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Ecosystem Services into Water Resource Planning and Management  

Phoebe Koundouri, Angelos Alamanos, Kostas Dellis, Conrad Landis, and Artemis Stratopoulou

The broad economic notion of ecosystem services (ES) refers to the benefits that humans derive, directly or indirectly, from ecosystem functions. Provisioning ES refer to human-centered benefits that can be extracted from nature (e.g., food, drinking water, timber, wood fuel, natural gas, oils, etc.), whereas regulating ES include ecosystem processes that moderate natural phenomena (pollination, decomposition, flood control, carbon storage, climate regulation, etc.). Cultural ES entail nonmaterial benefits accruing to the cultural advancement of people, such as the role of ecosystems in national and supranational cultures, recreation, and the spur of knowledge and creativity (music, art, architecture). Finally, supporting ES refer to the main natural cycles that nature needs to function, such as photosynthesis, nutrient cycling, the creation of soils, and the water cycle. Most ES either depend on or provide freshwater services, so they are linked to water resources management (WRM). The concept of ES initially had a pedagogical purpose to raise awareness on the importance of reasonable WRM; later, however, it started being measured with economic methods, and having policy implications. The valuation of ES is an important methodology aimed at achieving environmental, economic and sustainability goals. The total economic value of ecosystems includes market values (priced) as well as nonmarket values (not explicit in any market) of different services for humanity’s benefit. The valuation of ES inherently reflects human preferences and perceptions regarding the contribution of ecosystems and their functions to the economy and society. The ES concept and associated policies have been criticized on the technical weaknesses of the valuation methods, interdisciplinary conflicts (e.g., ecological vs. economic perception of value), and ethical aspects on the limits of economics, nature’s commodification, and its policy implications. Since valuation affects the incentives and policies aimed at conserving key ES, e.g., through payment schemes, it is important to understand the way that humans decide and develop preferences under uncertainty. Behavioral economics attempts to understand human behavior and psychology and can help to identify appropriate institutions and policies under uncertainty that enhance ecosystem services that are key to WRM.

Article

Hedging and Financial Tools for Water Management  

C. Dionisio Pérez-Blanco

The management of risky water episodes entails a comprehensive set of instruments that can be broadly divided into two groups: damage prevention and damage management. Damage prevention instruments aim at negating or minimizing the economic damage of water scarcity and water extremes, and they include hard and soft engineering, information and awareness campaigns, and regulations and economic incentives. Damage management instruments aim at compensating damages and facilitating recovery, and they include tort law and hedging and financial tools. The growing interconnections and cascading uncertainties across coupled human and water systems make it increasingly challenging to comprehensively predict and anticipate expected damages from water scarcity and extremes, which is giving higher prominence to the management of damages, notably through hedging and financial tools. Hedging and financial tools are a risk transfer mechanism by which a potential future damage is transferred from one party to another, typically in exchange of a pecuniary compensation (risk premium), albeit they can be also freely provided (e.g., state aid). Hedging and financial instruments are varied and include futures, options, insurance, self-capitalization, reinsurance, private actions such as charities or nongovernmental organizations, state aid, and solidarity funds. The first section of this document discusses the political context for disaster risk reduction efforts at an international level and provides key definitions. The second section presents a taxonomy for hedging and financial instruments; assesses their strengths and weaknesses, performance, and market penetration levels; and critically reviews reform propositions in the literature toward increasing their performance and adoption. The third section discusses the interconnections between hedging and financial tools and damage prevention tools, as well as how their design can enhance each other’s performance. The last section discusses barriers and enablers for the adoption of hedging and financial tools.

