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Several decision support systems have been developed and many of them are focused on efficient use of water and energy resources. These include the Irrigation Advisory Service, combined with the use of benchmarking techniques and energy audits. In Brazil, the pioneer project called the Irrigation Advisory Service has been developed in Baixo Acarau irrigation district, located in the north-east of Brazil. The main goal of the project is to transfer information about irrigation to farmers. The aim of this paper is to present the results of the Irrigation Advisory Service activities in Baixo Acarau irrigation district, during the 2011, 2012 and 2013 irrigation seasons. Moreover, the results from the estimation of several performance indicators and from energy: audits carried out are shown as a complementary activity developed by the Irrigation Advisory Service. At the beginning of the irrigation season, on-field emission of uniformity was close to 67%. During the following evaluations, after recommendations of the Irrigation Advisory Service about the necessity of corrections to the systems, emission of uniformity reached 85%. With regard to the use of performance indicators, some problems relating to maintenance of equipment were noted, resulting in malfunctioning pumps. Copyright © 2015 John Wiley & Sons, Ltd.

Crop coefficients were developed to determine crop water needs based on the evapotranspiration (ET) of a reference crop under a given set of meteorological conditions. Starting in the 1980s, crop coefficients developed through lysimeter studies or set by expert opinion began to be supplemented by remotely sensed vegetation indices (VI) that measured the actual status of the crop on a field-by-field basis. VIs measure the density of green foliage based on the reflectance of visible and near infrared (NIR) light from the canopy, and are highly correlated with plant physiological processes that depend on light absorption by a canopy such as ET and photosynthesis. Reflectance-based crop coefficients have now been developed for numerous individual crops, including corn, wheat, alfalfa, cotton, potato, sugar beet, vegetables, grapes and orchard crops. Other research has shown that VIs can be used to predict ET over fields of mixed crops, allowing them to be used to monitor ET over entire irrigation districts. VI-based crop coefficients can help reduce agricultural water use by matching irrigation rates to the actual water needs of a crop as it grows instead of to a modeled crop growing under optimal conditions. Recently, the concept has been applied to natural ecosystems at the local, regional and continental scales of measurement, using time-series satellite data from the MODIS sensors on the Terra satellite. VIs or other visible-NIR band algorithms are combined with meteorological data to predict ET in numerous biome types, from deserts, to arctic tundra, to tropical rainforests. These methods often closely match ET measured on the ground at the global FluxNet array of eddy covariance moisture and carbon flux towers. The primary advantage of VI methods for estimating ET is that transpiration is closely related to radiation absorbed by the plant canopy, which is closely related to VIs. The primary disadvantage is that they cannot capture stress effects or soil evaporation. Copyright © 2011 John Wiley & Sons, Ltd.

An efficient management of our precious water resources is not possible without acquiring a comprehensive and detailed understanding on water fluxes at irrigated areas. In the past few decades, agricultural water balance analyses have been carried out at a wide variety of temporal (from hourly to annual) and spatial (from plant root zone to basin) scales. Scheme-wide water balance analyses, in particular, provide information on the amount of water supplied to irrigation schemes and its fate. This paper attempts to summarise the results of previous studies in quantifying water balance components of irrigation schemes, as well as to present challenges and opportunities of conducting such research projects. With recent improvements in air- and space-borne imaging of land surfaces, remote sensing techniques nowadays can serve as a powerful tool in monitoring/modeling water movements, at or near real time. Remote sensing contribution to water balance studies could be as simple as developing crop classification maps, or as complicated as estimating the spatially distributed evapotranspiration, which is perhaps the most critical water flux in irrigated areas. Therefore, this paper also aims to review the few studies that have incorporated satellite-derived products in their water budget analysis. Finally, the results of a case study from the southern California are presented to better demonstrate the potential of remotely sensed data. Copyright © 2011 John Wiley & Sons, Ltd.

