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"Hydrologic models"
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Groundwater modelling in arid and semi-arid areas
\"Arid and semi-arid regions face major challenges in the management of scarce freshwater resources under pressures of population, economic development, climate change, pollution and over-abstraction. Groundwater is commonly the most important water resource in these areas. Groundwater models are widely used globally to understand groundwater systems and to guide decisions on management. However, the hydrology of arid and semi-arid areas is very different from that of humid regions, and there is little guidance on the special challenges of groundwater modelling for these areas. This book brings together the experience of internationally-leading experts to fill a gap in the scientific and technical literature. It introduces state-of-the-art methods for modelling groundwater resources, illustrated with a wide-ranging set of illustrative examples from around the world. The book is valuable for researchers, practitioners in developed and developing countries, and graduate students in hydrology, hydrogeology, water resources management, environmental engineering and geography\"-- Provided by publisher.
Book
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integrating PB model outputs with an ML algorithm known as long short-term memory (LSTM) network on their ability to simulate streamflow in 531 catchments representing diverse conditions across the Conterminous United States. Model performance of hybrid models as measured by Nash-Sutcliffe efficiency (NSE) improved relative to standalone PB and LSTM models. More importantly, hybrid models provide highest improvement in catchments where PB models fail completely (i.e. NSE < 0). However, all models performed poorly in catchments with extended low flow periods, suggesting need for additional research.
Journal Article
Depressional wetlands affect watershed hydrological, biogeochemical, and ecological functions
Depressional wetlands of the extensive U.S. and Canadian Prairie Pothole Region afford numerous ecosystem processes that maintain healthy watershed functioning. However, these wetlands have been lost at a prodigious rate over past decades due to drainage for development, climate effects, and other causes. Options for management entities to protect the existing wetlands, and their functions, may focus on conserving wetlands based on spatial location vis-à-vis a floodplain or on size limitations (e.g., permitting smaller wetlands to be destroyed but not larger wetlands). Yet the effects of such management practices and the concomitant loss of depressional wetlands on watershed-scale hydrological, biogeochemical, and ecological functions are largely unknown. Using a hydrological model, we analyzed how different loss scenarios by wetland size and proximal location to the stream network affected watershed storage (i.e., inundation patterns and residence times), connectivity (i.e., streamflow contributing areas), and export (i.e., streamflow) in a large watershed in the Prairie Pothole Region of North Dakota, USA. Depressional wetlands store consequential amounts of precipitation and snowmelt. The loss of smaller depressional wetlands (< 3.0 ha) substantially decreased landscape-scale inundation heterogeneity, total inundated area, and hydrological residence times. Larger wetlands act as hydrologic “gatekeepers,” preventing surface runoff from reaching the stream network, and their modeled loss had a greater effect on streamflow due to changes in watershed connectivity and storage characteristics of larger wetlands. The wetland management scenario based on stream proximity (i.e., protecting wetlands 30 m and ~450 m from the stream) alone resulted in considerable landscape heterogeneity loss and decreased inundated area and residence times. With more snowmelt and precipitation available for runoff with wetland losses, contributing area increased across all loss scenarios. We additionally found that depressional wetlands attenuated peak flows; the probability of increased downstream flooding from wetland loss was also consistent across all loss scenarios. It is evident from this study that optimizing wetland management for one end goal (e.g., protection of large depressional wetlands for flood attenuation) over another (e.g., protecting of small depressional wetlands for biodiversity) may come at a cost for overall watershed hydrological, biogeochemical, and ecological resilience, functioning, and integrity.
Journal Article
Urbanization impacts on flood risks based on urban growth data and coupled flood models
Urbanization increases regional impervious surface area, which generally reduces hydrologic response time and therefore increases flood risk. The objective of this work is to investigate the sensitivities of urban flooding to urban land growth through simulation of flood flows under different urbanization conditions and during different flooding stages. A sub-watershed in Toronto, Canada, with urban land conversion was selected as a test site for this study. In order to investigate the effects of urbanization on changes in urban flood risk, land use maps from six different years (1966, 1971, 1976, 1981, 1986, and 2000) and of six simulated land use scenarios (0%, 20%, 40%, 60, 80%, and 100% impervious surface area percentages) were input into coupled hydrologic and hydraulic models. The results show that urbanization creates higher surface runoff and river discharge rates and shortened times to achieve the peak runoff and discharge. Areas influenced by flash flood and floodplain increases due to urbanization are related not only to overall impervious surface area percentage but also to the spatial distribution of impervious surface coverage. With similar average impervious surface area percentage, land use with spatial variation may aggravate flash flood conditions more intensely compared to spatially uniform land use distribution.
Journal Article
Floods in a changing climate. Extreme precipitation
\"Measurement, analysis and modeling of extreme precipitation events linked to floods is vital in understanding changing climate impacts and variability. This book provides methods for assessment of the trends in these events and their impacts. It also provides a basis to develop procedures and guidelines for climate-adaptive hydrologic engineering. Academic researchers in the fields of hydrology, climate change, meteorology, environmental policy and risk assessment, and professionals and policy-makers working in hazard mitigation, water resources engineering and climate adaptation will find this an invaluable resource. This volume is the first in a collection of four books on flood disaster management theory and practice within the context of anthropogenic climate change. The others are: Floods in a Changing Climate: Hydrological Modeling by P. P. Mujumdar and D. Nagesh Kumar, Floods in a Changing Climate: Inundation Modeling by Giuliano Di Baldassarre and Floods in a Changing Climate: Risk Management by Slodoban Simonoviâc\"-- Provided by publisher.
Book
Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network
Land surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single‐value objective function. To address this problem, we propose a novel Generative Adversarial Network‐based Parameter Optimization (GAN‐PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network‐based surrogate models to make the physical model differentiable, we employ GAN‐PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN‐PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN‐PO excels in maintaining spatial consistency, outperforming the state‐of‐the‐art differentiable parameter learning (dPL) method in terms of model spatial performance. Key Points A novel generative adversarial network‐based parameter estimation method is proposed to calibrate distributed land surface hydrologic models By employing a discriminator to identify model spatial biases, this method contributes to effective and spatially coherent parameter estimation This method can substantially reduce model simulated errors at grid scale and achieve consistent spatial performance
Journal Article
A Deep State Space Model for Rainfall‐Runoff Simulations
The classical way of studying the rainfall‐runoff processes in the water cycle relies on conceptual or physically‐based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in the hydrology community for rainfall‐runoff simulations. However, the decades‐old Long Short‐Term Memory (LSTM) network remains the benchmark for this task, outperforming newer architectures like Transformers. In this work, we propose a State Space Model (SSM), specifically the Frequency Tuned Diagonal State Space Sequence (S4D‐FT) model, for rainfall‐runoff simulations. The proposed S4D‐FT is benchmarked against the established LSTM and a physically‐based Sacramento Soil Moisture Accounting model under in‐sample and out‐of‐sample simulation setups across 531 watersheds in the contiguous United States (CONUS). Results show that S4D‐FT is able to outperform the LSTM model across diverse regions under both simulation setups, especially for regions that feature snowmelt‐driven or intermittent flow regimes. In contrast, S4D‐FT tends to underperform in flashier, high‐magnitude flow regimes, likely due to its global state‐space convolution computation that emphasizes slow, storage‐driven dynamics, which makes it less effective at picking up short bursts and noisy spikes in the data. In summary, our pioneering introduction of the S4D‐FT for rainfall‐runoff simulations challenges the dominance of LSTM in the hydrology community and expands the arsenal of DL tools available for hydrological modeling.
Journal Article