Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques with evaluation against river flow at the global scale for the first time. The modelling procedure was challenging but doable, and even the first model version showed better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for >130 000 catchments (with an average resolution of 1000 km²), delineated to cover the Earth's landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, and the pathways between various compartments. Global parameter values were estimated using a stepwise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily and monthly time series (>10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, the World-Wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best (KGE >0.6) in the eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements, and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version. This first model version clearly indicates challenges in large-scale modelling, usefulness of open data, and current gaps in process understanding. However, we also found that catchment modelling techniques can contribute to advance global hydrological predictions. Setting up a global catchment model has to be a long-term commitment as it demands many iterations; this paper shows a first version, which will be subjected to continuous model refinements in the future. WWH is currently shared with regional/local modellers to appreciate local knowledge.

The 2015 Paris Agreement encourages stakeholders to implement sustainable forest management policies to mitigate anthropogenic emissions of greenhouse gases (GHG). The net effects of forest management on the climate and the environment are, however, still not completely understood, partially as a result of a lack of long-term measurements of GHG fluxes in managed forests. During the period 2010–2013, we simultaneously measured carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) fluxes using the flux-gradient technique at two clear-cut plots of different degrees of wetness, located in central Sweden. The measurements started approx. one year after clear-cutting, directly following soil scarification and planting. The study focused on robust inter-plot comparisons, spatial and temporal dynamics of GHG fluxes, and the determination of the global warming potential of a clear-cut boreal forest. The clear-cutting resulted in significant emissions of GHGs at both the wet and the dry plot. The degree of wetness determined, directly or indirectly, the relative contribution of each GHG to the total budgets. Faster establishment of vegetation on the wet plot reduced total emissions of CO₂ as compared to the dry plot but this was partially offset by higher CH₄ emissions. Waterlogging following clear-cutting likely caused both plots to switch from sinks to sources of CH₄. In addition, there were periods with N₂O uptake at the wet plot, although both plots were net sources of N₂O on an annual basis. We observed clear diel patters in CO₂, CH₄ and N₂O fluxes during the growing season at both plots, with the exception of CH₄ at the dry plot. The total three-year carbon budgets were 4107 gCO₂-equivalent m⁻² and 5274 gCO₂-equivalent m⁻² at the wet and the dry plots, respectively. CO₂ contributed 91.8% to the total carbon budget at the wet plot and 98.2% at the dry plot. For the only full year with N₂O measurements, the total GHG budgets were 1069.9 gCO₂-eqvivalents m⁻² and 1695.7 gCO₂-eqvivalents m⁻² at the wet and dry plot, respectively. At the wet plot, CH₄ contributed 3.7%, while N₂O contributed 7.3%. At the dry plot, CH₄ and N₂O contributed 1.5% and 7.6%, respectively. Our results emphasize the importance of considering the effects of the three GHGs on the climate for any forest management policy aiming at enhancing the mitigation potential of forests.

Recent advancements in catchment hydrology (such as understanding hydrological processes, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques at the global scale for the first time. The modelling procedure was challenging but doable and even the first model version show better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for > 130 000 catchments (with an average resolution of 1000 km²), delineated to cover the Earths landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, interfacial fluxes, and the pathways between various compartments. Global parameter values were estimated using a step-wise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily time-series (> 10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in an average monthly KGE of 0.4. However, the world-wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best in Eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows and constancy of daily flow. Nevertheless, there remains large potential for model improvements and we suggest both redoing the calibration and reconsidering parts of the model structure for the next WWH version. The calibration cycle should be repeated a couple of times to find robust values under new fixed parameter conditions. For the next iteration, special focus will be given to precipitation, evapotranspiration, soil storage, and dynamics from hydrological features, such as lakes, reservoirs, glaciers, and floodplains. This first model version clearly indicates challenges in large scale modelling, usefulness of open data and current gaps in processes understanding. Parts of the WWH can be shared with other modellers working at the regional scale to appreciate local knowledge, establish a critical mass of experts and improve the model in a collaborative manner. Setting up a global catchment model has to be a long-term commitment of continuous model refinements to achieve successful and truly useful results.

A user‐friendly high‐resolution intermediate complexity dynamic and spatially distributed flow model is crucial in urban flood modeling. Planners and consultants need to improve the accuracy of floods and estimation of risks. A new flow model will serve as a rapid tool to improve identification of these. This article provides a detailed explanation of a model based on a multiple flow algorithm. Model testing was performed on selected urban and rural areas. Additionally, a sensitivity analysis is conducted to analyze functionality. The model includes basic hydrological processes and is therefore less complex than fully physical models. The data needed to set up and run the new model include spatially and temporally distributed basic geometric and hydrologic variables (i.e., digital elevation model, precipitation, infiltration, and surface roughness). The model is implemented using open‐source coding and can easily be applied to any selected area. Outputs are water volumes, depths, and velocities at different modeling times. Using GIS, results can be visualized and utilized for further analyses. The test, applied in urban as well as rural areas, demonstrates its user‐friendliness, and that the estimated distributed water depths and water velocity at any time step can be saved and visualized.

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This presentation provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator.Some 5338 gauges from these river flow time series (> 10 years) were used in the evaluation of the Worldwide-HYPE (WWH) hydrological model at the global scale (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, WWH performance varies widely spatially and with the target flow signature. The model performs best (KGE > 0.6) in Eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. It also shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version.References:Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., Arheimer, B., (2019). Lessons learnt from checking the quality of openly accessible river flow data worldwide. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2019.1659509Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., Pineda, L., (accepted). Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation. Hydrology and Earth System Sciences Discussions. In press. https://doi.org/10.5194/hess-2019-111

The risk of pluvial flooding is going to increase as climate change causes an increase in intense precipitation along with urbanisation leading to an increase in impermeable surfaces. In the last decade, cities such as Malmö and Copenhagen have already experienced severe pluvial flooding that has caused extensive damage. Adapting to climate change by creating flood resilient urban areas is therefore important and blue-green infrastructure (BGI) may be one measure to accomplish this. A hydrological model called TFM-DYN has been used to investigate whether BGI can aid the mitigation of pluvial flooding. TFM-DYN can also assist in selecting the best locations of BGIs. The problem of modeling urban floods using distributed high resolution hydrological models while considering the hydrological process in the upstream area is difficult due to the limited current computation capacity. However, coupling a distributed hydrological model (TFM-DYN) with an other semi distributed models (HYPE) is crucial to enable simulate, predict and map floods with high-resolution for an urban area while considering its catchment area. With the using of the new suggested coupled hydrological model, it is possible to connect and use the output results from HYPE model as an input to a distributed model (TFM-DYN). The interaction between HYPE and TFM-DYN will consider the hydrologic process occurred outside the model boundary of the interested urban area. The coupling of the two models will help initiating the model with real water depth data that may lead to more realistic simulation. The procedure of input data manipulation using the two model interactions is explained in details. The model is tested on a selected urban area to dynamically simulate the changes in the water depth with time using high resolution gridded data. The new coupled model can be of a great tool for wide range of user and stakeholders as an example to municipalities, water experts, insurance companies and to all other interested water organizations who have access to regional catchment models and in need for a high-resolution, flood simulation and mapping model.