The main objective of this study was to analyse the trends in 20 annual extreme indices of temperature and precipitation for Utah, USA. The analyses were conducted for 28 meteorological stations, during the period from 1930 to 2006, characterized by a long-term and high-quality dataset. The software used to process the data was the RClimdex 1.0. The analyses of extreme temperature indices have identified an increase in air temperature in Utah during the last century. Meanwhile, the analyses of precipitation indices showed a large variation throughout the studied area and time period, and, in general, with few statistically significant trends. Thus, it was not possible to conclude that significant changes in precipitation have occurred in this region over the last century. Copyright © 2010 Royal Meteorological Society

Landscape water conservation is an important issue for municipalities throughout the Western United States, and especially in Utah as rapid growth strains existing water supplies. We conducted interdisciplinary research in Layton, Utah, that aimed at understanding patterns of landscape water use among households and businesses. The research project involved three basic tasks. First, a landscape “water budget” was developed by producing a calibrated and classified mosaic of landscape type and area from airborne multispectral digital imagery, integrating this information with Layton City parcel boundary data to determine landscape vegetated areas per lot, and estimating irrigation needs derived from reference evapotranspiration (ETo) obtained using weather data for the Salt Lake City metropolitan region. Second, utilizing Layton water billing data, water use for each household and business was identified and categorized as “conserving,”“acceptable” or “wasteful” by determining how much the water applied varied from actual landscape plant need. Third, surveys were administered to a random stratified sample of households and businesses in the study area to investigate various factors that were hypothesized to be predictive of wasteful watering practices. This paper primarily focuses on analysis of the household and business survey data, which explores factors affecting urban landscape water use from a human behavioral perspective. We found that the most significant factors predicting actual water use were the type of irrigation system and whether the location was a household or business. Attitudinal and motivational characteristics were not consistently associated with water use. We found that wasteful watering is the result of many factors embedded in the complex context of urban landscapes. This implies that water conservation programs should identify potential wasteful users through analyses of water billing data and direct water conservation measures at these users by focusing on site-specific evaluations and recommendations. Water audits or water checks are one such tool that some communities have employed to help people understand and assess the quantity of water needed by and applied to their landscapes. This approach provides an opportunity to evaluate situational constraints at particular locations and design appropriate strategies for reducing water waste.

We present a conceptual model for identifying restoration sites for riparian wetlands and discuss its application to reaches within the Upper Arkansas River basin in Colorado. The model utilizes a Geographic Information System (GIS) to analyze a variety of spatial data useful in characterizing geomorphology, hydrology, and vegetation of riparian wetland sites. The model focuses on three basic properties of riparian wetland sites: relative soil moisture, disturbance regime, and vegetative characteristics. A relative wetness index is used to define nominal soil moisture classes within the watershed. These classes generally coincide with uplands (low), channel margins (moderate), and channels or open water (high). Vegetative conditions are characterized using color infrared aerial photographs. Land cover types are grouped into five major land cover classes: riparian, moist herbaceous, bare ground, upland, and open water. Disturbance regime is characterized by a reach-based index of specific power (ω). Preliminary results indicate that reaches within the Upper Arkansas River basin can be classified as high energy (ω ≥ 8 W/m 2 ) or low energy (ω ≤ 3W/m 2 ), using discharge estimates that reflect the 10-year flood event. Field surveys of channel and flood-plain conditions plain conditions show that high-energy reaches (ω ≥ 8 W/m 2 ) are characterized by sites where the channel occupies a large proportion of the valley bottom. By contrast, low-energy reaches (ω ≤ 3 W/m 2 ) are characterized by meandering channels with wide alluvial valleys. Restoration potential is evaluated as a combination of nominal scores from wetness, land cover, and disturbance indices. Application of these methods to field sites within the Upper Arkansas River basin identifies a wide range of riparian wetland sites for preservation or restoration. Potential sites within identified reaches are prioritized using size and proximity criteria.

Airborne multispectral videography has become a useful tool for classifying and mapping riparian vegetation because its spectral and digital format has high spatial resolution. This paper explains the processing steps and procedures required to use this type of spectral imagery for riparian vegetation classification and mapping. A description of the Utah State University airborne system is given, along with the image processing steps used for developing finalized products, because these are used in other articles in this volume. Examples of riparian applications using high-resolution imagery are included.

