To date, few MAR site suitability studies have conducted a sensitivity analysis or validation of recommended sites . Previous MAR suitability assessment studies have used indirect methods to validate MAR locations , while few have used numerical models and in situ observations . With this study, we propose to guide selection of suitable MAR sites by ensuring quantifiable benefits to groundwater levels, storage, water quality, and land subsidence. Although water management agencies maintain multiple MAR basins in the southern CV, most of these facilities have not been implemented to benefit the domestic water supply to rural communities. The Tulare Irrigation District has a 42 ha MAR basin located south of the Okieville community that has been operational since the 1940 . The recharge basin overlays the capture zone of the community’s southern groundwater wells. Its location was accurately identified by this study as suitable Ag-MAR location . Data from Okieville domestic wells show groundwater quality improvements from MAR, including lower nitrate, uranium and arsenic concentrations, which are well below the groundwater concentrations of nearby communities . These indicate that our methodology has positively identified locations where recharge can improve the drinking water supply of rural communities in a region of our study area. Although many studies have used GIS-based MCDA for MAR suitability studies, there is no consensus on appropriate criteria, weights, and methods as these are generally dependent on the study objective, data availability,planting blueberries in containers and local experience . The assignment of weights to each thematic layer or feature is one of the most subjective factors of MCDA and thus, one of the main sources of uncertainty .
To address this issue, AHP is increasingly used to convert subjective assessments of relative importance into a set of weights , though sometimes the relative importance of themes may not be discernable . In this study, local experts in hydrology and human ecology similarly recommended the use of equal weights for thematic layers in both the site suitability and community vulnerability analyses. However, future iterations of these analyses will require the active involvement of local stakeholders , a process that may benefit greatly from the integration of AHP into the GIS-based MCDA . One main difficulty when estimating suitable recharge areas is the spatial and temporal variability of the physical system. We acknowledge that our analysis mainly uses land surface characteristics to determine suitable Ag-MAR sites, while subsurface characteristics were not directly included. Other factors not accounted for in our analysis include water availability, water quality, unsaturated zone transport, and willingness of landowners to flood agricultural land. Although robust quality control measures were taken, the accuracy of our results relies on the integrity of input data. Issues of accuracy and completeness of proprietary, hand-digitized, or self-reported data are inevitable, hence field-level studies of local surface and subsurface characteristics should be completed as part of project scoping and pilot testing. They are also essential to assess soil surface conditions, the presence of potential unprotected wellheads, capacity of connected surface water conveyance systems, feasible Ag-MAR water application amounts , and cropping and agro-chemical application history to determine potential legacy contaminant loading in the unsaturated zone that could be mobilized by recharge . Although nitrate loading to groundwater has been assessed at larger scales in California’s CV , parcel-level data on fertilizer application rates and nitrogen removal by crops is not publicly available, preventing the assessment of legacy nitrate loading in the unsaturated zone.
Future improvements of this methodology should include the addition of contaminant transport modeling or site-specific simulation of drinking water contaminants to address this gap. Climate projections and impacts on surface water availability for recharge require further investigation . As shown by Bachand et al. , despite its semiarid climate, the southern CV faces frequent flood risks. Along the Kings River, flows have exceeded the flood stage almost once every 7 years in the last 4 decades, creating total losses exceeding $1.2 billion . Kocis & Dahlke showed that excess surface water from high flows occur on average every 4.7 out of 10 years with total amounts reaching up to 1.6 km3 between November and April in years when high flows are available. Water scarcity is expected to increase as the southern CV experiences more frequent and longer droughts and more frequent extreme events during wet years . Integrated water management solutions like Ag-MAR are urgently needed to stabilize groundwater supplies in the region.Motorized UAS were introduced as a potential remote sensing tool for scientific research in the late 1970s. However, due to a variety of limitations these platforms had few practical applications . For years, UAS technology was led by military needs and applications. The relatively few applications in research and agriculture included deployments in Japan for crop dusting and in Australia for meteorological studies . In the past decade, several factors have greatly increased the utility and ease of use of UAS, while prices have fallen. Consumer demand drove the hobby craft industry to make major improvements in UAS vehicles. Integrating improved battery technology, miniature inertia measurement units , GPS and customizable apps for smartphones and tablets has delivered improved flight longevity, reliability, ease of use and the ability to better utilize cameras and other sensors needed for applications in agriculture and natural resources . Innovations in sensor technology now include dozens of models of lightweight visible-spectrum and multi-spectrum cameras capable of capturing reliable, scientifically valid data from UAS platforms .
