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Deriving evapotranspiration is crucial for determining the water requirements of crops and for efficiently allocating water resources for irrigation. Various experiments and methods have proven that earth observation (EO) is a useful tool for estimating evapotranspiration and supporting irrigation and water resource management at different scales. This study presents a framework based on the Penman-Monteith big leaf model and Shuttleworth-Wallace sparse canopy model for estimating the evapotranspiration in irrigated crops with partial and full-canopy conditions. The approach fully utilizes the high-resolution and multi-spectral capabilities of the Sentinel-2 (S2) sensors for the derivation of surface parameters such as hemispherical shortwave albedo(α), Leaf Area Index (LAI), and the water status of the soil-canopy ensemble by using the OPTRAM model. Proposed by Sadeghi [1], the OPTRAM model uses the pixel distribution in the Shortwave Infrared Transformed Reflectance (STR)-NDVI space, where the water content of the soil-canopy system is linearly correlated to the STR index. In detail, the proposed approach estimates the contributions of soil and canopy to the total evapotranspiration by incorporating the OPRAM model to assess the water status of the surface and adjust the resistance terms in the combination equation [2] The results are validated by using Eddy Covariance data collected during the GRAPEX (Grape Remote Sensing Atmospheric Profile Evapotranspiration eXperiment) project [3], T-REX (Tree crop Remote sensing of Evapotranspiration eXperiment) project, and COALA (COpernicus Applications and services for Low impact agriculture in Australia) project [4]. These projects are conducted respectively in irrigated vineyards and almond orchards in California, and in irrigated maize and alfalfa in Australia. [1] Sadeghi, Morteza, Scott B. Jones, and William D. Philpot.: A linear physically-based model for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment 164, 66-76 (2015). [2] D’Urso, G., Bolognesi, S. F., Kustas, W. P., Knipper, K. R., Anderson, M. C., Alsina, M. M., ... & Belfiore, O. R.: Determining evapotranspiration by using combination equation models with sentinel-2 data and comparison with thermal-based energy balance in a California irrigated Vineyard. Remote Sensing, 13(18), 3720 (2021). [3] Kustas, W.P., Anderson, M.C., Alfieri, J.G., Knipper, K., Torres-Rua, A., Parry, C.K., Nieto, H., Agam, N., White, W.A., Gao, F. The grape remote sensing atmospheric profile and evapotranspiration experiment. Bulletin of the American Meteorological Society 2018, 99, 1791-1812. [4] COALA project. https://www.coalaproject.eu/

