Impacts of climate and vegetation leaf area index changes on global terrestrial water storage from 2002 to 2016

2020 ◽  
Vol 724 ◽  
pp. 138298 ◽  
Author(s):  
Fulu Tao ◽  
Yi Chen ◽  
Bojie Fu
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Area Index ◽  
Terrestrial Water ◽  
Index Changes

The Impact of Noah-MP Physical Parameterizations on Modeling Water Availability during Droughts in the Texas-Gulf Region

2021 ◽  
Author(s):  
Wen-Ying Wu ◽  
Zong-Liang Yang ◽  
Michael Barlage
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Availability ◽  
Water Storage ◽  
Rooting Depth ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Area Index ◽  
Terrestrial Water ◽  
The Impact

AbstractTexas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multi-parameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

The efficacy of seasonal terrestrial water storage forecasts for predicting vegetation activity over Africa

2021 ◽  
Author(s):  
Benjamin I Cook ◽  
Kimberly Slinski ◽  
Christa Peters-Lidard ◽  
Amy McNally ◽  
Kristi Arsenault ◽  
...  
Keyword(s):  
Regression Model ◽  
Southern Africa ◽  
Water Storage ◽  
Dry Season ◽  
Forecast Skill ◽  
Terrestrial Water Storage ◽  
Wet Season ◽  
Area Index ◽  
Vegetation Activity ◽  
Terrestrial Water

AbstractTerrestrial water storage (TWS) provides important information on terrestrial hydroclimate and may have value for seasonal forecasting because of its strong persistence. We use the NASA Hydrological Forecast and Analysis System (NHyFAS) to investigate TWS forecast skill over Africa and assess its value for predicting vegetation activity from satellite estimates of leaf area index (LAI). Forecast skill is high over East and Southern Africa, extending up to 3–6 months in some cases, with more modest skill over West Africa. Highest skill generally occurs during the dry season or beginning of the wet season when TWS anomalies from the previous wet season are most likely to carry forward in time. In East Africa, this occurs prior to and during the transition into the spring “Long Rains” from January–March, while in Southern Africa this period of highest skill starts at the beginning of the dry season in April and extends through to the start of the wet season in October. TWS is highly and positively correlated with LAI, and a logistic regression model shows high cross-validation skill in predicting above or below normal LAI using TWS. Combining the LAI regression model with the NHyFAS forecasts, 1-month lead LAI predictions have high accuracy over East and Southern Africa, with reduced but significant skill at 3-month leads over smaller sub-regions. This highlights the potential value of TWS as an additional source of information for seasonal forecasts over Africa, with direct applications to some of the most vulnerable agricultural regions on the continent.

Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach

2020 ◽  
Author(s):  
David Chaparro ◽  
Thomas Jagdhuber ◽  
Dara Entekhabi ◽  
María Piles ◽  
Anke Fluhrer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Water Content ◽  
Leaf Area ◽  
Optical Depth ◽  
Time Lag ◽  
Water Storage ◽  
Water Dynamics ◽  
Area Index ◽  
L Band

<p>Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.</p><p>Our study spans from April 2015 to March 2019 and is structured in two parts:</p><p>Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (<em>mg</em>) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and <em>mg</em> independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.</p><p>Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference.</p><p> </p><p>References</p><p>[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.</p><p>[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.</p><p>[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353</p><p>[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.</p><p>[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.</p>

scholarly journals The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages

2019 ◽  
Author(s):  
Xinxuan Zhang ◽  
Viviana Maggioni ◽  
Azbina Rahman ◽  
Paul Houser ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Global Water

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.

scholarly journals Investigating the leaf area index changes in response to climate change (case study: Kasilian catchment, Iran)

