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In the Netherlands, the peat soils degradation is estimated to contribute annually from 4.6 to 7 Mt CO2, representing around 3% of the annual national GHG emissions. Following the Paris Agreement, the Dutch government presented a national Climate agreement in 2019; reducing net CO2 emission of fen meadows by 1 Mt CO2 per year is part of the objectives for 2030. In order to comply with this, the Ministry of Agriculture, Nature and Food Quality set up a research consortium, The Dutch National Research Programme on Greenhouse Gases in Peatlands. The NOBV implemented an intensive GHG monitoring network mainly based on gas chambers, on-site and airborne Eddy-Covariance measurements. Mapping these emissions according to the diversity of peat, edaphic conditions, grassland management, and water table management is one of the challenges of this research programme.25 measurement sites are part of the NOBV Eddy-Covariance Network and are currently investigated using Mobile EC towers; using mobile EC towers instead of permanent ones is a pragmatic solution to embrace this site diversity. These mobile station set-ups include meteorological variables measurement, these alternate between closely located sites with different characteristics, assuming the meteorological variation is weak between both. Constructing annual GHG budgets requires a robust gap-filling method, able to operate for large gaps; the traditional gap-filling algorithms require long-term measurements, while this project occurs during a limited time window. These algorithms also usually fail at predicting fluxes after abrupt changes. By combining external data sources, remote-sensing and data mining, the objective is to decrease the uncertainties introduced by these gaps in a consistent way.More than time-series gap-filling, this approach provides site-specific data-driven Ecosystem Response Functions, it constitutes the first step to a bottom-up approach that will take into account more site-specific parameters. The interpretation of purely data-driven models is not as straightforward as process-based models, requiring the use of more ML-oriented tools, such as Shapley values. Another challenge is the partitioning of fluxes between the peat degradation-related emissions and the plant photosynthetic curves based on these data-driven models, highlighting the effect of external drivers such as soil moisture/temperature and water table depth.

As contractor and sub-contractor for C3S, SMHI and WU-DES have set up a multi-model operational service for hydrological seasonal forecasts. The service currently produces forecasts of monthly mean river discharge for a pan-European domain using several hydrological models, namely E-HYPEcatch, on irregular catchment delineations, and E-HYPEgrid and VIC-WUR on regular 5km gridded drainage network of EFAS. Also, the EFAS-Lisflood forecasts from the separate ECMWF production line are included in the final data set. All forecasts use the SEAS5 meteorological forecasts with 51 ensemble members. The data from all models are available from the CDS data portal for a re-forecast (https://doi.org/10.24381/cds.13c18212) and a forecast period (https://doi.org/10.24381/cds.52f45864). Further, the most probable forecast is displayed in an online application (https://cds.climate.copernicus.eu/cdsapp#!/software/app-hydrology-seasonal-forecast-explorer?tab=app).Ongoing work is aimed at introducing a second meteorological forecasting system, and adding more output variables at a higher daily temporal resolution. The quality of the forecasts is at this point mainly addressed by working on the input data and statistical downscaling and bias adjustment. The new system is expected to be operational in mid-2024.

Following the Paris Agreement (2015) that aims to limit climate warming, the Dutch government presented a National Climate Agreement in 2019. This agreement stated the overall ambition of reducing the national greenhouse gas emission by 49% in 2030 (compared to 1990) and allocates this reduction target to different sectors, such as industry, mobility, agriculture or land use. Within the latter sector, the peatland meadows are currently estimated to contribute ~4.6 to 7 Mton per year of CO2 to the national Dutch greenhouse gas emission. In the National Climate Agreement, the aim is to reduce the net CO2 emission from the peatland meadows with 1 Mton per year by 2030. To comply with the greenhouse gas emission reduction targets for peatlands, a set of measures that raise groundwater levels are currently being proposed and tested in pilots. The Dutch National Research Programme on Greenhouse Gas Emissions from Peat Meadows (NOBV) investigates the effects of the proposed measures on the greenhouse gas emission balance under different environmental conditions. In the National Climate Agreement, it was decided that annual progress made in reducing greenhouse gas emissions needs to be monitored. The NOBV consortium is developing a registration system for this monitoring and presents it current status and ideas for future development in this contribution.The registration system SOMERS (Subsurface Organic Matter Emission Registration System) is based on a multi-model ensemble approach. Using numerical models that simulate groundwater and carbon dynamics, the CO2 emission as a result of peat decomposition is calculated. Within SOMERS, existing models are supplemented by two newly developed models for assessing groundwater dynamics and peat decomposition, that require limited data input and have a short runtime. The new models simulate at parcel resolution and together are used to make a multi-model ensemble estimate of annual, national peatland greenhouse gas emissions since 2016 (the reference year). The new models are tested with annual carbon flux estimates. In the long run, we envisage to fully couple the modelling approach with the automated field measurements that are being collected in a new national measurement network.In this contribution, SOMERS will be introduced, and the calibration and validation approach will be discussed. We present predictions, under idealized average weather conditions, to establish effects of proposed mitigation measures. This directly serves policy development in regional spatial plans for the Dutch peatland meadows. Lastly, a first national peatland CO2 emission budget based on SOMERS is presented, which after some further development may support LULUCF-sector reporting in the Netherlands.

Drained organic soils are an important source of greenhouse gases worldwide. Also in the low lying areas of the Netherlands drainage of its organic soils, with the aim to enable more intensive usage of the land, leads to oxidation of organic material, significant carbon dioxide release and subsequent land subsidence. As part of climate mitigation policies the Dutch government seeks to reduce these emissions by about 25% in 2030. In support of these policies, the National Research programme on Greenhouse gas emissions from Fen meadow areas (in Dutch NOBV: https://www.nobveenweiden.nl/) aims to investigate the effects of various mitigation measures on total greenhouse gas balance of the targeted areas.One approach, complementing multi-site ground based measurements using various techniques, is to use repeated airborne surveys to measure in-situ turbulent CO2 exchange. The push propellor aircraft is a SkyArrow 650 TCNS equipped with a BAT probe in combination with a Licor 7500 for eddy covariance fluxes of momentum, sensible and latent heat and CO2, augmented by onboard PAR and net radiation sensors. Survey altitude is 200ft/60m nominally, guaranteeing minimal flux divergence between surface and flight level for well-developed boundary layers. Covariances were spatially integrated over 2 km.In 2020 and 2021 flights were made twice weekly, weather permitting, to cover three major fen meadow landscapes in the Netherlands: the so-called ‘Groene Hart’ area in the west between the cities of Amsterdam, Utrecht and Rotterdam, predominantly used for intensive dairy farming; the ‘Kop van Overijssel’ between Zwolle, Meppel and Vollenhove with large tracts of nature areas besides dairy farming; and finally the South West of the province of Fiesland between Heerenveen, Drachten and Sneek. Flight patterns were designed such that crosswind, parallel flight tracks, separated ~2km, made typical flux footprints overlapping ensuring full spatial coverage of the respective areas.We will present first analyses and scaling of airborne flux data for each of the three regions in relation to explanatory variables from vegetation and soil characteristics, land and water management (EO and map based) and weather, using machine learning algorithms. Source partitioning based on high frequency airborne covariance data will be used to separate soil and vegetation fluxes. We aim to ultimately provide a data driven regional greenhouse gas balances for the different fen meadow areas of the Netherlands.

