Climate Change Impacts on Irish River Flows: High Resolution Scenarios and Comparison with CORDEX and CMIP6 Ensembles

Climate change is likely to impact water quality, resource availability and riverine ecosystems. While large ensembles are available to assess future impacts (e.g., the Coupled Model Intercomparison Projects (CMIP) and/or CORDEX) many countries have developed their own high-resolution ensembles. This poses a selection problem with robust adaptation dependent on plausible ranges of change being adequately quantified. Therefore, it is important to compare projected changes from available ensembles. Here we assess future climate change impacts for 26 Irish catchments. Using a high-resolution national ensemble of climate models projected impacts in mean, low and high flows are assessed and uncertainties in future projections related to catchment size. We then compare future impacts from CORDEX and CMIP6 ensembles for a subset of catchments. Results suggest increases in winter flows (-3.29 to 59.63%), with wide ranges of change simulated for summer (-59.18 to 31.23%), low (-49.30 to 22.37%) and food (-19.31 to 116.34%) flows across catchments under RCP8.5 by the 2080s. These changes would challenge water management without adaptation. Smaller catchments tend to show the most extreme impacts and widest ranges of change in summer, low and food flow changes. Both the ensemble mean and range of changes from the national ensemble were more modest and narrower than the CMIP6 and CORDEX ensembles, especially for summer mean and low flows, highlighting the importance of evaluating impacts across ensembles to ensure adaptation decisions are informed by plausible ranges of change

Using a scenario-neutral framework to avoid potential maladaptation to future flood risk

This study develops a coherent framework to detect those catchment types associated with ahigh risk of maladaptation to futureflood risk. Using the“scenario‐neutral”approach to impactassessment the sensitivity of Irish catchments tofluvialflooding is examined in the context of nationalclimate change allowances. A predefined sensitivity domain is used to quantifyflood responses to +2 °Cmean annual temperature with incremental changes in the seasonality and mean of the annual precipitationcycle. The magnitude of the 20‐yearflood is simulated at each increment using two rainfall‐runoff models(GR4J, NAM), then concatenated as response surfaces for 35 sample catchments. A typology of catchmentsensitivity is developed using clustering and discriminant analysis of physical attributes. The same attributesare used to classify 215 ungauged/data‐sparse catchments. To address possible redundancies, the exposure ofdifferent catchment types to projected climate is established using an objectively selected subset of theCoupled Model Intercomparison Project Phase 5 ensemble. Hydrological model uncertainty is shown tosignificantly influence sensitivity and have a greater effect than ensemble bias. A nationalflood riskallowance of 20%, considering all 215 catchments is shown to afford protection against ~48% to 98% of theuncertainty in the Coupled Model Intercomparison Project Phase 5 subset (Representative ConcentrationPathway 8.5; 2070–2099), irrespective of hydrological model and catchment type. However, results indicatethat assuming a standard national or regional allowance could lead to local over/under adaptation. Herein,catchments with relatively less storage are sensitive to seasonal amplification in the annual cycle ofprecipitation and warrant special attention

Understanding hydrological flow paths in conceptual catchment models using uncertainty and sensitivity analysis

Increasing pressures on water quality due to intensification of agriculture have raised demands for environmental modeling to accurately simulate the movement of diffuse (nonpoint) nutrients in catchments. As hydrological flows drive the movement and attenuation of nutrients, individual hydrological processes in models should be adequately represented for water quality simulations to be meaningful. In particular, the relative contribution of groundwater and surface runoff to rivers is of interest, as increasing nitrate concentrations are linked to higher groundwater discharges. These requirements for hydrological modeling of groundwater contribution to rivers initiated this assessment of internal flow path partitioning in conceptual hydrological models. In this study, a variance based sensitivity analysis method was used to investigate parameter sensitivities and flow partitioning of three conceptual hydrological models simulating 31 Irish catchments. We compared two established conceptual hydrological models (NAM and SMARG) and a new model (SMART), produced especially for water quality modelling. In addition to the criteria that assess streamflow simulations, a ratio of average groundwater contribution to total streamflow was calculated for all simulations over the 16 year study period. As observations time-series of groundwater contributions to streamflow are not available at catchment scale, the groundwater ratios were evaluated against average annual indices of base flow and deep groundwater flow for each catchment. The exploration of sensitivities of internal flow path partitioning was a specific focus to assist in evaluating model performances. Results highlight that model structure has a strong impact on simulated groundwater flow paths. Sensitivity to the internal pathways in the models are not reflected in the performance criteria results. This demonstrates that simulated groundwater contribution should be constrained by independent data to ensure results within realistic bounds if such models are to be used in the broader environmental sustainability decision making context.Environmental Protection AgencyLeverhulme Trus

