利用GRACE卫星重力数据监测关中地区地下水储量变化

关中地区作为一带一路重要的工农业发达地区之一,开展针对该地区地下水储量变化的监测和分析工作对揭示地下水储量变化特征与经济社会发展具有重要现实意义.本文基于2003—2014年GRACE卫星重力场模型数据,采用组合滤波及单一尺度因子方法反演了关中地区陆地水储量变化,扣除GLDAS地表水平均结果,对关中地区地下水储量变化进行了监测分析.将陆地水储量变化与GLDAS进行相关性分析,将地下水储量变化与WGHM地下水模型及实测地下水位结果进行对比分析.研究结果表明：①关中地区陆地水变化与GLDAS模型结果具有较强的相关性,相关系数多数大于0.7,其中与模型平均结果的相关系数可达0.8.② 2003—2008年关中地区地下水呈正增长趋势,增加速率为0.25 cm·a^-1,与同期实测数据变化趋势一致;但2003—2013年地下水存在长期亏损,亏损速率为-0.37 cm·a^-1等效水高,这与同时期WGHM估算结果-0.35 cm·a^-1十分吻合.③关中地区地下水存在明显的年变化特征,在2003—2014年期间地下水减少速率为-0.44 cm·a^-1,与该地区降雨量有较好的一致性,在降雨偏少的2008、2012和2013年,地下水也显著减少.

     

The influence of groundwater representation on hydrological simulation and its assessment using satellite‐based water storage variation

The interaction between surface water and groundwater is an important aspect of hydrological processes. Despite its importance, groundwater is not well represented in many land surface models. In this study, a groundwater module with consideration of surface water and groundwater dynamic interactions is incorporated into the distributed biosphere hydrological (DBH) model in the upstream of the Yellow River basin, China. Two numerical experiments are conducted using the DBH model: one with groundwater module active, namely, DBH_GW and the other without, namely, DBH_NGW. Simulations by two experiments are compared with observed river discharge and terrestrial water storage (TWS) variation from the Gravity Recovery and Climate Experiment (GRACE). The results show that river discharge during the low flow season that is underestimated in the DBH_NGW has been improved by incorporating the groundwater scheme. As for the TWS, simulation in DBH_GW shows better agreement with GRACE data in terms of interannual and intraseasonal variations and annual changing trend. Furthermore, compared with DBH_GW, TWS simulated in DBH_NGW shows smaller decreases during autumn and smaller increases in spring. These results suggest that consideration of groundwater dynamics enables a more reasonable representation of TWS change by increasing TWS amplitudes and signals and as a consequence, improves river discharge simulation in the low flow seasons when groundwater is a major component in runoff. Additionally, incorporation of groundwater module also leads to wetter soil moisture and higher evapotranspiration, especially in the wet seasons.     

Application of GRACE to the estimation of groundwater storage change in a data-poor region: A case study of Ngadda catchment in the Lake Chad Basin

The present study is to explore the feasibility of GRACE-based estimation of a groundwater storage change in a data-poor region using a case study of the Ngadda catchment in the Lake Chad Basin. Although the Ngadda catchment has only one set of in situ time series data of groundwater from 2006 to 2009 and a limited number of groundwater measurements in 2005 and 2009, GRACE-based groundwater storage change can be evaluated against the in situ groundwater measurements combined with specific yield data. The cross-correlation analysis in the Ngadda catchment shows that maximum rainfall reached in July and August, whereas both the maximum total water storage anomaly and the maximum groundwater storage anomaly occurred 2months later. Whereas the mean annual amplitude of total water storage anomaly is about 17cm from both the average total water storage anomaly from three mascon products and the one from three spherical harmonic products, the mean annual amplitude of soil moisture storage anomaly is substantially varied from 5.58cm for CLM to about 14cm for NOAH and Mosaic. The goodness-of-fit tests show that CLM soil moisture produces the closest estimation of groundwater storage anomaly to the in situ groundwater measurements. The present study shows that GRACE-based estimation for groundwater storage anomaly can be a cost-effective and alternative tool to observe how groundwater changes in a basin scale under the limitation of modelling and in situ data availability.     

