Managing landscapes to increase agricultural productivity and environmental stewardship requires spatially distributed models that can integrate data and operate at spatial and temporal scales that are intervention-relevant. This paper presents Cycles-L, a landscape-scale, coupled agroecosystem hydrologic modeling system. Cycles-L couples a 3-D land surface hydrologic model, Flux-PIHM, with a 1-D agroecosystem model, Cycles. Cycles-L takes the landscape and hydrology structure from Flux-PIHM and most agroecosystem processes from Cycles. Consequently, Cycles-L can simulate landscape level processes affected by topography, soil heterogeneity, and management practices, owing to its physically-based hydrologic component and ability to simulate horizontal and vertical transport of mineral nitrogen (N) with water. The model was tested at a 730-ha agricultural experimental watershed within the Mahantango Creek watershed in Pennsylvania. Cycles-L simulated well stream water discharge and N exports (Nash-Sutcliffe coefficient 0.55 and 0.58, respectively), and grain crop yield (root mean square error 1.01 Mg ha−1), despite some uncertainty in the accuracy of survey-based input data. Cycles-L outputs are as good if not better than those obtained with the uncoupled Flux-PIHM (water discharge) and Cycles (crop yield) models. Model predicted spatial patterns of N fluxes clearly show the combined control of crop management and topography. Cycles-L spatial and temporal resolution fills a gap in the availability of analytical models at an operational scale relevant to evaluate costly strategic and tactical interventions in silico, and can become a core component of tools for applications in precision agriculture, precision conservation, and artificial intelligence-based decision support systems.
Estimates of net primary (NPP) and ecosystem production (NEP) are needed for tropical savanna, which is structurally diverse but understudied compared to tropical rainforest. Estimates of NPP and NEP are available from eddy covariance and inventory methods, but both approaches have errors and uncertainties. We used both methods to estimate carbon (C) fluxes for an upland mixed grassland and a seasonally flooded forest to determine the correspondence in C cycling components derived from these methods and assess the contribution of the various C cycling components to the overall NEP. Both techniques provided similar estimates of NPP, NEP, and gross primary production (GPP). Belowground NPP accounted for 49-53% of the total NPP for both ecosystems, followed by aboveground litter (26-27%) and wood (16-17%) production. Increases in water availability increased the potential for C storage, but the mechanism was different in the savanna types with an increase in soil moisture causing higher NPP in the mixed grassland but lower ecosystem respiration (Reco) in the Cerrado forest. Compared to other savanna ecosystems, the mixed grassland had a similar rate of Reco but lower productivity and C use efficiency (CUE = NPP/GPP = 0.28). The Cerrado forest had a high CUE (0.58) and similar C flux rates to other tropical savanna forests and woodlands. While our measurements are spatially and temporally limited, the agreement in C fluxes estimated using inventory and eddy covariance methods suggest that the C cycle estimates for these savanna ecosystems are robust.
Agriculture is a significant source of carbon dioxide (CO2) and methane (CH4) and is the dominant source of anthropogenic nitrous oxide (N2O) emissions. Changes in agricultural land management practices that reduce overall greenhouse gas (GHG) emissions have been suggested to help mitigate climate change, but a better understanding of the timing, magnitude, and drivers of GHG fluxes is needed. Alfalfa agroecosystems may be significant sources of N2O given their ability to increase N inputs through symbiotic N2 fixation and frequent irrigation events that create conditions for hot moments of N2O production. However, few studies have explored long-term N2O emissions and their associated drivers in alfalfa ecosystems. We collected over 108,000 CO2, CH4 and N2O soil flux measurements over four years using cavity ring-down spectroscopy from a conventional flood-irrigated alfalfa field in California, USA. This ecosystem was a consistent source of N2O (annual mean: 624.4 ± 27.8 mg N2O m-2 yr-1, range: 263.6. ± 5.6 to 901.9 ± 74.5 mg N2O m-2 yr-1) and a small net sink of CH4 (annual mean: -53.5 ± 2.5 mg CH4 m-2 yr-1, range: -78.2 ± 8.8 to -31.6 ± 2.5 mg CH4 m-2 yr-1). Soil CO2 fluxes averaged 4925.9 ± 13.5 g CO2 m-2 yr-1 and were greater than other alfalfa ecosystem estimates, likely driven by elevated temperatures and plant productivity throughout the growing season. Hot moments of N2O emissions represented only 0.2% to 1.1% of annual measurements but were 31.6% to 56.8% of the annual flux. We found that both the magnitude and the contribution of N2O hot moments to annual emissions decreased over time. Normalized difference vegetation index (NDVI), soil temperature, moisture, and O2 were all significantly correlated with soil CO2, N2O, and CH4 fluxes, although associations varied across both soil depth and timescales. Our results suggest that flood-irrigated alfalfa is a significant source of agricultural N2O emissions, and that plant productivity and soil moisture effects on O2 availability may modulate the net GHG budget of alfalfa agroecosystems.
