1,720,976 research outputs found
Hydrologic sustainability of a mediterranean tree-grass ecosystem under climate change
Full text linkIn Mediterranean dryland ecosystems climate change is occurring with an increase in air temperature and a decrease (mainly in wet seasons) of precipitation, which are key for grass and tree growth. We investigated an attractive Sardinian case study with a typical tree-grass ecosystem, where wild olives and seasonal grass species grow on thin surface soil layer overlaying a fractured rock sublayer. A very long-term database with almost 60 years of data is available, with micrometeorological and meteorological measurements, and remote sensing data, providing a unique opportunity to analyze the response of tree-grass ecosystems to historical climate and land cover changes. We proposed an ecohydrological model that was able to reproduce the soil, vegetation, and atmosphere interactions and dynamics, and their long-term evolution. The model accurately predicted the longterm dynamics of the tree cover fraction, which was drastically reduced ( 0.10) by a human-induced fire about 50 years ago, and restored naturally in almost 20 years, reaching the equilibrium value ( 0.33). The Sardinian tree-grass ecosystem suffered a historically significant reduction in rain and a significant increase in air temperature in the last century. The predicted future scenarios are even more severe, with a further decrease of mean annual precipitation (MAP) of up to 400 mm, and an increase of air temperature of +4 degrees C, which will cause a reduction of the tree cover fraction of up to 0.10 and a strong decrease of the tree LAI. At present, the developed tree cover percentage of the Sardinian site is sustainable with the historical MAP (>600 mm/y), thanks to the tree hydraulic redistribution contribution to transpiration (up to 80 %). In the predicted future scenarios, the increase of dry conditions with a wetness index (precipitation/potential evaporation) below 0.005 will increase the hydraulic redistribution contribution, reaching 91 % of tree transpiration, which, however, will be not enough to support tree growth and maintenance. The soil is predicted to become drier, with less grass and vegetation in general, with consequences for the landscape aspect, becoming more and more a savanna-like ecosystem
On the prediction of the Toce Alpine Basin Floods with Distributed Hydrologic Models
No full textWith the objective of improving flood predictions, in recent years sophisticated continuous hydrologic models including complex land surface sub-models have been developed. This has produced a significant increase in parameterization; consequently, applications of distributed models to ungaged basins lacking specific data from field campaigns may become redundant.
The objective of this paper is to produce a parsimonious and robust distributed hydrologic model for flood predictions in Italian Alpine basins. Application is made to the Toce basin (area of 1534 km2). The Toce basin was a case study of the RAPHAEL European Union research project, during which a comprehensive set of hydrologic, meteorological and physiographic data were collected, including the hydrologic analysis of the 1996-1997 period. Two major floods occurred during this period. We compare the FEST04 event model, which computes rainfall abstraction and antecedent soil moisture conditions through the simple SCS-CN method, and two continuous hydrologic models, SDM and TDM, which differ in soil water balance scheme, and base flow and runoff generation computations.
The simple FEST04 event model demonstrated good performance in the prediction of the 1997 flood, but shows limits in the prediction of the long and moderate 1996 flood. More robust predictions are obtained with the parsimonious SDM continuous hydrologic model, which uses a simple one-layer soil water balance model and an infiltration excess mechanism for runoff generation, and demonstrates good performance in both long-term runoff modeling and flood predictions. Instead, the use of a more sophisticated continuous hydrologic model, the TDM, that simulates soil moisture dynamics in two-layers of soil, and computes runoff and base flow using some TOPMODEL concepts, does not seem to be advantageous for this Alpine basi
On the Impacts of Historical and Future Climate Changes to the Sustainability of the Main Sardinian Forests
Full text linkThe Mediterranean Basin is affected by climate changes that may have negative effects on forests. This study aimed to evaluate the ability of 17 forests located in the Island of Sardinia to resist or adapt to the past and future climate. Sardinia is experiencing a decreasing anthropic pressure on forests, but drought-triggered dieback in trees was recently observed and confirmed by the analysis of 20 years of satellite tree-cover data (MOD44B). Significant negative trends in yearly tree cover have affected the broad-leaved vegetation, while significative positive trends were found in the bushy sclerophyllous vegetation. Vegetation behavior resulted in being related to the mean annual precipitation (MAP); for MAP < 700 mm, we found a decline in the tall broad-leaved stands and an increase in the short ones, and the opposite was found for bushy sclerophyllous vegetations. In forests with MAP > 700 mm, both stands are stable, regardless of the growing trends in the vapor-pressure deficit (VPD) and