Nature-based Solutions (NbS) and other small-scale, decentralized infrastructure elements are increasingly recognized for their potential to reduce flood and drought impacts. Systematic evaluation approaches are now needed to identify effective spatial configurations and temporal staging for the implementation of NbS. This study develops and applies a hydrologic modeling framework to evaluate five NbS pathways combining alien invasive vegetation removal and replacement by indigenous grasslands or forest, and targeted soil conductivity modification to enhance infiltration. Pathways were assessed using multi-criteria metrics including hydrologic performance, feasibility, cost, and co-benefits for enhancing dry season flows. The five adaptation pathways were developed as representative combinations of short-, mid-, and long-term NbS interventions selected based on modeled hydrologic performance, feasibility, and stakeholder-identified implementation priorities and constraints. Pathway 1 and Pathway 2 emphasize the alien invasive vegetation removal and replacement with indigenous grasslands in different spatial sequences; Pathway 3 combines the alien invasive vegetation replacement with grasslands and soil modification; Pathway 4 combines the alien invasive vegetation replacement with grasslands and indigenous forest in different locations; and Pathway 5 combines the alien invasive vegetation replacement with indigenous grasslands and forest with soil modification at high-performing locations. Results show that each of the five adaptation pathways improves water availability relative to the baseline, with variations in their performance, feasibility, cost, and co-benefits. Across pathways, modeled average daily flow at the outlet increased from 6.0 m(3)/s (baseline) to 6.8-7.4 m(3)/s (+0.8-1.4 m(3)/s; similar to 13-23%) under full-year conditions. Improvements were more pronounced during water-stressed periods: dry-season flow increased from 3.2 m(3)/s to 3.8-4.0 m(3)/s (similar to 19-25%), and flow in the driest year increased from 0.6 m(3)/s to 1.0-2.9. Pathway 5 enhanced the dry season flows the most while mitigating peak flows and providing ecosystem benefits. This research advances the integration of hydrologic modeling of decentralized land use changes and water system infrastructure elements with stakeholder-driven decision-making, thereby contributing to the adaptation planning and Adaptative Pathways Planning literature.

We present the Network Analysis and Data Integration (NADI) System for extracting, organizing, analyzing, and visualizing river data with upstream/downstream relationships. The NADI System consists of a Geographical Information System (GIS) tool that uses spatial methods to generate the network, and a Domain Specific Language (DSL) that provides a concise and intuitive syntax for data analysis and is extensible through a plugin system. We demonstrate the capabilities of NADI using a case study of the Ohio River basin, showing it to be well-suited for large-scale metadata analysis based on river connections. The result of the case study shows that approximately half of the USGS streamflow gages in the Ohio Basin were constructed after dam(s) upstream, and only 35% of the gages remain without any dams upstream. These unaffected gages only account for 1.2% of the measured streamflow, showing the scarcity of natural streamflow data.

We present the Network Analysis and Data Integration (NADI) System for extracting, organizing, analyzing, and visualizing river data with upstream/downstream relationships. The NADI System consists of a Geographical Information System (GIS) tool that uses spatial methods to generate the network, and a Domain Specific Language (DSL) that provides a concise and intuitive syntax for data analysis and is extensible through a plugin system. We demonstrate the capabilities of NADI using a case study of the Ohio River basin, showing it to be well-suited for large-scale metadata analysis based on river connections. The result of the case study shows that approximately half of the USGS streamflow gages in the Ohio Basin were constructed after dam(s) upstream, and only 35% of the gages remain without any dams upstream. These unaffected gages only account for 1.2% of the measured streamflow, showing the scarcity of natural streamflow data.

