Potential climate change impacts on water resources have been extensively assessed in Norway due to substantial changes in climate in the recent decades. However, the combined and isolated effects of forest and forest management have been rarely considered in the climate impact studies in Norway although about 38% of the land area is covered by forest. This study aims to improve hydrological impact projections in forest dominant catchments by considering the effects of forest growth and management and to attribute hydrological changes to climate and forest changes. The eco-hydrological model SWIM (Soil and Water Integrated Model) was applied to simulate hydrological processes and extremes for two micro-scale, two meso-scale and two macro-scale catchments, accounting for the effects of spatial scale. The climate projections were generated by three EURO-CORDEX (Coordinated Downscaling Experiment for the European domain) regional climate models (RCMs) for two RCPs (Representative Concentration Pathways, RCP2.6 and RCP4.5) and were bias corrected using the quantile-mapping method. Forest development over time was simulated as a function of climate determining growth and SSP-dependent harvest levels determining wood outtake. The simulations were initialized with the forest status of the year 2020 and different forest types are distinguished according to structural characteristics represented by three key parameters: leaf area index, mean tree height and surface albedo. Preliminary simulation results show that there are minor changes (within ±5%) in hydrological processes under the combinations of the climate and forest scenarios for these catchments. Climate change is the major driver of hydrological change at the catchment scale whereas forest development mainly influences the spatial distribution of the hydrological fluxes. The results further indicate that forest growth under a warming climate helps to reduce the risk of the floods and drought slightly by reducing surface runoff in wet periods and increasing base flow in dry periods, respectively.

Study region: Six forest dominant catchments in Norway: two are micro- (< 10 km2), two are meso- (< 1000 km2) and two are macro-scale (> 10000 km2) catchments. Study focus: This study focuses on the combined climate and forest impacts on streamflow, hydrological components as well as flood and low flow levels. In addition, the relative contributions of climate and forest impacts are distinguished. New hydrological insights for the region: This study provides the first hydrological projections in Norwegian catchments driven by both the climate projections and their corresponding forest projections. Due to warmer climate and higher precipitation under the Representative Concentration Pathway scenarios (RCP2.6 and RCP4.5), continuous increase in forest timber volume is projected in five out of six catchments. The combined effects of climate and forest development lead to median changes in annual streamflow ranging from −2 % to 8 %. Climate is the major driver of streamflow changes, and forest growth slightly offsets the increase in streamflow caused by climate and reduces runoff generation locally. Forest growth reduces the flood levels caused by climate by up to 3 % in all catchments except one with large clear-cutting areas. Forest growth leads to increase in low flow levels in three coniferous forest dominant catchments while it aggravates the low flow conditions in the catchments with high coverage of deciduous forest in the summer half-year.

Water, energy, and food security are threatened by changes in climate. Shifts in rainfall patterns and increases in temperature affect availability of catchment water resources, particularly when hydrological regimes are rainfall-limited, snow-dominant or influenced by glaciers. The sectors dependent on those water resources are therefore more at risk. To evaluate resource availability, sector interdependencies and overall vulnerability of a catchment, a nexus approach can be used. More holistic solutions can then be developed, increasing the catchment’s resilience to changes in future. However, difficulties lie in capturing the dynamics of climate, land, energy, and water systems together. In Norway for instance, this often includes snow, glaciers, and the management of reservoirs for hydropower production, and few nexus methods include these features. To address this, we selected the Community Water Model (CWatM) and made several new developments. CWatM is a widely available, easily adjustable hydrological model on a 1km x 1km daily resolution. It has the facility to include multiple crop types, and domestic, agriculture and industry water demands, therefore highly suitable for nexus assessment. The new developments to CWatM mean that seasonal changes in both reservoir and glacier water storage can now be assessed, so how these have affected, and may affect resilience to changes in climate in future could be evaluated. To test the model developments, we applied the CWatM model to the Otta catchment in Innlandet, Norway. Three large glacial bodies, and four hydropower reservoirs provide water storage to an otherwise rain-limited catchment (~300mm/year). Water resources are required for consistent hydropower production throughout the year, agriculture, and forestry, as well as white water rafting-dependent tourism. These competing demands, alongside the melting of glaciers due to climate change, have the potential to put a large amount of strain on the limited water resources. Results showed that CWatM with the new developments successfully represented the dynamics of stream discharge, glaciers, and reservoir water storage in the Otta catchment. Future work will focus on assessing the vulnerability and resilience of the Otta catchment to climatic extremes given historic and potential future changes in storage with climate change. Wider application of CWatM and the new developments could improve nexus evaluation of other catchments in Norway and worldwide and highlight opportunities for greater resilience to changes in climate.

