Inter-Sectoral Impact Model Intercomparison Project Research Articles

Global warming level indicators of climate change and hotspots of exposure

Abstract In the 21st century, a growing population will be exposed to various hazards caused by a warming climate. Here we present a new database of 12 climate change indicators with a total of 42 variants at different global warming levels (GWLs) (1.2 °C–3.5 °C), which is based on global climate and hydrological model data from the Coupled Model Intercomparison Project round 6 and the Inter-Sectoral Impact Model Intercomparison Project round 3b at 0.5° spatial resolution. It comprises of indicators relating to temperature and precipitation extremes, heatwave events, and hydrological variability. To facilitate the comparison of hazards from different indicators, including for an audience without a scientific background, we have developed a bivariate hazard score which is applied on the grid cell level and incorporates statistics on both the absolute hazard (e.g. low or high precipitation) and the relative change under global warming compared to the reference period (e.g. a large change from low to high precipitation). Additionally, we calculate exposed land area and population through the 21st century for a large set of countries and regions by combining this score with gridded projections of population from the Shared Socioeconomic Pathways (SSPs). The datasets are intended for use by the wider research community and analysts seeking digestible climate hazard and exposure data summarized by GWLs. To illustrate potential uses of the data, in a preliminary analysis we find that even at 1.5 °C large parts of the land area and population face substantial unavoidable risks from multiple indicators, with 86% of the world’s population exposed to at least three indicators with at least medium hazard using the population projections for SSP2 in the year 2050. This picture only worsens with increasing warming, as the land areas facing the highest number of impacts coincide with some of the most densely populated parts of the world.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models’ representation of PCI. Moreover, the models’ poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

Current status and obstacles of narrowing yield gaps of four major crops.

Maize, wheat, rice and soybean production are intimately linked to food security. Identifying the key factors affecting crop yields and determining the countries where increased irrigation and nitrogen application most effectively enhance yields are essential steps towards achieving sustainable development goals and ensuring food security. Identifying these areas is crucially dependent on yield gaps. However, the lack of comparability between different regions in current regional-scale yield gap studies stems from varied methodologies. Moreover, global yield gap research, relying on statistical models and regression methods, tends to neglect the crop growth process. In this study, we used a random forest model, based on statistical and meteorological data, to pinpoint the key factors influencing crop yields. Subsequently, using unified yield data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), derived from crop models simulations, we applied the yield gap method to calculate the potential yield increase for four crops across countries, under conditions of full irrigation and nitrogen application. Our research finds that nitrogen application is the main factor affecting yields globally, while irrigation plays a crucial role in the major producing countries. The countries with high potential for yield increases are located at the border between Africa and Eurasia. The global average yield of the four major crops increased 13.7-29.8% under full irrigation, 2.9-39.1% under full nitrogen application and 29.4-97.8% under both conditions. This study provides crucial insights into global crop yield changes and their determinants, which are highly important for global sustainable agriculture and food security efforts. © 2024 Society of Chemical Industry.

Global wheat planting suitability under the 1.5°C and 2°C warming targets.

The potential distribution of crops will be impacted by climate change, but there is limited research on potential wheat distributions under specific global warming targets. This study employed the Maxent model to predict the potential distribution of wheat under the 1.5°C and 2°C warming targets based on data from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) multimodel ensemble, and the effect of global warming on wheat planting suitability was analyzed. Our results indicated global warming would significantly change wheat planting suitability. Over half of the areas experienced changes in wheat planting suitability under two warming targets, and the effect became more pronounced with increasing temperatures. Additionally, global warming might promote wheat planting in more regions. The area with an increase in wheat planting suitability was observed to be 9% higher than those experiencing a decrease on average. Moreover, global warming could exacerbate the disparity between global wheat supply and demand in countries/regions. Traditional wheat-producing countries/regions are poised to benefit from the warming effects of climate change, while less developed and wheat import-dependent countries/regions may face greater challenges in achieving wheat self-sufficiency. To address this potential challenge, the promotion and inter-regional exchange of agronomic technologies, and the development of more rational trade standards are urgently needed. Since socioeconomic factors have a significant impact on wheat cultivation, further investigation is required to determine how the wheat planting distribution may change in the future under the combined impact of climate change, supply-demand relationship, and policy.