temperature. No significative correlation between bushy sclerophyllous tree cover and the climate drivers was found, while broad-leaved tree cover is positively related to MAP1990–2019 and negatively related to the growing annual VPD. We modeled those relationships, and then we used them to coarsely predict the effects of twelve future scenarios (derived from HADGEM2-AO (CMIP5) and HadGEM3-GC31-LL (CMIP6) models) on forest tree covers. All scenarios show an annual VPD increase, and the higher its increase, the higher the trees-cover loss. The future changes in precipitation were contrasting. SC6, in line with past precipitation trends, predicts a further drop in the mean annual precipitation (−7.6%), which would correspond to an average 2.1-times-greater reduction in the tree cover (−16.09%). The future changes in precipitation for CMIP6 scenarios agree on a precipitation reduction in the range of −3.4% (SC7) to −14.29% (S12). However, although the reduction in precipitation predicted in SC12 is almost double that predicted in SC6, the consequent average reduction in TC is comparable and stands at −16%. On the contrary, SC2 predicts a turnaround with an abrupt increase of precipitation (+21.5%) in the upcoming years, with a reduction in the number of forests in water-limited areas and an increase in the percentage of tree cover in almost all forests
Soil moisture estimates in a grass field using sentinel-1 radar data and an assimilation approach
Full text linkThe new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6–12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Our objective was to develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, and to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field. However, for soil moisture retrievals from Sentinel 1 observations in grasslands, there is still the need to identify robust and parsimonious solutions, accounting for the effects of vegetation attenuation and their seasonal variability. In a grass experimental site in Sardinia, where field measurements of soil moisture were available for the 2016–2018 period, three common retrieval methods have been compared to estimate soil moisture from Sentinel 1 data, with increasing complexity and physical interpretation of the processes: the empirical change detection method, the semi-empirical Dubois model, and the physically-based Fung model. In operational approaches for soil moisture mapping from remote sensing, the parame-terization simplification of soil moisture retrieval techniques is encouraged, looking for parameter estimates without a priori information. We have proposed a simplified approach for estimating a key parameter of retrieval methods, the surface roughness, from the normalized difference vegetation index (NDVI) derived by simultaneous Sentinel 2 optical observations. Soil moisture was estimated better using the proposed approach and the Dubois model than by using the other methods, which accounted vegetation effects through the common water cloud model. Furthermore, we successfully merged radar-based soil moisture observations and a land surface model, through a data assimilation approach based on the Ensemble Kalman filter, providing robust predictions of soil moisture
The root-zone soil moisture spectrum in a mediterranean ecosystem
Full text linkStorage of water within soil pores of the root zone introduce memory effects in the dynamics of soil moisture that are considerably longer than the integral timescale of many atmospheric processes. Thus, hydro-climatic states can be “sustained” through land-surface heat and water vapor fluxes primarily because they can “feed off” on this long-term soil moisture memory. Root-zone soil moisture memory is only but one feature characterizing the spectrum of soil moisture dynamics, which is analyzed here using a combination of long-term measurements and models. In particular, the spectrum of root-zone soil moisture content in a Mediterranean ecosystem is examined using 14-years of half-hourly measurements. A distinguishing hydro-climatic feature in such ecosystems is that sources (mainly rainfall) and sinks (mainly evapotranspiration) of soil moisture are roughly out of phase with each other. For over 4 decades of time scales and 7 decades of energy, the canonical shape of the measured soil moisture spectrum is shown to be approximately Lorentzian determined by the soil moisture variance and its memory but with two exceptions: the occurrences of a peak at diurnal-to-daily time scales and a weaker peak at near annual time scales. Model calculations and spectral analysis demonstrate that diurnal and seasonal variations in hydroclimate forcing responsible for variability in evapotranspiration had minor impact on the normalized shape of the soil moisture spectrum. However, their impact was captured by adjustments in the temporal variance. These findings indicate that precipitation and not evapotranspiration variability dominates the multi-scaling properties of soil moisture variability consistent with prior climate model simulations. Furthermore, the soil moisture memory inferred by the annual peak of soil moisture (340 d) is consistent with climate model simulations, while the memory evaluated from the loss function of a linearized mass balance approach leads to a smaller value (50 d), highlighting the effect of weak non-stationarity on soil moisture variability
