Bias-corrected Climate Projections Research Articles

Future temperature-related mortality in the UK under climate change scenarios: impact of population ageing and bias-corrected climate projections

BackgroundExposure to heat and cold poses a serious threat to human health. In the UK, hotter summers, milder winters and an ageing population will shift how populations experience temperature-related health burdens. Estimating future burdens can provide insights on the drivers of temperature-related health effects and removing biases in temperature projections is an essential step to generating these estimates, however, the impact of various methods of correction is not well examined. MethodsWe conducted a detailed health impact assessment by estimating mortality attributable to temperature at a baseline period (2007-2018) and in future decades (2030s, 2050s and 2070s). Epidemiological exposure-response relationships were derived for all England regions and UK countries, to quantify cold and heat risk, and temperature thresholds where mortality increases. UK climate projections 2018 (UKCP18)were bias-corrected using three techniques: correcting for mean bias (shift or SH), variability (bias-correction or BC) and extreme values (quantile mapping or QM). These were applied in the health impact assessment, alongside consideration of population ageing and growth to estimate future temperature-related mortality. FindingsIn the absence of adaptation and assuming a high-end emissions scenario (RCP8.5), annual UK temperature-related mortality is projected to increase, with substantial differences in raw vs. calibrated projections for heat-related mortality, but smaller differences for cold-related mortality. The BC approach gave an estimated 29 deaths per 100,000 in the 2070s, compared with 50 per 100,000 using uncorrected future temperatures. We also found population ageing may exert a bigger impact on future mortality totals than the impact from future increases in temperature alone. Estimating future health burdens associated with heat and cold is an important step towards equipping decision-makers to deliver suitable care to the changing population. Correcting inherent biases in temperature projections can improve the accuracy of projected health burdens to support health protection measures and long-term resilience planning.

Assessing Climate Change Effects on Winter Wheat Production in the 3H Plain: Insights from Bias-Corrected CMIP6 Projections

Climate change exerts significant impacts on regional agricultural production. This study assesses the implications of climate change on winter wheat yields in the Huang-Huai-Hai Plain (3H Plain), utilizing bias-corrected climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for mid-21st century (2041–2060) and late 21st century (2081–2100) periods under two shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5). These projections were incorporated into the decision support system for agrotechnology transfer (DSSAT) CERES-Wheat model to forecast potential alterations in winter wheat production. Initial findings reveal that uncorrected CMIP6 projections underestimated temperature and precipitation while overestimating solar radiation across the southern 3H Plain. Following bias correction through the equidistant cumulative distribution function (EDCDF) method, the regional average biases for temperature, precipitation, and solar radiation were reduced by 18.3%, 5.6%, and 30.7%, respectively. Under the SSP2–4.5 and SSP5–8.5 scenarios, mid-21st century simulations predicted a 13% increase in winter wheat yields. Late 21st century projections indicated yield increases of 11.3% and 3.6% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, with a notable 8.4% decrease in yields south of 36° N under the SSP5-8.5 scenario. The analysis of climate change factors and winter wheat yields in the 3H Plain under both scenarios identified precipitation as the key contributing factor to yield increases in the northern 3H Plain, while temperature limitations were the primary constraint on yields in the southern region. Consequently, adaptive strategies are essential to mitigate climate change impacts, with a particular focus on addressing the challenges posed by elevated temperature in the southern 3H Plain.

High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections

We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901–1930, 1931–1960, 1961–1990, and 1991–2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041–2070 and 2071–2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO2-induced warming rates. We estimate that from 1901–1930 to 1991–2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991–2020 to 2071–2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen.

Large sex differences in vulnerability to circulatory-system disease under current and future climate in Bucharest and its rural surroundings