Global hydrological models continue to overestimate river discharge

Global hydrological models (GHMs) are widely used to assess the impact of climate change on streamflow, floods, and hydrological droughts. For the ‘model evaluation and impact attribution’ part of the current round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), modelling teams generated historical simulations based on observed climate and direct human forcings with updated model versions. Here we provide a comprehensive evaluation of daily and maximum annual discharge based on ISIMIP3a simulations from nine GHMs by comparing the simulations to observational data from 644 river gauge stations. We also assess low flows and the effects of different river routing schemes. We find that models can reproduce variability in daily and maximum annual discharge, but tend to overestimate both quantities, as well as low flows. Models perform better at stations in wetter areas and at lower elevations. Discharge routed with the river routing model CaMa-Flood can improve the performance of some models, but for others, variability is overestimated, leading to reduced model performance. This study indicates that areas for future model development include improving the simulation of processes in arid regions and cold dynamics at high elevations. We further suggest that studies attributing observed changes in discharge to historical climate change using the current model ensemble will be most meaningful in humid areas, at low elevations, and in places with a regular seasonal discharge as these are the regions where the underlying dynamics seem to be best represented.

Quantifying and communicating uncertain climate change hazards in participatory climate change adaptation processes

Abstract. Participatory processes for identifying local climate change adaptation measures have to be performed worldwide. As these processes require information about context-specific climate change hazards, we show in this study how to quantify climate change hazards with their uncertainties in regions all around the globe and how to best communicate the potential hazards with their uncertainties in order to identify local climate change adaptation strategies. In a participatory process on water-related adaptation in a biosphere reserve in Germany, we used the freely available output of a multi-model ensemble provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) initiative, which provides global coverage, to quantify the wide range of potential future changes in (ground)water resources. Our approach for quantifying the range of potential climate change hazards can be applied worldwide for local to regional study areas and also for adaptations in agriculture, forestry, fisheries, and biodiversity. We evaluated our approach to communicating uncertain local climate change hazards by means of questionnaires that the stakeholders in the participatory process and the audiences from the general public of two project result presentations answered. To support the stakeholders in participatory climate change adaptation processes, we propose the use of percentile boxes rather than boxplots for visualizing the range of potential future changes. This helps the stakeholders identify the future changes they wish to adapt to, depending on the problem (e.g., resource scarcity vs. resource excess) and their risk aversion. The general public is best informed by simple ensemble averages of potential future changes together with the model agreement on the sign of change. Using or adapting our quantification and communication approach, flexible climate change adaptation strategies can and should be developed worldwide in a participatory and transdisciplinary manner, involving stakeholders and scientists.

The perfect storm? Co-occurring climate extremes in East Africa

Abstract. Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes within the same location and calendar year over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We analyse their co-occurrence on a yearly timescale because some of the climate extremes we consider play out over timescales up to several months. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves and wildfires (+940 % by the end of the century under RCP6.0 relative to present day), followed by river floods and heatwaves (+900 %) and river floods and wildfires (+250 %). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these co-occurring events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that co-occurring extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.

Future changes and distribution of water resources in China under global warming levels of 1.5–4.5 °C

Study regionChina (excluding Hong Kong, Macao, and Taiwan). Study focusFew studies have nationally evaluated China's spatiotemporal distribution of future runoff and per capita water resources (PCWR). Based on the SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5 scenarios, the future runoff and PCWR in China under global warming levels of 1.5–4.5 °C are predicted. The calibrated Water and Energy Budget-based Distributed Hydrological Model with improved Snow physics for Global simulation (WEB-DHM-SG) was driven by the Inter-Sectoral Impact Model Intercomparison Project 3b (ISIMIP3b) protocol data to simulate runoff and future population data were used to predict the PCWR. Furthermore, the pre-industrial scenario was compared with future development scenarios to analyse the impact of human-induced climate change (HICC). New hydrological insights for the regionThe average annual runoff would increase by 6.9–27.6 % nationally and increase in 28 provinces, whereas it would decrease in the other 3 provinces. Seasonal runoff decreased in winter but increased in the other three seasons. Runoff is affected by HICC by 4.1–30.0 %. The national average PCWR would change by −7.5 % to 20.4 %. Water shortage conditions might be further intensified in nine provinces but relatively alleviated in the other 22 provinces. Our results can provide scientific support for the future national and regional water planning and management in China.

