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.@en

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.@en

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.

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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.

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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.@en

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.

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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.@en

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.

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Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the “nexus”. There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice.@en

Study region: Upper Cauvery river basin, India Study focus: Reservoir construction is one of the major contributors to changes in natural river flow regime characteristics. This study aims to understand the hydrological alterations resulting from the construction of reservoirs and water abstraction in the upper regions of the basin. The impacts of dams on river flow regimes where data is available only for periods after the construction of the dams is assessed. A landscape-based hydrological model, FLEX-Topo, is used to model the flows contributed by the upstream and downstream areas of four major reservoirs in the study area. A separate reservoir operation model is developed for each of the reservoirs. Next, the hydrological model is integrated with the reservoir model and the modelled flow at the downstream streamflow gauging station of each of the corresponding four sub-basins is calibrated. The modelled flow regimes with and without reservoirs are then compared using the indicators of hydrological alterations to understand the degree to which the flows have been altered by the reservoirs. New hydrological insights for the region: The results indicate that flow regimes have been modified from their natural state following reservoir impoundment and water abstractions. Significant impacts are observed in median monthly flow, 1-day minimum flow and low pulses. Such information could provide a reference to water managers to replicate the natural flow regimes, help sustain natural biota and thus contribute toward the sustainable management of river basins in India.

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Study region: The study region is the Kamadhiya catchment (1150 km²), located in the Saurashtra region of the western state of Gujarat, India. The region has seen intensive development of check dams (CDs) for groundwater recharge with an estimated 27,000 CDs constructed up until 2018. Study focus: The impact of CDs on groundwater storage, food production and resilience are assessed for Kamadhiya catchment by estimating and comparing changes, across periods of low and high CD development, in potential recharge from CDs, rainfall trends, and irrigation demand. The analysis is carried out for the period from 1983 to 2015. New hydrological insights for the region: Groundwater storage gains observed following CD development can partly be attributed to an increase in high rainfall years after several drought years. Groundwater demand for irrigation has increased substantially, outweighing increase in groundwater recharge from CDs. This deficit in supply relative to demand is greatest in dry years, and when considered together with the low inter-annual carry-over storage of the region's hardrock aquifers, means that CDs capacity to enhance groundwater storage and mitigate the negative impacts of drought remains limited. Findings suggest that a standalone focus on MAR, unless complemented by greater emphasis on management of water demand and groundwater resources more broadly, may not be sufficient to achieve the long-term goals of sustainable groundwater and concurrently expanding agricultural crop production.

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Over abstraction for agricultural production and droughts in the Bakhtegan basin in southwest Iran has led to decreased lake volumes and has even dried in some years. These problems have occurred in the basin as a result of neglecting the roles that humans have played in lake desiccation. This study developed a socio-hydrological model to understand such dynamic interactions between the economy and community's sensitivity to the environment in the period of 1999 to 2013. The WEAP model was used to simulate the hydrological system that is (bi-directionally) coupled to a society model simulating community sensitivity. A multi-objective optimization algorithm was used to estimate the parameters of the coupled model. The results of the model calibration and validation in estimating the simulated discharges show good performance of the model in simulating observed streamflows (R² and NSE ranging between 0.73 and 0.99 and 0.31 to 0.99 respectively), agriculture water supplied (R² and NSE ranging between 0.00 and 0.69 and −7.08 to 0.58 respectively), migration and population (R² and NSE for migration: 0.29 and 0.63). The results of the integrated model on community response showed that attention to environmental conditions, such as lake levels, was heightened right after the drought events of 2000–2001 and 2008–2010. The effect of the latter event was even accentuated by several human interventions such as operationalization of Sivand and Mollasadra dams and transfer of pristine lands to agriculture. This led to major contraction of the lake. The model interprets that heightened community sensitivity as a result led to public policy dialogue and change. Water was taken away from agriculture and given to the environment leading to temporary lake revival. This was also confirmed by reviewing government decisions in 2011, further validating the value of socio-hydrological models in interpreting feedbacks between community sensitivity and environmental flows.

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Rainwater harvesting systems (RWHs) are implemented globally to bridge the frequent water supply-demand gaps. This study explores, through farmer household surveys (n = 492), how farmers perceived the benefits of RWHs, the equitability of benefits, and the role of contextual and psychological factors towards the behaviour of maintaining such systems. The study is carried out in a semi-arid catchment in the Indian state of Gujarat where RWHs, in the form of Check dams (CDs), have been implemented extensively. Results show that the benefits of CDs are perceived in good rainfall years through enhanced availability of water for expanding crops and irrigated areas. Farmers reported limited benefits of CDs in dry years. This is because of limited runoff and no carryover of stored groundwater, due to underlying shallow hard rock aquifer with little primary porosity, from wet years to dry years. Overall, ∼ 40%–50% of sampled farmers reported no benefits from CDs and the benefits decreased with distance. This reflects a spatially inequitable distribution of benefits skewed towards the farmers nearest to the CDs. The sustainability of CDs is a challenge with already ∼40% of CDs reportedly not working and 72.8% of farmers reported doing no maintenance activity. This is because 91.2% of farmers reported playing no role in its construction. The results show contextual (participation during construction, economic indicators) and sociopsychological factors (attention to CD condition, maintenance effort) significantly affect the behaviour towards maintaining the CDs. This highlights the need to complement RWHs with wider drought management and water demand management interventions to achieve drought resilience, and adherence to project exit protocols to secure the sustainability of investments.@en

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.

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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.

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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.

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Psychological frameworks are rarely used to understand irrigation adoption behaviour in developing countries. A Bayesian belief network (BBN) model was developed that integrated socio-economic characteristics and psychological factors to understand farmer behaviours with respect to irrigation practices in four districts of Maharashtra, India. Strong norms, risk perceptions of water scarcity, and attitude play roles in the adoption of irrigation technology and practices. Critically, it was found that no one factor can explain adoption behaviour; rather, an ensemble of factors is needed to understand farmer behaviour. A farmer who is highly educated, middle-aged, and moderately wealthy with a significant level of family help and an open well as their main water source, while receiving low promotional information related to water scarcity and irrigation adoption, is most likely to adopt irrigation technology. The application of the BBN in this study enables stakeholders and policymakers to better understand the linkages between different factors and behaviour.

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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.

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Socio-hydrology has expanded and been effective in exposing the hydrological community to ideas and approaches from other scientific disciplines, and social sciences in particular. Yet it still has much to explore regarding how to capture human agency and how to combine different methods and disciplinary views from both the hydrological and the social sciences to develop knowledge. A useful starting ground is noting that the complexity of human–water relations is due to interactions not only across spatial and temporal scales but also across different organizational levels of social systems. This calls for consideration of another analytical scale, the human organizational scale, and interdisciplinarity in study methods. Based on the papers published in this journal’s Special Issue Advancing Socio-hydrology over 2019–2022, this paper illuminates how the understanding of coupled human–water systems can be strengthened by capturing the multi-level nature of human decision making and by applying an interdisciplinary multi-method approach.

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