HydroBlocks: a field‐scale resolving land surface model for application over continental extents

Land surface spatial heterogeneity plays a significant role in the water, energy, and carbon cycles over a range of temporal and spatial scales. Until now, the representation of this spatial heterogeneity in land surface models has been limited to over simplistic schemes because of computation and environmental data limitations. This study introduces HydroBlocks – a novel land surface model that represents field‐scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). HydroBlocks is a coupling between the Noah‐MP land surface model and the Dynamic TOPMODEL hydrologic model. The HRUs are defined by clustering proxies of the drivers of spatial heterogeneity using high‐resolution land data. The clustering mechanism allows for each HRU's results to be mapped out in space, facilitating field‐scale application and validation. The Little Washita watershed in the USA is used to assess HydroBlocks' performance and added benefit from traditional land surface models. A comparison between the semi‐distributed and fully distributed versions of the model suggests that using 1000 HRUs is sufficient to accurately approximate the fully distributed solution. A preliminary evaluation of model performance using available in situ soil moisture observations suggests that HydroBlocks is generally able to reproduce the observed spatial and temporal dynamics of soil moisture. Model performance deficiencies can be primarily attributed to parameter uncertainty. HydroBlocks' ability to explicitly resolve field‐scale spatial heterogeneity while only requiring an increase in computation of one to two orders of magnitude when compared with existing land surface models is encouraging – ensemble field‐scale land surface modelling over continental extents is now possible. Copyright © 2016 John Wiley & Sons, Ltd.     

Incorporating probabilistic approach into local multi-criteria decision analysis for flood susceptibility assessment

Flood risk management can be enhanced by integrating geographic information system (GIS) with multi-criteria decision analysis (MCDA). However, the conventional, deterministic MCDA methods ignore uncertainty in the decision-making process and fail to account for local variability in criteria values and preferences. Therefore, a spatially explicit MCDA model which effectively incorporates spatial heterogeneity is required. In this paper, a probabilistic or stochastic MCDA method which incorporates the uncertainty into a local weighted linear combination (WLC) was utilized to evaluate flood susceptibility; and an application case in Gucheng County, Central China, was developed. A GIS database of geomorphological and hydro-meteorological criteria contributing to flood susceptibility analysis was constructed using six conditioning factors: digital elevation model (DEM), slope (SL), maximum three-day precipitation (M3DP), topographic wetness index (TWI), distance from the river (DR), and Soil Conservation Service Curve Number (SCS-CN). The results of local WLC were compared with those of the global WLC. It shows that the local WLC model can provide much more valuable information about the spatial patterns of criterion values, ranges, weights, trade-offs and overall scores, whereas the global WLC can only depict the spatial distribution of criterion values and overall scores. The local WLC can also help to prioritize the most susceptible locations within a neighborhood when navigating the disaster assistance process. Moreover, the uncertainty analysis of criteria weights increases the degree of confidence in the model output. It is concluded that the presented approach can provide more insights and understanding of the nature of the flood susceptibility than global WLC.     

Impact of lateral flow and spatial scaling on the simulation of semi‐arid urban land surfaces in an integrated hydrologic and land surface model

Understanding and representing hydrologic fluxes in the urban environment is challenging because of fine scale land cover heterogeneity and lack of coherent scaling relationships. Here, the impact of urban land cover heterogeneity, scale, and configuration on the hydrologic and surface energy budget (SEB) is assessed using an integrated, coupled land surface/hydrologic model at high spatial resolutions. Archetypes of urban land cover are simulated at varying resolutions using both the National Land Cover Database (NLCD; 30 m) and an ultra high‐resolution land cover dataset (0.6 m). The analysis shows that the impact of highly organized, yet heterogeneous, land cover typical of the urban domain can cause large variations in hydrologic and energy fluxes within areas of similar land cover. The lateral flow processes that occur within each simulation create variations in overland flow of up to ±200% and ±4% in evapotranspiration. The impact on the SEB is smaller and largely restricted to the wet season for our semi‐arid forcing scenarios. Finally, we find that this seasonal bias, predominantly caused by lateral flow, is displaced by a systematic diurnal bias at coarser resolutions caused by deficiencies in the method used for scaling of land surface and hydrologic parameters. As a result of this research, we have produced land surface parameters for the widely used NLCD urban land cover types. This work illustrates the impact of processes that remain unrepresented in traditional high‐resolutions land surface models and how they may affect results and uncertainty in modeling of local water resources and climate. Copyright © 2015 John Wiley & Sons, Ltd.     

