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Power demands are set to increase by two-fold within the current century and a high fraction of that demand should be met by carbon free sources. Among the renewable energies, solar energy is among the fastest growing; therefore, a comprehensive and accurate design methodology for solar systems and how they interact with the local environment is vital. This paper addresses the environmental effects of solar panels on an unirrigated pasture that often experiences water stress. Changes to the microclimatology, soil moisture, water usage, and biomass productivity due to the presence of solar panels were quantified. The goal of this study was to show that the impacts of these factors should be considered in designing the solar farms to take advantage of potential net gains in agricultural and power production. Microclimatological stations were placed in the Rabbit Hills agrivoltaic solar arrays, located in Oregon State campus, two years after the solar array was installed. Soil moisture was quantified using neutron probe readings. Significant differences in mean air temperature, relative humidity, wind speed, wind direction, and soil moisture were observed. Areas under PV solar panels maintained higher soil moisture throughout the period of observation. A significant increase in late season biomass was also observed for areas under the PV panels (90% more biomass), and areas under PV panels were significantly more water efficient (328% more efficient).

There are few methods to provide high-resolution in-situ characterization of flow in aquifers and reservoirs. We present a method that has the potential to quantify lateral and vertical (magnitude and direction) components of flow with spatial resolution of about one meter and temporal resolution of about one day. A fiber optic distributed temperature sensor is used with a novel heating system. Temperatures before heating may be used to evaluate background geothermal gradient and vertical profile of thermal diffusivity. The innovation presented is the use of variable energy application along the well, in this case concentrated heating at equally-spaced (2 m) localized areas (0.5 m). Relative to uniform warming this offers greater opportunity to estimate water movement, reduces required heating power, and increases practical length that can be heated. Numerical simulations are presented which illustrate expected behaviors. We estimate relative advection rates near the well using the times at which various locations diverge from a heating trajectory expected for pure conduction in the absence of advection. The concept is demonstrated in a grouted 600 m borehole with 300 heated patches, though evidence of vertical water movement was not seen.

Land-use and land-cover changes are accelerating. Such changes can homogenize the water cycle and undermine planetary resilience. Policymakers and practitioners must consider water–vegetation interactions in their land-management decisions.

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom’s development.

Hydrologic research is a very demanding application of fiber-optic distributed temperature sensing (DTS) in terms of precision, accuracy and calibration. The physics behind the most frequently used DTS instruments are considered as they apply to four calibration methods for single-ended DTS installations. The new methods presented are more accurate than the instrument-calibrated data, achieving accuracies on the order of tenths of a degree root mean square error (RMSE) and mean bias. Effects of localized non-uniformities that violate the assumptions of single-ended calibration data are explored and quantified. Experimental design considerations such as selection of integration times or selection of the length of the reference sections are discussed, and the impacts of these considerations on calibrated temperatures are explored in two case studies.

Increasingly, satellite-derived rainfall data are used for climate research and action in Africa. In this study, we use 6 years of rain gauge data from 596 stations operated by the Trans-African Hydrometeorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products}CHIRPS, TAMSAT, and GSMaP_wGauge}and one satellite-only rainfall product, GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, southern Africa, and West Africa at daily, pentadal, and monthly time scales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5% (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7%). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29%; wet bias of 26.3 mm), TAMSAT in southern Africa (13%; dry bias of 10.4 mm), and GSMaP in West Africa (23%; wet bias of 19.6 mm). Considerable biases in seasonal rainfall are observed in all subregions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1-km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple time scales relative to CHIRPS and TAMSAT.

Recession analysis is a classical method in hydrology to assess watersheds' hydrological properties by means of the receding limb of a hydrograph, frequently expressed as the rate of change in discharge (-dQ/dt) against discharge (Q). This relationship is often assumed to take the form of a power law -dQ/dt=aQb, where a and b are recession parameters. Recent studies have highlighted major differences in the estimation of the recession parameters depending on the method, casting doubt on our ability to properly evaluate and compare hydrological properties across watersheds based on recession analysis of -dQ/dt vs. Q. This study shows that estimation based on collective recessions as an average watershed response is strongly affected by the distributions of event inter-arrival time, magnitudes, and antecedent conditions, implying that the resulting recession parameters do not represent watershed properties as much as they represent the climate. The main outcome from this work highlights that proper evaluation of watershed properties is only ensured by considering independent individual recession events. While average properties can be assessed by considering the average (or median) values of a and b, their variabilities provide critical insight into the sensitivity of a watershed to the initial conditions involved prior to each recharge event.

Understanding of hydroclimatic processes in Africa has been hindered by the lack of in situ precipitation measurements. Satellite-based observations, in particular, the TRMM Multisatellite Precipitation Analysis (TMPA) have been pivotal to filling this void. The recently released Integrated Multisatellite Retrievals for GPM (IMERG) project aims to continue the legacy of its predecessor, TMPA, and provide higher-resolution data. Here, IMERG-V04A precipitation data are validated using in situ observations from the Trans-African Hydro-Meteorological Observatory (TAHMO) project. Various evaluation measures are examined over a select number of stations in West and East Africa. In addition, continent-wide comparisons are made between IMERG and TMPA. The results show that the performance of the satellite-based products varies by season, region, and the evaluation statistics. The precipitation diurnal cycle is relatively better captured by IMERG than TMPA. Both products exhibit a better agreement with gauge data in East Africa and humid West Africa than in the southern Sahel. However, a clear advantage for IMERGis not apparent in detecting the annual cycle. Although all gridded products used here reasonably capture the annual cycle, some differences are evident during the short rains inEast Africa. Direct comparison between IMERG and TMPA over the entire continent reveals that the similarity between the two products is also regionally heterogeneous. Except for Zimbabwe and Madagascar, where both satellite-based observations present a good agreement, the two products generally have their largest differences over mountainous regions. IMERG seems to have achieved a reduction in the positive bias evident in TMPA over Lake Victoria.

Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.

Over the past five years, Distributed Temperature Sensing (DTS) along fiber optic cables using Raman backscattering has become an important tool in the environmental sciences. Many environmental applications of DTS demand very accurate temperature measurements, with typical RMSE < 0.1 K. The aim of this paper is to describe and clarify the advantages and disadvantages of double-ended calibration to achieve such accuracy under field conditions. By measuring backscatter from both ends of the fiber optic cable, one can redress the effects of differential attenuation, as caused by bends, splices, and connectors. The methodological principles behind the double-ended calibration are presented, together with a set of practical considerations for field deployment. The results from a field experiment are presented, which show that with double-ended calibration good accuracies can be attained in the field.