A new surface runoff parameterization with subgrid-scale soil heterogeneity for land surface models

Soil heterogeneity plays an important role in determining surface runoff generation mechanisms. At the spatial scales represented by land surface models used in regional climate model and/or global general circulation models (GCMs) for numerical weather prediction and climate studies, both infiltration excess (Horton) and saturation excess (Dunne) runoff may be present within a studied area or a model grid cell. Proper modeling of surface runoff is essential to a reasonable representation of feedbacks in the land–atmosphere system. In this paper, a new surface runoff parameterization that dynamically represents both Horton and Dunne runoff generation mechanisms within a model grid cell is presented. The new parameterization takes into account of effects of soil heterogeneity on Horton and Dunne runoff. A series of numerical experiments are conducted to study the effects of soil heterogeneity on Horton and Dunne runoff and on soil moisture storage under different soil and precipitation conditions. The new parameterization is implemented into the current version of the hydrologically based variable infiltration capacity (VIC) land surface model and tested over three watersheds in Pennsylvania. Results show that the new parameterization plays a very important role in partitioning the water budget between surface runoff and soil moisture in the atmosphere–land coupling system. Significant underestimation of the surface runoff and overestimation of subsurface runoff and soil moisture could be resulted if the Horton runoff mechanism were not taken into account. Also, the results show that the Horton runoff mechanism should be considered within the context of subgrid-scale spatial variability of soil properties and precipitation. An assumption of time-invariant spatial distribution of potential infiltration rate may result in large errors in surface runoff and soil moisture. In addition, the total surface runoff from the new parameterization is less sensitive to the choice of the soil moisture shape parameter of the distribution.     

Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models

A land surface hydrology parameterization for use in atmospheric GCMs is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large scale parameterizations: water balance calculations are performed for a number of intervals of the topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged land surface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well at the dynamics of land surface-atmosphere interactions.     

鄱阳湖流域径流模型

流域径流是鄱阳湖主要来水,建立鄱阳湖流域径流模型对揭示湖泊水量平衡及其受流域自然和人类活动的影响具有重要意义.针对鄱阳湖-流域系统的特点:流域面积大(16.22×104km2)、多条入湖河流、湖滨区坡面入湖径流等,研究了相应的模拟方法,建立了考虑流域土壤属性和土地利用空间变化的鄱阳湖流域分布式径流模型.采用6个水文站1991-2001年的实测河道径流对模型进行了率定和验证.结果显示,模型整体模拟精度较高.其中,赣江、信江和饶河均取得了较好的模拟结果,月效率系数为0.82-0.95;抚河和修水模拟精度略低,为0.65-0.78.模型揭示了研究时段内年平均入湖径流总量为1623×108m3,其中,赣江最多,占47%,其次为信江和抚河,分别占13%和12%,湖滨区坡面入湖径流约占4%,其余24%来自饶河、修水以及其它入湖支流.模型将用于评估流域下垫面或气候变化引起的入湖水量变化,为湖泊水量平衡计算提供依据.     

Comparison of simple runoff models used in Korea for small watersheds

This study analysed runoff characteristics for three small watersheds in the Su‐Young River basin in Korea using the storage function method, the linear reservoir cascade model, and the discrete linear input–output model. The models performed well for the watersheds. The runoff analysis of the observed flood events showed that the storage function method was the most accurate of the three, and was followed by the linear reservoir cascade model and the discrete linear input–output model. Copyright © 1999 John Wiley & Sons, Ltd.     

Assessment of models predicting anthropogenic interventions and climate variability on surface runoff of the Lower Zab River

Multi-regression, hydrologic sensitivity and hydrologic model simulations were applied to quantify the climate change and anthropogenic intervention impacts on the Lower Zab River basin (LZRB). The Pettitt, precipitation-runoff double cumulative curve (PR-DCC) and Mann–Kendall methods were used for the change points and significant trend analyses in the annual streamflow. The long-term runoff series from 1979 to 2013 was first divided into two main periods: a baseline (1979–1997) and an anthropogenic intervention period (1998–2013). The findings show that the mean annual streamflow changes were consistent using the three methods. In addition, climate variability was the main driver, which led to streamflow reduction with contributions of 66–97% during 2003–2013, whereas anthropogenic interventions caused reductions of 4–34%. Moreover, to enhance the multi-model combination concept and explore the simple average method (SAM), Hydrologiska Byrans Vattenbalansavdelning (HBV), Génie Rural a Daily 4 parameters (GR4J) and Medbasin models have been successfully applied.     

