Quantifying the Contribution of Global Precipitation Product Uncertainty to Ensemble Discharge Simulations and Projections: A Case Study in the Liujiang Catchment, Southwest China

Abstract. The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash–Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

An ensemble-based dynamic Bayesian averaging approach for discharge simulations using multiple global precipitation products and hydrological models

Using multiple satellite-gauge merged precipitation products ensemble for hydrologic uncertainty analysis over the Huaihe River basin

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture.

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

In order to quantify the amount of carbonate, precipitated as calcium-carbonate in the shells of blue mussel (Mytilus edulis) in a temperate climate, an existing Dynamic Energy Budget (DEB) model for the blue mussel was adapted by separating shell growth from soft tissue growth. Hereby, two parameters were added to the original DEB-model, a calcification cost [J/mgCaCO3] and an energy allocation fraction [-], which resulted in the energy allocated for structural growth being divided between shell and meat growth. As values for these new parameters were lacking, they were calibrated by fitting the model to field data. Calibration results showed that an Energy allocation fraction of 0.5 and a calcification cost of 0.9 J/mgCaCO3, resulted in the best fit when fitted on 2017 and 2018 field data separately. These values however, show the best fit for data obtained within the first couple of years of the shellfish life, and do not take later years into account. Also it could be discussed that some parameters vary throughout the lifespan of the species. The results were compared to a regular DEB model, where the shell output was calculated through a simple allometric relationship. It is sometimes assumed that the carbon storage in shell material as calcium carbonate could be regarded as a form of carbon sequestration, with a positive impact on the atmospheric CO2 concentrations. However, studies on the physical-chemical processes related to shell formation have shown that from an oceanographic perspective, shell formation should be regarded as a source of atmospheric CO2 rather than a sink. The removal of carbonates, through the biocalcification process, reduces the buffer capacity (alkalinity) of the water to store CO2. As a result CO2 is released from the water to the atmosphere when shell material is formed. The actual amount of CO2 that escapes from the water to the atmosphere as a result of biocalcification depends strongly on local water characteristics. In this study, the effect of calcification by mussels on the CO2 flux to the atmosphere is studied using an adapted DEB model where energy costs of calcification are modelled explicitly. The model was subsequently run under two future climate scenarios, (RCP 4.5 and RCP 8.3) with elevated temperature and decreased pH, and the total released CO2 as a result of shell formation was calculated with the SeaCarb model. This showed growth of mussels, under future climate conditions to be slower, and with that the cumulative shell mass and carbonate precipitated to CaCO3 to decrease. Yet the amount of CO2 released, due to biocalcification, increased. This is due to the fact that the amount of CO2 released/gr of CaCO3 precipitated will be higher, as a result of the decreased buffering capacity of seawater under future climatic environmental conditions. In summary the conclusions of the project were: • Biocalcification (shell formation) of marine organisms, such as bivalves, cannot be regarded as a process resulting in negative CO2 emission to the atmosphere; • The actual amount of CO2 that, due to biocalcification, is released from the water to the atmosphere depends on the physicochemical characteristics of the water, which are influenced by (future) climate conditions; • Our first model calculations suggest that at future climate conditions mussel’s grow rate will be somewhat reduced. While the amount of CO2 that due to biocalcification, escapes to the atmosphere during its life-time will slightly increase. Making the ratio of g CO2 release/g CaCO3 precipitated slightly higher; • Our model calculations should be considered an exercise rather than a definite prediction of how mussels will respond to future climate scenarios. Additional information/experimentation is strongly needed to validate the model settings, and to test the validity of the above mentioned outcome of the model.

<p>We present findings from an analysis of weather regimes over the North Atlantic and Europe in present and future climate conditions. Weather regimes strongly influence the statistical distribution of surface weather variables. We use a recently developed, all-season North Atlantic - European weather regime classification with seven regimes. These regimes were originally identified in ERA-Interim reanalyses and, in this study, we investigate how they are represented in climate simulations using the CESM1 large ensemble for present-day and future (RCP8.5) climate conditions. With these regimes, the classification of the flow conditions in the considered region goes beyond the classical categorization according to the North Atlantic oscillation index; the weather regimes explicitly capture different flavors of strong zonal flows and the occurrence of blocking over Greenland, Scandinavia, and Central Europe, respectively. In ERA-Interim they explain 70% of the variability in geopotential height at 500 hPa year-round. Our analysis quantifies how well CESM1 represents the statistics of the weather regimes in present-day climate and how strongly their frequencies change in the future climate scenario. In addition, we identify statistical relationships between weather regimes and their resulting impacts on spatial patterns of surface variables such as precipitation. We compare those patterns and characteristics of the weather regimes identified in ERA-Interim to their characteristics in simulations of present and future climate conditions.</p><p>This analysis leads to insight into the representation of and changes in atmospheric circulation in one particular climate model, and, at the same time, it quantifies how well the climate model captures the observed link between surface weather and weather regimes. This approach contributes to improving our understanding of atmospheric circulation changes and their impact on a regional scale, and it may benefit the interpretation and communication of climate projections.</p>

