A modified trapezoid framework model for partitioning regional evapotranspiration

While evapotranspiration (ET) is normally measured as one hydrologic component, evaporation (E), and transpiration (T) result from different physical-biological processes. Using a two-source model, a trapezoid framework has been widely applied in recent years. The key to applying the trapezoid framework model is the determination of the dry/wet boundaries of the land surface temperature-fractional vegetation coverage trapezoid (LST-f_c). Although algorithms have been developed to characterize the two boundaries, there remains a significant uncertainty near the wet boundary which scatters in a discrete and uneven manner. It is therefore difficult to precisely locate the wet boundary. To address this problem, a Wet Boundary Algorithm (WBA) was developed in this study with the algorithm applied in the region of Huang-Huai-Hai plain of China, using the Pixel Component Arranging and Comparing Algorithm (PCACA) to retrieve ET from MODerate-resolution Imaging Spectroradiometer (MODIS) Data. The eddy covariance (EC) measurements from Yucheng station was used to verify the modified model where the root mean square error (RMSE) of 17.8 W/m², Bias of −7.2 W/m² for latent heat flux (LE) simulation in 28 cloudless test days. The ratio of transpiration to evapotranspiration (T/ET) varied between 0.48 and 0.81 over the Huang-Huai-Hai plain. The spatial and temporal distribution of ET revealed that agriculture practices have a significant influence on the hydrological cycle, where crop growth promotes the magnitude of ET. Likewise, harvesting activities significantly reduce ET. The proposed WBA algorithm significantly reduces the uncertainty of the trapezoid ET model caused by wet edge positioning. The analysis of the impact of agricultural activities on ET provide a better understanding how human activities change the hydrological cycle at regional scales.     

Determination of regional distribution of crop transpiration and soil water use efficiency using quantitative remote sensing data through inversion

As is widely known, there is a severe shortage of water resources in North China. There have been frequent droughts in recent years. Developing water saving measures, especially in agricul-ture, has become an urgent task. In water-saving agriculture, one …     

Investigating the uncertainty and transferability of parameters in SWAT model under climate change

ABSTRACT

There is an implicit assumption in most work that the parameters calibrated based on observations remain valid for future climatic conditions. However, this might not be true due to parameter instability. This paper investigates the uncertainty and transferability of parameters in a hydrological model under climate change. Parameter transferability is investigated with three parameter sets identified for different climatic conditions, which are: wet, intermediate and dry. A parameter set based on the baseline period (1961–1990) is also investigated for comparison. For uncertainty analysis, a k-simulation set approach is proposed instead of employing the traditional optimization method which uses a single best-fit parameter set. The results show that the parameter set from the wet sub-period performs the best when transferred into wet climate condition, while the parameter set from the baseline period is the most appropriate when transferred into dry climate condition. The largest uncertainty of simulated daily high flows for 2011–2040 is from the parameter set trained in the dry sub-period, while that of simulated daily medium and low flows lies in the parameter set from the intermediate calibration sub-period. For annual changes in the future period, the uncertainty with the parameter set from the intermediate sub-period is the largest, followed by the wet sub-period and dry sub-period. Compared with high and medium flows/runoffs, the uncertainty of low flows/runoffs is much smaller for both simulated daily flows and annual runoffs. For seasonal runoffs, the largest uncertainty is from the intermediate sub-period, while the smallest is from the dry sub-period. Apart from that, the largest uncertainty can be observed for spring runoffs and the lowest one for autumn runoffs. Compared with the traditional optimization method, the k-simulation set approach shows many more advantages, particularly being able to provide uncertainty information to decision support for watershed management under climate change.

EDITOR Z.W. Kundzewicz ASSOCIATE EDITOR not assigned     

Simulating precipitation in the Northeast United States using a climate-informed K-nearest neighbour algorithm

Decadal prediction using climate models faces long-standing challenges. While global climate models may reproduce long-term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate-informed K-nearest neighbour (K-NN)-based stochastic modelling approach to capture the long-term trend and variability while replicating intra-annual statistics. The climate-informed K-NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near-term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate-informed K-NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K-NN weather generator and ISIMIP-2b GFDL general circulation model bias-corrected output in a cross-validation mode. Results indicate that the climate-informed K-NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within-year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra-annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near-term streamflow, which in turn can be used for short-term water resources planning and management.     

