ALBERT

Description: Understanding how tropical rainforests respond to elevated atmospheric CO 2 concentration (eCO 2 ) is essential for predicting Earth's carbon, water and energy budgets under future climate change. Here we use long-term (1982-2010) precipitation ( P ) and runoff ( Q ) measurements to infer runoff coefficient ( Q / P ) and evapotranspiration ( E ) trends across 18 unimpaired tropical rainforest catchments. We complement that analysis by using satellite observations coupled with ecosystem process modelling (using both ‘top-down’ and ‘bottom-up’ perspectives) to examine trends in carbon uptake and relate that to the observed changes in Q / P and E . Our results show there have been only minor changes in the satellite-observed canopy leaf area over 1982-2010, suggesting that eCO 2 has not increased vegetation leaf area in tropical rainforests and therefore any plant response to eCO 2 occurs at the leaf-level. Meanwhile, observed Q / P and E also remained relatively constant in the 18 catchments, implying an unchanged hydrological partitioning and thus approximately conserved transpiration under eCO 2 . For the same period, using a ‘top-down’ model based on gas-exchange theory, we predict increases in plant assimilation ( A ) and light-use efficiency ( ε ) at the leaf-level under eCO 2 , the magnitude of which is essentially that of eCO 2 ( i.e ., ~12% over 1982-2010). Simulations from ten state-of-the-art ‘bottom-up’ ecosystem models over the same catchments also show the direct effect of eCO 2 is to mostly increase A and ε with little impact on E . Our findings add to the current limited pool of knowledge regarding the long-term eCO 2 impacts in tropical rainforests.

Description: Current state-of-the-art models typically applied at continental to global scales (hereafter called macro-scale) tend to use a priori parameters, resulting in suboptimal streamflow ( Q ) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10000 km 2 ) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q , after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the ten most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q , which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially-uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Köppen-Geiger climate types and even for evaluation catchments 〉 5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macro-scale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via http://water.jrc.ec.europa.eu/HBV/ . This article is protected by copyright. All rights reserved.

Description: Abstract Spatio‐temporally continuous global river discharge estimates across the full spectrum of stream orders are vital to a range of hydrologic applications, yet they remain poorly constrained. Here we present a carefully‐designed modeling effort (VIC land surface model and RAPID river routing model) to estimate global river discharge at very high resolutions. The precipitation forcing is from a recently published 0.1° global product that optimally merged gauge‐, reanalysis‐, and satellite‐based data. To constrain runoff simulations, we use a set of machine learning‐derived, global runoff characteristics maps (i.e., runoff at various exceedance probability percentiles) for grid‐by‐grid model calibration and bias correction. To support spaceborne discharge studies, the river flowlines are defined at their true geometry and location as much as possible – approximately 2.94 million vector flowlines (median length 6.8 km) and unit catchments are derived from a high‐accuracy global DEM at 3 arc‐second resolution (~90 m), which serves as the underlying hydrography for river routing. Our 35‐year daily and monthly model simulations are evaluated against over 14,000 gauges globally. Among them, 35% (64%) have a percentage bias within ±20% (±50%), and 29% (62%) have a monthly Kling‐Gupta Efficiency ≥0.6 (0.2), showing data robustness at the scale the model is assessed. This reconstructed discharge record can be used as a priori information for the Surface Water and Ocean Topography (SWOT) satellite mission's discharge product, thus named “Global Reach‐level A priori Discharge Estimates for SWOT (GRADES)”. It can also be used in other hydrologic applications requiring spatially‐explicit estimates of global river flows.

Description: Recent discharge observations are lacking for most rivers globally. Discharge can be estimated from remotely sensed floodplain and channel inundation area, but there is currently no method that can be automatically extended to many rivers. We examined whether automated monitoring is feasible by statistically relating inundation estimates from moderate to coarse (〉0.05°) resolution remote sensing to monthly station discharge records. Inundation extents were derived from optical MODIS data and passive microwave sensors, and compared to monthly discharge records from over 8000 gauging stations and satellite altimetry observations for 442 reaches of large rivers. An automated statistical method selected grid cells to construct ‘satellite gauging reaches' (SGRs). MODIS SGRs were generally more accurate than passive microwave SGRs, but there were complementary strengths. The rivers widely varied in size, regime and morphology. As expected performance was low ( R 〈0.7) for many (86%), often small or regulated, rivers, but 1263 successful SGRs remained. High monthly discharge variability enhanced performance: a standard deviation of 100-1000 m 3 s −1 yielded ca. 50% chance of R 〉0.6. The best results ( R 〉0.9) were obtained for large unregulated lowland rivers, particularly in tropical and boreal regions. Relatively poor results were obtained in arid regions, where flow pulses are few and recede rapidly, and in temperate regions, where many rivers are modified and contained. Where discharge variations produce clear changes in inundated area and gauge records are available for part of the satellite record, SGRs can retrieve monthly river discharge values back to around 1998 and up to present. This article is protected by copyright. All rights reserved.