Trends in hydrological and climatic variables affected by four variations of the Mann-Kendall approach in Urmia Lake basin,Iran

ABSTRACT

The trends in hydrological and climatic time series data of Urmia Lake basin in Iran were examined using the four different versions of the Mann-Kendall (MK) approach: (i) the original MK test; (ii) the MK test considering the effect of lag-1 autocorrelation; (iii) the MK test considering the effect of all autocorrelation or sample size; and (iv) the MK test considering the Hurst coefficient. Identification of hydrological and climatic data trends was carried out at monthly and annual time scales for 25 temperature, 35 precipitation and 35 streamflow gauging stations selected from the Urmia Lake basin. Mann-Kendall and Pearson tests were also applied to explore the relationships between temperature, precipitation and streamflow trends. The results show statistically significant upward and downward trends in the annual and monthly hydrological and climatic variables. The upward trends in temperature, unlike streamflow, are much more pronounced than the downward trends, but for precipitation the behaviour of trend is different on monthly and annual time scales. Furthermore, the trend results were affected by the different approaches. Specifically, the number of stations showing trends in hydrological and climatic variables decreased significantly (up to 50%) when the fourth test was considered instead of the first and the absolute value of the Z statistic for most of the time series was reduced. The results of correlations between streamflow and climatic variables showed that the streamflow in Urmia Lake basin is more sensitive to changes in temperature than those of precipitation. The observed decreases in streamflow and increases in temperature in the Urmia Lake basin in recent decades may thus have serious implications for water resources management under the warming climate with the expected population growth and increased freshwater consumption in this region.

Editor Z. W. Kundzewicz; Associate editor Q. Zhang     

Glacial isostatic adjustment in Fennoscandia from GRACE data and comparison with geodynamical models

The Earth’s gravity field observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission shows variations due to the integral effect of mass variations in the atmosphere, hydrosphere and geosphere. Several institutions, such as the GeoForschungsZentrum (GFZ) Potsdam, the University of Texas at Austin, Center for Space Research (CSR) and the Jet Propulsion Laboratory (JPL), Pasadena, provide GRACE monthly solutions, which differ slightly due to the application of different reduction models and centre-specific processing schemes. The GRACE data are used to investigate the mass variations in Fennoscandia, an area which is strongly influenced by glacial isostatic adjustment (GIA). Hence the focus is set on the computation of secular trends. Different filters (e.g. isotropic and non-isotropic filters) are discussed for the removal of high frequency noise to permit the extraction of the GIA signal. The resulting GRACE based mass variations are compared to global hydrology models (WGHM, LaDWorld) in order to (a) separate possible hydrological signals and (b) validate the hydrology models with regard to long period and secular components. In addition, a pattern matching algorithm is applied to localise the uplift centre, and finally the GRACE signal is compared with the results from a geodynamical modelling. The GRACE data clearly show temporal gravity variations in Fennoscandia. The secular variations are in good agreement with former studies and other independent data. The uplift centre is located over the Bothnian Bay, and the whole uplift area comprises the Scandinavian Peninsula and Finland. The secular variations derived from the GFZ, CSR and JPL monthly solutions differ up to 20%, which is not statistically significant, and the largest signal of about 1.2 Gal/year is obtained from the GFZ solution. Besides the GIA signal, two peaks with positive trend values of about 0.8 Gal/year exist in central eastern Europe, which are not GIA-induced, and also not explainable by the hydrology models. This may indicate that the recent global hydrology models have to be revised with respect to long period and secular components. Finally, the GRACE uplift signal is also in quite good agreement with the results from a simple geodynamical modelling.     