基于单亲遗传算法的SAR图像混合像元分解

已有的遥感影像混合像元分解理论方法都要求遥感影像的通道数目大于地物种类,而合成孔径雷达(SAR)的自身特点决定了SAR图像不可能有过多的通道数目,为解决SAR图像地物种类大于通道数目情况下的混合像元分解问题,本文基于单亲遗传算法提出了一种新的混合像元分解方法,创建了一种新的染色体编码方式及进化迭代方式,新算法很好地实现混合像元的分解,可以分解出比通道数目更多的地物种类.并从北京地区ENVISAT-ASAR图像中截取天安门附近区域作为数据源进行实验,实验结果表明了本文算法的正确性和有效性.     

Mapping Potential Groundwater‐Dependent Ecosystems for Sustainable Management

Ecosystems which rely on either the surface expression or subsurface presence of groundwater are known as groundwater‐dependent ecosystems (GDEs). A comprehensive inventory of GDE locations at an appropriate management scale is a necessary first‐step for sustainable management of supporting aquifers; however, this information is unavailable for most areas of concern. To address this gap, this study created a two‐step algorithm which analyzed existing geospatial and remote sensing data to identify potential GDEs at both state/province and aquifer/basin scales. At the state/province scale, a geospatial information system (GIS) database was constructed for Texas, including climate, topography, hydrology, and ecology data. From these data, a GDE index was calculated, which combined vegetative and hydrological indicators. The results indicated that central Texas, particularly the Edwards Aquifer region, had highest potential to host GDEs. Next, an aquifer/basin scale remote sensing‐based algorithm was created to provide more detailed maps of GDEs in the Edwards Aquifer region. This algorithm used Landsat ETM+ and MODIS images to track the changes of NDVI for each vegetation pixel. The NDVI dynamics were used to identify the vegetation with high potential to use groundwater—such plants remain high NDVI during extended dry periods and also exhibit low seasonal and inter‐annual NDVI changes between dry and wet seasons/years. The results indicated that 8% of natural vegetation was very likely using groundwater. Of the potential GDEs identified, 75% were located on shallow soil averaging 45 cm in depth. The dominant GDE species were live oak, ashe juniper, and mesquite.     

Statistical corrections of spatially interpolated missing precipitation data estimates

Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd.     

In situ Mixing of Organic Matter Decreases Hydraulic Conductivity of Denitrification Walls in Sand Aquifers

In a previous study, a denitrification wall was constructed in a sand aquifer using sawdust as the carbon substrate. Ground water bypassed around this sawdust wall due to reduced hydraulic conductivity. We investigated potential reasons for this by testing two new walls and conducting laboratory studies. The first wall was constructed by mixing aquifer material in situ without substrate addition to investigate the effects of the construction technique (mixed wall). A second, biochip wall, was constructed using coarse wood chips to determine the effect of size of the particles in the amendment on hydraulic conductivity. The aquifer hydraulic conductivity was 35.4 m/d, while in the mixed wall it was 2.8 m/d and in the biochip wall 3.4 m/d. This indicated that the mixing of the aquifer sands below the water table allowed the particles to re-sort themselves into a matrix with a significantly lower hydraulic conductivity than the process that originally formed the aquifer. The addition of a coarser substrate in the biochip wall significantly increased total porosity and decreased bulk density, but hydraulic conductivity remained low compared to the aquifer. Laboratory cores of aquifer sand mixed under dry and wet conditions mimicked the reduction in hydraulic conductivity observed in the field within the mixed wall. The addition of sawdust to the laboratory cores resulted in a significantly higher hydraulic conductivity when mixed dry compared to cores mixed wet. This reduction in the hydraulic conductivity of the sand/sawdust cores mixed under saturated conditions repeated what occurred in the field in the original sawdust wall. This indicated that laboratory investigations can be a useful tool to highlight potential reductions in field hydraulic conductivities that may occur when differing materials are mixed under field conditions.     

