Quantitative assessment of sewer overflow performance with climate change in northwest England

Changes in rainfall patterns associated with climate change can affect the operation of a combined sewer system, with the potential increase in rainfall amount. This could lead to excessive spill frequencies and could also introduce hazardous substances into the receiving waters, which, in turn, would have an impact on the quality of shellfish and bathing waters. This paper quantifies the spilling volume, duration and frequency of 19 combined sewer overflows (CSOs) to receiving waters under two climate change scenarios, the high (A1FI), and the low emissions (B1) scenarios, simulated by three global climate models (GCMs), for a study catchment in northwest England. The future rainfall is downscaled, using climatic variables from HadCM3, CSIRO and CGCM2 GCMs, with the use of a hybrid generalized linear–artificial neural network model. The results from the model simulation for the future in 2080 showed an annual increase of 37% in total spill volume, 32% in total spill duration, and 12% in spill frequency for the shellfish water limiting requirements. These results were obtained, under the high emissions scenario, as projected by the HadCM3 as maximum. Nevertheless, the catchment drainage system is projected to cope with the future conditions in 2080 by all three GCMs. The results also indicate that under scenario B1, a significant drop was projected by CSIRO, which in the worst case could reach up to 50% in spill volume, 39% in spill duration and 25% in spill frequency. The results further show that, during the bathing season, a substantial drop is expected in the CSO spill drivers, as predicted by all GCMs under both scenarios

A Mixed-Level Factorial Inference Approach for Ensemble Long-Term Hydrological Projections over the Jing River Basin

Significance statement: Increasing concerns have been paid to climate change due to its aggravating impacts on the hydrologic regime, leading to water-related disasters. Such impacts can be investigated through long-term hydrological projection under climate change. However, it is not well understood what factor plays a dominant role in inducing extensive uncertainties associated with the long-term hydrological projections due to plausible meteorological forcings, multiple hydrologic models, and internal variability. The stepwise cluster Bayesian ensemble method and mixed-level factorial inference approach are employed to quantify the contribution of multiple uncertainty sources. We find that the total variance of changes in monthly precipitation, potential evapotranspiration, and streamflow can be mainly explained by the model choices. The identified dominant factor accounting for projection uncertainties is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management. It is suggested that more reliable models should be taken into consideration in order to improve the projection robustness from a perspective of the Loess Plateau.Data availability statement. The climate datasets presented in this research are available from the Climate Change Data Portal (http://ccdp.network/). The observations are acquired from the National Meteorological Information Center (http://data.cma.cn/). The elevation datasets are obtained from the hydrological data and maps website (https://www.hydrosheds.org/). The vegetation data are retrieved from the AVHRR Global Land Cover Classification (https://www.arcgis.com/home/item.html?id=70c54b0b7b344c418dee4af9029fe6f2). The soil parameters are collected from the Harmonized World Soil Database (https://www.fao.org/soils-portal/data-hub/soil-maps-anddatabases/harmonized-world-soil-database-v12/en/).Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow.Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060302), the Natural Science Foundation (U2040212, 52279002, 52279003, 52221003), the special fund of State Key Joint Laboratory of Environment Simulation and Pollution Control, the Fundamental Research Funds for the Central Universities, MWR/CAS Institute of Hydroecology, and Natural Science and Engineering Research Council of Canada

Statistical Downscaling For The Northern Great Plains: A Comparison Of Bias Correction And Redundancy Analysis

The climate of the Earth is changing, and is primarily a result of our rampant industrialization over the past two centuries. These changes have manifested themselves in many ways over the whole of the Earth’s surface and sub-systems, leading to the need to understand the changes and predict future outcomes. Coupled climate and general circulation - Earth system models (GCMs) allow for the analysis of dynamically active simulations over the whole of the planet, yet are limited by computational power. The model grids are coarse by design to perform within these computational constraints, which enables them to function and provide information at continental and larger scales, but which limit their ability to offer information for regional and local environments. Dynamical models created with higher resolutions allow for regional climate modeling yet are also limited by computational constraints and require detailed information to run. Statistical downscaling seeks to bridge the gap between coarse GCM grids by utilizing observational data and statistical models to remove the biases from the data at the local level. There have been several types of statistical methods applied to this task over many different regions with some success. The goal of this study is to utilize two methods in particular, bias-corrected spatial disaggregation (BCSD) and redundancy analysis (RDA), to downscale maximum and minimum temperature, as well as precipitation, for the Northern Great Plains (NGP) region. These methods are calibrated over the period 1950 – 1970 using a 1/8 degree gridded dataset for 17 GCMs, then applied to a verification period (1970 – 1999) and compared to observations over that period to assess the downscaled models skill in capturing local NGP variability. These methods are also applied to future model runs forced via the representative concentration pathways (RCPs) low end (2.6), median (4.5) and high end (8.5) 21st Century forcings, which provides possible outlooks for local stakeholders over the coming decades. It is found that BCSD does well in downscaling temperature and precipitation, as well as their various metrics. RDA provides more mixed success, with good skill demonstrated for temperatures but a strong wet bias in precipitation. It is noted, however, that RDA yielded better correlations to the observations. Future scenarios show broad ranges of projected outcomes that, as expected, increase with increasing forcing, though temperature shows stronger changes than precipitation, and BCSD exhibits higher sensitivity than RDA. Future research may help further constrain the results of these downscaling methods, particularly RDA, by adopting further bias correction to the results

