Nitrous oxide (N₂O) is a long-lived greenhouse gas that contributes to global warming. Emissions of N₂O mainly stem from agricultural soils. This review highlights the principal factors from peer-reviewed literature affecting N₂O emissions from agricultural soils, by grouping the factors into three categories: environmental, management and measurement. Within these categories, each impact factor is explained in detail and its influence on N₂O emissions from the soil is summarized. It is also shown how each impact factor influences other impact factors. Process-based simulation models used for estimating N₂O emissions are reviewed regarding their ability to consider the impact factors in simulating N₂O. The model strengths and weaknesses in simulating N₂O emissions from managed soils are summarized. Finally, three selected process-based simulation models (Daily Century (DAYCENT), DeNitrification-DeComposition (DNDC), and Soil and Water Assessment Tool (SWAT)) are discussed that are widely used to simulate N₂O emissions from cropping systems. Their ability to simulate N₂O emissions is evaluated by describing the model components that are relevant to N₂O processes and their representation in the model.

The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km²) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

Environmental and socio-economic developments induce land-use changes with potentially negative impacts on human well-being. To counteract undesired developments, a profound understanding of the complex relationships between drivers, land use, and ecosystem services is needed. Yet, national studies examining extended time periods are still rare. Based on the Special Report on land use, land management and climate change by the Austrian Panel on Climate Change (APCC), we use the Driver-Pressure-State-Impact-Response (DPSIR) framework to (1) identify the main drivers of land-use change, (2) describe past and future land-use changes in Austria between 1950 and 2100, (3) report related impacts on ecosystem services, and (4) discuss management responses. Our findings indicate that socio-economic drivers (e.g., economic growth, political systems, and technological developments) have influenced past land-use changes the most. The intensification of agricultural land use and urban sprawl have primarily led to declining ecosystem services in the lowlands. In mountain regions, the abandonment of mountain grassland has prompted a shift from provisioning to regulating services. However, simulations indicate that accelerating climate change will surpass socio-economic drivers in significance towards the end of this century, particularly in intensively used agricultural areas. Although climate change-induced impacts on ecosystem services remain uncertain, it can be expected that the range of land-use management options will be restricted in the future. Consequently, policymaking should prioritize the development of integrated land-use planning to safeguard ecosystem services, accounting for future environmental and socio-economic uncertainties.

Study regionBavaria, Germany.Study focusThe Altmühl River is prone to nutrient inputs from agricultural activities. Quantifying nitrate nitrogen (NO3−-N) and total phosphorus (TP) concentrations due to potential future changes in the watershed is necessary for managing water quality and adhering to water policy directives. The Soil and Water Assessment Tool (SWAT) was used to provide stakeholders with support in determining the impacts of climate change (CC) in combination with crop land use change (LUC) scenarios on streamflow, NO3−-N and TP to the 2050 time horizon. The CC simulations stemmed from RCMs and the LUC scenarios were developed with stakeholders.New hydrological insights for the regionWhen CC was combined with LUC, mean annual NO3−-N loads increased 3-fold, and TP loads 8-fold, compared to the CC simulations alone. Nutrient loads were higher in several months due to the future increased annual precipitation plus the additional fertilizer input in the land use scenarios. The maize areas above the Altmühl Lake contributed greatly to TP loads, while winter wheat areas mainly contributed to NO3−-N loads. When CC was combined with LUC, the in-stream nutrient concentrations exceeded ministerial guidelines of 11mgTP/L and 0.05mgNO3−-N/L every month at the outlet. CC simulations combined with LUC scenarios demonstrated non-linear dynamics whereby the direction and the magnitude of impacts were not predictable from the individual changes alone