Abstract. Subirrigation has been proposed as a water table management practice to maintain appropriate soil water content during periods of high crop water demand on subsurface drained croplands in the Corn Belt. Subirrigation takes advantage of the subsurface drainage systems already installed on drained agricultural lands to remove excess soil water after precipitation events. However, limited information regarding corn yield under subirrigation is available to promote its implementation at a large scale. In addition, its performance within the backdrop of climate change has not been explored in the context of the Corn Belt. DRAINMOD was calibrated and validated for a location with Nappanee loam soil in the Maumee River Basin in Northwest Ohio. The model was then used to investigate relative corn yield differences between subirrigation and free subsurface drainage for historical (1984-2013) and projected future (2041-2070) climate conditions. For historical conditions, the mean relative corn yield increased by 26.5% under subirrigation. Under free subsurface drainage, corn relative yields were lower for future climate conditions than for historical climate conditions. For the projected future climate conditions, overall relative corn yields under subirrigation increased by 36.5% compared to relative yields under free subsurface drainage. Subirrigation contributed in sustaining relative corn yields under future climate conditions.

The National Center for Atmospheric Research and the National Renewable Energy Laboratory (NREL) collaborated to develop a method to assess the interannual variability of wind and solar power over the contiguous United States under current and projected future climate conditions, for use with NREL’s Regional Energy Deployment System (ReEDS) model. The team leveraged a reanalysis-derived database to estimate the wind and solar power resources and their interannual variability under current climate conditions (1985–2005). Then, a projected future climate database for the time range of 2040–69 was derived on the basis of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) simulations driven by free-running atmosphere–ocean general circulation models. To compare current and future climate variability, the team developed a baseline by decomposing the current climate reanalysis database into self-organizing maps (SOMs) to determine the predominant modes of variability. The current climate patterns found were compared with those of an NARCCAP-based future climate scenario, and the CRCM–CCSM combination was chosen to describe the future climate scenario. The future climate scenarios’ data were projected onto the Climate Four Dimensional Data Assimilation reanalysis SOMs. The projected future climate database was then created by resampling the reanalysis on the basis of the frequency of occurrence of the future SOM patterns, adjusting for the differences in magnitude of the wind speed or solar irradiance between the current and future climate conditions. Comparison of the changes in the frequency of occurrence of the SOM modes between current and future climate conditions indicates that the annual mean wind speed and solar irradiance could be expected to change by up to 10% (increasing or decreasing regionally).

With regard to global climate change due to increasing concentration in greenhouse gases, particularly carbon dioxide (CO2), it is important to examine its potential impact on crop development and production. We used statistically-downscaled climate data from 28 Global Climate Models (GCMs) and the Agricultural Production Systems sIMulator (APSIM)–Wheat model to simulate the impact of future climate change on wheat production. Two future scenarios (RCP4.5 and RCP8.5) were used for atmospheric greenhouse gas concentrations during two different future periods (2031–2060 referred to as 40S and 2071–2100 referred to as 80S). Relative to the baseline period (1981–2010), the trends in mean daily temperature and radiation significantly increased across all stations under the future scenarios. Furthermore, the trends in precipitation increased under future climate scenarios. Due to climate change, the trend in wheat phenology significantly advanced. The early flowering and maturity dates shortened both the vegetative growth stage (VGP) and the whole growth period (WGP). As the advance in the days of maturity was more than that in flowering, the length of the reproductive growth stage (RGP) of spring wheat was shortened. However, as the advance in the date of maturity was less than that of flowering, the RGP of winter wheat was extended. When the increase in CO2 concentration under future climate scenarios was not considered, the trend in change in wheat production for the baseline declined. In contrast, under increased CO2 concentration, the trend in wheat yield increased for most of the stations (except for Nangong station) under future climatic conditions. Winter wheat and spring wheat evapotranspiration (ET) decreased across all stations under the two future climate scenarios. As wheat yield increased with decreasing water consumption (as ET) under the future climatic conditions, water use efficiency (WUE) significantly improved in the future period.