Precision irrigation management requires operational monitoring of crop water status. However, there is still some controversy on how to account for crop water stress. To address this question, several physiological, several physiological metrics have been proposed, such as the leaf/stem water potentials, stomatal conductance, or sap flow. On the other hand, thermal remote sensing has been shown to be a promising tool for efficiently evaluating crop stress at adequate spatial and temporal scales, via the Crop Water Stress Index (CWSI), one of the most common indices used for assessing plant stress. CWSI relates the actual crop evapotranspiration ET (related to the canopy radiometric temperature) to the potential ET (or minimum crop temperature). However, remotely sensed surface temperature from satellite sensors includes a mixture of plant canopy and soil/substrate temperatures, while what is required for accurate crop stress detection is more related to canopy metrics, such as transpiration, as the latter one avoids the influence of soil/substrate in determining crop water status or stress. The Two-Source Energy Balance (TSEB) model is one of the most widely used and robust evapotranspiration model for remote sensing. It has the capability of partitioning ET into the crop transpiration and soil evaporation components, which is required for accurate crop water stress estimates. This study aims at evaluating different TSEB metrics related to its retrievals of actual ET, transpiration and stomatal conductance, to track crop water stress in a vineyard in California, part of the GRAPEX experiment. Four eddy covariance towers were deployed in a Variable Rate Irrigation system in a Merlot vineyard that was subject to different stress periods. In addition, root-zone soil moisture, stomatal conductance and leaf/stem water potential were collected as proxy for in situ crop water stress. Results showed that the most robust variable for tracking water stress was the TSEB derived leaf stomatal conductance, with the strongest correlation with both the measured root-zone soil moisture and stomatal conductance gas exchange measurements. In addition, these metrics showed a better ability in tracking stress when the observations are taken early after noon., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant no. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Optimizing water management has become one of the biggest challenges for grapevine growers in California, especially during drought conditions. Monitoring grapevine water status and stress level across the whole vineyard is an essential step for precision irrigation management of vineyards to conserve water. We developed a unified machine learning model to map leaf water potential ($${\psi }_{\mathrm{leaf}}$$ψleaf), by combining high-resolution multispectral remote sensing imagery and weather data. We conducted six unmanned aerial vehicle (UAV) flights with a five-band multispectral camera from 2018 to 2020 over three commercial vineyards, concurrently with ground measurements of sampled vines. Using vegetation indices from the orthomosaiced UAV imagery and weather data as predictors, the random forest (RF) full model captured 77% of$${\psi }_{\mathrm{leaf}}$$ψleafvariance, with a root mean square error (RMSE) of 0.123 MPa, and a mean absolute error (MAE) of 0.100 MPa, based on the validation datasets. Air temperature, vapor pressure deficit, and red edge indices such as the normalized difference red edge index (NDRE) were found as the most important variables in estimating$${\psi }_{\mathrm{leaf}}$$ψleafacross space and time. The reduced RF models excluding weather and red edge indices explained 52–48% of$${\psi }_{\mathrm{leaf}}$$ψleafvariance, respectively. Maps of the estimated$${\psi }_{\mathrm{leaf}}$$ψleaffrom the RF full model captured well the patterns of both within- and cross-field spatial variability and the temporal change of vine water status, consistent with irrigation management and patterns observed from the ground sampling. Our results demonstrated the utility of UAV-based aerial multispectral imaging for supplementing and scaling up the traditional point-based ground sampling of$${\psi }_{\mathrm{leaf}}$$ψleaf. The pre-trained machine learning model, driven by UAV imagery and weather data, provides a cost-effective and scalable tool to facilitate data-driven precision irrigation management at individual vine levels in vineyards.

Water conservation efforts for California’s agricultural industry are critical to its sustainability through severe droughts like the current one and others experienced over the last two decades. This is most critical for perennial crops, such as vineyards and orchards, which are costly to plant and maintain and constitute a significant fraction of the regional water use. It is no longer feasible to access groundwater for irrigation to replace deficit surface water resources during drought due to a significant overdraft of aquifers and new regulation limiting its use. To achieve significant water savings, the actual crop water use or evapotranspiration (ET) needs to be mapped from field to regional scales on a daily basis. This can only be achieved using remote sensing-based models, particularly thermal-based energy balance models that are sensitive to deficit irrigation conditions. The two-source energy balance (TSEB) model has been successfully applied over vineyards in California, but challenges still remain. In particular, much of the irrigated cropland in the California Central Valley is affected by advection of hot dry air masses from surrounding non-irrigated areas and the TSEB model appears to need modifications to adequately estimate ET under such conditions, as well as the partitioning between evaporation and transpiration. This study investigates the application of the TSEB model, using local observations in a vineyard having significant advection. Four versions of the transpiration algorithm in TSEB are applied and evaluated with tower eddy covariance measurements spanning 4 growing seasons. The results suggest the performance of the original transpiration algorithm based on Priestley–Taylor used in TSEB is satisfactory in all but the most extreme advective conditions, while a transpiration algorithm based on Shuttleworth–Wallace with a canopy resistance formula, which relates maximum stomata conductance to vapor pressure deficit (VPD), performs well in all cases. These modifications have potential for improving regional applications of the TSEB model in support of water management in the Central Valley., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service.