Generation of regional climate change scenarios using general circulation models and empirical downscaling

Thesis (Ph.D.) - Indiana University, Department of Geography, 2004Coupled ocean-atmosphere general circulation models (GCMs) are the best tools available for examination of climate change due to increases in atmospheric greenhouse gas concentrations. Due to large computational requirements, these numerical models run at horizontal resolutions that are inadequate for climate impact studies and, hence, require parameterization of many small-scale processes important for characterization of regional climate. The aim of this research was to develop and evaluate a methodology for generating regional climate change scenarios for the Midwest region of the USA using GCM simulations and empirical downscaling methods. The research focuses on (1) identification of relationships between large-scale predictors and three surface parameters (local maximum and minimum daily surface air temperature and total daily precipitation) at 84 stations in the study region, (2) evaluation of variables simulated by two GCMs, and (3) development and evaluation of empirical downscaling tools to generate projections of the surface parameters for the 21st century. The results of the analysis indicate that the large-scale atmospheric predictors explain a large proportion of the variability in the surface parameters, but that GCM simulations of the large-scale predictors do not exhibit an acceptable level of agreement with observations at the grid point level. Therefore, the downscaling models applied in this study are based on (1) relationships between GCM simulated variables and the surface parameters and (2) spatially aggregated predictor information. The downscaled climate change scenarios indicate strong warming at most stations consistent with projected increases in greenhouse gases. Averaged over all stations, the downscaled results indicate year-round warming, but the magnitude of the 21st century temperature change is inconsistent between results downscaled from the two GCMs used. These results show that, under the emissions scenarios used by the GCMs, important climate change impacts such as increases in heat wave frequency may be realized, although there is a high degree of uncertainty associated with these findings. The downscaled precipitation scenarios are less consistent than those for temperature (in terms of both the direction and magnitude of precipitation change and its spatial coherence), resulting in lower confidence for the precipitation scenarios relative to those for temperature

Exposure and sensitivity of ponderosa pine to climate change in mountainous western North American landscapes

Climate change has emerged as one of the most potent threats to forests across the globe. This study examined the exposure and sensitivity of ponderosa pine (Pinus ponderosa) to climate change from landscape to continental scales across its geographic range in western North America. We began by developing a framework for assessing climate change exposure based on climatic water deficit (CWD), a metric of unmet evaporative demand and strong predictor of plant species distributions. The framework combined change in average annual CWD and frequency of departure from the local historical range of variability in annual CWD. We applied this framework to Tejon Ranch, a mountainous landscape in the Tehachapi Mountains of Southern California. We found disproportionate climate change exposure at high elevations due to projected losses in snowpack associated with warmer winters. Next, we assessed long-term relationships between climate and ponderosa pine growth at Tejon Ranch. Interannual variability in tree growth was explained by a combination of climatic water deficit over the current and preceding water-year (Oct 1 – Sep 30), March precipitation, July maximum and January minimum air temperatures (adjusted R² = 0.55-0.57). In general, growth is expected to decline under future climate change in current stands, but heterogeneous topography offered potential favorable growing habitat under all climate projections, particularly on north-facing slopes at higher elevations. Under warmer and drier projections, overall habitat availability decreased in terms of distance to the nearest suitable patch from current stands for both mid- (2040-2069) and end-of-century (2070-2099) periods. Spatiotemporal climate variability, however, created suitable patches within average seed dispersal distance of current stands, potentially offering ephemeral windows of opportunity for local range shifts without long-distance dispersal. Finally, we examined the sensitivity of ponderosa pine to climate variability across its range in western North America by combining the Tejon Ranch tree rings and 159 published chronologies from the International Tree Ring Data Bank. We encountered heterogeneous climate sensitivities across the species range to a suite of limiting climate variables. Our results indicated that position along environmental gradients interacts with genetically based local adaptation to determine climate sensitivity of individual ponderosa pine populations. Although all ponderosa pine populations will likely be exposed to locally novel climate regimes in the 21st Century, the species’ overall wide variability in climate sensitivity will likely buffer some populations from negative effects of climate change. Future conservation efforts for ponderosa pine and other wide-ranging species should consider the mediating role of geographic patterns of genetic structure in within-species climate sensitivities