In this study, we examined the site-specific effects of a large episodic flood on the riparian vegetation within the floodplain of the San Luis Rey River in southern California. Using multispectral airborne videography, we quantified percent cover of riparian vegetation, cultivated agricultural land, urban surfaces, upland vegetation, bare soil, and water within 22 sections of flood-plain, both before and after a large flood (January 13, 1993). We also quantified the amount of these cover types within bands of the watershed 1 km wide × 5 km long directly upstream of each floodplain site. The amount of riparian vegetation destroyed by the flood within each section varied from nearly zero to almost 40% of pre-flood coverage. The magnitude of loss in riparian vegetation was most strongly related to the amount of riparian vegetation initially present in the floodplain and the amount of urban surfaces in the nearby watershed. These results suggest that riparian vegetation within the San Luis Rey River floodplain is generally at high risk of destruction from large floods, and that this risk is exaggerated in areas with high urban development. We infer from these results that sites near existing large areas of intact riparian vegetation and away from urban development will have the highest potential for successful long-term restoration.

Setback levees, in which levees are reconstructed at a greater distance from a river channel, are a promising restoration technique particularly for alluvial rivers with broad floodplains where river-floodplain connectivity is essential to ecological processes. Documenting the ecological outcomes of restoration activities is essential for assessing the comparative benefits of different restoration approaches and for justifying new restoration projects. Remote sensing of aquatic habitats offers one approach for comprehensive, objective documentation of river and floodplain habitats, but is difficult in glacial rivers because of high suspended-sediment concentrations, braiding and a lack of large, well-differentiated channel forms such as riffles and pools. Remote imagery and field surveys were used to assess the effects of recent and planned setback levees along the Puyallup River and, more generally, the application of multispectral imagery for classifying aquatic and riparian habitats in glacial-melt water rivers. Airborne images were acquired with a horizontal ground resolution of 0.5 m in three spectral bands (0.545-0.555, 0.665-0.675 and 0.790-0.810 μm) spanning from green to near infrared (NIR) wavelengths. Field surveys identified river and floodplain habitat features and provided the basis for a comparative hydraulic analysis. Broad categories of aquatic habitat (smooth and rough water surface), exposed sediment (sand and boulder) and vegetated surfaces (herbaceous and deciduous shrub/forest) were classified accurately using the airborne images. Other categories [e.g. conifers, boulder, large woody debris (LWD)] and subdivisions of broad categories (e.g. riffles and runs) were not successfully classified either because these features did not form large patches that could be identified on the imagery or their spectral reflectances were not distinct from those of other habitat types. Airborne imagery was critical for assessing fine-scale aquatic habitat heterogeneity including shallow, low-velocity regions that were not feasible or practical to map in the field in many cases due to their widespread distribution, small size and poorly defined boundaries with other habitat types. At the reach-scale, the setback levee affected the amount and distribution of riparian and aquatic habitats: (1) the area of all habitats was greater where levees had been set back and with relatively more vegetated floodplain habitat and relatively less exposed sediment and aquatic habitat, (2) where levees confine the river, less low-velocity aquatic habitat is present over a range of flows with a higher degree of bed instability during high flows. As river restoration proceeds in the Pacific Northwest and elsewhere, remotely acquired imagery will be important for documenting its effects on the amount and distribution of aquatic and floodplain habitats, complimenting field data as a quantitative basis for evaluating project efficacy. Copyright © 2008 John Wiley & Sons, Ltd.

One of the fundamental issues with lidar-derived evapotranspiration estimates is its reliance on tower-based measurements of Monin-Obukhov similarity variables, specifically the Obukhov length (L) and the friction velocity (u * ). Our study indicates that L can be derived in the atmospheric surface layer directly from lidar range-height scans by estimating the integral length scale (ILS). Data from both three-dimensional sonic anemometers mounted on towers and lidar data collected during two subsequent field experiments were analyzed using autocorrelation analysis to estimate the ILS. The ILS values were then transformed into L values using a power-law similarity model and were compared to coincident tower-based observations. The comparisons between tower-based eddy covariance sensors and lidar data show that the lidar-derived L values are within the expected uncertainty and variability of standard point sensor measured observations. An additional model for estimating the friction velocity from the Obukhov length was also derived, and both L and u * were used to calculate the latent energy flux from lidar without external measurements. The evaporative fluxes from the standard method and the new advanced method were compared with eddy covariance fluxes, and it was found that the advanced method is superior.

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (T B S) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the T B signature. SSM/I T B S of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover.