Climate variability poses great challenges to food security in West Africa, a region heavily dependent on rainfall for farming. Identifying sowing strategies that minimize yield losses for farmers in the region is crucial to securing their livelihood. In this paper, we investigate three sowing strategies to assess their ability to identify safe sowing windows for smallholder farmers in the Sudanian region of West Africa (WA) in the context of a changing climate. The GIS version of the FAO crop model, AquaCrop-GIS, is used to simulate the yield response of maize (Zea mays L.) to varying sowing dates throughout the rainy season across WA. Based on an average of 38 years of data per grid cell, we identify safe sowing windows across the Sudanian region that secure at least 90% of maximal yield. We find that current sowing strategies, based on minimum thresholds for rainfall accumulated over a period that are widely applied in the region, carry a higher risk of yield failure, especially at the beginning of the rainy season. This analysis shows that delaying sowing for a month to mid-June in the central region (east of Lon 8.5°W), and to early August in the semi-arid areas is a safer strategy that ensures optimal yields. A comparison between the periods 1982–1991 and 1992–2019 shows a negative shift for LO10 mm and LO20 mm, suggesting a wetter regime compared to the dry periods of the 1970s and 1980s. On the contrary, we observe a positive shift in the safe window strategy, highlighting the need for precautions due to erratic rainfall at the beginning of the season. The precipitation-based strategies hold a high risk, while the safe sowing window strategy, easily accessible to smallholder farmers, is more fitting, given the current climate.@en

Wind machines are increasingly used to mitigate spring frost damage in agricultural sectors. Complementing quasi-3D temperature measurements to quantify the warming effects of wind machines (Dai et al., 2023), this study develops a numerical model to quantify warming effects on air and plant tissues and resolve the dynamic interplay between turbulent rotating plumes and canopy structure. We implement an integrated model in a large-eddy simulation and validate the model against field observations. Simulation results show remarkable agreement with the air mixing and warming effects observed during wind machine operation in Dai et al. (2023). Simulation results reveal significant air and leaf warming near the wind machine due to direct jet-mixing. Beyond 20 m from the machine (3–4 rotor diameters), while wind velocities drop rapidly, the warming is sustained and gradually decreases over distance. This sustained warming, without direct jet mixing, likely results from the advection of jet-entrained warm air. The warming extends 150 m upstream and 550 m downstream, influenced by the background wind. This difference is attributed to the interaction between the machine-induced jet and the background wind, forming convergence patterns when jets oppose the wind and extended warming plumes in wave-like patterns when jets align with the wind. Cross-stream warming symmetrically extends about 250 m. Within these warming regions, leaf temperatures closely follow air temperatures due to strong turbulent heat exchanges. Outside the warming zone, radiative cooling prevails, bringing the leaf–air temperature difference back to approximately 1 degree. These findings collectively give new insights into interactions between the induced warming plumes and air flows within the canopy and provide a useful tool to optimize operational wind machine deployment. This integrated model uniquely provides a full, multi-process representation of outdoor reality with respect to wind machine operation in orchards.

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The Delft Measures Rain Citizen-Science programme has been running for several years in the city of Delft, the Netherlands. Within this programme, interested citizens can apply to receive a low-cost Alecto WS5500 weather station, to measure local meteorological parameters in their own garden. Currently there are over 45 of these citizen-science weather stations spread across neighbourhoods in Delft, capturing rainfall variability in different urban microclimates. However, the scientific quality of these specific stations has never been tested, and from previous work we know that rigorous quality assurance is necessary in order to get meaningful (precipitation) data. Thus we have installed 8 Alecto stations in The Green Village outdoors urban climate field lab at the TU Delft. Stations have been explicitly installed in ways that a citizen might do: slightly tilted, next to a wall (simulating the limited open garden space of a Dutch urban residence), on top of a shed as well as free-standing. These different measurement setups, combined with a row of stations installed in the same way right next to one another, allow us to investigate the bias caused by less-than-ideal station installation, as well as systematic errors related to the tipping bucket mechanism and sensor drifts. Initial results show a general overestimation of the Alecto compared to reference stations and radar observations, and a discernible negative bias caused by sheltering effects of plants and, to a lesser extent by walls.@en

