Community-based water purification technologies provide a pathway to safe drinking water in rural India, where infrastructure and service reliability are often limited. This study investigates direct and indirect psychological determinants as well as contextual factors of adoption of Jivamritam, a community-based water purification technology, as a regular source of drinking water. Guided by a user-focused theory of change framework, cross-sectional survey data from 671 households across 40 communities in six Indian states were analyzed using generalized linear mixed models. Results revealed that five psychological determinants were significantly associated with adoption: perceived severity of waterborne disease (odds ratio [OR] = 1.74), perceived ease of access (OR = 2.22), perceived cost-effectiveness (OR = 2.17), perceived descriptive norms (OR = 2.80) and trust in the implementing agency (OR = 2.14). Among the contextual factors, assistance from family members was directly associated with adoption (OR = 24.96) as well as indirectly via perceived ease of access (OR = 4.77). Trust in the water committee was associated with adoption via perceived ease of access and perceived cost-effectiveness (OR = 2.08). Our findings emphasize the need to account for both direct and indirect behavioral pathways when seeking to understand water purification technology’s adoption. Moreover, by quantifying these behavioral pathways, this study provides actionable, empirically grounded targets for interventions aimed at promoting the adoption of such technologies.

Sociohydrology aims to deliver user-inspired solutions to water challenges, often through model-based understanding and simulation of local realities. However, sociohydrological modeling methodologies used to understand such complex human-water systems remain difficult to apply to many real-world case studies. Sociohydrological model predictions at daily to annual time scales of decision- making remain a challenge due to often difficult-to-acquire social sciences data, and missing or unknown feedbacks that lead to model structural errors, among other issues. This paper assesses and reduces model structural deficiencies of a smallholder sociohydrological (SH) model when applied to a case study of small-scale agricultural production in India, where variables from a farmer survey help alleviate structural deficiencies. A structural error model is proposed based on a regression model of nonlinear projection of the these variables to a Kernel space, called Kernel Principal Component Analysis (KPCA) based model. Based on this, a hybrid model that is a sum of the SH model and the structural error model is proposed. It offers significantly better yield predictions on ‘unseen’ (to the model) survey data than the SH only model. The hybrid model also performs better on yield prediction than a KPCA model alone, which predicts yields without any SH dynamics. This is because the hybrid model combines the structural error model that learns from the spatial pattern of observed yields with the temporal dynamics explained by the SH model alone. The results indicate that the structure of the SH model can be improved by further incorporation of irrigation and adaptive behaviour of farmers.

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.

Understanding the behavioral drivers of technology adoption is critical to promoting public health in rural areas, particularly in the context of safe drinking water. This study investigates the psychological determinants of adopting a community-based water purification technology deployed in 300 rural communities. Using a two-stage regression framework, we correct for endogeneity in behavioral models, showing that adoption itself can reshape psychological drivers such as perceived benefits and descriptive norms. Cultural factors, measured through Hofstede's dimensions and World Values Survey constructs, serve as instrumental variables to address reverse causality. Our findings reveal that cultural factors such as generalized morality, belief in hard work, and collectivism indirectly shape adoption behavior by influencing psychological perceptions. These results offer methodological and practical contributions by demonstrating how culturally informed interventions, aligned with community values, can enhance the long-term adoption of water purification initiatives.

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.

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.

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.

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.

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.

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.