Streamflow simulations for continental-scale river basins in a global atmospheric general circulation model

Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

Future climate projection data are increasingly employed to evaluate the potential impacts of global warming across a wide range of domains, including meteorological variables (e.g., temperature and precipitation), hydrological processes, ecosystems, human health, and societal activities. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides an extensive dataset produced through international collaboration, incorporating multiple General Circulation Models (GCMs), diverse future scenarios, and numerous initial conditions. Despite the comprehensive nature of these datasets, most impact assessments rely on a limited subset of realizations, with no standardized methodology guiding their selection. This lack of consensus introduces potential biases into the outcomes of impact studies. This study quantitatively assesses the influence of realization selection on future climate impact assessments. Monthly precipitation and temperature data from CMIP6 were analyzed for both historical experimental periods and multiple Shared Socioeconomic Pathways (SSP) scenarios. Comparisons were conducted between outcomes obtained using all available realizations for each GCM and those derived from a single realization per GCM. Additionally, combinations of GCMs and realizations commonly used in prior studies were evaluated for their representativeness. The findings reveal that global average monthly precipitation is consistently higher when all realizations are utilized compared to scenarios based on a single realization. The inclusion of all realizations captures a broader range of variability, whereas subsets exhibit narrower variability and more localized trends. These results emphasize the significant impact of realization selection on future climate prediction outcomes. Moreover, an analysis of existing studies indicates that while selected datasets often reflect average trends, their overall representativeness requires further scrutiny. This research highlights the necessity of adopting uncertainty-aware methodologies in climate change studies. The findings offer valuable insights for improving the robustness and reliability of future climate impact assessments, paving the way for more informed decision-making in addressing climate change challenges.

A compositing scheme that predicts changes in tropical precipitation under climate change from changes in near-surface relative humidity (RH) and temperature is presented. As shown by earlier work, regions of high tropical precipitation in general circulation models (GCMs) are associated with high near-surface RH and temperature. Under climate change, it is found that high precipitation continues to be associated with the highest surface RH and temperatures in most CMIP5 GCMs, meaning that it is the “rank” of a given GCM grid box with respect to others that determines how much precipitation falls rather than the absolute value of surface temperature or RH change, consistent with the weak temperature gradient approximation. Further, it is demonstrated that the majority of CMIP5 GCMs are close to a threshold near which reductions in land RH produce large reductions in the RH ranking of some land regions, causing reductions in precipitation over land, particularly South America, and compensating increases over ocean. Recent work on predicting future changes in specific humidity allows the prediction of the qualitative sense of precipitation change in some GCMs when land surface humidity changes are unknown. However, the magnitudes of predicted changes are too small. Further study, perhaps into the role of radiative and land–atmosphere feedbacks, is necessary.

We investigate the causes of temperature dependent changes in global precipitation in contemporary General Circulation Models (GCMs) subjected to a doubling of atmospheric CO2 concentration. By analyzing the energy budget of the troposphere, we find that changes are dominated by processes robustly simulated by GCMs. Importantly, shortwave cloud feedbacks, whose uncertainty is largely responsible for the wide range of GCM temperature climate sensitivities, are shown to have little effect. This is because these mainly arise from the scattering of shortwave radiation that has little impact on the tropospheric heating that controls precipitation. Hence, we expect that the range of simulated precipitation sensitivities to temperature will not change greatly in future GCMs, despite the recent suggestion that satellite observations indicate that GCM precipitation changes are significantly in error.

Assessment of climate change impact in the hydrological regime of River Pinios Basin, central Greece