Improvements of statistical downscaling methods and evaluation of their contributions to the uncertainty of hydrologic impacts in a changing climate

The most important impacts of climate change will likely be linked to water resources. Hydropower companies throughout the world increasingly realize that they must deal with future climate change. To evaluate future impacts, realistic climate projections that encompass the uncertainty linked to climate change are needed. Given the relatively large biases of General Circulation Model (GCM) outputs, particularly for precipitation and to a lesser extent for temperatures at the regional scale, it is necessary to perform some postprocessing to improve these global-scale models for hydrologic and water resource management studies. The two most commonly used approaches, dynamical and statistical downscaling, each have significant advantages and drawbacks. It is not a simple task to select one over the other. This work aims at coupling global and regional climate models and statistical downscaling into a new hybrid method by merging stochastic weather generators with climate models that quantify the hydrological impacts of climate change for a Canadian river basin. The performances of stochastic weather generators were first improved. A statistical downscaling method combining attributes of both stochastic weather generator and change factor (CF) methods was then developed. Several aspects of statistical downscaling were also evaluated. Moreover, global uncertainty and the downscaling uncertainty were outlined in quantifying the hydrological impacts of climate change. A spectral correction method and integration scheme resulted in a weather generator that can accurately produce the low-frequency variability of precipitation and temperatures, as well as the auto- and cross-correlations of and between maximum and minimum temperature (Tmax and Tmin). A large number of atmospheric predictors were used to assess the ability of statistical methods to downscale precipitation to the station scale. The downscaling of daily precipitation occurrence was mostly unsuccessful with both linear regression methods and using discriminant analysis, even though the latter was much better. Explained variances were very low for regression-based downscaling of precipitation, although results were consistently improved as the climate model resolution was made progressively finer. Even when going to the 15-km resolution Canadian Reginal Climate Model (CRCM), the predictors still explained less than 50% of the total site precipitation variance. Despite the added complexity, the weather typing approach was not much better at downscaling precipitation than the approaches without classification. The weather generator was used as a downscaling tool to downscale outputs of the CRCM (45km scale) to catchment scale. Its performance was further compared with the CF method for quantifying the hydrological impacts of climate change. Both downscaling methods suggested increases in annual and seasonal discharges for the 2025-2084 period. The weather generator-based method predicts more increase in spring (AMJ) discharge, as well as smaller increases in summer-autumn (JASON) and winter (DJFM) discharges than the CF method. Moreover, both methods indicated increases in mean annual and seasonal low flows, while there are considerable differences between their predictions. All downscaling methods including dynamical and statistical approaches suggested general increases in winter discharge (November - April) and decreases in summer discharge for the 2071-2099 horizon. Winter flows would be especially large for regression-based methods, which also predicted the largest temperature increases in autumn and winter. Peak discharges would appear earlier for all downscaling methods, but their timing varies according to the downscaling method. A GCM was consistently a major uncertainty contributor when quantifying the hydrological impacts of climate change. However, other sources of uncertainty such as the choice of downscaling method and natural variability, as represented by GCM ensemble runs, also had a comparable and even larger uncertainty affect depending on the criteria. For example, the downscaling method was the largest source of uncertainty with respect to spring discharge magnitude, annual low flow and peak discharge; while GCM initial conditions (which were a member of the ensemble runs) dominated the uncertainty for the time to peak discharge and the time to the end of flood. Uncertainties linked to greenhouse gase emission scenarios (GGES) and hydrological model structure also played an important role in hydrological predictions, but these were somewhat less than those related to GCMs and the downscaling method. Uncertainties due to the hydrological model parameters had less impact than those of the other five sources. Overall, combining Regional Climate Models (RCMs) and statistical downscaling in a unified approach appeared to have significant advantages in quantifying the hydrological impacts of climate change. Any management and adaptation of water resource systems should consider the effects of future climate change, as well as all sources of uncertainty

Statistical/climatic models to predict and project extreme precipitation events dominated by large-scale atmospheric circulation over the central-eastern China

