Ecological models at fish community and species level to support effective river restoration

RESUMEN Los peces nativos son indicadores de la salud de los ecosistemas acuáticos, y se han convertido en un elemento de calidad clave para evaluar el estado ecológico de los ríos. La comprensión de los factores que afectan a las especies nativas de peces es importante para la gestión y conservación de los ecosistemas acuáticos. El objetivo general de esta tesis es analizar las relaciones entre variables biológicas y de hábitat (incluyendo la conectividad) a través de una variedad de escalas espaciales en los ríos Mediterráneos, con el desarrollo de herramientas de modelación para apoyar la toma de decisiones en la restauración de ríos. Esta tesis se compone de cuatro artículos. El primero tiene como objetivos modelar la relación entre un conjunto de variables ambientales y la riqueza de especies nativas (NFSR), y evaluar la eficacia de potenciales acciones de restauración para mejorar la NFSR en la cuenca del río Júcar. Para ello se aplicó un enfoque de modelación de red neuronal artificial (ANN), utilizando en la fase de entrenamiento el algoritmo Levenberg-Marquardt. Se aplicó el método de las derivadas parciales para determinar la importancia relativa de las variables ambientales. Según los resultados, el modelo de ANN combina variables que describen la calidad de ribera, la calidad del agua y el hábitat físico, y ayudó a identificar los principales factores que condicionan el patrón de distribución de la NFSR en los ríos Mediterráneos. En la segunda parte del estudio, el modelo fue utilizado para evaluar la eficacia de dos acciones de restauración en el río Júcar: la eliminación de dos azudes abandonados, con el consiguiente incremento de la proporción de corrientes. Estas simulaciones indican que la riqueza aumenta con el incremento de la longitud libre de barreras artificiales y la proporción del mesohabitat de corriente, y demostró la utilidad de las ANN como una poderosa herramienta para apoyar la toma de decisiones en el manejo y restauración ecológica de los ríos Mediterráneos. El segundo artículo tiene como objetivo determinar la importancia relativa de los dos principales factores que controlan la reducción de la riqueza de peces (NFSR), es decir, las interacciones entre las especies acuáticas, variables del hábitat (incluyendo la conectividad fluvial) y biológicas (incluidas las especies invasoras) en los ríos Júcar, Cabriel y Turia. Con este fin, tres modelos de ANN fueron analizados: el primero fue construido solamente con variables biológicas, el segundo se construyó únicamente con variables de hábitat y el tercero con la combinación de estos dos grupos de variables. Los resultados muestran que las variables de hábitat son los ¿drivers¿ más importantes para la distribución de NFSR, y demuestran la importancia ecológica de los modelos desarrollados. Los resultados de este estudio destacan la necesidad de proponer medidas de mitigación relacionadas con la mejora del hábitat (incluyendo la variabilidad de caudales en el río) como medida para conservar y restaurar los ríos Mediterráneos. El tercer artículo busca comparar la fiabilidad y relevancia ecológica de dos modelos predictivos de NFSR, basados en redes neuronales artificiales (ANN) y random forests (RF). La relevancia de las variables seleccionadas por cada modelo se evaluó a partir del conocimiento ecológico y apoyado por otras investigaciones. Los dos modelos fueron desarrollados utilizando validación cruzada k-fold y su desempeño fue evaluado a través de tres índices: el coeficiente de determinación (R2 ), el error cuadrático medio (MSE) y el coeficiente de determinación ajustado (R2 adj). Según los resultados, RF obtuvo el mejor desempeño en entrenamiento. Pero, el procedimiento de validación cruzada reveló que ambas técnicas generaron resultados similares (R2 = 68% para RF y R2 = 66% para ANN). La comparación de diferentes métodos de machine learning es muy útil para el análisis crítico de los resultados obtenidos a través de los modelos. El cuarto artículo tiene como objetivo evaluar la capacidad de las ANN para identificar los factores que afectan a la densidad y la presencia/ausencia de Luciobarbus guiraonis en la demarcación hidrográfica del Júcar. Se utilizó una red neuronal artificial multicapa de tipo feedforward (ANN) para representar relaciones no lineales entre descriptores de L. guiraonis con variables biológicas y de hábitat. El poder predictivo de los modelos se evaluó con base en el índice Kappa (k), la proporción de casos correctamente clasificados (CCI) y el área bajo la curva (AUC) característica operativa del receptor (ROC). La presencia/ausencia de L. guiraonis fue bien predicha por el modelo ANN (CCI = 87%, AUC = 0.85 y k = 0.66). La predicción de la densidad fue moderada (CCI = 62%, AUC = 0.71 y k = 0.43). Las variables más importantes que describen la presencia/ausencia fueron: radiación solar, área de drenaje y la proporción de especies exóticas de peces con un peso relativo del 27.8%, 24.53% y 13.60% respectivamente. En el modelo de densidad, las variables más importantes fueron el coeficiente de variación de los caudales medios anuales con una importancia relativa del 50.5% y la proporción de especies exóticas de peces con el 24.4%. Los modelos proporcionan información importante acerca de la relación de L. guiraonis con variables bióticas y de hábitat, este nuevo conocimiento podría utilizarse para apoyar futuros estudios y para contribuir en la toma de decisiones para la conservación y manejo de especies en los en los ríos Júcar, Cabriel y Turia.Olaya Marín, EJ. (2013). Ecological models at fish community and species level to support effective river restoration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/28853TESI