Author(s): Bambach, NE; Rhoades, AM; Hatchett, BJ; Jones, AD; Ullrich, PA; Zarzycki, CM | Abstract: We introduce variable-resolution enabled Community Earth System Model (VR-CESM) results simulating historical and future climate conditions at 28 km over South America and 14 km over the Andes. Three 30-year simulations are performed: a historic (1985–2014), a near future (2030–2059), and an end-century (2070–2099) simulation under the RCP8.5 scenario. Historic results compare favourably to several temperature and precipitation reanalysis products, though local biases are present, particularly during austral summer. Future simulations highlight broad warming patterns (+3–6°C by end-century) and heterogeneous precipitation responses across South America that qualitatively agree with prior modelling efforts. Our results reveal that the interaction between temperature and precipitation changes produce shifts in several Koppen–Geiger climates. Notable changes include the near-elimination of the Andean Tundra or Alpine climates, a 15% decrease in Tropical Rainforests and a Tropical Savannah expansion of 20%. To provide a regionally focused analysis of projected climate change and to illustrate the benefits of variable resolution modelling, we analyse changes in the magnitude and trend in seasonal and daily temperature and precipitation in Chile. We also examined several metrics [e.g., snow water equivalent (SWE), temperatures on wet days, and days below 0°C] to evaluate potential impacts of climate change on the Chilean cryosphere between the end-of-century and historic periods, finding wide-ranging indications of cryospheric decline. These changes are interpreted through reductions in the timing (1–2.5 months earlier peak SWE) and magnitude (200–1,000 mm SWE decreases) of water stored as snow in the Andes, a 10–30% decrease in number of cool season wet days with temperatures below 1°C, and 50–200 fewer days (annually) with minimum temperatures below 0°C. Our aim in producing a high-resolution dataset of climate projections from VR-CESM is to support analyses of climate change throughout South America but especially in vulnerable montane regions and to provide additional results for comparison with previous, ongoing, and upcoming modelling efforts.

Knowledge about physiological stress thresholds provides crucial information about plant performance and survival under drought. In this study, we report on the triphasic nature of the relationship between plant water potential (Ψ) at predawn and midday and describe a method that predicts Ψ at stomatal closure and turgor loss exclusively from this water potential curve (WP curve). The method is based on a piecewise linear regression model that was developed to predict the boundaries (termed Θ1 and Θ2) separating the three phases of the curve and corresponding slope values. The method was tested for three economically important woody species. For all species, midday Ψ was much more negative than predawn Ψ during phase I (mild drought), reductions in midday Ψ were minor while predawn Ψ continued to decline during phase II (moderate drought), and midday and predawn Ψ reached similar values during phase III (severe drought). Corresponding measurement of leaf gas exchange indicated that boundary Θ1 between phases I and II coincided with Ψ at stomatal closure. Data from pressure-volume curves demonstrated that boundary Θ2 between phases II and III predicted Ψ at leaf turgor loss. The WP curve method described here is an advanced application of the Scholander-type pressure chamber to categorize plant dehydration under drought into three distinct phases and to predict Ψ thresholds of stomatal closure and turgor loss.

High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model’s algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps.

Societies and ecosystems within and downstream of mountains rely on seasonal snowmelt to satisfy their water demands. Anthropogenic climate change has reduced mountain snowpacks worldwide, altering snowmelt magnitude and timing. Here, the global warming level leading to widespread and persistent mountain snowpack decline, termed low-to-no snow, is estimated for the world's most latitudinally contiguous mountain range, the American Cordillera. We show a combination of dynamical, thermodynamical, and hypsometric factors results in an asymmetric emergence of low-to-no snow conditions within the midlatitudes of the American Cordillera. Low-to-no snow emergence occurs approximately 20 years earlier in the Southern Hemisphere, at a third of the local warming level, and coincides with runoff efficiency declines in both dry and wet years. Prevention of a low-to-no snow future in either hemisphere requires the level of global warming to be held to, at most, +2.5 °C.

Efficient use of available water resources is key to sustainable viticulture management in California (CA) and other regions with limited water availability in the western US and abroad. This requi...

Landsat-based monitoring of seasonal and near real-time evapotranspiration (ET) in California vineyards is currently challenged by its low temporal revisit period and missing data under cloudy cond...

A new approach is proposed to derive evapotranspiration (E) and irrigation requirements by implementing the combination equation models of Penman–Monteith and Shuttleworth and Wallace with surface parameters and resistances derived from Sentinel-2 data. Surface parameters are derived from Sentinel-2 and used as an input in these models; namely: the hemispherical shortwave albedo, leaf area index and water status of the soil and canopy ensemble evaluated by using a shortwave infrared-based index. The proposed approach has been validated with data acquired during the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) in California irrigated vineyards. The E products obtained with the combination equation models are evaluated by using eddy covariance flux tower measurements and are additionally compared with surface energy balance models with Landsat-7 and -8 thermal infrared data. The Shuttleworth and Wallace (S-W S-2) model provides an accuracy comparable to thermal-based methods when using local meteorological data, with daily E errors < 1 mm/day, which increased from 1 to 1.5 mm/day using meteorological forcing data from atmospheric models. The advantage of using the S-W S-2 modeling approach for monitoring ET is the high temporal revisit time of the Sentinel-2 satellites and the finer pixel resolution. These results suggest that, by integrating the thermal-based data fusion approach with the S-W S-2 modeling scheme, there is the potential to increase the frequency and reliability of satellite-based daily evapotranspiration products.

