Impact of climate change on renewable groundwater resources : assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections

Reduction of greenhouse gas (GHG) emissions to minimize climate change requires very significant societal effort. To motivate this effort, it is important to clarify the benefits of avoided emissions. To this end, we analysed the impact of four emissions scenarios on future renewable groundwater resources, which range from 1600 GtCO2 during the 21st century (RCP2.6) to 7300 GtCO2 (RCP8.5). Climate modelling uncertainty was taken into account by applying the bias-corrected output of a small ensemble of five CMIP5 global climate models (GCM) as provided by the ISI-MIP effort to the global hydrological model WaterGAP. Despite significant climate model uncertainty, the benefits of avoided emissions with respect to renewable groundwater resources (i.e. groundwater recharge (GWR)) are obvious. The percentage of projected global population (SSP2 population scenario) suffering from a significant decrease of GWR of more than 10% by the 2080s as compared to 1971–2000 decreases from 38% (GCM range 27–50%) for RCP8.5 to 24% (11–39%) for RCP2.6. The population fraction that is spared from any significant GWR change would increase from 29% to 47% if emissions were restricted to RCP2.6. Increases of GWR are more likely to occur in areas with below average population density, while GWR decreases of more than 30% affect especially (semi)arid regions, across all GCMs. Considering change of renewable groundwater resources as a function of mean global temperature (GMT) rise, the land area that is affected by GWR decreases of more than 30% and 70% increases linearly with global warming from 0 to 3 ° C. For each degree of GMT rise, an additional 4% of the global land area (except Greenland and Antarctica) is affected by a GWR decrease of more than 30%, and an additional 1% is affected by a decrease of more than 70%

Evaluation of two new-generation global soil databases for macro-scale hydrological modelling in Norway

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.publishedVersio

Simple Models Outperform More Complex Big-Leaf Models of Daily Transpiration in Forested Biomes

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%; R2 = 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.publishedVersio

Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide - A synthesis

An intercomparison of climate change impacts projected by nine regional-scale hydrological models for 12 large river basins on all continents was performed, and sources of uncertainty were quantified in the framework of the ISIMIP project. The models ECOMAG, HBV, HYMOD, HYPE, mHM, SWAT, SWIM, VIC and WaterGAP3 were applied in the following basins: Rhine and Tagus in Europe, Niger and Blue Nile in Africa, Ganges, Lena, Upper Yellow and Upper Yangtze in Asia, Upper Mississippi, MacKenzie and Upper Amazon in America, and Darling in Australia. The model calibration and validation was done using WATCH climate data for the period 1971–2000. The results, evaluated with 14 criteria, are mostly satisfactory, except for the low flow. Climate change impacts were analyzed using projections from five global climate models under four representative concentration pathways. Trends in the period 2070–2099 in relation to the reference period 1975–2004 were evaluated for three variables: the long-term mean annual flow and high and low flow percentiles Q 10 and Q 90, as well as for flows in three months high- and low-flow periods denoted as HF and LF. For three river basins: the Lena, MacKenzie and Tagus strong trends in all five variables were found (except for Q 10 in the MacKenzie); trends with moderate certainty for three to five variables were confirmed for the Rhine, Ganges and Upper Mississippi; and increases in HF and LF were found for the Upper Amazon, Upper Yangtze and Upper Yellow. The analysis of projected streamflow seasonality demonstrated increasing streamflow volumes during the high-flow period in four basins influenced by monsoonal precipitation (Ganges, Upper Amazon, Upper Yangtze and Upper Yellow), an amplification of the snowmelt flood peaks in the Lena and MacKenzie, and a substantial decrease of discharge in the Tagus (all months). The overall average fractions of uncertainty for the annual mean flow projections in the multi-model ensemble applied for all basins were 57% for GCMs, 27% for RCPs, and 16% for hydrological models

Klimaframskrivninger i Tønsberg og Drammen - Styrking av kommunenes kart over blå og grønne verdier

Denne rapporten gjør rede for metoder brukt for å fremstille temakart for framskrivninger av klima i Tønsberg og Drammen for perioden 2040-2070. Kartene skal gjøres tilgjengelige i kommunenes kartportaler. Temakartene går inn i en større leveranse av temakart over arealdekkets rolle i arbeidet med klimatilpasning, samt klimagassutslipp fra arealbruk og arealbruksendringer for Tønsberg og Drammen kommune. Oppdragene er finansiert med Klimasats-ordningen til Miljødirektoratet

Fear of relapse in schizophrenia: a mixed-methods systematic review

Introduction: Fears of relapse in people diagnosed with schizophrenia have long been recognised as an impediment to recovery and wellbeing. However, the extent of the empirical basis for the fear of relapse concept is unclear. A systematic review is required to collate available evidence and define future research directions. Methods: A pre-registered systematic search (PROSPERO CRD42020196964) of four databases (PubMED, MEDLINE-Ovid, PsycINFO-Ovid, and Cochrane Central Register of Controlled Trials) was conducted from their inception to 05/04/2021. Results: We found nine eligible studies. Five were quantitative (4 descriptive and 1 randomised controlled trial), and four were qualitative. The available quantitative evidence suggests that fear of relapse may have concurrent positive relationships with depression (r = 0.72) and suicide ideation (r = 0.48), and negative relationship with self-esteem (r = 0.67). Qualitative synthesis suggests that fear of relapse is a complex phenomenon with behavioural and emotional components which has both direct and indirect effects on wellbeing. Conclusions: Evidence in this area is limited and research with explicit service user and carer involvement is urgently needed to develop new and/or refine existing measurement tools, and to measure wellbeing rather than psychopathology. Nonetheless, clinicians should be aware that fear of relapse exists and appears to be positively associated with depression and suicide ideation, and negatively associated with self-esteem. Fear of relapse can include fears of losing personal autonomy and/or social/occupational functioning. It appears to impact carers as well as those diagnosed with schizophrenia

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained

Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scale, due to a lack of observations at local and seasonal time scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5 degree) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that global total water withdrawal has increased significantly during 1971–2010, mainly driven by the increase of irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of annual total irrigation water withdrawal in mid and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual scales. The reconstructed gridded water withdrawal dataset is open-access, and can be used for examining issues related to water withdrawals at fine spatial, temporal and sectoral scales

Adjustment of global precipitation data for enhanced hydrologic modeling of tropical Andean watersheds

Global gridded precipitation is an essential driving input for hydrologic models to simulate runoff dynamics in large river basins. However, the data often fail to adequately represent precipitation variability in mountainous regions due to orographic effects and sparse and highly uncertain gauge data. Water balance simulations in tropical montane regions covered by cloud forests are especially challenging because of the additional water input from cloud water interception. The ISI-MIP2 hydrologic model ensemble encountered these problems for Andean sub-basins of the Upper Amazon Basin, where all models significantly underestimated observed runoff. In this paper, we propose simple yet plausible ways to adjust global precipitation data provided by WFDEI, the WATCH Forcing Data methodology applied to ERA-Interim reanalysis, for tropical montane watersheds. The modifications were based on plausible reasoning and freely available tropics-wide data: (i) a high-resolution climatology of the Tropical Rainfall Measuring Mission (TRMM) and (ii) the percentage of tropical montane cloud forest cover. Using the modified precipitation data, runoff predictions significantly improved for all hydrologic models considered. The precipitation adjustment methods presented here have the potential to enhance other global precipitation products for hydrologic model applications in the Upper Amazon Basin as well as in other tropical montane watersheds