Statistical scale-up of 3D particle-tracking simulation for non-Fickian dispersive solute transport modeling

Numerical techniques for subsurface flow and transport modeling are often limited by computational limitations including fine mesh and small time steps to control artificial dispersion. Particle-tracking simulation offers a robust alternative for modeling solute transport in subsurface formations. However, the modeling scale usually differs substantially from the rock measurement scale, and the scale-up of measurements have to be made accounting for the pattern of spatial heterogeneity exhibited at different scales. Therefore, it is important to construct accurate coarse-scale simulations that are capable of capturing the uncertainties in reservoir and transport attributes due to scale-up. A statistical scale-up procedure developed in our previous work is extended by considering the effects of unresolved (residual) heterogeneity below the resolution of the finest modeling scale in 3D. First, a scale-up procedure based on the concept of volume variance is employed to construct realizations of permeability and porosity at the (coarse) transport modeling scale, at which flow or transport simulation is performed. Next, to compute various effective transport parameters, a series of realizations exhibiting detailed heterogeneities at the fine scale, whose domain size is the same as the transport modeling scale, are generated. These realizations are subjected to a hybrid particle-tracking simulation. Probabilistic transition time is considered, borrowing the idea from the continuous time random walk (CTRW) technique to account for any sub-scale heterogeneity at the fine scale level. The approach is validated against analytical solutions and general CTRW formulation. Finally, coarse-scale transport variables (i.e., dispersivities and parameterization of transition time distribution) are calibrated by minimizing the mismatch in effluent history with the equivalent averaged models. Construction of conditional probability distributions of effective parameters is facilitated by integrating the results over the entire suite of realizations. The proposed method is flexible, as it does not invoke any explicit assumption regarding the multivariate distribution of the heterogeneity. In contrast to other hierarchical CTRW formulation for modeling multi-scale heterogeneities, the proposed approach does not impose any length scale requirement regarding sub-grid heterogeneities. In fact, it aims to capture the uncertainty in effective reservoir and transport properties due to the presence of heterogeneity at the intermediate scale, which is larger than the finest resolution of heterogeneity but smaller than the representative elementary volume, but it is often comparable to the transport modeling scale.     

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.     

A review of continental scale hydrological models and their suitability for drought forecasting in (sub-Saharan) Africa

The aim of this review is to provide a basis for selecting a suitable hydrological model, or combination of models, for hydrological drought forecasting in Africa at different temporal and spatial scales; for example short and medium range (1–10 days or monthly) forecasts at medium to large river basin scales or seasonal forecasts at the Pan-African scale. Several global hydrological models are currently available with different levels of complexity and data requirements. However, most of these models are likely to fail to properly represent the water balance components that are particularly relevant in arid and semi-arid basins in sub-Saharan Africa. This review critically looks at weaknesses and strengths in the representation of different hydrological processes and fluxes of each model. The major criteria used for assessing the suitability of the models are (1) the representation of the processes that are most relevant for simulating drought conditions, such as interception, evaporation, surface water-groundwater interactions in wetland areas and flood plains and soil moisture dynamics; (2) the capability of the model to be downscaled from a continental scale to a large river basin scale model; and (3) the applicability of the model to be used operationally for drought early warning, given the data availability of the region. This review provides a framework for selecting models for hydrological drought forecasting, conditional on spatial scale, data availability and end-user forecast requirements. Among 16 well known hydrological and land surface models selected for this review, PCR-GLOBWB, GWAVA, HTESSEL, LISFLOOD and SWAT show higher potential and suitability for hydrological drought forecasting in Africa based on the criteria used in this evaluation.     