Afforestation and land use changes have turned Southern China into one of the largest carbon sinks globally, which sequesters carbon from the atmosphere thus mitigating climate change. However, forest growth saturation and available land that can be forested limit the longevity of this carbon sink, and while a plethora of studies have quantified vegetation changes over the last decades, the remaining carbon sink potential of this area is currently unknown. Here, we train a model with multiple predictors characterizing the heterogeneous landscapes of Southern China and predict the carbon carrying capacity of the region for 2002-2017. We compare observed and predicted carbon density and find that during two decades of afforestation, 2.34 Pg C have been sequestered between 2002 and 2017, and a total of 5.32 Pg carbon can potentially still be sequestrated. This means that the region has reached 75% of its carbon carrying capacity in 2017, which is 12% more than in 2002, equal to a decrease of 0.83% per year. We identify potential afforestation areas that can still sequester 2.39 Pg C, while old and new forests have reached 87% of their potential with 1.85 Pg C remaining. Our work locates areas where vegetation has not yet reached its full potential but also shows that afforestation is not a long-term solution for climate change mitigation.
Freshwater ecosystems are globally significant sources of greenhouse gases (GHG) to the atmosphere. Generally, we assume that in-situ production of GHG in streams is limited by turbulent reaeration and high dissolved oxygen concentrations, so stream GHG flux is highest in headwater streams that are connected to their watersheds and serve as conduits for the release of terrestrially derived GHG. Low-gradient streams contain pool structures with longer residence times conducive to the in-situ production of GHG, but these streams, and the longitudinal heterogeneity therein, are seldom studied. We measured continuous ecosystem metabolism alongside concentrations and fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from autumn to the following spring along an eight kilometer segment of a low-gradient third order stream in the North Carolina Piedmont. We characterized spatial and temporal patterns of GHG in the context of channel geomorphology, hydrology, and ecosystem metabolic rates using linear mixed effects models. We found that stream metabolic cycling was responsible for most of the CO2 flux over this period, and that in-channel aerobic metabolism was a primary driver of both CH4 and N2O fluxes as well. Long water residence times, limited reaeration, and substantial organic matter from terrestrial inputs foster conditions conducive to the in-stream accumulation of CO2 and CH4 from microbial respiration. Streams like this one are common in landscapes with low topographic relief, making it likely that the high contribution of instream metabolism to GHG fluxes that we observed is a widespread yet understudied behavior of many small streams.
Weather and climate forecast predictability relies on Land-Atmosphere (L-A) interactions occurring at different time scales. However, evaluation of L-A coupling parameterizations in current land surface models (LSMs) is challenging since the physical processes are complex, and large-scale observations are scarce and uncommon. Recent advancements in satellite observations, in this light, provide a unique opportunity to evaluate the models’ performances at large spatial scales. Using 5-year soil moisture memory (SMM) from Soil Moisture Active and Passive (SMAP) observations, we evaluate L-A coupling performances in 4 prevailing LSMs with both coupled and offline simulations. Multi-model mean comparison at the global scale shows that current LSMs tend to overestimate SMM that is controlled by water-limited processes and vice versa. Large model spreads in SMM are also observed between individual models. The SMM biases are highly dependent on models’ parameterizations, while showing minor relevance to the models’ soil layer depths or the models’ online/offline simulating schemes. Further analyses of two important terrestrial water cycle-related variables indicate current LSMs may underestimate soil moisture that is directly available for evapotranspiration and global flood risks. Finally, a comparison of two soil moisture thresholds indicates that the soil parameters employed in LSMs play an essential role in producing the model’s biases. The satellite estimation of ET at the water-limited stage and soil hydraulic parameters provides readily available information to constrain LSMs, which are essentially important to improve the models’ L-A coupling simulations, as well as other land surface processes such as terrestrial hydrological cycles.