Global warming has posed non-negligible effects on regional extreme precipitation changes and increased the uncertainties when meteorologists predict such extremes. More importantly, floods, landslides, and waterlogging caused by extreme precipitation have had catastrophic societal impacts and led to steep economic damages across the world, in particular over central-eastern China (CEC), where heavy precipitation due to the Meiyu-front and typhoon activities often causes flood disaster. There is mounting evidence that the anomaly atmospheric circulation systems and water vapor transport have a dominant role in triggering and maintaining the processes of regional extreme precipitation. Both understanding and accurately predicting extreme precipitation events based on these anomalous signals are hot issues in the field of hydrological research. In this thesis, the self-organizing map (SOM) and event synchronization were used to cluster the large-scale atmospheric circulation reflected by geopotential height at 500 hPa and to quantify the level of synchronization between the identified circulation patterns with extreme precipitation over CEC. With the understanding of which patterns were associated with extreme precipitation events, and corresponding water vapor transport fields, a hybrid deep learning model of multilayer perceptron and convolutional neural networks (MLP-CNN) was proposed to achieve the binary predictions of extreme precipitation. The inputs to MLP-CNN were the anomalous fields of GP at 500 hPa and vertically integrated water vapor transport (IVT). Compared with the original MLP, CNN, and two other machine learning models (random forest and support vector machine), MLP-CNN showed the best performance. Additionally, since the coarse spatial resolution of global circulation models and its large biases in extremes precipitation estimations, a new precipitation downscaling framework that combination of ensemble-learning and nonhomogeneous hidden Markov model (Ensemble-NHMM) was developed, to improve the reliabilities of GCMs in historical simulations and future projection. The performances of downscaled precipitation from reanalysis and GCM datasets were validated against the gauge observations and also compared with the results of traditional NHMM. Finally, the Ensemble-NHMM downscaling model was applied to future scenario data of GCM. On the projections of change trends in precipitation over CEC in the early-, medium- and late- 21st centuries under different emission scenarios, the possible causes were discussed in term of both thermodynamic and dynamic factors. Main results are enumerated as follows. (1) The large-scale atmospheric circulation patterns and associated water vapor transport fields synchronized with extreme precipitation events over CEC were quantitatively identified, as well as the contribution of circulation pattern changes to extreme precipitation changes and their teleconnection with the interdecadal modes of the ocean. Firstly, based on the nonparametric Pettitt test, it was found that 23% of rain gauges had significant abrupt changes in the annual extreme precipitation from 1960 to 2015. The average change point in the annual extreme precipitation frequency and amount occurred near 1989. Complex network analysis showed that the rain gauges highly synchronized on extreme precipitation events can be clustered into four clusters based on modularity information. Secondly, the dominant circulation patterns over CEC were robustly identified based on the SOM. From the period 1960–1989 to 1990–2015, the categories of identified circulation patterns generally remain almost unchanged. Among these, the circulation patterns characterized by obvious positive anomalies of 500 hPa geopotential height over the Eastern Eurasia continent and negative values over the surrounding oceans are highly synchronized with extreme precipitation events. An obvious water vapor channel originating from the northern Indian Ocean driven by the southwesterly airflow was observed for the representative circulation patterns (synchronized with extreme precipitation). Finally, the circulation pattern changes produced an increase in extreme precipitation frequency from 1960–1989 to 1990–2015. Empirical mode decomposition of the annual frequency variation signals in the representative circulation pattern showed that the 2–4 yr oscillation in the annual frequency was closely related to the phase of El Niño and Southern Oscillation (ENSO); while the 20–25 yr and 42–50 yr periodic oscillations were responses to the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. (2) A regional extreme precipitation prediction model was constructed. Two deep learning models-MLP and CNN were linearly stacked and used two atmospheric variables associated with extreme precipitation, that is, geopotential height at 500 hPa and IVT. The hybrid model can learn both the local-scale information with MLP and large-scale circulation information with CNN. Validation results showed that the MLP-CNN model can predict extreme or non-extreme precipitation days with an overall accuracy of 86%. The MLP-CNN also showed excellent seasonal transferability with an 81% accuracy on the testing set from different seasons of the training set. MLP-CNN significantly outperformed over other machine learning models, including MLP, CNN, random forest, and support vector machine. Additionally, the MLP-CNN can be used to produce precursor signals by 1 to 2 days, though the accuracy drops quickly as the number of precursor days increases. (3) The GCM seriously underestimated extreme precipitation over CEC but showed convincing results for reproducing large-scale atmospheric circulation patterns. The accuracies of 10 GCMs in extreme precipitation and large-scale atmospheric circulation simulations were evaluated. First, five indices were selected to measure the characteristics of extreme precipitation and the performances of GCMs were compared to the gauge-based daily precipitation analysis dataset over the Chinese mainland. The results showed that except for FGOALS-g3, most GCMs can reproduce the spatial distribution characteristics of the average precipitation from 1960 to 2015. However, all GCMs failed to accurately estimate the extreme precipitation with large underestimation (relative bias exceeds 85%). In addition, using the circulation patterns identified by the fifth-generation reanalysis data (ERA5) as benchmarks, GCMs can reproduce most CP types for the periods 1960–1989 and 1990–2015. In terms of the spatial similarity of the identified CPs, MPI-ESM1-2-HR was superior. (4) To improve the reliabilities of precipitation simulations and future projections from GCMs, a new statistical downscaling framework was proposed. This framework comprises two models, ensemble learning and NHMM. First, the extreme gradient boosting (XGBoost) and random forest (RF) were selected as the basic- and meta- classifiers for constructing the ensemble learning model. Based on the top 50 principal components of GP at 500 hPa and IVT, this model was trained to predict the occurrence probabilities for the different levels of daily precipitation (no rain, very light, light, moderate, and heavy precipitation) aggregated by multi-sites. Confusion matrix results showed that the ensemble learning model had sufficient accuracy (>88%) in classifying no rain or rain days and (>83%) predicting moderate precipitation events. Subsequently, precipitation downscaling was done using the probability sequences of daily precipitation as large-scale predictors to NHMM. Statistical metrics showed that the Ensemble-NHMM downscaled results matched best to the gauge observations in precipitation variabilities and extreme precipitation simulations, compared with the result from the one that directly used circulation variables as predictors. Finally, the downscaling model also performed well in the historical simulations of MPI-ESM1-2-HR, which reproduced the change trends of annual precipitation and the means of total extreme precipitation index. (5) Three climate scenarios with different Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs) were selected to project the future precipitation change trends. The Ensemble-NHMM downscaling model was applied to the scenario data from MPI-ESM1-2-HR. Projection results showed that the CEC would receive more precipitation in the future by ~30% through the 2075–2100 period. Compared to the recent 26-year epoch (1990–2015), the frequency and magnitude of extreme precipitation would increase by 21.9–48.1% and 12.3–38.3% respectively under the worst emission scenario (SSP585). In particular, the south CEC region is projected to receive more extreme precipitation than the north. Investigations of thermodynamic and dynamic factors showed that climate warming would increase the probability of stronger water vapor convergence over CEC. More wet weather states due to the enhanced water vapor transport, as well as the increased favoring large-scale atmospheric circulation and the strengthen pressure gradient would be the factors for the increased precipitation