Economic evaluation using cost benefit analysis (CBA) focuses on the allocation of scarce resources to competing needs and often requires a multi-objective approach for accurate analysis. However, known and unforeseen project uncertainties hinder the monetary quantification of all project objectives. Future cost and benefit projections of civil infrastructure such as water resources projects are influenced by uncertainties in societal preferences and behavioral patterns, policies and their implementation, as well as distribution and sustainability of benefits under changing climate. It is especially difficult to measure the projected benefits of public goods like water infrastructure because they have no direct or standard valuation approach. With advancement in technology, improvement to analytical tools and methods, and the science of decision making under uncertainty, there has been more research interest in CBA under uncertainty. This paper: (1) reviews the historical evolution of analytical approaches for water infrastructure evaluation; (2) explains the limitations on CBA imposed by uncertainties such as climate change; (3) describes analytical techniques applied to manage uncertainty in CBA; and (4) proposes methods for improved incorporation of socio-economic factors into economic evaluation. Our review of 302 manuscripts showed that standard practice differs from academic assumptions. For instance, there is limited uptake of analytical techniques that support robust characterization of deep uncertainties such as climate change (e.g. the climate stress test) and the incorporation of socio-economic factors such as equity. The paper suggests ways to reconcile academia and practice, provides direction for future work, and presents recommendations for improving current approaches to CBA. To strengthen water resource management efforts and improve policy making, we recommend more collaborative partnerships that identify opportunities to integrate academic recommendations into standard practice. Better documentation of systematic approaches to water project evaluation under uncertainty and holistic consideration of equity at the various stages of water project planning are also required.

Traditional vulnerability assessments of climate change impacts often rely on randomised precipitation scenarios that lack a strong physical science basis. Furthermore, the limitations of General Circulation Models (GCMs) in accurately representing local precipitation fields undermine their utility for projecting future hydroclimatic extremes. To address these gaps in climate risk management, this study explores the role of large-scale atmospheric circulation patterns, known as weather regimes (WRs), in explaining local and regional precipitation dynamics in South Africa. Utilising a Non-Homogeneous Hidden Markov Chain approach, we identified six primary WRs for South Africa, each exhibiting distinct seasonal patterns. The results show that winter precipitation near Cape Town is dominated by three WRs linked to higher rainfall, whilst summer precipitation is influenced by two WRs associated with drier conditions. The WR-precipitation relationship in South Africa appears to be influenced by topographic features (e.g., The Great Escarpment and Cape Fold Mountains) and ocean currents (Agulhas and Benguela), leading to distinct spatial precipitation responses to regional WR configurations. Importantly, significant shifts in seasonal WR frequencies have been observed over the past two decades, particularly a marked change since 2010. Notably, a WR historically associated with rainfall in Cape Town has been replaced by a drier WR, contributing to worsening drought conditions, including the 2015-2017 "Day Zero" drought. During this period, the WR associated with dryness in Cape Town occurred more frequently than the historical average, whilst wetter years before and after the drought were characterised by low-pressure (low 500 hPa geopotential height anomalies) WRs conducive to precipitation. In contrast, the drought years were dominated by high-pressure (high 500 hPa geopotential height anomaly) WRs associated with dry conditions. This WR-precipitation analysis underscores the critical link between atmospheric circulation patterns and regional hydroclimatic extremes. These findings can inform the development of WR-based rainfall generators, providing valuable tools for vulnerability assessments and climate change adaptation planning.

Study region:: The Ohio River in the northeast United States (US). Study focus:: Low streamflows are critical for urban water security, agricultural irrigation, water quality regulatory thresholds, navigation passage, and ecological well-being. However, there is insufficient understanding of the natural low flow conditions in rivers containing dams and artificial reservoirs, in part because we have inadequate records of natural flows prior to the introduction of the water control infrastructure. We demonstrate an improved technique for estimation of human impact on low flow, and describe the analytical innovations necessary to apply the technique. The primary innovation necessary was the development of a parsimonious streamflow routing algorithm to aggregate naturalized flow from reservoirs operated by the US Army Corps of Engineers (USACE) to locations of concern along the Ohio River mainstem. New hydrological insights for the region:: This study shows that, in dry years, releases from USACE reservoirs during autumn months account for up to approximately half of the mainstem flow, and the influence of USACE water control infrastructure is more pronounced on many of the Ohio River’s tributaries. The Flow Duration Curves also show significant differences in low flow throughout the mainstem. This has implications for dry season river functionality in all the categories listed above; if the infrastructure were to fail, or lose effectiveness due to climate change, these river functions would be threatened.