With over 700 million km2 Siberia is the largest expanse of the northern boreal forest—deciduous-needleleaf larch. Temperatures are increasing across this region, but the consequences to carbon balances are not well understood for larch forests. We present flux measurements from a larch forest near the southern edge of Central-Siberia where permafrost degradation and ecosystem shifting are already observed. Results indicate net carbon exchanges are influenced by the seasonality of permafrost active layers, temperature and humidity, and soil water availability. During periods when surface soils are fully thawed, larch forest is a significant carbon sink. During the spring-thaw and fall-freeze transition, there is a weak signal of carbon uptake at mid-day. Net carbon exchanges are near-zero when the soil is fully frozen from the surface down to the permafrost. We fit an empirical ecosystem functional model to quantify the dependence of larch-forest carbon balance on climatic drivers. The model provides a basis for ecosystem carbon budgets over time and space. Larch differs from boreal evergreens by having higher maximum productivity and lower respiration, leading to an increased carbon sink. Comparison to previous measurements from another northern larch site suggests climate change will result in an increased forest carbon sink if the southern larch subtype replaces the northern subtype. Observations of carbon fluxes in Siberian larch are still too sparse to adequately determine age dependence, inter-annual variability, and spatial heterogeneity though they suggest that boreal larch accounts for a larger fraction of global carbon uptake than has been previously recognized.
A common viewpoint across the Earth science community is that global soil moisture estimates from satellite L-band (1.4 GHz) measurements represent moisture only in the shallow soil layers (0-5 cm) and are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Here, we argue that such a viewpoint is flawed for two reasons. First, microwave soil emission theory and statistical considerations of vertically correlated soil moisture information together indicate that L-band measurements are typically representative of soil moisture within at least the top 15-25 cm, or 3-5 times deeper than commonly thought. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a global prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. While shrub and tree species tend to draw deeper soil moisture, these plants often still preferentially or seasonally draw water from the upper soil layers. Therefore, L-band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated, and we encourage their application across terrestrial hydrosphere and biosphere studies.
The plant hydrodynamic approach represents a recent advancement to land surface modeling, in which stomatal conductance responds to water availability in the xylem rather than in the soil. To provide a realistic representation of tree hydrodynamics, hydrodynamic models must resolve processes at the level of a single modelled tree, and then scale the resulting fluxes to the canopy and land surface. While this tree-to-canopy scaling is trivial in a homogeneous canopy, mixed-species canopies require careful representation of the species properties and a scaling approach that results in a realistic description of both the canopy and individual-tree hydrodynamics, as well as leaf-level fluxes from the canopy and their forcing. Here, we outline advantages and pitfalls of three commonly used approaches for representing mixed-species forests in land surface models, and present a new framework for scaling vegetation characteristics and fluxes in mixed-species forests. The new formulation scales fluxes from the tree- to canopy-level in an energy- and mass-conservative way and allows for a consistent multi-species canopy description for hydrodynamic models.
Rooting depth is an ecosystem trait that determines the extent of soil development and carbon (C) and water cycling. Recent hypotheses propose that human-induced changes to Earth’s biogeochemical cycles propagate deeply due to rooting depth changes from agricultural and climate-induced land cover changes. Yet, the lack of a global-scale quantification of rooting depth responses to human activity limits knowledge of hydrosphere-atmosphere-lithosphere feedbacks in the Anthropocene. Here we use land cover datasets to demonstrate that root depth distributions are changing globally as a consequence of agricultural expansion truncating depths above which 99% of root biomass occurs (D99) by ~60 cm, and woody encroachment linked to anthropogenic climate change extending D99 in other regions by ~38 cm. The net result of these two opposing drivers is a global reduction of D99 by 5%, or ~8 cm, representing a loss of ~11,600 km3 of rooted volume. Projected land cover scenarios in 2100 suggest additional future D99 shallowing of up to 30 cm, generating further losses of rooted volume of ~43,500 km3, values exceeding root losses experienced to date and suggesting that the pace of root shallowing will quicken in the coming century. Losses of Earth’s deepest roots — soil-forming agents — suggest unanticipated changes in fluxes of water, solutes, and C. Two important messages emerge from our analyses: dynamic, human-modified root distributions should be incorporated into earth systems models, and a significant gap in deep root research inhibits accurate projections of future root distributions and their biogeochemical consequences.
