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.

Addressing global water crises demands effective communication across diverse audiences, especially in initiatives such as the scientific decade HELPING by the International Association of Hydrological Sciences (IAHS). This study synthesizes insights from the hydrological community, gathered through interviews, workshops and a digital survey. We identify key challenges and best practices across three inter-related domains of communication: science–society interactions, policy–science interfaces and transdisciplinary research communication. Effective science–society interaction depends on community trust-building, transparent communication of uncertainty and inclusive engagement strategies. Strong policy–science interfaces benefit from bridging institutions and dedicated knowledge brokers. Transdisciplinary work improves when disciplinary siloing is reduced through common language and co-production. We summarize our findings in the FUSS framework, which promotes messages that are few, unambiguous, short and well-structured. We argue that advancing hydrological science in the face of water crises requires moving beyond one-way communication towards more dialogic, inclusive and context-sensitive approaches.

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.

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.

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.

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.