Article

Machine Learning Tools for Water Resources Modeling and Management  

Giorgio Guariso and Matteo Sangiorgio

The pervasive diffusion of information and communication technologies that has characterized the end of the 20th and the beginning of the 21st centuries has profoundly impacted the way water management issues are studied. The possibility of collecting and storing large data sets has allowed the development of new classes of models that try to infer the relationships between the variables of interest directly from data rather than fit the classical physical and chemical laws to them. This approach, known as “data-driven,” belongs to the broader area of machine learning (ML) methods and can be applied to many water management problems. In hydrological modeling, ML tools can process diverse data sets, including satellite imagery, meteorological data, and historical records, to enhance predictions of streamflow, groundwater levels, and water availability and thus support water allocation, infrastructure planning, and operational decision-making. In water demand management, ML models can analyze historical water consumption patterns, weather data, and socioeconomic factors to predict future water demands. These models can support water utilities and policymakers in optimizing water allocation, planning infrastructure, and implementing effective conservation strategies. In reservoir management, advanced ML tools may be used to determine the operating rule of water structures by directly searching for the management policy or by mimicking a set of decisions with some desired properties. They may also be used to develop surrogate models that can be rapidly executed to determine the optimal course of action as a component of a decision-support system. ML methods have revolutionized water management studies by showing the power of data-driven insights. Thanks to their ability to make accurate forecasts, enhanced monitoring, and optimized resource allocation, adopting these tools is predicted to expand and consistently modify water management practices. Continued advancements in ML tools, data availability, and interdisciplinary collaborations will further propel the use of ML methods to address global water challenges and pave the way for a more resilient and sustainable water future.

Article

A Review of Alternative Water Supply Systems in ASEAN  

Cecilia Tortajada, Kristopher Hartley, Corinne Ong, and Ojasvee Arora

Climate change, water scarcity and pollution, and growing water demand across all sectors are stressing existing water supply systems, highlighting the need for alternative water supply (AWS) systems. AWS systems are those that have not typically existed in the traditional supply portfolio of a given service area but may be used to reduce the pressure on traditional water resources and potentially improve the system’s resilience. AWS systems have been used for decades, often where traditional systems are unable to maintain sufficient quantity and quality of water supply. Simpler forms of AWS systems, like rainwater harvesting, have been used for centuries. As human population and water demand have increased, AWS systems now play a larger role in the broader supply portfolio, but these systems alone are not able to fully resolve the increasingly complex mix of problems contributing to water stress. Entrenched challenges that go beyond technical issues include low institutional capacity for developing, operating, and maintaining AWS systems; monitoring water quality; more efficiently using available resources; and establishing clear responsibilities among governments, service providers, and property owners. Like traditional water supply systems, AWS systems should be developed within a sustainability-focused framework that incorporates scenario planning to account for evolving natural and institutional conditions. In ASEAN, the adoption of AWS systems varies among countries and provides context-specific lessons for water management around the world. This article provides an overview of AWS systems in the region, including rainwater harvesting, graywater recycling, wastewater reclamation, desalination, and stormwater harvesting.

Article

Basin Development Paths: Lessons From the Colorado and Nile River Basins  

Kevin Wheeler

Complex societies have developed near rivers since antiquity. As populations have expanded, the need to exploit rivers has grown to supply water for agriculture, build cities, and produce electricity. Three key aspects help to characterize development pathways that societies have taken to expand their footprint in river basins including: (a) the evolution of the information systems used to collect knowledge about a river and make informed decisions regarding how it should be managed, (b) the major infrastructure constructed to manipulate the flows of water, and (c) the institutions that have emerged to decide how water is managed and governed. By reflecting on development pathways in well-documented transboundary river basins, one can extract lessons learned to help guide the future of those basins and the future of other developing basins around the world.