基于ANSYS的新型乡村抗震防灾适宜性规划模型分析

当前新型乡村抗震防灾适宜性规划分析中通常采用地质分区方法对勘测点进行分析,在分析过程中忽略了GIS空间的复杂性,且未对评价指标加权分析,导致抗震适宜性评价指标量化过程过于主观,存在计算结果与实际结果拟合度低的问题。据此,提出基于ANSYS的新型乡村抗震防灾适宜性规划模型分析。考虑到GIS的空间复杂性,采用ANSYS在GIS空间进行有限元结构场修正操作,结合Logistic非线性回归模型,对乡村土地抗震防灾适宜性规划中的二分类变量数据进行非线性回归分析。为了防止计算数值过于主观,采用组合熵系数模型对Logistic方程计算得来的评价指标加权,由此完成基于ANSYS的新型乡村抗震防灾适宜性规划模型分析。经过实例分析证明,所提方法求出的计算结果与实际结果拟合度较高,能成功完成评价指标的量化,对乡村抗震防灾适宜性规划分析更加客观。     

Model structure and uncertainty for stochastic non-point source modelling applications

Abstract

The uncertainty associated with a rainfall–runoff and non-point source loading (NPS) model can be attributed to both the parameterization and model structure. An interesting implication of the areal nature of NPS models is the direct relationship between model structure (i.e. sub-watershed size) and sample size for the parameterization of spatial data. The approach of this research is to find structural limitations in scale for the use of the conceptual NPS model, then examine the scales at which suitable stochastic depictions of key parameter sets can be generated. The overlapping regions are optimal (and possibly the only suitable regions) for conducting meaningful stochastic analysis with a given NPS model. Previous work has sought to find optimal scales for deterministic analysis (where, in fact, calibration can be adjusted to compensate for sub-optimal scale selection); however, analysis of stochastic suitability and uncertainty associated with both the conceptual model and the parameter set, as presented here, is novel; as is the strategy of delineating a watershed based on the uncertainty distribution. The results of this paper demonstrate a narrow range of acceptable model structure for stochastic analysis in the chosen NPS model. In the case examined, the uncertainties associated with parameterization and parameter sensitivity are shown to be outweighed in significance by those resulting from structural and conceptual decisions.

Citation Parker, G. T. Rennie, C. D. & Droste, R. L. (2011) Model structure and uncertainty for stochastic non-point source modelling applications. Hydrol. Sci. J. 56(5), 870–882.     

Hydrological modelling uncertainty analysis for different flow quantiles: a case study in two hydro-geographically different watersheds

This study focuses on analysis of hydrological model parameter uncertainty at varying sub-basin spatial scales. It was found that the variation in sub-basin spatial scale had little influence on the entire flow simulations. However, the different sub-basin spatial scales had a significant impact on the reproduction of the flow quantiles. The coarser sub-basin spatial scale provided a better coverage of most prediction uncertainty in observations. However, the finer sub-basin spatial scale produced the best single simulation output closer to the observations. In general, the optimal sub-basin spatial scales (ratio to the entire watershed size) in the two test watersheds were found to be in the ranges 14–19% and 2–4% for good simulation of high and low flows, respectively. It is therefore worthwhile to put more effort into reproducing different flow quantiles by investigating an appropriate sub-basin spatial scale.     

Critical Sensitivity and Trans-scale Fluctuations in Catastrophic Rupture

-- Rupture in the heterogeneous crust appears to be a catastrophe transition. Catastrophic rupture sensitively depends on the details of heterogeneity and stress transfer on multiple scales. These are difficult to identify and deal with. As a result, the threshold of earthquake-like rupture presents uncertainty. This may be the root of the difficulty of earthquake prediction. Based on a coupled pattern mapping model, we represent critical sensitivity and trans-scale fluctuations associated with catastrophic rupture. Critical sensitivity means that a system may become significantly sensitive near catastrophe transition. Trans-scale fluctuations mean that the level of stress fluctuations increases strongly and the spatial scale of stress and damage fluctuations evolves from the mesoscopic heterogeneity scale to the macroscopic scale as the catastrophe regime is approached. The underlying mechanism behind critical sensitivity and trans-scale fluctuations is the coupling effect between heterogeneity and dynamical nonlinearity. Such features may provide clues for prediction of catastrophic rupture, like material failure and great earthquakes. Critical sensitivity may be the physical mechanism underlying a promising earthquake forecasting method, the load-unload response ratio (LURR).     

