To better understand the increasing human impact on the water cycle and the feedbacks between hydrology and society, the International Association of Hydrological Sciences (IAHS) organized the scientific decade “Panta Rhei–Everything Flows: Change in hydrology and society” (2013–2022). A key finding is the need to use integrated approaches to assess the co-evolution of human–water systems in order to avoid unintended consequences of human interventions over long periods of time. Additionally, substantial progress has been made in leveraging new data sources on human behaviour, e.g. through text mining of social media posts. Much has been learned about detecting hydrological changes and attributing them to their drivers, e.g. quantifying climate effects on floods. To achieve further progress, we recommend broadening the understanding, the discipline and training activities, while at the same time pursuing synthesis by focusing on key themes, developing innovative approaches and finding sustainable solutions to the world’s water problems.

Smallholder farmers are critical to global food production and natural resource management. Due to increased competition for water resources and variability in rainfall due to climate change, chronic irrigation water scarcity is rising particularly in drought-prone regions. Improving the awareness of climatic risk to yields and incomes is critical to sustainable agricultural intensification. However, adopting a new technology represents a certain level of risk for the farmers, who invest time and economic resources in changing their practices. We have developed a mobile application, currently for cotton, that would allow farmers to actualize the risk of growing cotton. By implementing a sociohydrological dynamic model with a kernel principal component analysis structural error model, the software provides a risk forecast of the yield and profit the user can expect at the end of the season. The mobile app not only processes social and agricultural information provided by the user but also retrieves and continually updates climate datasets from the web, as well as market prices. The users can request the execution of the sociohydrological model to the servers from their own mobile devices. By following an agile methodology, the mobile app has been tested with ∼100 farmers in order to get feedback from real users; this brought the opportunity to redesign the functionality based on the correct understanding of information and, a fast and clear management of the tool and helping in the adoption of the technology. This was combined with existing knowledge around communicating risk by using multiple modes of communication - text, graphics, sound and video - all of which were implemented to reinforce the knowledge communicated and ensure sufficient redundancy. This turned out to be beneficial for farmers with low prior knowledge and higher acceptability of the mobile app by the users as evidenced through feedback rounds with them. This study exemplifies an approach to address the gap in communicating risks in agriculture using a user-friendly mobile application.

Mobile applications have the potential to revolutionise agricultural advisories, providing farmers with real-time information and insights for improved decision-making. However, the adoption of such apps is influenced by various behavioural factors, necessitating a participatory approach of development with the stakeholders. This study proposes a framework that begins with a prototype app informed by a literature review and the identification of behavioral determinants of app adoption. Iterative participatory feedback, grounded in these determinants, is employed to refine the app. The framework is demonstrated through the case study of Makara, an app providing risk advisories for farm yield, income, and risk mitigating practices in Maharashtra, India. A user-focused Theory of Change (ToC) was used to design a survey to identify socio-economic and behavioral drivers of agricultural app adoption. Data collected from 1354 farmers across four districts of Maharashtra during April–May 2023 informed a linear regression model that identified significant explanatory factors. Building on these findings, multiple feedback sessions with farmers were conducted over a year to iteratively co-develop the app's features. Key behavioral determinants, including norms, trust, abilities, and attitudes towards adopting mobile-based agricultural advisories, significantly influenced adoption. The participatory design process addressed these factors, incorporating features such as multi-lingual support, intercropping and multi-cropping options, and multi-component budgeting to enhance trust and perceived ease in using the app. User-friendliness was further improved through redundant communication of risks, combining textual and audio-visual formats. This paper presents a mixed-methods approach to integrating behavioral drivers of agricultural (advisory app) technology adoption into a participatory co-design framework (of such an app), enabling considerations for inclusivity and scaling in the design process of the app itself.

The agricultural sector, particularly in rural areas, faces numerous challenges, including labor shortages, fluctuating costs, and unpredictable weather patterns. The Makara app emerges as a pioneering digital solution, specifically designed to address the multifaceted needs of small-scale farmers. This chapter presents a case study on the Makara app, highlighting its role in transforming agricultural practices through digital innovation. The app provides a comprehensive platform for farmers to manage their land, crops, and financials effectively. It offers detailed land and crop management, budgeting, and activity management. Additionally, Makara’s day-to-day advisory service and risk prediction module assist farmers in optimizing resource use and enhancing productivity. The app’s multilingual interface and offline mode ensure accessibility and usability in remote areas. This study analyzes the software development, farmer engagement, feedback collection, software refinement, and deployment process of the Makara app among the select farmers of the Nagpur region in Maharashtra, India. The Makara app exemplifies the potential of digital tools in promoting sustainable and profitable farming practices in rural communities.