利用GRACE数据联合新型尺度因子校正法提高陆地水储量变化准确性

本文围绕GRACE数据在信号处理过程中存在泄露误差开展了探索性研究.第一,在传统尺度因子法的基础上,根据模型与CSR-SHc数据的均方根误差和相关性赋予权重,构建了新型尺度因子校正法.第二,以长江流域为例,评估该方法的校正效果,研究结果表明:新型尺度因子校正法校正结果综合GLDAS(Global Land Data A...     

Quantifying the impact of landscape changes on hydrological variables in the alpine and cold region using hydrological model and remote sensing data

Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC-CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200–5900 m), and mainly consumed in the mid-low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low-coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid-coverage grassland (MG; 15.6%), bare land (12.5%), high-coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water.     

关于利用重力卫星对青藏高原水储量年际变化和季节性变化进行监测并用微波数据产品进行验证的研究

本文利用GRACE重力卫星和被动微波传感器TMI,AMSR-E的数据产品对青藏高原的水储量的月平均变化进行了研究.首先介绍了对青藏高原进行水储量变化研究的意义,指出了目前研究手段的不足.然后利用GRACE重力卫星的数据计算了青藏高原的月平均水储量变化,并对计算的结果用微波数据进行解释.结果表明：利用重力数据计算的青藏高原的月平均水储量的时间分布,可以很好的用微波数据产品进行定性的解释.最后还对计算的结果进行了简单的误差分析.     

Unpacking some of the linkages between uncertainties in observational data and the simulation of different hydrological processes using the Pitman model in the data scarce Zambezi River basin

The main objective of this study was to use an uncertainty version of a widely used monthly time step, semi-distributed model (the Pitman model) to explore the equifinalities in the way in which the main hydrological processes are simulated and any identifiable linkages with uncertainties in the available observational data. The study area is the Zambezi River basin and 17 gauged sub-basins have been included in the analyses. Unfortunately, it is not generally possible to quantify some of the observational uncertainties in such a data scarce area and mostly we are limited to identifying where these data are clearly deficient (i.e., erroneous or non-representative). The overall conclusion is that the equifinalities in the model are hugely dominant in terms of the uncertainties in the relative occurrence of different runoff generating processes, although water use uncertainties in the semi-arid parts of the basin can contribute to these uncertainties. The identification of landscape features that suggest the occurrence of saturation excess surface runoff provides some information to constrain the model. Improved independent estimates of groundwater recharge is also identified as a key source of observational data that would help a great deal in constraining the model parameter space and therefore reducing some of the model equifinality.     

Mapping groundwater quality variables using PCA and geostatistics: a case study of Bajo Andarax,southeastern Spain

Abstract

A case study is presented for the application of statistical and geostatistical methods to the problem of estimating groundwater quality variables. This methodology has been applied to the investigation of the detrital aquifer of the Bajo Andarax (Almería, Spain). The use of principal components analysis is proposed, as a first step, for identifying relevant types of groundwater and the processes that bring about a change in their quality. As a result of this application, three factors were obtained, which were used as three new variables (VI: sulphate influence; V2: thermal influence; and V3: marine influence). Analysis of their spatial distribution was performed through the calculation of experimental and theoretical variograms, which served as input for geostatistical modelling using ordinary block kriging. This analysis has allowed a probabilistic representation of the data to be obtained by mapping the three variables throughout the aquifer for each sampling point. In this way, one can evaluate the spatial and temporal variation of the principal physico-chemical processes associated with the three variables VI, V2 and V3 implicated in the groundwater quality of the detrital aquifer.     

