Lund-Potsdam-Jena Managed Land Research Articles

Drivers of global irrigation expansion: the role of discrete global grid choice

Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

Connecting competitor, stress-tolerator and ruderal (CSR) theory and Lund Potsdam Jena managed Land 5 (LPJmL 5) to assess the role of environmental conditions, management and functional diversity for grassland ecosystem functions

Abstract. Forage offtake, leaf biomass and soil organic carbon storage are important ecosystem services of permanent grasslands, which are determined by climatic conditions, management and functional diversity. However, functional diversity is not independent of climate and management, and it is important to understand the role of functional diversity and these dependencies for ecosystem services of permanent grasslands, since functional diversity may play a key role in mediating impacts of changing conditions. Large-scale ecosystem models are used to assess ecosystem functions within a consistent framework for multiple climate and management scenarios. However, large-scale models of permanent grasslands rarely consider functional diversity. We implemented a representation of functional diversity based on the competitor, stress-tolerator and ruderal (CSR) theory and the global spectrum of plant form and function into the Lund Potsdam Jena managed Land (LPJmL) dynamic global vegetation model (DGVM) forming LPJmL-CSR. Using a Bayesian calibration method, we parameterised new plant functional types (PFTs) and used these to assess forage offtake, leaf biomass, soil organic carbon storage and community composition of three permanent grassland sites. These are a temperate grassland and a hot and a cold steppe for which we simulated several management scenarios with different defoliation intensities and resource limitations. LPJmL-CSR captured the grassland dynamics well under observed conditions and showed improved results for forage offtake, leaf biomass and/or soil organic carbon (SOC) compared to the original LPJmL 5 version at the three grassland sites. Furthermore, LPJmL-CSR was able to reproduce the trade-offs associated with the global spectrum of plant form and function, and similar strategies emerged independent of the site-specific conditions (e.g. the C and R PFTs were more resource exploitative than the S PFT). Under different resource limitations, we observed a shift in the community composition. At the hot steppe, for example, irrigation led to a more balanced community composition with similar C, S and R PFT shares of aboveground biomass. Our results show that LPJmL-CSR allows for explicit analysis of the adaptation of grassland vegetation to changing conditions while explicitly considering functional diversity. The implemented mechanisms and trade-offs are universally applicable, paving the way for large-scale application. Applying LPJmL-CSR for different climate change and functional diversity scenarios may generate a range of future grassland productivities.

Climate-induced shifts in irrigation water demand and supply during sensitive crop growth phases in South Asia

This study investigated the shifts in irrigation water demand and supply of the major staple and water-intensive crops (wheat and rice) in the Indus, Ganges and Brahmaputra (IGB) river basins of South Asia under the combined impacts of climate change and socio-economic development during the period 1981–2100. It explores irrigation water usage during climate-sensitive crop growth phases (i.e. vegetative and reproductive which required ~ 60% of the total seasonal (sowing to harvest) water demand), which is supposed to be crucial for long-term integrated crop water management. A hydrology vegetation model Lund Potsdam Jena Managed Land is forced with an ensemble of eight downscaled (5 arc-min) global climate model’s using the RCP (Representative concentration pathways) -SSP (Shared socio-economic pathways) framework, i.e. RCP4.5-SSP1 and RCP8.5-SSP3. To investigate phase-specific crop water projections, trend analysis is performed. It shows a significant (p<0.001) increase in irrigation water demand during the vegetative phase of wheat (6 mm) and reproductive phase of rice (26 mm) and a decrease during the reproductive phase of wheat (13 mm) and vegetative phase of rice (11 mm) in selected study sites. The large decrease in projected irrigation demand for wheat can be explained by a shortening of the growing season length as a result of rising temperatures and increased precipitation. Whereas, an increase in irrigation demand for rice is a combined effect of higher temperatures and less precipitation during the reproductive phase in the region. At the same time, irrigation supply by surface water and groundwater is likely to change in future due to warmer and drier growing periods, causing a significant increase in groundwater irrigation, mainly for rice. Our major research findings show the importance of crop water assessments during the sensitive crop growth phases of wheat and rice which vary in space and time. Including crop phase-specific, climate impact assessments of regional and global projection will help improve the region’s existing crop-water management strategies and adaptation practices.

The role of cover crops for cropland soil carbon, nitrogen leaching, and agricultural yields – a global simulation study with LPJmL (V. 5.0-tillage-cc)

Abstract. Land management practices can reduce the environmental impact of agricultural land use and production, improve productivity, and transform cropland into carbon sinks. In our study we assessed the biophysical and biogeochemical impacts and the potential contribution of cover crop practices to sustainable land use. We applied the process-based, global dynamic vegetation model LPJmL (Lund–Potsdam–Jena managed Land) V. 5.0-tillage-cc with a modified representation of cover crops to simulate the growth of grasses on cropland in periods between two consecutive main crops' growing seasons for near-past climate and land use conditions. We quantified simulated responses of agroecosystem components to cover crop cultivation in comparison to bare-soil fallowing practices on global cropland for a period of 50 years. For cover crops with tillage, we obtained annual global median soil carbon sequestration rates of 0.52 and 0.48 t C ha−1 yr−1 for the first and last decades of the entire simulation period, respectively. We found that cover crops with tillage reduced annual nitrogen leaching rates from cropland soils by medians of 39 % and 54 % but also the productivity of the following main crop by an average of 1.6 % and 2 % for the 2 analyzed decades. The largest reductions in productivity were found for rice and modestly lowered ones for maize and wheat, whereas the soybean yield revealed an almost homogenously positive response to cover crop practices replacing bare-soil fallow periods. The obtained simulation results of cover crop with tillage practices exhibit a good ability of the model version to reproduce observed effects reported in other studies. Further, the results suggest that having no tillage is a suitable complementary practice to cover crops, enhancing soil carbon sequestration and the reduction in nitrogen leaching, while reducing potential trade-offs with the main-crop productivity due to their impacts on soil nitrogen and water dynamics. The spatial heterogeneity of simulated impacts of cover crops on the variables assessed here was related to the time period since the introduction of the management practice as well as to environmental and agronomic conditions of the cropland. This study supports findings of other studies, highlighting the substantial potential contribution of cover crop practices to the sustainable development of arable production.

A prototype Earth system impact metric that accounts for cross-scale interactions

Human activities are disrupting the Earth system’s biophysical processes, which underlie human wellbeing. The planetary boundary framework sets ‘safe’ global limits on these pressures, but a sub-global assessment of these pressures, their interactions and subsequent systemic effects is needed to enable corporate and public entities to assess the systemic environmental impacts of their decisions. Here, we developed a prototype Earth system impact metric that is savvy to Earth system interactions. First, we quantified sub-global interactions between climate change, surface water runoff, and vegetation cover using the global dynamic vegetation model LPJmL (Lund-Potsdam-Jena managed Land). Second, we used a feedback model to study how these interactions amplify environmental impacts. We found, for example, that interactions more than double the Earth system impacts of deforestation in some tropical forests. Finally, we combined these amplification factors with an assessment of the current state of the Earth system to create a prototype Earth system impact metric. We envision that future versions of our prototype metric will allow corporate and public actors to better assess the systemic environmental impacts of their decisions. Our ambition is that these results catalyse further scientific work to extend and improve this metric, as well as action by investors, companies, cities, and governments to deliver sustainable outcomes across the private and public sectors.

Trade-offs between water needs for food, utilities, and the environment—a nexus quantification at different scales

With a growing population and a changing climate, competition for water resources in the water-energy-food (WEF) nexus is expected to increase. In this study, competing water demands between food production, freshwater ecosystems and utilities (energy, industries and households) are quantified. The potential trade-offs and related impacts are elaborated for different SSP scenarios with the integrated assessment model IMAGE, which includes the global vegetation and hydrology model Lund-Potsdam-Jena managed Land (LPJmL). Results for the 2045–2054 period are evaluated at the global scale and for a selection of 14 hotspot basins and coastal zones. On the global scale, we estimate that an additional 1.7 billion people could potentially face severe water shortage for electricity, industries and households if food production and environmental flows would be prioritized. Zooming in on the hotspots, this translates to up to 70% of the local population. Results furthermore show that up to 33% of river length in the hotspots risks not meeting environmental targets when prioritizing other water demands in the nexus. For local food production, up to 41% might be lost due to competing water demands. The potential trade-offs quantified in this study highlight the competition for resources in the WEF nexus, for which impacts are most notably felt at local scales. This emphasizes the need to simultaneously consider different dimensions of the nexus when developing scenarios that aim to achieve multiple sustainability targets.

Global cotton production under climate change – Implications for yield and water consumption

Abstract. Being an extensively produced natural fiber on earth, cotton is of importance for economies. Although the plant is broadly adapted to varying environments, the growth of and irrigation water demand on cotton may be challenged by future climate change. To study the impacts of climate change on cotton productivity in different regions across the world and the irrigation water requirements related to it, we use the process-based, spatially detailed biosphere and hydrology model LPJmL (Lund–Potsdam–Jena managed land). We find our modeled cotton yield levels in good agreement with reported values and simulated water consumption of cotton production similar to published estimates. Following the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) protocol, we employ an ensemble of five general circulation models under four representative concentration pathways (RCPs) for the 2011–2099 period to simulate future cotton yields. We find that irrigated cotton production does not suffer from climate change if CO2 effects are considered, whereas rainfed production is more sensitive to varying climate conditions. Considering the overall effect of a changing climate and CO2 fertilization, cotton production on current cropland steadily increases for most of the RCPs. Starting from ∼65 million tonnes in 2010, cotton production for RCP4.5 and RCP6.0 equates to 83 and 92 million tonnes at the end of the century, respectively. Under RCP8.5, simulated global cotton production rises by more than 50 % by 2099. Taking only climate change into account, projected cotton production considerably shrinks in most scenarios, by up to one-third or 43 million tonnes under RCP8.5. The simulation of future virtual water content (VWC) of cotton grown under elevated CO2 results for all scenarios in less VWC compared to ambient CO2 conditions. Under RCP6.0 and RCP8.5, VWC is notably decreased by more than 2000 m3 t−1 in areas where cotton is produced under purely rainfed conditions. By 2040, the average global VWC for cotton declines in all scenarios from currently 3300 to 3000 m3 t−1, and reduction continues by up to 30 % in 2100 under RCP8.5. While the VWC decreases by the CO2 effect, elevated temperature acts in the opposite direction. Ignoring beneficial CO2 effects, global VWC of cotton would increase for all RCPs except RCP2.6, reaching more than 5000 m3 t−1 by the end of the simulation period under RCP8.5. Given the economic relevance of cotton production, climate change poses an additional stress and deserves special attention. Changes in VWC and water demands for cotton production are of special importance, as cotton production is known for its intense water consumption. The implications of climate impacts on cotton production on the one hand and the impact of cotton production on water resources on the other hand illustrate the need to assess how future climate change may affect cotton production and its resource requirements. Our results should be regarded as optimistic, because of high uncertainty with respect to CO2 fertilization and the lack of implementing processes of boll abscission under heat stress. Still, the inclusion of cotton in LPJmL allows for various large-scale studies to assess impacts of climate change on hydrological factors and the implications for agricultural production and carbon sequestration.

The Impacts of Climate Variability on Crop Yields and Irrigation Water Demand in South Asia

Accurate (spatio-temporal) estimation of the crop yield relation to climate variables is essential in the densely populated Indus, Ganges, and Brahmaputra (IGB) river basins of South Asia for devising appropriate adaptation strategies to ensure regional food and water security. This study examines wheat (Triticum aestivum) and rice (Oryza sativa) crop yields’ sensitivity to primary climate variables (i.e., temperature and precipitation) and related changes in irrigation water demand at different spatial (i.e., province/state, districts and grid cell) and temporal (i.e., seasonal and crop growth phase) scales. To estimate the climate driven variations in crop yields, observed and modelled data applying the Lund-Potsdam-Jena managed Land (LPJmL) model are used for six selected study sites in the IGB river basins over the period 1981–2010. Our statistical analysis underscores the importance of impacts assessments at higher spatio-temporal scales. Our grid cell (aggregated over study sites) scale analysis shows that 27–72% variations in wheat and 17–55% in rice crop yields are linked with temperature variations at a significance level of p &lt; 0.001. In the absence of irrigation application, up to 39% variations in wheat and up to 75% variations in rice crop yields are associated with precipitation changes in all study sites. Whereas, observed crop yields show weak correlations with temperature at a coarser resolution, i.e., up to 4% at province and up to 31% at district scales. Crop yields also showed stronger sensitivity to climate variables at higher temporal scale (i.e., vegetative and reproductive phases) having statistically strong negative relationship with temperature and positive with precipitation during the reproductive phase. Similarly, crop phase-specific variations in climate variables have considerable impacts (i.e., quantity and timing) on irrigation water demand. For improved crop water planning, we suggest integrated climate impact assessments at higher spatio-temporal scales which can help to devise appropriate adaptation strategies for sustaining future food demand.

Modeling forest plantations for carbon uptake with the LPJmL dynamic global vegetation model

Abstract. We present an extension of the dynamic global vegetation model, Lund–Potsdam–Jena Managed Land (LPJmL), to simulate planted forests intended for carbon (C) sequestration. We implemented three functional types to simulate plantation trees in temperate, tropical, and boreal climates. The parameters of these functional types were optimized to fit target growth curves (TGCs). These curves represent the evolution of stemwood C over time in typical productive plantations and were derived by combining field observations and LPJmL estimates for equivalent natural forests. While the calibrated model underestimates stemwood C growth rates compared to the TGCs, it represents substantial improvement over using natural forests to represent afforestation. Based on a simulation experiment in which we compared global natural forest versus global forest plantation, we found that forest plantations allow for much larger C uptake rates on the timescale of 100 years, with a maximum difference of a factor of 1.9, around 54 years. In subsequent simulations for an ambitious but realistic scenario in which 650 Mha (14 % of global managed land, 4.5 % of global land surface) are converted to forest over 85 years, we found that natural forests take up 37 PgC versus 48 PgC for forest plantations. Comparing these results to estimations of C sequestration required to achieve the 2 ∘C climate target, we conclude that afforestation can offer a substantial contribution to climate mitigation. Full evaluation of afforestation as a climate change mitigation strategy requires an integrated assessment which considers all relevant aspects, including costs, biodiversity, and trade-offs with other land-use types. Our extended version of LPJmL can contribute to such an assessment by providing improved estimates of C uptake rates by forest plantations.

Assessing Long-Term Hydrological Impact of Climate Change Using an Ensemble Approach and Comparison with Global Gridded Model-A Case Study on Goodwater Creek Experimental Watershed

Potential impacts of climate change on the hydrological components of the Goodwater Creek Experimental Watershed were assessed using climate datasets from the Coupled Model Intercomparison Project Phase 5 and Soil and Water Assessment Tool (SWAT). Historical and future ensembles of downscaled precipitation and temperature, and modeled water yield, surface runoff, and evapotranspiration, were compared. Ensemble SWAT results indicate increased springtime precipitation, water yield, surface runoff and a shift in evapotranspiration peak one month earlier in the future. To evaluate the performance of model spatial resolution, gridded surface runoff estimated by Lund–Potsdam–Jena managed Land (LPJmL) and Jena Diversity-Dynamic Global Vegetation model (JeDi-DGVM) were compared to SWAT. Long-term comparison shows a 6–8% higher average annual runoff prediction for LPJmL, and a 5–30% lower prediction for JeDi-DGVM, compared to SWAT. Although annual runoff showed little change for LPJmL, monthly runoff projection under-predicted peak runoff and over-predicted low runoff for LPJmL compared to SWAT. The reasons for these differences include differences in spatial resolution of model inputs and mathematical representation of the physical processes. Results indicate benefits of impact assessments at local scales with heterogeneous sets of parameters to adequately represent extreme conditions that are muted in global gridded model studies by spatial averaging over large study domains.