A limiter-based well-balanced discontinuous Galerkin method for shallow-water flows with wetting and drying: One-dimensional case

An important part in the numerical simulation of tsunami and storm surge events is the accurate modeling of flooding and the appearance of dry areas when the water recedes. This paper proposes a new algorithm to model inundation events with piecewise linear Runge–Kutta discontinuous Galerkin approximations applied to the shallow water equations. This study is restricted to the one-dimensional case and shows a detailed analysis and the corresponding numerical treatment of the inundation problem.The main feature is a velocity based “limiting” of the momentum distribution in each cell, which prevents instabilities in case of wetting or drying situations. Additional limiting of the fluid depth ensures its positivity while preserving local mass conservation. A special flux modification in cells located at the wet/dry interface leads to a well-balanced method, which maintains the steady state at rest. The discontinuous Galerkin scheme is formulated in a nodal form using a Lagrange basis. The proposed wetting and drying treatment is verified with several numerical simulations. These test cases demonstrate the well-balancing property of the method and its stability in case of rapid transition of the wet/dry interface. We also verify the conservation of mass and investigate the convergence characteristics of the scheme.     

Impacts of hydraulic redistribution on eco-hydrological cycles: A case study over the Amazon basin

Hydraulic redistribution(HR)refers to the process of soil water transport through the low-resistance pathway provided by plant roots.It has been observed in field studies and proposed to be one of the processes that enable plants to resist water limitations.However,most land-surface models(LSMs)currently do not include this underground root process.In this study,a HR scheme was incorporated into the Community Land Model version 4.5(CLM4.5)to investigate the effect of HR on the eco-hydrological cycle.Two paired numerical simulations(with and without the new HR scheme)were conducted for the Tapajos National Forest km83(BRSa3)site and the Amazon.Simulations for the BRSa3 site in the Amazon showed that HR during the wet season was small,0.1 mm day~(–1),transferring water from shallow wet layers to deep dry layers at night;however,HR in the dry season was more obvious,up to 0.3 mm day~(–1),transferring water from deep wet layers to shallow dry layers at night.By incorporating HR into CLM4.5,the new model increased gross primary production(GPP)and evapotranspiration(ET)by 10%and 15%,respectively,at the BRSa3 site,partly overcoming the underestimation.For the Amazon,regional analysis also revealed that vegetation responses(including GPP and ET)to seasonal drought and the severe drought of 2005 were better captured with the HR scheme incorporated.     

Estimation of soil hydraulic properties and their uncertainty through the Beerkan infiltration experiment

The Beerkan method based on in situ single‐ring water infiltration experiments along with the relevant specific Beerkan estimation of soil transfer parameters (BEST) algorithm is attractive for simple soil hydraulic characterization. However, the BEST algorithm may lead to erroneous or null values for the saturated hydraulic conductivity and sorptivity especially when there are only few infiltration data points under the transient flow state, either for sandy soil or soils in wet conditions. This study developed an alternative algorithm for analysis of the Beerkan infiltration experiment referred to as BEST‐generalized likelihood uncertainty estimation (GLUE). The proposed method estimates the scale parameters of van Genuchten water retention and Brooks–Corey hydraulic conductivity functions through the GLUE methodology. The GLUE method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the goodness‐of‐fit between modelled and observed data. The results showed that using a combination of three different likelihood measurements based on observed transient flow, steady‐state flow and experimental steady‐state infiltration rate made the BEST‐GLUE procedure capable of performing an efficient inverse analysis of Beerkan infiltration experiments. Therefore, it is more applicable for a wider range of soils with contrasting texture, structure, and initial and saturated water content. Copyright © 2015 John Wiley & Sons, Ltd.     

Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment

ABSTRACT

Climate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important.

Editor M.C. Acreman; Associate editor S. Kanae     

Sparse representation-based color visualization method for hyperspectral imaging

In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.     

