Evaluating the spatial uncertainty of future land abandonment in a mountain valley (Vicdessos, Pyrenees-France) : insights form model parameterization and experiments

International audienceEuropean mountains are particularly sensitive to climatic disruptions and land use changes. The latter leads to high rates of natural reforestation over the last 50 years. Faced with the challenge of predicting possible impacts on ecosystem services, LUCC models offer new opportunities for land managers to adapt or mitigate their strategies. Assessing the spatial uncertainty of future LUCC is crucial for the defintion of sustainable land use strategies. However, the sources of uncertainty may differ, including the input parameters, the model itself, and the wide range of possible futures. The aim of this paper is to propose a method to assess the probability of occurrence of future LUCC that combines the inherent uncertainty of model parameterization and the ensemble uncertainty of the future based scenarios. For this purpose, we used the Land Change Modeler tool to simulate future LUCC on a study site located in the Pyrenees Mountains (France) and 2 scenarios illustratins 2 land use strategies. The model was parameterized with the same driving factors used for its calibration. The defintion of static vs. dynamic and quantitative vs. qualitative (discretized) driving factors, and their combination resulted in 4 parameterizations. The combination of model outcomes produced maps of spatial uncertainty of future LUCC. This work involves literature to future-based LUCC studies. It goes beyond the uncertainty of simulation models by integrating the unceertainty of the future to provide maps to help decision makers and land managers

Integration of remotely sensed soil sealing data in landslide susceptibility mapping

Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC(area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter "soil sealing aggregation" significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures

Numerical simulations of snowfall events: sensitivity analysis of physical parameterizations

Accurate estimation of snowfall episodes several hours or even days in advance is essential to minimize risks to transport and other human activities. Every year, these episodes cause severe traffic problems on the northwestern Iberian Peninsula. In order to analyze the influence of different parameterization schemes, 15 snowfall days were analyzed with the Weather Research and Forecasting (WRF) model, defining three nested domains with resolutions of 27, 9, and 3 km. We implemented four microphysical parameterizations (WRF Single‐Moment 6‐class scheme, Goddard, Thompson, and Morrison) and two planetary boundary layer schemes (Yonsei University and Mellor‐Yamada‐Janjic), yielding eight distinct combinations. To validate model estimates, a network of 97 precipitation gauges was used, together with dichotomous data of snowfall presence/absence from snowplow requests to the emergency service of Spain and observatories of the Spanish Meteorological Agency. The results indicate that the most accurate setting of WRF for the study area was that using the Thompson microphysical parameterization and Mellor‐Yamada‐Janjic scheme, although the Thompson and Yonsei University combination had greater accuracy in determining the temporal distribution of precipitation over 1 day. Combining the eight deterministic members in an ensemble average improved results considerably. Further, the root mean square difference decreased markedly using a multiple linear regression as postprocessing. In addition, our method was able to provide mean ensemble precipitation and maximum expected precipitation, which can be very useful in the management of water resources. Finally, we developed an application that allows determination of the risk of snowfall above a certain threshold.This paper was supported by the following grants: TEcoAgua, METEORISK PROJECT(RTC‐2014‐1872‐5), Granimetro(CGL2010‐15930) and MINECO(CGL2011‐25327, RTC‐2014‐1872‐5 and ESP2013‐47816‐C4‐4P), and LE220A11‐2 and LE003B009 awarded by the Junta de Castilla and León