Drylands in Africa, consisting of arid, semi-arid and dry sub-humid areas, are particularly vulnerable to land degradation, due to large climate variability and water scarcity, and land degradation is still largely present in these areas. On top of that, the number of people living in the drylands is expected to increase rapidly, especially in Sub-Saharan Africa, which further increases the number of people subject to land degradation in the future. On the bright side, land degradation in dryland Africa as well as in other parts of the world has not gone unnoticed and several restoration initivatives have emerged to reduce, reverse and prevent further degradation through practices such as reforestation, natural regeneration or agroforestry. Through these practices they aim to improve soil quality, contribute to carbon sequestration, improve the local climate and therefore the overall livelihood of the local people.In line with this development, the number of land restoration projects has increased rapidly over the past years. However, only a small part of the organisations monitor the trees after planting. On top of that, the organisations that do monitor the projects, often report small survival rates of the plants. In combination with the fact that a complete and open database of land restoration projects does, to our knowledge, not exist, there is a large lack of information on the amount, and effectiveness, of regreening after the implementation of these projects. This negatively affects much needed reflection on the effectiveness of land restoration projects.Remote sensing can be a practical alternative to detect greening due to land restoration, as vegetation indices like the NDVI are able to detect changes in vegetation greenness over large areas and long time series. Vegetation greenness does, however, not only change through land management, but also through processes such as CO2 fertilisation, nitrogen deposition, climate change and feedbacks between those, which makes it challenging to directly measure the greening effects of land restoration projects. The aim of this study is to detect regreening trends in semi-arid environments in Africa using remote sensing while correcting for natural climate variability.To this end, an analysis is performed in Google Earth engine, where MODIS NDVI 16-day time series are pixel-wise compared to a time series created by averaging the neighbourhood of the respective pixel. Because climate induced changes in NDVI are expected to act on a larger scale than changes in land management, subtracting the neighbourhood NDVI from the pixel NDVI corrects the time series for climate induced changes. Next, a BFAST algorithm is applied to the corrected time series to detect breakpoints and trends in NDVI. This method then allows for the detection of small scale greening hotspots across semi-arid Africa. In addition, the method is applied to several case study restoration projects in semi-arid Africa to illustrate the method on smaller scales.Preliminary results show that small scale regreening hotspots, i.e. increases in NDVI compared to the surrounding area, are more prominent in semi-arid environments than in humid and hyper-arid environments in Africa.

Urban emissions of N2O and CH4 may be an important part of their total anthropogenic emissions. In this study, we aimed to independently estimate the fluxes based on direct observations focusing on two urban regions. We developed a new active AirCore (~6 L) system that is able to continuously collect air samples aboard aircraft. The sampling can last 2.5 hours with a typical flow rate of 40 mL/min, and the spatial resolution dependent on diffusion in the tubing as well is ~ 1800 m with a typical flight speed of 40 m/s. Several flights were conducted with the new active AirCore aboard a SkyArrow aircraft over the Groningen and Utrecht regions in 2020 and 2021. During a few of those flights, both the active AirCore and a commercially available LICOR-7810 analyzer for high precision CH4 were flown together. The in situ LICOR CH4 measurements were used to optimize the AirCore retrieval algorithm. The optimized AirCore CH4 showed a high agreement with the in situ LICOR CH4 measurements (R2 = 0.9998). Furthermore, a mass balance approach was utilized to derive CH4 fluxes. The preliminary results show that the estimated CH4 emission rate from three flights over the Groningen region is 41±28 mol/s, much higher than the yearly average emission rate (3.3 mol/s) from the EDGARv6.0 inventory in 2018, and we localize one potential source to be southwest outside the Groningen city. The CH4 estimated emission rate from one flight over the Utrecht region is 30 mol/s, also higher than the EDGARv6.0 mean value 2.2 mol/s. Since the N2O signals are weak, we will explore whether it will be feasible to estimate the N2O emission rates based on these flights.

Natural peatlands and fen meadows have the potential to sequester CO2 from the atmosphere but can also form a major source of CH4 emissions. However, their flux dynamics, showing the diurnal and annual variation of GHG exchange depend on site characteristics such as soil/peat type, water dynamics andmanagement practices. It is thus essential, that carbon fluxes of different locations are individually quantified in order to assess if, from a climate perspective, CO2 uptake outweighs CH4 emission for these areas.We deployed five movable eddy covariance measurement stations to chart dynamics of CO2 and CH4 fluxes in an array of peat soil sites. The fluxes are measured directly, alternating every few weeks between the different sites. One aim of the study is to examine the feasibility of these moveable stations, as they may reduce the relatively high investment costs of EC measurements per site. We show that moveable stations are feasible from a practical point of view, as the stations can be relocated relatively easily within the time span of a few hour.The resulting carbon budgets provide insight into an array of site specific GHG exchanges over typically small temporal and spatial scales. Meteorological observations are permanently performed at all selected locations as well, along with other supportive measurements such as soil/water temperature, moisture and water level.Since the measurement stations alternate between locations, robust gap filling methods are needed to obtain a complete picture of the variability of the flux dynamics over the entire year for each location. The main objective of this study is to identify most suitable and robust gap filling methods. As suchmeasurements from the permanent meteorological stations serve to force several gap-filling methods such as interpolation based on observed ecosystem responses, the look up table approach and more established methods. We also investigate in the use of more process-based empirical models as the gaps between measurement periods are longer. Results show that the mobile eddy covariance approach does allowidentification of significant differences in GHG flux between sites as well as meaningful aggregation to annual budgets.Ultimately, enabling the monitoring at more locations than with static systems may serve as a basis for policy makers and land managers to shape nature conservation or agricultural practices that achieve a net mitigation of greenhouse warming potential.

Land-climate interactions are driven by causal relations that are difficult to ascertain given the complexity and high dimensionality of the systems. Many methods of statistical and mechanistic models exist to identify and quantify the causality in such highly-interacting systems. Recent advances in remote sensing development allowed people to investigate the land-climate interaction with spatially and temporally continuous data. In this study, we present a new approach to measure how climatic factors interact with each other under land cover change. The quantification method is based on the correlation analysis of the different order derivatives, with the canonical mathematical definitions developed from the theories of system dynamics and practices of the macroscopic observations. We examined the causal relationship between the interacting variables on both spatial and temporal dimensions based on macroscopic observations of land cover change and surface climatic factors through a comparative study in the different grassland ecosystems of China. The results suggested that the interaction of land-climate could be used to explain the temporal lag effect in the comparison of the three grassland ecosystems. Significant spatial correlations between the vegetation and the climatic factors confirmed feedback mechanisms described in the theories of eco-climatology, while the uncertain temporal synchronicity reflects the causality among the key indicators. This has been rarely addressed before. Our research show that spatial correlations and the temporal synchronicity among key indicators of the land surface and climatic factors can be explained by a novel method of causality quantification using derivative analysis.

There are many initiatives to re-wet drained nature or former agricultural land. These young wetlands provide a natural habitat for a range of endangered species, while serving as a natural climate buffer by retaining water, regulating air temperature, and sequestering CO2 from the atmosphere. However, wetlands may also emit CH4, which has a global warming potential (GWP) of about 30. Thus, all carbon fluxes need to be quantified in order to assess if, from a climate perspective, CO2 uptake outweighs CH4 emission.To assess the net effect of young wetlands on Greenhouse Gas exchange, we study the CO2 and CH4 fluxes of two recently rewetted areas near Groningen, the Netherlands. The fluxes are measured directly using the Eddy Covariance (EC) technique on a moveable station, alternating between the two sites. Meteorological observations are performed at these stations as well, along with other supportive measurements such as soil/water temperature. The alternating time gaps are filled by interpolation based on observed ecosystem responses. Footprint analysis provides insight into the role of various vegetation types inside these swamps. The resulting carbon budgets provide insight into GHG exchange over typically small temporal and spatial scales.The study also examines the feasibility of these moveable stations, as they may reduce the relatively high research costs of EC measurements. The data from moveable stations is reliable if the data is regular, as the time gaps are filled by interpolation. At this stage, the timeseries is too short to draw any conclusions upon the reliability of the data. However, the moveable stations appear to be feasible from a practical point of view, as the station can be relocated relatively easy within the time span of a day.The first results suggest both substantial CO2 uptake and CH4 emissions but a full year of data was not collected yet. Observed exchange compares well to similar studies previously performed. Ultimately, annual budgets of the carbon exchange response will be correlated to weather conditions but also to hydrological measures such as water levels. This should allow extrapolation of the data, which may serve as a basis for policy makers to manage the carbon balance when re-wetting nature to achieve net mitigation of greenhouse warming potential.