Boreal forest soils are a critical terrestrial carbon (C) reservoir, with soil organic carbon (SOC) stocks playing a key role in global C cycling. In this study, we generated high-resolution (16 m) spatial predictions of SOC stocks in Norwegian forests for three depth intervals: (1) soil surface down to 100 cm depth, (2) forest floor (LFH layer), and (3) 0-30 cm into the mineral soil. Our predictions were based on legacy soil data collected between 1988 and 1992 from a subset (n = 1014) of National Forest Inventory plots. We used boosted regression tree models to generate SOC estimates, incorporating environmental predictors such as land cover, site moisture, climate, and remote sensing data. Based on the resulting maps, we estimate total SOC stocks of 1.57-1.87 Pg C down to 100 cm, with 0.55-0.66 Pg C stored in the LFH layer and 0.68-0.80 Pg C in the upper mineral soil. These correspond to average SOC densities of 15.3, 5.4, and 6.6 kg C m-2, respectively. We compared the predictive performance of these models with another set, supplemented by soil chemistry variables. These models showed higher predictive performance (R2 = 0.65-0.71) than those used for mapping (R2 = 0.44-0.58), suggesting that the mapping models did not fully capture environmental variability influencing SOC stock distributions. Within the spatial predictive models, Sentinel-2 Normalized Difference Vegetation Index, depth to water table, and slope contributed strongly, while soil nitrogen and manganese concentrations had major roles in models incorporating soil chemistry. Prediction uncertainties were related to soil depth, soil types, and geographical regions, and we compared the spatial prediction against external SOC data. The generated maps of this offer a valuable starting point for identifying forest areas in Norway where SOC may be vulnerable to climate warming and management-related disturbances, with implications for soil CO2 emissions.

Forests, especially in the northern latitudes, are vulnerable ecosystems to climate change, and tree-ring data offer insights into growth-climate relationships as an important effect. Using the National Forest Inventory plot network, we analysed these correlations for the two dominant conifer species in Norway - Norway spruce and Scots pine - for the 1960-2020 period. For both species, the June climate was an important driver of radial growth during this period. Countrywide, the climate-growth correlations divided the Norwegian forests into spatial clusters following a broad shift from temperature- to water-sensitivity of growth with latitude and altitude. The clusters were delineated by a mean 1960-2020 June temperature of ca. 12 degrees C for Norway spruce and Scots pine. The annual mean growing season and July temperatures - but not June temperature - has increased by 1.0 degrees C between the 1960-1990 and 1990-2020 periods, with a slight increase in precipitation. Despite this warming and wetting trend, the long-term growth-climate relationship has remained relatively stable between 1960 and 1990 and 1990-2020 for both species. The threshold between temperature and water-sensitive growth has not changed in the last two 31-year periods, following the stability of the June temperature compared with other months during the growing season. These findings highlight geographically coherent regions in Norway, segregating between temperature- and water-sensitive radial growth for the two major conifer species, temporally stable in the long-term for the 1960-2020 period studied.

Forest restoration and improved forest management are seen as options to enhance terrestrial carbon dioxide removal in many regions, yet concerns surrounding their potentially adverse surface albedo impacts exist, particularly in high latitude and altitude regions. Such concerns are often based on generalized conclusions rooted in analyses carried out over broad spatial extents at coarse resolutions. The impacts of surface albedo change are highly sensitive to local environmental factors governing both the surface albedo and solar radiation budgets, and many previous assessments either do not sufficiently deal with such sensitivities or do not qualify the conditions under which they are relevant. Using the country of Norway with its diverse gradients in topography and climate as an ideal case study region, we seek clarity to the question of whether surface albedo is relevant to consider in forestry planning, and if so, what are the important factors determining it. We find that the adverse impact of a forest’s albedo outweighs its carbon cycle benefit on only ∼4% of Norway’s total forested area, reducing to <∼1% when future climate changes are considered. Our findings challenge the common perception that surface albedo concerns are highly relevant to forestry planning at high latitudes and emphasize the importance of carrying out albedo impact assessments at spatial scales aligning with those of local forestry planning.

ABSTRACT The decision support indicators (DSIs) are specifically designed to inform local and regional stakeholders on the characteristics of a predicted event to facilitate decision-making. They can be classified as conventional, impact-based and event-based DSIs. This study aims to develop methodologies for calculating event-based DSIs and to evaluate the usefulness of different classes of DSIs for climate impact assessment and climate actions by learning about users' perceptions. The DSIs are calculated based on an ensemble of hydrological projections in western Norway under two representative concentration pathway (RCP) scenarios. The definitions, methodologies and results of the indicators are summarized in questionnaires and evaluated by key stakeholders in terms of understandability, importance, plausibility and applicability. Based on the feedback, we conclude that the conventional DSIs are still preferred by stakeholders and an appropriate selection of conventional DSIs may overcome the understanding problems between the scientists and stakeholders. The DSIs based on well-known historical events are easy to understand and can be a useful tool to convey climate information to the public. However, they are not readily implemented by stakeholders in the decision-making process. The impact-based DSI is generally easy to understand and important but it can be restricted to specific impact sectors.

Decision Support Indicators (DSIs) are metrics designed to inform local and regional stakeholders about the characteristics of a predicted (or ongoing) event to facilitate decision-making. In this paper, the DSI concept was developed to clarify the different aims of different kinds of indicators by naming them, and a framework was developed to describe and support the usage of such DSIs. The framework includes three kinds of DSI: hydroclimatic DSIs which are easy to calculate but hard to understand by non-experts; impact-based DSIs which are often difficult to calculate but easy to understand by non-experts; and event-based DSIs, which compare a current or projected state to a locally well-known historical event, where hydroclimatic and impact-based DSIs are currently mainly used. Tables and figures were developed to support the DSI development in collaboration with stakeholders. To develop and test the framework, seven case studies, representing different hydrological pressures on three continents (South America, Asia, and Europe), were carried out. The case studies span several temporal and spatial scales (hours-decades; 70-6,000 km2) as well as hydrological pressures (pluvial and riverine floods, drought, and water scarcity), representing different climate zones. Based on stakeholder workshops, DSIs were developed for these cases, which are used as examples of the conceptual framework. The adaptability of the DSI framework to this wide range of cases shows that the framework and related concepts are useful in many contexts.

&lt;p&gt;Downwelling longwave irradiance (&lt;em&gt;LW&lt;/em&gt;&lt;em&gt;&lt;sub&gt;&amp;#8595;&lt;/sub&gt;&lt;/em&gt;) is challenging and expensive to measure and is often estimated indirectly with parametric modeling of routinely measured surface-level meteorological variables.&amp;#160; Modeling &lt;em&gt;LW&lt;sub&gt;&amp;#8595;&lt;/sub&gt;&lt;/em&gt; under all-sky conditions typically involves &amp;#8220;correcting&amp;#8221; a clear- (or non-overcast) sky model estimate using solar-irradiance-based proxies of cloud cover in lieu of actual cloud cover given uncertainties and measurement challenges of the latter.&amp;#160; While such approaches are deemed sound, their application in time and space is inherently limited.&amp;#160; Here, we present a correction model free of cloud variables applicable at the true daily (24-hr.) and global scale that &amp;#8211; irrespective of the underlying clear-sky model &amp;#8211; yields errors over land that are lower than those from stand-alone models and on par with daytime errors from the prevailing solar-based cloud proxy corrections (rRMSD = ~7%; rMAD = ~5.5%).&amp;#160; We document and critically assess its performance over land and ocean independently, as well as in high elevation and cold environments representing two notoriously challenging conditions.&amp;#160; The cloud-free correction is found to perform better than stand-alone approaches at all subsets; however, within-subset performance differences were evident and attributable to the underlying clear-sky model, reinforcing previous findings surrounding performance thresholds of parametric models with globally-tuned parameters.&amp;#160;&lt;/p&gt;

Abstract Parametric modeling of downwelling longwave irradiance under all‐sky conditions (LW↓) typically involves “correcting” a clear‐ (or non‐overcast) sky model estimate using solar‐irradiance‐based proxies of cloud cover in lieu of actual cloud cover given uncertainties and measurement challenges of the latter. While such approaches are deemed sound, their application in time and space is inherently limited. We report on a correction model free of solar irradiance‐derived cloud proxies that is applicable at the true daily (24 hr) and global scales. The new “cloud‐free” correction model demonstrates superior performance in a range of environments relative to existing cloud‐free modeling approaches and to corrections based on solar‐derived cloudiness proxies. Literature‐based performance benchmarking indicates a performance that is often comparable to—and in some cases superior to—performances yielded by conventional parametric modeling approaches employing locally or regionally calibrated parameters, as well as to performances of satellite‐based algorithms.

Transpiration (T) makes up the bulk of total evaporation over vegetated land yet remains challenging to predict at landscape-to-global scale. Model improvements often occur at the expense of model parsimony and an increased dependence on input data that is difficult to acquire at large scale. T models intended for these scales should ideally be easily scalable using routine meteorological and/or remote sensing data as input.Here, we critically evaluate several “big leaf”-type models ranging in their complexity to simulate daily T in a variety of forest biomes. All these models use input data streams furnished by readily available global reanalysis or satellite-based remote sensing products. We develop and evaluate a novel moisture stress method based on the Antecedent Precipitation Index (API) serving as proxy for soil moisture supply, motivated by the challenge of acquiring reliable soil moisture and other soil physical property data at large spatial and temporal scales.We rely on independent estimates of T derived from co-located sap flow and eddy-covariance measurement systems. The triple collocation technique is employed to quantify error metrics when treating modeled T as a third, independent measurement.Preliminary results suggests that models that explicitly account for the aerodynamic coupling between canopy surfaces and the atmosphere generally perform better than those that do not, and that the API-based approach to modeling constraints related to soil moisture stress appears as a valid alternative when soil moisture information is unavailable.

Transpiration makes up the bulk of total evaporation in forested environments yet remains challenging to predict at landscape-to-global scales. We harnessed independent estimates of daily transpiration derived from co-located sap flow and eddy-covariance measurement systems and applied the triple collocation technique to evaluate predictions from big leaf models requiring no calibration. In total, four models in 608 unique configurations were evaluated at 21 forested sites spanning a wide diversity of biophysical attributes and environmental backgrounds. We found that simpler models that neither explicitly represented aerodynamic forcing nor canopy conductance achieved higher accuracy and signal-to-noise levels when optimally configured (rRMSE = 20%; R-2 = 0.89). Irrespective of model type, optimal configurations were those making use of key plant functional type dependent parameters, daily LAI, and constraints based on atmospheric moisture demand over soil moisture supply. Our findings have implications for more informed water resource management based on hydrological modeling and remote sensing.

Lack of national soil property maps limits the studies of soil moisture (SM) dynamics in Norway. One alternative is to apply the global soil data as input for macro-scale hydrological modelling, but the quality of these data is still unknown. The objectives of this study are 1) to evaluate two recent global soil databases (Wise30sec and SoilGrids) in comparison with data from local soil profiles; 2) to evaluate which database supports better model performance in terms of river discharge and SM for three macro-scale catchments in Norway and 3) to suggest criteria for the selection of soil data for models with different complexity. The global soil databases were evaluated in three steps: 1) the global soil data are compared directly with the Norwegian forest soil profiles; 2) the simulated discharge based on the two global soil databases is compared with observations and 3) the simulated SM is compared with three global SM products. Two hydrological models were applied to simulate discharge and SM: the Soil and Water Integrated Model (SWIM) and the Variable Infiltration Capacity (VIC) model. The comparison with data from local soil profiles shows that SoilGrids has smaller mean errors than Wise30sec, especially for upper soil layers, but both soil databases have large root mean squared errors and poor correlations. SWIM generally performs better in terms of discharge using SoilGrids than using Wise30sec and the simulated SM has higher correlations with the SM products. In contrast, the VIC model is less sensitive to soil input data and the simulated SM using Wise30sec is higher correlated with the SM products than using SoilGrids. Based on the results, we conclude that the global soil databases can provide reasonable soil property information at coarse resolutions and large areas. The selection of soil input data should depend on the characteristics of both models and study areas.

Water consumption along value chains of goods and services has increased globally and led to increased attention on water footprinting. Most global water consumption is accounted for by evaporation (E), which is connected via bridges of atmospheric moisture transport to other regions on Earth. However, the resultant source-receptor relationships between different drainage basins have not yet been considered in water footprinting. Based on a previously developed data set on the fate of land evaporation, we aim to close this gap by using comprehensive information on evaporation recycling in water footprinting for the first time. By considering both basin internal evaporation recycling (BIER; >5% in 2% of the world's basins) and basin external evaporation recycling (BEER; >50% in 37% of the world's basins), we were able to use three types of water inventories (basin internal, basin external, and transboundary inventories), which imply different evaluation perspectives in water footprinting. Drawing on recently developed impact assessment methods, we produced characterization models for assessing the impacts of blue and green water evaporation on blue water availability for all evaluation perspectives. The results show that the negative effects of evaporation in the originating basins are counteracted (and partly overcompensated) by the positive effects of reprecipitation in receiving basins. By aggregating them, combined net impacts can be determined. While we argue that these offset results should not be used as a standalone evaluation, the water footprint community should consider atmospheric moisture recycling in future standards and guidelines.

WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model (WGHM). Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be achieved with WaterGAP 2.2d model output.

Robust projections of changes in the hydrological cycle in a non-stationary climate rely on trustworthy estimates of the water balance elements. Additional drivers than precipitation and temperature, namely wind, radiation, and humidity are known to have a significant influence on processes such as evaporation, snow accumulation, and snow-melt. A gridded version of the rainfall-runoff HBV model is run at a 1 × 1 km scale for mainland Norway for the period 1980–2014, with the following alterations: (i) the implementation of a physically based evaporation scheme; (ii) a net radiation-restricted degree-day factor for snow-melt, and (iii) a diagnostic precipitation phase threshold based on temperature and humidity. The combination of improved forcing data and model alterations allowed for a regional calibration with fewer calibrated parameters. Concurrently, modeled discharge showed equally good or better validation results than previous gridded model versions constructed for the same domain; and discharge trend patterns, snow water equivalent, and potential evaporation compared fairly to observations. Compared with previous studies, lower precipitation and evaporation values for mainland Norway were found. The results suggest that a more robust and more physically based model for climate change studies has been obtained, although additional studies will be needed to further constrain evaporation estimates.

As a carbon dioxide removal measure, the Norwegian government is currently considering a policy of large-scale planting of spruce (Picea abies (L) H. Karst) on non-forested lands (i.e., aff-/reforestation) and secondary forested lands dominated by early successional broadleaved tree species (i.e., improved forest management). Given the need to achieve net zero emissions in the latter half of the 21st century in effort to limit the global mean temperature rise to “well below” 2 °C, the mitigation potential of such a policy is unclear given relatively slow tree growth rates in the region. Further convoluting the picture is the magnitude and relevance of surface albedo changes linked to such projects, which typically counter the benefits of an enhanced forest CO2 sink in high latitude regions. Here, we carry out a rigorous empirical assessment of the terrestrial carbon dioxide removal (tCDR) potential of large-scale aff-/reforestation (AR) and improved forest management (IFM) projects in Norway, taking into account transient developments in both terrestrial carbon sinks and surface albedo over the 21st century and beyond. We find that surface albedo changes would likely play a negligible role in counteracting the carbon cycle benefit of tCDR, yet given slow forest growth rates in the region, meaningful tCDR benefits from AR and IFM projects would not be realized until the end of the 21st century, with maximum benefits occurring around 2150. We estimate Norway’s total accumulated tCDR potential at 2100 and 2150 (including surface albedo changes) to be 447 (± 240) and 852 (± 295) Mt CO2-eq. at mean costs of US$ 29 (± 18) and US$ 26 (± 14) per ton CDR, respectively. For perspective, the accumulated tCDR potential at 2100 represents around 8 years of Norway’s total current annual production-based (i.e., territorial) CO2-eq. emissions.