Evaluating resistance surfaces for modeling wildlife movement and connectivity

The continued growth of human populations and associated development in many areas of the world is causing persistent fragmentation of natural habitats. In response, wildlife corridors are often promoted as essential for the conservation of wildlife species. Wildlife corridors allow for the movement of individuals between habitat patches and confer many benefits including the maintenance of metapopulations and metapopulation dynamics, the maintenance of seasonal migratory routes, genetic exchange, and the potential for individuals and populations to shift their ranges in response to climate change. Wildlife corridors are modeled across a resistance-to-movement surface where resistance represents the willingness of an organism to cross a particular environment, the physiological cost of moving through a particular environment, or the reduction in survival for the organism moving through a particular environment. Resistance surfaces can be estimated using a wide variety of methods yet, to date, there has been no in-depth methodological comparison of these methods and their appropriateness for modeling connectivity. My dissertation has two main objectives. The first was to determine the sensitivity of species-habitat models, resistance surfaces and corridors for pumas (Puma concolor) in southern California to six key factors: (1) data type used (point, step, or path data); (2) Statistical models employed; (3) Behavioral state of the individuals; (4) Spatial scale of analysis; (5) GPS collar acquisition interval; and (6) Thematic resolution and richness of the underlying geospatial layers. The second objective was to determine which combination of factors results in the most appropriate resistance surfaces for connectivity modeling. I found that species-habitat models, resistance surfaces and corridors were extremely sensitive to all six of these factors – to the point where using one scale versus another or one data type versus another resulted in conflicting conclusions about habitat use and differences in the location of corridors. I recommend that, for modeling movement and corridors, path data be used in a context-dependent multi-scale modeling framework. I also recommend that many different geospatial layers at different thematic resolutions be examined to identify the most appropriate landscape definition for the species and study area of interest

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)

Impact of Land Management Practices on Water Balance and Sediment Transport in the Morogoro Catchment, Uluguru Mountains (Tanzania)

Tanzania, like other developing countries in the tropics is severely affected by the degradation of water resources owing to improper land management practices. Such practices affect water supply through soil erosion which does not only cause sedimentation of rivers and water bodies but also leads to a reduction in the rainwater infiltration capacity of soils. This thesis seeks to demonstrate how the implementation of proper land management measures can reduce soil erosion and increase water supply in the Morogoro River catchment (Uluguru Mountains). The proper practices referred to are the soil and water conservation (SWC) approaches which include contour farming, fanya juu terracing and bench terracing. The thesis combines social science and geoscience methods in a synergetic manner to address this research problem. To understand how and to what degree SWC methods affect water fluxes and sediment yields, the hydrological model SWAT (Soil and Water Assessment Tool) was applied. Before carrying out the modelling procedures, it was necessary to examine the level of SWC adoption among farmers and factors influencing the process so as to establish the baseline. To this end, biophysical and socio-economic factors assumed to affect farmers’ adoption tendency were examined using a household questionnaire. Modelling results indicate that if correctly implemented contour farming, fanya juu terracing and bench terracing would significantly reduce sediment yield at different rates. The reduction would range approximately between 1% - 85% with the highest percentage change achieved by practicing the three SWC methods simultaneously. However, such SWC measures would not increase water flow annually owing to evapotranspiration losses. Nevertheless, according to modelling results groundwater storage would be increased by about 14% and hence contributing to water supply during the dry season. The household questionnaire survey suggests that the adoption of SWC methods in the study area is very low and complex. While age of the head of household, access to extension (professional) services, household annual income and proximity to the farm significantly influenced farmers’ decision to adopt SWC, gender of the head of household, slope characteristics of the farm, number of adults in the household and farmer’s perception on soil erosion effects had no considerable influence on adoption. Therefore, to successfully realize the SWC benefits demonstrated by the modelling results, smallholder farmers upstream of the catchment should be incentivized to implement proper land management practices. Payment for ecosystem services scheme appears to be a suitable strategy. To make this operational, the Tanzanian government should establish a national water fund which will finance watershed management activities. The methodological approach employed in this thesis is transferrable to other sites with problems comparable to the studied catchment

Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale

REVISITING BID DESIGN ISSUES IN CONTINGENT VALUATION

A uniform bid design from a predetermined uniform distribution is proposed as a practical and robust alternative to existing optimal or naïve bid designs. Analytics and simulations show that the uniform design provides efficiency better than naïve designs under ideal conditions and outperforms optimal designs with poor initial information.Research Methods/ Statistical Methods,