In this study, we present an extension to the Monin–Obukov similarity theory (MOST) for the roughness sublayer (RSL) over short vegetation. We test our theory using temperature measurements from fiber optic cables in an array-shaped set-up. This provides a high vertical measurement resolution that enables us to measure the sharp temperature gradients near the surface. It is well-known that MOST is invalid in the RSL as the flow is distorted by roughness elements. However, to derive the surface temperature, it is common practice to extrapolate the logarithmic profiles down to the surface through the RSL. Instead of logarithmic behaviour defined by MOST near the surface, our observations show near-linear temperature profiles. This log-to-linear transition is described over an aerodynamically smooth surface by the Van Driest equation in classical turbulence literature. Here we propose that the Van Driest equation can also be used to describe this transition over a rough surface, by replacing the viscous length scale with a surface length scale L_s that represents the size of the smallest eddies near the grass structures. We show that L_s scales with the geometry of the vegetation and that the model shows the potential to be scaled up to tall canopies. The adapted Van Driest model outperforms the roughness length concept in describing the temperature profiles near the surface and predicting the surface temperature.

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Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments (“longest dry period”, “sequential model training”) are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations (> 90 % true positive rate). The zero-model and random forest are shown to be unable to predict contamination episodes if pollution episodes are not present in the training data. Our research shows that the investigated Bayesian approaches reduce the risk of missed pollution events, thereby improving bathing water safety management. Additionally, the approaches provide a transparent solution for setting minimum data quality requirements under various conditions. The proposed approaches open the way for developing data-driven models for bathing water quality prediction against the reality that data scarcity is common problem at existing and prospective bathing waters.

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West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products continue to show weak correlations with ground measurements. This paper aims to investigate whether water vapor (WV) observations together with temporal information can complement thermal infrared (TIR) data for satellite rainfall retrieval in a Deep Learning (DL) framework. This is motivated by the fact that water vapor plays a key role in the highly seasonal West African rainfall dynamics. We present a DL model for satellite rainfall detection based on WV and TIR channels of Meteosat Second Generation and temporal information. Results show that the WV inhibition of low-level features enables the depiction of strong convective motions usually related to heavy rainfall. This is especially relevant in areas where convective rainfall is dominant, such as the tropics. Additionally, WV data allow us to detect dry air masses over our study area, that are advected from the Sahara Desert and create discontinuities in precipitation events. The developed DL model shows strong performance in rainfall binary classification, with less false alarms and lower rainfall overdetection (FBias <2.0) than the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run.@en

Wind machines have been increasingly used for frost damage mitigation in the agricultural community. During radiative frost nights, wind machines are used to erode near-surface thermal inversion by air mixing. The underlying mixing processes remain poorly understood. A full picture of warming effects caused by air mixing requires measurements with wide coverage and high resolution. Our study aimed to quantify the magnitude and area of warming by air mixing and identify the characteristic mixing processes downwind and upwind. We installed 9 km of fiber optic cables in a 6.75 ha orchard block, creating two horizontal planes and three vertical profiles. Quasi-3D temperature responses with spatial sampling and temporal resolution of 25 cm and 10 s, respectively, were obtained before and during machine operation. We found a 50% reduction of the local inversion strength (8 K) over 0.42 ha at 1 m and 0.46 ha at 2 m height. The warming area for a 30% reduction extends to 2.81 and 2.52 ha, respectively. As the propeller rotates 360°, the weak background wind substantially impacts the air mixing processes downwind and upwind. When jets blow along with background wind, the warming plumes arrive earlier than the jet due to horizontal advection from earlier warmed sections. The warming plumes consequently accumulate downwind and penetrate deep into the canopy. In contrast, in upwind direction, wind drag resistance causes warming plumes arrive later than the jet. Quadrant analysis reveals that flux transport during the machine operation is dominated by sweeping and ejection motions. Intermittent downdrafts of warm air and updrafts of cool air result in efficient vertical heat exchange. This feature makes wind machines highly effective in raising canopy airspace temperature to mitigate frost damage.

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Food and economic security in West Africa rely heavily on rainfed agriculture and are threatened by climate change and demographic growth. Accurate rainfall information is therefore crucial to tackling these challenges. Particularly, information about the occurrence and length of droughts as well as the onset date of the rainy season is essential for agricultural planning. However, existing rainfall models fail to accurately represent the highly variable and sparsely monitored West African rainfall patterns. In this paper, we show the potential of deep learning (DL) to model rainfall in the region and propose a methodology to develop DL models in data-scarce areas. We built two DL models for satellite rainfall (rain/no-rain) detection over northern Ghana from Meteosat TIR data based on standard DL architectures: Convolutional neural networks (CNNs) and convolutional long short-term memory neural networks (ConvLSTM). The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System (PERSIANN-CCS) products are used as benchmarks. We use rain gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) for model development and performance evaluation. We show that our models compare well against existing products despite being considerably simpler, developed with a small training dataset—i.e., 8 stations covering 2.5 years with 20.4% of the data missing—and using TIR data alone. Concretely, our models consistently outperform PERSIANN-CCS for rain/no-rain detection at a sub-daily timescale. While IMERG is the overall best performer, the DL models perform better in the second half of the rainy season despite their simplicity (i.e., up to 120 k parameters). Our results suggest that DL-based regional models are a promising alternative to state-of-the-art global products for providing regional rainfall information, especially in meteorologically complex regions such as the (sub)tropics, which are poorly covered by ground-based rainfall observations.@en

