Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations

Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions. In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate

The sensitivity of satellite solar‐induced chlorophyll fluorescence (SIF) to meteorological drought

Solar‐induced chlorophyll fluorescence (SIF) could provide information on plant physiological response to water stress (e.g., drought). There are growing interests to study the effect of drought on SIF. However, to what extent SIF responds to drought and how the responses vary under different precipitation, temperature and potential evapotranspiration conditions are not clear. In this regard, we evaluated the relationship between satellite‐based SIF product and four commonly used meteorological drought indices (Standardized Precipitation‐Evapotranspiration Index, SPEI; Standardized Precipitation Index, SPI; Temperature Condition Index, TCI; and Palmer Drought Severity Index, PDSI) under diverse climate regions in the continental United States. The four drought indices were used because they estimate meteorological drought conditions from either single or combined meteorological factors such as precipitation, temperature, and potential evapotranspiration, representing different perspectives of drought. The relationship between SIF and meteorological drought varied spatially and differed for different ecosystem types. The high sensitivity occurred in dry areas characterized by a high mean annual growing season temperature and low vegetation productivity. Through random forest regression analyses, we found that temperature, gross primary production, precipitation, and land cover are the major factors affecting the relationships between SIF and meteorological drought indices. Taken together, satellite SIF is highly sensitive to meteorological drought but the high sensitivity is constrained to dry regions

A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available

Spatial and temporal variations of tap water 17O-excess in China

Compared to tap water δ2H and δ18O, tap water 17O-excess preserves additional information about source water dynamics. In this study, we provide the first report of 17O-excess variations of tap water across China (652 samples). Annual 17O-excess of tap waters at the national scale did not show obvious spatial pattern, and was almost unaffected by local environmental factors except in the Qinghai-Tibet Plateau region with a strong latitudinal trend. The mean 17O-excess values in different seasons were not significantly different. The isotopic compositions of most of the tap waters at the annual and seasonal scale were likely influenced by the equilibrium fractionation effect (δ′18O-δ′17O slope ranged from 0.5277 to 0.5301), except for the northwest region in the summer (slope = 0.5264) influenced by kinetic fractionation associated with re-evaporation effect. Based on the information of tap water source distribution, site aridity index and the known precipitation δ18O values, a subset of the tap water can be considered as precipitation proxy. Different from the obvious spatial characteristics of precipitation δ18O, precipitation 17O-excess did not show a clear spatial pattern. But it revealed much detailed precipitation formation mechanisms related to different climate regions and geographical conditions. The lower 17O-excess values of the precipitation-sourced tap waters were caused by kinetic fractionation associated with supersaturation process in snow or glacier formation and re-evaporation effect in some arid regions. The higher 17O-excess values of the precipitation-sourced tap waters in the inland were caused by continental moisture recycling, while likely caused by multiple factors in the southeast coastal region including short transport from ocean source and the humid local environment. Overall, this study provides a unique tap water 17O-excess dataset across China, and probes the precipitation formation mechanisms using tap waters

Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future

Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future

Convergent vegetation fog and dew water use in the Namib Desert

Nonrainfall water inputs (e.g., fog and dew) are the least studied hydrological components in ecohydrology. The importance of nonrainfall waters on vegetation water status in arid ecosystems is receiving increasing attention. However, a clear understanding on how common plant water status benefits from nonrainfall waters, the impacts of different types of fog and dew events on vegetation water status, and the vegetation uptake mechanisms of nonrainfall waters is still lacking. In this study, we used concurrent leaf and soil water potential measurements from 3 years to investigate the species‐specific capacity to utilize moisture from fog and dew within the Namib Desert. Eight common plant species in the Namib Desert were selected. Our results showed that both fog and dew significantly increased soil water potential. Seven of the eight plant species studied responded to fog and dew events, although the magnitude of the response differed. Plants generally showed stronger responses to fog than to dew. Fog timing seemed to be an important factor determining vegetation response; for example, night fog did not affect plant water potential. We also found that Euclea pseudebenus and Faidherbia albida likely exploit fog moisture through foliar uptake. This study provides a first comprehensive assessment of the effects of nonrainfall waters on plant water status within the Namib Desert. Furthermore, this study highlights the importance of concurrent leaf and soil water potential measurements to identify the pathways of nonrainfall water use by desert vegetation. Our results fill a knowledge gap in dryland ecohydrology and have important implications for other drylands

Satellite Observed Positive Impacts of Fog on Vegetation

Fog is an important water source for many ecosystems, especially in drylands. Most fog‐vegetation studies focus on individual plant scale; the relationship between fog and vegetation function at larger spatial scales remains unclear. This hinders an accurate prediction of climate change impacts on dryland ecosystems. To this end, we examined the effect of fog on vegetation utilizing both optical and microwave remote sensing‐derived vegetation proxies and fog observations from two locations at Gobabeb and Marble Koppie within the fog‐dominated zone of the Namib Desert. Significantly positive relationships were found between fog and vegetation attributes from optical data at both locations. The positive relationship was also observed for microwave data at Gobabeb. Fog can explain about 10%–30% of variability in vegetation proxies. These findings suggested that fog impacts on vegetation can be quantitatively evaluated from space using remote sensing data, opening a new window for research on fog‐vegetation interactions

Investigating the role of evaporation in dew formation under different climates using 17O-excess

With increasing aridity in many regions, dew is likely to play an increasingly important role in the ecohydrological processes in many ecosystems, especially in arid and semiarid regions. Few studies investigated the role of evaporation during dew formation and how it varies under different climate settings. 17O-excess, as a new tracer, could be used to extract information of evaporation dynamics from natural water samples (e.g., precipitation, river, and lake). Therefore, to fill the knowledge gap in evaporation mechanisms during dew formation, we report the isotopic variation (δ2H, δ18O, δ17O, and 17O-excess) of dew and precipitation from three distinct climatic regions (i.e., Gobabeb in the central Namib Desert, Nice in France with Mediterranean climate, and Indianapolis in the central United States with humid continental climate). We examined whether dew formed in different climate settings was affected by different degree of evaporation using observed isotopic values and evaporation models during the formation processes, and modeled the effects of key meteorological variables (i.e., temperature and relative humidity) on 17O-excess variations. The results showed that dew in Gobabeb experienced kinetic fractionation associated with evaporation under non-steady state conditions during dew formation with enriched δ18O and low 17O-excess values. Dew formations with temperatures over 14.7 °C in Indianapolis were also influenced by evaporation under non-steady state conditions. However, dew formation in Nice did not experience significant evaporation. Evaporation processes (equilibrium or kinetic fractionation) occurring during nights with heavy dew under three climate settings were mainly related to the variation of atmosphere relative humidity. The 17O-excess tracer provides a new method to distinguish the different evaporation processes (equilibrium or kinetic fractionation) during dew formation and our result provides an improved understanding of dew formation

A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze

The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment

Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p 2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p > 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future