Hydrological post-processing of streamflow forecasts issued from single-model and multimodel ensemble prediction systems

Les simulations et prévisions hydrologiques sont sujettes à diverses sources d'incertitudes, qui sont malheureusement inévitables. La cascade d'incertitude provient de différentes composantes de la chaîne de prévision, telles que la nature chaotique de l'atmosphère, diverses conditions initiales et limites, une modélisation hydrologique conceptuelle nécessairement inexacte et des paramètres stationnaires incohérents avec un environnement en mutation. La prévision d'ensemble s'avère un outil puissant pour représenter la croissance des erreurs dans le système dynamique et pour capter les incertitudes associées aux différentes sources. Thiboult et al. (2016) ont construit un grand ensemble de 50,000 membres qui tient compte de l'incertitude des prévisions météorologiques, de celle des conditions initiales et l’incertitude structurale. Ce vaste ensemble de 50,000 membres peut également être séparé en sous-composants afin de démêler les trois principales sources d’incertitude mentionnées ci-dessus. Emixi Valdez a donc généré un autre H-EPS multimodèles et calibré pour différents bassins hydrographiques suivant un modèle similaire. Cependant, les résultats obtenus ont été simplement agrégés, en considérant les membres équiprobables. Bien que les systèmes de prévision hydrologique multimodèles puissent être considérés comme un système très complet, ils sont néanmoins exposés à d'autres incertitudes. Par exemple, les prévisions météorologiques des recherches de Thiboult et al. (2016) ont été pré-testées sur certains bassins versants. Ces tests ont montré que les performances dues à l'assimilation de données s'estompent rapidement avec l’horizon de prévision. De plus, en réalité, les utilisateurs peuvent ne pas être en mesure d’utiliser parfaitement tous les outils de prévision (c’est-à-dire les prévisions météorologiques d’ensemble, l’assimilation de données et le schéma multimodèle) conjointement. Par conséquent, il existe toujours une place pour l'amélioration permettant d'augmenter la fiabilité et la résolution des prévisions grâce à un post-traitement statistique approprié. L'objectif global de cette recherche est d'explorer l'utilisation appropriée et les compétences prévisionnelles de divers algorithmes statistiques afin de post-traiter séparément les prévisions de débit provenant d’un modèle unique ainsi que les prévisions multimodèles. Premièrement, nous avons testé l’efficacité de méthodes depost-traitement telles que le Affine Kernel Dressing (AKD) et le Non-dominated sorting genetic algorithm II (NSGA-II) en comparant les prévisions post-traitées par ces méthodes aux soties brutes de systèmes de prévision à modèle unique. Ces deux méthodes sont théoriquement / techniquement distinctes, mais partagent toutefois la même caractéristique, à savoir qu’elles ne nécessitent pas d’hypothèse paramétrique concernant la distribution des membres de la prévision d’ensemble. Elles peuvent donc être considérées comme des méthodes de post-traitement non paramétriques. Dans cette étude, l'analyse des fronts de Pareto générés avec NSGA-II a démontré la supériorité de l'ensemble post-traité en éliminant efficacement les biais des prévisions et en maintenant une bonne dispersion pour tous les horizons de prévision. Deux autres méthodes de post-traitement, à savoir le Bayesian Model Averaging (BMA) et le Copula-BMA, ont également été comparées. Ces deux méthodes ont permis d’obtenir des distributions prédictives à partir de prévisions de débit journalier émises par cinq systèmes de prévision d'ensemble hydrologiques différents. Les poids obtenus par la méthode du BMA quantifient le niveau de confiance que l'on peut avoir à l'égard de chaque modèle hydrologique candidat et conduisent à une fonction de densité prédictive (PDF) contenant des informations sur l'incertitude. Le BMA améliore la qualité globale des prévisions, principalement en maintenant la dispersion de l'ensemble avec l’horizon de prévision. Il a également la capacité d’améliorer la fiabilité des systèmes multimodèles qui n’incluent que deux sources d’incertitudes. Le BMA est donc efficace pour améliorer la fiabilité et la résolution des prévisions hydrologiques. Toutefois, le BMA souffre de limitations dues au fait que les fonctions de densité de probabilité conditionnelle (PDF) doivent suivre une distribution paramétrique connue (ex., normale, gamma). Par contre, le modèle prédictif Copula-BMA ne requiert pas une telle hypothèse et élimine aussi l'étape de transformation de puissance, qui est nécessaire pour le BMA. Dans cette étude, onze types de distributions marginales univariées et six fonctions de copule de différents