Soil moisture monitoring is essential for a variety of applications including agriculture, forestry, and environmental monitoring. However, soil moisture sensors may be expensive and require batteries or other energy sources, making them unsuitable for remote or off-grid locations and farmers. Improper e-waste management of short-lived sensing components can reveal the contradictions of solutions aimed at environmental sustainability, which also degrade environmental health. Therefore, the development of low-cost, off-grid, biodegradable in-situ soil moisture sensing system (SMSS) is necessary for these regions. This article provides an overview of the current state-of-the-art in low-cost, off-grid, and biodegradable in-situ soil moisture sensing. It highlights low-cost SMSS components including hardware (microcontrollers and communication modules), software, and off-grid ambient energy sources. It also highlights the current research in biodegradable polymers used for moisture sensing. The challenges in combining low-cost, off-grid, and biodegradable soil moisture sensing are identified as a research gap. Finally, the underlining question of the “perfect” choice of SMSS is explored based on the trade-offs of performance, operational feasibility, and the newly proposed aspect of biodegradability, consequently suggesting context-specific decisions by consciously managing these tradeoffs.

The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions–whether it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes.

Complex contextual and sociopsychological factors influence the adoption of agricultural technologies like irrigation. This study used the sociopsychological Risk-Attitude-Norms-Abilities-Self-Regulation (RANAS) framework to examine the factors impacting irrigation adoption in Maharashtra (India). Logistic regression modelling was conducted based on cross-sectional surveys in 2019 and 2022, with interim interventions promoting risk-awareness and irrigation technology training. Effects of the interventions on the psychological variables in 2022 were corrected using instrumental variable regression. While micro-irrigation adoption rose from 36.9% to 62.8%, overall irrigation counterproductively decreased from 81.6% to 70.4%. Results indicated that wealth and risk-aversion remained relevant, while self-perceived ability and attitude towards irrigation became non-significant to irrigation adoption. This study highlights the unintended consequences of interventions and the necessity to also transform attitudes, and promote psychological ownership and trust to sustain irrigation technology adoption behaviour. These results could support stakeholders (e.g., policy makers, water authorities) in designing and implementing more sustainable interventions.

The adoption of agricultural water interventions for climate change adaptation has been slow and limited despite their established efficacy and benefits. While several studies have identified socio-economic, biophysical, technological and institutional factors that influence adoption, psychological factors have often been overlooked. This study examines the socio-economic and psychological factors, using RANAS behavioral model, that influence the adoption of agricultural water interventions in the semi-arid region of Saurashtra in India. Two contrasting and dominating agricultural water interventions in the area: drip irrigation and borewells are evaluated. Despite subsidies being available for drip irrigation systems, the adoption rate remains low (~16% adopting rate) compared to borewells (~24.5% adoption) with no subsidies reflecting farmer’s preference for supply augmentation measures over demand management. Incorporating psychological factors in the analysis improved the explanatory power of the logistic model by almost threefold, underscoring the significance of psychological factors in explaining farmers’ adoption decisions. Based on the logistic model, major factors determining farmers adoption behaviour identified are farmer’s perceived ability, risk preference and positive beliefs about the technologies along with socio-economic (e.g., land size) and biophysical factors (e.g., proximity to water). The study recommends a multi-pronged approach to increase the adoption of interventions, including augmenting subsidies with efforts on extension services, post-adoption services, training, and awareness campaigns to build farmers’ capacity and raise awareness.

