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In order to achieve climate change mitigation, long-term decisions are required that must be reconciled with other societal goals that draw on the same resources. For example, ensuring food security for a growing population may require an expansion of crop land, thereby reducing natural carbon sinks or the area available for bio-energy production. Here, we show that current impact-model uncertainties pose an important challenge to long-term mitigation planning and propose a new risk-assessment and decision framework that accounts for competing interests.
Based on cross-sectorally consistent simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) we discuss potential gains and limitations of additional irrigation and trade-offs of the expansion of agricultural land as two possible response measures to climate change and growing food demand. We describe an illustrative example in which the combination of both measures may close the supply demand gap while leading to a loss of approximately half of all natural carbon sinks.
We highlight current limitations of available simulations and additional steps required for a comprehensive risk assessment.
A new global crop water model was developed to compute blue (irrigation) water requirements and crop evapotranspiration from green (precipitation) water at a spatial resolution of 5 arc minutes by 5 arc minutes for 26 different crop classes. The model is based on soil water balances performed for each crop and each grid cell. For the first time a new global data set was applied consisting of monthly growing areas of irrigated crops and related cropping calendars. Crop water use was computed for irrigated land and the period 1998 – 2002. In this documentation report the data sets used as model input and methods used in the model calculations are described, followed by a presentation of the first results for blue and green water use at the global scale, for countries and specific crops. Additionally the simulated seasonal distribution of water use on irrigated land is presented. The computed model results are compared to census based statistical information on irrigation water use and to results of another crop water model developed at FAO.
In global hydrological models, groundwater storages and flows are generally simulated by linear reservoir models. Recently, the first global gradient-based groundwater models were developed in order to improve the representation of groundwater-surface water interactions, capillary rise, lateral flows and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. The impact of data quality is presented by showing the sensitivity of a groundwater model to changes in the only available global hydraulic conductivity data-set. To better understand the sensitivity of model output to uncertain spatially distributed parameter inputs, we present the first application of a global sensitivity method for a global-scale groundwater model using nearly 2000 steady-state model runs of the global gradient-based groundwater model G3M. By applying the Morris method in a novel domain decomposition approach that identifies global hydrological response units, spatially distributed parameter sensitivities are determined for a computationally expensive model. Results indicate that globally simulated hydraulic heads are equally sensitive to hydraulic conductivity, groundwater recharge and surface water body elevation, though parameter sensitivities vary regionally. For large areas of the globe, rivers are simulated to be either losing or gaining, depending on the parameter combination, indicating a high uncertainty of simulating the direction of flow between the two compartments. Mountainous and dry regions show a high variance in simulated head due to numerical difficulties of the model, limiting the reliability of computed sensitivities in these regions. This instability is likely caused by the uncertainty in surface water body elevation. We conclude that maps of spatially distributed sensitivities can help to understand complex behaviour of models that incorporate data with varying spatial uncertainties. The findings support the selection of possible calibration parameters and help to anticipate challenges for a transient coupling of the model.
Drought is understood as both a lack of water (i.e., a deficit compared to demand) and a temporal anomaly in one or more components of the hydrological cycle. Most drought indices, however, only consider the anomaly aspect, i.e., how unusual the condition is. In this paper, we present two drought hazard indices that reflect both the deficit and anomaly aspects. The soil moisture deficit anomaly index, SMDAI, is based on the drought severity index, DSI (Cammalleri et al., 2016), but is computed in a more straightforward way that does not require the definition of a mapping function. We propose a new indicator of drought hazard for water supply from rivers, the streamflow deficit anomaly index, QDAI, which takes into account the surface water demand of humans and freshwater biota. Both indices are computed and analyzed at the global scale, with a spatial resolution of roughly 50 km, for the period 1981–2010, using monthly time series of variables computed by the global water resources and the model WaterGAP 2.2d. We found that the SMDAI and QDAI values are broadly similar to values of purely anomaly-based indices. However, the deficit anomaly indices provide more differentiated spatial and temporal patterns that help to distinguish the degree and nature of the actual drought hazard to vegetation health or the water supply. QDAI can be made relevant for stakeholders with different perceptions about the importance of ecosystem protection, by adapting the approach for computing the amount of water that is required to remain in the river for the well-being of the river ecosystem. Both deficit anomaly indices are well suited for inclusion in local or global drought risk studies.
Drought is understood as both a lack of water (i.e., a deficit as compared to some requirement) and an anomaly in the condition of one or more components of the hydrological cycle. Most drought indices, however, only consider the anomaly aspect, i.e., how unusual the condition is. In this paper, we present two drought hazard indices that reflect both the deficit and anomaly aspects. The soil moisture deficit anomaly index, SMDAI, is based on the drought severity index, DSI, but is computed in a more straightforward way that does not require the definition of a mapping function. We propose a new indicator of drought hazard for water supply from rivers, the streamflow deficit anomaly index, QDAI, which takes into account the surface water demand of humans and freshwater biota. Both indices are computed and analyzed at the global scale, with a spatial resolution of roughly 50 km, for the period 1981-2010, using monthly time series of variables computed by the global water resources and the model WaterGAP2.2d. We found that the SMDAI and QDAI values are broadly similar to values of purely anomaly-based indices. However, the deficit anomaly indices provide more differentiated, spatial and temporal patterns that help to distinguish the degree of the actual drought hazard to vegetation health or the water supply. QDAI can be made relevant for stakeholders with different perceptions about the importance of ecosystem protection, by adapting the approach for computing the amount of water that is required to remain in the river for the well being of the river ecosystem. Both deficit anomaly indices are well suited for inclusion in local or global drought risk studies.
Flow velocity in rivers has a major impact on residence time of water and thus on high and low water as well as on water quality. For global scale hydrological modeling only very limited information is available for simulating flow velocity. Based on the Manning-Strickler equation, a simple algorithm to model temporally and spatially variable flow velocity was developed with the objective of improving flow routing in the global hydrological model of Water- GAP. An extensive data set of flow velocity measurements in US rivers was used to test and to validate the algorithm before integrating it into WaterGAP. In this test, flow velocity was calculated based on measured discharge and compared to measured velocity. Results show that flow velocity can be modeled satisfactorily at selected river cross sections. It turned out that it is quite sensitive to river roughness, and the results can be optimized by tuning this parameter. After the validation of the approach, the tested flow velocity algorithm has been implemented into the WaterGAP model. A final validation of its effects on the model results is currently performed.
Global-scale assessments of freshwater fluxes and storages by hydrological models under historic climate conditions are subject to a variety of uncertainties. Using the global hydrological model WaterGAP 2.2, we investigated the sensitivity of simulated freshwater fluxes and water storage variations to five major sources of uncertainty: climate forcing, land cover input, model structure, consideration of human water use and calibration (or no calibration). In a modelling experiment, five variants of the standard version of WaterGAP 2.2 were generated that differed from the standard version only regarding the investigated source of uncertainty. Sensitivity was analyzed by comparing water fluxes and water storage variations computed by the variants to those of the standard version, considering both global averages and grid cell values for the time period 1971–2000. The basin-specific calibration approach for WaterGAP, which forces simulated mean annual river discharge to be equal to observed values at 1319 gauging stations (representing 54% of global land area except Antarctica and Greenland), has the highest effect on modelled water fluxes and leads to the best fit of modelled to observed monthly and seasonal river discharge. Alternative state-of-the-art climate forcings rank second regarding the impact on grid cell specific fluxes and water storage variations, and their impact is ubiquitous and stronger than that of alternative land cover inputs. The diverse model refinements during the last decade lead to an improved fit to observed discharge, and affect globally averaged fluxes and storage values (the latter mainly due to modelling of groundwater depletion) but only affect a relatively small number of grid cells. Considering human water use is important for the global water storage trend (in particular in the groundwater compartment) but impacts on water fluxes are rather local and only important where water use is high. The best fit to observed time series of monthly river discharge (Nash–Sutcliffe criterion) or discharge seasonality is obtained with the standard WaterGAP 2.2 model version which is calibrated and driven by a sequence of two time series of daily observation-based climate forcings, WFD/WFDEI. Discharge computed by a calibrated model version using monthly CRU 3.2 and GPCC v6 climate input reduced the fit to observed discharge for most stations. Taking into account the investigated uncertainties of climate and land cover data, we estimate that the global 1971–2000 discharge into oceans and inland sinks is between 40 000 and 42 000 km3 yr−1. The range is mainly due differences in precipitation data that affect discharge in uncalibrated river basins. Actual evapotranspiration, with approximately 70 000 km3 yr−1, is rather unaffected by climate and land cover in global sum but differs spatially. Human water use is calculated to reduce river discharge by approximately 1000 km3 yr−1. Thus, global renewable water resources are estimated to range between 41 000 and 43 000 km3 yr−1. The climate data sets WFD (available until 2001) and WFDEI (starting in 1979) were found to be inconsistent with respect to short wave radiation data, resulting in strongly different potential evapotranspiration. Global assessments of freshwater fluxes and storages would therefore benefit from the development of a global data set of consistent daily climate forcing from 1900 to current.
