Groundwater-dependent ecosystems: Recent insights from satellite and field-based studies

© 2015 Author(s). Groundwater-dependent ecosystems (GDEs) are at risk globally due to unsustainable levels of groundwater extraction, especially in arid and semi-arid regions. In this review, we examine recent developments in the ecohydrology of GDEs with a focus on three knowledge gaps: (1) how do we locate GDEs, (2) how much water is transpired from shallow aquifers by GDEs and (3) what are the responses of GDEs to excessive groundwater extraction? The answers to these questions will determine water allocations that are required to sustain functioning of GDEs and to guide regulations on groundwater extraction to avoid negative impacts on GDEs. We discuss three methods for identifying GDEs: (1) techniques relying on remotely sensed information; (2) fluctuations in depth-to-groundwater that are associated with diurnal variations in transpiration; and (3) stable isotope analysis of water sources in the transpiration stream. We then discuss several methods for estimating rates of GW use, including direct measurement using sapflux or eddy covariance technologies, estimation of a climate wetness index within a Budyko framework, spatial distribution of evapotranspiration (ET) using remote sensing, groundwater modelling and stable isotopes. Remote sensing methods often rely on direct measurements to calibrate the relationship between vegetation indices and ET. ET from GDEs is also determined using hydrologic models of varying complexity, from the White method to fully coupled, variable saturation models. Combinations of methods are typically employed to obtain clearer insight into the components of groundwater discharge in GDEs, such as the proportional importance of transpiration versus evaporation (e.g. using stable isotopes) or from groundwater versus rainwater sources. Groundwater extraction can have severe consequences for the structure and function of GDEs. In the most extreme cases, phreatophytes experience crown dieback and death following groundwater drawdown.We provide a brief review of two case studies of the impacts of GW extraction and then provide an ecosystem-scale, multiple trait, integrated metric of the impact of differences in groundwater depth on the structure and function of eucalypt forests growing along a natural gradient in depth-to-groundwater. We conclude with a discussion of a depth-to-groundwater threshold in this mesic GDE. Beyond this threshold, significant changes occur in ecosystem structure and function

Assessment and Development of Remotely Sensed Evapotranspiration Modeling Approaches

Remote sensing has been a promising approach to extracting distributed evapotranspiration (ET) information at varying spatial and temporal scales. Performances of several vegetation index (VI) based and remotely sensed surface energy balance (RSEB) models were evaluated to identify simple and accurate models and apply them to study ET variations from field to regional scales. A simple VI model using a single Landsat image to estimate annual ET was evaluated and successfully captured inter-annual riparian ET variations along a section of the Colorado River, U.S. The study showed the applicability of a simple and accurate approach for annual ET estimation with fewer data and resources. A modeling framework was developed to derive daily time series of ET maps using a RSEB model, satellite imagery, and ground-based weather data. The daily and annual ET maps obtained from the modeling framework successfully captured spatial and temporal ET variations across Oklahoma, U.S. The model also identified the regions that are more susceptible to droughts. Finally, five RSEB models were evaluated for their performance in estimating daily ET of winter wheat under variable grazing and tillage practices in central Oklahoma. The surface energy balance algorithm for land (SEBAL) had the best agreement whit eddy covariance estimates. The daily ET estimates from SEBAL captured the field-scale ET variations within grazing/tillage managements. All studies conducted based on VI and RSEB models over different land covers and spatial/temporal scales identified advantages and limitations of models and developed a framework to construct time series of ET maps, which has a wide range of applications

Improving regional climate simulations based on a hybrid data assimilation and machine learning method

The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.</p

Climate Change Impacts on Net Ecosystem Productivity in a Subtropical Shrubland of Northwestern México

The sensitivity of semiarid ecosystems to climate change is not well understood due to competing effects of soil and plantâ mediated carbon fluxes. Limited observations of net ecosystem productivity (NEP) under rising air temperature and CO2 and altered precipitation regimes also hinder climate change assessments. A promising avenue for addressing this challenge is through the application of numerical models. In this work, we combine a mechanistic ecohydrological model and a soil carbon model to simulate soil and plant processes in a subtropical shrubland of northwest México. Due to the influence of the North American monsoon, the site exhibits net carbon losses early in the summer and net carbon gains during the photosynthetically active season. After building confidence in the simulations through comparisons with eddy covariance flux data, we conduct a series of climate change experiments for nearâ future (2030â 2045) scenarios that test the impact of meteorological changes and CO2 fertilization relative to historical conditions (1990â 2005). Results indicate that reductions in NEP arising from warmer conditions are effectively offset by gains in NEP due to the impact of higher CO2 on water use efficiency. For cases with higher summer rainfall and CO2 fertilization, climate change impacts lead to an increase of ~25% in NEP relative to historical conditions (mean of 66 g C mâ 2). Net primary production and soil respiration derived from decomposition are shown to be important processes that interact to control NEP and, given the role of semiarid ecosystems in the global carbon budget, deserve attention in future simulation efforts of ecosystem fluxes.Key PointsModel simulation accurately captured the seasonality of vegetation activityNet ecosystem productivity decreased under reduced summer rainfall and increased temperature scenariosElevated CO2 scenarios offset the negative impacts of meteorological conditionsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142969/1/jgrg20992_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142969/2/jgrg20992.pd

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Evaluating the influence of the land surface and air temperature gradient on terrestrial flux estimates derived using satellite earth observation data.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in pdf

Prevalence and magnitude of groundwater use by vegetation:A global stable isotope meta-analysis

The role of groundwater as a resource in sustaining terrestrial vegetation is widely recognized. But the global prevalence and magnitude of groundwater use by vegetation is unknown. Here we perform a meta-analysis of plant xylem water stable isotope (δ(2)H and δ(18)O, n = 7367) information from 138 published papers – representing 251 genera, and 414 species of angiosperms (n = 376) and gymnosperms (n = 38). We show that the prevalence of groundwater use by vegetation (defined as the number of samples out of a universe of plant samples reported to have groundwater contribution to xylem water) is 37% (95% confidence interval, 28–46%). This is across 162 sites and 12 terrestrial biomes (89% of heterogeneity explained; Q-value = 1235; P < 0.0001). However, the magnitude of groundwater source contribution to the xylem water mixture (defined as the proportion of groundwater contribution in xylem water) is limited to 23% (95% CI, 20–26%; 95% prediction interval, 3–77%). Spatial analysis shows that the magnitude of groundwater source contribution increases with aridity. Our results suggest that while groundwater influence is globally prevalent, its proportional contribution to the total terrestrial transpiration is limited