niveaux de complexité ont été explorés dans un cadre Copula-BMA. Cela a permis de représenter de manière exhaustive la structure de dépendance entre des couples de débits prévus et observés. Les résultats démontrent la supériorité du Copula-BMA par rapport au BMA pour réduire le biais dans les prévisions et maintenir une dispersion appropriée pour tous les horizons de prévision.Hydrological simulations and forecasts are subject to various sources of uncertainties. Forecast uncertainties are unfortunately inevitable when conducting the deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, necessarily imperfect hydrologic modeling, and the inconsistent stationnarity assumption in a changing environment. Ensemble forecasting is a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. Thiboult et al. (2016) constructed a 50,000-member great ensemble that accounts for meteorological forcing uncertainty, initial conditions uncertainty, and structural uncertainty. This large ensemble can also be separated into sub-components to untangle the three main sources of uncertainties mentioned above. In asimilar experiment, another multimodel hydrological ensemble forecasting system implemented for different catchments was produced by Emixi Valdez. However,in the latter case, model outputs were simply pooled together, considering the members equiprobable. Although multimodel hydrological ensemble forecasting systems can be considered very comprehensive, they can still underestimate the total uncertainty. For instance, the meteorological forecasts in there search of Thiboult et al. (2016) were pre-tested on some watersheds. It was found out that the forecasting performance of data assimilation fades away quickly as the lead time progresses. In addition, operational forecasts users may not able to perfectly utilize all the forecasting tools (i.e., meteorological ensemble forcing, data assimilation, and multimodel) jointly. Therefore, there is still room for improvement to enhance the forecasting skill of such systems through proper statistical post-processing.The global objective of this research is to explore the proper use and predictive skill of various statistical post-processing algorithms by testing them on single-model and multimodel ensemble stream flow forecasts. First, we tested the post-processing skills of Affine kernel dressing (AKD) and Non-dominated sorting genetic algorithm II (NSGA-II) over single-model H-EPSs. Those two methods are theoretically/technically distinct yet are both non-parametric. They do not require the raw ensemble members to follow a specific parametric distribution.AKD-transformed ensembles and the Pareto fronts generated with NSGA-II demonstrated the superiority of post-processed ensembles compared to raw ensembles. Both methods where efficient at eliminating biases and maintaining a proper dispersion for all forecasting horizons. For multimodel ensembles, two post-processors, namely Bayesian model averaging (BMA) and the integrated copula-BMA, are compared for deriving a pertinent joint predictive distribution of daily streamflow forecasts issued by five different single-model hydrological ensemble prediction systems (H-EPSs). BMA assign weights to different models. Forecasts from all models are then combined to generate more skillful and reliable probabilistic forecasts. BMA weights quantify the level of confidence one can have regarding each candidate hydrological model and lead to a predictive probabilistic density function (PDF) containing information about uncertainty. BMA improves the overall quality of forecasts mainly by maintaining the ensemble dispersion with the lead time. It also improves the reliability and skill of multimodel systems that only include two sources of uncertainties compared to the 50,000-member great ensemble from Thiboult et al (2016). Furthermore, Thiboult et al. (2016) showed that the meteorological forecasts they used were biased and unreliable on some catchments. BMA improves the accuracy and reliability of the hydrological forecasts in that case as well.However, BMA suffers from limitations pertaining to its conditional probability density functions (PDFs), which must follow a known parametric distribution form (e.g., normal, gamma). On the contrary, Copula-BMA predictive model fully relaxes this constraint and also eliminates the power transformation step. In this study, eleven univariate marginal distributions and six copula functions are explored in a Copula-BMA framework for comprehensively reflecting the dependence structure between pairs of forecasted and observed streamflow. Results demonstrate the superiority of the Copula-BMAcompared to BMA in eliminating biases and maintaining an appropriate ensemble dispersion for all lead-times