Identifying the potential loss of monitoring wells using an uncertainty analysis

From the mid-1940s through the 1980s, large volumes of waste water were discharged at the Hanford Site in southeastern Washington State, causing a large-scale rise (>20 m) in the water table. When waste water discharges ceased in 1988, ground water mounds began to dissipate. This caused a large number of wells to go dry and has made it difficult to monitor contaminant plume migration. To identify monitoring wells that will need replacement, a methodology has been developed using a first-order uncertainty analysis with UCODE, a nonlinear parameter estimation code. Using a three-dimensional, finite-element ground water flow code, key parameters were identified by calibrating to historical hydraulic head data. Results from the calibration period were then used to check model predictions by comparing monitoring wells' wet/dry status with field data. This status was analyzed using a methodology that incorporated the 0.3 cumulative probability derived from the confidence and prediction intervals. For comparison, a nonphysically based trend model was also used as a predictor of wells' wet/dry status. Although the numerical model outperformed the trend model, for both models, the central value of the intervals was a better predictor of a wet well status. The prediction interval, however, was more successful at identifying dry wells. Predictions made through the year 2048 indicated that 46% of the wells in the monitoring well network are likely to go dry in areas near the river and where the ground water mound is dissipating.     

Trend and persistence of precipitation under climate change scenarios for Kansabati basin,India

With increasing uncertainties associated with climate change, precipitation characteristics pattern are receiving much attention these days. This paper investigated the impact of climate change on precipitation in the Kansabati basin, India. Trend and persistence of projected precipitation based on annual, wet and dry periods were studied using global climate model (GCM) and scenario uncertainty. A downscaling method based on Bayesian neural network was applied to project precipitation generated from six GCMs using two scenarios (A2 and B2). The precipitation values for any of three time periods (dry, wet and annual) do not show significant increasing or decreasing trends during 2001–2050 time period. There is likely an increasing trend in precipitation for annual and wet periods during 2051–2100 based on A2 scenario and a decreasing trend in dry period precipitation based on B2 scenario. Persistence during dry period precipitation among stations varies drastically based on historical data with the highest persistence towards north‐west part of the basin. Copyright © 2009 John Wiley & Sons, Ltd.     

卫星遥感数据评估黄土高原陆面干湿程度研究

卫星遥感数据具有估算时空尺度上地表参量的优势,在陆地环境状况评估和监测等方面有很大的应用潜力.本文利用美国地球观测系统卫星搭载中等分辨率成像光谱仪（EOS/MODIS）在黄土高原2002-2010年期间获取的每16天归一化植被指数（NDVI）和每日地表温度（LST）数据,分析了黄土高原地区LST-NDVI空间的基本特征.结果发现：当研究区域足够大且遥感数据时间序列足够长时,LST-NDVI空间中（NDVI,LST）散点并非呈三角形或梯形分布.为了能够利用EOS/MODIS的NDVI和LST数据正确地评估陆面的干湿状况,本文给出了利用数据集合法确定LST-NDVI空间中干边和湿边的数值,即在LST-NDVI空间中,利用NDVI等值区间内LST最大值和最小值的集合代表干边和湿边的数值,并进一步证明了在LST-NDVI空间中干边和湿边数值并非呈线性关系.在分析LST-NDVI空间特征的基础上,通过构建地表温度-植被干旱指数（TVDI）,探讨其在评估黄土高原地区陆面的干湿状况的应用潜力.结果表明：由TVDI距平表征的陆面的干湿程度与局地降水距平有很好的关联性,二者在时空分布上有较好的对应关系.在我国陇东黄土高原塬区,TDVI数值与地面观测的表层土壤湿度有很好的相关性,相关系数在0.67以上,并通过显著性为1%的检验.由此说明：如果合理选取干边和湿边的数值,TDVI可应用于区域陆面干湿程度的客观评估.     

