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Climate variability places significant pressure on climate-sensitive sectors such as agriculture. Information on crop-related climate and water indicators under climate change becomes essential. The Copernicus Climate Change Service (C3S) provides reliable information about climate change based on climate models, Earth Observations, and in situ data to support adaptation policies. Within this C3S context, the Global Agriculture Sectoral Information System (SIS) has developed a new product based on global crop productivity indicators to document the effects of climate variability on crop yield variability. This study focuses on the evaluation of climate-enhanced, earth observation (EO) based crop productivity indicators i.e., Total Weight Storage Organs (TWSO, equivalent to yield), Total Above Ground Production (TAGP). These variables provide insights into the Spatio-temporal variability of yield, productivity, and development of four major staple crops (rice, maize, soybean, and wheat) for their main production regions at the global scale for the period 2000–2018. In this study, the evaluation was carried out spatially and temporally for the United States of America, India, and China for the period 2000–2018, using reported yield statistics aggregated to the lowest available administrative level of each region. For almost all the crops in the three countries, the skilled error can be reduced by more than 25% for both the indicators TAGP and TWSO compared to the trend of reported yield, suggesting considerable performance in assessing interannual yield variability. Results indicated both indicators individually performed well in many important producing areas of the USA, India, and China. However, if we compare both the indicator's performance in terms of production totals in the USA, India, and China, TAGP contributes ∼27–50% of total crop production in the USA and India, and ∼47–75% in China. While, TWSO contributes ∼24–41% in the USA and India, and 5–65% in China.

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.

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 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

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.

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 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.

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.

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.

Following the convective triggering potential (CTP)–humidity index (HIlow) framework by Findell and Eltahir, the sensitivity of atmospheric convection to soil moisture conditions is studied for India. Using the same slab model as Findell and Eltahir, atmospheric conditions in which the land surface state affects convective precipitation are determined. For India, CTP–HIlow thresholds for land surface–atmosphere feedbacks are shown to be slightly different than for the United States. Using atmospheric sounding data from 1975 to 2009, the seasonal and spatial variations in feedback strength have been assessed. The patterns of feedback strengths thus obtained have been analyzed in relation to the monsoon timing. During the monsoon season, atmospheric conditions where soil moisture positively influences precipitation are present about 25% of the time. During onset and retreat of the monsoon, the south and east of India show more potential for feedbacks than the north. These feedbacks suggest that large-scale irrigation in the south and east may increase local precipitation. To test this, precipitation data (from 1960 to 2004) for the period about three weeks just before the monsoon onset date have been studied. A positive trend in the precipitation just before the monsoon onset is found for irrigated stations. It is shown that for irrigated stations, the trend in the precipitation just before the monsoon onset is positive for the period 1960–2004. For nonirrigated stations, there is no such upward trend in this period. The precipitation trend for irrigated areas might be due to a positive trend in the extent of irrigated areas, with land–atmosphere feedbacks inducing increased precipitation.

Aircraft measurements of turbulent fluxes are generally being made with the objective to obtain an estimate of regional exchanges between land surface and atmosphere, to investigate the spatial variability of these fluxes, but also to learn something about the fluxes from some or all of the land cover types that make up the landscape. In this study we develop a method addressing this last objective, an approach to disentangle blended fluxes from a landscape into the component fluxes emanating from the various land cover classes making up that landscape. The method relies on using a footprint model to determine which part of the landscape the airborne flux observation refers to, using a high resolution land cover map to determine the fractional covers of the various land cover classes within that footprint, and finally using multiple linear regression on many such flux/fractional cover data records to estimate the component fluxes. The method is developed in the context of three case studies of increasing complexity and the analysis covers three scalar fluxes: sensible and latent heat fluxes and carbon dioxide flux, as well as the momentum flux. A basic assumption under the dis-aggregation method is that the composite flux, i.e. the landscape flux, is a linear average of the component fluxes, i.e. the fluxes from the various land elements. We test and justify this assumption by comparing linear averages of component fluxes in simple ‘binary landscapes’, weighted by their relative area, with directly aircraft observed fluxes. In all case studies dis-aggregation of mixed values for fluxes from heterogeneous areas into component land cover class specific fluxes is feasible using robust least squares regression, both in simple binary ‘landscapes’ and in more complex cases. Both the differences between land cover classes and the differences between synoptic conditions can be resolved, for those land cover classes that make up sufficiently large fractions of the landscape. The regression F -statistic and the closely associated p -values are good indicators for this latter prerequisite and for other sources of uncertainty in the dis-aggregated flux estimates that render it meaningful or not. An analysis of the effect of various sources of errors in input data, footprint estimates and of skewed land cover class distributions is presented. A validation of flux estimates obtained through the dis-aggregation method against independent ground data proved satisfactorily. Recommendations for the use of the method are given as are suggestions for further development.

