scholarly journals Numerical analysis of coupled hydrosystems based on an object-oriented compartment approach

2008 ◽  
Vol 10 (3) ◽  
pp. 227-244 ◽  
Author(s):  
Olaf Kolditz ◽  
Jens-Olaf Delfs ◽  
Claudius Bürger ◽  
Martin Beinhorn ◽  
Chan-Hee Park
Keyword(s):  
Numerical Solution ◽  
Land Surface ◽  
Overland Flow ◽  
Drainage Basin ◽  
Spatial Scales ◽  
Shallow Water Equation ◽  
Object Oriented ◽  
Richards Equation ◽  
Saturated Flow ◽  
Flow Processes

In this paper we present an object-oriented concept for numerical simulation of multi-field problems for coupled hydrosystem analysis. Individual (flow) processes modelled by a particular partial differential equation, i.e. overland flow by the shallow water equation, variably saturated flow by the Richards equation and saturated flow by the groundwater flow equation, are identified with their corresponding hydrologic compartments such as land surface, vadose zone and aquifers, respectively. The object-oriented framework of the compartment approach allows an uncomplicated coupling of these existing flow models. After a brief outline of the underlying mathematical models we focus on the numerical modelling and coupling of overland flow, variably saturated and groundwater flows via exchange flux terms. As each process object is associated with its own spatial discretisation mesh, temporal time-stepping scheme and appropriate numerical solution procedure. Flow processes in hydrosystems are coupled via their compartment (or process domain) boundaries without giving up the computational necessities and optimisations for the numerical solution of each individual process. However, the coupling requires a bridging of different temporal and spatial scales, which is solved here by the integration of fluxes (spatially and temporally). In closing we present three application examples: a benchmark test for overland flow on an infiltrating surface and two case studies – at the Borden site in Canada and the Beerze–Reusel drainage basin in the Netherlands.

Review of numerical solution of Richardson–Richards equation for variably saturated flow in soils

2019 ◽  
Vol 6 (5) ◽  
Author(s):  
Yuanyuan Zha ◽  
Jinzhong Yang ◽  
Jicai Zeng ◽  
Chak‐Hau M. Tso ◽  
Wenzhi Zeng ◽  
...  
Keyword(s):  
Numerical Solution ◽  
Richards Equation ◽  
Saturated Flow

scholarly journals A Conjunctive Surface–Subsurface Flow Representation for Mesoscale Land Surface Models

2013 ◽  
Vol 14 (5) ◽  
pp. 1421-1442 ◽  
Author(s):  
Hyun Il Choi ◽  
Xin-Zhong Liang ◽  
Praveen Kumar
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Moisture Transport ◽  
Overland Flow ◽  
Spatial Scales ◽  
Subsurface Flow ◽  
Land Surface Models ◽  
Local Soil ◽  
Common Land Model ◽  
Surface Models

Abstract Most current land surface models used in regional weather and climate studies capture soil-moisture transport in only the vertical direction and are therefore unable to capture the spatial variability of soil moisture and its lateral transport. They also implement simplistic surface runoff estimation from local soil water budget and ignore the role of surface flow depth on the infiltration rate, which may result in significant errors in the terrestrial hydrologic cycle. To address these issues, this study develops and describes a conjunctive surface–subsurface flow (CSSF) model that comprises a 1D diffusion wave model for surface (overland) flow fully interacted with a 3D volume-averaged soil-moisture transport model for subsurface flow. The proposed conjunctive flow model is targeted for mesoscale climate application at relatively large spatial scales and coarse computational grids as compared to the traditional coupled surface–subsurface flow scheme in a typical basin. The CSSF module is substituted for the existing 1D scheme in the common land model (CoLM) and the performance of this hydrologically enhanced version of the CoLM (CoLM+CSSF) is evaluated using a set of offline simulations for catchment-scale basins around the Ohio Valley region. The CoLM+CSSF simulations are explicitly implemented at the same resolution of the 30-km grids as the target regional climate models to avoid downscaling and upscaling exchanges between atmospheric forcings and land responses. The results show that the interaction between surface and subsurface flow significantly improves the stream discharge prediction crucial to the terrestrial water and energy budget.

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

Bi-directional Coupling of an Unstructured Triangular Meshes-based Integrated Hydrodynamic Model for Heterogeneous Feature-based Urban Flood Simulation

2021 ◽  
Author(s):  
Guoqiang Peng ◽  
Zhuo Zhang ◽  
Tian Zhang ◽  
Zhiyao Song ◽  
Arif Masrur
Keyword(s):  
Water Flow ◽  
Urban Areas ◽  
Overland Flow ◽  
Coupled Model ◽  
Flood Simulation ◽  
Urban Flood ◽  
Essential Condition ◽  
Flow Processes ◽  
Feature Based ◽  
Heterogeneous Feature

Abstract Urban pluvial flash floods have become a matter of widespread concern, as they severely impact people’s lives in urban areas. Hydrological and hydraulic models have been widely used for urban flood management and urban planning. Traditionally, to reduce the complexity of urban flood modelling and simulations, simplification or generalization methods have been used; for example, some models focus on the simulation of overland water flow, and some models focus on the simulation of the water flow in sewer systems. However, the water flow of urban floods includes both overland flow and sewer system flow. The overland flow processes are impacted by many different geographical features in what is an extremely spatially heterogeneous environment. Therefore, this article is based on two widely used models (SWMM and ANUGA) that are coupled to develop a bi-directional method of simulating water flow processes in urban areas. The open source overland flow model uses the unstructured triangular as the spatial discretization scheme. The unstructured triangular-based hydraulic model can be better used to capture the spatial heterogeneity of the urban surfaces. So, the unstructured triangular-based model is an essential condition for heterogeneous feature-based urban flood simulation. The experiments indicate that the proposed coupled model in this article can accurately depict surface waterlogged areas and that the heterogeneous feature-based urban flood model can be used to determine different types of urban flow processes.

Structural, Hydrologic, and Sediment Connectivity in a Shrub-Encroached and Restored Semiarid Grassland

2021 ◽  
Author(s):  
Justin Johnson ◽  
Jason Williams ◽  
Phillip Guertin ◽  
Steven Archer ◽  
Philip Heilman ◽  
...  
Keyword(s):  
Sediment Yield ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Overland Flow ◽  
Spatial Scales ◽  
Structural Connectivity ◽  
Post Treatment ◽  
Semiarid Grassland ◽  
Concentrated Flow ◽  
Shrub Canopy

<p>Shrub encroachment of semiarid grasslands is influenced by connected runoff and erosion patterns that preferentially accumulate resources under vegetated patches (canopy microsites) and deplete interspaces. Soil loss from dryland hillslopes results when areas of bare ground become structurally and functionally connected through overland flow. Although these patterns have been well-described, uncertainty remains regarding how these feedbacks respond to restoration practices. This study compared the structure and hydrologic function of a shrub-encroached semiarid grassland treated five years prior with the herbicide, tebuthiuron, to that of an adjacent untreated grassland. Through a series of hydrologic experiments conducted at increasing spatial scales, vegetation and soil structural patterns were related to runoff and erosion responses. At a fine scale (0.5 m<sup>2</sup>), rainfall simulations (120 mm·h<sup>-1</sup> rainfall intensity; 45 min) showed herbicided shrub canopy microsites had greater infiltration capacities (105 and 71 mm·h<sup>-1</sup> terminal infiltration rates) and were less susceptible to splash-sheet erosion (3 and 26 g sediment yield) than untreated shrub canopy microsites, while interspaces were statistically comparable between study sites. Concentrated flow simulations at a coarse scale (~9 m<sup>2</sup>) revealed that gaps between the bases of vegetation (i.e. basal gaps) > 2 m<sup></sup>were positively related to both concentrated flow runoff (r = 0.72, p = 0.008) and sediment yield (r = 0.70, p = 0.012). Modeled hillslope-scale (50 m<sup>2</sup>) runoff and erosion (120 mm·h<sup>-1</sup> rainfall intensity; 45 min) indicated less soil loss in the tebuthiuron-treated site (1.78 Mg·ha<sup>-1</sup> tebuthiuron; 3.19 Mg·ha<sup>-1</sup> untreated), even though runoff was similar between sites. Our results suggest interspaces in shrub-encroached grasslands continue to be runoff sources following herbicide-induced shrub mortality and may be indicators of runoff responses at larger spatial scales. In contrast, sediment sources are limited post-treatment due to lesser sediment detachment from sheet-splash and concentrated flow processes. Reduced sediment supplies provide evidence that connectivity feedbacks that sustain a shrub-dominant ecological state may have been dampened post-treatment. Our study also highlights the utility of simple measures of structural connectivity, such as basal gaps, as an indicator of hillslope susceptibility to increased runoff and erosion.</p>

scholarly journals The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

2018 ◽  
Vol 22 (9) ◽  
pp. 4649-4665 ◽  
Author(s):  
Anouk I. Gevaert ◽  
Ted I. E. Veldkamp ◽  
Philip J. Ward
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Propagation Time ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Surface Models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.

scholarly journals Global downscaling of remotely sensed soil moisture using neural networks

2018 ◽  
Vol 22 (10) ◽  
pp. 5341-5356 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatiotemporal Scales ◽  
Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

scholarly journals Modified Richards’ Equation to Improve Estimates of Soil Moisture in Two-Layered Soils after Infiltration

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1174 ◽  
Author(s):  
Honglin Zhu ◽  
Tingxi Liu ◽  
Baolin Xue ◽  
Yinglan A. ◽  
Guoqiang Wang
Keyword(s):  
Soil Moisture ◽  
Overland Flow ◽  
Hydrological Cycle ◽  
Soil Depth ◽  
Controlling Factors ◽  
Moisture Distribution ◽  
Richards Equation ◽  
Infiltration Process ◽  
Soil Moisture Distribution

Soil moisture distribution plays a significant role in soil erosion, evapotranspiration, and overland flow. Infiltration is a main component of the hydrological cycle, and simulations of soil moisture can improve infiltration process modeling. Different environmental factors affect soil moisture distribution in different soil layers. Soil moisture distribution is influenced mainly by soil properties (e.g., porosity) in the upper layer (10 cm), but by gravity-related factors (e.g., slope) in the deeper layer (50 cm). Richards’ equation is a widely used infiltration equation in hydrological models, but its homogeneous assumptions simplify the pattern of soil moisture distribution, leading to overestimates. Here, we present a modified Richards’ equation to predict soil moisture distribution in different layers along vertical infiltration. Two formulae considering different controlling factors were used to estimate soil moisture distribution at a given time and depth. Data for factors including slope, soil depth, porosity, and hydraulic conductivity were obtained from the literature and in situ measurements and used as prior information. Simulations were compared between the modified and the original Richards’ equations and with measurements taken at different times and depths. Comparisons with soil moisture data measured in situ indicated that the modified Richards’ equation still had limitations in terms of reproducing soil moisture in different slope positions and rainfall periods. However, compared with the original Richards’ equation, the modified equation estimated soil moisture with spatial diversity in the infiltration process more accurately. The equation may benefit from further solutions that consider various controlling factors in layers. Our results show that the proposed modified Richards’ equation provides a more effective approach to predict soil moisture in the vertical infiltration process.

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