Tidal salt marshes are widespread along the World’s coasts, and are ecologically and economically important as they provide several valuable ecosystem services. In particular, their significant primary production, coupled with sustained vertical accretion rates, enables marshes to sequester and store large amounts of organic carbon and makes them one of the most carbon-rich ecosystems on Earth. Organic carbon accumulation results from the balance between inputs, i.e. organic matter produced by local plants or imported, and outputs through decomposition and erosion. Additionally, organic matter deposition actively contributes to marsh vertical accretion, thus critically affecting the resilience of marsh ecosystems to rising relative sea levels. A better understanding of organic-matter dynamics in salt marshes is key to address salt-marsh conservation issues and to elucidate marsh importance within the global carbon cycle. Toward this goal, we empirically derived rates of organic matter decomposition by burying 712 commercially available tea bags at different marshes in the microtidal Venice Lagoon (Italy), and by analyzing them following the Tea Bag Index protocol. We find values of the decomposition rate (k) and stabilization factor (S) equal to 0.012±0.003 day-1 and 0.15±0.063, respectively. Water temperature critically affects organic matter decomposition, enhancing decomposition rates by 8% per °C on average. We argue that, at least in the short term, the amount of undecomposed organic matter that actively contributes to carbon sequestration and marsh vertical accretion strongly depends on the initial organic matter quality, which is a function of marsh and vegetation characteristics.

Understanding three-dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field-grown roots remains a challenge due to the unknown tradeoff between 3D root-model quality and 3D root-trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state-of-the-art open-source 3D model reconstruction pipelines on 12 contrasting genotypes of field-grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch-based Multi-view Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi-View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. Thus, in the second test, we compared the accuracy of 3D root-trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP-based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM-based 3D reconstruction (Liu et al., 2021) on the same dataset of 12 genotypes, with 5~10 replicates per genotype. The results revealed that, 1) the average number of images needed to build a denser 3D model was reduced from 3000~3600 (DIRT/3D [VisualSFM-based 3D reconstruction]) to 300~600 (DIRT/3D [COLMAP-based 3D reconstruction]); 2) denser 3D models helped improve the accuracy of the 3D root-trait measurement; 3) reducing the number of images can help resolve data storage capacity problems. The updated DIRT/3D (COLMAP-based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root-trait measurements.

While Hg in sediments is increasingly used as a proxy for deep-time volcanic activity, the behaviour of Hg in OM-rich sediments as they undergo thermal maturation is not well understood. In this study, we evaluate the effects of thermal maturation on sedimentary Hg contents and, thereby, the impact of thermal maturity on the use of the Hg/TOC proxy for large igneous province (LIP) volcanism. We investigate three cores (marine organic matter) with different levels of thermal maturity in lowermost Toarcian sediments (Posidonienschiefer) from the Lower Saxony Basin in Germany. We present Hg content, bulk organic geochemistry, and total sulfur in three cores with different levels of thermal maturity. The comparison of Hg data between the three cores indicates that Hg content in the mature/overmature sediments have increased > 2-fold compared to Hg in the immature deposits. Although difficult to confirm with the present data, we speculate that redistribution within the sedimentary sequence caused by the mobility and volatility of the element under relatively high temperatures may have contributed to Hg enrichment in distinct stratigraphic levels of the mature cores. Regardless of the exact mechanism, elevated Hg content together with organic-carbon loss by thermal maturation exaggerate the value of Hg/TOC in mature sediments, suggesting that thermal effects have to be considered when using TOC-normalised Hg as a proxy for far-field volcanic activity.

Around the world, water is considered a fundamental factor, and plays a role in public health and economic growth. Both the water development rates and the proportion of the population are directly related to water needs. Water quality regarding physiochemistry and microbiology is important in dietary needs. Drinking water is considered one of the most important food products. Therefore, the water should meet the recommended quality standards. So, it should be free of bacteria, parasites, all kinds of microorganisms, and chemical substances which are dangerous to human health. This research focused on five cities of the Alkalaa Municipal Community, which forms 43% of the inhabitants of this community, in the Bint Jbeil district south of Lebanon. The goal of this study is to determine the fundamental physicochemical and microbiological water properties of eight distinct sites, as well as the amount of pollution. These tests were carried out in accordance with World Health Organization criteria (WHO). The collected data were utilized to assess the level of pollution in the examined zone.

