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.
Forest restoration is being scaled-up globally to deliver critical ecosystem services and biodiversity benefits, yet we lack rigorous comparison of co-benefit delivery across different restoration approaches. In a global synthesis, we use 25,950 matched data pairs from 264 studies in 53 countries to assess how delivery of climate, soil, water, and wood production services as well as biodiversity compares across a range of tree plantations and native forests. Carbon storage, water provisioning, and especially soil erosion control and biodiversity benefits are all delivered better by native forests, with compositionally simpler, younger plantations in drier regions performing particularly poorly. However, plantations exhibit an advantage in wood production. These results underscore important trade-offs among environmental and production goals that policymakers must navigate in meeting forest restoration commitments.
[1] Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1 resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km 2 ) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions. Citation: van Dijk, A. I. J. M., J. L. Peña-Arancibia, E. F. Wood, J. Sheffield, and H. E. Beck (2013), Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide, Water Resour.
Abstract. The understanding of low flows in rivers is paramount more than ever as demand for water increases on a global scale. At the same time, limited streamflow data to investigate this phenomenon, particularly in the tropics, makes the provision of accurate estimations in ungauged areas an ongoing research need. This paper analysed the potential of climatic and terrain attributes of 167 tropical and sub-tropical unregulated catchments to predict baseflow recession rates. Daily streamflow data (m 3 s −1 ) from the Global River Discharge Center (GRDC) and a linear reservoir model were used to obtain baseflow recession coefficients (k bf ) for these catchments. Climatic attributes included annual and seasonal indicators of rainfall and potential evapotranspiration. Terrain attributes included indicators of catchment shape, morphology, land cover, soils and geology. Stepwise regression was used to identify the best predictors for baseflow recession coefficients. Mean annual rainfall (MAR) and aridity index (AI) were found to explain 49% of the spatial variation of k bf . The rest of climatic indices and the terrain indices average catchment slope (SLO) and tree cover were also good predictors, but co-correlated with MAR. Catchment elongation (CE), a measure of catchment shape, was also found to be statistically significant, although weakly correlated. An analysis of clusters of catchments of smaller size, showed that in these areas, presumably with some similarity of soils and geology due to proximity, residuals of the regression could be explained by SLO and CE. The approach used provides a potential alternative for k bf parameterisation in ungauged catchments.
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