Circulatory-system diseases (CSDs) are responsible for 50–60% of all deaths in Romania. Due to its continental climate, with cold winters and very warm summers, there is a strong temperature dependence of the CSD mortality. Additionally, within its capital Bucharest, the urban heat island (UHI) is expected to enhance (reduce) heat (cold)-related mortality. Using distributed lag non-linear models, we establish the relation between temperature and CSD mortality in Bucharest and its surroundings. A striking finding is the strong temperature-related response to high urban temperatures of women in comparison with men from the total CSDs mortality. In the present climate, estimates of the CSDs attributable fraction (AF) of mortality at high temperatures is about 66% higher in Bucharest than in its rural surroundings for men, while it is about 100% times higher for women. Additionally, the AF in urban areas is also significantly higher for elderly people, and for those with hypertensive and cerebrovascular diseases than in the rural surroundings. On the other hand, in rural areas, men but especially women are currently more vulnerable with respect to low temperatures than in the urban environment. In order to project future thermal-related mortality, we have used five bias-corrected climate projections from regional circulation models under two climate-change scenarios, RCP4.5 and RCP8.5. Analysis of the temperature-mortality associations for future climate reveals the strongest signal under the scenario RCP8.5 for women, elderly people as well as for groups with hypertensive and cerebrovascular diseases. The net AF increase is much larger in urban agglomeration for women (8.2 times higher than in rural surroundings) and elderly people (8.5 times higher than in rural surroundings). However, our estimates of thermal attributable mortality are most likely underestimated due to the poor representation of UHI and future demography.

Drought impacts on hydrology and water quality under climate change

The Intergovernmental Panel on Climate Change (IPCC) has predicted that droughts are projected to affect global hydrology and water quality in varying ways, resulting in a considerable challenge to water availability for society, environment, and ecosystems. This study employed the Soil and Water Assessment Tool to evaluate how drought affects hydrology and water quality in the Miyun Reservoir watershed, coupled with bias-corrected climate projections in the Representative Concentration Pathway 8.5 scenario, accommodating the intercoupling effects of precipitation shifts and rising temperatures. The standardized precipitation index (SPI), standardized runoff index (SRI), and standardized soil moisture index (SSWI) were used to characterize meteorological, hydrological, and agricultural droughts that occur in the different phases in the hydrological cycle. Climate change had the most significant impact on agricultural drought. SSWI were projected to considerably increase in intensity, frequency, and duration in most subbasins by up to 15 %, 55 %, and 45 %, respectively, and showed a strong correlation with meteorological and hydrological droughts (correlation coefficients r = 0.54, 0.57, and 0.60 with SPI for the baseline, near future and far future periods, and 0.91, 0.87, and 0.89 with SRI for the three periods, respectively). Hydrological components, sediment export, and nutrient loss were highly correlated with changes in drought indexes, with r ranging between −0.68 and 0.34 in the near future period and −0.62 and 0.53 in the far future period. Drought conditions of surface runoff and soil water dominated the changes in sediment export, and hydrological drought was the major cause for reduced nutrient loads. In addition to drought impacts, the synergistic effects of increasing precipitation and rising temperature led to a certain degree of increase in sediment and nutrient exports. The results of this study emphasize the need to enhance the resilience of watershed systems to the predicted increases in the intensity, frequency, and duration of droughts.

Assessment of Climate Change Impact on the Annual Maximum Flood in an Urban River in Dublin, Ireland

Hydrological modelling to address the problem of flood risk corresponding to climate change can play an important role in water resources management. This paper describes the potential impact of climate change on an urban river catchment using a physically based hydrological model called Soil Water Assessment Tool (SWAT). The study area considered is the Dodder River basin located in the southern part of Dublin, the capital city of Ireland. Climate projections from three regional climate models and two representative concentration pathways (RPC 4.5 and RCP 8.5) were used to evaluate the impact of flooding corresponding to different climate change scenarios. Annual maximum flow (AMF) is generated by combining the bias-corrected climate projections with the calibrated and validated SWAT model to understand the projected changes in flood patterns for the year 2021–2100. The expected changes in flood quantiles were estimated using a generalised extreme value distribution. The results predicted up to 12% and 16% increase in flood quantiles corresponding to 50 years and 100 years return periods. Based on the flood quantiles, flood inundation maps were developed for the study area.

Contrasting changes in hydrological processes of the Volta River basin under global warming

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of 12 general circulation models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) that are dynamically downscaled by five regional climate models (RCMs) from the Coordinated Regional-climate Downscaling Experiment (CORDEX)-Africa is used. In total, 43 RCM–GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The bias-corrected climate projections are then used as input to the mesoscale Hydrologic Model (mHM) for hydrological projections over the 21st century (1991–2100). Results reveal contrasting dynamics in the seasonality of rainfall, depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture, and terrestrial water storage) is only projected under RCP8.5. High- and low-flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. The evolutions of streamflow indicate a future delay in the date of occurrence of low flows up to 11 d under RCP8.5, while high flows could occur 6 d earlier (RCP2.6) or 5 d later (RCP8.5), as compared to the historical period. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water–energy–food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision-makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socioeconomic development.