Multi-scale heterogeneity of soil moisture following snow thawing in Haloxylon ammodendron shrubland

Spatial variation of soil moisture after snow thawing in South Gurbantunggut was quantitatively studied using ANOVA and geostatistics at various scales. The results show that the soil moisture heterogeneity varies along with spatial scales. At the shrub individual scale, there is a gradient in soil moisture from shrub-canopied area to canopy margin and to the interspaces between shrubs. At the community scale, soil moisture is highly autocorrelated and the semivariogram is fitted as spherical model, with an 89.6% structural variance and a range of 4.02 m. In addition, Kringing map indicates that the soil moisture distribution pattern after snow thawing is highly consistent with the shrub patch pattern. At the typical inter-dune transect scale, soil moisture presents a pattern of high value at inter-dune depression and low value at dune, and this variation is fitted as Gaussian model with a structural variance of 95.8% and a range of 66.16 m. The range is comparable with the scale of topography zoning, suggesting that the topography pattern controls the pattern of snowmelt at this scale. The evidence indicates that the heterogeneity of soil moisture at various scales is controlled by various land surface processes after snow thawing. For Gurbantunggut Desert, the spatial heterogeneity of snowmelt at various scales is ecologically valuable, because it promotes the utilization efficiency of the snowmelt for the desert vegetation.     

Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia

Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model.     

Multi-scale heterogeneity of soil moisture following snow thawing in <Emphasis Type="Italic">Haloxylon ammodendron</Emphasis> shrubland

Spatial variation of soil moisture after snow thawing in South Gurbantunggut was quantitatively studied using ANOVA and geostatistics at various scales. The results show that the soil moisture heterogeneity varies along with spatial scales. At the shrub individual scale, there is a gradient in soil moisture from shrub-canopied area to canopy margin and to the interspaces between shrubs. At the community scale, soil moisture is highly autocorrelated and the semivariogram is fitted as spherical model, with an 89.6% structural variance and a range of 4.02 m. In addition, Kringing map indicates that the soil moisture distribution pattern after snow thawing is highly consistent with the shrub patch pattern. At the typical inter-dune transect scale, soil moisture presents a pattern of high value at inter-dune depression and low value at dune, and this variation is fitted as Gaussian model with a structural variance of 95.8% and a range of 66.16 m. The range is comparable with the scale of topography zoning, suggesting that the topography pattern controls the pattern of snowmelt at this scale. The evidence indicates that the heterogeneity of soil moisture at various scales is controlled by various land surface processes after snow thawing. For Gurbantunggut Desert, the spatial heterogeneity of snowmelt at various scales is ecologically valuable, because it promotes the utilization efficiency of the snowmelt for the desert vegetation.     

Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

Sediment modelling with fine temporal and spatial resolution for a hilly catchment

The suitability of the physically based model SHETRAN for simulating sediment generation and delivery with a high degree of spatial (20 m) and temporal (sub‐hourly) resolution was assessed through application of the model to a 167‐km² catchment leading to an estuary in New Zealand. By subdividing the catchment and conducting calculations on a computer cluster for a 6‐month hydrology initialisation period, it was possible to simulate a large rainfall event and its antecedent conditions in 24 h of computation time. The model was calibrated satisfactorily to catchment outlet flow and sediment flux for a large rainfall event in two subcatchments (~2 km²). Validation for a separate subcatchment was successful for flow (Nash–Sutcliff efficiency of 0.84) with a factor 2.1 over‐prediction for sediment load. Validation for sediment at full catchment scale using parameters from the subcatchment scale was good for flow but poor for sediment, with gross under‐estimation of the dominant stream sources of sediment. After recalibration at catchment scale, validation for a separate event gave good results for flow (Nash–Sutcliff efficiency of 0.93) and sediment load within a factor of two of measurements. An exploratory spatially explicit landslide model was added to SHETRAN, but it was not possible to test this fully because no landslides were observed in the study period. Application to climate change highlighted the non‐linear response to extreme rainfall. However, full exploration of land use and climate change and the evaluation of uncertainty were severely constrained by computational limitations. Subdivision of the catchment with separate stream routing is suggested as a way forward to overcome these limitations. Copyright © 2011 John Wiley & Sons, Ltd.     

