Multi-decadal trends in global terrestrial evapotranspiration and its components

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle

The WACMOS-ET project, part 2 : evaluation of global terrestrial evaporation data sets

The WAter Cycle Multi-mission Observation Strategy - EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman-Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation

Contrasting changes in hydrological processes of the Volta River basin under global warming

A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of 12 general circulation models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) that are dynamically downscaled by five regional climate models (RCMs) from the Coordinated Regional-climate Downscaling Experiment (CORDEX)-Africa is used. In total, 43 RCM–GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The bias-corrected climate projections are then used as input to the mesoscale Hydrologic Model (mHM) for hydrological projections over the 21st century (1991–2100). Results reveal contrasting dynamics in the seasonality of rainfall, depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture, and terrestrial water storage) is only projected under RCP8.5. High- and low-flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. The evolutions of streamflow indicate a future delay in the date of occurrence of low flows up to 11 d under RCP8.5, while high flows could occur 6 d earlier (RCP2.6) or 5 d later (RCP8.5), as compared to the historical period. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water–energy–food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision-makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socioeconomic development

Global Analysis, Interpretation and Modelling: An Earth Systems Modelling Program

The Goal of the GAIM is: To advance the study of the coupled dynamics of the Earth system using as tools both data and models; to develop a strategy for the rapid development, evaluation, and application of comprehensive prognostic models of the Global Biogeochemical Subsystem which could eventually be linked with models of the Physical-Climate Subsystem; to propose, promote, and facilitate experiments with existing models or by linking subcomponent models, especially those associated with IGBP Core Projects and with WCRP efforts. Such experiments would be focused upon resolving interface issues and questions associated with developing an understanding of the prognostic behavior of key processes; to clarify key scientific issues facing the development of Global Biogeochemical Models and the coupling of these models to General Circulation Models; to assist the Intergovernmental Panel on Climate Change (IPCC) process by conducting timely studies that focus upon elucidating important unresolved scientific issues associated with the changing biogeochemical cycles of the planet and upon the role of the biosphere in the physical-climate subsystem, particularly its role in the global hydrological cycle; and to advise the SC-IGBP on progress in developing comprehensive Global Biogeochemical Models and to maintain scientific liaison with the WCRP Steering Group on Global Climate Modelling

Evaluating the Contribution of Remote Sensing Data Products for Regional Simulations of Hydrological Processes in West Africa using a Multi-Model Ensemble

Water is a crucial resource for human health, agricultural production and economic development. This holds especially true in West Africa, where large parts of the population work as self-sustaining farmers. Accurate knowledge of available water resources is therefore essential to properly manage this valuable commodity. Hydrologic modeling is seen as a key aspect in generating predictions of available resources. However, the overall availability of in situ data for model parametrization in West Africa has been steadily declining since the 1990s. When observations are available, they often contain errors and gaps. This lack of data severely hinders the application of hydrologic models in the region. Nowadays, many global and regional remote sensing and reanalysis data products exist which may be used to overcome these problems. A thorough analysis of the contribution of these products to regional simulations of hydrologic processes in West Africa has so far not been conducted. The purpose of this study is to close this gap. The study area spans from 3 to 24° latitude and -18 to 16° longitude and encompasses, among others, the Niger, Volta, and Senegal river basins. This study focuses on three key aspects, namely how the performance of remotely sensed and reanalyzed products can be validated without the availability of in situ data for the region; to what extent semi-distributed hydrologic models of the region can be parameterized and validated using these data; and how a fully distributed, grid-based model can be set up, calibrated and validated for sparsely-gauged river basins using multivariate data inputs. Comparisons of remote sensing and reanalysis precipitation products for the region show strong variability. A hydrologic evaluation was conducted, during which the skill of each precipitation dataset to accurately reproduce observed streamflow in HBV-light simulations was tested. Best results are achieved by products which include satellite infrared and microwave measurements as well as bias-correction based on in situ observations. Averaged Nash-Sutcliffe Efficiencies (NSE) of 0.66 were reached during the calibration of the CMORPH CRT and PERSIANN CDR products over six subbasins. In a next step, three SWAT models were set up for the region using multiple remote sensing and reanalysis data products and then calibrated and validated against observed river discharge with global and local approaches. While streamflow results differ within models and model regions, they are mostly satisfactory with coefficient of determination (R2) values of 0.52 and 0.51 for calibrations and 0.63 and 0.61 for validations. In a multivariate validation framework, the skill of the model in simulating variables not included in the calibration is further evaluated against remote sensing observations of actual evapotranspiration, soil moisture dynamics, and total water storage anomaly. Here, it has been shown that the models perform robustly and reach a good agreement in relation to observations. Furthermore, the grid-based mHM model was applied to several river basins in the south of the study area. After the quality of precipitation and evapotranspiration inputs was tested, a multivariate calibration was conducted. Models were calibrated using discharge observations (Q) and, to further constrain model boundary conditions, discharge in combination with remote sensing actual evapotranspiration observations (Q/ET). Finally, the quality of the simulations was tested against streamflow data as well as against remote sensing actual evapotranspiration, soil moisture, and total water storage anomaly data. Streamflow simulations performed well with averaged Kling-Gupta Efficiencies (KGE) of 0.53 for the first (Q) and 0.49 for the second (Q/ET) calibration. Further variables tested during the multiobjective validation were within good predictive ranges, especially during the Q/ET calibration. When SWAT and mHM model results are compared against each other and against external data products, results show that while both models perform robustly, mHM predictions outperform SWAT results. This study furthers the understanding of the contribution of remote sensing, reanalysis and global data products in regional simulations of hydrologic processes in West Africa. Specific modeling strategies and routines were developed to further increase predictive capabilities of hydrologic models of the region using these freely-available datasets

Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3&amp;ndash;5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations