Integrated Environmental Modelling Framework for Cumulative Effects Assessment

Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment

Semantic array programming in data-poor environments: assessing the interactions of shallow landslides and soil erosion

This research was conducted with the main objective to better integrate and quantify the role of water-induced shallow landslides within soil erosion processes, with a particular focus on data-poor conditions. To fulfil the objectives, catchment-scale studies on soil erosion by water and shallow landslides were conducted. A semi-quantitative method that combines heuristic, deterministic and probabilistic approaches is here proposed for a robust catchment-scale assessment of landslide susceptibility when available data are scarce. A set of different susceptibility-zonation maps was aggregated exploiting a modelling ensemble. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques such as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN), and two different landslide-susceptibility techniques based on the infinite slope stability model. The good performance of the ensemble model, when compared with the single techniques, make this method suitable to be applied in data-poor areas where the lack of proper calibration and validation data can affect the application of physically based or conceptual models. A new modelling architecture to support the integrated assessment of soil erosion, by incorporating rainfall induced shallow landslides processes in data-poor conditions, was developed and tested in the study area. This proposed methodology is based on the geospatial semantic array programming paradigm. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. By analysing modelling results within the study catchment, each year, on average, mass movements are responsible for a mean increase in the total soil erosion rate between 22 and 26% over the pre-failure estimate. The post-failure soil erosion rate in areas where landslides occurred is, on average, around 3.5 times the pre-failure value. These results confirm the importance to integrate landslide contribution into soil erosion modelling. Because the estimation of the changes in soil erosion from landslide activity is largely dependent on the quality of available datasets, this methodology broadens the possibility of a quantitative assessment of these effects in data-poor regions

Assessment and formulation of data assimilation techniques for a 3D Richards equation-based hydrological model

The main objectives of de DAUFIN project are: to develop a unifying modeling framework applicable at the catchment scale and based on rigorous conservation equations for the study of hydrological processes in the stream channel, land surface, soil, and groundwater components of a river basin; to implement data assimilation methodologies within this modeling framework and for other control models to enable the optimal use of remote sensing, ground-based, and simulation data; to test and apply the models and the data assimilation methods at various catchment scales, including hillslopes and subcatchment of the Ourthe water shed in Belgium and the entire Meuse river basin, one of the major basins in Europe with a drainage area of 33000 km² that comprises the Ourthe

Assessment of different modelling studies on the spatial hydrological processes in an arid alpine catchment

To assess the model description of spatial hydrological processes in the arid alpine catchment, SWAT and MIKE SHE were jointly applied in Yarkant River basin located in northwest China. Not only the simulated daily discharges at the outlet station but also spatiotemporal distributions of runoff, snowmelt and evapotranspiration were analyzed contrastively regarding modules' structure and algorithm. The simulation results suggested both models have their own strengths for particular hydrological processes. For the stream runoff simulation, the significant contributions of lateral interflow flow were only reflected in SWAT with a proportion of 41.4 %, while MIKE SHE simulated a more realistic distribution of base flow from groundwater with a proportion of 21.3 %. In snowmelt calculation, SWAT takes account of more available factors and got better correlations between snowmelt and runoff in temporal distribution, however, MIKE SHE presented clearer spatial distribution of snowpack because of fully distributed structure. In the aspect of water balance, less water was evaporated because of limitation of soil evaporation and less spatially distributed approach in SWAT, on another hand, the spatial distribution of actual evapotranspiration (ETa) in MIKE SHE clearly expressed influence of land use. Whether SWAT or MIKE SHE, without multiple calibrations, the model's limitation might bring in some biased opinions of hydrological processes in a catchment scale. The complementary study of combined results from multiple models could have a better understanding of overall hydrological processes in arid and scarce gauges alpine region

In Pursuit of Improving Peak Flow Prediction in the Canadian Prairies

The prairies were subjected to multiple unprecedented floods over the past decade that caused major damages to agricultural and residential areas. Accurate prediction of the magnitude and timing of floods is important as it is an essential component of flood risk management programs. However, the accuracy of predicting floods and the associated flooding extents have not drawn much attention in the prairies due to difficulties in predicting prairie streamflow in general. Such difficulties are caused, mainly, by the limitations of the currently available modeling approaches in handling the pothole complexities – a dominant feature in prairie watersheds. This thesis focuses on improving the prediction of floods (peak flows), in particular, and the streamflow in general, along with the associated landscape pluvial and nival flooding extents that frequently occur in the complex pothole-dominated environment of the Canadian prairies. This aim is achieved through adapting/developing a set of models that are built and tested for the prairies to contribute to solving the flood prediction problem in the prairies. The first model is a new Hydrological model for the Prairie Region (HYPR), which is proposed as an engineering solution for the prediction of the flood peak in the prairies. HYPR is a modified version of the HBV model, developed by coupling the conceptual HBV model, for hydrological processes representation, and the Probability Distribution Model based RunOFf generation algorithm (PDMROF) for pothole representation. The second model is a novel Prairie Region Inundation MApping model (PRIMA), which is developed as a distributed hydrologic routing model for more accurate and comprehensive storage dynamics simulation and inundation mapping in the prairies. PRIMA uses a set of rules along with Manning’s equation (iteratively) to route the water over the landscape. The third model is the Modelisation Environmentale Communautaire (MEC)—Surface and Hydrology (MESH), which is modified by coupling it with PRIMA to improve the non-contributing area and potholes dynamic representation in complex land surface models for better prediction of peak flows and the associated flooding extents. In this model, called MESH-PRIMA, MESH handles the vertical fluxes calculations based on physically based equations and PRIMA routes the water over the landscape and accounts for the effect of potholes on changing the net runoff reaching the stream network. HYPR shows good simulation of the overall hydrograph and peak flows, on a daily resolution, as indicated by the Nash-Sutcliffe Efficiency (NSE) of 0.72 and NSE for flows over threshold (NSEOT) of 0.78, respectively, averaged over multiple prairie watersheds for the entire simulation period. Although HYPR’s process representation is simple, it shows acceptable simulation of internal hydrologic variables (e.g., accumulated snow on ground) when compared against field measurements. HYPR can be useful when data or computational resources are limited. As for PRIMA, it shows potential for simulating the inundation extents when compared against remote sensing observations of water extents with an accuracy of 85 % averaged over two prairie basins in Saskatchewan, Canada. PRIMA is three to eight times as computationally efficient as the recently developed Wetland DEM Ponding Model (WDPM). The MESH-PRIMA model shows an improved hydrograph and flood simulation on a daily resolution (NSE = 0.55 and NSEOT = 0.60, respectively) compared to the MESH model with its current prairie algorithm (NSE = 0.49 and NSEOT = 0.55, respectively) for the entire simulation period. More importantly, MESH-PRIMA can identify the spatial distribution of water over the landscape and quantify the spatial non-contributing area for different flood events. The proposed models in this thesis can be used for efficient pothole storage dynamics simulation, inundation mapping, streamflow, and peak flow prediction in the prairies. The models can be used for a wide spectrum of hydrologic or hydraulic purposes ranging from limited data, conceptual-lumped-operational mode (e.g., HYPR) to detailed data, physically based research mode (e.g., MESH-PRIMA). These models, especially MESH-PRIMA, improve our understanding of the complexities of the prairie hydrology and the impacts of land depressions on changing the watershed response. More importantly, the methods proposed in MESH-PIMA can be explicitly used in most land-surface schemes within earth system models, allowing for important application in climate change and numerical prediction systems that typically ignore this important prairie phenomenon