On the significance of crop‐type information for the simulation of catchment hydrology

Spatially distributed hydrological models require information on the land cover pattern, as it directly influences the generation of run‐off. However, it is not clear which detail of land cover information is suitable for simulating the catchment hydrology. A better understanding of the relationship between the land cover detail and the hydrological processes is therefore required as it would enhance a successful application of the hydrological model. This study investigates the relevance of accurate information about the crop types in the catchment for the simulation of run‐off, baseflow and evapotranspiration. Results reveal that adding knowledge about the crop type in the model simulation is redundant when area‐averaged water flux predictions are intended. On the contrary, when a spatial distribution of water flux predictions is desired, it is meaningful to increase the land cover detail because an increased heterogeneity in the land cover map induces an increased heterogeneity in the hydrological response and locally reduces the uncertainty in the model prediction up to 50%. To assess the land cover uncertainty and to locate the regions for which the reduction in prediction uncertainty is the highest, the thematic uncertainty map (constructed through fuzzy logic) is shown to be a useful tool. Copyright © 2014 John Wiley & Sons, Ltd.     

Modelling the hydrologic effects of dynamic land‐use change using a distributed hydrologic model and a spatial land‐use allocation model

This study develops a novel approach for modelling and examining the impacts of time–space land‐use changes on hydrological components. The approach uses an empirical land‐use change allocation model (CLUE‐s) and a distributed hydrological model (DHSVM) to examine various land‐use change scenarios in the Wu‐Tu watershed in northern Taiwan. The study also uses a generalized likelihood uncertainty estimation approach to quantify the parameter uncertainty of the distributed hydrological model. The results indicate that various land‐use policies—such as no change, dynamic change and simultaneous change—have different levels of impact on simulating the spatial distributions of hydrological components in the watershed study. Peak flow rates under simultaneous and dynamic land‐use changes are 5·71% and 2·77%, respectively, greater than the rate under the no land‐use change scenario. Using dynamic land‐use changes to assess the effect of land‐use changes on hydrological components is more practical and feasible than using simultaneous land‐use change and no land‐use change scenarios. Furthermore, land‐use change is a spatial dynamic process that can lead to significant changes in the distributions of ground water and soil moisture. The spatial distributions of land‐use changes influence hydrological processes, such as the ground water level of whole areas, particularly in the downstream watershed. Copyright © 2010 John Wiley & Sons, Ltd.     

Multi-scale heterogeneity of soil moisture following snow thawing in Haloxylon ammodendron shrubland

Spatial variation of soil moisture after snow thawing in South Gurbantunggut was quantitatively studied using ANOVA and geostatistics at various scales. The results show that the soil moisture heterogeneity varies along with spatial scales. At the shrub individual scale, there is a gradient in soil moisture from shrub-canopied area to canopy margin and to the interspaces between shrubs. At the community scale, soil moisture is highly autocorrelated and the semivariogram is fitted as spherical model, with an 89.6% structural variance and a range of 4.02 m. In addition, Kringing map indicates that the soil moisture distribution pattern after snow thawing is highly consistent with the shrub patch pattern. At the typical inter-dune transect scale, soil moisture presents a pattern of high value at inter-dune depression and low value at dune, and this variation is fitted as Gaussian model with a structural variance of 95.8% and a range of 66.16 m. The range is comparable with the scale of topography zoning, suggesting that the topography pattern controls the pattern of snowmelt at this scale. The evidence indicates that the heterogeneity of soil moisture at various scales is controlled by various land surface processes after snow thawing. For Gurbantunggut Desert, the spatial heterogeneity of snowmelt at various scales is ecologically valuable, because it promotes the utilization efficiency of the snowmelt for the desert vegetation.

     

Assessment of rural community and agricultural development using geomorphological–geological factors and GIS in the Trikala prefecture (Central Greece)

In this study, the potential land use planning for rural communities and agricultural development is examined with a multi-criteria analysis and Geographical Information System. For this purpose, geological, geomorphological and socio-economic data and natural hazard maps were chosen as major factors affecting both land uses. The Analytical Hierarchical Process method was applied to evaluate these factors and the uncertainty of their weight alterations estimated. Three scenarios were developed for each land use to examine the effect of uncertainty to the suitability assessment results, leading to the corresponding potential suitability maps. The areas of very high suitability are distributed mainly at the plain part of the study area. The proposed methodology comprises a case application concerning physical factors in conjunction with natural hazard maps in the land use planning procedure.