Forest soils are often a sink for atmospheric methane (CH4) and are thus worth special attention in the context of mitigation of greenhouse gases (GHGs) and offset of agricultural GHG emissions at farm to national levels. The litter layer influences the exchange of GHGs between soil and atmosphere; however, most studies focus on the contribution of only soil to the CH4 cycle. In order to improve the inventory of this gas, it is worth investigating how litter influences the exchange of GHGs. Its effect on CH4 uptake may vary in deciduous and coniferous sites due to the different properties of litter. Field experiments were carried out to assess the CH4 uptake capability in 5 different soil types (with and without litter) under different forest types (deciduous, coniferous, and mixed) in Poland. During summer 2019, the highest CH4 uptake (about 2 mg C m-2 day-1) in a variant without litter on the ground was detected in Dystric Cambisol (with the highest C/N ratio) under a 100-year-old coniferous forest and in Albic Luvisol under a 58-year-old mixed forest. The presence of the litter level reduced the CH4 flux in the range of 6-27% in these locations. Methane consumption was the lowest in silty soils (~ 0.4 – 1 mg C m-2 day-1) in the mixed forest and decreased by 13-29% when covered with the litter layer. The negative effect of the litter layer on CH4 absorption was the lowest (~ 3-4%) in sandy Eutric Gleysol under a 75-year-old deciduous forest with 90% of oak and 10% of European hornbeam. The dry conditions in the summer 2019 (with total rainfall 163 mm during the tested months in the studied region) resulted in low moisture in both the litter and soil. However, even low-humidity litter (below 10%) reduced CH4 consumption rates in the measured sites.Research was partially conducted under the project financed by Polish National Centre for Research and Development within of ERA-NET CO-FUND ERA-GAS Programme (ERA-GAS/I/GHG-MANAGE/01/2018).

This contribution deals with the skill of a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to South America and earlier to Europe (see Greuell et al., hess-23-371-2019). The system basically consists of the Variable Infiltration Capacity (VIC) hydrological model forced with output from ECMWF’s Seasonal Forecasting System 5 (SEAS5). We analyse skill in runoff and discharge hindcasts both with real observations and with so-called pseudo-observations, i.e. with discharge data generated with VIC forced with historical meteorological observations (1981-2015). At the continental scale discrimination skill in runoff shows characteristics that are similar to Europe. Especially, even at the longest lead time (7 months) significant skill remains in 20-30% of both continents. However, in the first lead month there is less significant skill in South America, due to absence of skill in its very dry and very wet regions, than in Europe, where similar extremes do not exist. To explain the skill in runoff, we performed two suites of specific hydrological hindcasts akin to Ensemble Streamflow Predictions (ESP), which each isolate a different source of skill (meteorological forcing and initial conditions). We find that in South America the contribution to skill by forcing is larger than in Europe, which can be ascribed to differences in the skill in the precipitation forcing. Even at a lead time of 7 months, the precipitation hindcasts have significant skill in 15-30% of South America while in Europe skill is almost confined to the first lead month. Discharge hindcasts for grid cells with a sufficient amount of observations were post-processed with ensemble model output statistics (EMOS). This procedure successfully increased reliability but resulted in a small decrease of discrimination skill. Nevertheless, for the location of the Itaipu dam, used to produce 18% of Brazil’s electricity, discrimination skill is highly significant for the post-processed discharge, e.g. at all lead times in the last two months of the year.

With the increasing need to mitigate rising atmospheric greenhouse gas (GHG) concentrations more attention is being directed at the quantification of the GHG exchange characteristics of heterogenous landscape assemblages that vary in land cover and land use. Whilst emission-limiting or uptake-enhancing management actions are often being proposed for specific land use most remain to be experimentally tested and validated at the landscape scale. This is a challenge because the typical size of different landscape elements (fields, afforested areas and unmanaged land at hectare scale) or experimental fields where emission reduction measures are being tested, is at the lower limit of what micrometeorological techniques such as eddy covariance measurements can deal with. With large heterogeneity the use of chamber measurements is also limited. The investments to be made in equipment are a challenge for operational monitoring of GHG budgets.To address this we assess the feasibility of several options to acquire appropriate data in a way that is achievable for stakeholders, such as land managers and regional authorities. We use existing and new flux data from an agricultural landscape in the North of the Netherlands to: 1) compare paired eddy covariance (EC) data and automatic chamber (AC) data to test the representativity of small footprints. Results from a test site on drained meadows show almost identical CO2 fluxes. Future research should compare grass length and soil moisture of EC- and AC footprints; 2) test simplified alternatives to EC, such as those relying on concentration variances. Data from the peat meadow site suggest that time-averaged fluxes can be estimated in an empirical way with reasonable accuracy from concentration variances; 3) analyze the value of information gathered with mobile, roving/temporary EC approaches interpolated with gap filling models. The indications are that the values and variability of fluxes is largely conserved and predictable within seasons In all these analyses, we will consider the tradeoffs between the need for accuracy and pragmatism in operational practice.

Climate change and socio-economic development increase variations in water availability and water use in the Pearl River Basin (PRB), China. This can potentially result in conflicts over water resources between water users, and cause water shortage in the dry season. To assess and manage water shortage in the PRB, we first explored two water availability and three water use scenarios. Next, four different strategies to allocate water were defined. These water allocation strategies prioritized upstream water use, Pearl River Delta water use, irrigation water use, and manufacturing water use, respectively. The impact of the four strategies on water use and related economic output was assessed under different water availability and water use scenarios. Results show that almost all the regions in the PRB are likely to face water shortage under the four strategies. The increasing water demand contributes twice as much as the decreasing water availability to water shortage. All four water allocation strategies are insufficient to solve the water scarcity in the PRB. The economic losses differ greatly under the four water allocation strategies. Prioritizing the delta region or manufacturing production would result in lower economic losses than the other two strategies. However, all of them are rather extreme strategies. Development of water resources management strategies requires a compromise between different water users.

This study evaluates the potential use of the ECMWF System-4 seasonal forecasts (S4) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15-member ensemble of 7-month forecasts initiated every month, and tested forecast skill of precipitation (tp), near-surface air temperature (tas) and surface downwelling shortwave radiation (rsds). We validated the 30-year (1981–2010) hindcast version of S4 against the WFDEI reanalysis (WATCH Forcing Data ERA-Interim) and to independent relevant observational data sets. Probabilistic skill is assessed using anomaly correlation, ranked probability skill score (RPSS) and the relative operating curve skill score (ROCSS) at both grid cell and over six distinct homogeneous rainfall regions for the three growing seasons of East Africa (i.e. MAM, JJA and OND). S4 exhibits a wet bias in OND, a dry bias in MAM and a mix of both in JJA. Temperature biases are similar in all seasons, constant with lead-time and correlate with elevation. Biases in rsds correlate with cloud/rain patterns. Bias correction clears biases but does not affect probabilistic skills. Predictability of the three variables varies with season, location and lead-time. The choice of validating dataset plays little role in the regional patterns and magnitudes of probabilistic skill scores. The OND tp forecasts show skill over a larger area up to 3 months lead-time compared to MAM and JJA. Upper- and lower-tercile tp forecasts are 20–80% better than climatology. Temperature forecasts are skillful for at least 3 months lead-time and they are 40–100% better than climatology. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher in all lead months against the alternative datasets. The forecast system captures El-Nino Southern Oscillation (ENSO)-related anomalous years with region-dependent skill.

This paper uses hindcasts (1981–2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mainly of noise which enhances degradation of the skill.