Assessment of the impact of spatio‐temporal attributes of wetlands on stream flows using a hydrological modelling framework: a theoretical case study of a watershed under temperate climatic conditions

Wetlands play a significant role on the hydrological cycle, reducing flood peaks through water storage functions and sustaining low flows through slow water release ability. However, their impacts on water resources availability and flood control are mainly driven by wetland type (e.g. isolated wetland—IW—and riparian wetland—RW) and location within a watershed. Consequently, assessing the qualitative and quantitative impact of wetlands on hydrological regimes has become a relevant issue for scientists as well as stakeholders and decision‐makers. In this study, the distributed hydrological model, HYDROTEL, was used to investigate the role and impact of the geographic distribution of isolated and RWs on stream flows of the Becancour River watershed of the St Lawrence Lowlands, Quebec, Canada. The model was set up and calibrated using available datasets (i.e. DEM, soil, wetland distribution, climate, land cover, and hydrometeorological data for the 1969–2010 period). Different wetland theoretical location tests (WTLT) were simulated. Results were used to determine whether stream flow parameters, related to peak flows and low flows, were related to: (i) geographic location of wetlands, (ii) typology of wetlands, and (iii) seasonality. The contribution of a particular wetland was assessed using intrinsic characteristics (e.g. surface area, typology) and extrinsic factors (e.g. location in the watershed landscape and seasonality). Through these investigations, the results suggest, to some extent, that both IWs and RWs impact landscape hydrology. The more IWs are located in the upper part of the watershed, the greater their effect on both on high flow damping and low flow support seems to be. The more RWs are connected to a main stream, the greater their effect is. Our modelling results indicate that local landscape conditions may influence the wetland effect; promoting or limiting their efficiency, and thus their impacts on stream flows depend on a combined effect of wetland and landscape attributes. Copyright © 2015 John Wiley & Sons, Ltd.     

联合星载GPS和KBRR星间测速数据反演GRACE时变重力场模型

高精度GRACE卫星时变重力场反演一直是卫星重力测量中的难题.为了恢复高精度的时变地球重力场模型,本文联合GRACE卫星的星载GPS和KBR星间测速观测数据,在对GRACE卫星进行精密定轨的同时,解算出60阶月平均地球重力场模型.通过对GRACE卫星的定轨精度、星载GPS相位和KBR星间测速数据的拟合残差以及时变地球重力场模型解算精度等分析,表明:(1)与美国宇航局喷气推进实验室(JPL)发布的约化动力学精密轨道相比,本文确定GRACE卫星轨道三维位置误差小于5 cm.(2)星载GPS相位数据拟合残差为5~8 mm,KBR星间测速数据拟合残差为0.18~0.30μm·s~(-1).(3)解算的月平均重力场模型与美国德克萨斯大学空间研究中心(CSR)、德国地学研究中心(GFZ)和JPL发布的RL05模型精度接近,时变信号在全球范围内具有很好的空间分布一致性.通过计算亚马逊流域和长江流域的水储量变化,本文与上述三个机构的计算结果无明显差异,且相关系数均达0.9以上.可见,本文建立的卫星轨道与重力场同解算法具有反演高精度GRACE时变重力场能力,为我国卫星重力场反演提供了重要的技术支持.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Simulation of the spatio‐temporal extent of groundwater flooding using statistical methods of hydrograph classification and lumped parameter models

This article presents the development of a relatively low cost and rapidly applicable methodology to simulate the spatio‐temporal occurrence of groundwater flooding in chalk catchments. In winter 2000/2001 extreme rainfall resulted in anomalously high groundwater levels and groundwater flooding in many chalk catchments of northern Europe and the southern United Kingdom. Groundwater flooding was extensive and prolonged, occurring in areas where it had not been recently observed and, in places, lasting for 6 months. In many of these catchments, the prediction of groundwater flooding is hindered by the lack of an appropriate tool, such as a distributed groundwater model, or the inability of models to simulate extremes adequately. A set of groundwater hydrographs is simulated using a simple lumped parameter groundwater model. The number of models required is minimized through the classification and grouping of groundwater level time‐series using principal component analysis and cluster analysis. One representative hydrograph is modelled then transposed to other observed hydrographs in the same group by the process of quantile mapping. Time‐variant groundwater level surfaces, generated using the discrete set of modelled hydrographs and river elevation data, are overlain on a digital terrain model to predict the spatial extent of groundwater flooding. The methodology is applied to the Pang and Lambourn catchments in southern England for which monthly groundwater level time‐series exist for 52 observation boreholes covering the period 1975–2004. The results are validated against observed groundwater flood extent data obtained from aerial surveys and field mapping. The method is shown to simulate the spatial and temporal occurrence of flooding during the 2000/2001 flood event accurately. British Geological Survey © NERC 2011. Hydrological Processes © 2011 John Wiley & Sons, Ltd.     

