Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin

Extreme flooding impacts millions of people that live within the Amazon floodplain. Global hydrological models (GHMs) are frequently used to assess and inform the management of flood risk, but knowledge on the skill of available models is required to inform their use and development. This paper presents an intercomparison of eight different GHMs freely available from collaborators of the Global Flood Partnership (GFP) for simulating floods in the Amazon basin. To gain insight into the strengths and shortcomings of each model, we assess their ability to reproduce daily and annual peak river flows against gauged observations at 75 hydrological stations over a 19-year period (1997–2015). As well as highlighting regional variability in the accuracy of simulated streamflow, these results indicate that (a) the meteorological input is the dominant control on the accuracy of both daily and annual maximum river flows, and (b) groundwater and routing calibration of Lisflood based on daily river flows has no impact on the ability to simulate flood peaks for the chosen river basin. These findings have important relevance for applications of large-scale hydrological models, including analysis of the impact of climate variability, assessment of the influence of long-term changes such as land-use and anthropogenic climate change, the assessment of flood likelihood, and for flood forecasting systems

Подсистема автономного программно-аппаратного комплекса для индуктивного долгосрочного прогноза осредненных значений метеопараметров

The research of the inductive method of long-term (forestalling to 0,5 year) prognosis of average decade air s temperature on the basis of principle of analogies was executed and it s sufficient was shown. The research of the offered approach was also conducted: in the base of spatial models without principle of analogies; in the polynomial harmonic base; the analysis of middle quality of the inductive prognostic method for cases of the analogue principle usage and without it

Evaluation of a sub-kilometre NWP system in an Arctic fjord-valley system in winter

Terrain challenges the prediction of near-surface atmospheric conditions, even in kilometre-scale numerical weather prediction (NWP) models. In this study, the ALADIN-HIRLAM NWP system with 0.5 km horizontal grid spacing and an increased number of vertical levels is compared to the 2.5-km model system similar to the currently operational NWP system at the Norwegian Meteorological Institute. The impact of the increased resolution on the forecasts’ ability to represent boundary-layer processes is investigated for the period from 12 to 16 February 2018 in an Arctic fjord-valley system in the Svalbard archipelago. Model simulations are compared to a wide range of observations conducted during a field campaign. The model configuration with sub-kilometre grid spacing improves both the spatial structure and overall verification scores for the near-surface temperature and wind forecasts compared to the 2.5-km experiment. The subkilometre experiment successfully captures the wind channelling through the valley and the temperature field associated with it. In a situation of a cold-air pool development, the sub-kilometre experiment has a particularly high near-surface temperature bias at low elevations. The use of measurement campaign data, however, reveals some encouraging results, e.g. the sub-kilometre system has a more realistic vertical profile of temperature and wind speed, and the surface temperature sensitivity to the net surface energy is closer to the observations. This work demonstrates the potential of sub-kilometre NWP systems for forecasting weather in complex Arctic terrain, and also suggests that the increase in resolution needs to be accompanied with further development of other parts of the model system

The physical sustainability of the coastal zone of the Ganges-Brahmaputra-Meghna delta under climatic and anthropogenic stresses

The Ganges-Brahmaputra-Meghna (GBM) delta is one of the world’s largest deltas, and consists of large areas of low flat lands formed by the deposition of sediment from the GBM rivers. However, recent estimates have projected between 200~1000 mm of climate-driven sea-level rise by the end of the 21st century, at an average rate of ~6 mm/yr. Eustatic sea-level rise is further compounded by subsidence of the delta, which in the coastal fringes varies from 0.2 to 7.5 mm/yr, at an average value of ~2.0 mm/yr. Therefore, the combined effect of sea-level rise and subsidence (termed relative sea-level rise, RSLR) is around 8.0 mm/yr. Such high values of RSLR raise the question of whether sediment deposition on the surface of the delta is sufficient to maintain the delta surface above sea level. Moreover, as the total fluvial sediment influx to the GBM delta system is known to be decreasing, the retained portion of fluvial sediment on the delta surface is also likely decreasing, reducing the potential to offset RSLR. Within this context, the potential of various interventions geared at promoting greater retention of sediment on the delta surface is explored using numerical experiments under different flow-sediment regime and anthropogenic interventions. We find that for the existing, highly managed, conditions, the retained portion of fluvial sediment on the delta surface varies between 22% and 50% during average (when about 20% of the total floodplain in the country is inundated) and extreme (> 60% of the total floodplain in the country is inundated) flood years, respectively. However, the degree to which sediment has the potential to be deposited on the delta surface increases by up to 10% when existing anthropogenic interventions such as polders that act as barriers to delta-plain sedimentation are removed. While dismantling existing interventions is not a politically realistic proposition, more quasi-natural conditions can be reestablished through local- sediment management using tidal river management, cross dams, dredging, bandal-like structures and/or combinations of the above measures

Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain

Densely populated floodplains downstream of Asia’s mountain ranges depend heavily on mountain water resources, in particular for irrigation. An intensive and complex multi-cropping irrigated agricultural system has developed here to optimize the use of these mountain water resources in conjunction with monsoonal rainfall. Snow and glacier melt thereby modulate the seasonal pattern of river flows and, together with groundwater, provide water when rainfall is scarce. Climate change is expected to weaken this modulating effect, with potentially strong effects on food production in one of the world’s breadbaskets. Here we quantify the space-, time- and crop-specific dependence of agriculture in the Indo-Gangetic Plains on mountain water resources, using a coupled state-of-the-art, high-resolution, cryosphere–hydrology–crop model. We show that dependence varies strongly in space and time and is highest in the Indus basin, where in the pre-monsoon season up to 60% of the total irrigation withdrawals originate from mountain snow and glacier melt, and that it contributes an additional 11% to total crop production. Although dependence in the floodplains of the Ganges is comparatively lower, meltwater is still essential during the dry season, in particular for crops such as sugar cane. The dependency on meltwater in the Brahmaputra is negligible. In total, 129 million farmers in the Indus and Ganges substantially depend on snow and glacier melt for their livelihoods. Snow and glacier melt provides enough water to grow food crops to sustain a balanced diet for 38 million people. These findings provide important information for agricultural and climate change adaptation policies in a climate change hot spot where shifts in water availability and demand are projected as a result of climate change and socio-economic growth

Attribution of global lake systems change to anthropogenic forcing

Lake ecosystems are jeopardized by the impacts of climate change on ice seasonality and water temperatures. Yet historical simulations have not been used to formally attribute changes in lake ice and temperature to anthropogenic drivers. In addition, future projections of these properties are limited to individual lakes or global simulations from single lake models. Here we uncover the human imprint on lakes worldwide using hindcasts and projections from five lake models. Reanalysed trends in lake temperature and ice cover in recent decades are extremely unlikely to be explained by pre-industrial climate variability alone. Ice-cover trends in reanalysis are consistent with lake model simulations under historical conditions, providing attribution of lake changes to anthropogenic climate change. Moreover, lake temperature, ice thickness and duration scale robustly with global mean air temperature across future climate scenarios (+0.9 °C °Cair–1, –0.033 m °Cair–1 and –9.7 d °Cair–1, respectively). These impacts would profoundly alter the functioning of lake ecosystems and the services they provide

Output-based assessment of herd-level freedom from infection in endemic situations:Application of a Bayesian Hidden Markov model

International audienceCountries have implemented control programmes (CPs) for cattle diseases such as bovine viral diarrhoea virus (BVDV) that are tailored to each country-specific situation. Practical methods are needed to assess the output of these CPs in terms of the confidence of freedom from infection that is achieved. As part of the STOC free project, a Bayesian Hidden Markov model was developed, called STOC free model, to estimate the probability of infection at herd-level. In the current study, the STOC free model was applied to BVDV field data in four study regions, from CPs based on ear notch samples. The aim of this study was to estimate the probability of herd-level freedom from BVDV in regions that are not (yet) free. We additionally evaluated the sensitivity of the parameter estimates and predicted probabilities of freedom to the prior distributions for the different model parameters. First, default priors were used in the model to enable comparison of model outputs between study regions. Thereafter, country-specific priors based on expert opinion or historical data were used in the model, to study the influence of the priors on the results and to obtain country-specific estimates.The STOC free model calculates a posterior value for the model parameters (e.g. herd-level test sensitivity and specificity, probability of introduction of infection) and a predicted probability of infection. The probability of freedom from infection was computed as one minus the probability of infection. For dairy herds that were considered free from infection within their own CP, the predicted probabilities of freedom were very high for all study regions ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The priors did have more influence on two of the model parameters, herd-level sensitivity and the probability of remaining infected, due to the low prevalence and incidence of BVDV in the study regions. The advantage of STOC free model compared to scenario tree modelling, the reference method, is that actual data from the CP can be used and estimates are easily updated when new data becomes availabl

Global water scarcity including surface water quality and expansions of clean water technologies

Water scarcity threatens people in various regions, and has predominantly been studied from a water quantity perspective only. Here we show that global water scarcity is driven by both water quantity and water quality issues, and quantify expansions in clean water technologies (i.e. desalination and treated wastewater reuse) to ‘reduce the number of people suffering from water scarcity’ as urgently required by UN’s Sustainable Development Goal 6. Including water quality (i.e. water temperature, salinity, organic pollution and nutrients) contributes to an increase in percentage of world’s population currently suffering from severe water scarcity from an annual average of 30% (22%–35% monthly range; water quantity only) to 40% (31%–46%; both water quantity and quality). Water quality impacts are in particular high in severe water scarcity regions, such as in eastern China and India. In these regions, excessive sectoral water withdrawals do not only contribute to water scarcity from a water quantity perspective, but polluted return flows degrade water quality, exacerbating water scarcity. We show that expanding desalination (from 2.9 to 13.6 billion m3 month−1) and treated wastewater uses (from 1.6 to 4.0 billion m3 month−1) can strongly reduce water scarcity levels and the number of people affected, especially in Asia, although the side effects (e.g. brine, energy demand, economic costs) must be considered. The presented results have potential for follow-up integrated analyses accounting for technical and economic constraints of expanding desalination and treated wastewater reuse across the world