Modeling Nutrient Legacies and Time Lags in Agricultural Landscapes: A Midwestern Case Study

Land-use change and agricultural intensification have increased food production but at the cost of polluting surface and groundwater. Best management practices implemented to improve water quality have met with limited success. Such lack of success is increasingly attributed to legacy nutrient stores in the subsurface that may act as sources after reduction of external inputs. These legacy stores have built up over decades of fertilizer application and contribute to time lags between the implementation of best management practices and water quality improvement. However, current water quality models lack a framework to capture these legacy effects and corresponding lag times. The overall goal of this thesis is to use a combination of data synthesis and modeling to quantify legacy stores and time lags in intensively managed agricultural landscapes in the Midwestern US. The specific goals are to (1) quantify legacy nitrogen accumulation using a mass balance approach from 1949 - 2012 (2) develop a SWAT model for the basin and demonstrate the value of using crop yield information to increase model robustness (3) modify the SWAT (Soil Water Assessment Tool) model to capture the effect of nitrogen (N) legacies on water quality under multiple land-management scenarios, and (4) use a field-scale carbon-nitrogen cycling model (CENTURY) to quantify the role of climate and soil type on legacy accumulation and water quality. For objectives 1 and 2, the analysis was performed in the Iowa Cedar Basin (ICB), a 32,660 km2 watershed in Eastern Iowa, while for objective 3, the focus has been on the South Fork Iowa River Watershed (SFIRW), a 502 km2 sub-watershed of the ICB, and for objective 4 the focus was at the field scale. For the first objective, a nitrogen mass balance analysis was performed across the ICB to understand whether legacy N was accumulating in this watershed and if so, the magnitude of accumulation. The magnitude of N inputs, outputs, and storage in the watershed was quantified over 64 years (1949 – 2012) using the Net Anthropogenic Nitrogen Inputs (NANI) framework. The primary inputs to the system were atmospheric N deposition (9.2 ± 0.35 kg/ha/yr), fertilizer N application (48 ± 2 kg/ha/yr) and biological N fixation (49 ± 3 kg/ha/yr) and while the primary outputs from the system was net food and feed that was estimated as 42 ± 4.5 kg/ha/yr. The Net Anthropogenic Nitrogen Input (NANI) to the system was estimated to be 64 ± 6 kg/ha/yr. Finally, an estimated denitrification rate constant of 12.7 kg/ha/yr was used to estimate the subsurface legacy nitrogen storage as 33.3 kg/ha/yr. This is a significant component of the overall mass budget and represents 48% of the NANI and 31% of the fertilizer added to the watershed every year. For the second objective, the effect of crop yield calibration in increasing the robustness of the hydrologic model was analyzed. Using a 32,660 km2 agricultural watershed in Iowa as a case study, a stepwise model refinement was performed to show how the consideration of additional data sources can increase model consistency. As a first step, a hydrologic model was developed using the Soil and Water Assessment Tool (SWAT) that provided excellent monthly streamflow statistics at eight stations within the watershed. However, comparing spatially distributed crop yield measurements with modeled results revealed a strong underestimation in model estimates (PBIAS Corn = 26%, PBIAS soybean = 61%). To address this, the model was refined by first adding crop yield as an additional calibration target and then changing the potential evapotranspiration estimation method -- this significantly improved model predictions of crop yield (PBIAS Corn = 3%, PBIAS soybean = 4%), while only slightly improving streamflow statistics. As a final step, for better representation of tile flow, the flow partitioning method was modified. The final model was also able to (i) better capture variations in nitrate loads at the catchment outlet with no calibration and (ii) reduce parameter uncertainty, model prediction uncertainty, and equifinality. The findings highlight that using additional data sources to improve hydrological consistency of distributed models increases their robustness and predictive ability. For the third objective, the SWAT model was modified to capture the effects of nitrogen (N) legacies on water quality under multiple land-management scenarios. My new SWAT-LAG model includes (1) a modified carbon-nitrogen cycling module to capture the dynamics of soil N accumulation, and (2) a groundwater travel time distribution module to capture a range of subsurface travel times. Using a 502 km2 SFIR watershed as a case study, it was estimated that, between 1950 and 2016, 25% of the total watershed N surplus (N Deposition + Fertilizer + Manure + N Fixation – Crop N uptake) had accumulated within the root zone, 14% had accumulated in groundwater, while 27% was lost as riverine output, and 34% was denitrified. In future scenarios, a 100% reduction in fertilizer application led to a 79% reduction in stream N load, but the SWAT-LAG results suggest that it would take 84 years to achieve this reduction, in contrast to the two years predicted in the original SWAT model. The framework proposed here constitutes a first step towards modifying a widely used modeling approach to assess the effects of legacy N on time required to achieve water quality goals. The above research highlighted significant uncertainty in the prediction of biogeochemical legacies -- to address this uncertainty in the last objective the field scale CENTURY model was used to quantify SON accumulation and depletion trends using climate and soil type gradients characteristic of the Mississippi River Basin. The model was validated using field-scale data, from field sites in north-central Illinois that had SON data over 140 years (1875-2014). The study revealed that across the climate gradient typical of the Mississippi River Basin, SON accumulation was greater in warmer areas due to greater crop yield with an increase in temperature. The accumulation was also higher in drier areas due to less N lost by leaching. Finally, the analysis revealed an interesting hysteretic pattern, where the same levels of SON in the 1930s contributed to a lower mineralization flux compared to current