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing.@en

Climate change is exacerbating adverse impacts of water stress in rainfed agriculture. This paper seeks to identify safe sowing windows for smallholder farmers in the Sudanian region of West Africa (WA). We hypothesize that the traditional focus on the onset of the season to start sowing leads to crop losses in years of high rainfall intermittency. AquaCrop, an FAO crop model, is used to simulate the yield response of maize (Zea mays L.) to sowing dates ranging from the 1st of May to the 30th of November at 20 locations in WA. We find that sowing directly after the first rains carries a higher risk of water stress, hampering crop development due to insufficient buildup of soil water storage to overcome dry spells. Based on three years of data per station on average, we identify safe sowing windows across the Sudanian region that secure optimal yield in 97% of all cases. We find that delaying sowing to mid-June (savanna and western part of the region) and to July (semi-arid region) ensures optimal yields. Of the three commonly applied local onset approaches covered in our evaluation, only LO10mm (10 mm/day on four consecutive days) achieves a similar yield result. The advantage of the safe window approach is that it is accessible for smallholders, who in many cases do not have access to local rainfall information.@en

To mitigate spring frost damage, fruit farmers use wind machines to mix warm overlying air down to the vegetation. Up to this point, studies on wind machine efficiency have focused on air temperatures. The temperature of different plant organs during operation remains unknown, while critical for the actual degree of frost damage. With Distributed Temperature Sensing we measured vertical in-canopy air temperature profiles in a pear orchard in the Netherlands and thermistors were installed to determine the plant tissue temperatures. We found that to optimize wind machine operation, it is important to consider two effects of a wind machine: (1) mixing of stratified air above and into the canopy layer and (2) erosion of the leaf boundary layer to facilitate plant–air heat exchange. We show how foliage reduces plume penetration to the ground with distance to the wind machine. Due to this blocking at least 15 rotations (∼ 75 min) are needed for optimal mixing. Leaf temperatures lag behind air temperatures, due to strong radiative cooling. We found that over the rotation cycle of a wind machine the temperature difference between leaf and air is variable as convective warming repeatedly dominates over radiative cooling. This is different for flowers and shoots due to different heat capacities. Thin flower petals store little heat and are almost in direct equilibrium with air temperature changes. Shoots, with their higher heat capacity and lower surface/volume ratio, store more heat during the day that is slowly released at night. This discrepancy between plant and air temperature should be considered for frost damage prediction.@en

To ensure a sustainable future and combat food scarcity, we must boost agricultural productivity, improve climate resilience and optimize resource usage. There is untapped potential for dense wireless sensor networks in agriculture that can increase yields and support resilient production when linked to smart decision and control systems.@en

Plant roots are highly adaptable, but their adaptability is not included in crop and land surface models. They rely on a simplified representation of root growth, which is independent of soil moisture availability. Data of subsurface processes and interactions, needed for model setup and validation, are scarce. Here we investigated soil-moisture-driven root growth. To this end, we installed subsurface drip lines and small soil moisture sensors (0.2 L measurement volume) inside rhizoboxes (length × width × height of 45 × 7.5 × 45 cm). The development of the vertical soil moisture and root growth profiles is tracked with a high spatial and temporal resolution. The results confirm that root growth is predominantly driven by vertical soil moisture distribution, while influencing soil moisture at the same time. Besides support for the functional relationship between the soil moisture and the root density growth rate, the experiments also suggest that the extension of the maximum rooting depth will stop if the soil moisture at the root tip drops below a threshold value. We show that even a parsimonious one-dimensional water balance model, driven by the water input flux (irrigation), can be convincingly improved by implementing root growth driven by soil moisture availability.@en