Climate change and climate variability pose high risks to the agri-food business as it threatens ecosystems and impacts the natural capital on which companies depend. Agri-food companies have already suffered weather–related impacts in recent years, and most have seen an intensification of such impacts. Meanwhile, farming- and other communities on which the agri-food businesses depend for their supplies of agricultural commodities, raw materials and work force are also highly affected. Recognizing the problem, identifying climate related risks in the supply chain and responding with adaptation measures can help agri-food businesses to minimize their risks and strengthen resilience. Responding to the effects of climate change will also provide opportunities for innovative climate resilient products and services and open up new markets. For example, insurance agencies are already developing innovative climate-insurance products for communities at increased risk of weather-related natural disasters. New water-use efficient irrigation technologies are being developed and deployed to address increased water stress. This report describes a set of services WEnR can deliver to assist stakeholders in an agri-food chain to assess climatic risks and potential adaptation strategies to mitigate these risks. These services include development of regionalised / bias corrected climate change projections, climate change impact assessments, assessment of climate /weather related risks, assessment of potential business cases mitigating climatic risks. The services i.e. climate risk assessment described in this report are current being delivered in the Climate Smart Agriculture - East Africa project, but do have wider application opportunities as being described in the last chapter. Some of the climate risk activities are being carried out by WEnR, others by project partners.---Dit rapport beschrijft een aanpak en services die geleverd kunnen worden door medewerkers van Wageningen Environmental Research om partijen binnen agrifood ketens te helpen inzicht te krijgen in risico’s die hun business loopt als gevolg van klimaatverandering en hoe men deze risico’s zou kunnen verminderen en/of vermijden. De diensten die WEnR kan leveren omvatten: de ontwikkeling en visualisatie van klimaatprojecties voor specifieke regio’s, het bepalen van effecten van klimaatverandering op water beschikbaarheid en gewasproductie en het analyseren van risico’s voor ketenpartijen en hun business als gevolg van klimaatverandering. Ook kan WEnR helpen bij het identificeren en analyseren van businessideeën die klimaatrisico’s kunnen verminderen, maar die ook nieuwe kansen kunnen bieden. De ‘climate risk assessment’ aanpak die in dit rapport wordt beschreven wordt op dit moment toegepast in het Climate Smart Africa – East Africa project waarin WEnR participeert. In dit project worden sommige zogenaamde ‘climate risk assesssment activiteiten’ uitgevoerd door WEnR, anderen door projectpartners. Dit rapport beschrijft een aanpak en services die geleverd kunnen worden door medewerkers van Wageningen Environmental Research om partijen binnen agrifood ketens te helpen inzicht te krijgen in risico’s die hun business loopt als gevolg van klimaatverandering en hoe men deze risico’s zou kunnen verminderen en/of vermijden. De diensten die WEnR kan leveren omvatten: de ontwikkeling en visualisatie van klimaatprojecties voor specifieke regio’s, het bepalen van effecten van klimaatverandering op water beschikbaarheid en gewasproductie en het analyseren van risico’s voor ketenpartijen en hun business als gevolg van klimaatverandering. Ook kan WEnR helpen bij het identificeren en analyseren van businessideeën die klimaatrisico’s kunnen verminderen, maar die ook nieuwe kansen kunnen bieden. De ‘climate risk assessment’ aanpak die in dit rapport wordt beschreven wordt op dit moment toegepast in het Climate Smart Africa – East Africa project waarin WEnR participeert. In dit project worden sommige zogenaamde ‘climate risk assesssment activiteiten’ uitgevoerd door WEnR, anderen door projectpartners.

Land Surface Models which determine evapotranspiration (ET) by neglecting the sub-grid heterogeneity of land-atmosphere parameters will cause aggregation biases in spatially-averaged ET estimates, considering the nonlinear dependences of ET on the heterogeneous land-atmosphere parameters. One frequently adopted strategy clusters the heterogeneous surface within a model grid into several tiles, assumed to be homogeneous, usually based on high-resolution land cover data. While the differences in bulk-averaged parameters between different tiles are considered, the heterogeneity within each tile is neglected. This study evaluated in detail the aggregation biases in the tile mean ET estimates due to applying bulk-averages of the Saturation degree of surface Soil Moisture (SSM) and Leaf Area Index (LAI) for each tile through numerical experiments. Four types of Probability Distribution Function (PDF) were used to simulate different scenarios on the heterogeneity (within a tile) of SSM and LAI, i.e., from water scarcity to wet, and from sparse to dense vegetation covered surfaces. Aggregation bias was calculated by comparing ET estimates based on bulk-averaged SSM and LAI with the one obtained by aggregation of the flux estimates based on the PDFs, which complies with energy conservation. In addition, a wide range of meteorological conditions was applied in our numerical experiments and the impacts were evaluated by binning results according to the reference ET (ET0). We found that potentially significant bias can be found in semi-arid areas. Neglecting the actual spatial variability of both SSM and LAI within tiles can lead to both large relative error (> 20%) and absolute error (> 1 mm/day) in the estimated ET. A negative bias is expected at low ET / ET0 and a positive bias is expected at large ET / ET0, regardless of climate conditions (i.e., ET0).

The conversion of primary forest (PF) to other types of land cover, such as pasture and agriculture, in Amazonia, affects regional carbon and water balances, significantly contributing to increased carbon emissions and reduced evapotranspiration. However, secondary forest (SF) growth, resulting from the abandonment of low-productivity pasture areas, offers a potential alternative to counterbalance the effects of deforestation on carbon release to the atmosphere and evapotranspiration reduction. In this work, we present four years of eddy flux measurements of a SF that is approximately 20 years old, located in Central Amazonia, and we compare these measurements with those of a PF in the same region, analyzing daily and seasonal variations in evapotranspiration, gross primary productivity of carbon and water use efficiency. On average, evapotranspiration is 20% higher in the SF (3.6 mm day−1) than in the PF (3.1 mm day−1), while gross primary productivity is only 5% higher in the SF (8.1 gC m−2 day−1) than in the PF (7.8 gC m−2 day−1). Despite robust evidence of higher evapotranspiration and gross primary productivity in SF, the estimated uncertainty range of WUE is large to reach definite conclusions about the differences on carbon gain per water loss between the sites. Nonetheless, the significantly higher evapotranspiration and gross primary productivity of SF may counterbalance both water and C losses from deforestation and has important implications for regional budgets.

To better understand and manage climate risks in climate-sensitive sectors such as agriculture, it is essential to have access to consistent and reliable data and information products. Tailoring these products to the needs of the users they want to serve facilitate informed decision-making and downstream applications. This requires an in-depth understanding of users' needs and the context in which these users operate. Considering the diversity of the economic sectors and their actors it is extremely challenging if not outright impossible to promote the emergence of climate services without empowering a plethora of intermediate users who can act as one of the steps in a potential long knowledge brokers chain that connect the climate data providers and the end-users. In this context, Copernicus Climate Change Service (C3S) has been designed around the Climate Data Store (CDS), a unique entry point to a huge variety of quality-controlled climate data and high-level utilities to process that data to develop user-driven applications. Through the Sectoral Information System, C3S has then developed a series of sector specific applications, which show how the infrastructure can be used to address specific users’ needs. This paper presents the key elements of the CDS and selected cases of sectoral application of C3S in agriculture. Keywords: Copernicus Climate Change Services (C3S), Climate Data Store (CDS), User-driven applications, Agriculture sector

The Naga Foundation aims to implement durable re-greening interventions to increase local soil sustainability and regional water availability. When this is done on a large enough scale such landscape changes may also lead to positive regional climate impacts. Naga is developing a plan to re-green 15 large areas in Eastern Africa, creating a so-called hydrological corridor. Four potential hydrological corridors have been identified in Kenya and Tanzania, all four of them around Mount Kilimanjaro. To select the most promising corridor, a method was developed to support a decision in a situation where few data are available. The method is based on maps, models and literature from four different disciplines concerning soil, water, climate and social institutions. The findings favour the Tanzanian corridors and especially the Tanzania-East one, to start with re-greening projects. In that region many applicable land management options combine with a high potential for restoring soil organic matter, the highest rainfall recycling potential exists in the more favourable long rains season, while finally also the Tanzanian governments both at national and at local level seem more dependable for supporting hydrological corridor implementation.