Antarctic lakes with perennial ice covers provide the opportunity to investigate in-lake processes without direct atmospheric interaction, and to study their ice-cover sensitivity to climate conditions. In this study, a numerical model – driven by radiative, atmospheric and turbulent heat fluxes from the water body beneath the ice cover – was implemented to investigate the impact of climate change on the ice covers from two Antarctic lakes: west lobe of Lake Bonney (WLB) and Crooked Lake. Model results agreed well with measured ice thicknesses of both lakes (WLB – RMSE= 0.11 m over 16 years of data; Crooked Lake – RMSE= 0.07 m over 1 year of data), and had acceptable results with measured ablation data at WLB (RMSE= 0.28 m over 6 years). The differences between measured and modeled ablation occurred because the model does not consider interannual variability of the ice optical properties and seasonal changes of the lake's thermal structure. Results indicate that projected summer air temperatures will increase the ice-cover annual melting in WLB by 2050, but that the ice cover will remain perennial through the end of this century. Contrarily, at Crooked Lake the ice cover becomes ephemeral most likely due to the increase in air temperatures.

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.

Solar-induced fluorescence (SIF) is a promising tool to estimate photosynthesis across scales; however, there has been limited research done at the leaf level to investigate the relationship between SIF and photosynthesis. To help bridge this gap, a LI-COR LI-6800 gas exchange instrument was modified with a visible-near-infrared (VIS-NIR) spectrometer to measure active and passive fluorescence simultaneously. The system was adapted by drilling a hole into the bottom plate of the leaf chamber and inserting a fibre-optic to measure passive steady-state fluorescence (Ft,λ, analogous to SIF) from the abaxial surface of a leaf. This new modification can concurrently measure gas exchange, passive fluorescence and active fluorescence over the same leaf area and will allow researchers to measure leaf-level Ft,λ in the field to validate tower-based and satellite measurements. To test the modified instrument, measurements were performed on leaves of well-watered and water-stressed walnut plants at three light levels and a constant air temperature. Measurements on these same plants were also conducted using a similarly modified Walz GFS-3000 gas exchange instrument to compare results. We found a positive linear correlation between Ft,λ measurements from the modified LI-6800 and GFS-3000 instruments. We also report a positive linear relationship between Ft,λ and normalized steady-state chlorophyll fluorescence (Ft/Fo) from the pulse-amplitude modulation (PAM) fluorometer of the LI-6800 system. Accordingly, this modification will inform the link between spectrally resolved Ft,λ and gas exchange—leading to improved interpretation of how remotely sensed SIF tracks changes in the light reactions of photosynthesis.

More frequent, intense, and more extended droughts and heatwaves are challenging agricultural productivity and management. This study introduces a new modeling approach to represent the impact of heat and water stress on energy and mass fluxes from orchards. A photosynthesis model that explicitly accounts for dynamic responses of Rubisco and RuBP limitations to stress was linked to the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). ACASA is a multilayer soil-vegetation-atmosphere numerical model based on higher-order closure of turbulence equations to calculate plant-atmosphere exchanges of carbon dioxide, water, and heat. Field measurements of leaf area index, stomatal conductance, and photosynthesis were performed to estimate model parameters. Model results were compared to data from an eddy covariance flux tower deployed at an almond orchard. Overall, this new approach, ACASA-DynPM, is in closer agreement with observations of H, λE, and Fc compared to ACASA. On days of high temperatures and water stress, ACASA-DynPM outperforms ACASA. Thus, we argue that accounting for plant physiological stress might be especially relevant when estimating energy and mass fluxes over intensively irrigated agricultural regions. Such biases can have implications for the modeled degree of crop stress and water demands, and eventually, it can impact climate regional estimates when coupled with an atmospheric model.

Crimson Seedless is one of the most important table grape cultivars in the world, but often exhibits uneven berry color when grown in warm climates. Deficit irrigation is used extensively by growers during the ripening phase to advance fruit maturity and color, but there is a lack of information about the relationship between irrigation practices and fruit ripening variability. We imposed deficit irrigation in a commercial Crimson Seedless vineyard in the Maipo Valley, Chile, from veraison to harvest in two consecutive seasons. The fruit was tested for uniformity of Brix, firmness, and the color parameters “L”, “a”, “b”, and the Color Index of Red Grapes using analysis of variance on absolute residuals (Levene’s test). Postveraison water stress increased water productivity, Brix, and slightly improved berry coloration, but did not affect berry weight, size, or firmness. Moderate levels of water stress improved color uniformity at harvest, as lower values of leaf water potential were associated with a lower percentage of green berries that never matured. These results confirmed the role of deficit irrigation in the table grape ripening process. In contrast, extending the fruit ripening period by delaying harvest beyond 18 Brix did not increase color accumulation, but did increase berry shatter.

Kiwifruit is widely recognized as a fruit crop sensitive to water stress due to low stomatal regulation. Unfortunately, many of the most important kiwifruit producing areas have been affected by increasing water scarcity due to climate change. Protected cultivation may be used in kiwifruit vines not only to mitigate water stress and potential reductions in fruit quality but also to increase intrinsic water use efficiency. At the beginning of fruit maturation, two environmental conditions (uncovered and covered with a transparent plastic covering) were assessed in mature kiwifruit plants (Actinidia deliciosa Chev. cv. Hayward) subjected to conventional and deficit irrigation regimes in San Nicolas, Chile, for two consecutive seasons. The results showed that covered plants under deficit irrigation required twice the time to exhibit severe water stress levels (~−1.3 MPa) than plants under open-field conditions. Despite changes in solar radiation quantity and quality due to the transparent plastic covering, differences in rates of water stress occurrence between cover treatments in deficit-irrigated vines were not explained by differences in soil desiccation or stomatal conductance. The delay in severe water stress onset led to considerable water savings and caused no reductions in either yield or fruit quality, which increased water productivity between 21% and 71%. Fruit from covered plants subjected to deficit irrigation exhibited higher firmness at greater maturity (>7.0 Brix). The increase in water productivity in severely water-stressed kiwifruit vines, when using late-season plastic canopy cover, confirms that protected cultivation can be an excellent tool to reduce the impact of limited irrigation in many kiwifruit producing areas affected by water scarcity.

Plant physiological responses to stress and subsequent damage have not been successfully integrated into leaf, plant or land surface models. Water deficit leads to a series of plant responses: stomatal closure decreases evaporative cooling leading to higher leaf temperature, which it can affect photosynthetic dissipation of absorbed energy, especially under high irradiance. Excess absorbed energy by PSII results in photoinhibition, which is not typically included when modeling photosynthesis using the Farquhar-von Caemmerer-Berry (FvCB) model. We introduce a novel approach to represent plant-atmosphere interactions: a non-steady state model of photoinhibition linked to the RuBP-regeneration component of the FvCB model that accounts for the interactions between high temperature, irradiance, and water deficits. Gas exchange and chlorophyll fluorescence dynamics showed that, under severe water stress, the maximum quantum efficiency of PSII (Fv/Fm) quickly decreases, and it takes days to recover. We found a clear relationship between maximum electron transport rate and Fv/Fm and that short-term acclimation to irradiance affected photosynthetic FvCB model parameters. Fv/Fm functioned well as a coupling variable, able to scale RuBP-regeneration limited photosynthesis parameters proportional to photoinhibition. These results indicate that plant stress, photo-inhibitory damage, and recovery can be explicitly represented when modeling photosynthesis.

