Changes in the Distribution of Broadleaf Tree Species in the Central Part of the Southern Urals since the 1970s

Russian Journal of Ecology - Changes in the distribution of broadleaf tree species—Tilia cordata Mill., Quercus robur L., Acer platanoides L., and Ulmus glabra Huds.—in the central part...     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Characteristics that make trophy hunting of giant pandas inconceivable

In November 1928, Theodore Jr. and Kermit Roosevelt led an expedition to China with the expressed purpose of being the first Westerners to kill the giant panda (Ailuropoda melanoleuca). The expedition lasted 8 months and resulted in the brothers shooting a giant panda in the mountains of Sichuan Province. Given the concurrent attention in the popular press describing this celebrated expedition, the giant panda was poised to be trophy hunted much like other large mammals around the world. Today, however, the killing of giant pandas, even for the generation of conservation revenue, is unthinkable for reasons related to the species itself and the context, in time and space, in which the species was popularized in the West. We found that the giant panda's status as a conservation symbol, exceptional charisma and gentle disposition, rarity, value as a nonconsumptive ecotourism attraction, and endemism are integral to the explanation of why the species is not trophy hunted. We compared these intrinsic and extrinsic characteristics with 20 of the most common trophy-hunted mammals to determine whether the principles applying to giant pandas are generalizable to other species. Although certain characteristics of the 20 trophy-hunted mammals aligned with the giant panda, many did not. Charisma, economic value, and endemism, in particular, were comparatively unique to the giant panda. Our analysis suggests that, at present, exceptional characteristics may be necessary for certain mammals to be excepted from trophy hunting. However, because discourse relating to the role of trophy hunting in supporting conservation outcomes is dynamic in both science and society, we suspect these valuations will also change in future.     

Experimental investigation of a line plume in a filling box

Environmental Fluid Mechanics - A series of experiments were conducted to quantify the dynamics of a filling box driven by a line plume that spans the full width of the enclosure. Three...     

Fine-resolution mapping of wildfire fuel types for Canada: Fuzzy logic modeling for an Alberta pilot area

The Canadian Forest Fire Danger Rating System (CFFDRS) is used daily across Canada for evaluating forest fire danger. Fuel-type information is one of the inputs required by the models used in the CFFDRS. In this project, three fuel-type maps with a 25 m resolution were produced for a pilot study area located in Alberta using land cover only; land cover and biomass; and, land cover, biomass and leaf area index data derived from satellite imagery. The relationships between inputs and fuel types were determined mainly by the opinions of forest fire scientists and incorporated into a computer program using fuzzy set methodology. Not all the CFFDRS fuel types could be distinguished using these inputs; three of the coniferous types had to be grouped into one common fuel type. Overall accuracy was between 74 and 83% based on ground-truth comparisons. The most accurate map resulted from land cover and biomass data. Detailed accuracy assessment indicated that the overall accuracy increased up to 86% if ambiguous fuel type identification was considered. No combination of inputs was able to define a fuel type with absolute certainty, which is a reflection of differing expert opinions and the small number of inputs used to produce the maps.     

Modelling Phosphorus Retention in Lakes and Reservoirs

Steady-state models for the prediction of P retention coefficient (R) in lakes were evaluated using data from 93 natural lakes and 119 reservoirs situated in the temperate zone. Most of the already existing models predicted R relatively successfully in lakes while it was seriously under-estimated in reservoirs. A statistical analysis indicated the main causes of differences in R between lakes and reservoirs: (a) distinct relationships between P sedimentation coefficient, depth, and water residence time; (b) existence of significant inflow–outflow P concentration gradients in reservoirs. Two new models of different complexity were developed for estimating R in reservoirs: , where τ is water residence time (year), was derived from the Vollenweider/Larsen and Mercier model by adding a calibrated parameter accounting for spatial P non-homogeneity in the water body, and is applicable for reservoirs but not lakes, and , where [P_in] is volume-weighted P concentration in all inputs to the water body (μg l⁻¹), was obtained by re-calibrating the OECD general equation, and is generally applicable for both lakes and reservoirs. These optimised models yield unbiased estimates over a large range of reservoir types.     

Implications of changing national policies on land use in the Chittagong Hill Tracts of Bangladesh

Land use in the Chittagong Hill Tracts (CHT) of Bangladesh had undergone changes over the past several centuries. The landscape, which was mostly covered with forest with interspersed shifting cultivation plots until the beginning of the colonial period, has gradually changed into a landscape with a blend of land uses. Overall, the forest area has gradually declined, while the area under shifting cultivation and sedentary agriculture has expanded. The process of the change was multi-directional. National forestry, land use, land taxation, population migration policies, and development activities, such as construction of a hydroelectric dam and roads, played an important role in this process. Shifting cultivation had inflicted little damage on the forest until the beginning of the colonial period. The pace of deforestation accelerated with the nationalization of forests which abolished tribal people's customary use and management rights to the forest, and allowed large-scale commercial logging both legally and illegally. The pace was further intensified by the policy encouraging population migration to CHT and construction of a reservoir on the Karnafuli River. Efforts were made to replace shifting cultivation with more productive types of sedentary agriculture. However, much change could not take place in the absence of secure land rights, supportive trade policies, and the required support services and facilities, including infrastructure. Locationally suitable land use evolved in areas where transportation facilities were available and farmers were granted land title with the necessary extension services and credit facilities. These findings have important policy implications for the promotion of environmentally and economically sound land use in CHT.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

Impacts of land cover on stream hydrology in the West Georgia Piedmont, USA

The southeastern United States is experiencing rapid urban development. Consequently, Georgia's streams are experiencing hydrologic alterations from extensive development and from other land use activities such as livestock grazing and silviculture. A study was performed to assess stream hydrology within 18 watersheds ranging from 500 to 2500 ha. Study streams were first, second, or third order and hydrology was continuously monitored from 29 July 2003 to 23 September 2004 using InSitu pressure transducers. Rating curves between stream stage (i.e., water depth) and discharge were developed for each stream by correlating biweekly discharge measurements and stage data. Dependent variables were calculated from discharge data and placed into 4 categories: flow frequency (i.e., the number of times a predetermined discharge threshold is exceeded), flow magnitude (i.e., maximum and minimum flows), flow duration (i.e., the amount of time discharge was above or below a predetermined threshold), and flow predictability and flashiness. Fine resolution data (i.e., 15-min interval) were also compared to daily discharge data to determine if resolution affected how streams were classified hydrologically. Urban watersheds experienced flashy discharges during storm events, whereas pastoral and forested watersheds showed less flashy hydrographs. Also, in comparison to all other flow variables, flow frequency measures were most strongly correlated to land cover. Furthermore, the stream hydrology was explained similarly with both the 15-min and daily data resolutions.