We tested the usefulness of seasonal climate predictions for impacts prediction in eastern Africa. In regions where these seasonal predictions showed skill we tested if the skill also translated into maize yield forecasting skills. Using European Centre for Medium-Range Weather Forecasts (ECMWF) system-4 ensemble seasonal climate hindcasts for the period 1981–2010 at different initialization dates before sowing, we generated a 15-member ensemble of yield predictions using the World Food Studies (WOFOST) crop model implemented for water-limited maize production and single season simulation. Maize yield predictions are validated against reference yield simulations using the WATCH Forcing Data ERA-Interim (WFDEI), focussing on the dominant sowing dates in the northern region (July), equatorial region (March-April) and in the southern region (December). These reference yields show good anomaly correlations compared to the official FAO and national reported statistics, but the average reference yield values are lower than those reported in Kenya and Ethiopia, but slightly higher in Tanzania. We use the ensemble mean, interannual variability, mean errors, Ranked Probability Skill Score (RPSS) and Relative Operating Curve skill Score (ROCSS) to assess regions of useful probabilistic prediction. Annual yield anomalies are predictable 2-months before sowing in most of the regions. Difference in interannual variability between the reference and predicted yields range from ±40%, but higher interannual variability in predicted yield dominates. Anomaly correlations between the reference and predicted yields are largely positive and range from +0.3 to +0.6. The ROCSS illustrate good pre-season probabilistic prediction of above-normal and below-normal yields with at least 2-months lead time. From the sample sowing dates considered, we concluded that, there is potential to use dynamical seasonal climate forecasts with a process based crop simulation model WOFOST to predict anomalous water-limited maize yields.

Expansion of urban areas has profound effects on land surface characteristics. As such, the land surface can exert influence on atmospheric parameters that might alter precipitation amounts or patterns. In this study, precipitation observations near urban areas along the West coast of the Netherlands are investigated throughout the 1951–2010 period. An innovative analysis methodology is used to deal with the small and fragmented urban areas in the Netherlands. The results show that daily precipitation totals downwind of urban areas are, on average, about 7% higher than precipitation in the rest of the Dutch West coast. Precipitation enhancements up to 20% are found depending on wind direction and time period. These results are comparable with studies from around the globe and show that the influence of relatively small fragmented urban areas, as are present in the Netherlands, can be similar to the influence of large metropolitan areas on precipitation.

Most land surface models require information on aerodynamic roughness length and its temporal and spatial variability. This research presents a practical approach for determining the aerodynamic roughness length at fine temporal and spatial resolution over the landscape by combining remote sensing and ground measurements. The basic framework of Raupach, with the bulk surface parameters redefined by Jasinski et al., has been applied to optical remote sensing data collected by the HJ-1A/1B satellites. In addition, a method for estimating vegetation height was introduced to derive the aerodynamic roughness length, which is preferred by users over the height-normalized form. Finally, mapping different vegetation classes was validated taking advantage of the data-dense field experiments conducted in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. Overall, the roughness model performed well against the measurements collected at most HiWATER flux tower sites. However, deviations still occurred at some sites, which have been further analyzed.

Bolivia's forests contribute to the global carbon and water cycle, as well as to global biodiversity. The survival of these forests may be at risk due to climate change. To explore the associated mechanisms and uncertainties, a regionally adapted dynamic vegetation model was implemented for the Bolivian case, and forced with two contrasting climate change projections. Changes in carbon stocks and fluxes were evaluated, factoring out the individual contributions of atmospheric carbon dioxide ([CO2]), temperature, and precipitation. Impacts ranged from a strong increase to a severe loss of vegetation carbon (cv), depending on differences in climate projections, as well as the physiological response to rising [CO2]. The loss of cv simulated for an extremely dry projection was primarily driven by a reduction in gross primary productivity, and secondarily by enhanced emissions from fires and autotrophic respiration. In the wet forest, less precipitation and higher temperatures equally reduced cv, while in the dry forest, the impact of precipitation was dominating. The temperature-related reduction of cv was mainly due to a decrease in photosynthesis and only to lesser extent because of more autotrophic respiration and less stomatal conductance as a response to an increasing atmospheric evaporative demand. Under an extremely dry projection, tropical dry forests were simulated to virtually disappear, regardless of the potential fertilizing effect of rising [CO2]. This suggests a higher risk for forest loss along the drier southern fringe of the Amazon if annual precipitation will decrease substantially.

Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the second of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. Whereas the first paper presents the development and the skill evaluation of the system, this paper provides explanations for the skill. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with meteorological forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4 (S4). WUSHP is probabilistic. For the assessment of skill, hindcast simulations (1981–2010) were carried out. To explain skill, we first looked at the forcing and found considerable skill in the precipitation forecasts of the first lead month but hardly any significant skill for later lead months. Seasonal forecasts for temperature have more skill. Skill in summer temperature is related to climate change and more or less independent of lead time. Skill in February and March is unrelated to climate change. Sources of skill in runoff were isolated with Ensemble Streamflow Prediction (ESP) experiments. These revealed that beyond the second lead month simulations with forcing that is identical for all years (ESPall) produce more skill in runoff than the simulations forced with S4 output (Full Hindcasts). This occurs because interannual variability of the S4 forcing has insufficient skill while it adds noise. Other ESP-experiments show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, at the end of the melt season, initial conditions of snow contribute significantly to the skill, also when forecasts start much earlier. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Finally, predictability of evapotranspiration was analysed in some detail, leading among others to the conclusion that it is due to all potential sources of skill but mostly to forcing.

Significant increases in precipitation have been observed in The Netherlands over the last century. At the same time persistent spatial variations are apparent. The objective of this study is to analyse and explain these spatial patterns, focussing on changes in means and extremes for the period 1951–2009. To investigate different possibilities for the causes of spatial variations, a distinction was made between six regions based on mean precipitation, soil type and elevation, and four zones at different distances to the coast. Spatial maxima in mean precipitation inland and over elevated areas are mainly formed in winter and spring, while maxima along the coast are generated in autumn. Daily precipitation maxima are found in the central West coast and over elevated areas. Upward trends in daily precipitation are highest from February to April and lowest from July to September. The strongest and most significant increases are found along the coast. For several seasonal and climatological periods diverging behaviour between coastal and inland zones is observed. We find that distance to the coast gives a more consistent picture for the seasonal precipitation changes than a classification based on surface characteristics. Therefore, from the investigated surface factors, we consider sea surface temperature to have the largest influence on precipitation in The Netherlands.

Bolivia is facing numerous climate-related threats, ranging from water scarcity due to rapidly retreating glaciers in the Andes to a partial loss of the Amazon forest in the lowlands. To assess what changes in climate may be expected in the future, 35 global circulation models (GCMs) from the third and fifth phases of the Coupled Model Intercomparison Project (CMIP3/5) were analyzed for the Bolivian case. GCMs were validated against observed surface air temperature, precipitation, and incoming shortwave (SW) radiation for the period 1961–90. Weighted ensembles were developed, and climate change projections for five emission scenarios were assessed for 2070–99. GCMs revealed an overall cold, wet, and positive-SW-radiation bias and showed no substantial improvement from the CMIP3 to the CMIP5 ensemble for the Bolivian case. Models projected an increase in temperature (2.5°–5.9°C) and SW radiation (1%–5%), with seasonal and regional differences. In the lowlands, changes in annual rainfall remained uncertain for CMIP3 whereas CMIP5 GCMs were more inclined to project decreases (−9%). This pattern also applied to most of the Amazon basin, suggesting a higher risk of partial biomass loss for the CMIP5 ensemble. Both ensembles agreed on less rainfall (−19%) during drier months (June–August and September–November), with significant changes in interannual rainfall variability, but disagreed on changes during wetter months (January–March). In the Andes, CMIP3 GCMs tended toward less rainfall (−9%) whereas CMIP5 tended toward more (+20%) rainfall during parts of the wet season. The findings presented here may provide inputs for studies of climate change impact that assess how resilient human and natural systems are under different climate change scenarios.

The relative contribution of topography and land use on precipitation is analyzed in this paper for a forested area in the Netherlands. This area has an average yearly precipitation sum that can be 75–100 mm higher than the rest of the country. To analyze this contribution, different configurations of land use and topography are fed into a mesoscale model. The authors use the Regional Atmospheric Modeling System (RAMS) coupled with a land surface scheme simulating water vapor, heat, and momentum fluxes [Soil–Water–Atmosphere Plant System–Carbon (SWAPS-C)]. The model simulations are executed for two periods that cover varying large-scale synoptic conditions of summer and winter periods. The output of the experiments leads to the conclusion that the precipitation maximum at the Veluwe is forced by topography and land use. The effect of the forested area on the processes that influence precipitation is smaller in summertime conditions when the precipitation has a convective character. In frontal conditions, the forest has a more pronounced effect on local precipitation through the convergence of moisture. The effect of topography on monthly domain-averaged precipitation around the Veluwe is a 17% increase in the winter and a 10% increase in the summer, which is quite remarkable for topography with a maximum elevation of just above 100 m and moderate steepness. From this study, it appears that the version of RAMS using Mellor–Yamada turbulence parameterization simulates precipitation better in wintertime, but the configuration with the medium-range forecast (MRF) turbulence parameterization improves the simulation of precipitation in convective circumstances.

