Colorado Senate Bill 24-037 (SB24-037) directs the University of Colorado and Colorado State University, with the Colorado Department of Public Health and Environment (CDPHE), to evaluate the feasibility of alternative compliance programs using green infrastructure (GI) and to establish up to three pilot projects. This technical note examines how water quality trading (WQT)-a regulatory framework allowing NPDES point sources to meet permit obligations through offset credits-implemented in other states can inform SB24-037 pilot design. We define GI and nature-based solutions in Colorado's context and develop three pilot archetypes spanning the state's compliance pressures, relevant out-of-state analogues, and the financing structures used in US WQT practice: riparian restoration for temperature compliance, nutrient offset trading in urbanizing watersheds, and stormwater retention credit banking. Drawing on Oregon temperature trading, Wisconsin and Chesapeake Bay nutrient trading, and the DC stormwater retention credit program, we find WQT can provide a regulatory pathway for GI-based compliance, but realistic expectations are essential: most US trading programs have generated fewer trades than initially projected. SB24-037 pilots should prioritize clear permit integration, conservative credit quantification, intermediate monitoring indicators, multi-year maintenance funding, and explicit accounting for interactions with existing conservation subsidy programs.

Rural households in Rwanda face intensifying risks from climate variability, including erratic rainfall, recurrent droughts, and flooding that threaten food and water security. Households in areas with limited infrastructure and constrained livelihood options are particularly vulnerable due to reduced adaptive capacity. Drawing on longitudinal data from over 60,000 household surveys and remote sensing observations, this study investigated the co-occurrence of household food and water insecurity and evaluated associations between geographical, biophysical, and socioeconomic factors and food insecurity outcomes. Approximately 19.4% of surveyed households—equivalent to about 2.8 million people nationally—experienced simultaneous food and water insecurity, with co-occurrence higher in the control group (21.1%) than in the intervention group (18.3%). A mixed-effects logistic regression revealed strong associations between food insecurity and water insecurity, household size, and income, with water insecurity increasing the odds of food insecurity by approximately 60%. These findings underscore the need for climate resilience programs in Rwanda to explicitly address the interplay between food and water insecurity alongside the mediating roles of climatic and non-climatic factors in shaping household well-being.

Microbial contamination of recreational and source waters poses persistent public health risks, yet conventional monitoring based on laboratory culture methods provides delayed results and limited temporal resolution. These constraints hinder timely identification of short-duration contamination events and effective management of recreation advisories. This study evaluates the performance of a continuous, in-situ tryptophan-like fluorescence (TLF) monitoring system for high-frequency assessment of microbial water quality across laboratory experiments, site-specific field deployments, and a global multi-site modeling framework. Site-specific analyses employed conventional linear regression, while a machine learning approach was used to develop a global model trained across multiple deployments and evaluated using temporal holdouts.Under controlled laboratory conditions, the system reproducibly demonstrated sub-ppb sensitivity that exceeds the stated detection limits of many commercially available TLF instruments. Field deployments in recreational surface waters showed strong agreement between sensor-derived Escherichia coli estimates and laboratory measurements obtained using the industry-standard Colilert method. Approximately 75% of linear, continuous predictions fell within the analytical uncertainty bounds of Colilert with a mean absolute percentage error of 7% in log-transformed concentration space. Binary classification at management-relevant thresholds for a deployment on the Seine in Paris achieved a balanced accuracy of greater than 90% for time-blocked test holdout data. A global model demonstrated a mean absolute percentage error of approximately 19% on temporally held-out test data in log space. Quantitative agreement was strongest at moderate to high concentrations, with increased uncertainty at low concentrations reflecting limitations of both fluorescence sensing and culture-based reference methods.Together, these results demonstrate that continuous TLF-based measurement can complement laboratory monitoring by providing real-time screening and decision support for recreational water management. As additional deployments and training data are incorporated, the global model is expected to further improve, enhancing the scalability and operational value of continuous microbial water quality monitoring.