This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund–Potsdam–Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations.

Eddy fluxes were measured over different ecosystems, winter and summer crops, a maritime pine forest at different stages of development and grassland, from 17 May to 26 June 2005 in the southwestern region of France. During the experiment, summer crops started growing whereas winter crops and grassland achieved their senescence. Comparatively, the other ecosystems had a much slower growth emphasized by soil water deficit at forest sites. The ten ecosystems showed different partitioning of available energy. Net radiation was the highest above the maritime pine forest, followed, in decreasing order, by the crops, the vineyard and the grassland. Over the whole campaign period, the Bowen ratio (β=H/LE) was larger above the forest sites than for the other sites. The various vegetation types also showed contrasting net ecosystem exchange (NEE) dynamics following their growth status and respective behaviour in response to drought. Both the clearcut and summer crops before irrigation and plant growth behaved as sources of CO2, whereas the vineyard, the mature forest and winter crops acted as sinks. However the maize crops became substantial sinks of CO2 after the start of irrigation and canopy growth, with fluxes twice as large as for the mature pine forest. Finally, throughout the experiment, forest, grassland and crops sequestrated from about 50 gC m−2 to 230 gC m−2, while the cleacut and the beans crop rejected about 30 gC m−2. These results support the idea that converting a mature forest to a clearcut or bare soil available to agricultural use enhances the sensible heat flux and shifts the ecosystem from a sink to a source of carbon.

The CarboEurope Regional Experiment Strategy (CERES) was designed to develop and test a range of methodologies to assess regional surface energy and mass exchange of a large study area in the South-Western part of France. This paper describes a methodology to estimate sensible and latent heat fluxes on the basis of net radiation, surface radiometric temperature measurements and information obtained from available products derived from the Meteosat Second Generation (MSG) geostationary meteorological satellite, weather stations and ground-based eddy covariance towers. It is based on a simplified bulk formulation of sensible heat flux that considers the degree of coupling between the vegetation and the atmosphere and estimates latent heat as the residual term of net radiation. Estimates of regional energy fluxes obtained in this way are validated at the regional scale by means of a comparison with direct flux measurements made by airborne eddy-covariance. The results show an overall good matching between airborne fluxes and estimates of sensible and latent heat flux obtained from radiometric surface temperatures that holds for different weather conditions and different land use types. The overall applicability of the proposed methodology to regional studies is discussed.

Water use projections are crucial to safeguard sustainable access to freshwater in the future. The Water Futures and Solution initiative (WFaS) has developed a set of global water-use scenarios consistent with the recent Assessment Report framework of the Intergovernmental Panel on Climate Change, notably the Shared Socioeconomic Pathways (SSPs), and applying a hydro-economic classification that links a socioeconomic dimension with hydrologic complexity. Here we present regional water use projections for the Pearl River Delta (PRD) in China consistent with the WFaS global assessment. Using two different downscaling techniques for developing regional water-use scenarios based on the national assumptions made for China in the WFaS assessment, we investigate PRD’s water-use projections. The findings indicate significant differences in the PRD’s regional development trends compared to China’s national SSP. The regionalized scenarios project lower water use because of the PRD’s lower share of the manufacturing sector in total Gross Domestic Product (GDP) and higher rates of technological improvement, compared to national development trend assumptions. Nevertheless, hydrological challenges remain for the PRD. Its total water use would still increase by up to 54% in 2030 under the regionalized scenarios. Although uncertainties related to scarce data remain, we provide a scientifically sound and feasible method to generate regional scenarios that can capture the regional sectorial water uses development as well as being consistent with national water-use scenarios developed by global assessment.