Development of the universal and simplified soil model coupling heat and water transport

It is very important to develop a universal soil model with higher simplicity and more accuracy, which can be widely applied to very general cases such as wet or dry soil, frozen or unfrozen soil and homogeneous or heterogeneous soil. Firstly in this study, based on analysis of both magnitude order and the numerical simulation results, the universal and simplified soil model (USSM) coupling heat and mass transport processes is developed. Secondly, in order to avoid the greater uncertainty caused by the phase change term in numerical iteration process for the model solution obtaining, new version of the universal simplified soil model (NUSSM) is further derived through variables transformation, and accordingly a more efficient numerical scheme for the new version is designed well. The simulation results from the NUSSM agree with the results from more complicated and accurate soil model very well, also reasonably reproduce the observed data under widely real conditions. The new version model, because of its simplicity, will match for the development of land surface model.     

Effect of zero measurements on the spatial correlation structure of rainfall

In this study, the effect of zero measurements on the spatial correlation function of rainfall is analyzed for the quantification of a rainfall field. The use of a bivariate mixed distribution function made it possible to analyze and compare the spatial correlation functions for these three different data sets: only the positive measurements at both gauge locations, positive measurements at either one or both gauge locations, and all measurements including zero at both locations. As an example, the spatial correlation functions are derived for the Geum River Basin, Korea and evaluated for the wet and dry seasons, respectively. Results show that the effect of zero measurements on spatial correlation structures is significant during the wet season, when the inter-station correlations were estimated significantly lower than those during the dry season. It was also found that only the case considering positive measurements are valid for the quantification of rainfall field. Even during the wet season, the inter-station correlation coefficients derived by considering the zero measurements show their high variability along with many abnormally looking high estimates, which made the quantification of the spatial correlation function become very ambiguous.     

Non-monotonic optimal damper placement via steepest direction search

An efficient and systematic procedure is proposed for finding the optimal damper positioning to minimize the dynamic compliance of a 3-D shear building model. The dynamic compliance is expressed in terms of the transfer function amplitudes of the local interstorey drifts evaluated at the undamped fundamental natural frequency. The dynamic compliance is minimized subject to a constraint on the sum of the damping coefficients of added dampers. Optimality criteria are derived and the optimal damper positioning is determined via an original steepest direction search algorithm. This algorithm enables one to find an optimal damper positioning sequentially for gradually increasing damper capacity levels. A non-monotonic design path with respect to the total damper capacity level often appears in the application of this algorithm. A new augmented algorithm via parameter switching is devised to find this non-monotonic design path. Copyright © 1999 John Wiley & Sons, Ltd.     

综合InSAR和应变张量估计2016年MW7.0熊本地震同震三维形变场

利用合成孔径雷达(Synthetic Aperture Radar,SAR)影像提取与地质活动相关的三维地表形变场,对深入理解地质灾害的形成机制及其潜在灾害风险评估非常重要.目前,利用SAR影像的同震三维形变场提取主要利用单个像素点的多次观测构建观测方程,然后基于加权最小二乘(Weighted Least Squares,WLS)方法分解从而获得同震三维形变场,因此该方法缺乏对相邻像素点空间相关性的约束.考虑相邻同震位移点的应力连续性,研究学者提出了顾及大地测量应变张量和卫星形变观测的SAR同震三维形变场方法(Extended Simultaneous and Integrated Strain Tensor Estimation from geodetic and satellite deformation Measurements,ESISTEM).本文以2016年MW7.0熊本地震为例,收集了覆盖此次地震的ALOS-2卫星升降轨影像,利用传统差分InSAR(DInSAR)方法和子孔径雷达干涉测量(Multiple Aperture InSAR,MAI)方法分别对升降轨SAR影像对进行处理,得到视线向(LOS)形变和方位向形变,最后利用ESISTEM方法获取此次地震的三维同震形变场.此外,利用GPS和野外考察观测对本文的三维形变场结果进行结果精度分析.研究结果表明,与传统WLS方法相比,ESISTEM方法不仅能有效抑制奇异像素点对形变结果的干扰,同时对近断层的失相干信号能进行较好的恢复,更有助于解释地表破裂区的地震形变特征和掌握地震发生机制.本文确定的三维同震形变场结果显示主形变区发生在Futagawa断层中部和Hinagu断层最北端,最大水平位移为2m,抬升为0.55m.断层破裂以NE-SW走向的右旋走滑为主兼有部分正断成分.应变张量分析表明发震断层处受到了明显的收缩力和剪切力的作用.