Dynamic-stochastic models of rainfall and snowmelt runoff formation

Abstract

Possibilities for the development of dynamic-stochastic models of runoff formation with random inputs are discussed. Two models are described: the first allows the calculation of the statistical distribution of the maximum discharges of rainfall floods, and the second the statistical distribution of snowmelt flood volumes. Meteorological inputs are generated by the Monte- Carlo method. Physically-based models are used for the transformation of input data into runoff. The various models are applied to observation data from two watersheds.     

Investigating the ability of a land surface model to reproduce river runoff with the accuracy of hydrological models

The ability of the physically-based Soil Water-Atmosphere-Plants model, describing the processes of heat and water exchange between the land surface and the near-surface atmosphere, to reproduce hydrographs of daily river runoff is examined and compared with the Sacramento conceptual hydrological model, which has demonstrated the best performance in the International Model Parameter Estimation Experiment. Model simulations were carried out for 12 river basins with the area of ～10³ km² in the southeastern USA for the period of 1960–1998, of which the first 20 years were used to calibrate both models, while the last 19 years were used to validate them. The daily runoff hydrographs reproduced by the Soil Water-Atmosphere-Plants model, calibrated using different methods with the aim to maximize its accuracy, were compared with observational data and the results from the Sacramento model.     

稻田氮磷径流流失模型研究进展

水稻是全球最重要的粮食作物,但种植过程中不合理的水肥管理引起了稻田氮磷径流流失,对邻近水体造成污染风险.稻田氮磷径流流失模型是理解区域水体污染排放时空特征和评估水污染风险的有效手段.论文阐述了稻田氮磷径流流失的发生机制和关键过程,梳理了统计经验模型、物理机理模型、水文过程模型和生态系统模型的研发历程.结果表明,当前模型...     

Assessing rainfall–runoff models for the management of Lake Verbano

Growing human pressure and potential change in precipitation pattern induced by climate change require a more efficient and sustainable use of water resources. Hydrological models can provide a fundamental contribution to this purpose, especially as increasing availability of meteorological data and forecast allows for more accurate runoff predictions. In this article, two models are presented for describing the flow formation process in a sub‐alpine catchment: a distributed parameter, physically based model, and a lumped parameter, empirical model. The scope is to compare the two modelling approaches and to assess the impact of hydrometeorological information, either observations or forecast, on water resources management. This is carried out by simulating the real‐time management of the regulated lake that drains the catchment, using the inflow predictions provided by the two models. Copyright © 2010 John Wiley & Sons, Ltd.     

Assessment of alternative adsorption models and global sensitivity analysis to characterize hexavalent chromium loss from soil to surface runoff

We investigate our ability to assess transfer of hexavalent chromium, Cr(VI), from the soil to surface runoff by considering the effect of coupling diverse adsorption models with a two‐layer solute transfer model. Our analyses are grounded on a set of two experiments associated with soils characterized by diverse particle size distributions. Our study is motivated by the observation that Cr(VI) is receiving much attention for the assessment of environmental risks due to its high solubility, mobility, and toxicological significance. Adsorption of Cr(VI) is considered to be at equilibrium in the mixing layer under our experimental conditions. Four adsorption models, that is, the Langmuir, Freundlich, Temkin, and linear models, constitute our set of alternative (competing) mathematical formulations. Experimental results reveal that the soil samples characterized by the finest grain sizes are associated with the highest release of Cr(VI) to runoff. We compare the relative abilities of the four models to interpret experimental results through maximum likelihood model calibration and four model identification criteria (i.e., the Akaike information criteria [AIC and AIC_C] and the Bayesian and Kashyap information criteria). Our study results enable us to rank the tested models on the basis of a set of posterior weights assigned to each of them. A classical variance‐based global sensitivity analysis is then performed to assess the relative importance of the uncertain parameters associated with each of the models considered, within subregions of the parameter space. In this context, the modelling strategy resulting from coupling the Langmuir isotherm with a two‐layer solute transfer model is then evaluated as the most skilful for the overall interpretation of both sets of experiments. Our results document that (a) the depth of the mixing layer is the most influential factor for all models tested, with the exception of the Freundlich isotherm, and (b) the total sensitivity of the adsorption parameters varies in time, with a trend to increase as time progresses for all of the models. These results suggest that adsorption has a significant effect on the uncertainty associated with the release of Cr(VI) from the soil to the surface runoff component.     