Anthropogenic impacts and climate change modify instream flow, altering ecosystem services and impacting on aquatic ecosystems. Alpine rivers and streams on the Qinghai-Tibet Plateau (QTP), are especially vulnerable to disturbance due to a limited taxonomic complexity. The effects of variations in flow have been studied using specific taxa, however, the flow-biota relationships of assemblages are poorly understood. A multi-metric habitat suitability model (MM-HSM) was developed, using biological integrity measures of macroinvertebrate assemblages to substitute for habitat suitability indices (HSI) derived from individual taxa. The MM-HSM was trained using macroinvertebrate data from three representative alpine rivers (the Yarlung Tsangpo, the Nujiang, and the Bai Rivers) on the QTP, and was verified using data from the Lanmucuo River. The model produced reliable predictions using the training dataset (R2 = 0.587) and the verification dataset (R2 = 0.489), and was robust to inter-basin differences and changes in dataset size. By coupling the MM-HSM with hydrodynamic simulations, the relationship between weighted usable area (WUA) and flow variations (0.11–1.99 m3/s) for macroinvertebrates was established, and a unimodal response pattern (optimal flow Q = 1.21 m3/s) was observed for macroinvertebrate assemblages from the Lanmucuo River. This was in contrast to the skewed unimodal or monotonically increasing relationships observed for individual indicator taxa, supporting our hypothesis that biological integrity varies with changing flow and conforms to the intermediate disturbance hypothesis. The MM-HSM provides a novel framework to quantify species-environment relationships, which may be used for integrated river basin management.
Access to groundwater leaves riparian plants in drylands resistant to atmospheric drought but vulnerable to changes in climate or water use that reduce streamflow and groundwater tables. Despite the vulnerability of riparian vegetation to water balance changes few extensible methods have been developed to automatically map riparian plants at the scale of individual stands or stream reaches, to assess their response to changes in moisture due to drought and climate change, and to contrast those responses across plant functional types. We used LiDAR and a sub-annual timeseries of NDVI to map vegetation and then assessed drought response by comparing a drought index to variation in a remotely sensed metric of plant health. First, a random forest model was built to classify vegetation communities based on phenological changes in Sentinel-2 NDVI. This model produced community classes with an overall accuracy of 97.9%; accuracy for the riparian vegetation class was 98.9%. Following this initial classification, LiDAR measurements of vegetation height were used to split the riparian class into structural subclasses. Multiple Endmember Spectral Mixture Analysis was applied to a timeseries of Landsat imagery from 1984 to 2018, producing annual sub-pixel fractions of green vegetation, non-photosynthetic vegetation, and soil. Relationships were assessed within structural subclasses between mid-summer green vegetation fraction (GV) and the Standardized Precipitation-Evapotranspiration Index (SPEI), a measure of soil moisture drought. Among riparian vegetation subclasses, all groups showed significant positive correlations between SPEI and GV, indicating an increase in healthy plant material during wetter years. However, the relationship was strongest for herbaceous plants (R^2=0.509, m=0.0278), intermediate for shrubs (R^2=0.339, m=0.0262), and weakest for the largest trees (R^2=0.1373, m=0.0145). This implies decoupling of larger riparian plants from the impacts of atmospheric drought due to subsidies provided by groundwater resources. Our method was extended successfully to multiple climatically-dissimilar dryland systems in the American Southwest, and the results provide a basis for ongoing studies on the fine-scale drought response and climatic vulnerability of riparian woodlands.
Whole-stream metabolism models are generally implemented with a steady flow assumption that does not hold true for many systems with sub-daily flow variation, such as river sections downstream of dams. The steady flow assumption has confined metabolism estimation to a limited range of river environments, thus limiting our understanding about the influence of hydrology on biological production in rivers. Therefore, we couple a flow routing model with the two-station stream metabolism model to estimate metabolism under unsteady flow conditions in rivers. The model’s applicability is further extended by including advection-dispersion processes to facilitate metabolism estimation in transient storage zones. Metabolism is estimated using two approaches: (1) an accounting approach similar to the conventional two-station method and (2) an inverse approach that estimates metabolism parameters using least-squares minimisation method. Both approaches are complementary since we use outputs of the accounting approach to constrain the inverse model parameters. The model application is demonstrated using a case study of an 11 km long stretch downstream of a hydropower plant in the River Otra in southern Norway. We present and test different formulations of the model to show that users can make an appropriate selection that best represents hydrology and solute transport mechanism in the river system of interest. The inclusion of unsteady flows and transient storage zones in the model unlocks new possibilities for studying metabolism controls in altered river ecosystems.