The aim of this study is to present the application of statistical methods to assess the possibility of grouping precipitation according to their elevation above sea level and precipitation levels in the temperate climate of Poland. The country was divided into regions with similar levels of precipitation using cluster analysis by Ward’s method. The study was performed with meteorological data on average monthly precipitation of 53 meteorological stations from 1981 to 2010. The selection of stations was dictated by the need to consider the variability in amounts of annual precipitation throughout the country. The data was used to calculate average annual precipitation total as well as average precipitation total in the summer half-year (from May to October) and the winter half-year (from November to April) for Poland, from 1981 to 2010. Statistical method was performed and six clusters were generated, for the elevation of the stations and average annual precipitation as well as average precipitation for hydrological half-years. The average annual precipitation for the clusters ranged from 530 mm (cluster 5) to 820 mm (cluster 2). The average precipitation for the winter hydrological half-year ranged from 190.1 mm (cluster 1) to 288.8 mm (cluster 5). The average precipitation for the summer hydrological half-year ranged from 326.3 mm (cluster 5) to 605 mm (cluster 2). The conclusions from the analysis carried out in the study are that: (1) grouping by means of Ward’s method can be used to distinguish homogeneous areas with the same levels of precipitation; (2) both precipitation and the elevation, at which meteorological stations are located, are the basis for distinguishing clusters in Ward’s method.

This study aims at evaluating the hydrologic impacts of climate change on the Châteauguay River basin in the province of Quebec, Canada. Three global climate models (GCMs) covering a range of climate sensitivities were selected, and their output was employed to adjust the parameters of a stochastic weather generator using simple transformation rules for precipitation and temperature. Values of monthly precipitation and temperature were extracted from the GCMs for the current (1960–1990) and future (2040–2060) climate. The International Panel on Climate Change emission scenario known as B2 was selected. It represents an average scenario and corresponds approximately to a doubling of the atmospheric CO2 concentration. Resorting to stochastically generated climate scenarios allowed assessing whether the modelled effects of climate change on flows were statistically significant. Results indicate that spring and summer–fall peak flows were reduced on average by 30% and 12%, respectively, using the Echam4 model derived scenarios. The Hadcm3 model produced a weaker signal that was not statistically significant. The CGCM2 model produced a statistically significant reduction in spring peak flows of 8% on average, whereas the simulated reduction in summer flows was not statistically significant for many of the return periods considered. Many sources of uncertainties were partially considered in this study. One is the downscaling of the GCM climatology at the watershed scale. The approach employed to generate the future climate scenarios changed the precipitation variability through an adjustment of the parameters of the Gamma distribution function used to model precipitation amounts. Whether this approach is truly typical of climate change effect remains to be ascertained. Using more physically based hydrological models would help reduce uncertainties in climate change impacts studies.Key words: climate change, weather generator, flood, frequency analysis, hydrological modelling.

Impact of solar activity on precipitation in the United States

Rainfall-Runoff modeling for Semi-arid and trans-boundary Yarmouk River Basin

The representation of extratropical cyclones (ETCs) precipitation in general circulation models (GCMs) and a weather research and forecasting (WRF) model is analyzed. This work considers the link between ETC precipitation and dynamical strength and tests if parameterized convection affects this link for ETCs in the North Atlantic Basin. Lagrangian cyclone tracks of ETCs in ERA-Interim reanalysis (ERAI), the GISS and GFDL CMIP5 models, and WRF with two horizontal resolutions are utilized in a compositing analysis. The 20-km resolution WRF model generates stronger ETCs based on surface wind speed and cyclone precipitation. The GCMs and ERAI generate similar composite means and distributions for cyclone precipitation rates, but GCMs generate weaker cyclone surface winds than ERAI. The amount of cyclone precipitation generated by the convection scheme differs significantly across the datasets, with GISS generating the most, followed by ERAI and then GFDL. The models and reanalysis generate relatively more parameterized convective precipitation when the total cyclone-averaged precipitation is smaller. This is partially due to the contribution of parameterized convective precipitation occurring more often late in the ETC life cycle. For reanalysis and models, precipitation increases with both cyclone moisture and surface wind speed, and this is true if the contribution from the parameterized convection scheme is larger or not. This work shows that these different models generate similar total ETC precipitation despite large differences in the parameterized convection, and these differences do not cause unexpected behavior in ETC precipitation sensitivity to cyclone moisture or surface wind speed.