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

Abstract. Grassland management affects the carbon fluxes of one-third of the global land area and is thus an important factor for the global carbon budget. Nonetheless, this aspect has been largely neglected or underrepresented in global carbon cycle models. We investigate four harvesting schemes for the managed grassland implementation of the dynamic global vegetation model (DGVM) Lund–Potsdam–Jena managed Land (LPJmL) that facilitate a better representation of actual management systems globally. We describe the model implementation and analyze simulation results with respect to harvest, net primary productivity and soil carbon content and by evaluating them against reported grass yields in Europe. We demonstrate the importance of accounting for differences in grassland management by assessing potential livestock grazing densities as well as the impacts of grazing, grazing intensities and mowing systems on soil carbon stocks. Grazing leads to soil carbon losses in polar or arid regions even at moderate livestock densities (&lt; 0.4 livestock units per hectare – LSU ha−1) but not in temperate regions even at much higher densities (0.4 to 1.2 LSU ha−1). Applying LPJmL with the new grassland management options enables assessments of the global grassland production and its impact on the terrestrial biogeochemical cycles but requires a global data set on current grassland management.

Mediterranean irrigation under climate change: more efficient irrigation needed to compensate for increases in irrigation water requirements

Abstract. Irrigation in the Mediterranean is of vital importance for food security, employment and economic development. This study systematically assesses how climate change and increases in atmospheric CO2 concentrations may affect irrigation requirements in the Mediterranean region by 2080–2090. Future demographic change and technological improvements in irrigation systems are taken into account, as is the spread of climate forcing, warming levels and potential realization of the CO2-fertilization effect. Vegetation growth, phenology, agricultural production and irrigation water requirements and withdrawal were simulated with the process-based ecohydrological and agro-ecosystem model LPJmL (Lund–Potsdam–Jena managed Land) after an extensive development that comprised the improved representation of Mediterranean crops. At present the Mediterranean region could save 35 % of water by implementing more efficient irrigation and conveyance systems. Some countries such as Syria, Egypt and Turkey have a higher savings potential than others. Currently some crops, especially sugar cane and agricultural trees, consume on average more irrigation water per hectare than annual crops. Different crops show different magnitudes of changes in net irrigation requirements due to climate change, the increases being most pronounced in agricultural trees. The Mediterranean area as a whole may face an increase in gross irrigation requirements between 4 and 18 % from climate change alone if irrigation systems and conveyance are not improved (4 and 18 % with 2 °C global warming combined with the full CO2-fertilization effect and 5 °C global warming combined with no CO2-fertilization effect, respectively). Population growth increases these numbers to 22 and 74 %, respectively, affecting mainly the southern and eastern Mediterranean. However, improved irrigation technologies and conveyance systems have a large water saving potential, especially in the eastern Mediterranean, and may be able to compensate to some degree for the increases due to climate change and population growth. Both subregions would need around 35 % more water than today if they implement some degree of modernization of irrigation and conveyance systems and benefit from the CO2-fertilization effect. Nevertheless, water scarcity may pose further challenges to the agricultural sector: Algeria, Libya, Israel, Jordan, Lebanon, Syria, Serbia, Morocco, Tunisia and Spain have a high risk of not being able to sustainably meet future irrigation water requirements in some scenarios. The results presented in this study point to the necessity of performing further research on climate-friendly agro-ecosystems in order to assess, on the one hand, their degree of resilience to climate shocks and, on the other hand, their adaptation potential when confronted with higher temperatures and changes in water availability.

Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model.

Functional diversity is critical for ecosystem dynamics, stability and productivity. However, dynamic global vegetation models (DGVMs) which are increasingly used to simulate ecosystem functions under global change, condense functional diversity to plant functional types (PFTs) with constant parameters. Here, we develop an individual- and trait-based version of the DGVM LPJmL (Lund-Potsdam-Jena managed Land) called LPJmL- flexible individual traits (LPJmL-FIT) with flexible individual traits) which we apply to generate plant trait maps for the Amazon basin. LPJmL-FIT incorporates empirical ranges of five traits of tropical trees extracted from the TRY global plant trait database, namely specific leaf area (SLA), leaf longevity (LL), leaf nitrogen content (Narea ), the maximum carboxylation rate of Rubisco per leaf area (vcmaxarea), and wood density (WD). To scale the individual growth performance of trees, the leaf traits are linked by trade-offs based on the leaf economics spectrum, whereas wood density is linked to tree mortality. No preselection of growth strategies is taking place, because individuals with unique trait combinations are uniformly distributed at tree establishment. We validate the modeled trait distributions by empirical trait data and the modeled biomass by a remote sensing product along a climatic gradient. Including trait variability and trade-offs successfully predicts natural trait distributions and achieves a more realistic representation of functional diversity at the local to regional scale. As sites of high climatic variability, the fringes of the Amazon promote trait divergence and the coexistence of multiple tree growth strategies, while lower plant trait diversity is found in the species-rich center of the region with relatively low climatic variability. LPJmL-FIT enables to test hypotheses on the effects of functional biodiversity on ecosystem functioning and to apply the DGVM to current challenges in ecosystem management from local to global scales, that is, deforestation and climate change effects.