A simple two‐parameter model for scaling hillslope surface runoff

The objective of this research was to develop and parameterise a physically justified yet low‐parameter model to quantify observed changes in surface runoff ratios with hillslope length. The approach starts with the assumption that a unit of rainfall‐excess runoff generated at a point is a fraction β of precipitation P (m) which travels some variable distance down a slope before reinfiltrating, depending on the local rainfall, climate, soils, etc. If this random distance travelled Y is represented by a distribution, then a survival function will describe the probability of this unit of runoff travelling further than some distance x (m). The total amount of per unit width runoff Q (m²) flowing across the lower boundary of a slope of length λ (m) may be considered the sum of all the proportions of the units of rainfall excess runoff integrated from the lower boundary x = 0 to the upper boundary x = λ of the slope. Using these assumptions we derive a model Q(λ) = βPμλ/(μ + λ), (μ > 0, 0 ≤ β ≤ 1, λ ≥ 0) that describes the change in surface runoff with slope length, where μ (m) is the mean of the random variable Y. Dividing both sides of this equation by Pλ yields a simple two‐parameter equation for the dimensionless hillslope runoff ratio Q_h(λ) = βμ/(μ + λ). The model was parameterised with new rainfall and runoff data collected from three replicates of bounded 2 m wide plots of four different lengths (0.5, 1.0, 2.0 and 4.0 m) for 2 years from a forested SE Australian site, and with 32 slope length–runoff data sets from 12 other published studies undertaken between 1934 and 2010. Using the parameterised model resulted in a Nash and Sutcliffe statistic between observed and predicted runoff ratio (for all data sets combined) of 0.93, compared with –2.1 when the runoff ratio was fixed at the value measured from the shortest plot. Copyright © 2014 John Wiley & Sons, Ltd.     

Analysis of objective functions used in urban runoff models

The objective functions used in parameter estimation in urban runoff models are compared by using a method proposed by Diskin and Simon¹ and the urban runoff model ILLUDAS. Two sets of objective functions, the first one used by Diskin and Simon in their study and a second one which includes other objective functions are used. Rainfall-runoff data from urban watersheds in the US are used in the study. The results indicate that the least squares criterion is the best among those studied.     

A simplified model for estimating field‐scale surface runoff hydrographs

The problem of obtaining field‐scale surface response to rainfall events is complicated by the spatial variability of infiltration characteristics of the soil and rainfall. In this paper, we develop and test a simplified model for generating surface runoff over fields with spatial variation in both rainfall rate and saturated hydraulic conductivities. The model is able to represent the effects of local variation in infiltration, as well as the run‐on effect that controls infiltration of excess water from saturated upstream areas. The effective rainfall excess is routed to the slope outlet using a simplified solution of the kinematic wave approximation. Model results are compared to averaged hydrographs from numerically‐intensive Monte–Carlo simulations for observed and design rainfall events and soil patterns that are typical of Central Italy. The simplified model is found to yield satisfactory results at a relatively small computational expense. A proposal to include a simple channel routing scheme is also presented as a prelude to extend this conceptualization to watershed scales. Copyright © 2006 John Wiley & Sons, Ltd.     

The introduction of runoff routing into large-scale hydrological models

Two alternative schemes are presented that are appropriate for the representation of runoff routing in large-scale grid-based hydrological models and atmospheric general circulation models (AGCMs). The first scheme characterizes routing processes as a single conceptual store. The second scheme, developed by Naden (1992), uses the normalized network width function to characterize the channel network form and a linear solution to the convective diffusion equation of one-dimensional flow to characterize the routing effect of a single channel. Both schemes are applied to the Severn catchment at the daily time-scale for the period 1981 to 1990 using a grid resolution of 40 km. Comparable results were obtained using both schemes (efficiencies were of the order of 80% in both cases). A combined model using a conceptual reservoir to represent hillslope routing and the network-based scheme to represent channel routing was developed to investigate the relative roles of hillslope and channel routing at the catchment scale. The application of this model demonstrated the important role of hillslope routing in reproducing the low frequency component of the catchment response. However, in terms of goodness-of-fit there was little to choose between the three schemes. Consequently, it is recommended that additional a priori knowledge of the routing processes should be used to condition the choice of model structure. © 1997 John Wiley & Sons, Ltd.     