An N–PLS regression technique was tested as an empirical downscaling method. Average monthly near-ground air temperature (t), specific humidity (q), and sea-level pressure (p) fields across Central and Western Europe were used as predictors for average monthly air temperature (T), dew temperature (D), and precipitation amount (P) at 4 locations in Slovenia. The empirical downscaling models (EM) were developed by means of available predictand data from the ARSO archive and predictor data from the NCEP/NCAR reanalysis project, for the period 1951–2002, separately for single months. Using the combination of t and p as predictors, the EM for T explained from 73% to 95% of predictand variability, for D from 74% to 97% of predictand variability, and for P from 31% to 76% of predictand variability. The use of q as an additional predictor did not improve the quality of the EM considerably. Developed EM using p and t as predictors were applied to the results of 5 general circulation models (GCM): CSIRO/Mk2, CCC/CGCM2, UKMO/HadCM3, DOE-NCAR/PCM, and MPI-DMI/ECHAM4-OPYC3. Only the simulations based on SRES A2 and B2 emission scenarios were considered in our calculations. Available mean monthly values of predictors for the period 1951–2100 were used. All the projections of GCM results indicate an increase in T and D and decrease in P in the 21st century at all 4 locations. The expected range of changes in T, D, and P is wide due to the different response of GCM to identical changes in the atmospheric composition, and represents a source of uncertainty in empirical downscaling results. Another important source of uncertainty in empirical downscaling studies, especially when temperature dependent predictors are used, is the problem of extrapolation. By using the proper mathematical approach for EM development we only reduce a part of the uncertainty related to the quality of empirical models that also strongly depend on the quality of input data and predictor selection. The N–PLS regression seems to be a suitable choice of mathematical method, as the feature selection from a large number of predictor time series is not predictand independent. Finally, any climate change and impact studies for the future are affected by many other uncertainties that we have to be fully aware of, while interpreting their results.

<p>The present study analyses the various uncertainties and nonstationarity in the streamflow projections of Subarnarekha river basin in Eastern India using two widely used hydrological climate models: 1) general circulation model (GCM), and 2) forcing climate change scenarios. These two climate models are used to force the ArcSWAT model. Subsequently this model is calibrated using SUFI-2 optimization technique. The downscaled and bias-corrected data from an ensemble of 10 climate projections with representative concentration pathways (RCP) 4.5 and 8.5 scenarios (five each) were used in first model, whereas in second model a total of 63 (7 perturbed precipitations and 9 perturbed temperatures) combinations of hypothetical climate change scenarios were used. The results show very good correlation during monthly calibration time steps and relatively good agreement between the observed and simulated streamflows in daily calibration time steps. The uncertainties are expressed in probabilistic terms using probability density function (PDF) and cumulative distribution function (CDF) as they provide significant information for decision process in climate change adaptation in the river basin. The uncertainties associated with climate models, return periods and streamflow extremes are also analysed in the present work. The RCP 8.5 scenarios seem more appropriate than RCP 4.5 scenarios in quantifying the uncertainties under nonstationarity assumptions. The mean values of water balance components and their percentage variation for both historic and future periods reveal that the water balance components get affected significantly due to climate change in a future period. Consequently, the streamflows are likely to decline in the river basin. The present study also highlights the comprehensive approaches that are being planned to facilitate adaptation to climate change as well as those that are specific to the water resources management in the study region. The findings in this work are useful for overall well-being of people in the study area.</p>

A multidisciplinary study of precipitation and streamflow data and paleohydrologic studies of channel features was made to analyze the flood hydrology of foothill and mountain streams in the Front Range of Colorado, with emphasis on the Big Thompson River basin, because conventional hydrologic analyses do not adequately characterize the flood hydrology. In the foothills of Colorado, annual floodflows are derived from snowmelt at high elevations in the mountain regions, from rainfall at low elevation in the plains or plateau regions, or from a combination of rain falling on snow or mixed population hydrology. Above approximately 7,500 ft, snowmelt dominates; rain does not contribute to the flood potential. Regional flood-frequency relations were developed and compared with conventional flood-estimating technique results, including an evaluation of the magnitude and frequency of the probable maximum flood. Evaluation of streamflow data and paleoflood investigations provide an alternative for evaluating flood hydrology and the safety of dams. The study indicates the need for additional data collection and research to understand the complexities of the flood hydrology in mountainous regions, especially its effects on flood-plain management and the design of structures in the flood plain. (USGS)