AbstractAs extreme precipitation intensifies under climate change, traditional risk models based on the ‘100-year return period’ concept are becoming inadequate in assessing real-world risks. In response, this nationwide study explores shifting extremes under non-stationary warming using high-resolution data across the contiguous United States. Results reveal pronounced variability in 100-year return levels, with Coastal and Southern regions displaying the highest baseline projections, and future spikes are anticipated in the Northeast, Ohio Valley, Northwest, and California. Exposure analysis indicates approximately 53 million residents currently reside in high-risk zones, potentially almost doubling and tripling under 2 °C and 4 °C warming. Drought frequency also rises, with over 37% of major farmland vulnerable to multi-year droughts, raising agricultural risks. Record 2023 sea surface temperature anomalies suggest an impending extreme El Niño event, demonstrating the need to account for natural climate variability. The insights gained aim to inform decision-makers in shaping adaptation strategies and enhancing the resilience of communities in response to evolving extremes.

A broad set of tools, frameworks, and guidance documents are available for water resources project planning, design, evaluation, and implementation in an ever-evolving world. The principles underlying most of these resources aim to advance the practice of water systems engineering under uncertainty, preserve and enhance project benefits, and achieve investment goals. Approaches to financial and economic evaluation under climate uncertainty in civil infrastructure investments, in particular, are currently being reviewed by academics and practitioners in the field to assess their ability to deliver resilience, sustainability, and equity. In climate-sensitive projects, adaptation measures that help mitigate the adverse effects of climate change and preserve project benefits are required, and stakeholder willingness-to-pay (WTP) for these must be assessed. Typically, stakeholders and decision-makers utilize the outcomes of economic assessment methods such as cost–benefit analysis (CBA) to justify large capital investments. Synthesizing previous advancements in water resources planning and evaluation, this study illustrates how a CBA framework can be augmented by applying a Climate-informed Robustness Index (CRI). The analytics underpinning the CRI, as well as the summary metric itself, help characterize project climate vulnerability, while conducting CBA with and without potential adaptation measures can be used to estimate WTP of investors for adaptation to the identified climate vulnerabilities. The case study of a planned irrigated agriculture project in Lesotho highlights critical climate conditions for which adaptation measures such as integrated catchment management (ICM) plans can be introduced to safeguard project robustness.

The fundamental water resources problem to be solved in adaptation to climate change is increasing rainfall variability: generally, flood peaks are increasing, and dry-season water availability is decreasing. Large, centralized adaptation strategies such as reservoirs impounded behind tall dams typically perform well at both attenuation of flood peaks, and augmentation of dry season flows. They come with substantial costs, however, in terms of capital cost outlays and damages to local ecological and human environments. The Intergovernmental Panel on Climate Change, the United States (US) Government, the European Union, the United Nations, and many other institutions and agencies are currently advocating for the adoption of “nature-based solutions” (NbS), which are believed able to reduce adverse climate change impacts at community scale, while supporting biodiversity and securing ecosystem services. However, the potential of NbS to provide the intended benefits has not been rigorously assessed. This talk presents a preliminary assessment of climate change vulnerabilities for the Chimanimani biosphere reserve in Zimbabwe, and an evaluation of the tradeoffs between conventional “hard” civil infrastructure and decentralized NbS.