Following wildfires, riverine water quality in forested watersheds is prone to degradation, impacting drinking water treatment and potentially causing increased carbon emissions because of additional electricity consumption during treatment. We explore the potential for climate-based financing to support wildfire mitigation and watershed restoration by reducing potential water treatment energy demand following a fire within the Provo River watershed, Utah, USA. We model pre- and post-wildfire erosion and water quality in the Provo River using GeoWEPP. We use energy data from a water treatment plant in the watershed and literature data to estimate the increase in energy use for treating degraded water. We find that most watershed areas are not subject to large treatment demand changes, but a few hotspots are prone to increased sediment loads. In the Provo River watershed, on average, a fire in a single 12-digit hydrologic unit code (HUC) subwatershed corresponds to an additional 350 metric tonnes of carbon-dioxide-equivalent (CO2e) emissions for one year following a wildfire event due to increased energy required by the water treatment plant. If wildfire risk is reduced, the avoided emissions can generate a potential of $88,500 annually in carbon credit revenue (at $10/CO2e credit) for the contributing HUC8 sub-basin.

Over half the world lacks access to safe drinking water, with fecal contamination cited as a primary source of pollution. E. coli is a widely used indicator for fecal contamination, however, existing monitoring methods limit spatially and temporally dense data collection. Developed by our research group, the E. coli sensor uses tryptophan-like fluorescence (TLF), a wavelength of fluorescence correlated with microbial activity, fused with machine learning analytics to estimate $E$. coli contamination. Raw sensor data are matched with E. coli enumerated in samples using EPA-approved methods to train the model. The technology outputs quantitative E. coli levels, is designed for continuous in-situ deployment, and utilizes internet connectivity to upload data in real time. Ongoing sensor deployments to validate the sensor performance are being conducted with partners across diverse environmental and social contexts. In Kenya, the sensor monitors borehole system functionality for community drinking water systems, where detection of contaminated water can inform health advisories and infrastructure repair. In Kigali, Rwanda sensors are installed in community water filters to validate filter functionality and support carbon credit generation. In the US, sensors monitor surface waters for compliance with regulations and to protect recreation along Boulder Creek in Colorado, Chicago River in Illinois and Charles River in Massachusetts.

This study investigates the barriers and opportunities for implementing nature-based solutions to improve water quality in the United States, utilizing a mixed-methods approach. Data were collected through key informant interviews (n = 27), focus group discussions, and an online survey (n = 167). The triangulation of these methods provided a comprehensive understanding of stakeholder perspectives across various sectors including water treatment plant managers, government officials, regulators, and landowners. Key themes include regulatory hurdles, funding challenges, and the necessity for robust water quality monitoring systems. Regulatory constraints were consistently identified as a primary barrier, highlighting the need for policy reforms to facilitate green infrastructure. Funding availability was another critical challenge, with stakeholders emphasizing the importance of new financing models and incentive-based programs. Continuous water quality monitoring to establish baselines and measure the impacts of restoration projects is also emphasized. Efforts to improve local policy and regulatory frameworks could significantly bolster watershed restoration practices, enhancing riverine water quality and providing broader environmental and social benefits.

Agricultural monitoring is least developed for smallholders in low- and middle-income countries—communities most likely to be impacted by hunger, poverty, and climate change. Recent efforts to monitor smallholder productivity are limited in spatial and temporal scope, but here, we provide an end-to-end machine learning pipeline built on Google Earth Engine for high-resolution, wall-to-wall time series mapping of crop area and yield, demonstrated for maize at every 10 m pixel in Rwanda over 2019–2023. Gradient boosted tree models were built from more than 60 000 field-level labels, 9000 yield measurements, and satellite-derived inputs. Maize was classified with 83% accuracy, precision of 0.70, and recall of 0.44 and total maize cover was predicted within 4% of national statistics. Yields aggregated to districts had an RMSE of 370 kg ha ^−1 (nRMSE: 27%). Our data compare favorably to other smallholder maize classification and yield estimation products for sub-Saharan Africa while being accessible, low-cost, standardized, and observed over time; thus, being more likely to enable technology transfer and downstream analyses.