Projected Streamflow and Sediment Supply under Changing Climate to the Coast of the Kalu River Basin in Tropical Sri Lanka over the 21st Century

Tropical countries are already experiencing the adverse impacts of climate change. This study presents projections of climate change-driven variations in hydrology and sediment loads in the Kalu River Basin, Sri Lanka. Bias-corrected climate projections (i.e., precipitation and temperature) from three high resolution (25 km) regional climate models (viz., RegCM4-MIROC5, MPI-M-MPI-ESM-MR, and NCC-NORESM1-M) are used here to force a calibrated hydrological model to project streamflow and sediment loads for two future periods (mid-century: 2046–2065, and end of the century: 2081–2099) under two representative concentration pathways (i.e., RCPs 2.6 and 8.5). By the end of the century under RCP 8.5, all simulations (forced with the three RCMs) project increased annual streamflow (67–87%) and sediment loads (128–145%). In general, streamflow and sediment loads are projected to increase more during the southwest monsoon season (May–September) than in other periods. Furthermore, by the end of the century, all simulations under the RCP 8.5 project a shift of streamflow and sediment loads in the southwest monsoon peak from May to June, while preserving the peak in the inter-monsoon 2 (in October). The projected changes in annual sediment loads are greater than the projected changes in annual streamflow (in percentage) for both future periods.

Building resiliency to climate change uncertainty through bioretention design modifications

Climate stationarity is a traditional assumption in the design of the urban drainage network, including green infrastructure practices such as bioretention cells. Predicted deviations from historic climate trends associated with global climate change introduce uncertainty in the ability of these systems to maintain service levels in the future. Climate change projections are made using output from coarse-scale general circulation models (GCMs), which can then be downscaled using regional climate models (RCMs) to provide predictions at a finer spatial resolution. However, all models contain sources of error and uncertainty, and predicted changes in future climate can be contradictory between models, requiring an approach that considers multiple projections. The performance of bioretention cells were modeled using USEPA's Storm Water Management Model (SWMM) to determine how design modifications could add resilience to these systems under future climate conditions projected for Knoxville, Tennessee, USA. Ten downscaled climate projections were acquired from the North American Coordinated Regional Downscaling Experiment program, and model bias was corrected using Kernel Density Distribution Mapping (KDDM). Bias-corrected climate projections were used to assess bioretention hydrologic function in future climate conditions. Several scenarios were evaluated using a probabilistic approach to determine the confidence with which design modifications could be implemented to maintain historic performance for both new and existing (retrofitted) bioretention cells. The largest deviations from current design (i.e., concurrently increasing ponding depths, thickness of media layer, media conductivity rates, and bioretention surface areas by 307%, 200%, 200%, and 300%, respectively, beyond current standards) resulted in the greatest improvements on historic performance with respect to annual volumes of infiltration and surface overflow, with all ten future climate scenarios across various soil types yielding increased infiltration and decreased surface overflow compared to historic conditions. However, lower performance was observed for more conservative design modifications; on average, between 13-82% and 77–100% of models fell below historic annual volumes of infiltration and surface overflow, respectively, when ponding zone depth, media layer thickness, and media conductivity were increased alone. Findings demonstrate that increasing bioretention surface area relative to the contributing catchment provides the greatest overall return on historic performance under future climate conditions and should be prioritized in locations with low in situ soil drainage rates. This study highlights the importance of considering local site conditions and management objectives when incorporating resiliency to climate change uncertainty into bioretention designs.

Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

Impacts of hydrological model calibration on projected hydrological changes under climate change\u2014a multi-model assessment in three large river basins

This study aimed to investigate the influence of hydrological model calibration/validation on discharge projections for three large river basins (the Rhine, Upper Mississippi and Upper Yellow). Three hydrological models (HMs), which have been firstly calibrated against the monthly discharge at the outlet of each basin (simple calibration), were re-calibrated against the daily discharge at the outlet and intermediate gauges under contrast climate conditions simultaneously (enhanced calibration). In addition, the models were validated in terms of hydrological indicators of interest (median, low and high flows) as well as actual evapotranspiration in the historical period. The models calibrated using both calibration methods were then driven by the same bias corrected climate projections from five global circulation models (GCMs) under four Representative Concentration Pathway scenarios (RCPs). The hydrological changes of the indicators were represented by the ensemble median, ensemble mean and ensemble weighted means of all combinations of HMs and GCMs under each RCP. The results showed moderate (5–10%) to strong influence (> 10%) of the calibration methods on the ensemble medians/means for the Mississippi, minor to moderate (up to 10%) influence for the Yellow and minor (< 5%) influence for the Rhine. In addition, the enhanced calibration/validation method reduced the shares of uncertainty related to HMs for three indicators in all basins when the strict weighting method was used. It also showed that the successful enhanced calibration had the potential to reduce the uncertainty of hydrological projections, especially when the HM uncertainty was significant after the simple calibration.