Development of clustered polynomial chaos expansion model for stochastic hydrological prediction

Data availability: The data that support the findings of this study are available from https://www.researchgate.net/publication/342065388_Yuanjiaan1981-1987. The code used in this paper are available from the corresponding author upon reasonable request.Supplementary data are available online at https://www.sciencedirect.com/science/article/pii/S002216942100069X?via%3Dihub#s0075 .This study introduced a clustered polynomial chaos expansion (CPCE) model to reveal random propagation and dynamic sensitivity of uncertainty parameters in hydrologic prediction. In the CPCE model, the random characteristics of the streamflow simulations resulting from parameter uncertainties are characterized through the polynomial chaos expansion (PCE) model based on the probabilistic collocation method. At the same time, a multivariate discrete non-functional relationship between PCE coefficients and hydrological model inputs is established based on stepwise cluster analysis. Therefore, compared with traditional PCE method, the developed CPCE model cannot only reflect uncertainty propagation in stochastic hydrological simulation, but also have the capability of random forecasting. Moreover, the dynamic sensitivities of model parameters are investigated through the multilevel factorial analyses. The developed approach was applied for streamflow forecasting for the Ruihe watershed, China. Results showed that with effective quantification for the random characteristics of hydrological processes, the CPCE model can directly predict runoff series and generate the associated probability distributions at different time periods. The dynamic sensitivity analysis indicates that the maximum soil moisture capacity within the catchment plays a key role in the accuracy of the low-flow forecasting, while the degree of spatial variability in soil moisture capacities has a remarkable impact on the accuracy of the high-flow forecasting in the studied watershed.National Key Research and Development Plan (2016YFC0502800), the Natural Sciences Foundation (51520105013, 51679087), the 111 Program (B14008) and the Natural Science and Engineering Research Council of Canada