The sensing of soil microbial and enzymatic activity continues to be a challenge, as current techniques are typically limited to the laboratory, and are time and labor intensive. In addition, such offsite assessments are not necessarily reflective of in situ bio-chemical-physical processes. We previously presented a novel printed decomposition sensor comprising a poly(hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and carbon composite material, wherein the sensor response correlated with the microbial activity in incubated soils [1]. In field trials carried out in the Yorkshire Dales (UK) these devices showed a clear correlation with measured soil microbial biomass carbon. These sensors consisted of a single fuse-like resistive element and as such were subject to stochastic effects in soil, requiring large numbers of devices to be used in order to address variability in field measurements. Here, we present a novel hardware solution to mitigate these stochastic effects by parallelizing multiple printed sensing elements on custom printed circuit boards (PCBs). The first instantiation of this approach showed to be effective at smoothing out sensor response in potato farms in the Upper Midwest region of the United States. In order to further shape the signal response of these decomposition sensors, we explored different parallel topologies by varying the number of sensing elements, element width, and element length. We discuss the advantages and disadvantages of these different topologies. [1] Atreya, M.; Desousa, S.; Kauzya, J.; Williams, E.; Hayes, A.; Dikshit, K.; Nielson, J.; Palmgren, A.; Khorchidian, S.; Liu, S.; Gopalakrishnan, A.; Bihar, E.; Bruns, C. J.; Bardgett, R.; Quinton, J. N.; Davies, J.; Neff, J. C.; Whiting, G. L. A Transient Printed Soil Decomposition Sensor Based on a Biopolymer Composite Conductor. Adv. Sci. 2022, 2205785, 1–10. https://doi.org/10.1002/advs.202205785.

The increase in global water insecurity is one of the first perceivable effects of climate change. Two billion people are now without access to safe drinking water, and four billion experience water stress at least once a year, primarily in low per-capita emission countries. This nexus between climate change and water insecurity has significant implications for the global economy, with the water sector contributing 10% of global emissions. Though traditionally a local issue, climate finance mechanisms like the voluntary carbon market (VCM) present opportunities for a global, sustainable, performance-based funding stream to address water insecurity. Since 2010, water-related carbon projects have yielded over 45 million emission reduction credits. Our analysis estimates a global potential of over 1.6 billion tCO2e per year across various water project subsectors. At a $10 per credit average, this could attract over $160 billion in investments over the next decade, enhancing global water security. However, barriers like high intervention costs and limited technologies hinder widespread implementation, creating a tension between standardized and bespoke credits. We present case studies, spanning drinking water initiatives to the wastewater treatment sector that illustrate VCM's role in channeling private sector capital for water security in climate-vulnerable regions.

Green infrastructure solutions can improve in-stream water quality in lieu of building electricity-consuming gray infrastructure. Permitted under the United States Clean Water Act, these programs allow regulated utilities to trade point-source water quality obligations with non-point source mitigation efforts in the watershed. Carbon financing can provide an incentive for water quality trading. Here we combine data on impaired waters, treatment technologies, and life cycle greenhouse gas emissions in the Contiguous United States, and compare traditional treatment technologies to alternative green infrastructure. We find green infrastructure could save $15.6 billion dollars, 21.2 terawatt-hours of electricity, and 29.8 million tonnes of carbon dioxide equivalent emissions per year while sequestering over 4.2 million tonnes CO2e per year over a 40 year time horizon. Green infrastructure solutions may have the potential to generate $679 million annually in carbon credit revenue (at $20 per credit), which represents a unique opportunity to help accelerate water quality trading. In the United States, green infrastructure may be less energy and carbon-intensive than gray infrastructure and generate substantial carbon credit revenue, accelerating water quality trading, according to an analysis of data on impaired waters, technologies, and life cycle accounting.

The Colorado and Mississippi River basins are crucial for water supply, agriculture, and ecological stability in the U.S., yet climate change, water management practices, and energy sector demands pose significant challenges to their sustainability. This paper highlights the potential of leveraging the Voluntary Carbon Market (VCM) to address these challenges by creating new revenue streams and incentivizing sustainable water management practices. It provides high-level estimates by extrapolating from existing literature. The paper finds that water projects in these basins could generate over 45 million carbon credits annually, potentially attracting around USD 4.5 billion in investments over the next decade. However, challenges such as high costs, complex regulations, and stakeholder coordination must be addressed. The paper also identifies opportunities for advancing water resiliency projects, including increasing public awareness, engaging corporations, and utilizing innovative financing mechanisms. Recommendations include promoting the VCM–water relationship, encouraging methodology innovation, developing pilot programs, investing in digital monitoring technologies, and conducting localized analysis to optimize carbon credit potential in water management. In conclusion, this paper quantifies the potential of water projects to generate carbon credits and indicates that integrating carbon markets with water management strategies can significantly contribute to global climate goals and improve water resilience in these critical regions.

Access to safe, reliable, and equitable water services in urban settings of low- and middle-income countries remains a critical challenge toward achieving Sustainable Development Goal 6.1, but progress has either slowed or stagnated in recent years. A pilot water kiosk network funded by the United States Millennium Challenge Corporation was implemented by the Sierra Leone Millennium Challenge Coordinating Unit into the intermittent piped water distribution network of Freetown, Sierra Leone, as a private-public partnership to improve water service provision for households without reliable piped water connections and to reduce non-revenue water. This study employs the use of high-frequency instrumentation to monitor, model, and assess the functionality of this water kiosk network over 2,947 kiosk-days. Functionality was defined via functionality levels on a daily basis through monitored stored water levels and modeled water withdrawals. The functionality levels across the kiosk network were found to be 34% operational, 30% offline, and 35% empty. Statistically significant (p<0.001) determinants of functionality were found for several predictors across the defined thresholds. Finally, modeling of water supply, water demand and withdrawal capacity, and water storage was conducted to further explain findings and provide additionally externally relevant support for kiosk operations.

Sanitation programs typically measure latrine access, which does not equate to use. We aimed to objectively measure latrine use with sensors among households enrolled in the sanitation and control groups of a randomized controlled trial (WASH Benefits) in Bangladesh. The intervention provided upgraded latrines and behavioral promotion. We recorded self-reported latrine use quarterly 1-3.5 years after intervention initiation. We installed motion sensors in household latrines in two annual waves (1.5-2.5 and 2.5-3.5 years after intervention initiation). We used zero-inflated negative binomial regression to compare sensor-measured daily latrine use events/person between (1) sanitation and control groups, and (2) households with different levels of selfreported latrine use. Households receiving the sanitation intervention had more sensor-measured daily latrine use events/person than controls in the first wave of sensor observations (ratio: 1.18, 1.06-1.32) but not in the second wave (ratio: 0.95, 0.86-1.05). In the sanitation group, households reporting exclusive latrine use (individuals >3 years always defecating in latrine) had a similar number of sensor-measured latrine use events as those not reporting exclusive use (ratio: 0.97, 0.86-1.09). In the control group, households reporting exclusive latrine use truly had more sensor-measured latrine use events than households not reporting exclusive use (ratio: 1.19, 1.03-1.37). We objectively demonstrate higher latrine use among sanitation intervention recipients than controls up to 2.5 but not 3.5 years after intervention initiation, indicating reduced uptake over time. Selfreported latrine use appears inflated among intervention recipients but not controls. Our findings underscore the importance of longitudinal follow-up and objective measurements in sanitation program assessments.