EVALUATING THE PERFORMANCE OF PROCESS-BASED AND MACHINE LEARNING MODELS FOR RAINFALL-RUNOFF SIMULATION WITH APPLICATION OF SATELLITE AND RADAR PRECIPITATION PRODUCTS

Hydrology Modeling using HEC-HMS (Hydrological Engineering Centre-Hydrologic Modeling System) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, Machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research\u27s high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. In addition, Point gauge observations have historically been the primary source of the necessary rainfall data for hydrologic models. However, point gauge observation does not provide accurate information on rainfall\u27s spatial and temporal variability, which is vital for hydrological models. Therefore, this study also evaluates the performance of satellite and radar precipitation products for hydrological analysis. The results revealed that integrated Machine Learning and physical-based model could provide more confidence in rainfall-runoff and flood depth prediction. Similarly, the study revealed that radar data performance was superior to the gauging station\u27s rainfall data for the hydrologic analysis in large watersheds. The discussions in this research will encourage researchers and system managers to improve current rainfall-runoff simulation models by application of Machine learning and radar rainfall data

Dynamics Of Flood Flow In Red River Basin

In recent decades, flooding has become a major issue in many areas of the Upper Midwest. Many rivers and streams in the region had considerable increases in mean annual peak flows during this period, which was driven by a combination of natural factors including discharge synchrony with the spring thaw, ice jams, glacial lake plain, and a decrease in gradient downstream. The Red River of the North is a prominent river in the United States and Canada\u27s Upper Midwest. It flows from its headwaters in Minnesota and North Dakota to Lake Winnipeg in Manitoba. The river is well-known for its spring floods, which can cause havoc on communities along its banks. There is an increasing need to improve the characterization and identification of precursors in the Red River basin that affect the hydrological conditions that cause spring snowmelt floods and improve predictions to reduce Red River flood damage. This dissertation has developed different research that concerns the dynamics of floods in the Red River basin by integrating hydrological, hydraulic, and machine-learning models. The primary objectives were to improve flood prediction accuracy by deriving the parameters of the Muskingum Routing method using discharge measurements obtained by an Autonomous Surface Vehicle, to predict scour potential of the river through HEC-RAS modeling, and to provide an estimate of the flood progression downstream based on the flow characteristics. The study also compared the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM) algorithms for flood prediction. Additionally, the research investigated the surface water area variation and response to wet and dry seasons across the entire Red River basin, which can inform the development of effective flood mitigation strategies. The results of this study contributed to a better understanding of flood control strategies in the Red River Basin and helped to inform policy decisions related to flood mitigation in the region. Ultimately, this research aimed to understand the complex dynamics of the RRB and derive hydrological and hydraulic models that could help to improve flood prediction. The first research developed a linear and nonlinear Muskingum model with lateral inflows for flood routing in the Red River Basin using Salp Swarm Algorithm (SSA). The distributed Muskingum model is introduced to improve the accuracy and efficiency of the calculations. The study focuses on developing a linear and nonlinear Muskingum model for the Grand Forks and Drayton USGS stations deriving the parameters of the Muskingum Routing method using discharge measurements based on spatial variable exponent parameters. The suggested approach minimizes the Sum of Square Errors (SSE) between observed and routed outflows. The results show for an icy river like Red River, the Muskingum method proposed is a convenient way to predict outflow hydrographs caused by snowmelt. The second study improved flood inundation mapping accuracy in flood-prone rivers, such as the Red River of the North, by using simulation tools in HEC-RAS for flood modeling and determining Manning\u27s n coefficient. An Autonomous Surface Vehicle (ASV) was used to collect bathymetry and discharge data, including a flood event with a 16.5-year return period in 2022. The results showed that Manning\u27s n-coefficient of 0.07 and 0.15 for the channel and overbanks, respectively, agreed well with the observed and simulated water level values under steady flow conditions. The study also demonstrated the efficiency of using ASVs for flood mapping and examined the scour potential and any local scour development in the streambed near the bridge piers. The third study of this dissertation used hourly level records from three USGS stations to evaluate water level predictions using three methods: SARIMA, RF, and LSTM. The LSTM method outperformed the other methods, demonstrating high precision for flood water level prediction. The results showed that the LSTM method was a reliable choice for predicting flood water levels up to one week in advance. This study contributes to the development of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. This last study focused on the spatiotemporal dynamics of surface water area in the Red River Basin (RRB) by using a high-resolution global surface water dataset to investigate the changes in surface water extent from 1990 to 2019. The results showed that there were four distinct phases of variation in surface water: wetting (1990-2001), dry (2002-2005), recent wetting (2006-2013), and recent drying (2014-2019). The transition from bare land to permanent and seasonal water area was observed during the wetting phase, while the other phases experienced relatively little fluctuation. Overall, this study contributes to a better understanding of the spatiotemporal variation of surface water area in the RRB and provides insights into the impact of recent wetting and drying periods on the lakes and wetlands of the RRB

Tools for Assessing Climate Impacts on Fish and Wildlife

Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management

Scientific knowledge and scientific uncertainty in bushfire and flood risk mitigation: literature review

EXECUTIVE SUMMARY The Scientific Diversity, Scientific Uncertainty and Risk Mitigation Policy and Planning (RMPP) project aims to investigate the diversity and uncertainty of bushfire and flood science, and its contribution to risk mitigation policy and planning. The project investigates how policy makers, practitioners, courts, inquiries and the community differentiate, understand and use scientific knowledge in relation to bushfire and flood risk. It uses qualitative social science methods and case studies to analyse how diverse types of knowledge are ordered and judged as salient, credible and authoritative, and the pragmatic meaning this holds for emergency management across the PPRR spectrum. This research report is the second literature review of the RMPP project and was written before any of the case studies had been completed. It synthesises approximately 250 academic sources on bushfire and flood risk science, including research on hazard modelling, prescribed burning, hydrological engineering, development planning, meteorology, climatology and evacuation planning. The report also incorporates theoretical insights from the fields of risk studies and science and technology studies (STS), as well as indicative research regarding the public understandings of science, risk communication and deliberative planning. This report outlines the key scientific practices (methods and knowledge) and scientific uncertainties in bushfire and flood risk mitigation in Australia. Scientific uncertainties are those ‘known unknowns’ and ‘unknown unknowns’ that emerge from the development and utilisation of scientific knowledge. Risk mitigation involves those processes through which agencies attempt to limit the vulnerability of assets and values to a given hazard. The focus of this report is the uncertainties encountered and managed by risk mitigation professionals in regards to these two hazards, though literature regarding natural sciences and the scientific method more generally are also included where appropriate. It is important to note that while this report excludes professional experience and local knowledge from its consideration of uncertainties and knowledge, these are also very important aspects of risk mitigation which will be addressed in the RMPP project’s case studies. Key findings of this report include: Risk and scientific knowledge are both constructed categories, indicating that attempts to understand any individual instance of risk or scientific knowledge should be understood in light of the social, political, economic, and ecological context in which they emerge. Uncertainty is a necessary element of scientific methods, and as such risk mitigation practitioners and researchers alike should seek to ‘embrace uncertainty’ (Moore et al., 2005) as part of navigating bushfire and flood risk mitigation

Predicting Landslides in Costa Rica Using Self-Organizing Map Machine Learning

Landslides are natural hazards commonly understated in both number of occurrences and cost of economic impacts. The Costa Rican terrain is predominately geologically young and therefore, severely impacted by landslides. It has limited resources and infrastructure and with a large portion of the population being poor, this causes communities to build in hazardous locations and infrastructure that can be easily crippled by landslides. Being able to identify where and when landslides are going to occur is key to mitigating the effects, either by stabilizing the slope or by evacuating communities. Machine learning is one method that has been increasingly used to monitor and predict landslides in recent times. These methods do not have the shortcomings of traditional analytical methods and can be easily adapted for different locations, changing or missing data, and number of factors studied. This research proposes that Self Organizing Maps (SOM) can be used as a versatile and effective method for landslide prediction. The results of this study have shown how SOM can be used for multi scale susceptibility analysis and for prediction with use of precipitation data, by producing significant results identifying high risk areas with a varying number and combination of variables. It has also shown that when precipitation data is used, it can identify high risk locations based on precipitation amounts and static variables (slope, TWI, curvature, NDVI, etc.). At the five-time scales tested, four of the tests produced correlations between increased precipitation and higher landslides risk (6 hour r2 = 0.38, 12 hour r2 = 0.36, 1 day r2 = 0.24, 1 month r2 = 0.33). This study has shown the versatility and effectiveness of SOM by producing significant results, as well as being able to use current weather conditions to produce landslide prediction analysis