Lebanon’s natural water resources are facing serious problems and approaches exhaustion. One of these issues is deteriorating performance, which is linked to unregulated resource planning and rising demand. There are many different types of consumption, such as residential, industrial, and irrigation. Surface and groundwater are both referred to designate water resources. However, due to the obvious accessibility of exploitation, surface water resources such as rivers, lakes, and basins are primarily used. The Ras El-Ain basin is 6 km far south of Tyr, Lebanon. The Lebanese state dedicated it, along with other reservoirs, to supply potable water for Tyr and the surrounding villages. Today, these basins’ water quality has deteriorated significantly because of unrestricted liquid and soil waste dumping. As a result, contaminants develop in the basin water. Aside from laboratory testing for water quality, contamination can be seen through direct observations, odors, watercolors, and patterns. The purpose of this study is to assess the level of pollution in the Ras El-Ain basin. This basin has been progressively subjected to a variety of quality degradation characteristics. This includes the most important physiochemical properties. As a result, the physicochemical and microbiological water characteristics of five selected samples from each basin were tested. These tests were performed in accordance with European Standard Methods and World Health Organization guidelines (WHO). The effect of pollutant disposal in the Ras El-Ain basin was studied using multivariate approaches. The obtained results were used to evaluate the pollution degree in various regions of the basin.

Poem to imagine the “essence” of water as it circulates through the Earth universe, synergistically supporting all environments and living ecosystems, forming, and shaping land and life. The poem links key elements of the interactive global water cycle and international programs to sustainably manage natural, and socioeconomic resources, given the challenge of climate change. It is in awareness of: –Essential Water Variables (EWVs) of the Group on Earth Observations (GEO) Global Water Sustainability (GEOGLOWS) initiative; Earth Observations (EO) for the Water-Energy-Food Nexus (EO4WEF) community activity; UN Sustainable Development Goals (UN SDGs), UNFCCC–Climate Change. The poem hopes to bring water to the forefront of consciousness. Readers are invited to comment on the intangible “feelings” evoked by the poem.

A document by Julián Peláez Quiñones. Click on the document to view its contents.

Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM2.5) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM2.5 machine-learning model covering the contiguous US from 2003 through 2021. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. Averaging across all years in site-level cross-validation, the weighted mean absolute error of predictions (MAE) was 2.13 μg/m3, a substantial improvement over the mean absolute deviation from the median, which was 4.23 μg/m3. Comparing XIS to a leading product from the US Environmental Protection Agency, the Fused Air Quality Surface Using Downscaling (FAQSD), we obtained a 22% reduction in MAE. We also found a stronger relationship between PM2.5 and social vulnerability with XIS than with the FAQSD. Thus, XIS has potential for reconstructing environmental exposures, and its predictions have applications in environmental justice and human health.

The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low-resolution (roughly 110 km) and high-resolution (roughly 25 km) configurations — as established by the High Resolution Model Intercomparison Project protocol — to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes to the water cycle come from reductions in precipitation and evapotranspiration, the latter of which results from decreases in evaporative fraction. While the HR simulation is warmer than the LR, moisture convergence decreases despite the increased atmospheric water vapor, suggesting circulation biases are an important factor. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.