High-Resolution Climate Projections for a Densely Populated Mediterranean Region

The present study projected future climate change for the densely populated Central North region of Egypt (CNE) for two representative concentration pathways (RCPs) and two futures (near future: 2020–2059, and far future: 2060–2099), estimated by a credible subset of five global climate models (GCMs). Different bias correction models have been applied to correct the bias in the five interpolated GCMs’ outputs onto a high-resolution horizontal grid. The 0.05° CNE datasets of maximum and minimum temperatures (Tmx, and Tmn, respectively) and the 0.1° African Rainfall Climatology (ARC2) datasets represented the historical climate. The evaluation of bias correction methodologies revealed the better performance of linear and variance scaling for correcting the rainfall and temperature GCMs’ outputs, respectively. They were used to transfer the correction factor to the projections. The five statistically bias-corrected climate projections presented the uncertainty range in the future change in the climate of CNE. The rainfall is expected to increase in the near future but drastically decrease in the far future. The Tmx and Tmn are projected to increase in both future periods reaching nearly a maximum of 5.50 and 8.50 °C for Tmx and Tmn, respectively. These findings highlighted the severe consequence of climate change on the socio-economic activities in the CNE aiming for better sustainable development.

Assessment of water and energy scarcity, security and sustainability into the future for the Three Gorges Reservoir using an ensemble of RCMs

The looming impacts of changing climate and ever increasing water and energy demands make it important to quantify expected water and energy availabilities and develop strategies to mitigate expected shortfalls. Keeping these aspects in mind, in this paper, hydrological modeling is performed on the Upper Yangtze River Basin (UYRB) to simulate the inflows to the Three Gorges Reservoir (TGR) based on the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model and a Multiple-input single-output Linear Systematic Model (MLSM). These models are derived for historical (1960–2005) and future (near: 2021–2050 and far: 2061–2090) time periods using bias corrected climate projections from an ensemble of 6 RCMs available through the COordinated Regional Downscaling EXperiment in East Asia (CORDEX-EA) under Representative Concentration Pathway (RCP) 4.5 and 8.5. Simulating with and without snow, hydrological responses to both the historical and future climates are fed into a daily reservoir simulation model where the operation of the TGR follows the designed operating rule curves which can be regarded as a standard operating policy (SOP). The results indicate marginal reduction in mean annual precipitation, inflow and hydropower generation and mean hydropower generation reliability for the future scenarios under RCP 8.5 with the decreases for far future being more prominent than those for near future. The inflow decreases strongly reduce the hydropower generation of the TGR in November and May and have limited impact on other months because of the regulation ability of the SOP. Hydropower generation responses to extreme variations in annual inflow are projected to aggravate the water and energy security stress of the TGR. The without snow projections alter the inflow patterns as well as the hydropower generation patterns of the TGR with respect to the with snow projections and are likely to have positive impact on the waterimpounding and hydropower generation for both the historical and future time periods.

Adapting water resources systems to climate change in tropical areas: Ecuadorian coast

Climate change is expected to increase rainfall and temperature in the tropical areas of the Ecuadorian coast. The increase in temperature will also increase evapotranspiration therefore, future water balance on Ecuadorian coast will have a slight variation. Changes in precipitation patterns and evapotranspiration will produce an increase in the water requirements for current crops, so an imbalance in the water resources systems between natural resources and water demands is expected. This study presents water resources management as an adaptation measure to climate change for reducing vulnerability in tropical areas.Twelve bias-corrected climate projections are used, from: two AR5 General Circulation Models (GCMs), two Representative Concentration Pathways, 4.5–8.5 scenarios, and three time periods, short-term (2010–2039), medium-term (2040–2069) and long-term (2070–2099). These data were incorporated into the Lumped Témez Hydrological Model. Climate change scenarios predict for the long-term period both a mean rainfall and temperature increases up to 22%-2.8 °C, respectively. Besides, the potential evapotranspiration will increase until 12% by Penman-Monteith method and 60% by Thornthwaite method. Therefore, natural water resources will finally have an increase of 19% [8–30%]. Additionally, water requirements for crops will increase around 4% and 45%.As this research shows, in tropical regions, currently viable water resources systems could become unsustainable under climate change scenarios. To guarantee the water supply in the future additional measures are required as reservoir operation rules and irrigation efficiency improvement of system from 0.43 to 0.65, which it involves improving the distribution and application system. In study area future irrigation areas have been estimated for 13,268 ha, which under climate change scenarios is unsustainable, only 11,500 ha could be expanded with a very high irrigation efficiency of 0.73. Therefore, in tropical areas the effect of climate change on expansion projects for irrigated areas should be considered to ensure the functioning systems.