Meanwhile, the Federal Aviation Administration has helped facilitate increased UAS use, with rule changes adopted in August 2016 that lowered what have previously been significant regulatory obstacles to the legal use of UAS for research and commercial purposes . UC faculty throughout California are using UAS in a wide range of agricultural and environmental research projects — from grazed range lands to field crops and orchards, forests, lakes and even the ice sheets of Greenland . UAS also have become a part of the curriculum across the UC system, and are increasingly used by campus staff in departments from facilities to athletics to marketing . UAS are already in wide use in agriculture, and the sector is projected to continue to account for a large share — 19% in the near term, per a recent FAA report — of the commercial UAS market in the United States. The use of UAS for research, particularly remote sensing and mapping, is soaring: A search in Scopus finds 3,079 articles focused on UAS or UAV applications in 2015, compared with 769 in 2005. Across all commercial uses, the FAA estimates 2016 sales of commercial UAS at 600,000 units and expects that figure to balloon to 2.5 million units annually as soon as 2017 . Despite the growing ubiquity of UAS, a variety of practical and scientific challenges remain to using the technology effectively. Collecting and processing data that is useful for management decisions requires a disparate range of skills and knowledge — understanding the relevant regulations, determining what sensing technology and UAS to use for the problem at hand, developing a data collection plan, safely piloting the UAS, managing the large data sets generated by the sensors, selecting and then using the appropriate image-processing and mapping software, and interpreting the data. In addition, as highlighted in the research cases presented below, much science remains to be done to develop reliable methods for interpreting and processing the data gathered by UAS sensors, so that a user can know with confidence that the changes or patterns detected by a UAS camera reflect reality. The UC Agriculture and Natural Resources Informatics and GIS program has recently incorporated drone services into the portfolio of support that it offers to UC ANR and its affiliated UC Agricultural Experiment Station faculty.Working closely with UC Office of the President, Center of Excellence on Unmanned Aircraft System Safety , IGIS has also developed a workshop curriculum around UAS technology,container growing raspberries regulations and data processing, which is open to members of the UC system as well as the public. Please check the IGIS website to learn about upcoming training events around the state in 2017, including a three day “DroneCamp” that will intensively cover drone technology, regulations and data processing.When a tree is stressed — whether due to pest infestation, nutrient deficiency or insufficient water — its leaves change. These changes may be detectable in the visible light spectrum — a shift in a leaf’s shade of green. They can also be “seen” in other bands of the electromagnetic spectrum — for example, a change in the texture of a leaf’s waxy coating may alter how infrared light is reflected.
Different types of stress generate unique electromagnetic “signatures.” If these signatures can be reliably correlated with specific causes, a UAS could be deployed to quickly scan a large orchard for signs of trouble, enabling early detection and treatment of pest infestations and other problems. Christian Nansen, a professor of entomology and nematology at UC Davis, leads a team working to refine this monitoring technique. They use hyperspectral camera, which generates a very high-resolution signature across a wide range of wavelengths. One of the challenges is that the electromagnetic signatures often contain high degrees of data “noise” — due to shadows, dust on leaves, differences between leaves and other factors — making it difficult to discern a clear signal associated with the stress that the tree is experiencing. To address this problem, Nansen’s team is refining a combination of advanced calibration, correction and data filtering techniques. As entomologists, they are also working to understand in fine detail the interactions between different pest species and tree stress, and how those affect the electromagnetic signature of a tree’s leaves .Rapid detection of water stress can help farmers optimize irrigation water applications and improve crop yields. In an orchard, precise assessments of water stress typically require manual measurements at individual trees using a device known as a pressure bomb that measures water tension in individual leaves. Tiebiao Zhao, a graduate student at UC Merced’s Mechatronics, Embedded Systems and Automation Laboratory, is collaborating with UC ANR Merced County pomology farm advisor David Doll with the goal of developing UAS-based tools to assess water stress across a large almond orchard at a high level of accuracy. Water stress can be detected by relatively low-cost multi-spectral cameras due to changes in how the canopy reflects near-infrared light. This project is building a database of canopy spectral signatures and water-stress measurements with the objective of developing indices that can be used to reliably translate UAS imagery into useful water-stress information. In a related experiment, Zhao is working with Dong Wang of the USDA Agriculture Research Service San Joaquin Valley Agricultural Sciences Center to detect the effects of varying irrigation levels and biomass soil amendments on crop development and yield in onions. As in Zhao’s almond experiment, the researchers are comparing spectral signatures gathered by low-cost UAS-mounted multi-spectral cameras with ground-truth data to better understand the relationship between the two .The Greenland ice sheet covers 656,000 square miles and holds roughly 2.3 trillion acre-feet of water — the sea level equivalent of 24 feet. As the climate warms, ice sheet melt accelerates; therefore, understanding the processes involved is important. This knowledge can help to refine predictions about the ice sheet’s future and its contribution to global sea level rise. A team of researchers led by UCLA professor of geography Laurence Smith is using UAS-based imaging technologies to map and monitor meltwater generation, transport and export. The group’s UAS carry multiband visible and near-infrared digital cameras that capture sub-meter resolution data, from which the researchers create multiple orthomosaics of the ice surface and perimeter over time. They are using the data to analyze a number of different cryohydrologic processes and features, including mapping rivers on the ice surface from their origins to their termination at moulins — vertical conduits that connect the ice surface with en- and sub-glacial drainage networks — and melt water outflow to the ocean. The team is also generating digital elevation models of the ice surface to extract hydrologic features, micro topography and drainage divides. In addition, they are working towards mapping ice surface impurities and albedo at high resolution using multi-band visible and near-infrared images.