Canopy conductance ( g c ) is the main controller of plant-atmospheric interaction and a key element in understanding how plants cope with drought. Empirical g c models provide a good inference as to how environmental forcing affects surface water vapor and CO 2 gas exchange. However, when facing water scarcity, soil moisture or plant water availability becomes the primary controller. We studied g c in an Acacia caven (Mol) savannah in Central Chile under Mediterranean-type climate conditions that present distinguishable wet and dry seasons. We calibrated an empirical g c , in order to account for whole canopy gas exchange with g c measurements from three different data sets: (1) an inversion of the Penman–Monteith equation in combination with a Shuttleworth and Wallace model (PMSW) for evapotranspiration from sparse canopies; (2) an inversion of the Penman–Monteith (PM) based on the big leaf approach and (3) a set of leaf stomatal conductance ( g s ) ground based measurements taken throughout the season and scaled up to the canopy level. Then the semi-empirical Farquhar–Ball–Berry (FBB) g c model was added to the comparison to evaluate if the inclusion of a mechanistic component for photosynthesis would improve the prediction of g c . Models performance was assessed with ground based leaf gas exchange measurements during both wet and dry seasons. Acacia ’ s g c showed a high synchronicity with soil moisture, exhibiting the typical isohydric behavior of this kind of vegetation. The addition of the Shuttleworth and Wallace modifier to the Penman–Monteith equation did not yield a better calibration for the multiplicative model when compared to the one calibrated with the PM g c data set, however this does not directly certifies that PM itself is a better estimator of g c in sparse canopies. Furthermore, scaling issues such as ecosystem heterogeneity and patchiness must be considered when applying these estimations to a watershed level for both eco and hydrological reasons. These empirical models demonstrated to be a good tool for predicting stomatal behavior for this kind of vegetation. Nevertheless, the effect of deep soil moisture on plant water status must be integrated in g c estimations in order to improve model’s performance.

Motivated by recent extreme flow events in the Mataquito River located in the Mediterranean region of Chile, we performed a detailed trend analysis of critical hydroclimatic variables based on observed daily flow, precipitation and temperature within the basin. For the period 1976–2008, positive trends in temperature were observed, especially during spring and summer months. At the same time, we found negative trends in the frequency and intensity of precipitation, especially during spring months. We observed an increasing difference between average streamflow in the rainy season as compared to the snowmelt season. Part of this trend is caused by larger flows during autumn months, although no positive precipitation trends are observed for these months. Finally, significant reductions in minimum flow during spring/summer and a disproportionate concentration of high-flow events occurring in the last 10 years were also identified. These high-flow events tend to happen during autumn months, and are as...

The Mediterranean region of Chile is considered a biodiversity hot spot. An increase in temperature and decrease in precipitation, as projected for the end of this century by global circulation models, would likely change the distribution of the sclerophyllous thorny shrubland and woodland. In order to assess those potential impacts, the MAXENT algorithm was used to project potential changes in the distribution of the Mediterranean ecosystem. Ecological niche models were fitted and used to project the potential distribution of these forest ecosystems by the end of the century. Projections were made using data from the PRECIS model for the A2 and B2 climate change scenarios and two strategies of occupancy: free migration and non-migration. Distribution models of sclerophyllous, woodland and shrubland performed accurately representing current species’ distribution. When we assume non-migration responses under climate change scenarios, results reveal a decrease in the distribution area for all the species. The areas where the highest reduction in a suitable environment was found are located along the coastline, where higher temperature increases have been projected. For native ecosystems from the Andean Range region, such as communities dominated by thorny species, a stable habitat was found, associated with a higher adaptation capability to future climatic projections. Hence, in the future, buffer zones originated by “topo-climatic” conditions might play a key role in protecting Central Chile biodiversity.

Agricultural regions located in snowmelt-dominated Mediterranean climate basins have been identified as being highly vulnerable to the impacts of climate change. The Maipo basin in central Chile is one such region. Projections of future climate conditions suggest major challenges for this basin. Precipitation levels are projected to decrease by the end of the century, and temperature levels in the mountains are expected to increase by around 3–4°C. Such changes would affect both river discharge and irrigation water demand. This paper illustrates potential climate change impacts on the hydroclimatological regime of the Maipo basin, focusing on irrigated agriculture and its demands on water use rights. The impact assessment was carried out by combining a multisite stochastic weather generator with a disaggregation technique for historical monthly flows of the Maipo river at El Manzano. Demand for irrigation was simulated with a daily water budget model. Data showed that water demands from irrigated ...