Spatiotemporal Variability in Future Extreme Temperatures and Rainfall in the Yangtze River Basin: Update Using Bias-Corrected Climate Projections Fitted by Stationary and Nonstationary Model

AbstractThe Yangtze River is the third largest river basin in the world. With the advancement of research methods and data quality, the understanding of extreme climate changes in the Yangtze River...

Climate change induced impact and uncertainty of rice yield of agro-ecological zones of India

An innovative approach of using agro-ecological zones (AEZs), instead of using political boundaries, has been adopted for climate change impact analysis on rice production of India. The analysis has been carried out by using a process-based Crop Simulation Model (CSM)-CERES-Rice fed with improved state of art bias corrected climate projections from eight Global Climate Models (GCMs) for four expected climatic scenarios- Representative Concentration Pathways (RCP 2.6, 4.5, 6.0 and 8.5). Using weather-soil-crop information along with year-wise effect of CO2 increase assumption for different RCPs as input to the crop model, simulations were performed for the base period (1976–2005) as well as three future periods (2020s: 2006–2035, 2050s: 2036–2065, and 2080s: 2066–2095) for insight understanding of climate change impact on rice yield. Model simulated rice yields of future periods were compared with that of the base period to quantify the climate change impact. Results based on multi-GCM ensemble show expected increase in rice yield of most of the AEZs in RCP 2.6 but as on moving towards RCP 8.5 through RCP 4.5 and 6.0, the positive impact on rice yield in RCP 2.6, in major rice producing zones, is expected to mitigate and lead to the negative impact by 2080s. Large spatiotemporal variability is expected in most of the zones with humongous variability in arid and hilly zones. The overall change in spatial rice yield in India taking all used GCM-RCP combinations in consideration is expected to vary from 1.2 to 8.8%, 0.7 to 12.6% and −2.9 to 17.8% due to the expected climate change in 2020s, 2050s and 2080s, respectively.

Hydrological impacts of moderate and high-end climate change across European river basins

Study regionTo provide a picture of hydrological impact of climate change across different climatic zones in Europe, this study considers eight river basins: Tagus in Iberian Peninsula; Emån and Lule in Scandinavia; Rhine, Danube and Teteriv in Central and Eastern Europe; Tay on the island of Great Britain and Northern Dvina in North-Eastern Europe. Study focusIn this study the assessment of the impacts of moderate and high-end climate change scenarios on the hydrological patterns in European basins was conducted. To assess the projected changes, the process-based eco-hydrological model SWIM (Soil and Water Integrated Model) was set up, calibrated and validated for the basins. The SWIM was driven by the bias-corrected climate projections obtained from the coupled simulations of the Global Circulation Models and Regional Climate Models. New hydrological insights for the regionThe results show robust decreasing trends in water availability in the most southern river basin (Tagus), an overall increase in discharge in the most northern river basin (Lule), increase in the winter discharge and shift in seasonality in Northern and Central European catchments. The impacts of the high-end climate change scenario RCP 8.5 continue to develop until the end of the century, while those of the moderate climate change scenario RCP 4.5 level-off after the mid-century. The results of this study also confirm trends, found previously with mostly global scale models.