The construction of dams threatens the health of watershed ecosystems. The purpose of this study is to show how multiple dams in a basin can impact hydrological flow regimes and subsequently aquatic ecosystems that depend on river flows. The approach assesses the ecosystem services (ESs), including the tradeoffs between economic and ecological services due to altered flow regimes. It uses a previously developed model that integrates a landscape-based hydrological model with a reservoir operations model on a basin scale. The approach is novel because not only does it offer the analysis of alterations in ecosystem services on a daily scale when pre-dam data are unavailable but also allows for dams to be synthetically placed anywhere in the river network and the corresponding alterations in flow regimes to be simulated in a flexible manner. As a proof of concept, we analyse the economic and ecological performances of different spatial configuration of existing reservoirs instead of synthetically placed reservoirs in the upper Cauvery River basin in India. Such a study is timely and conducted for the first time, especially in light of calls to assess the cascade of reservoirs in India and regions elsewhere where pre-dam data are unavailable. The hydrological impact of different configurations of reservoirs is quantified using indicators of hydrologic alteration (IHAs). Additionally, the production of two major ecosystem services that depend on the flow regime of the river, as indicated by irrigated agricultural production and the normalized fish diversity index (NFDI), is estimated, and a tradeoff curve, i.e. a production possibility frontier, for the two services is established. Through the lens of the indices chosen for the ecosystem services, the results show that smaller reservoirs on lower-order streams are better for the basin economy and the environment than larger reservoirs. Cultivating irrigated crops of higher value can maximize the value of stored water and, with lower storage, generate a better economic value than cultivating lower-value crops while reducing hydrological alterations. The proposed approach, especially when simulating synthetic spatial configurations of reservoirs, can help water and river basin managers to understand the provision of ecosystem services in hydrologically altered basins, optimize dam operations, or even prioritize dam removals with a goal of achieving a balanced provision of ecosystem services.

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability. This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r² = 0.76, RMSE = 3.13 %, validation r² = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters. This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r² = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r² = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r² = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.

Introduction:
Water scarcity is a significant global challenge that frequently manifests as inadequate water supply for domestic purposes. However, domestic water insecurity can occur even in regions where water is naturally abundant. Despite Colombia’s plentiful surface water resources, rural and peri-urban communities often experience limited access to water. Existing water supply systems are frequently susceptible to poor maintenance, particularly in remote areas where much of the infrastructure remains outdated. Consequently, water is often lost through leaks or unintentional non-domestic use. Although a regulatory framework for water usage exists, it does not consistently translate into effective implementation.

Methodology:
Based on an extensive survey of approximately 1000 households in four rural and four peri-urban communities in the Valle del Cauca Department, Colombia, we identified the factors underlying inefficient water supply and use. Perceived water use at the household level, based on self-reported time spent on various use types, such as bathing, and water supplied at the system level, was estimated.

Results and discussion:
Household size, education level, age and occupation were found to be critical factors influencing end water use and water supply. This not only elucidates why water is supplied and used inefficiently in rural systems (e.g., due to non-domestic use), but also accounts for the variability of perceived water use within peri-urban systems. The water use perceived by households in the rural systems was statistically similar across the rural systems studied and was significantly lower than that in the peri-urban systems. Most rural systems exhibited very low ratios of perceived water use to water supplied, indicating that either water is lost in conveyance or that water is used for non-domestic purposes. Peri-urban users, who perceived to use more water than users in rural areas, were associated with younger and more educated households. Higher education levels were also associated with better financial capacity and technical ability to manage water systems; therefore, peri-urban systems were better managed.

Groundwater depletion has become increasingly challenging, and many cities worldwide have adopted drastic policies to relieve water stress due to socioeconomic growth. Located on the declining aquifer of the North China Plain, Beijing, for example, has developed plans to limit the size of the city’s population. However, the effect of population displacement under uncertain macroeconomic and climate change remains ambiguous. We adopt a sociohydrological model, with explicit consideration of the dynamics of human-water interactions, to explore the groundwater vulnerability of Beijing. We investigate how human response might shape the development trajectories of the groundwater-population-economy system under different macroscale economic and climate scenarios. Furthermore, we use a machine learning algorithm to identify the decisive factors to be considered for reducing groundwater vulnerability. Our results show that while rapid external economic development or larger annual average precipitation would enable recovery of the groundwater table in the short term, they may slacken human water shortage awareness and result in more acute groundwater depletion in the long run. Strengthening policymaker perceptions of groundwater depletion would prompt timely response policies for controlling population size. Improving the quantity and quality of labor force input to economic development would avoid downturns in the economy due to labor shortages. The outcomes of this study suggest that these strategies would effectively reduce groundwater vulnerability in the long run without causing severe socioeconomic recession. These findings highlight the importance of endogenizing human behavioral dynamics in sustainable urban water management.