This paper presents an analysis of regional fluxes obtained with a small aircraft over heterogeneous terrain in the south-west of France, during the large scale field experiment CERES’07. We use a method combining variable flight-path segmentation with basic airborne footprint analysis. The segmentation is based on topography, land use and soil type, using a.o. satellite imagery and digital maps.This paper presents an analysis of regional fluxes obtained with a small aircraft over heterogeneous terrain in the south-west of France, during the large scale field experiment CERES'07. We use a method combining variable flight-path segmentation with basic airborne footprint analysis. The segmentation is based on topography, land use and soil type, using a.o. satellite imagery and digital maps. The segments are delineated using an average footprint length, based on all flights, and segment lengths, which are variable in space but not in time. The method results in segment averaged carbon and energy fluxes, which are shown to be representative of regional fluxes. Our analysis is focussed on carbon dioxide, heat and evaporative fluxes around solar noon. We will show that spatial and seasonal variations in the fluxes can be linked to the underlying landscape. In addition, a comparison between the airborne data and ground flux data is made to support our results. However, due to the incompleteness of ground data for some predominant vegetation types (even in such a data dense context), upscaling of ground data to regional fluxes was not possible. Without the comparison, we are still able to demonstrate that aircraft can provide direct and meaningful estimates of regional fluxes of energy and carbon dioxide.

Climate variability is an important driver for regionally anomalous production levels of especially rainfed crops, with implication for food security of subsistence farmers and economic performance for market oriented agriculturalists. In large parts of the tropics, modern seasonal ensemble forecast systems have useful levels of skill, that open up the possibilty to develop climate services that assist agriculturalist and others in the food chain (farm suppliers, commodity traders, aid organisations) to anticipate on expected anomalous conditions. In this thesis we explore the forecast skill at various steps in the modelling chain for seasonal maize yield anomalies in East Africa. First, we analyse the skill of ECMWF System-4 (S4) climate forecasts for primary meteorological variables against gridded observations and findboth potential and real skill for rainfall and temperature in typical cropping seasons in eastern Africa. However, forecast skill is a function of geographical region, season, climate variable (i.e. higher skill in temperature, rainfall, downwelling shortwave radiation in that order) and forecast lead-time, as such skill assessment should not be generalized over a large geographical area. Next we analyse correlations between reported production and anomalous weather conditions, using a range of climate indicators relevant for arable farming, such as growing and killing degree days, and rainfall amount, evenness, random independent events (unevenness), and timing during consequent maize growth phases in two case study regions. In this case significant levels of correlation and skill are revealed that open up the potential for statistical forecasting by use of climate forecast derived variables. Sensitivity of yields to climate indicators depend on geographical location, for example, higher sensitivity to rainfall is found in northern Ethiopia while in a location in equatorial-western Kenya, there is higher sensitivity to temperature indicators. At the next level of complexity we explore the use of full process based crop models forced by seasonal climate forecasts to forecast anomalous water-limited maize yield in the region, and find again potentially useful levels of skill with at least two months lead before planting, in most agricultural regions. But this again depends on regions, for example yield forecasts in Tanzania, in the season starting October did not, have skill. Finally, we try to attribute skill levels to physiographic characteristics (soils, maize cultivars, geographical region etc.) and address some issues of scale of aggregation for two case study regions in Kenya and Ethiopia. The results showed that skill assessment at national boundaries and high resolution crop simulation units may inform both maize production related policy decisions at regional or national levels, and also support maize production decisions at specific cropping locations such as farm management decisions made by farmers, We conclude with a synthesis discussing further on found skill levels in relation to potential climate services aimed at the agricultural sector in East Africa and beyond.