Article

Statistical Scaling of Randomly Fluctuating Hierarchical Variables  

Shlomo P. Neuman, Monica Riva, Alberto Guadagnini, Martina Siena, and Chiara Recalcati

Environmental variables tend to fluctuate randomly and exhibit multiscale structures in space and time. Whereas random fluctuations arise from variations in environmental properties and phenomena, multiscale behavior implies that these properties and phenomena possess hierarchical structures. Understanding and quantifying such random, multiscale behavior is critical for the analysis of fluid flow as well as mass and energy transport in the environment. The multiscale nature of randomly fluctuating variables that characterize a hierarchical environment (or process) tends to be reflected in the way their increments vary in space (or time). Quite often such increments (a) fluctuate randomly in a highly irregular fashion; (b) possess symmetric, non-Gaussian frequency distributions characterized by heavy tails, which sometimes decay with separation distance or lag; (c) exhibit nonlinear power-law scaling of sample structure functions (statistical moments of absolute increments) in a midrange of lags, with breakdown in such scaling at small and large lags; (d) show extended power-law scaling (linear relations between log structure functions of successive orders) at all lags; (e) display nonlinear scaling of power-law exponent with order of sample structure function; and (f) reveal various degrees of anisotropy in these behaviors. Similar statistical scaling is known to characterize many earth, ecological, biological, physical, astrophysical, and financial variables. The literature has traditionally associated statistical scaling behaviors of the aforementioned kind with multifractals. This is so even though multifractal theory (a) focuses solely on statistical scaling of variable increments, unrelated to statistics of the variable itself, and (b) explains neither observed breakdown in power-law scaling at small and large lags nor extended power-law scaling of such increments. A novel Generalized sub-Gaussian scaling model is introduced that does not suffer from such deficiencies, and some of its key aspects are illustrated on microscale surface measurements of a calcite crystal fragment undergoing dissolution reaction due to contact with a fluid solution.

Article

Smart One Water: An Integrated Approach for the Next Generation of Sustainable and Resilient Water Systems  

Sunil K. Sinha, Meghna Babbar-Sebens, David Dzombak, Paolo Gardoni, Bevlee Watford, Glenda Scales, Neil Grigg, Edgar Westerhof, Kenneth Thompson, and Melissa Meeker

Quality of life for all people and communities is directly linked to the availability of clean and abundant water. Natural and built water systems are threatened by crumbling infrastructure, floods, drought, storms, wildfires, sea-level rise, population growth, cybersecurity breaches, and pollution, often in combination. Marginalized communities feel the worst impacts, and responses are hampered by fragmented and antiquated governance and management practices. A standing grand challenge for the water sector is transitioning society to a future where current silos are transformed into a significantly more efficient, effective, and equitable One Water system-of-systems paradigm—in other words, a future where communities are able to integrate the governance and management of natural and engineered water systems at all scales of decision-making in a river basin. Innovation in digital technologies that connect data, people, and organizations can be game changers in addressing this societal grand challenge. It is envisioned that advancing digital capabilities in the water sector will require a Smart One Water approach, one that builds upon new technologies and research advancements in multiple disciplines, including those in engineering, computer science, and social science. However, several fundamental knowledge gaps at the nexus of physical, social, and cyber sciences currently exist on how a nationwide Smart One Water approach can be created, operationalized, and maintained. Convergent research is needed to investigate these gaps and improve our current understanding of Smart One Water approaches, including the costs, risks, and benefits to diverse communities in the rural-to-urban continuum. At its core, implementing the Smart One Water approach requires a science-based, stakeholder-driven, and artificial intelligence (AI)–enabled cyberinfrastructure platform, one that can provide a robust framework to support networks of river-basin collaborations. We refer to this envisioned cyberinfrastructure foundation as the digital research and operational platform (DROP). DROP is envisioned to exploit advances in data analytics, machine learning, information, communication, and decision support technologies for the management of One Water systems via AI-enabled digital twins of river-basin systems. Deploying DROP at a large-basin scale requires an understanding of (a) physical water systems (natural and engineered) at the basin scale, which interact with each other in a dynamic environment affected by climate change and other societal trends and whose data, functions, and processes must be integrated to create digital twins of river basins; (b) the social aspects of One Water systems by understanding the values and perspectives of stakeholders, costs and benefits of water management practices and decisions, and the specific needs of disadvantaged populations in river basin communities; (c) approaches for developing and deploying the digital technologies, analytics, and AI required to efficiently operate and manage Smart One Water systems in small to large communities; (d) strategies for training and advancing the next-generation workforce who have expertise on cyber, physical, and social aspects of One Water systems; and (e) lessons learned from testing and evaluating DROP in diverse testbeds. The article describes a strategic plan for operationalizing Smart One Water management and governance in the United States. The plan is based on five foundational pillars: (a) river-basin scale governance, (b) workforce development, (c) innovation ecosystem, (d) diversity and inclusion, and (e) stakeholder engagement. Workshops were conducted for each foundational pillar among diverse stakeholders representing federal, state, and local governments; utilities; industry; nongovernmental organizations; academics; and the general public. The workshops confirmed the strong desire of water communities to embrace, integrate, and grow the Smart One Water approach. Recommendations were generated for using the foundational pillars to guide strategic plans to implement a national-scale Smart One Water program and facilitate its adoption by communities in the United States, with global applications to follow.