Climate Change Impacts on the Water Resources and Vegetation Dynamics of a Forested Sardinian Basin through a Distributed Ecohydrological Model
Full text linkClimate change is impacting Mediterranean basins, bringing warmer climate conditions. The Marganai forest is a natural forest protected under the European Site of Community Importance (Natura 2000), located in Sardinia, an island in the western Mediterranean basin, which is part of the Fluminimaggiore basin. Recent droughts have strained the forest′s resilience. A long-term hydrological database collected from 1922 to 2021 shows that the Sardinian forested basin has been affected by climate change since the middle of the last century, associated with a decrease in winter precipitation and annual runoff, reduced by half in the last century, and an increase of ~1 °C in the mean annual air temperature. A simplified model that couples a hydrological model and a vegetation dynamics model for long-term ecohydrological predictions in water-limited basins is proposed. The model well predicted almost one century of runoff observations. Trees have suffered from the recent warmer climate conditions, with a tree leaf area index (LAI) decreasing systematically due to the air temperature and a vapor pressure deficit (VPD) rise at a rate of 0.1 hPa per decade. Future climate scenarios of the HadGEM2-AO climate model are predicting even warmer conditions in the Sardinian forested basin, with less annual precipitation and higher air temperatures and VPD. Using these climate scenarios, we predicted a further decrease in runoff and tree transpiration and LAI in the basin, with a reduction of tree LAI by half in the next century. Although the annual runoff decreases drastically in the worst scenarios (up to 26%), runoff extremes will increase in severity, outlining future scenarios that are drier and warmer but, at the same time, with an increased flood frequency. The future climate conditions undermine the forest’s sustainability and need to be properly considered in water resources and forest management plans
Parsimonious Modeling of Vegetation Dynamics for Ecohydrologic Studies of Water-Limited Ecosystems
No full textThe structure and function of vegetation regulate fluxes across the biosphere-atmosphere interface with large effects in water-limited ecosystems. Vegetation dynamics are often neglected in hydrological modeling except for simple prescriptions of seasonal phenology. However, changes in vegetation densities, influencing the partitioning of incoming solar energy into sensible and latent heat fluxes, can result in long-term changes in both local and global climates with resulting feedbacks on vegetation growth. This paper seeks a simple vegetation dynamics model (VDM) for simulation of the leaf area index (LAI) dynamics in hydrologic models. Five variants of a VDM are employed, with a range of model complexities. The VDMs are coupled to a land surface model (LSM), with the VDM providing the LAI evolution through time and the LSM using this to compute the land surface fluxes and update the soil water contents. We explore the models through case studies of water-limited grass fields in California (United States) and North Carolina (United States). Results show that a simple VDM, simulating only the living aboveground green biomass (i.e., with low parameterization), is able to accurately simulate the LAI. Results also highlight the importance of including the VDM in the LSM when studying the climate-soil-vegetation interactions over moderate to long timescales. The inclusion of the VDM in the LSM is demonstrated to be essential for assessing the impact of interannual rainfall variability on the water budget of a water limited region
Hydrological Sustainability of Dam-Based Water Resources in a Mediterranean Basin Undergoing Climate Change
Full text linkThe Flumendosa dams are a key part of the water resources system of the island of Sardinia. The analysis of a long-term (1922-2022) hydrological database showed that the Flumendosa basin has been affected by climate change since the middle of the last century, associated with a decrease in winter precipitation and annual runoff (Mann-Kendall tau = -0.271), reduced by half in the last century, and an increase in the mean annual air temperature (Mann-Kendall tau = +0.373). We used a spatially distributed ecohydrological model and a water resources management model (WARGI) to define the economic efficiency and the optimal water allocation in the water system configurations throughout the evaluation of multiple planning and management rules for future climate scenarios. Using future climate scenarios, testing land cover strategies (i.e., forestation/deforestation), and optimizing the use of water resources, we predicted drier future scenarios (up to the end of the century) with an alarming decrease in water resources for agricultural activities, which could halt the economic development of Sardinia. In the future hydrological conditions (2024-2100), irrigation demands will not be totally satisfied, with up to 74% of future years being in deficit for irrigation, with a mean deficit of up to 52% for irrigation
Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem
Full text linkData assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic–land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil–water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions
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