Land surface impacts on precipitation in the Netherlands This thesis addresses the effects of historical and projected land use changes on precipitation in the Netherlands. The thesis first deals with the observed increase in precipitation in the last century and indicates that this is most likely related to an increase in sea surface temperature. It is also found that along the Dutch West coast (the Randstad) precipitation is enhanced downwind of urban areas. Next, the atmospheric Weather Research and Forecasting (WRF) model is used to investigate the sensitivity of precipitation to the land surface in the Netherlands in spring and summer. Land use conversions from historic to present, and present to future, led to a small decrease in precipitation. In all, precipitation will likely continue to increase over the coming decades, more so as a result of climate change than from land conversion. Nevertheless, in the Netherlands the influence of land surface changes is not negligible and counters the effect of climate change on precipitation.

De afgelopen jaren is er een grotere vraag ontstaan naar klimaat- en weergegevens op lokaal niveau, nu en in de toekomst. Van regionale klimaatmodellen wordt verwacht dat zij dit kunnen geven met verbeterde informatie wat betreft extremen. Heterogeniteit moet daartoe verder uitgewerkt worden. Vier verschillende voorbeelden worden behandeld om het begrip te verbeteren van de processen en de terugkoppelingen op lokaal/regionaal niveau. Terugkoppelingen tussen aardoppervlak en de atmosfeer. Drie voorbeelden stammen uit Nederland (gematigd klimaat), de vierde is afkomstig uit het (semi-)aride Saoedi Arabië. Dit proefschrift doet aanbevelingen om regionale atmosferische modellen te verbeteren.