Article

Groundwater Development Paths in the U.S. High Plains  

Renata Rimšaitė and Nicholas Brozović

The High Plains Aquifer is the largest aquifer in the United States and the major source of groundwater withdrawals in the region. Although regionally abundant, groundwater availability for agriculture and other uses is not uniform across the area. Three separate states comprising the most significant portion of the aquifer have distinct climate and hydrologic characteristics, water law systems, and institutional groundwater governance leading to different concerns about water policy issues across the area. The northern, largest, and most saturated part of the High Plains Aquifer is located under Nebraska. The state has the largest irrigated area in the United States, most of which is groundwater irrigated. Nebraska is the home of the largest companies in the center pivot irrigation industry. Center pivot technology has had a fundamental role in expanding groundwater-fed irrigation. Nebraska is not free from groundwater depletion issues, but these issues are more important in central and south-central parts of the aquifer underlying large, primarily agricultural, lands of Kansas and Texas. The natural aquifer recharge is much lower in the south-central parts of the region, which has caused large groundwater extractions to have more significant water declines than in Nebraska. In the United States, the greatest portion of water quantity management regulatory oversight is left to individual states and local government agencies. Each of the three states has a unique legal system, which highly influences the framework of groundwater management locally. In Nebraska, groundwater is governed following two doctrines: correlative and reasonable use, which, in times of water shortage, lead to a proportional reduction of everyone’s allocation. Kansas uses the prior appropriation doctrine to manage groundwater, which applies the seniority principle when there is scarcity in water availability, making junior water rights holders bear the greatest risk. The absolute ownership doctrine is used to govern groundwater in Texas, which allows landowners to drill wells on their property and extract as much water as needed. Institutional groundwater governance in Nebraska is performed by the system of 23 locally elected Natural Resources Districts having full regulatory power to manage the state’s groundwater. The local governments use a variety of regulatory and incentive-based groundwater management tools to achieve local groundwater management goals. In Kansas, the Chief Engineer in the Kansas Department of Agriculture is in charge of water administration for the state. The Kansas legislature established five Groundwater Management Districts to address groundwater depletion issues, which can make policy recommendations but do not have the power to regulate. Groundwater Conservation Districts were created in Texas to provide protection from uncontrolled water mining in the state. The districts gained more power to regulate and enforce rules over time; however, significant groundwater depletion issues remain. Multiple lessons have been learned across the region since the beginning of groundwater development. Some of these could be applied in other areas seeking to address negative consequences of groundwater use. Forward-looking perspectives about groundwater management in the region vary from strong government-led solutions in Nebraska to various producer-initiated innovative approaches in Kansas and Texas.

Article

Virtual Water  

Francesca Greco, Martin Keulertz, and David Dent

Virtual water is the water contained in food, understood not only as the physical amount within the product but also as the amount of water required to generate it over time, from planting to final harvest. Despite Tony Allan defined virtual water in the context of the water needed to produce agricultural commodities, the concept has been subsequently expanded to include the water needed to produce non-agricultural commodities and industrial goods by Arjen Hoekstra, the creator of the water footprint indicator. Virtual water is a revolutionary concept because it describes something never conceptualized before: the water “embedded” in a product. Allan used virtual water “food water” and “embedded water” as interchangeable terms. Virtual water “trade” is the result of food trade: where agricultural goods are traded across countries, the water needed to produce that product in country A is, in fact, consumed in country B. Country B is therefore not consuming its own local resources when consuming imported food. Allan believed that this mechanism could alleviate irrigation water needs in water-scarce areas when food imports are in place. The virtual water content of a product (measured in liters per kilo) is provided not only by the sum of the irrigation water that has been withdrawn from surface and underground sources in order to grow crops—called “blue water.” Virtual water is also composed of the rainwater consumed by plants and persisting in agricultural soil moisture, which does not percolate down to the aquifers or go back to rivers and lakes. This second component is called “green water.” The green- and blue-water components form the total amount of water embedded in crops, and they are the two components of virtual water. Allan borrowed the concepts of green and blue water from the work of Malin Falkenmark. Virtual water and virtual water “trade” have been largely explored and studied at both local and global levels, becoming the subjects of thousands of papers between 1993 and 2022, which helped uncover global appropriation of a local resource that is unevenly distributed by nature and very often unequally “traded” by humans: water.

Article

Business Models for Sustainability  

Nancy Bocken

Human activity is increasingly impacting the environment negatively on all scales. There is an urgent need to transform human activity toward sustainable development. Business has a key role to play in this sustainability transition through technological, product and service, and process innovations, as well as innovative business models. Business models can enable new technologies, and vice versa. These models are therefore important in the transition to sustainability. Business models for sustainability, or synonymously, sustainable business models, take holistic views on how business is operated in relation to its stakeholders, including the society and the natural environment. They incorporate economic, environmental, and social aspects in an organization’s purpose and performance measures; consider the needs of all stakeholders rather than giving priority to owner and shareholder expectations; treat “nature” as a stakeholder; and take a system as well as a firm-level perspective on the way business is conducted. The research field of sustainable business models emerged from fields such as service business models, green and social business models, and concepts such as sharing and circular economy. Academics have argued that the most service-oriented business models can achieve a “factor 10” environmental impact improvement if designed the right way. Researchers have developed various conceptualizations, typologies, tools, and methods and reviews on sustainable business models. However, sustainable business models are not yet mainstream. Important research areas include the following: (a) tools, methods, and experimentation; (b) the assessment of sustainability impact and rebounds for different stakeholders; (c) sufficiency and degrowth; and (d) the twin revolution of sustainability and digital transition. First, a plethora of tools and approaches are available for inspiration and for creation of sustainable business model designs. Second, in the field of assessment, methods have been based on life cycle thinking considering the supply chain and how a product is (re)used and eventually disposed of. In the field of sufficiency, authors have recognized the importance of moderating consumption through innovative business models to reduce the total need for products, reducing the impact on the environment. Finally, researchers have started to investigate the important interplay between sustainability and digitalization. Because of the potential to achieve a factor 10 environmental impact improvement, sustainable business models are an important source of inspiration for further work, including the upscaling of sustainable business models in established businesses and in new ventures. Understanding how to design better business models and preempting their usage in practice are essential to achieve a desired positive impact. In the field of sufficiency, the macro-impacts of individual and business behavior would need to be better understood. In the area of digital innovation, environmental, societal, and economic values need scrutinization. Researchers and practitioners can leverage the popularity of this field by addressing these important areas to support the development and roll-out of sustainable business models with significantly improved economic, environmental, and societal impact.