A comprehensive assessment framework for attributing trends in streamflow and groundwater storage to climatic and anthropogenic changes: A case study in the typical semi-arid catchments of southern India

The clearest signs of hydrologic change can be observed from the trends in streamflow and groundwater levels in a catchment. During 1980–2007, significant declines in streamflow (−3.03 mm/year) and groundwater levels (−0.22 m/year) were observed in Himayat Sagar (HS) catchment, India. We examined the degree to which hydrologic changes observed in the HS catchment can be attributed to various internal and external drivers of change (climatic and anthropogenic changes). This study used an investigative approach to attribute hydrologic changes. First, it involves to develop a model and test its ability to predict hydrologic trends in a catchment that has undergone significant changes. Second, it examines the relative importance of different causes of change on the hydrologic response. The analysis was carried out using Modified Soil and Water Assessment Tool (SWAT), a semi-distributed rainfall-runoff model coupled with a lumped groundwater model for each sub- catchment. The model results indicated that the decline in potential evapotranspiration (PET) appears to be partially offset by a significant response to changes in rainfall. Measures that enhance recharge, such as watershed hydrological structures, have had limited success in terms of reducing impacts on the catchment-scale water balance. Groundwater storage has declined at a rate of 5 mm/y due to impact of land use changes and this was replaced by a net addition of 2 mm/y by hydrological structures. The impact of land use change on streamflow is an order of magnitude larger than the impact of hydrological structures and about is 2.5 times higher in terms of groundwater impact. Model results indicate that both exogenous and endogenous changes can have large impacts on catchment hydrology and should be considered together. The proposed comprehensive framework and approach demonstrated here is valuable in attributing trends in streamflow and groundwater levels to catchment climatic and anthropogenic changes.     

Improving the reliability of groundwater monitoring networks using combined numerical,geostatistical and neural network-based simulation models

ABSTRACT

A new methodology is proposed for improving the accuracy of groundwater-level estimations and increasing the efficiency of groundwater-level monitoring networks. Three spatio-temporal (S-T) simulation models, numerical groundwater flow, artificial neural network and S-T kriging, are implemented to simulate water-table level variations. Individual models are combined using model fusion techniques and the more accurate of the individual and combined simulation models is selected for the estimation. Leave-one-out cross-validation shows that the estimation error of the best fusion model is significantly less than that of the three individual models. The selected fusion model is then considered for optimal S-T redesign of the groundwater monitoring network of the Dehgolan Plain (Iran). Using a Bayesian maximum entropy interpolation technique, soft data are included in the geostatistical analyses. Different scenarios are defined to incorporate economic considerations and different levels of precision in selecting the best monitoring network; a network of 37 wells is proposed as the best configuration. The mean variance estimation errors of all scenarios decrease significantly compared to that of the existing monitoring network. A reduction in equivalent uniform annual costs of different scenarios is achieved.     

Spatial and temporal variations of nutrient concentration in the groundwater of a floodplain: effect of hydrology,vegetation and substrate