Objectives In this early retrospective cohort study, a total of 26 patients with SARS-CoV-2 were treated with bamlanivimab or casirivimab/imdevimab, and the reduction of the viral load associated with the developed clinical symptoms was analyzed.
Methods: Patients in the intervention groups received bamlanivimab or casirivimab/imdevimab. Patients without treatment served as control. Outcomes were assessed by clinical symptoms and change in log viral load from baseline based on the cycle threshold over a period of 18 days.
Results: Median log viral load decline was higher in both intervention groups after 3 and 6 days compared to control. However, at later time points, the decline of the viral load was more distinct in the control group. Mild symptoms of COVID-19 were observed in 6.3% of the intervention groups and in no patient of the control. No patients treated with bamlanivimab, 18.8% treated with casirivimab/imdevimab, and 14.2% in the control group developed moderate symptoms. Severe symptoms were recorded only in the control group (14.2%), including one related death.
Conclusion: Treatment with monoclonal SARS-CoV-2 antibodies seems to accelerate decline of virus loads, especially in the first 6 days after administration, compared to control. This may be associated with a reduced likeliness of a severe course of COVID-19.
The design of rainwater harvesting based gardens requires considering current climate but also climate change during the lifespan of the facility. The goal of this study is to present an approach for designing garden variants that can be safely supplied with harvested rainwater, taking into account climate change and adaptation measures. In addition, the study presents a methodology to quantify the effects of climate change on rainwater harvesting based gardening. Results of the study may not be accurate due to the assumptions made for climate projections and may need to be further refined. We used a tank flow model and an irrigation water model. Then we established three simple climate scenarios and analyzed the impact of climate change on harvested rain and horticulture production for a semi-arid region in northern Namibia. In the two climate scenarios with decreased precipitation and medium/high temperature increase; adaptation measures are required to avoid substantial decreases in horticulture production. The study found that the most promising adaptation measures to sustain yields and revenues are a more water efficient garden variant and an enlargement of the roof size. The proposed measures can partly or completely compensate the negative impacts of climate change.
Local climate change risk assessments (LCCRAs) are best supported by a quantitative integration of physical hazards, exposures and vulnerabilities that includes the characterization of uncertainties. We propose to use Bayesian Networks (BNs) for this task and show how to integrate freely-available output of multiple global hydrological models (GHMs) into BNs, in order to probabilistically assess risks for water supply. Projected relative changes in hydrological variables computed by three GHMs driven by the output of four global climate models were processed using MATLAB, taking into account local information on water availability and use. A roadmap to set up BNs and apply probability distributions of risk levels under historic and future climate and water use was co-developed with experts from the Maghreb (Tunisia, Algeria, Morocco) who positively evaluated the BN application for LCCRAs. We conclude that the presented approach is suitable for application in the many LCCRAs necessary for successful adaptation to climate change world-wide.