil water deficit index) based on soil water availability have been developed for agricultural drought monitoring. However, when developing these indices, it was generally assumed that soil water availability to crops was equal throughout the root zone, and the effects of root distribution and crop growth stage on soil water uptake were ignored. This article aims to incorporate root distribution into a soil moisture-based index and to evaluate the performance of the improved soil moisture index for agricultural drought monitoring. The Huang-Huai-Hai Plain of China was used as the study area. Overall, soil moisture indices were significantly correlated with the crop moisture index (CMI), and the improved root-weighted soil moisture index (RSMI) was more closely related to the CMI than averaged soil moisture indices. The RSMI correctly identified most of the observed drought events and performed well in the detection of drought levels. Furthermore, the RSMI had a better performance than averaged soil moisture indices when compared to crop yield. In conclusion, soil moisture indices could improve agricultural drought monitoring by incorporating root distribution.

Improving soil moisture estimation by assimilating remotely sensed data into crop growth model for agricultural drought monitoring

Soil moisture is an effective variable for agricultural drought monitoring, and data assimilation is a useful tool to improve soil moisture estimates. In this study, we assimilated remotely sensed soil moisture (SM) and leaf area index (LAI) into DSSAT-CSM-Wheat crop growth model to estimate soil moisture. The results showed that compared to open-loop scenario, assimilating LAI independently could slightly improve soil moisture accuracy with reduction in average RMSE (root mean square error) by 4%, and the average RMSEs were decreased by 7% and 10% for SM and LAI+SM assimilations, respectively. The yield differences with observation were decreased by 379 kg/ha for LAI assimilation, 592 kg/ha for SM assimilation and 866 kg/ha for LAI+SM assimilation. Assimilating LAI and SM jointly received best performances in soil moisture and yield estimation. Hence, assimilating remotely sensed data into crop growth model provides a robust method to improve soil moisture for agricultural drought monitoring.