Urbanizing delta regions face seasonal water shortages induced by rising salt intrusion. Decreasing river discharge is readily listed as the major cause of water shortage events. Yet, observations of river discharge often fail to support this attribution. Evidence of the association between severe salt intrusion and water use is weak and inconclusive. The present study asks to what extent water use contributes to salt intrusion and freshwater shortages. Moreover, it asks whether management of water use rather than water supply can be part of mitigating salt intrusion. The contribution of water use in causing severe salt intrusion events is assessed by first quantifying monthly sectorial water use and next comparing it with threshold discharges from the graded salt intrusion warning system. The case study region is the Pearl River Delta, China. Sectorial water use is found to substantially vary between months. In particular in the dry month in which water shortages are reported, water use can be more than 25% of discharge and thus exacerbates salt intrusion. Evaluation of coping strategies shows that improved water use can alleviate salt intrusion by up to one level in the warning system, thus preventing problems at a number of water abstraction points.

The effects of historic and future land use on precipitation in the Netherlands are investigated on 18 summer days with similar meteorological conditions. The days are selected with a circulation type classification and a clustering procedure to obtain a homogenous set of days that is expected to favor land impacts. Changes in precipitation are investigated in relation to the present-day climate and land use, and from the perspective of future climate and land use. To that end, the weather research and forecasting (WRF) model is used with land use maps for 1900, 2000, and 2040. In addition, a temperature perturbation of +1 °C assuming constant relative humidity is imposed as a surrogate climate change scenario. Decreases in precipitation of, respectively, 3–5 and 2–5 % are simulated following conversion of historic to present, and present to future, land use. The temperature perturbation under present land use conditions increases precipitation amounts by on average 7–8 % and amplifies precipitation intensity. However, when also considering future land use, the increase is reduced to 2–6 % on average, and no intensification of extreme precipitation is simulated. In all, the simulated effects of land use changes on precipitation in summer are smaller than the effects of climate change, but are not negligible.

Considering the high rates of evapotranspiration of Amazonian forests, understanding the impacts of deforestation on water loss rates is important for assessing those impacts on a regional and global scale. This paper quantifies evapotranspiration rates in two different pasture sites in Amazonia and evaluates the differences between the sites. In both places, measured evapotranspiration varies seasonally, decreasing during the dry season. The decrease is higher at the southwestern Amazonia site, while at the central Amazonia site, the decrease is less pronounced. During the dry season, average values of evapotranspiration are around 2.2 ± 0.6 mm day−1 in central Amazonia and 2.4 ± 0.6 mm day−1 in southwestern Amazonia, while during the wet season, those values are 2.1 ± 0.6 mm day−1 in central Amazonia and 3.5 ± 0.8 mm day−1 in southwestern Amazonia. On an annual basis, the pasture in southwestern Amazonia has higher evapotranspiration than in central Amazonia. We conclude that the main reason for this difference is the lower available energy in the wet season at the central Amazonian site, combined with a lower leaf area index at this site during the whole year. Still, the evapotranspiration is significantly controlled by the vegetation, which is well coupled with the local moisture conditions in the dry season.

Terrestrial gross primary production (GPP) is an important parameter to explore and quantify carbon fixation by plant ecosystems at various scales. Remote sensing (RS) offers a unique possibility to investigate GPP in a spatially explicit fashion; however, budgeting of terrestrial carbon cycles based on this approach still remains uncertain. To improve calculations, spatio-temporal variability of GPP must be investigated in more detail on local and regional scales. The overarching goal of this study is to enhance our knowledge on how environmentally induced changes of photosynthetic light-use efficiency (LUE) are linked with optical RS parameters. Diurnal courses of sun-induced fluorescence yield (FSyield) and the photochemical reflectance index of corn were derived from high-resolution spectrometric measurements and their potential as proxies for LUE was investigated. GPP was modeled using Monteith’s LUE-concept and optical-based GPP and LUE values were compared with synoptically acquired eddy covariance data. It is shown that the diurnal response of complex physiological regulation of photosynthesis can be tracked reliably with the sun-induced fluorescence. Considering structural and physiological effects, this research shows for the first time that including suninduced fluorescence into modeling approaches improves their results in predicting diurnal courses of GPP. Our results support the hypothesis that air- or spaceborne quantification of sun-induced fluorescence yield may become a powerful tool to better understand spatio-temporal variations of fluorescence yield, photosynthetic efficiency and plant stress on a global scale. Nomenclature

According to general consensus, the global climate is changing, which may also affect agricultural and livestock production. The potential impact of climate change on food security is a widely debated and investigated issue. Nonetheless, the specific impact on safety of food and feed for consumers has remained a less studied topic. This review therefore identifies the various food safety issues that are likely to be affected by changes in climate, particularly in Europe. Amongst the issues identified are mycotoxins formed on plant products in the field or during storage; residues of pesticides in plant products affected by changes in pest pressure; trace elements and/or heavy metals in plant products depending on changes in their abundance and availability in soils; polycyclic aromatic hydrocarbons in foods following changes in long-range atmospheric transport and deposition into the environment; marine biotoxins in seafood following production of phycotoxins by harmful algal blooms; and the presence of pathogenic bacteria in foods following more frequent extreme weather conditions, such as flooding and heat waves. Research topics that are amenable to further research are highlighted.

Atmospheric CO2 modeling in interaction with the surface fluxes, at the regional scale is developed within the frame of the European project CarboEurope-IP and its Regional Experiment component. In this context, five meso-scale meteorological models at 2 km resolution participate in an intercomparison exercise. Using a common experimental protocol that imposes a large number of rules, two days of the CarboEurope Regional Experiment Strategy (CERES) campaign are simulated. A systematic evaluation of the models is done in confrontation with the observations, using statistical tools and direct comparisons. Thus, temperature and relative humidity at 2 m, wind direction, surface energy and CO2 fluxes, vertical profiles of potential temperature as well as in-situ CO2 concentrations comparisons between observations and simulations are examined. These comparisons reveal a cold bias in the simulated temperature at 2 m, the latent heat flux is often underestimated. Nevertheless, the CO2 concentrations heterogeneities are well captured by most of the models. This intercomparison exercise shows also the models ability to represent the meteorology and carbon cycling at the synoptic and regional scale in the boundary layer, but also points out some of the major shortcomings of the models.

Measurements of regional net ecosystem exchange (NEE) were made over a period of 21 days in summer 2002 in the South-Central part of the Netherlands and extrapolated to an area of 13 000 km2 using a combination of flux measurements made by a Sky Arrow ERA research aircraft, half-hourly eddy covariance data from four towers, half-hourly weather data recorded by three weather stations and detailed information on regional land use. Measurements of regional net ecosystem exchange (NEE) were made over a period of 21 days in summer 2002 in the South-Central part of the Netherlands and extrapolated to an area of 13 000 km(2) using a combination of flux measurements made by a Sky Arrow ERA research aircraft, half-hourly eddy covariance data from four towers, half-hourly weather data recorded by three weather stations and detailed information on regional land use. The combination of this type of information allowed to estimate the net contribution of the terrestrial ecosystems to the overall regional carbon flux and to map dynamically the temporal and spatial variability of the fluxes. A regional carbon budget was calculated for the study period and the contributions of the different land uses to the overall regional flux, were assessed. Ecosystems were, overall, a small source of carbon to the atmosphere equivalent to to 0.23 +/- 0.025 g C m(-2) day(-1). When considered separately, arable and grasslands were a source of, respectively, 0.68 +/- 0.022 and 1.28 +/- 0.026 g C m(-2) day(-1). Evergreen and deciduous forests were instead a sink of -1.42 +/- 0.015 g C m(-2) day(-1). During the study period, forests offset approximately 3.5% of anthropogenic carbon emission estimates obtained from inventory data. Lacking of a robust validation, NEE values obtained with this method were compared with independent state of art estimates of the regional carbon balance that were obtained by applying a semi-empirical model of NEE driven by MODIS satellite fAPAR data. The comparison showed an acceptable matching for the carbon balance of forest that was a sink in both cases, while a much larger difference for arable and grassland was found. Those ecosystems were a sink for satellite-based estimates while they were a source for the combined aircraft and tower estimates. Possible causes of such differences are discussed and partly addressed. The importance of new methods for determining carbon balance at the regional scale, is outlined.

In this paper, the Weather Research and Forecasting (WRF) Model is used to investigate the sensitivity of precipitation to soil moisture and urban areas in the Netherlands. The average output of a 4-day event during 10–13 May 1999 for which the individual days had similar synoptical forcing is analyzed. Four simulations are conducted to test the impact of soil moisture changes on precipitation. A positive soil moisture–precipitation feedback is found, that is, wet (dry) soils increase (decrease) the amount of precipitation. Three additional experiments are executed, two in which urban areas in the Netherlands are expanded and one where urban areas are completely removed. Expansion of urban areas results in an increase of the sensible heat flux and a deeper planetary boundary layer, similar to reducing soil moisture. Expanding urban areas reduces precipitation over the Netherlands as a whole, but the local response is not clear. Within existing urban areas, mean and maximum temperature increases of 0.4 and 2 K, respectively, are found under an urban coverage scenario for 2040. The ratio of evapotranspiration to precipitation (a measure of the soil moisture–precipitation feedback) in the urbanization experiments is only about one-third (23%) of that in the soil moisture experiments (67%). Triggering of precipitation, on the other hand, is relatively high in the urban expansion experiments. The effects of reduced moisture availability and enhanced triggering in the urban expansion experiments compensate each other, leading to the moderate reduction in precipitation.

Assessing and managing water use is crucial for supporting sustainable river basin management and regional development. The first consistent and comprehensive assessment of sectorial water use in the Pearl River Delta (PRD) is presented by analysing homogenized annual water use data from 2000 to 2010 in relation to socio economic statistics for the same period. An abstraction of water use, using the concept of water use intensity, and based on equations inspired by those used in global water resource models, is developed to explore the driving forces underlying water use changes in domestic, industrial and agricultural sectors. We do this at both the level of the region as a whole, as well as for the nine cities that constitute the PRD separately. We find that, despite strong population and economic growth, the PRD managed to stabilize its absolute water use by significant improvements in industrial water use intensities, and early stabilisation of domestic water use intensities. Results reveal large internal differentiation of sectorial water use among the cities in this region, with industrial water use intensity varying from -80 to +95% and domestic water use itensity by +/- 30% compared to the PRD average. In general, per capita water use is highest in the cities that industrialised first. Yet, all cities except Guangzhou are expected to approach a saturation value of per capita water use much below what is suggested in recent global studies. Therefore, existing global assessments probably have overestimated future domestic water use in developing countries. Although scarce and uncertain input data and model limitations lead to a high level of uncertainty, the presented conceptualization of water use is useful in exploring the underlying driving forces of water use trends.

On continental to regional scales feedbacks between landuse and landcover change and climate have been widely documented over the past 10–15 years. In the present study we explore the possibility that also vegetation changes over much smaller areas may affect local precipitation regimes. Large scale (∼ 105 ha) irrigated plantations in semi-arid environments under particular conditions may affect local circulations and induce additional rainfall. Capturing this rainfall ‘surplus’ could then reduce the need for external irrigation sources and eventually lead to self-sustained water cycling. This concept is studied in the coastal plains in South West Saudi Arabia where the mountains of the Asir region exhibit the highest rainfall of the peninsula due to orographic lifting and condensation of moisture imported with the Indian Ocean monsoon and with disturbances from the Mediterranean Sea. We use a regional atmospheric modeling system (RAMS) forced by ECMWF analysis data to resolve the effect of complex surface conditions in high resolution (Δx = 4 km). After validation, these simulations are analysed with a focus on the role of local processes (sea breezes, orographic lifting and the formation of fog in the coastal mountains) in generating rainfall, and on how these will be affected by large scale irrigated plantations in the coastal desert. The validation showed that the model simulates the regional and local weather reasonably well. The simulations exhibit a slightly larger diurnal temperature range than those captured by the observations, but seem to capture daily sea-breeze phenomena well. Monthly rainfall is well reproduced at coarse resolutions, but appears more localized at high resolutions. The hypothetical irrigated plantation (3.25 105 ha) has significant effects on atmospheric moisture, but due to weakened sea breezes this leads to limited increases of rainfall. In terms of recycling of irrigation gifts the rainfall enhancement in this particular setting is rather insignificant.

International audience; Future climate evolution is of primary importance for the societal, economical, political orientations and decision-making. It explains the increasing use of climate projections as input for quantitative impact studies, assessing vulnerability and defining adaptation strategies in different sectors. Here we analyse 17 national and representative use cases so as to identify the diversity of the demand for climate information depending on user profiles as well as the best practices, methods and tools that are needed to answer the different requests. A particular emphasis is put on the workflow that allows to translate climate data into suitable impact data, the way to deal with the different sources of uncertainty and to provide a suited product to users. We identified three complementary tools to close the gap between climate scientists and user needs: an efficient interface between users and providers; an optimized methodology to handle user requests and a portal to facilitate access to data and elaborated products. We detail in the paper how these three tools can limit the intervention of experts, educate users, and lead to the production of useful information. This work provides the basis on which the ENES (European Network for Earth System Modelling) Portal Interface for the Climate Impact Communities is built.

The effect of large-scale irrigation in India on the moisture budget of the atmosphere was investigated using three regional climate models and one global climate model, all of which performed an irrigated run and a natural run without irrigation. Using a common irrigation map, year-round irrigation was represented by adding water to the soil moisture to keep it at 90% of the maximum soil moisture storage capacity, regardless of water availability. For two focus regions, the seasonal cycle of irrigation matched that of the reference dataset, but irrigation application varied between the models by up to 0.8 mm day−1. Because of the irrigation, evaporation increased in all models, but precipitation decreased because of a strong decrease in atmospheric moisture convergence. A moisture tracking scheme was used to track individual evaporated moisture parcels through the atmosphere to determine where these lead to precipitation. Up to 35% of the evaporation moisture from the Ganges basin is recycling within the river basin. However, because of a decreased moisture convergence into the river basin, the total amount of precipitation in the Ganges basin decreases. Although a significant fraction of the evaporation moisture recycles within the river basin, the changes in large-scale wind patterns due to irrigation shift the precipitation from the eastern parts of India and Nepal to the northern and western parts of India and Pakistan. In these areas where precipitation increases, the relative precipitation increase is larger than the relative decrease in the areas where precipitation decreases. It is concluded 1) that the direct effects of irrigation on precipitation are small and are not uniform across the models; 2) that a fraction of up to 35% of any marginal evaporation increase (for example, due to irrigation) will recycle within the river basin; and 3) that when irrigation is applied on a large scale, the dominant effect will be a change in large-scale atmospheric flow that decreases precipitation in eastern India and increases it in western and northern India.