Freshwater availability is of vital importance for humans, freshwater biota and ecosystem functions. In the past decades, global hydrological models (GHMs) were developed to improve understanding of the global freshwater situation in a globalized word, by filling gaps in observational coverage and assessing scenarios of the future under consideration of different socioeconomic developments and climate change. The Water Global Assessment and Prognosis (WaterGAP) model was one of the first GHMs developed to evaluate freshwater resources and their use for both historical and future conditions. It consists of five water use models (for irrigation, domestic, cooling of thermal power plants, manufacturing, and livestock sectors) and the WaterGAP Global Hydrology Model (WGHM). Recently, the latest model version, WaterGAP 2.2d, was finalized, containing a number of enhancements and revisions such as a river storage-based flow velocity approach, improvements in modelling groundwater recharge in dry environments and integration of historical development of irrigated areas. This presentation provides an overview about the WaterGAP 2.2d scheme and features, assesses global freshwater resources (runoff and streamflow) and water balance components, and provides insights to evaluation results against observed streamflow, GRACE total water storage and the AQUASTAT database.

Various studies investigated the fate of evaporation and the origin of precipitation. The more recent studies among them were often carried out with the help of numerical moisture tracking. Many research questions could be answered within this context, such as dependencies of atmospheric moisture transfers between different regions, impacts of land cover changes on the hydrological cycle, sustainability-related questions, and questions regarding the seasonal and interannual variability of precipitation. In order to facilitate future applications, global datasets on the fate of evaporation and the sources of precipitation are needed. Since most studies are on a regional level and focus more on the sources of precipitation, the goal of this study is to provide a readily available global dataset on the fate of evaporation for a fine-meshed grid of source and receptor cells. The dataset was created through a global run of the numerical moisture tracking model Water Accounting Model-2layers (WAM-2layers) and focused on the fate of land evaporation. The tracking was conducted on a 1.5∘×1.5∘ grid and was based on reanalysis data from the ERA-Interim database. Climatic input data were incorporated in 3- to 6-hourly time steps and represent the time period from 2001 to 2018. Atmospheric moisture was tracked forward in time and the geographical borders of the model were located at ±79.5∘ latitude. As a result of the model run, the annual, the monthly and the interannual average fate of evaporation were determined for 8684 land grid cells (all land cells except those located within Greenland and Antarctica) and provided via source–receptor matrices. The gained dataset was complemented via an aggregation to country and basin scales in order to highlight possible usages for areas of interest larger than grid cells. This resulted in data for 265 countries and 8223 basins. Finally, five types of source–receptor matrices for average moisture transfers were chosen to build the core of the dataset: land grid cell to grid cell, country to grid cell, basin to grid cell, country to country, basin to basin. The dataset is, to our knowledge, the first ready-to-download dataset providing the overall fate of evaporation for land cells of a global fine-meshed grid in monthly resolution. At the same time, information on the sources of precipitation can be extracted from it. It could be used for investigations into average annual, seasonal, and interannual sink and source regions of atmospheric moisture from land masses for most of the regions in the world and shows various application possibilities for studying interactions between people and water, such as land cover changes or human water consumption patterns. The dataset is accessible under https://doi.org/10.1594/PANGAEA.908705 (Link et al., 2019a) and comes along with example scripts for reading and plotting the data.