Wildfires expose populations to increased morbidity and mortality due to increased air pollutant concentrations. Data included burned area, particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind-speed, aerosol optical depth (AOD) and mortality rates due to Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease (COPD), and Asthma (ASMA). Only the months of the 2011-2020 wildfire season (June-July-August-September-October) with burned area greater than 1000 ha were considered. Multivariate statistical methods were used to reduce the dimensionality of the data to create two fire-pollution-meteorology indices (PBI, API), which allow us to understand how the combination of these variables affect cardio-respiratory mortality. Cluster analysis applied to PBI-API-Mortality divided the data into two Clusters. Cluster 1 included the months with lower temperatures, higher relative humidity, and high PM10, PM2.5, and NO2 concentrations. Cluster 2 included the months with more extreme weather conditions such as higher temperatures, lower relative humidity, larger forest fires, high PM10, PM2.5, O3, and CO concentrations, and high AOD. The two clusters were subjected to linear regression analysis to better understand the relationship between mortality and the PBI and API indices. The results showed statistically significant (p-value < 0.05) correlation (r) in Cluster 1 between RSDxPBI (rRSD = 0.539), PNEUxPBI (rPNEU = 0.644). Cluster 2 showed statistically significant correlations between RSDxPBI (rRSD = 0.464), PNEUxPBI (rPNEU = 0.442), COPDxPBI (rCOPD = 0.456), CSDxAPI (rCSD = 0.705), RSDxAPI (rCSD = 0.716), PNEUxAPI (rPNEU = 0.493), COPDxAPI (rPNEU = 0.619).

Stable carbon (δ¹³C) and oxygen (δ¹⁸O) isotope measurements in lacustrine ostracodes are widely used to infer past climatic conditions. Previous work has used individual ostracode valves to resolve seasonal and subdecadal climate signals, yet environmental controls on geochemical variability within co-occurring specimens from modern samples are poorly constrained. Here we focus on individual ostracode valves in modern-aged Lake Turkana sediments, an alkaline desert lake in tropical East Africa. We present individual ostracode valve analyses (IOVA) of δ¹³C and δ¹⁸O measurements (n = 329) of extant species Sclerocypris clavularis from 17 sites spanning the entire lake (n-avg ~19 specimens per site). We demonstrate that the pooled statistics of individual valve measurements at each site overcome inter-specimen isotopic variance and are driven by hydrological variability in the lake. Mean IOVA-δ¹³C and -δ¹⁸O across the sites exhibit strong spatial trends with higher values at more southerly latitudes, modulated by distance from the inflow of the Omo River. Whereas the latitudinal δ¹³C gradient reflects low riverine δ¹³C and decreasing lacustrine productivity towards the southern part of the lake, the δ¹⁸O gradient is controlled by evaporation superimposed on the waning influence of low-δ¹⁸O Omo River waters, sourced from the Ethiopian highlands. We show that ostracode δ¹⁸Oproximal to Omo River inflow is deposited under near-equilibrium conditions and that inter-specimen δ¹⁸O variability across the basin is consistent with observed temperature and lake water δ¹⁸O variability. IOVA can provide skillful constraints on high-frequency paleoenvironmental signals and, in Omo-Turkana sediments, yield quantitative insights into East African paleohydrology.

We describe a framework for the simultaneous estimation of model parameters in a partial differential equation using sparse observations. Monte Carlo Markov Chain (MCMC) sampling is used in a Bayesian framework to estimate posterior probability distributions for each parameter. We describe the necessary components of this approach and its broad potential for application in models of unsteady processes. The framework is applied to three case studies, of increasing complexity, from the field of cohesive sediment transport. We demonstrate that the framework can be used to recover posterior distributions for all parameters of interest and the results agree well with independent estimates (where available). We also demonstrate how the framework can be used to compare different model parameterizations and provide information on the covariance between model parameters.

The transport of meltwater through porous snow is a fundamental process in hydrology that remains poorly understood but essential for more robust prediction of how the cryosphere will respond under climate change. Here we propose a continuum model that resolves the nonlinear coupling of preferential melt flow and the nonequilibrium thermodynamics of ice-melt phase change at the Darcy scale. We assume that the commonly observed unstable melt infiltration is due to the gravity fingering instabililty, and capture it using the modified Richards equation that is extended with a higher-order term in saturation. Our model accounts for changes in porosity and the thermal budget of the snowpack caused by melt refreezing at the continuum scale, based on a mechanistic estimate of the ice-water phase change kinetics formulated at the pore scale. We validate the model in 1D against field data and laboratory experiments of infiltration in snow and find generally good agreement. Compared to existing theory of stable melt infiltration, our 2D simulation results show that preferential infiltration delivers melt faster to deeper depths, and as a result, changes in porosity and temperature can occur at deeper parts of the snow. The simulations also capture the formation of vertical low porosity annulus known as ice pipes, which have been observed in the field but lack mechanistic understanding to date. Our results demonstrate how melt refreezing and unstable infiltration reshape the porosity structure of snow and impacts thermal and mass transport in highly nonlinear ways, which are not captured by simpler models.

Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrients sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies on nutrient source attribution have focused on large watersheds or counties at long time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a portable network model framework for phosphorus source attribution at the subwatershed (HUC-12) scale. Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow dynamics simulated by the SWAT model, and in-stream water quality measurements into a probabilistic framework and apply Approximate Bayesian Computation to attribute phosphorus contributions from subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach adopted by environmental agencies. Phosphorus release is higher during spring planting than the growing period, with manure contributing more than inorganic fertilizer. By enabling source attribution at high spatiotemporal resolution, our lightweight and portable model framework is suitable for broad applications in environmental regulation and enforcement for other regions and pollutants.

Climate models still need to be improved in their capability of reproducing the present climate at both global and regional scale. The assessment of their performance depends on the datasets used as comparators. Reanalysis and gridded (homogenized or not homogenized) observational datasets have been frequently used for this purpose. However, none of these can be considered a reference dataset. Here, for the first time, using in-situ measurements from NOAA U.S. Climate Reference Network (USCRN), a network of 139 stations with high-quality instruments deployed across the continental U.S, daily temperature, and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA-Interim) involved in NA-CORDEX are assessed. The assessment is extended also to the most recent and modern widely used reanalysis (ERA5, ERA-Interim, MERRA2, NARR) and gridded observational datasets (Daymet, PRISM, Livneh, CPC). Results show that biases for the different datasets are mainly seasonal and subregional dependent. On average, reanalysis and in-situ-based datasets are generally warmer than USCRN year-round, while models are colder (warmer) in winter (summer). In-situ-based datasets provide the best performance in most of the CONUS regions compared to reanalysis and models, but still have biases in regions such as the Midwest mountains and the Northwestern Pacific. Results also highlight that reanalysis does not outperform RCMs in most of the U.S. subregions. Likewise, for both reanalysis and models, temperature and precipitation biases are also significantly depending on the orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.

Based on the long-term climatological data from Ny Alesund, Svalbard Airport – Longyearbyen and Hornsund Polish Polar Station, we undertook an analysis of drought indices on West Spitsbergen Island, Svalbard for the period 1979-2019. The features and causes of spatio-temporal variability of atmospheric drought on Svalbard were identified, as expressed by the Standardised Precipitation Evapotranspiration Index (SPEI). It was possible to indicate several-years long periods with the SPEI indicating a domination of drought or wet conditions. Long-term variability of annual and half-year (May-October) values of SPEI showed a prevalence of droughts in the 80-ties and in the first decade of the 21st century while wet seasons were frequent in the 90-ties and in the second decade of the 21st century. Seasonal SPEIs were characteristic of great inter-annual variability. In MAM and JJA droughts were more frequent after 2000; in the same period in SON and DJF, the frequency of wet seasons increased. The most remarkable changes in the scale of the entire research period were estimated for autumn where negative values of SPEI occur more often in the first part of the period and positive values dominate in the last 20 years. The long-term course of the variables in subsequent seasons between 1979-2019 indicates strong relationships between the SPEI drought index and anomalies of precipitable water and somewhat weaker relationships with anomalies of sea level pressure.

The possibility to exploit the muon scattering for the elemental discrimination of materials in a given volume is well known. When more than one material is present along the muon path, it is often important to discern the order in which they are stacked. The scattering angle due to the target volume can be split into two interior angles in the tomographic setups based on the muon scattering, and we call this property as the triangular correlation where the sum of these two interior angles is equal to the scattering angle. In this study, we apply this triangular correlation for a multi-block material configuration that consist of concrete, stainless steel, and uranium. By changing the order of this material set, we employ the GEANT4 simulations and we show that the triangular correlation is valid in the multi-block material setups, thereby providing the possibility of supportive information for the coarse prediction of the material order in such configurations.

Albeit slow and not without its challenges, lead (Pb) emissions and sources in the United States (U.S.) have decreased immensely over the past several decades. Despite the prevalence of childhood Pb poisoning throughout the 20th century, most U.S. children born in the last two decades are significantly better off than their predecessors in regards to Pb exposure. However, this is not equal across demographic groups and challenges remain. Modern atmospheric emissions of Pb in the U.S. are nearly negligible since the banning of leaded gasoline in vehicles and regulatory controls on Pb smelting plants and refineries. This is evident in the rapid decrease of atmospheric Pb concentrations across the U.S over the last four decades. One of the most significant remaining contributors to air Pb is aviation gasoline (avgas), which is minor compared to former Pb emissions. However, continual exposure risks to Pb exist in older homes and urban centers, where leaded paint and/or historically contaminated soils+dusts can still harm children. Thus, while effective in eliminating nearly all primary sources of Pb in the environment, the slow rate of U.S. Pb regulation has led to legacy, secondary sources of Pb in the environment. More proactive planning, communication, and research of commonly used emerging contaminants of concern that can persist in the environment long after their initial use (i.e., PFAS) should be prioritized so that the same mistakes are not made again.

1. This study combines two approaches to explore the utility of Monod growth kinetics to predict competition outcomes between freshwater cyanobacteria and chlorophytes at low iron Fe. Fe threshold concentrations (FeT) below which growth ceases, and growth affinities (slope of Fe concentration vs growth rate near FeT) were estimated for three large-bodied cyanobacteria (two N-fixers and Microcystis) and two chlorophytes in batch cultures. 2. Mean FeT for N-replete cyanobacteria, N-deplete (when N-fixing) cyanobacteria and chlorophytes were 0.076, 0.736 and 0.245 nmol L-1 , respectively. Mean affinities were 0.937, 0.597 and 0.412 L nmol-1 d-1 , respectively. Assuming that the mean affinities are representative of their groups, affinities predict that N-replete cyanobacteria are more efficient at acquiring Fe than chlorophytes and should dominate when Fe is low but greater than their FeT. 3. A second study evaluated the competitive abilities of a pico-cyanobacterium and a third chlorophyte in dual species, serial dilution culture. The pico-cyanobacterium was dominant at 50 nmol L-1 total Fe (which limited both taxa) and 500 nmol L-1 total Fe. At 0.5 nmol L-1 total Fe, a stressful concentration below FeT during most of the incubation, growth rates and cell densities were extremely low but neither had washed out after several months. 4. These results show that Monod kinetics can successfully predict competition outcomes in laboratory settings at low Fe. While important, Monod kinetics are only one mechanism governing competition between cyanobacteria and eukaryotes in natural systems. Observed deviations from Monod predictions can be partially explained with known mechanisms.

To increase drought preparedness in semi-arid regions many small and medium reservoirs have been built in recent decades. Together these reservoirs form a Dense Reservoir Network (DRN) and its presence generates numerous challenges for water management. Most of the reservoirs that constitute the network are unmonitored and unregistered, posing questions on their cumulative effects on strategic reservoirs and water distribution at watershed scale. Their influence on hydrological drought propagation is thus largely unexplored. The objective of this study is then to assess the DRN effects on droughts both in time and space. This study utilized a mesoscale semi-distributed hydrological model to reproduce the DRN in a large-scale tropical semiarid watershed (19,530 km2), which presents both a network of large strategic reservoirs and a DRN. To investigate the effects in time and space generated by the network’s presence, the differences between multiple network scenarios were analyzed. Results show that the presence of the DRN accelerates the transition from meteorological to hydrological drought phases by 20% on average and slows down the recharge in strategic reservoirs by 25%, leading to a 12% increase of periods in hydrological drought conditions in a highly strategic basin and 26% without strategic reservoirs. In space, the DRN shifts upstream the basin’s water storage capacity by 8%, but when both large and small reservoirs are present the stored volume distribution behavior is not straightforward. The findings confirm the need to consider small reservoirs when addressing drought management policies at regional scale.