Given the increasing demand for high-quality food and protein, global food security remains a challenge, particularly in the face of global change. However, since agriculture, food and water security are inextricably linked, they need to be examined via an interdisciplinary lens. Sociohydrology was introduced from a post-positivist perspective to explore and describe the bidirectional feedbacks and dynamics between human and water systems. This review situates sociohydrology in the agricultural domain, highlighting its contributions in explaining the unintended consequences of water management interventions, addressing climate change impacts due to/on agriculture and incorporating human behaviour into the description of agricultural water systems. Sociohydrology has combined social and psychological insights with novel data sources and diverse multi-method approaches to model human behaviour. However, as agriculture and agriculturalists face global change, sociohydrology can better use concepts from resilience thinking more explicitly to identify gaps in terms of desirable properties in resilient agricultural water systems, potentially informing more holistic climate adaptation policy.

In a recent editorial in the journal Nature Sustainability, the editors raised the concern that journal submissions on water studies appear too similar. The gist of the editorial: “too many publications and not enough ideas.” In this response, we contest this notion, and point to the numerous new ideas that result from taking a broader view of the water science field. Drawing inspiration from a recently hosted conference geared at transcending traditional disciplinary silos and forging new paradigms for water research, we are, in fact, enthusiastic and optimistic about the ways scientists are investigating political, economic, historical, and cultural intersections toward more just and sustainable human-water relations and ways of knowing.

Increased variability of the water cycle manifested by climate change is a growing global threat to agriculture with strong implications for food and livelihood security. Thus, there is an urgent need for adaptation in agriculture. Agricultural water management (AWM) interventions, interventions for managing water supply and demand, are extensively promoted and implemented as adaptation measures in multiple development programs globally. Studies assessing these adaptation measures overwhelmingly focus on positive impacts, however, there is a concern that these studies may be biased towards well-managed and successful projects and often miss out on reporting negative externalities. These externalities result from coevolutionary dynamics of human-water systems as AWM interventions impact hydrological flows and their use and adoption is shaped by the societal response. We review the documented externalities of AWM interventions and present a conceptual framework classifying negative externalities linked to water and human systems into negative hydrological externalities and unexpected societal feedbacks. We show that these externalities can lead to long term unsustainable and inequitable outcomes. Understanding how the externalities lead to undesirable outcomes demands rigorous modeling of the feedbacks between human and water systems, for which we discuss the key criteria that such models should meet. Based on these criteria, we showcase that differentiated and limited inclusion of key feedbacks in current water modeling approaches (e.g. hydrological models, hydro-economic, and water resource models) is a critical limitation and bottleneck to understanding and predicting negative externalities of AWM interventions. To account for the key feedback, we find agent-based modeling (ABM) as the method that has the potential to meet the key criteria. Yet there are gaps that need to be addressed in the context of ABM as a tool to unravel the negative externalities of AWM interventions. We carry out a systemic review of ABM application to agricultural systems, capturing how it is currently being applied and identifying the knowledge gaps that need to be bridged to unravel the negative externalities of AWM interventions. We find that ABM has been extensively used to model agricultural systems and, in many cases, the resulting externalities with unsustainable and inequitable outcomes. However, gaps remain in terms of limited use of integrated surface-groundwater hydrological models, inadequate representation of farmers’ behavior with heavy reliance on rational choice or simple heuristics and ignoring heterogeneity of farmers’ characteristics within a population.

Endogeneity or reverse causality in regression analysis results in biased estimation of the effects of independent variables on the dependent variable and leads to inaccurate interpretations. However, the biased estimation of the effects of psychological factors on water–related behaviours are rarely discussed. This study investigated the endogeneity of psychological factors in water-related behaviour using an instrument variable (IV) approach. Data from eight household water treatment (HWT) studies in Asia, Africa, and South America were utilized. A combination of several socio-economic characteristics, such as education and accessibility, was used as a control variable and three psychological factors, i.e., perception of risk, attitude towards HWT, and social norms, were used as the predictors of the adoption of HWT. Variables related to institutional quality of the countries, based on the World Governance Indicators of the World Bank were used as IVs to predict the psychological factors. The results suggest that endogeneity exists in water-related behavioural studies. Institutions were found to be valid IV for psychological factors attitude and norms, but not for the perception of risk. This suggests that the institutional quality influences households’ attitude and norms regarding behaviour. If the feedback effect of actual behaviour on the psychological factors were not considered, the effects of attitude and norms on HWT adoption were underestimated by 59% and 40%, respectively. Finally, despite the challenge of finding valid IV, the endogeneity effect of psychological factors needs to be controlled when estimating the effect of psychological factors on water-related behaviour in future water-related behavioural studies.

In the face of the rapidly dwindling carbon budgets, negative emission technologies are widely suggested as required to stabilize the Earth’s climate. However, finding cost-effective, socially acceptable, and politically achievable means to enable such technologies remains a challenge. We propose solutions based on negative emission technologies to facilitate wealth creation for the stakeholders while helping to mitigate climate change. This paper comes up with suggestions and guidelines on significantly increasing carbon sequestration in coffee farms. A coffee and jackfruit agroforestry-based case study is presented along with an array of technical interventions, having a special focus on bioenergy and biochar, potentially leading to “negative emissions at negative cost.” The strategies for integrating food production with soil and water management, fuel production, adoption of renewable energy systems and timber management are outlined. The emphasis is on combining biological and engineering sciences to devise a practically viable niche that is easy to adopt, adapt and scale up for the communities and regions to achieve net negative emissions. The concerns expressed in the recent literature on the implementation of emission reduction and negative emission technologies are briefly presented. The novel opportunities to alleviate these concerns arising from our proposed interventions are then pointed out. Our analysis indicates that 1 ha coffee jackfruit-based agroforestry can additionally sequester around 10 tonnes of CO2-eq and lead to an income enhancement of up to 3,000–4,000 Euros in comparison to unshaded coffee. Finally, the global outlook for an easily adoptable nature-based approach is presented, suggesting an opportunity to implement revenue-generating negative emission technologies on a gigatonne scale. We anticipate that our approach presented in the paper results in increased attention to the development of practically viable science and technology-based interventions in order to support the speeding up of climate change mitigation efforts.

Freshwater resources in coastal areas are under intense pressure from excessive groundwater extraction, which amplifies saltwater intrusion (SWI) into coastal freshwater aquifers, such as in the Mekong Delta. Studies that combine socioenvironmental data and households' psychological factors next to salinity measurement data to design groundwater conservation strategies are rare. In this study, these aspects are combined to explore their influence on the public willingness to conserve groundwater using a Bayesian belief network model. We analyzed 313 household survey data spread over three districts in the coastal province of Tra Vinh, located in the Vietnamese Mekong Delta. The level of salinity is significantly correlated with the willingness to conserve groundwater. The top three socioenvironmental characteristics that influence willingness are the level of salinity, type of employment - i.e., being a farmer - and frequency of being exposed to groundwater or SWI promotional activities. Social norm, i.e., perceived social pressure, is the most influential psychological factor that determines willingness. This study reveals an urgency for the local government to intervene and create social pressure regarding the issue.

Low technology adoption through agricultural extension may be a consequence of providing generic information without sufficient adaptation to local conditions. Data-rich paradigms may be disruptive to extension services and can potentially change farmer-advisor interactions. This study fills a gap in pre-existing, generic advisory programs by suggesting an approach to “diagnose” farm-specific agricultural issues quantitatively first in order to facilitate advisors in developing farm-centric advisories. A user-friendly Farm Agricultural Diagnostics (FAD) tool is developed in Microsoft Excel VBA that uses farmer surveys and soil testing to quantify current agricultural performance, classify farms into different performance categories relative to a localized performance target, and visualize farm performance within a user-friendly interface. The advisory diagnostics approach is tested in Kanpur, representative of an intensively managed rural landscape in the Ganga river basin in India. The developed open-source tool is made available online to generate data-based agricultural advisories. During the field testing in Kanpur, the tool identifies 24% farms as nutrient-limited, 34% farms as water-limited, 27% farms with nutrient and water co-limitations, and the remaining farms as satisfactory compared to the localized performance target. It is recommended to design advisories in terms of water and nutrient recommendations which can fulfill the farm needs identified by the tool. The tool will add data-based value to pre-existing demand based advisory services in agricultural extension programs. The primary users of the tools are academic, governmental and non-governmental agencies working in the agricultural sector, whose rigorous scientific research, soil testing capacity, and direct stakeholder engagement, respectively, can be harnessed to generate more data-based and customized advisories, potentially improving farmer uptake of agricultural advisories.