Development of Integrated Water Resources Planning Model for Dublin using WEAP21

Population growth, urbanisation, and climate change are predicted to impose huge pressure on water resource systems of many cities around the world including Dublin. Integrated water resources management is seen as a viable approach to address these challenges. This approach examines the water resources system in a more interconnected manner, focusing on reducing water demands, reducing reliance on fresh water supplies, reducing discharges into receiving water bodies, and creating water supply assets from storm water and wastewater. The role of mathematical modelling in designing an integrated water resources management plan is paramount as it provides a tool whereby performances of alternative water management plans can be predicted and evaluated under future scenarios of population growth, urban development and climate. There is a lack of an integrated water resources management model for Dublin that integrates the main components of the water resources system including water supply sources, sectoral water uses, wastewater disposal, urban runoff and associated infrastructure. Previous models also did not consider water management options such as rainwater harvesting, greywater reuse, and groundwater recharge - which are important for the implementation of an integrated water resources management approach. Moreover, integration of uncertainty analysis into water resources modelling helps understand associated uncertainties and hence reduce the

Understanding and quantifying channel transmission loss processes in the Limpopo River Basin

Water availability is one of the major societal issues facing the world. The ability to understand and quantify the impact of key hydrological processes on the availability of water resources is therefore integral to ensuring equitable and sustainable resource management. A review of previous hydrological studies conducted in the Limpopo River Basin has revealed a gap in the understanding of surface water-groundwater interactions, particularly channel transmission loss processes. These earlier studies, focused largely on the Limpopo River’s main stem, have attributed the existence of these streamflow losses to the presence of significant alluvial aquifers and indicated that the losses account for about 30 percent (or 1000 Mm3 a-1) of the basin’s water balance. The work conducted in this dissertation reports on the delineation of alluvial aquifers across three sub-basins of the Limpopo River Basin namely, the Mokolo (South Africa), Motloutse (Botswana) and Mzingwane (Zimbabwe) sub-basins and the estimation of potential channel transmission losses based on the alluvial aquifer properties. Additionally, an assessment of the different approaches that can be applied to simulate these channel transmission losses in the Pitman Model is presented. To delineate alluvial aquifers, general land cover classes including alluvial aquifers were produced from Landsat-8 imagery through image classification. The areal extent of the delineated alluvial aquifers was calculated using ArcMap 10.3. To quantify channel transmission losses and determine the effects on regional water resources, three approaches using the Pitman model were applied. The three approaches include an explicit transmission loss function, the use of a wetland function to represent channel-floodplain storage exchanges and the use of a ‘dummy’ reservoir to represent floodplain storage and evapotranspiration losses. Results indicate that all three approaches were able to simulate channel transmission losses, although with differing magnitudes. Observed monthly flow data were used to as a means of validating loss simulations however for each sub-basin, medium and low flows were over-simulated which accounts for water uses that were inefficiently represented due to lack of data. Knowledge of the structure of the transmission loss function dictates that it is better at representing the dynamics of channel transmission losses, as it takes into account the contribution of losses