In the context of global warming, agricultural production and social and economic development are significantly affected by drought. The future change of climate conditions is uncertain; thus, it is of great importance to clarify the aspects of drought in order to define local and regional drought adaptation strategies. In this study, the meteorological data from 1976 to 2005 was used as a historical reference, and nine Global Climate Models (GCMs), downscaling to meteorological stations from 2039 to 2089, were used as future climate data. Based on Penman–Monteith, the reference crop Evapotranspiration (ET0) and Standardized Precipitation Evapotranspiration Index (SPEI) of the reference crop in three emission scenarios of RCP2.6, RCP4.5, and RCP8.5, under future climate conditions, were calculated. A non-parameter Mann–Kendall trend test was performed on temperature, precipitation, ET0, and SPEI to analyze the drought spatiotemporal distribution traits under upcoming climate scenarios. The results showed that, under future climate conditions, SPEI values in most areas of the Huang-Huai-Hai region would continuously increase year by year, and drought would be alleviated to some extent at the same pace. However, with the increase of greenhouse gas concentration in the emission scenarios, SPEI values continued to decline. In the RCP8.5 scenario, the area of severe drought was large. To sum up, in the future climate scenario, the degree of drought in the Huang-Huai-Hai region will be alleviated to some extent with the increase of rainfall, but with the increase of greenhouse gas concentration, the degree of drought will be further intensified, posing a huge challenge to agricultural water use in the region. This study provides a theoretical foundation for alleviating drought in the Huang-Huai-Hai region in future climate scenarios.

Ongoing climate change, resulting in heavier rainfall and potentially higher flood peaks, can challenge flood risk management in many European regions. In particular, flood design values and flood hazard and risk maps can be challenged by future climate conditions. The devastating July 2021 floods in western Europe highlighted the need for transboundary cooperation in adapting flood risk management to climate change. In the JCAR-ATRACE Initiative (Joint Cooperation programme on Applied scientific Research – Accelerate Transboundary Regional Adaptation to Climate Extremes), we review and synthesize how climate change information is integrated into flood risk management in regions of Germany, the Netherlands, Belgium, and Luxembourg. We assess whether regions have published flood policy papers, developed future climate and flood scenarios, and translated these scenarios to flood hazard and risk maps and/or flood design values. Our findings reveal that while all 17 sub-national regions have adaptation plans addressing climate change, only 6 regions have developed future flood projections, with even fewer (3) incorporating climate-adjusted design values and only one providing flood hazard and risk maps under future climate scenarios. Practices vary widely: for example, Flanders in Belgium uses a full range of emission scenarios (CMIP5 RCP2.6 to RCP8.5), while Baden-Württemberg and Bavaria in Germany rely on the high-end scenario (CMIP5 RCP8.5) only. The Netherlands adopts a robust approach using 33 CMIP6 global climate models and a dynamic adaptation pathway framework to address uncertainties. Some regions like Saxony in Germany argue that the spread of projections is too large to derive design values and emphasize the need for standardized scenarios and methods. In summary, our synthesis highlights substantial gaps in incorporating climate change projections into flood risk management and significant regional variation in approaches. The synthesis will hopefully contribute to cross-border learning and foster uptake of climate change adaptation in flood risk management in Europe.

With the continuous emergence of remote sensing technologies and atmospheric models, multi-source precipitation products (MSPs) are increasingly applied in hydrometeorological research, especially in ungauged or data-scarce regions. This study comprehensively evaluates the reliability of MSPs and quantifies the uncertainty of sources in streamflow simulation. Firstly, the performance of seven state-of-the-art MSPs is assessed using rain gauges and the Block-wise use of the TOPMODEL (BTOP) hydrological model under two calibration schemes over Jialing River Basin, China. Then, a variance decomposition approach (Analysis of variance, ANOVA) is employed to quantify the uncertainty contribution of precipitation products, model parameters, and their interaction in streamflow simulation. The MSPs include five satellite-based (GSMaP, IMERG, PERCDR, CHIRPS, CMORPH), one reanalysis (ERA5L), and one ensembled product (PXGB2). The results of precipitation evaluation show that the MSPs have temporal and spatial variability and PXGB2 has the best performance. The hydrologic utility of MSPs is different under different calibration methods. When using gauge-based calibration parameters, the PXGB2-based simulation performs best, whereas CHIRPS, PERCDR, and ERA5L show relatively poor performance. In comparison, the model recalibrated by individual MSPs significantly improves the simulation accuracy of most MSPs, with GSMaP having the best performance. The ANOVA results reveal that the contribution of precipitation products to the streamflow uncertainty is larger than model parameters and their interaction. The impact of interaction suggests that a better simulation attributes to an optimal combination of precipitation products and model parameters rather than solely relying on the best MSPs. These new findings are valuable for improving the suitability of MSPs in hydrologic applications.

