Forensic isotope analysis to refine a hydrologic conceptual model

Water resources in the arid southwestern United States are frequently the subject of conflict from competing private and public interests. Legal remedies may remove impasses, but the technical analysis of the problem often determines the future success of legal solutions. In Owens Valley, California, the source of water for the Los Angeles Aqueduct (LAA) is flow diverted from the Owens River and its tributaries and ground water from valley aquifers. Future management of ground water delivered to the LAA needs technical support regarding quantity available, interconnection of shallow and confined aquifers, impact on local springs, and rate of recharge. Ground water flow models and ground water composition are tools already in use, but these have large uncertainty for local interpretations. This study conducted targeted sampling of springs and wells to evaluate the hydrologic system to corroborate conceptual and numerical models. The effort included measurement of intrinsic isotopic composition at key locations in the aquifers. The stable isotopic data of boron (delta(11)B), sulfur (delta(34)S), oxygen (delta(18)O), hydrogen (delta D), and tritium ((3)H) supported by basic chemical data provided rules for characterizing the upper and the lower aquifer system, confirmed the interpretation of ground water flow near faults and flow barriers, and detected hydraulic connections between the LAA and the perennial springs at key locations along the unlined reach of the LAA. This study exemplifies the use of forensic isotopic approaches as independent checks on the consistency of interpretations of conceptual models of a ground water system and the numerical hydrologic simulations.     

Electromagnetic induction prediction of soil salinity and groundwater properties in a Tunisian Saharan oasis

Abstract

Electromagnetic induction measurements (EM) were taken in a saline gypsiferous soil of the Saharan-climate Fatnassa oasis (Tunisia) to predict the electrical conductivity of saturated soil extract (ECe) and shallow groundwater properties (depth, Dgw, and electrical conductivity, ECgw) using various models. The soil profile was sampled at 0.2 m depth intervals to 1.2 m for physical and chemical analysis. The best input to predict the log-transformed soil salinity (lnECe) in surface (0–0.2 m) soil was the EMh/EMv ratio. For the 0–0.6 m soil depth interval, the performance of multiple linear regression (MLR) models to predict lnECe was weaker using data collected over various seasons and years (R _a ² = 0.66 and MSE = 0.083 dS m^-1) as compared to those collected during the same period (R _a ² = 0.97, MSE = 0.007 dS m^-1). For similar seasonal conditions, for the Dgw–EMv relationship, R ² was 0.88 and the MSE was 0.02 m for Dgw prediction. For a validation subset, the R ² was 0.85 and the MSE was 0.03 m. Soil salinity was predicted more accurately when groundwater properties were used instead of soil moisture with EM variables as input in the MLR.

Editor D. Koutsoyiannis; Associate editor K. Heal

Citation Bouksila, F., Persson, M., Bahri, A., and Berndtsson, R., 2012. Electromagnetic induction predictions of soil salinity and groundwater properties in a Tunisian Saharan oasis. Hydrological Sciences Journal, 57 (7), 1473–1486.     

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub-grid or sub-watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation-runoff modelling system (NHM-PRMS) represents the sub-grid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the sub-grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day⁻¹ resulted in decreases in runoff ratio by 1.8% on average in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water-limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications.     

Development of a soil moisture‐based distributed hydrologic model for determining hydrologically based critical source areas

A simple grid cell‐based distributed hydrologic model was developed to provide spatial information on hydrologic components for determining hydrologically based critical source areas. The model represents the critical process (soil moisture variation) to run‐off generation accounting for both local and global water balance. In this way, it simulates both infiltration excess run‐off and saturation excess run‐off. The model was tested by multisite and multivariable evaluation on the 50‐km² Little River Experimental Watershed I in Georgia, U.S. and 2 smaller nested subwatersheds. Water balance, hydrograph, and soil moisture were simulated and compared to observed data. For streamflow calibration, the daily Nash‐Sutcliffe coefficient was 0.78 at the watershed outlet and 0.56 and 0.75 at the 2 nested subwatersheds. For the validation period, the Nash‐Sutcliffe coefficients were 0.79 at the watershed outlet and 0.85 and 0.83 at the 2 subwatersheds. The per cent bias was less than 15% for all sites. For soil moisture, the model also predicted the rising and declining trends at 4 of the 5 measurement sites. The spatial distribution of surface run‐off simulated by the model was mainly controlled by local characteristics (precipitation, soil properties, and land cover) on dry days and by global watershed characteristics (relative position within the watershed and hydrologic connectivity) on wet days when saturation excess run‐off was simulated. The spatial details of run‐off generation and travel time along flow paths provided by the model are helpful for watershed managers to further identify critical source areas of non‐point source pollution and develop best management practices.     

Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture

This paper examines the potential for improving Soil and Water Assessment Tool (SWAT) hydrologic predictions of root-zone soil moisture, evapotranspiration, and stream flow within the 341 km² Cobb Creek Watershed in southwestern Oklahoma through the assimilation of surface soil moisture observations using an Ensemble Kalman filter (EnKF). In a series of synthetic twin experiments assimilating surface soil moisture is shown to effectively update SWAT upper-layer soil moisture predictions and provide moderate improvement to lower layer soil moisture and evapotranspiration estimates. However, insufficient SWAT-predicted vertical coupling results in limited updating of deep soil moisture, regardless of the SWAT parameterization chosen for root-water extraction. Likewise, a real data assimilation experiment using ground-based soil moisture observations has only limited success in updating upper-layer soil moisture and is generally unsuccessful in enhancing SWAT stream flow predictions. Comparisons against ground-based observations suggest that SWAT significantly under-predicts the magnitude of vertical soil water coupling at the site, and this lack of coupling impedes the ability of the EnKF to effectively update deep soil moisture, groundwater flow and surface runoff. The failed attempt to improve stream flow prediction is also attributed to the inability of the EnKF to correct for existing biases in SWAT-predicted stream flow components.     