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Mohammad Reza Ramezani ◽  
Ali Reza Massah Bavani ◽  
Mostafa Jafari ◽  
Ali Binesh ◽  
Stefan Peters
Keyword(s):  
Climate Change ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Index Changes

scholarly journals Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States

2019 ◽  
Vol 20 (7) ◽  
pp. 1359-1377 ◽  
Author(s):  
Sujay V. Kumar ◽  
David M. Mocko ◽  
Shugong Wang ◽  
Christa D. Peters-Lidard ◽  
Jordan Borak
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Vegetation Changes ◽  
Area Index ◽  
Terrestrial Water

Abstract Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil moisture, evapotranspiration, snow depth, terrestrial water storage, and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the United States, where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high-quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

scholarly journals Supplementary material to "Leaf Area Index Changes Explain GPP Variation across an Amazon Drought Stress Gradient"

2019 ◽  
Author(s):  
Sophie Flack-Prain ◽  
Patrick Meir ◽  
Yadvinder Malhi ◽  
Thomas Luke Smallman ◽  
Mathew Williams
Keyword(s):  
Drought Stress ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Stress Gradient ◽  
Area Index ◽  
Supplementary Material ◽  
Index Changes

scholarly journals The influence of assimilating leaf area index in a land surface model on global water fluxes and storages

2020 ◽  
Vol 24 (7) ◽  
pp. 3775-3788 ◽  
Author(s):  
Xinxuan Zhang ◽  
Viviana Maggioni ◽  
Azbina Rahman ◽  
Paul Houser ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Canopy Interception ◽  
Area Index ◽  
Carbon Cycles ◽  
Global Water

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycles but also in the global water balance by controlling surface evapotranspiration (ET). Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water, energy, and carbon cycles. This study aims to assess the extent to which a land surface model (LSM) can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI into an LSM through an ensemble Kalman filter (EnKF) to estimate LAI, ET, canopy-interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework not only effectively reduces errors in LAI model simulations but also improves all the modeled water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet conditions). However, it tends to worsen some of the modeled water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the LSM is conservative, and the LAI assimilation introduces more vegetation, which requires more water than what is available within the soil.

scholarly journals Impact of Vegetation Leaf Area Change on Global Terrestrial Water Storage in Recent 30 Years Based on GRACE and Reconstruction Data

2021 ◽  
Author(s):  
Tao Hong ◽  
Xianbiao Kang ◽  
Junjie Wu ◽  
Min Yuan ◽  
Yunfeng Liu
Keyword(s):  
Leaf Area ◽  
Vegetation Change ◽  
Water Cycle ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Limited Region ◽  
Terrestrial Water ◽  
The Impact ◽  
Land Water ◽  
Semi Arid

Abstract Vegetation change has an important impact on land water cycle by changing transpiration and other water exchange between land and atmosphere. Terrestrial water storage (TWS) is an important component of global water cycle and freshwater resources. However, the impact of vegetation change on terrestrial water storage under the background of global climate change is still unclear. Based on the GRACE satellite observed and GRACE-REC reconstructed global terrestrial water storage data, this study investigated the impact of global vegetation leaf area change on terrestrial water storage in recent 30 years. The results show that there is a significant positive correlation between leaf area index (LAI) and terrestrial water storage in the demand-limited region. The sensitivity of TWS on LAI change is high mainly in Australia, central and southern Africa, South Asia, Mediterranean region, western United States, southern South America and other regions with high temperature and low precipitation, and the analysis of GRACE-REC shows the sensitivity in demand-limited region has an increasing trend. Compared with climate factors such as temperature and precipitation, the TWS trend caused by LAI is nearly the same, and has the same sign (all positive or all negative) as that of originally TWS in about 63.6% global land area, and the LAI-related TWS trend is high in the region with annual average precipitation of 500-1000mm. In the six different global land cover classes, the sensitivity of TWS to the LAI change is much higher in semi-arid, grass cop, sparsely vegetated regions, and LAI plays an important role in the interannual variations of TWS in semi-arid, grass cop regions. This study emphasizes the important role of vegetation change in the land water cycle, which is of great significance to the management and utilization of water resources in the future, especially in the arid and semi-arid regions.

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