Dynamic vegetation models have been used to assess the resilience of tropical forests to climate change, but the global application of these modeling experiments often misrepresents carbon dynamics at a regional level, limiting the validity of future projections. Here a dynamic vegetation model (Lund Potsdam Jena General Ecosystem Simulator) was adapted to simulate present-day potential vegetation as a baseline for climate change impact assessments in the evergreen and deciduous forests of Bolivia. Results were compared to biomass measurements (819 plots) and remote sensing data. Using regional parameter values for allometric relations, specific leaf area, wood density, and disturbance interval, a realistic transition from the evergreen Amazon to the deciduous dry forest was simulated. This transition coincided with threshold values for precipitation (1400 mm yr−1) and water deficit (i.e., potential evapotranspiration minus precipitation) (−830 mm yr−1), beyond which leaf abscission became a competitive advantage. Significant correlations were found between modeled and observed values of seasonal leaf abscission (R2 = 0.6, p

Information of temporal and spatial variation of aerodynamic roughness length is required in most land surface models. The current research presents a practical approach for determining spatially distributed vegetation aerodynamic roughness length with fine temporal and spatial resolution by combining remote sensing and ground measurements. The basic framework of Raupach (1992), with the bulk surface parameters revised by Jasinski et al. (2005) has been applied to optical remote sensing data of HJ-1A/1B missions. In addition, a method for estimating regional scale vegetation height was introduced, so the aerodynamic roughness length, which is more preferred by users than the height normalized form has been developed. Direct validation on different vegetation classes have finally been performed taking advantage of the data-dense field experiments of Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The roughness model had an overall good performance on most of Eddy Covariance sites of HiWATER. However, deviations still existed on different sites, and these have been further analyzed.

The Core Project Biospheric Aspects of the Hydrological Cycle (BAHC) of the International Geosphere Biosphere Programme (IGBP) addresses the biospheric aspects of the hydrological cycle through experiments and modelling of energy, water, carbon dioxide and sediment fluxes in the soil– vegetation–atmosphere system at a variety of spatial and temporal scales. Active regulation of water, energy and carbon dioxide fluxes by the vegetation make it an important factor in regulating the Earth’s hydrological cycle and in the formation of the climate. Consequently, human induced conversion of vegetation cover is an important driver for climate change. A number of recent studies, discussed in this paper, emphasise the importance of the terrestrial biosphere for the climate system. Initially, these studies demonstrate the influence of the land surface on tropical weather and climate, revealing the mechanisms, acting at various scales, that connect increasing temperatures and decreasing rainfall to large-scale deforestation and other forms of land degradation. More recently, the significance of the land surface processes for water cycle and for weather and climate in temperate and boreal zones was demonstrated. In addition the terrestrial biosphere plays a significant role in the carbon dioxide fluxes and in global carbon balance. Recent work suggests that many ecosystems both in the tropics and in temperate zones may act as a substantial sink for carbon dioxide, though the temporal variability of this sink strength is yet unclear. Further, carbon dioxide uptake and evaporation by vegetation are intrinsically coupled, leading to links and feedbacks between land surface and climate that are hardly explored yet. Earth’s vegetation cover and its changes owing to human impact have a profound influence on a lateral redistribution of water and transported constituents, such as nutrients and sediments, and acts therefore as an important moderator of Earth’s biogeochemical cycles. In the BAHC science programme, the importance of studying the influence of climate and human activities on mobilisation and river-borne transport of constituents is explicitly articulated. The terrestrial water and associated material cycles are studied as highly dynamic in space and time, and reflect a complex interplay among climatic forcing, topography, land cover and vegetation dynamics. Despite a large progress in our understanding of how the terrestrial biosphere interacts with Earth’s and climate system and with the terrestrial part of its hydrological cycle, a number of basic issues still remain unresolved. Limited to the scope of BAHC, the paper briefly assesses the present status and identifies the most important outstanding issues, which require further research. Two, arguably most important outstanding issues are identified: a limited understanding of natural variability, especially with respect to seasonal to inter-annual cycles, and of a complex ecosystem behaviour resulting from multiple feedbacks and multiple coupled biogeochemical cycles within the overall climate system. This leads to two major challenges for the future science agenda related to global change research. First, there is a need for a strong multidisciplinary integration of research efforts in both modelling and experiments, the latter extending to inter-annual timescales. Second, the ever increasing complexity in characterisation and modelling of the climate system, which is mainly owing to incorporation of the biosphere’s and human feedbacks, may call for a new approach in global change impact studies. Methodologies need to be developed to identify risks to, and vulnerability of environmental systems, taking into account all important interactions between atmospheric, ecological and hydrological processes at relevant scales. With respect to the influence of climate and human activities on mobilisation and river-borne transport of constituents, the main issues for the future are related to declining availability and quality of ground data for quantity and quality of water discharge. Such assessments as presented in this paper, in combination with community wide science evaluation, have lead to an update of the science agenda for BAHC, a summary of which is provided in the appendix.

The variation in soil texture, surface moisture or vertical soil moisture gradient in larger scale atmospheric models may lead to significant variations in simulated surface fluxes of water and heat. The parameterization of soil moisture fluxes at spatial scales compatible with the grid size of distributed hydrological models and mesoscale atmospheric models (∼ 100 km2) faces principal problems which relate to the underlying microscopic or field scale heterogeneity in soil characteristics. The most widely used parameterization in soil hydrology, the Darcy-Richards (DR) equation, is gaining increasing importance in mesoscale and climate modelling. This is mainly due to the need to introduce plant-interactive soil water depletion and stomatal conductance parameterizations and to improve the calculation of deep percolation and runoff. Covering a grid of several hundreds of square kilometres, the DR parameterization in soil-vegetation-atmosphere-transfer schemes (SVATs) is assumed to be scale-invariant. The parameters describing the non-linear, area-average soil hydraulic functions in this scale-invariant DR-equation should be treated as calibration-parameters, which do not necessarily have a physical meaning. The saturated hydraulic conductivity is one of the soil parameters to which the models show very high sensitivity. It is shown that saturated hydraulic conductivity can be scaled in both vertical and horizontal directions for large flow domains. In this paper, a distinction is made between effective and aggregated soil parameters. Effective parameters are defined as area-average values or distributions over a domain with a single, distinct textural soil type. They can be obtained by scaling or inverse modelling. Aggregated soil parameters represent grid-domains with several textural soil types. In soil science dimensional methods have been developed to scale up soil hydraulic characteristics. With some specific assumptions, these techniques can be extrapolated from classical field-scale problems in soil heterogeneity to larger domains, compatible with the grid-size of large scale models. Particularly promising is the estimation of effective soil hydraulic parameters from area averaging measurements through inverse modelling of the unsaturated flow. Techniques to scale and aggregate the soil characteristics presented in this paper qualify for direct or indirect use in large scale meteorological models. One of the interesting results is the effective behaviour of the reference curve, which can be obtained from similar media scaling. If the conclusions of this paper survive further studies, a relatively simple method will become available to parameterize soil variability at large scales. The inverse technique is found to provide effective soil parameters which perform well in predicting both the area-average evaporation and the area-average soil moisture fluxes, such as subsurface runoff. This is not the case for aggregated soil parameters. Obtained from regression relationships between soil textural composition and hydraulic characteristics, these aggregated parameters predict evaporation fluxes well, but fail to predict water balance terms such as percolation and runoff. This is a serious drawback which could eventually hamper the improvement of the representation of the hydrological cycle in mesoscale atmospheric models and in GCMs.

Climate-related disasters in Bolivia are frequent, severe, and manifold and affect large parts of the population, economy, and ecosystems. Potentially amplified through climate change, natural hazards are of growing concern. To better understand these events, homogenized daily observations of temperature (29 stations) and precipitation (68 stations) from 1960 to 2009 were analyzed in this study. The impact of the positive (+) and negative (−) phases of the three climate modes (i) Pacific decadal oscillation (PDO), (ii) El Niño–Southern Oscillation (ENSO) with El Niño (EN) and La Niña (LN) events, and (iii) Antarctic Oscillation (AAO) were assessed. Temperatures were found to be higher during PDO(+), EN, and AAO(+) in the Andes. Total amounts of rainfall, as well as the number of extreme events, were higher during PDO(+), EN, and LN in the lowlands. During austral summer [December–February (DJF)], EN led to drier conditions in the Andes with more variable precipitation. Temperatures increased at a rate of 0.1°C per decade, with stronger increases in the Andes and in the dry season. Rainfall totals increased from 1965 to 1984 [12% in DJF and 18% in June–August (JJA)] and decreased afterward (−4% in DJF and −10% in JJA), following roughly the pattern of PDO. Trends of climate extremes generally corresponded to trends of climate means. Findings suggest that Bolivia’s climate will be warmer and drier than average in the near-term future. Having entered PDO(−) in 2007, droughts and LN-related floods can be expected in the lowlands, while increasing temperatures suggest higher risks of drought in the Andes.