to groundwater recharge whereas the other two functions simply store water and release it back to the channel. Overall, the hydrological modelling results demonstrate the potential of each approach in reproducing the dynamics of channel transmission losses between channel and alluvial aquifer within an existing sub-basin scale hydrological model. It is believed that better quantification of losses and more efficient qualitative determination of the function which best represents transmission losses, can be attained with more reliable observed data. In conclusion, a study of this nature can be beneficial to water resource estimation programmes as it highlights the uncertainties related with quantifying channel transmission loss processes in a semi-arid environment

Understanding and quantifying channel transmission loss processes in the Limpopo River Basin

Water availability is one of the major societal issues facing the world. The ability to understand and quantify the impact of key hydrological processes on the availability of water resources is therefore integral to ensuring equitable and sustainable resource management. A review of previous hydrological studies conducted in the Limpopo River Basin has revealed a gap in the understanding of surface water-groundwater interactions, particularly channel transmission loss processes. These earlier studies, focused largely on the Limpopo River’s main stem, have attributed the existence of these streamflow losses to the presence of significant alluvial aquifers and indicated that the losses account for about 30 percent (or 1000 Mm3 a-1) of the basin’s water balance. The work conducted in this dissertation reports on the delineation of alluvial aquifers across three sub-basins of the Limpopo River Basin namely, the Mokolo (South Africa), Motloutse (Botswana) and Mzingwane (Zimbabwe) sub-basins and the estimation of potential channel transmission losses based on the alluvial aquifer properties. Additionally, an assessment of the different approaches that can be applied to simulate these channel transmission losses in the Pitman Model is presented. To delineate alluvial aquifers, general land cover classes including alluvial aquifers were produced from Landsat-8 imagery through image classification. The areal extent of the delineated alluvial aquifers was calculated using ArcMap 10.3. To quantify channel transmission losses and determine the effects on regional water resources, three approaches using the Pitman model were applied. The three approaches include an explicit transmission loss function, the use of a wetland function to represent channel-floodplain storage exchanges and the use of a ‘dummy’ reservoir to represent floodplain storage and evapotranspiration losses. Results indicate that all three approaches were able to simulate channel transmission losses, although with differing magnitudes. Observed monthly flow data were used to as a means of validating loss simulations however for each sub-basin, medium and low flows were over-simulated which accounts for water uses that were inefficiently represented due to lack of data. Knowledge of the structure of the transmission loss function dictates that it is better at representing the dynamics of channel transmission losses, as it takes into account the contribution of losses to groundwater recharge whereas the other two functions simply store water and release it back to the channel. Overall, the hydrological modelling results demonstrate the potential of each approach in reproducing the dynamics of channel transmission losses between channel and alluvial aquifer within an existing sub-basin scale hydrological model. It is believed that better quantification of losses and more efficient qualitative determination of the function which best represents transmission losses, can be attained with more reliable observed data. In conclusion, a study of this nature can be beneficial to water resource estimation programmes as it highlights the uncertainties related with quantifying channel transmission loss processes in a semi-arid environment