This paper presents an analysis of future research and development needs to assess the effectiveness of nature-based solutions for climate adaptation in watersheds at scale using hydrological models. Two main questions are addressed: to what extent are hydrological model approaches able to support decision making on nature-based solutions and adaptation, and how well is this hydrological analysis embedded in the broader planning process? To support the research, case studies in Bhutan, Zimbabwe and the Netherlands are presented. The Climate Risk Informed Decision Analysis approach is used to structure the planning process. All three case studies demonstrate how the hydrological system and full landscape of land and water use in watersheds can be simulated to better understand hydrometeorological hazards under current and future climate. Also, simulations of nature-based solutions are demonstrated, which need creativity and profound expert knowledge. In contrast to the assessment of grey infrastructure, no rules or guidance exists for the hydrological assessment of nature-based solutions. Physically-based models are better able to support the understanding of the functioning of the ecohydrological system and, therefore, the effectiveness of adaptation using nature-based solutions. There are however trade-offs between the computational complexity, the computation time and the multiple scenarios and sensitivity analyses of adaptation options needed for climate stress testing. Often there is a lack of monitoring data for verification of model outcomes. Several recommendations on how to improve modelling in an adaptation process are given. In addition, it is recommended to develop and rectify a set of nature-based solutions performance indicators, rules and algorithms to be adopted in models in order to quantify the effectiveness of these solutions.

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

Cost benefit analysis (CBA), a conventional method of assessing the cost and benefits of proposed investments, is often associated with uncertainty due to a wide range of factors that hinder the accurate projection of costs and benefits into the future. Using a case study of the Metolong Dam in Lesotho, this paper explores uncertainty in the economic analysis of projects under climate change via a bottom-up approach to economic evaluation. Streamflow is assessed as measurable metric of climate uncertainty upon which sensitivity analysis is performed, to assess the impact of climate uncertainty on the outflow of the Metolong Dam, which supports production at a textile factory. Although climate change was not identified as a near-term threat with GCM projections estimating precipitation changes to be within ±10% of current annual totals and less than 2°C rise in temperature over historical values, uncertainty in water allocations and rates of reservoir release proved consequential when combined with climate change. Following a vulnerability analysis, the project was found to be robust to approximately 10% of the considered future scenarios. The project is judged to have low climate risk, but high uncertainty relative to other factors, so flexible adaptation strategies that provide incremental robustness to the project are recommended to avoid infrastructure redundancy and resource waste.

The threat that climate change poses to water resource systems has led to a significant and growing number of impact studies. These studies tend to follow two general methodological approaches: (1) top-down, process-based studies driven by projections of future climate change supplied by downscaled general circulation models (GCMs), and (2) bottom-up, vulnerability-based studies driven by exploratory scenarios. Top-down studies generate realistic climate scenarios, but computational burdens limit the ensemble size. As a result, critical vulnerabilities may be left unexplored. Bottom-up approaches make it possible to assess a wide range of scenarios, but usually without connection to physically plausible climate processes, limiting their utility in adaptive planning. This study develops process-informed exploratory scenarios that bridge the gap between top-down and bottom-up methods. This hybrid approach yields several advantages. First, emerging vulnerabilities associated with non-linear hydrologic changes are linked to thermodynamic and dynamic climate drivers modeled in the GCMs with differential likelihoods and plausible ranges of change. This provides a transparent link between stakeholder defined vulnerabilities and climate processes that is often missing in bottom-up assessments. Second, non-linear shifts in vulnerability are directly linked to specific climate drivers, through the systematic perturbation of process informed climate variables. Making this connection in top-down assessments is difficult since the climate response to an emissions scenario is modeled as part of an endogenous process. The hybrid approach developed by this study is presented with a case study in the Tuolumne River watershed; through which thermodynamic and dyanamically guided climate scenarios were created by a process-informed stochastic weather generator to evaluate flood and drought related performance vulnerabilities at the New Don Pedro Dam near the watershed’s outlet. This case study finds that flood and drought performance at the dam is more sensitive to process-informed climate drivers than less theoretically grounded delta shifts precipitation, and non-linear system responses to climate drivers are revealed through the systematic perturbation process-informed climate variables.

Abstract The moisture maximization approach, the most common technique for estimating Probable Maximum Precipitation (PMP), assumes that the upper limit of extreme precipitation can be estimated by maximizing atmospheric moisture availability alone. However, several atmospheric variables often play a role in the occurrence of extreme precipitation. In this paper, a generalization is proposed to incorporate other relevant atmospheric variables with a two-step multifactor maximization approach. In the first step, the dominant atmospheric variables are screened. In the second step, the maximization ratio for each variable is calculated separately and then combined as a weighted average to obtain a combined maximization ratio. When applied to a case study in Nepal, the multifactor maximization approach results in PMP estimates substantially different (up to 4x) from the conventional moisture maximization approach in regions where the PMP is particularly sensitive to factors other than atmospheric moisture availability.

Translation of a system's performance goals into measurable quantities is fundamental for water systems planning. Frequently, the selected metrics are the same threshold-based metrics that have been common in water systems analysis for four decades. These are single statistic measures that characterize failure characteristics (e.g., frequency, duration, and magnitude) for a given simulation period where the failure threshold is a target delivery. At the same time, our systems are increasingly challenged by shifting water availability due to climate change and other anthropogenic pressures. Preparing our water systems to be resilient to such changes is a major challenge faced by water resources planners and managers. In particular, shifting mismatches between availability and target deliveries mean that for optimal design and control problems, metric choice implicitly becomes a statement of preference of failure type (i.e., the magnitude and/or duration of failure events). This usually occurs without explicit discussion of failure preference, resulting in unintended consequences that are not well understood. This study addresses the issue of unintended consequences in two ways. First, we introduce an approach to detect the threshold at which the balance between availability and delivery is no longer stable. Second, we characterize the consequences of metric choice when this threshold is exceeded. We find that early warning signals, and specifically the L moment estimator of variance, are able to detect the system's entry into a state of increasing failure. We also find that for target deliveries greater than this threshold there are increasing tradeoffs between failure frequency, duration, and magnitude and that the distributional characteristics of failure magnitude and duration are driven by metric selection, objective formulation, and where on the Pareto front the solution sets lies. This study establishes a way to detect critical system thresholds and characterizes the effects of metric and threshold selection on failure, both of which are requisite for analysts and decision makers to avoid unintended consequences of planning and management decisions in an uncertain and changing world. Metrics form the basis of comparison for water resources planning and management decisions. When availability and demand are mismatched, the use of the standard threshold-based metrics in optimization problems can easily lead to unintended consequences. This is of fundamental concern for water resources planning and management in a changing world. This study helps analysts and decision makers avoid unintended consequence of poor metric selection by (1) characterizing the influence of metric selection on the types of failure events the system may endure; and (2) introducing a measure to detect the threshold at which analysts and decision makers should begin to be cautious of such consequences.

The moisture maximization approach, the most common technique for estimating Probable Maximum Precipitation (PMP), assumes that the upper limit of extreme precipitation can be estimated by maximizing atmospheric moisture availability alone. However, several atmospheric variables often play a role in the occurrence of extreme precipitation. In this paper, a generalization is proposed to incorporate other relevant atmospheric variables with a two-step multifactor maximization approach. In the first step, the dominant atmospheric variables are screened. In the second step, the maximization ratio for each variable is calculated separately and then combined as a weighted average to obtain a combined maximization ratio. When applied to a case study in Nepal, the multifactor maximization approach results in PMP estimates substantially different (up to 4x) from the conventional moisture maximization approach in regions where the PMP is particularly sensitive to factors other than atmospheric moisture availability.

A river contamination risk (RANK) framework is developed to demonstrate the application of a Computational Fluid Dynamics model in risk assessment of contamination exposure in surface waters. The ultimate goal is to identify the factors responsible for potential future river contamination emergencies, and the use of that insight to inform strategic investments in resilience-enhancing infrastructure and policies. The RANK model is applied to preliminary assessment of a historical contamination event in the Ohio River. The results prove that with higher river velocity, plume passage becomes faster, with earlier peak time and shorter duration of plume at the point-of-interest. For the case under study, with increasing the initial spill duration by 100%, the plume duration at the point-of-interest may increase 85% or 65% depending on the toxicity level of the contaminant. The sensitivity analysis on hydraulic inputs implies that RANK can be utilized for climate-informed decision analysis in water quality applications.

