ALBERT

Description: Predicting magnitude and frequency of floods is a key issue in hydrology, with implications in many fields ranging from river science and geomorphology to the insurance industry. In this paper, a novel physically-based approach is proposed to estimate the recurrence intervals of seasonal flow maxima. The method links the extremal distribution of streamflows to the stochastic dynamics of daily discharge, providing an analytical expression of the seasonal flood-frequency curve. The parameters involved in the formulation embody climate and landscape attributes of the contributing catchment, and can be estimated from daily rainfall and streamflow data. Only one parameter, which is linked to the antecedent wetness condition in the watershed, needs to be calibrated on the observed maxima. The performance of the method is discussed through a set of applications in four rivers featuring heterogeneous daily flow regimes. The model provides reliable estimates of seasonal maximum flows in different climatic settings and is able to capture diverse shapes of flood-frequency curves emerging in erratic and persistent flow regimes. The proposed method exploits experimental information on the full range of discharges experienced by rivers. As a consequence, model performances do not deteriorate when the magnitude of events with return times longer than the available sample size is estimated. The approach provides a framework for the prediction of floods based on short data series of rainfall and daily streamflows that may be especially valuable in data scarce regions of the world.

Description: One way of estimating the projected impact of climate change on crops is to use crop models. There is a large variability in results of different crop models, but it has been observed that the mean or median of a multimodel ensemble (MME) often gives good agreement with observed data. We used empirical data from several MME studies, plus theoretical arguments, to better understand why and when the MME mean will be a good predictor. This should help modelers decide how to create and use MMEs for climate impact assessment. Abstract A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Description: The projected impact of 1.5 and 2.0°C warming above the pre‐industrial period on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impacts of warming of 〈2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. Abstract Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming 〉2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming 〈2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Description: The Amazon Basin contains large wetland ecosystems which are important sources of methane (CH 4 ). Space-borne observations of atmospheric CH 4 can provide constraints on emissions from these remote ecosystems, but lack of validation precludes robust estimates. We present the first validation of CH 4 columns in the Amazon from the Greenhouse gases Observing SATellite (GOSAT) using aircraft measurements of CH 4 over five sites across the Amazon Basin. These aircraft profiles, combined with stratospheric results from the TOMCAT chemical transport model, are vertically integrated allowing direct comparison to the GOSAT XCH 4 measurements (the column averaged dry air mole fraction of CH 4 ). The measurements agree within uncertainties or show no significant difference at three of the aircraft sites, with differences ranging from -1.9 ppb to 6.6 ppb, whilst at two sites GOSAT XCH 4 is shown to be slightly higher than aircraft measurements, by 8.1 ppb and 9.7 ppb. The seasonality in XCH 4 seen by the aircraft profiles is also well captured (correlation coefficients from 0.61 to 0.90). GOSAT observes elevated concentrations in the north-west corner of South America in the dry season and enhanced concentrations elsewhere in the Amazon Basin in the wet season; with the strongest seasonal differences coinciding with regions in Bolivia known to contain large wetlands. Our results are encouraging evidence that these GOSAT CH 4 columns are generally in good agreement with in situ measurements and understanding the magnitude of any remaining biases between the two will allow more confidence in the application of XCH 4 to constrain Amazonian CH 4 fluxes.

Description: The effective discharge constitutes a key concept in river science and engineering. Notwithstanding many years of studies, a full understanding of the effective discharge determinants is still challenged by the variety of values identified for different river catchments. The present paper relates the observed diversity of effective discharge to the underlying heterogeneity of flow regimes. An analytic framework is proposed, which links the effective ratio (i.e. the ratio between effective discharge and mean streamflow) to the empirical exponent of the sediment rating curve and to the streamflow variability, as resulting from climatic and landscape drivers. The analytic formulation predicts patterns of effective ratio versus streamflow variability observed in a set of catchments of the continental United States, and helps in disentangling the major climatic and landscape drivers of sediment transport in rivers. The findings highlight larger effective ratios of erratic hydrologic regimes (characterized by high flow variability) compared to those exhibited by persistent regimes, which are attributable to intrinsically different streamflow dynamics. The framework provides support for the estimate of effective discharge in rivers belonging to diverse climatic areas.

Description: We present an assessment of methane (CH 4 ) atmospheric concentrations over the Amazon Basin for 2010 and 2011 using a 3-D atmospheric chemical transport model, two wetland emission models and new observations made during bi-weekly flights made over four locations within the Basin. We attempt to constrain Basin-wide CH 4 emissions using the observations, and, since 2010 was an unusually dry year, we assess the effect of this drought on Amazonian methane emissions. We find that South American emissions contribute up to 150 ppb to concentrations at the sites, mainly originating from within the Basin. Our atmospheric model simulations agree reasonably well with measurements at three of the locations (0.28 ≤ r 2 ≤ 0.63, mean bias ≤ 9.5 ppb). Attempts to improve the simulated background CH 4 concentration through analysis of simulated and observed sulphur hexafluoride concentrations do not improve the model performance, however. Through minimisation of seasonal biases between the simulated and observed atmospheric concentrations, we scale our prior emission inventories to derive total Basin-wide methane emissions of 36.5-41.1 Tg(CH 4 )/yr in 2010 and 31.6-38.8 Tg(CH 4 )/yr in 2011. These totals suggest that the Amazon contributes significantly (up to 7%) to global CH 4 emissions. Our analysis indicates that factors other than precipitation, such as temperature variations or tree mortality, may have affected microbial emission rates. However, given the uncertainty of our emission estimates, we cannot say definitively whether the non-combustion emissions from the region were different in 2010 and 2011, despite contrasting meteorological conditions between the two years.

Description: The Amazon Basin is an important region for global CH 4 emissions. It hosts the largest area of humid tropical forests, and around 20% of this area is seasonally flooded. In a warming climate it is possible that CH 4 emissions from the Amazon will increase both as a result of increased temperatures and precipitation. To examine if there are indications of first signs of such changes we present here a 13-year (2000-2013) record of regularly measured vertical CH 4 mole fraction profiles above the eastern Brazilian Amazon, sensitive to fluxes from the region upwind of Santarém (SAN), between SAN and the Atlantic coast. Using a simple mass balance approach, we find substantial CH 4 emissions with an annual average flux of 52.8±6.8 mg CH 4 m -2 day -1 over an area of approximately 1 million km 2 . Fluxes are highest in two periods of the year: in the beginning of the wet season and during the dry season. Using a CO:CH 4 emission factor estimated from the profile data, we estimated an influence of biomass burning around 15% of the total flux in dry season, indicating that biogenic emissions dominate the CH 4 flux. This 13-year record shows that CH 4 emissions upwind of SAN varied over the years, with highest emissions in 2008 (around 25% higher than in 2007), mainly during the wet season, representing 19% of the observed global increase.

Description: Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g∙N∙m−2) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p 〈 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g∙N∙m−2) (r2 = 0.73; SE = 0.603; p 〈 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately.

Description: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. This article is protected by copyright. All rights reserved.