Simple SummaryIn the context of future global climate change, investigating the potential suitable habitats for species is essential for animal conservation, particularly for the most environmentally sensitive amphibians. The Northeastern China Salamander (Hynobius leechii), a member of the Hynobiidae family, is endemic to Northeastern China and is classified as a national Class II protected species. This study utilized distribution records of the Northeastern China Salamander to predict its suitable habitat distribution under both current and future climate conditions. The results indicated that several climate variables significantly influence habitat suitability, including precipitation during the warmest season, precipitation in the driest quarter, the seasonal variation coefficient of temperature, average temperature in the wettest season, and mean diurnal range. Currently, the Northeastern China Salamander is mainly distributed in the Liaodong Peninsula of Liaoning Province, the Changbai Mountain area of Jilin Province and a few areas of Heilongjiang Province. Under projected future climate scenarios, the area of suitable habitat is expected to gradually increase, with a continued expansion toward higher latitudes. The findings of this manuscript provide a valuable reference for amphibian conservation efforts in Northeastern China.The Northeastern China Salamander (Hynobius leechii) is classified as a rare, nationally protected Class II wild animal in China. Its population is declining, and its habitat is deteriorating. This study aimed to predict the distribution of suitable habitats for the Northeastern China Salamander under both current and future climate scenarios, utilizing the MaxEnt model optimized through ENMeval parameters. Species distribution data were collected from field surveys, existing literature, amphibian records in China, and the Global Biodiversity Information Network. A total of 97 records were compiled, with duplicate records within the ENMTools grid unit removed, ensuring that only one record existed within every 5 km. Ultimately, 58 distinct distribution points for the Northeastern China Salamander were identified. The R software package ‘ENMeval 2.0’ was employed to optimize the feature complexity (FC) and regularization multiplier (RM), and the optimized model was applied to assess the suitable distribution regions for the Northeastern China Salamander under present and future climate conditions. The findings indicated that rainfall and temperature are the primary environmental factors influencing Hynobius. Currently, the suitable habitat for the Northeastern China Salamander constitutes 6.6% of the total area of Northeastern China. Projections for the periods of 2050 and 2070 suggest that suitable habitats for the Northeastern China Salamander will continue to expand towards higher latitudes across three climate scenarios. While this study focuses solely on climate change factors and acknowledges certain limitations, it serves as a reliable reference and provides essential information for the distribution and conservation of the Northeastern China Salamander.

Climate and land use changes may significantly impact the habitat distribution of Gastrodia elata, an endangered traditional medicinal plant. Accurately predicting its future potential suitable habitats is crucial for its conservation and sustainable development. This study integrates current distribution data of G. elata with 56 environmental variables and uses the MaxEnt model to predict changes in its suitable habitats under current climate conditions and four future climate scenarios(SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that October precipitation and December minimum temperature are key environmental factors influencing its distribution. Under the current climate, optimal habitats for G. elata are concentrated in montane forest areas in Sichuan, Yunnan, Guizhou, and Hubei, which meet the species' requirements for understory growth. Across all future scenarios, the suitable habitat of G. elata consistently shows a stable northward shift, with a steady increase in suitable areas, extending to the middle and lower reaches of the Yangtze River and the Huang-Huai region, and even expanding into Liaoning, Jilin, and southern Heilongjiang. Land use analysis, taking into account the protection of arable land and the utilization of forest resources, indicates that by 2100, under future climate conditions, arable land in medium-to high-suitability areas is expected to increase by 30%-124%. While the conversion of non-suitable forest land into suitable habitats is projected to increase by 5%-52%, the growth of medium-to high-suitability areas within forests is relatively modest, ranging from 1% to 24%. These findings highlight the need to balance agricultural expansion with forest resource conservation to ensure the long-term sustainability of G. elata and provide scientific guidance for future suitable habitat management.