Norovirus infections are among the major causes of acute gastroenteritis worldwide. In Germany, norovirus infections are the most frequently reported cause of gastroenteritis, although only laboratory confirmed cases are officially counted. The high infectivity and environmental persistence of norovirus, makes the virus a relevant pathogen for water related infections. In the 2017 guidelines for potable water reuse, the World Health Organization proposes Norovirus as a reference pathogen for viral pathogens for quantitative microbial risk assessment (QMRA). A challenge for QMRA is, that norovirus data are rarely available over long monitoring periods to assess inter-annual variability of the associated health risk, raising the question about the relevance of this source of variability regarding potential risk management alternatives. Moreover, norovirus infections show high prevalence during winter and early spring and lower incidence during summer. Therefore, our objective is to derive risk scenarios for assessing the potential relevance of the within and between year variability of norovirus concentrations in municipal wastewater for the assessment of health risks of fieldworkers, if treated wastewater is used for irrigation in agriculture. To this end, we use the correlation between norovirus influent concentration and reported epidemiological incidence (R²=0.93), found at a large city in Germany. Risk scenarios are subsequently derived from long-term reported epidemiological data, by applying a Bayesian regression approach. For assessing the practical relevance for wastewater reuse we apply the risk scenarios to different irrigation patterns under various treatment options, namely “status-quo” and “irrigation on demand”. While status-quo refers to an almost all-year irrigation, the latter assumes that irrigation only takes place during the vegetation period from May - September. Our results indicate that the log-difference of infection risks between scenarios may vary between 0.8 and 1.7 log given the same level of pre-treatment. They also indicate that under the same exposure scenario the between-year variability of norovirus infection risk may be > 1log, which makes it a relevant factor to consider in future QMRA studies and studies which aim at evaluating safe water reuse applications. The predictive power and wider use of epidemiological data as a suitable predictor variable should be further validated with paired multi-year data.

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The low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at a higher spatial density. In this paper, hourly radar rainfall bias adjustment was applied using two different rain gauge networks: tipping buckets, measured by Thai Meteorological Department (TMD), and daily citizen rain gauges. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a two-step Kalman filter to incorporate the two gauge datasets of contrasting quality. Radar reflectivity data from the Sattahip radar station, gauge rainfall data from the TMD, and data from citizen rain gauges located in the Tubma Basin, Thailand, were used in the analysis. Daily data from the citizen rain gauge network were downscaled to an hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. Results show that an improvement in radar rainfall estimates was achieved by including the downscaled citizen observations compared with bias correction based on the conventional rain gauge network alone. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse.@en

Plant transpiration accounts for about half of all terrestrial evaporation. Plants need water for many vital functions including nutrient uptake, growth and leaf cooling. The regulation of plant water transport by stomata in the leaves leads to the loss of 97% of the water that is taken up via their roots, to the atmosphere. Measuring plant-water dynamics is essential to gain better insight into its roles in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for plant-water dynamics measurement and highlight several promising innovations for future monitoring. Some of the techniques we will cover include: gas exchange for stomatal conductance and transpiration monitoring, lysimetry, thermometry, heat-based sap flow monitoring, reflectance monitoring including satellite remote sensing, ultrasound spectroscopy, dendrometry, accelometry, scintillometry, stable water isotope analysis and eddy covariance. To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques are required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly account for spatial heterogeneity, a dense network sampling is needed.

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Low-cost dual-frequency receivers and antennas have created opportunities for a wide range of new applications, in regions and disciplines where traditional GNSS equipment is unaffordable. However, the major drawback of using low-cost antenna equipment is that antenna phase patterns are typically poorly defined. Therefore, the noise in tropospheric zenith delay and coordinate time series is increased and systematic errors may occur. Here, we present a field calibration method that fully relies on low-cost solutions. It does not require costly software, uses low-cost equipment (~500 Euros), requires limited specialist expertise, and takes complex processing steps into the cloud. The application is more than just a relative antenna calibration: it is also a means to assess the quality and performance of the antenna, whether this is at a calibration site or directly in the field. We cover PCV calibrations, important for deformation monitoring, GNSS meteorology and positioning, and the computation of PCOs when the absolute position is of interest. The method is made available as an online web service. The performance of the calibration method is presented for a range of antennas of different quality and price in combination with a low-cost dual-frequency receiver. Carrier phase residuals of the low-cost antennas are reduced by 11–34% on L1 and 19–39% on L2, depending on the antenna type and ground plane used. For the cheapest antenna, when using a circular ground plane, the L1 residual is reduced from 3.85 mm before to 3.41 mm after calibration, and for L2 from 5.34 mm to 4.3 mm. The calibration reduces the Median Absolute Deviations (MADs) of the low-cost antennas in the vertical direction using Post Processed Kinematic (PPK) by 20–24%. For the cheapest antenna, the MAD is reduced from 5.6 to 3.8 mm, comparable to a geodetic-grade antenna (3.5 mm MAD). The calibration also has a positive impact on the Precise Point Positioning (PPP) results, delivering more precise results and reducing height biases.@en