Renewable sources of electricity, such as solar and wind, need to be paired with sources of reliable baseload. Hydropower is a renewable, low‐emission source of electricity baseload available throughout much of the world as an alternative to electricity conventionally provided by thermal combustion of fossil fuels; however, the global hydropower sector as it stands relies upon surface water flows of substantial and predictable volume. This makes it vulnerable to climate change. The impact of climate change on the hydropower sector is difficult to predict, and not globally uniform. It might be positive, negative, or inconsequential depending upon the local timing and magnitude of changes, reservoir size, allocation priority, and the energy market. The secondary effects of climate change on glacier lake outbursts floods, landslides, and sediment load are poorly understood. In addition, when planning hydropower projects for the future, attention must be given to the greenhouse gas contribution of the impounded waters behind storage dams, and the impact of dams on water temperature. In the past decade, sovereign nations and international development agencies worldwide have evaluated the potential of hydropower as a cost‐effective, clean, sustainable option for baseload electricity supply. There is therefore a crucial need to assess the opportunities and risks hydropower poses across a wide range of potential future climate conditions. This review paper conducts a global survey of the literature on the effect of climate change on hydropower and identifies room for improvement in current approaches to evaluation of the net benefits of hydropower projects under climate change.

Model emulation has become an integral tool in scenario analysis, risk assessment, and calibration of environmental models. Of particular interest is dynamic emulation – the approximation of model outputs from inputs or processes that vary in time. This paper presents a method for data-driven dynamic emulation of high-dimensional model outputs that overcomes the logistical challenges from assumptions in traditional multivariate statistics concerning output covariance. In this method, outputs are subjected to principal component analysis, and Gaussian random fields are fit along new orthogonal axes to accommodate spatial heterogeneity and serial correlation. The technique is demonstrated on a regional groundwater model of metropolitan Mexico City, where it successfully emulates spatial and temporal dynamics of land subsidence and aquifer level fluctuation resulting from two management scenarios. In doing so, we introduce methodological advances to emulation techniques, which facilitate the use of models with high-dimensional outputs in computationally expensive planning and optimization applications.

The evaluation of water systems based on historical statistics is problematic when shifts in the hydrologic system occur due to a changing climate. An explicit link to thermodynamic and dynamic pathways in the climate system that may control water system performance is missing from current operational policies prescribed by regulation manuals within the US. In response, this study contributes an extended version of an existing weather regime (WR)-based stochastic weather generator (SWG) that allows (1) hourly simulation, (2) over the entire year, and (3) with a corrected representation of extremes. A range of climate scenarios is developed to demonstrate the insights that can be gained from linking the impacts of climate change to their thermodynamic and dynamic causal mechanisms, in this case for inflows to the Don Pedro Reservoir within the Tuolumne River Watershed of California. Application of the WR-SWG and water system modeling chain shows that the magnitude of flood events can be heavily influenced by antecedent hydrologic factors such as snow water equivalent (SWE) and soil moisture. Our results suggest that, under all climate change scenarios, SWE decreases as temperature increases and contributes more (sometimes up to 2.5 times more than the baseline) inflow as part of rain-on-snow events. The monthly reservoir inflows show the potential to cause extreme floods as the average rate of inflow increases by up to 80% with temperature increases, whereas SWE tends to increase by 50%, adding water to the stream during the high flow season. In addition to the temperature increase, if the water-holding capacity of the atmosphere increases with Clausius-Clapeyron scaling, reservoir inflows are projected to increase. This provides insight for risk-hedging policies: winter storm and spring snowmelt release and storage decisions that drive flood and drought risk, respectively. (C) 2022 American Society of Civil Engineers.