Climate change significantly alters the low-flow regimes of river basins worldwide and presents significant challenges to water-scarce regions, especially in dry regions. This current study investigates the impact of climate change on projected low-flow variations in the Maduru Oya River Basin in Sri Lanka, focusing on the reach to the Padiyathalawa stream-gauge station. The study utilizes a lumped hydrological modeling framework, which used the HEC-HMS rainfall-runoff model to simulate streamflow behavior considering anticipated climate scenarios. Projections for future precipitation were obtained from the CNRM-CM6-1 Global Climate Model (GCM), which is part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), and subsequently downscaled through the Long Ashton Research Station Weather Generator (LARS-WG) according to two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The precipitation data, downscaled to the local scale, were integrated into the HEC-HMS model to forecast future river discharge and investigate possible changes in low-flow characteristics. The 7Q10 low-flow index, which is defined as the minimum average flow in a continuous seven-day period with a recurrence interval of ten years was used for estimating and comparing low-flow characteristics. The model parameters were calibrated and validated using historical data from 1997 to 2019. Three objective functions namely: Nash-Sutcliffe Efficiency (NSE), Mean Relative Absolute Error (MRAE), and Percent Error in Peak Flow (PEPF) were used for optimizing model parameters. Future precipitation was projected for short-term (2021-2040), medium-term (2041-2060), and long-term (2061-2080) durations. The projected precipitation data was subsequently input into the developed HEC-HMS model to obtain future streamflow projections for the specified periods. The results of the climate change scenario analysis showed that precipitation may vary due to climate change within the range of -16 % to -5 % for the 2021-2040 period, - 4 % to 1 % for the 2041-2060 period, and 1 % to 21 % for the 2061-2080 period. The results indicated a likely increase in low-flow values across both SSP scenarios. The flow-duration analysis showed that the Q90 flow, representing the flow level that exceeds 90% of the time, is expected to increase, reflecting an upward change in streamflow for low-flow conditions. These findings are important for water resource managers working in the area to plan for and adapt to the impacts of altered low-flow regimes that can impact water supply, agriculture, and overall ecosystem health. Further studies should consider incorporating the use of hydrological models coupled with diverse climate scenarios to better capture the uncertainties related to climate predictions and land-use changes. These would provide a better understanding of the impacts of climate change on river basin hydrology in dry regions like the Maduru Oya River Basin.

<p>Selection of a suitable Global Climate Model (GCM) in hydrological research for basin-scale of monsoon affected regions under future climate projection scenarios is a great necessity. This study comprehensively evaluated the suitability of 25 available GCMs issued of  Coupled Model Intercomparison Project 6 (CMIP6) to choose the best performing GCMs in precipitation simulating skill over the whole main River Basin System in South Korea for the historical period of 1973–2014. Bilinear interpolation method was used for mapping the grid resolution of the simulated GCMs precipitation and observed precipitation with a 0.125<sup>0</sup> x 0.125<sup>0</sup> resolution. Where, the observed monthly precipitation at 56 automated weather stations from 1973 to 2014 were derived from the Korea Meteorological Administration (KMA). Multi-Criteria Decision Making (MCDM) approach based on four spatial metrics, Cramer’s V, Goodman-Kruskal (GK) Lambda, Mapcurves and TheilU were proposed to compare the simulated GCMs precipitation with the observed precipitation. To calculate the overall ranking of the GCMs and identify the best performing GCMs, this study applied Jenks Natural Break classification based on the Compromise Programming index. The results indicated that: 1) The GCMs performance was different with different spatial indices with the most suitable of GCMs ranking for each watershed. 2) The best performing GCMs well simulated the annual mean precipitation with a bias of less than 15% for southwestern watersheds and higher biases (30-50%) for remaining watersheds. 3) Majority of CMIP6 GCMs could be capture trends and the spatial distribution of annual, seasonal precipitation over South Korea. However, the result was also found that most GCMs underestimated summer precipitation and overestimated spring precipitation. Therefore, the selected GCMs with corrected biases can be usefully employed for analyzing future changes of hydrological pattern associated with climate change projections.</p><p><strong>Keywords: </strong>Global Climate Models (GCMs), CMIP6, Bilinear interpolation, Multi-Criteria Decision Making, Jenks Natural Break classification.</p>