Emulating global climate change impacts on crop yields

The potential effects of climate change on the environment and society are many. In order to effectively quantify the uncertainty associated with these effects, highly complex simulation models are run with detailed representations of ecosystem processes. These models are computationally expensive and can involve a computer run of several days. Computationally cheaper models can be obtained from large ensembles of simulations using statistical emulation. The purpose of this article is to construct a cheaper computational model (emulator) from simulations of the Lund-Potsdam-Jena managed Land (LPJmL), which is a dynamic global vegetation and crop model. This article focuses on statistical emulation of potential crop yields from LPJmL and an emulator is constructed using a combination of ordinary least squares, principal component analysis and weighted least squares methods. For five climate models, under cross-validation, the percentage of variance explained ranges from 60 to 88% for the rainfed crops and 62 to 93% for the irrigated crops. The emulator can be used to predict potential crop yield change under any future climate scenarios and management options.

Accounting for environmental flow requirements in global water assessments

Abstract. As the water requirement for food production and other human needs grows, quantification of environmental flow requirements (EFRs) is necessary to assess the amount of water needed to sustain freshwater ecosystems. EFRs are the result of the quantification of water necessary to sustain the riverine ecosystem, which is calculated from the mean of an environmental flow (EF) method. In this study, five EF methods for calculating EFRs were compared with 11 case studies of locally assessed EFRs. We used three existing methods (Smakhtin, Tennant, and Tessmann) and two newly developed methods (the variable monthly flow method (VMF) and the Q90_Q50 method). All methods were compared globally and validated at local scales while mimicking the natural flow regime. The VMF and the Tessmann methods use algorithms to classify the flow regime into high, intermediate, and low-flow months and they take into account intra-annual variability by allocating EFRs with a percentage of mean monthly flow (MMF). The Q90_Q50 method allocates annual flow quantiles (Q90 and Q50) depending on the flow season. The results showed that, on average, 37% of annual discharge was required to sustain environmental flow requirement. More water is needed for environmental flows during low-flow periods (46–71% of average low-flows) compared to high-flow periods (17–45% of average high-flows). Environmental flow requirements estimates from the Tennant, Q90_Q50, and Smakhtin methods were higher than the locally calculated EFRs for river systems with relatively stable flows and were lower than the locally calculated EFRs for rivers with variable flows. The VMF and Tessmann methods showed the highest correlation with the locally calculated EFRs (R2=0.91). The main difference between the Tessmann and VMF methods is that the Tessmann method allocates all water to EFRs in low-flow periods while the VMF method allocates 60% of the flow in low-flow periods. Thus, other water sectors such as irrigation can withdraw up to 40% of the flow during the low-flow season and freshwater ecosystems can still be kept in reasonable ecological condition. The global applicability of the five methods was tested using the global vegetation and the Lund-Potsdam-Jena managed land (LPJmL) hydrological model. The calculated global annual EFRs for fair ecological conditions represent between 25 and 46% of mean annual flow (MAF). Variable flow regimes, such as the Nile, have lower EFRs (ranging from 12 to 48% of MAF) than stable tropical regimes such as the Amazon (which has EFRs ranging from 30 to 67% of MAF).