Modeling input errors to improve uncertainty estimates for one-dimensional sediment transport models

Stochastic Environmental Research and Risk Assessment - Bayesian methods have recently been applied to one-dimensional sediment transport models to assess the uncertainty in model predictions due...     

A revisit of NRCS-CN inspired models coupled with RS and GIS for runoff estimation

The Natural Resource Conservation Service – Curve Number (NRCS-CN) methodology is a widely used tool for estimating surface runoff, which is of prime importance in hydrological engineering, agricultural planning and management, environmental impact assessment, flood forecasting, and others fields. This article reviews the methodology and associated hydrological models used for runoff estimation along with their advantages and limitations. Furthermore, discussion focuses on the potential applications of Remote Sensing (RS) and Geographical Information System (GIS) techniques for estimating hydrological variables, such as rainfall, soil moisture and CN required for the NRCS-CN methodology, as well as future research and opportunities for improved runoff estimation at the macro scale.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR A. Efstratiadis     

Quantifying relations between surface runoff and aridity after wildfire

Post‐wildfire runoff and erosion are major concerns in fire‐prone landscapes around the world, but these hydro‐geomorphic responses have been found to be highly variable and difficult to predict. Some variations have been observed to be associated with landscape aridity, which in turn can influence soil hydraulic properties. However, to date there has been no attempt to systematically evaluate the apparent relations between aridity and post‐wildfire runoff. In this study, five sites in a wildfire burnt area were instrumented with rainfall‐runoff plots across an aridity index (AI) gradient. Surface runoff and effective rainfall were measured over 10 months to allow investigation of short‐ (peak runoff) and longer‐term (runoff ratio) runoff characteristics over the recovery period. The results show a systematic and strong relation between aridity and post‐wildfire runoff. The average runoff ratio at the driest AI site (33.6%) was two orders of magnitude higher than at the wettest AI site (0.3%). Peak runoff also increased with AI, with up to a thousand‐fold difference observed during one event between the driest and wettest sites. The relation between AI, peak 15‐min runoff (Q₁₅) and peak 15‐min rainfall intensity (I₁₅) (both in mm h^‐1) could be quantified by the equation: Q₁₅ = 0.1086I₁₅ × AI ^2.691 (0.65<AI<1.80, 0<I₁₅<45) (adjusted r² = 0.84). The runoff ratios remained higher at drier AI sites (AI 1.24 and 1.80) throughout the monitoring period, suggesting higher AI also lengthens the window of disturbance after wildfire. The strong quantifiable link which this study has determined between AI and post‐wildfire surface runoff could greatly improve our capacity to predict the magnitude and location of hydro‐geomorphic processes such as flash floods and debris flows following wildfire, and may help explain aridity‐related patterns of soil properties in complex upland landscapes. Copyright © 2018 John Wiley & Sons, Ltd.     

Uncertainty estimation of pathlines in ground water models

A method is proposed to estimate the uncertainty of the location of pathlines in two-dimensional, steady-state confined or unconfined flow in aquifers due to the uncertainty of the spatially variable unconditional hydraulic conductivity or transmissivity field. The method is based on concepts of the semianalytical first-order theory given in Stauffer et al. (2002, 2004), which allows estimates of the lateral second moment (variance) of the location of a moving particle. However, this method is reformulated in order to account for nonuniform recharge and nonuniform aquifer thickness. One prominent application is the uncertainty estimation of the catchment of a pumping well by considering the boundary pathlines starting at a stagnation point. In this method, the advective transport of particles is considered, based on the velocity field. In the case of a well catchment, backtracking is applied by using the reversed velocity field. Spatial variability of hydraulic conductivity or transmissivity is considered by taking into account an isotropic exponential covariance function of log-transformed values with parameters describing the variance and correlation length. The method allows postprocessing of results from ground water models with respect to uncertainty estimation. The code PPPath, which was developed for this purpose, provides a postprocessing of pathline computations under PMWIN, which is based on MODFLOW. In order to test the methodology, it was applied to results from Monte Carlo simulations for catchments of pumping wells. The results correspond well. Practical applications illustrate the use of the method in aquifers.