As an important anthropogenic interference on the water cycle, reservoir operation behavior remains challenging to be properly represented in hydrologic models, thus limiting the capability of predicting streamflow under the interactions between hydrologic variability and operational preferences. Data-driven models provide a promising approach to represent reservoir operation rules by capturing relationships embedded in historical records. Similar to hydrologic processes vary across temporal scales, reservoir operations manifest themselves at different timescales, prioritizing different targets to mitigate streamflow variability at a given time scale. To capture interactions of reservoir operations across time scales, we proposed a hierarchical temporal scale framework to investigate the behaviors of over 300 major reservoirs across the Contiguous United States with a wide range of streamflow conditions. Machine learning models were constructed to simulate reservoir operation at daily, weekly, and monthly scales, where decisions at short-term scales interact with long-term decisions. We found that the hierarchical temporal scale configuration better captures reservoir releases than models constructed at a single time scale, especially for reservoirs with multiple operation targets. Model-based sensitivity analysis shows that for more than one third of the studied reservoirs, the release schemes, as a function of decision variables, vary at different time scales, suggesting that operators are commonly faced with complicated trade-offs to serve multiple purposes. The proposed hierarchical temporal scale approach is flexible to incorporate various data-driven models and decision variables to derive reservoir operation rule, providing a robust framework to understand the feedbacks between natural streamflow variability and human interferences across time scales.

A document by Adrià Barbeta Margarit. Click on the document to view its contents.

Transpiration is a key process driving energy, water and thus carbon dynamics. Global T products are fundamental for understanding and predicting vegetation processes. However, validation of these transpiration products is limited, mainly due to lack of suitable datasets. We propose a method to use SAPFLUXNET, the first quality-controlled global tree sap flow database, for evaluating transpiration products at global scale. Our method is based on evaluating temporal mismatches, rather than absolute values, by standardizing both transpiration and sap flow products. We evaluate how transpiration responses to hydro-meteorological variation from the Global Land Evaporation Amsterdam Model (GLEAM), a widely used global transpiration product, compare to in-situ responses from SAPFLUXNET field data. Our results show GLEAM and SAPFLUXNET temporal trends are in good agreement, but diverge under extreme conditions. Their temporal mismatches differ depending on the magnitude of transpiration and are not random, but linked to energy and water availability. Despite limitations, we show that the new global SAPFLUXNET dataset is a valuable tool to evaluate T products and identify problematic assumptions and processes embedded in models. The approach we propose can, therefore, be the foundation for a wider use of SAPFLUXNET, a new, independent, source of information, to understand the mechanisms controlling global transpiration fluxes.

Impact evaluation (IE) of large infrastructure presents numerous challenges, and investments in urban piped water and sanitation are no exception. Here we present methods for more systematic assessment of the implications of such interventions, discussing tradeoffs between validity, relevance and practicality that arise from alternative approaches. Then, to more clearly illustrate the many issues that typically arise in such IEs, we draw on an example application in Zarqa, Jordan, where the Millennium Challenge Corporation invested about US$275 million to upgrade and extend piped water and sewer networks, as well as increase the capacity of the country’s largest wastewater treatment plant. The theory of change for the intervention took a systems view of impacts: the project aimed to improve water supply to urban areas while maintaining flows to irrigators through enhanced wastewater reuse. The case adds valuable evidence on the impacts of large infrastructure investments and illustrates well the challenges of capturing spillovers, mitigating study contamination, maintaining statistical power, and determining overall welfare effects, in situations involving diverse market and nonmarket impacts. These limitations notwithstanding, the case highlights the high value of conducting IEs, and why applied researchers should not give up on pragmatic and interdisciplinary collaborations to evaluation in the face of complex interventions.