Global Water Availability and Requirements for Future Food Production

AbstractThis study compares, spatially explicitly and at global scale, per capita water availability and water requirements for food production presently (1971–2000) and in the future given climate and population change (2070–99). A vegetation and hydrology model Lund–Potsdam–Jena managed Land (LPJmL) was used to calculate green and blue water availability per capita, water requirements to produce a balanced diet representing a benchmark for hunger alleviation [3000 kilocalories per capita per day (1 kilocalorie = 4184 joules), here assumed to consist of 80% vegetal food and 20% animal products], and a new water scarcity indicator that relates the two at country scale. A country was considered water-scarce if its water availability fell below the water requirement for the specified diet, which is presently the case especially in North and East Africa and in southwestern Asia. Under climate (derived from 17 general circulation models) and population change (A2 and B1 emissions and population scenarios), water availability per person will most probably diminish in many regions. At the same time the calorie-specific water requirements tend to decrease, due mainly to the positive effect of rising atmospheric CO2 concentration on crop water productivity—which, however, is very uncertain to be fully realized in most regions. As a net effect of climate, CO2, and population change, water scarcity will become aggravated in many countries, and a number of additional countries are at risk of losing their present capacity to produce a balanced diet for their inhabitants.

Harvesting the sun: New estimations of the maximum population of planet Earth

The maximum population, also called Earth's carrying capacity, is the maximum number of people that can live on the food and other resources available on planet Earth. Previous investigations estimated the maximum carrying capacity as large as about 1 trillion people under the assumption that photosynthesis is the limiting process. Here we use a present state-of-the-art dynamic global vegetation model with managed planetary land surface, Lund-Potsdam-Jena managed Land (LPJmL), to calculate the yields of the most productive crops on a global 0.5° × 0.5° grid. Using the 2005 crop distribution the model predicts total harvested calories that are sufficient for the nutrition of 11.4 billion people. We define scenarios where humankind uses the whole land area for agriculture, saves the rain forests and the boreal evergreen forests or cultivates only pasture to feed animals. Every scenario is run in an extreme version with no allowance for urban and recreational needs and in two soft versions with a certain area per person for non-agricultural use. We find that there are natural limits of the maximum carrying capacity which are independent of any increase in agricultural productivity, if non-agricultural land use is accounted for. Using all land planet Earth can sustain 282 billion people. The save-forests-scenario yields 150 billion people. The scenario that cultivates only pasture to feed animals yields 96 billion people. Nevertheless, we should always have in mind that all our calculated numbers for the carrying capacity refer to extreme scenarios where humankind may only vegetate on this planet. Our numbers are considerably higher than the general median estimate of upper bounds of human population found in the literature in the order of 10 billion.

Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

Abstract. Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity. We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of MTE over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while MTE is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with MTE is feasible and able to extract global patterns of carbon flux variability.

Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins

Abstract 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.

Human alterations of the terrestrial water cycle through land management

Abstract. This study quantifies current and potential future changes in transpiration, evaporation, interception loss and river discharge in response to land use change, irrigation and climate change, by performing several distinct simulations within the consistent hydrology and biosphere modeling framework LPJmL (Lund-Potsdam-Jena managed Land). We distinguished two irrigation simulations: a water limited one in which irrigation was restricted by local renewable water resources (ILIM), and a potential one in which no such limitation was assumed but withdrawals from deep groundwater or remote rivers allowed (IPOT). We found that the effect of historical land use change as compared to potential natural vegetation was pronounced, including a reduction in interception loss and transpiration by 25.9% and 10.6%, respectively, whereas river discharge increased by 6.6% (climate conditions of 1991–2000). Furthermore, we estimated that about 1170 km3yr−1 of irrigation water could be withdrawn from local renewable water resources (in ILIM), which resulted in a reduction of river discharge by 1.5%. However, up to 1660 km3yr−1 of water withdrawals were required in addition under the assumption that optimal growth of irrigated crops was sustained (IPOT), which resulted in a slight net increase in global river discharge by 2.0% due to return flows. Under the HadCM3 A2 climate and emission scenario, climate change alone will decrease total evapotranspiration by 1.5% and river discharge by 0.9% in 2046–2055 compared to 1991–2000 average due to changes in precipitation patterns, a decrease in global precipitation amount, and the net effect of CO2 fertilization. A doubling of agricultural land in 2046–2055 compared to 1991–2000 average as proposed by the IMAGE land use change scenario will result in a decrease in total evapotranspiration by 2.5% and in an increase in river discharge by 3.9%. That is, the effects of land use change in the future will be comparable in magnitude to the effects of climate change in this particular scenario. On present irrigated areas future water withdrawal will increase especially in regions where climate changes towards warmer and dryer conditions will be pronounced.