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.

[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.

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.

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.

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP-Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depthfrequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus, more accurate hydrological applications than selecting any single product.

Although global variation in actual evapotranspiration has been widely investigated, it remains unclear how its two major components, transpiration and soil evaporation, are driven by climate drivers across global land surface. This paper uses a well‐validated, process‐based model that estimates transpiration and soil evaporation, and for the first time investigates and quantifies how the main global drivers, associated to vegetation process and the water and energy cycle, drive the spatiotemporal variation of the two components. The results show that transpiration and soil evaporation dominate the variance of actual evapotranspiration in wet and dry regions, respectively. Dry southern hemisphere from 13°S to 27°S is highlighted since it contributes to 21% global soil evaporation variance, with only 11% global land area. In wet regions, particularly in the humid tropics, there are strong correlations between transpiration, actual evapotranspiration, and potential evapotranspiration, with precipitation playing a relatively minor role, and available radiative energy is the major contributor to the interannual variability in transpiration and actual evapotranspiration in Amazonia. Conversely in dry regions, there are strong correlations between soil evaporation, actual evapotranspiration, and precipitation. Our findings highlight that ecohydrological links are highly related to climate regimes, and the small region such as Australia has important contribution to interannual variation in global soil evaporation and evapotranspiration, and anthropogenic activities strongly influence the variances in irrigation regions.

Land surface and global hydrological models are often used to characterize global water and energy fluxes and stores and to model their future trajectories. This study evaluates estimates of streamflow and evapotranspiration (ET) obtained with a priori parameterization from a land surface model [CSIRO Atmosphere Biosphere Land Exchange (CABLE)] and a global hydrological model (H08) against a global dataset of streamflow from 644 largely unregulated catchments and ET from 98 flux towers and benchmarks their performance against two lumped conceptual daily rainfall–runoff models [modèle du Génie Rural à 4 paramètres Journalier (GR4J) and a simplified version of the HYDROLOG model (SIMHYD)]. The results show that all four models perform poorly in simulating the monthly and annual runoff values, with the rainfall–runoff models outperforming both CABLE and H08. The model biases in runoff are generally reflected as a complementary opposite bias in ET. All models can generally reproduce the observed seasonal and interannual runoff variability. The correlations between the modeled and observed runoff time series are reasonable, with the rainfall–runoff models performing slightly better than CABLE and H08 at the monthly time scale and all four models performing similarly at the annual time scale. The results suggest that while the land surface and global hydrological models cannot adequately simulate the actual runoff time series and long-term average volumes, they can reasonably simulate the monthly and interannual runoff variability and trends and can therefore be reliably used for broadscale or comparative regional and global water and energy balance assessments and simulations of future trajectories. They can be improved through validating the models or calibrating some of the more sensitive and less physically based parameters.

Forests act as 'pumps' through their evapotranspiration (E tot ) and as 'sponges' by enhancing soil infiltration capacity and moisture retention. Tropical deforestation and poor post-forest land management generally result in lower E tot , but also reduce infiltration. Strongly diminished infiltration is typically accompanied by enhanced overland flow and can cause reduced groundwater recharge and baseflows. A grid-based land surface hydrological model (W3RA-LUM) was tailored to incorporate the trade-offs between the 'pump' and 'sponge' effects so to investigate where deforestation can be expected to have the greatest impacts on dry-season flows. Streamflow sensitivity analyses for scenarios with or without full forest cover and/or good versus poor surface infiltration were performed for: (i) selected tropical catchments with documented changes in streamflow after deforestation; and (ii) the tropics at large (23.5°N to 35°S, to include important seasonal montane forests). The catchment sensitivity analyses showed that W3A-LUM captured the streamflow response to imposed deforestation and changes in surface conditions reasonably well. For the tropics as a whole, an increase in mean annual streamflow of 18% was obtained for forest conversion (to pasture) only, versus 26% after an additional imposed reduction in surface infiltration. Much of the inferred flow increases concerned water-limited regions. A reduction in dry-season flows was predicted for nearly one-fifth of all grid cells (despite lower E tot after forest conversion) after impaired infiltration, potential 'hot spots' of hydrological change after deforestation and reduced infiltration. The affected grid cells shared the following key characteristics: (i) strong seasonality; (ii) high infiltration capacity under forested conditions;(iii) sufficient wet-season precipitation to recharge deep soil-and groundwater stores; (iv) sufficient soil water storage under forested conditions to 'carry over' infiltrated wet-season rainfall into the subsequent dry season; and (v) slow groundwater recession maintaining baseflow throughout the dry season.Our results demonstrate that forest removal in highly seasonal tropical catchments, whilst typically increasing mean annual water yield, can indeed decrease dry-season flows, depending on pre-and post-forest removal surface conditions and groundwater response times.