Thornthwaite's formula is globally an optimum candidate for large-scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (formerly FAO-56), which is the most data-demanding method and is commonly used as the benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (Ep) using as benchmark the ASCE-standardized reference evapotranspiration method (Er). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity, and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature, rainfall, and Er data of the period 1950-2000 at 30arcsec resolution (1km at Equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly Er/Ep ratios by setting the ratios' weight according to the monthly Er magnitude and by excluding colder months with monthly values of Er or Ep <45mm per month because their ratio becomes highly unstable for low temperatures. The validation of the correction coefficients was made using raw data from 525 stations of Europe; California, USA; and Australia including data up to 2020. The validation procedure showed that the corrected Thornthwaite formula Eps using local coefficients led to a reduction of RMSE from 37.2 to 30.0mmm-1 for monthly step estimations and from 388.8 to 174.8mmyr-1 for annual step estimations compared to Ep using as a benchmark the values of the Er method. The corrected Eps and the original Ep Thornthwaite formulas were also evaluated by their use in Thornthwaite and UNEP (United Nations Environment Program) aridity indices using as a benchmark the respective indices estimated by Er. The analysis was made using the validation data of the stations, and the results showed that the correction of the Thornthwaite formula using local coefficients increased the accuracy of detecting identical aridity classes with Er from 63% to 76% for the case of Thornthwaite classification and from 76% to 93% for the case of UNEP classification. The performance of both aridity indices using the corrected formula was extremely improved in the case of non-humid classes. The global database of local correction factors can support applications of reference evapotranspiration and aridity index assessment with the minimum data requirements (i.e., temperature) for locations where climatic data are limited.

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A new type of rainfall sensor (the intervalometer), which counts the arrival of raindrops at a piezo electric element, is implemented during the Tanzanian monsoon season alongside tipping bucket rain gauges and an impact disdrometer. The aim is to test the validity of the Poisson hypothesis underlying the estimation of rainfall rates using an experimentally determined raindrop size distribution parameterisation based on Marshall and Palmer (1948)'s exponential one. These parameterisations are defined independently of the scale of observation and therefore implicitly assume that rainfall is a homogeneous Poisson process. The results show that 28.3 % of the total intervalometer observed rainfall patches can reasonably be considered Poisson distributed and that the main reasons for Poisson deviations of the remaining 71.7 % are non-compliance with the stationarity criterion (45.9 %), the presence of correlations between drop counts (7.0 %), particularly at higher arrival rates (ρa>500 m−2s−1), and failing a χ2 goodness-of-fit test for a Poisson distribution (17.7 %). Our results show that whilst the Poisson hypothesis is likely not strictly true for rainfall that contributes most to the total rainfall amount, it is quite useful in practice and may hold under certain rainfall conditions. The parameterisation that uses an experimentally determined power law relation between N0 and rainfall rate results in the best estimates of rainfall amount compared to co-located tipping bucket measurements. Despite the non-compliance with the Poisson hypothesis, estimates of total rainfall amount over the entire observational period derived from disdrometer drop counts are within 4 % of co-located tipping bucket measurements. Intervalometer estimates of total rainfall amount overestimate the co-located tipping bucket measurement by 12 %. The intervalometer principle shows potential for use as a rainfall measurement instrument.@en

The Food-Energy-Water (FEW) nexus for urban sustainability needs to be analyzed via an integrative rather than a sectoral or silo approach, reflecting the ongoing transition from separate infrastructure systems to an integrated social-ecological-infrastructure system. As technology hubs can provide food, energy, water resources via decentralized and/or centralized facilities, there is an acute need to optimize FEW infrastructures by considering cost-benefit-risk tradeoffs with respect to multiple sustainability indicators. This paper identifies, categorizes, and analyzes global trends with respect to contemporary FEW technology metrics that highlights the possible optimal integration of a broad spectrum of technology hubs for possible cost-benefit-risk tradeoffs. The challenges related to multiscale and multiagent modeling processes for the simulation of urban FEW systems were discussed with respect to the aspects of scaling-up, optimization process, and risk assessment. Our review reveals that this field is growing at a rapid pace and the previous selection of analytical methodologies, nexus criteria, and sustainability indicators largely depended on individual FEW nexus conditions disparately, and full-scale cost-benefit-risk tradeoffs were very rare. Therefore, the potential full-scale technology integration in three ongoing cases of urban FEW systems in Miami (the United States), Marseille (France), and Amsterdam (the Netherlands) were demonstrated in due purpose finally.

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