Harmonizing human-hydrological system under climate change: A scenario-based approach for the case of the headwaters of the Tagus River

Conventional water management strategies, that serve solely socio-economic demands and neglect changing natural conditions of the river basins, face significant challenges in governing complex human-hydrological systems, especially in the areas with constrained water availability. In this study we assess the possibility to harmonize the inter-sectoral water allocation scheme within a highly altered human-hydrological system under reduction in water availability, triggered by projected climate change applying scenario-based approach. The Tagus River Basin headwaters, with significant disproportion in the water resources allocation between the environmental and socio-economic targets were taken as a perfect example of such system out of balance. We propose three different water allocation strategies for this region, including two conventional schemes and one imposing shift to sustainable water management and environmental restoration of the river. We combine in one integrated modelling framework the eco-hydrological process-based Soil and Water Integrated Model (SWIM), coupled with the conceptual reservoir and water allocation modules driven by the latest bias-corrected climate projections for the region and investigate possible water allocation scenarios in the region under constrained water availability in the future. Our results show that the socio-economic demands have to be re-considered and lowered under any water allocation strategy, as the climate impacts may significantly reduce water availability in the future. Further, we show that a shift to sustainable water management strategy and river restoration is possible even under reduced water availability. Finally, our results suggest that the adaptation of complex human-hydrological systems to climate change and a shift to a more sustainable water management are likely to be parts of one joint strategy to cope with climate change impacts.

Assessing changes of river discharge under global warming of 1.5 °C and 2 °C in the upper reaches of the Yangtze River Basin: Approach by using multiple- GCMs and hydrological models

Assessment of climate change impacts on regional hydrological processes is vital for effective water resource management and planning. The 2015 Paris Agreement includes a two-headed temperature goal: “holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C”. The current study assesses the potential effect of global warming of 1.5 °C and 2 °C on river discharge, and their differences in the Upper Yangtze River Basin (UYRB) based on three hydrological models (HBV, SWAT and VIC) driven by bias-corrected climate projections from five General Circulation models (GCMs), namely GFDL, HAD, IPSL, MIROC and NOR as abbreviated, and two Representative Concentration Pathways (RCP2.6 and RCP4.5). Results indicate that all discharges (except autumn) especially winter have slight decreasing tendency (−0.3%, −2.3%, −0.8%, −6.4%, −1.2%, −0.3% respectively for annual, spring, summer, winter, 90% percentile and 10% percentile discharges) under RCP2.6 during 2020–2039 (+1.5 °C global warming) comparing to the reference period of 1986–2005. However, all discharges (except autumn and winter) especially summer have an increasing tendency (1.1%, 1.1%, 3.7%, 0.9%, 2.1% respectively for annual, spring, summer, 90% percentile and 10% percentile discharges) under RCP4.5 during 2040–2059 (+2 °C global warming). Moreover, the occurrence frequency and strength of droughts in dry season under global warming of 1.5 °C will increase, while floods in wet season under global warming of 2 °C tend to increase in UYRB. Additionally, the difference of river discharge between global warming of 2 °C and 1.5 °C is positive for almost all the time scales (1.4%, 3.5%, 4.5%, 2.1%, 2.4% respectively for annual, spring, summer, 90% percentile and 10% percentile discharges), which suggests that the increment of 0.5 °C could lead to more flood events in the UYRB.

Providing peak river flow statistics and forecasting in the Niger River basin

Flooding is a growing concern in West Africa. Improved quantification of discharge extremes and associated uncertainties is needed to improve infrastructure design, and operational forecasting is needed to provide timely warnings. In this study, we use discharge observations, a hydrological model (Niger-HYPE) and extreme value analysis to estimate peak river flow statistics (e.g. the discharge magnitude with a 100-year return period) across the Niger River basin. To test the model's capacity of predicting peak flows, we compared 30-year maximum discharge and peak flow statistics derived from the model vs. derived from nine observation stations. The results indicate that the model simulates peak discharge reasonably well (on average + 20%). However, the peak flow statistics have a large uncertainty range, which ought to be considered in infrastructure design. We then applied the methodology to derive basin-wide maps of peak flow statistics and their associated uncertainty. The results indicate that the method is applicable across the hydrologically active part of the river basin, and that the uncertainty varies substantially depending on location. Subsequently, we used the most recent bias-corrected climate projections to analyze potential changes in peak flow statistics in a changed climate. The results are generally ambiguous, with consistent changes only in very few areas. To test the forecasting capacity, we ran Niger-HYPE with a combination of meteorological data sets for the 2008 high-flow season and compared with observations. The results indicate reasonable forecasting capacity (on average 17% deviation), but additional years should also be evaluated. We finish by presenting a strategy and pilot project which will develop an operational flood monitoring and forecasting system based in-situ data, earth observations, modelling, and extreme statistics. In this way we aim to build capacity to ultimately improve resilience toward floods, protecting lives and infrastructure in the region.