Exposure of African ape sites to climate change impacts

Large gaps remain in our understanding of the vulnerability of specific animal taxa and regions to climate change, especially regarding extreme climate impact events. Here, we assess African apes, flagship and highly important umbrella species for sympatric biodiversity. We estimated past (1981–2010) and future exposure to climate change impacts across 363 sites in Africa for RCP2.6 and RCP6.0 for near term (2021–2050) and long term (2071–2099). We used fully harmonized climate data and data on extreme climate impact events from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Historic data show that 171 sites had positive temperature anomalies for at least nine of the past ten years with the strongest anomalies (up to 0.56°C) estimated for eastern chimpanzees. Climate projections suggest that temperatures will increase across all sites, while precipitation changes are more heterogeneous. We estimated a future increase in heavy precipitation events for 288 sites, and an increase in the number of consecutive dry days by up to 20 days per year (maximum increase estimated for eastern gorillas). All sites will be frequently exposed to wildfires and crop failures in the future, and the latter could impact apes indirectly through increased deforestation. 84% of sites are projected to be exposed to heatwaves and 78% of sites to river floods. Tropical cyclones and droughts were only projected for individual sites in western and central Africa. We further compiled available evidence on how climate change impacts could affect apes, for example, through heat stress and dehydration, a reduction in water sources and fruit trees, and reduced physiological performance, body condition, fertility, and survival. To support necessary research on the sensitivity and adaptability of African apes to climate change impacts, and the planning and implementation of conservation measures, we provide detailed results for each ape site on the open-access platform A.P.E.S. Wiki.

Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution

Abstract. Rising seas are a threat to human and natural systems along coastlines. The relation between global warming and sea level rise is established, but the quantification of impacts of historical sea level rise on a global scale is largely absent. To foster such quantification, here we present a reconstruction of historical hourly (1979–2015) and monthly (1900–2015) coastal water levels and a corresponding counterfactual without long-term trends in sea level. The dataset pair allows for impact attribution studies that quantify the contribution of sea level rise to observed changes in coastal systems following the definition of the Intergovernmental Panel on Climate Change (IPCC). Impacts are ultimately caused by water levels that are relative to the local land height, which makes the inclusion of vertical land motion a necessary step. Also, many impacts are driven by sub-daily extreme water levels. To capture these aspects, the factual data combine reconstructed geocentric sea level on a monthly timescale since 1900, vertical land motion since 1900 and hourly storm-tide variations since 1979. The inclusion of observation-based vertical land motion brings the trends of the combined dataset closer to tide gauge records in most cases, but outliers remain. Daily maximum water levels get in closer agreement with tide gauges through the inclusion of intra-annual ocean density variations. The counterfactual data are derived from the factual data through subtraction of the quadratic trend. The dataset is made available openly through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) at https://doi.org/10.48364/ISIMIP.749905 (Treu et al., 2023a).

Historical climate impact attribution of changes in river flow and sediment loads at selected gauging stations in the Nile basin

The Nile basin is the second largest basin in Africa and one of the regions experiencing high climatic diversity with variability of precipitation and deteriorating water resources. As climate change is affecting most of the hydroclimatic variables across the world, this study assesses whether historical changes in river flow and sediment loads at selected gauges in the Nile basin can be attributed to climate change. An impact attribution approach is employed by constraining a process-based model with a set of factual and counterfactual climate forcing data for 69 years (1951–2019), from the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). To quantify the role of climate change, we use the non-parametric Mann-Kendall test to identify trends and calculate the differences in long-term mean annual river flow and sediment load simulations between a model setup using factual and counterfactual climate forcing data. Results for selected river stations in the Lake Victoria basin show reasonable evidence of a long-term historical increase in river flows (two stations) and sediment load (one station), largely attributed to changes in climate. In contrast, within the Blue Nile and Main Nile basins, there is a slight decrease of river flows at four selected stations under factual climate, which can be attributed to climate change, but no significant changes in sediment load (one station). These findings show spatial differences in the impacts of climate change on river flows and sediment load in the study area for the historical period.

Understanding climate change impacts on drought in China over the 21st century: a multi-model assessment from CMIP6

The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs’ simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO2 concentration under high-emission scenarios in the mid- and late-future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.

Historical and projected future runoff over the Mekong River basin

Abstract. The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p<0.05); e.g. 3.81 ± 3.47 m3s-1a-1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m3s-1a-1 (13 ± 10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)

Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Attribution of current trends in streamflow to climate change for 12 Central Asian catchments

This study investigates the attribution of climate change to trends in river discharge during six decades from 1955 until 2014 in 12 selected river catchments across six Central Asian countries located upstream of the main rivers. For this purpose, the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model) was firstly calibrated and validated for all study catchments. Attributing climate change to streamflow simulation trends was forced by factual (reanalysis) and counterfactual climate data (assuming the absence of anthropogenic influence) proposed in the framework of the ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) or ESM without anthropogenic forcing that were firstly tested and then compared. The trend analysis was performed for three variables: mean annual discharge and high flow (Q5) and low flow (Q95) indices. The results show that trends in the annual and seasonal discharge could be attributed to climate change for some of the studied catchments. In the three northern catchments (Derkul, Shagan, and Tobol), there are positive trends, and in two catchments (Sarysu and Kafirnigan), there are negative streamflow trends under the factual climate, which could be attributed to climate change. Also, our analysis shows that the average level of discharge in Murghab has increased during the historical study period due to climate change, despite the overall decreasing trend during this period. In addition, the study reveals a clear signal of shifting spring streamflow peaks in all catchments across the study area.

Evaluation of gridded precipitation data in water availability modeling in CONUS

Water availability estimates are affected by the quality of data used. In this study precipitation data from meteorological forcing datasets specifically compiled for large-scale hydrological modeling are evaluated over the Conterminous United States (CONUS). The performance of the datasets was evaluated using Pearson Correlation (r), Nash Sutcliff Efficiency (NSE), Percent Bias (PBIAS), and Kling Gupta Efficiency (KGE) criterion. Three observation-based gridded precipitation data from the Inter-Sectoral Impact Model Intercomparison Project – ISIMIP (EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI), WATCH Forcing Data methodologyapplied to ERA-Interim data (WFDEI), and WFDE5 over land merged with ERA5 over the ocean (W5E5)) and one regional dataset (GRIDMET) were compared. The datasets were compared using more than 3300 gage precipitation observations. For grid-level comparisons, the Climate Prediction Center's Unified gage-based gridded precipitation dataset (CPC) was used as a reference dataset after verification with gage observations. The grid-to-grid comparison was made on daily, monthly, annual, and seasonal time steps. Grid runoff and discharge were simulated using the Community Water Model (CWatM) model. The flow estimations using the datasets were compared with CPC estimations and with more than 900 discharge observations distributed over the nine climate regions of CONUS. Furthermore, the per capita water availability was estimated and compared. In comparison with gage precipitation observation, CPC had superior performance, followed by GRIDMET. CPC resulted in daily KGE, NSE, and r above 0.5, and PBIAS within 25 % in 79 %, 69 %, 87 %, and 90 % of the stations, respectively. Grid-to-grid precipitation comparisons resulted in overall comparable performance between the datasets. In grid discharge estimation, GRIDMET (median KGE = 0.68) had the highest correlation with CPC. WFDEI, W5E5, and EWEMBI show close performance to each other in estimating grid discharge with median KGE values of 0.43, 0.25, and 0.45 respectively. The performance of the datasets as compared to observed stream flow gages also showed comparable performance with above zero KGE in 73, 56, 71, 60, and 70 stations by GRIDMET, EWEMBI, W5E5, and WFDEI, and CPC respectively. In estimating water availability per capita, W5E5 showed an overestimation as compared to ISMIP datasets. The estimations from CPC and GRIDMET were close to each other. This study serves as a valuable resource for guiding the selection of a suitable dataset for large-scale modeling over the CONUS. By providing performance information for the compared datasets, it assists in making informed data selection and contributes to our understanding of how the selection of input datasets affect water availability modeling.

Assessment of climate change impacts for two tributary basins of the Irtysh River in Kazakhstan

Information about the climate change impacts on river discharge of 2 tributary basins of the Irtysh River, the Oba and Ulbi, is important for effective management of the water resources of the Shulba reservoir in Kazakhstan. The main aim of the study was to investigate potential changes in water resource availability in the study basins. To assess the projected changes, the process-based eco-hydrological model SWIM was used under 2 Representative Concentration Pathway (RCP) climate scenarios obtained from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Our results show an increase in mean annual temperature of 6.1°C, an increase in average annual precipitation of 7.5 to 14%, an increase in annual average discharge for the Oba (6 to 8%) and smaller discharge changes (below 5%) for the Ulbi at the end of the century under RCP8.5—a scenario of comparatively high greenhouse gas emissions—compared to the reference period (1981-2010). Seasonal variations are marked by the peak discharge occurring up to a month earlier, a decrease in the duration of snow accumulation and a reduction in discharge during the summer months. The peak discharge period is prolonged in time, and a slight increase in river discharge could be expected from October to December. Possible future changes in climate may have serious impacts on water resources, agriculture and economic development in the case study basins. The results of this study can be extrapolated to other neighboring basins in Kazakhstan with similar climatic and geophysical characteristics.

Attributing historical streamflow changes in the Jhelum River basin to climate change

Amid a heated debate on what are possible and what are plausible climate futures, ascertaining evident changes that are attributable to historical climate change can provide a clear understanding of how warmer climates will shape our future habitability. Hence, we detect changes in the streamflow simulated using three different datasets for the historical period (1901–2019) and analyze whether these changes can be attributed to observed climate change. For this, we first calibrate and validate the Soil and Water Integrated Model and then force it with factual (observed) and counterfactual (baseline) climates presented in the Inter-Sectoral Impact Model Intercomparison Project Phase 3a protocol. We assessed the differences in simulated streamflow driven by the factual and counterfactual climates by comparing their trend changes ascertained using the Modified Mann–Kendall test on monthly, seasonal, and annual timescales. In contrast to no trend for counterfactual climate, our results suggest that mean annual streamflow under factual climate features statistically significant decreasing trends, which are − 5.6, − 3.9, and − 1.9 m3s−1 for the 20CRv3-w5e5, 20CRv3, and GSWP3-w5e5 datasets, respectively. Such trends, which are more pronounced after the 1960s, for summer, and for high flows can be attributed to the weakening of the monsoonal precipitation regime in the factual climate. Further, discharge volumes in the recent factual climate dropped compared to the early twentieth-century climate, especially prominently during summer and mainly for high flows whereas earlier shifts found in the center of volume timings are due to early shifts in the nival regime. These findings clearly suggest a critical role of monsoonal precipitation in disrupting the hydrological regime of the Jhelum River basin in the future.

Changes in apparent temperature and PM2.5 around the Beijing–Tianjin megalopolis under greenhouse gas and stratospheric aerosol intervention scenarios

Abstract. Apparent temperature (AP) and ground-level aerosol pollution (PM2.5) are important factors in human health, particularly in rapidly growing urban centers in the developing world. We quantify how changes in apparent temperature – that is, a combination of 2 m air temperature, relative humidity, surface wind speed, and PM2.5 concentrations – that depend on the same meteorological factors along with future industrial emission policy may impact people in the greater Beijing region. Four Earth system model (ESM) simulations of the modest greenhouse emissions RCP4.5 (Representative Concentration Pathway), the “business-as-usual” RCP8.5, and the stratospheric aerosol intervention G4 geoengineering scenarios are downscaled using both a 10 km resolution dynamic model (Weather Research and Forecasting, WRF) and a statistical approach (Inter-Sectoral Impact Model Intercomparison Project – ISIMIP). We use multiple linear regression models to simulate changes in PM2.5 and the contributions meteorological factors make in controlling seasonal AP and PM2.5. WRF produces warmer winters and cooler summers than ISIMIP both now and in the future. These differences mean that estimates of numbers of days with extreme apparent temperatures vary systematically with downscaling method, as well as between climate models and scenarios. Air temperature changes dominate differences in apparent temperatures between future scenarios even more than they do at present because the reductions in humidity expected under solar geoengineering are overwhelmed by rising vapor pressure due to rising temperatures and the lower wind speeds expected in the region in all future scenarios. Compared with the 2010s, the PM2.5 concentration is projected to decrease by 5.4 µg m−3 in the Beijing–Tianjin province under the G4 scenario during the 2060s from the WRF downscaling but decrease by 7.6 µg m−3 using ISIMIP. The relative risk of five diseases decreases by 1.1 %–6.7 % in G4, RCP4.5, and RCP8.5 using ISIMIP but has a smaller decrease (0.7 %–5.2 %) using WRF. Temperature and humidity differences between scenarios change the relative risk of disease from PM2.5 such that G4 results in 1 %–3 % higher health risks than RCP4.5. Urban centers see larger rises in extreme apparent temperatures than rural surroundings due to differences in land surface type, and since these are also the most densely populated, health impacts will be dominated by the larger rises in apparent temperatures in these urban areas.