During the 20th century, an increasing population increased the demand for food. As a consequence, agricultural activity has expanded and become more intense. A part of this intensification is the use of irrigation systems to water crops. Due to this irrigation, dams and channeling systems, water can be made available for agriculture in places or during seasons with limited precipitation. In monsoon climates, such as India, the majority of the precipitation falls in one season. During the rest of the year, water that is stored in dammed reservoirs can be made available to spread the water availability more evenly over the year. Previous studies with large scale hydrological models have shown that as a consequence of human influences (such as dams and irrigation systems), the river flow decreases during the wet monsoon months, but the evaporation of water into the atmosphere increases during the dry months. However, these large scale hydrological models did not take into account the atmospheric effects of a changed land surface. This PhD research studies these atmospheric effects of large scale irrigation in India. Three perspectives are taken to determined the influence of irrigation: (1) the local effects of a moister land-surface on the triggering of precipitation (i.e. does the change in land surface wetness lead to a different amount of precipitation?), (2) the atmospheric fate of evaporation due to irrigation (i.e. where does the evaporation lead to (down-wind) precipitation?), and (3) the effects of a moister land-surface on the large scale (monsoon) moisture transport patterns (i.e. do the monsoon flows change significantly due to large scale irrigation?) In the first part (the first perspective), several land-atmosphere diagnostics are tested globally. The goal of these diagnostics is to determine the influence of the land surface on precipitation, based on surface and atmospheric conditions. Of these diagnostics, the CTP-HIlow framework (Convective Triggering Potential and Humidity Index of the LOWer atmosphere) of Findell and Eltahir (2003a) performed well globally and over the Indian region. The summertime atmospheric conditions were diagnosed using this framework and the presence of a land-atmosphere coupling hot-spot in the Indian peninsula, proposed by previous studies (Koster et al. (2004)), is confirmed. Secondly, the local perspective is taken in the Indian subcontinent. The CTP- HIlow framework is tested in India, using an atmospheric slab model (a simple, one- dimensional model of the atmosphere) combined with atmospheric soundings (balloon measurements of temperature and moisture of an atmospheric profile of up to 30 km). This model is run twice; once with a wet land surface and once with a dry land surface. The results of these model runs can have two outcomes; the land surface does not have an influence on precipitation or it does have an influence. The CTP-HIlow framework proves to be useful to classify the potential influence of the land surface. When the atmosphere is very wet (low values of HIlow ), precipitation will occur regardless of the land surface, when the atmosphere is very dry (high HIlow values) no precipitation will occur, regardless of the land surface. However, for intermediate HIlow values, the effect of the land surface depends on the stability or the amount of convective energy (CTP) in the atmosphere. The stability of the atmosphere is related to how fast a particle will ascend in the atmosphere, which depends mostly on the temperature profile. For positive, but low convective potentials (0200 J/kg), a dry land surface will produce more precipitation. For India, a small adaptation of the framework improved the performance in predicting the influence of the land surface on precipitation triggering. For India, the effect of the land surface on precipitation is seasonal. During the periods two months before the monsoon onset and after the monsoon retreat, precipitation triggering was found to be sensitive to land surface wetness. During those periods, a wet land surface is expected to increase precipitation. The atmospheric conditions under which a wet land surface is expected to decreases precipitation do not occur frequently in India. During the dry winter season, the atmosphere is too dry for the land surface to have an influence on precipitation. During the monsoon period, the atmosphere is too wet for the land surface to have an influence on precipitation, it will occur regardless of the land surface conditions. In the third part of the study, the moisture recycling perspective was taken and the atmospheric moisture budget of the Ganges basin is studied. A three-dimensional moisture tracing model is used to release moisture parcels from the Ganges basin, similarly to a class of school children releasing helium-filled balloons with their address on it. These parcels were transported along the wind patterns. During the trajectory of the parcel through the atmosphere, some moisture will precipitate out of it and contribute to the precipitation at that location. For each location, many parcels were released for every time step of 6 hours. Similar to the balloons of the school children that are hopefully sent back to them, the fate of the released moisture was accounted. The fraction of the evaporated moisture that subsequently falls as precipitation (recycles) within the Ganges basin shows a strong seasonality. During the winter months, practically all evaporation parcels were transported towards the Indian ocean and were lost for the Ganges basin. During the pre-monsoon months, the recycled fraction increased and was between 30-40%. During the monsoon months, the recycling peaks at up to 60%, after which it drops off again. The importance of recycled evaporation to the total precipitation peaks during the pre-monsoon and post-monsoon periods, when it contributes up to 15% of the precipitation. In the last part of the study, the effects from the local and moisture recycling perspectives are compared to those from the large scale perspective. Four atmospheric models were run with and without irrigation to test the large scale effects of irrigation on the Ganges basin atmospheric water budget and the influence on large scale atmospheric moisture transport. The local effects on precipitation were minimal and not uniform across the models. The Ganges river basin evaporation increased, as well as the amount of evaporation recycled within the river basin. However, the large scale wind patterns showed an uniform change across the models. Due to an increased flow in the direction of north-west India, the precipitation in east-India decreased while it increased in north-west India and Pakistan. Therefore, the Ganges basin precipitation decreased slightly. The conclusion of the work is that from the local perspective and the moisture recycling perspective, irrigation will lead to more precipitation in India. A wetter land surface will trigger some additional precipitation (especially just before and after the monsoon season) and a significant fraction of the evaporation will return to the same river basin as precipitation. However, from the large scale perspective, large scale irrigation will shift the wind patterns due to changes in the land-sea temperature contrast; precipitation will decrease slightly in the Ganges basin and be shifted towards the Indus basin and north-west India. The effects of irrigation on precipitation is small compared to the hydrological response of human influences simulated by the large scale hydrological models. Moreover, the spread in response across these hydrological models is large compared to the simulated effects of irrigation by the atmospheric models. Therefore, it is recommended to improve the large scale hydrological models and reduce their uncertainty before including the feedbacks of land use changes on their precipitation input.

To meet the food demand of a growing global population, agricultural production will have to more than double in this century. Agricultural land expansion combined with yield increases will therefore be required. This thesis investigates whether enough water resources will be available to sustain the future food production. Using a global scale hydrology and crop growth model, the combined effect of climate change and socio economic changes on water scarcity and food production were quantified. The first thing to explore was where water for agriculture is currently extracted. Reservoirs behind large dams are found to be very important for agriculture and contribute around 18% of the total irrigation water today. It is shown however that with current reservoir capacities and irrigation efficiencies, not enough water can be supplied to sustain an increased food production. Irrigation water shortage can lead to a loss of 20% of the irrigated crop production globally, but with important regional differences. Regions particularly at risk include basins in Southern Africa and South Asia, where production losses on irrigated cropland can become over 50%. This means that unless major investments are made towards improving irrigation efficiency and increasing storage capacity, water shortage will put a serious constraint on future food production.