Water is a major contributor to climate change-producing 10 percent of global emissions, largely from energy use for water treatment and transport, and organic and wastewater decomposition [1].And still, today two billion people live without access to safe drinking water, most notably in countries with among the lowest per capita emissions [2], and four billion experience water stress at least one month a year [3].The linkages between climate change and water insecurity are clear, as are the implications for the global economy.Yet in my experience, my water research colleagues in higher education often remain reluctant to directly engage with policymakers to take action toward global water security.Often researchers do discuss the potential policy implications of our research, but are either hesitant or uncertain about how to engage with policymakers.There is perhaps also an optimistic view of "if we build it they will come" with regards to the impact of research and technology products.While perhaps unfortunate, journal articles and online data dashboards do not, intrinsically, create change on their own without linking those tools to improved policies, incentives and management practices.There are emerging opportunities to both improve water security while reducing emissions-a 2024 report commissioned by WaterAid and the Voluntary Carbon Markets Integrity Initiative identified that 1.6 billion tonnes of CO2e, monetizable as carbon credits, could be avoided or removed within the water sector globally [4].Given the opportunities and increasing pressures wrought by climate inaction, in the past year my team and I have more often directly engaged with elected representatives both in the State of Colorado and in the US Congress, as well as leadership in major agencies such as the US Environmental Protection Agency (EPA), the US Agency for International Development (USAID) and the Colorado Department of Public Health and Environment (CDPHE) to translate our research into direct community-level water security actions.We have worked to link technological and community practice research with the economics of climate finance toward new approaches in water security.On a global level, our team has worked since 2007 [5] to demonstrate the potential of carbon markets and improved technologies to improve accountable, financially sustainable water service delivery in low income settings.USAID Administrator Samantha Power [6] highlighted our water security efforts at an early 2024 event celebrating the 10th anniversary of the Water for the World Act on Capitol Hill earlier this year, "When Kenya's recent drought sparked a spike in water-related conflicts among rural farmers, as it tends to do, Kenya faced the challenge of more efficiently delivering scarce water resources to hard-to-reach communities.USAID, working again with private companies and nonprofit organizations, helped upgrade more than one hundred water systems across the country and establish real-time monitoring and remote sensors to detect failing pipes and boreholes from hundreds of miles

High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning enabled analysis is used to design spatially explicit productivity targets that, if met, would simultaneously ensure national goals without leaving anyone behind.

Low-income urban residents of Freetown, Sierra Leone, have limited access to safely managed piped drinking water services. The Government of Sierra Leone, in partnership with the United States Millennium Challenge Corporation, implemented a demonstration project of ten water kiosks providing distributed, stored, treated water among two neighborhoods in Freetown. This study quantifies the impact of the water kiosk intervention by utilizing a quasi-experimental propensity score matched difference-in-differences study design. Results indicate a 0.6 % improvement in household microbial water quality and an 8.2 % improvement in surveyed water security within the treatment group. Furthermore, low functionality and adoption of the water kiosks were observed.

Flooding, an increasing risk in Rwanda, tends to isolate and restrict the mobility of rural communities. In this work, we developed a streamflow model to determine whether floods and rainfall anomalies explain variations in rural trail bridge use, as directly measured by in-situ motion-activated digital cameras. Flooding data and river flows upon which our investigation relies are not readily available because most of the rivers that are the focus of this study are ungauged. We developed a streamflow model for these rivers by exploring the performance of process-based and machine learning models. We then selected the best model to estimate streamflow at each bridge site to enable an investigation of the associations between weather events and pedestrian volumes collected from motion-activated cameras. The Gradient Boosting Machine model (GBM) had the highest skill with a Kling-Gupta Efficiency (KGE) score of 0.79 followed by the Random Forest model (RFM) and the Generalized Linear Model (GLM) with KGE scores of 0.73 and 0.66, respectively. The physically-based Variable Infiltration Capacity model (VIC) had a KGE score of 0.07. At the 50% flow exceedance threshold, the GBM model predicted 90% of flood events reported between 2013 and 2022. We found moderate to strong positive correlations between total monthly crossings and the total number of flood events at four of the seven bridge sites ( r = 0.36–0.84), and moderate negative correlations at the remaining bridge sites ( r = -0.33– -0.53). Correlation with monthly rainfall was generally moderate to high with one bridge site showing no correlation and the rest having correlations ranging between 0.15–0.76. These results reveal an association between weather events and mobility and support the scaling up of the trail bridge program to mitigate flood risks. The paper concludes with recommendations for the improvement of streamflow and flood prediction in Rwanda in support of community-based flood early warning systems connected to trail bridges.