Spatio‐temporal variations in nitrogen and phosphorus concentrations in groundwater were analysed and related to the variations in hydrological conditions, vegetation type and substrate in an alluvial ecosystem. This study was conducted in the Illwald forest in the Rhine Plain (eastern France) to assess the removal of nutrients from groundwater in a regularly flooded area. We compared both forest and meadow ecosystems on clayey‐silty soils with an anoxic horizon (pseudogley) at 1·5–2 m depth (eutric gley soil) and a forest ecosystem on a clayey‐silty fluviosoil rich in organic matter with a gley at 0·5 m depth (calcaric gley soil). Piezometers were used to measure the nutrient concentrations in the groundwater at 2 m depth in the root layer and at 4·5 m depth, below the root layer. Lower concentrations of nitrate and phosphate in groundwater were observed under forest than under meadow, which could be explained by more efficient plant uptake by woody species than herbaceous plants. Thus NO₃‐N inputs by river floods were reduced by 73% in the shallow groundwater of the forested ecosystem, and only by 37% in the meadow. Compared with the superficial groundwater layer, the lowest level of nitrate nitrogen (NO₃‐N) and the highest level of ammonium nitrogen (NH₄‐N) were measured in the deep layer (under the gley horizon at 2·5 m depth), which suggests that the reducing potential of the anoxic horizon in the gley soils contributes to the reduction of nitrate. Nitrate concentrations were higher in the groundwater of the parcel rich in organic matter than in the one poorer in organic matter. Phosphate (PO₄‐P) concentrations in both shallow and deep groundwater are less than 62 to 76% of those found in surface water which can be related to the retention capacity of the clay colloids of these soils. Moreover, the temporal variations in nutrient concentrations in groundwater are directly related to variations in groundwater level during an annual hydrological cycle. Our results suggest that variations in groundwater level regulate spatio‐temporal variations in nutrient concentrations in groundwater as a result of the oxidation–reduction status of soil, which creates favourable or unfavourable conditions for nutrient bioavailability. The hydrological variations are much more important than those concerning substrate and type of vegetation. Copyright © 1999 John Wiley & Sons, Ltd.     

Catchment-scale vulnerability assessment of groundwater pollution from diffuse sources using the DRASTIC method: a case study

Abstract

The catchment-scale groundwater vulnerability assessment that delineates zones representing different levels of groundwater susceptibility to contaminants from diffuse agricultural sources has become an important element in groundwater pollution prevention for the implementation of the EU Water Framework Directive (WFD). This paper evaluates the DRASTIC method using an ArcGIS platform for assessing groundwater vulnerability in the Upper Bann catchment, Northern Ireland. Groundwater vulnerability maps of both general pollutants and pesticides in the study area were generated by using data on the factors depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity, as defined in DRASTIC. The mountain areas in the study area have “high” (in 4.5% of the study area) or “moderate” (in 25.5%) vulnerability for general pollutants due to high rainfall, net recharge and soil permeability. However, by considering the diffuse agricultural sources, the mountain areas are actually at low groundwater pollution risk. The results of overlaying the maps of land use and the groundwater vulnerability are closer to the reality. This study shows that the DRASTIC method is helpful for guiding the prevention practices of groundwater pollution at the catchment scale in the UK.

Citation Yang, Y. S. & Wang, L. (2010 Yang, Y. S. and Wang, L. 2010. A review of modelling tools for implementation of the EU Water Framework Directive in handling diffuse water pollution. Water Resour. Manage., 24: 1819–1843.  [Google Scholar]) Catchment scale vulnerability assessment of groundwater pollution from diffuse sources using the DRASTIC method: a case study. Hydrol. Sci. J. 55(7), 1206–1216.     

Determination of upward/downward groundwater fluxes using transient variations of soil profile temperature: test of the method with Voyons (Aube,France) experimental data

The temperature variations recorded at several points of a vertical shallow‐depth profile are governed both by conductive and convective heat transfers and can be used to calculate the vertical component of the Darcy velocity and thermal diffusivity in the soil. This paper describes such calculations when transient variations over tens of days are considered and tests them using data collected at Voyons (Aube, France). The temperature was recorded during a year and a half period with a 1 h sampling time step at three different depths: 0·2, 0·4 and 0·75 m. By processing the annual variation of temperature, we obtained a value of the Darcy velocity in good agreement with the value of actual/potential evapotranspiration ratio. By processing transient variations, despite the limitation of the calculations due to the lack of sensitivity of the sensors, results obtained at Voyons were in good correlation with tensiometric data. Copyright © 2005 John Wiley & Sons, Ltd.     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.