Compared performance of a conceptual and a mechanistic hydrologic models of a green roof

In order to investigate the hydrologic response of a green roof system within the urban environment, a monitoring campaign is carried out at the green roof site of the University of Genova (Italy). Experimental data confirm that the green roof is able to significantly mitigate the generation of runoff with median values of retained volume and peak reduction, respectively, equal to 94 and 98%. A conceptual linear reservoir and a simple mechanistic (Hydrus‐1D) models are implemented to simulate the hydrologic behaviour of the system; each model is calibrated and validated based on experimental data collected at the green roof site. The hydrographs simulated with both hydrologic models reproduce with acceptable matching capabilities the experimental measurements, as confirmed by the Nash‐Sutcliffe Efficiency index generally greater than 0·60. Although the relative percentage differences evaluated for the selected hydrograph variables (the total effluent volume, the peak flow rate and the hydrograph centroid) demonstrate that the mechanistic model is more accurate, prediction errors of the conceptual model are generally limited when compared with the observed hydrologic performance. Results of the present comparison are useful in supporting conceptual model selection in case the hydrologic response is addressed for hydrologic performance assessment. Copyright © 2011 John Wiley & Sons, Ltd.     

An application of the distributed hydrologic model CASC2D to a tropical montane watershed

Increased stormflow in the Quebrada Estero watershed (2.5 km²), in the northwestern Central Valley tectonic depression of Costa Rica, reportedly has caused flooding of the city of San Ramón in recent decades. Although scientifically untested, urban expansion was deemed the cause and remedial measures were recommended by the Programa de Investigación en Desarrollo Humano Sostenible (ProDUS). CASC2D, a physically-based, spatially explicit hydrologic model, was constructed and calibrated to a June 10th 2002 storm that delivered 110.5 mm of precipitation in 4.5 h visibly exceeded the bankfull stage (0.9 m) of the Quebrada flooding portions of San Ramón. The calibrated hydrograph showed a peak discharge 16.68% (2.5 m³ s⁻¹) higher, an above flood stage duration 20% shorter, and time to peak discharge 11 min later than the same observed discharge hydrograph characteristics. Simulations of changing land cover conditions from 1979 to 1999 showed an increase also in the peak discharge, above flood stage duration, and time to peak discharge. Analysis using a modified location quotient identified increased urbanization in lower portions of the watershed over the time period studied. These results suggest that increased urbanization in the Quebrada Estero watershed have increased flooding peaks, and durations above threshold, confirming the ProDUS report. These results and the CASC2D model offer an easy-to-use, pragmatic planning tool for policymakers in San Ramón to assess future development scenarios and their potential flooding impacts to San Ramón.     

Urban hydrologic trend analysis based on rainfall and runoff data analysis and conceptual model calibration

Urban stormwater is a major cause of urban flooding and natural water pollution. It is therefore important to assess any hydrologic trends in urban catchments for stormwater management and planning. This study addresses urban hydrological trend analysis by examining trends in variables that characterize hydrological processes. The original and modified Mann‐Kendall methods are applied to trend detection in two French catchments, that is, Chassieu and La Lechere, based on approximately 1 decade of data from local monitoring programs. In both catchments, no trend is found in the major hydrological process driver (i.e., rainfall variables), whereas increasing trends are detected in runoff flow rates. As a consequence, the runoff coefficients tend to increase during the study period, probably due to growing imperviousness with the local urbanization process. In addition, conceptual urban rainfall‐runoff model parameters, which are identified via model calibration with an event based approach, are examined. Trend detection results indicate that there is no trend in the time of concentration in Chassieu, whereas a decreasing trend is present in La Lechere, which, however, needs to be validated with additional data. Sensitivity analysis indicates that the original Mann‐Kendall method is not sensitive to a few noisy values in the data series.     

Quantifying watershed surface depression storage: determination and application in a hydrologic model

Hydrologic models often require correct estimates of surface macro‐depressional storage to accurately simulate rainfall–runoff processes. Traditionally, depression storage is determined through model calibration or lumped with soil storage components or on an ad hoc basis. This paper investigates a holistic approach for estimating surface depressional storage capacity (DSC) in watersheds using digital elevation models (DEMs). The methodology includes implementing a lumped DSC model to extract geometric properties of storage elements from DEMs of varying grid resolutions and employing a consistency zone criterion to quantify the representative DSC of an isolated watershed. DSC obtained using the consistency zone approach is compared to DSC estimated by “brute force” (BF) optimization method. The BF procedure estimates optimal DSC by calibrating DRAINMOD, a quasi‐process based hydrologic model, with observed streamflow under different climatic conditions. Both methods are applied to determine the DSC for relatively low‐gradient coastal plain watersheds on forested landscape with slopes less than 3%. Results show robustness of the consistency zone approach for estimating depression storage. To test the adequacy of the calculated DSC values obtained, both methods are applied in DRAINMOD to predict the daily watershed flow rates. Comparison between observed and simulated streamflow reveals a marginal difference in performance between BF optimization and consistency zone estimated DSCs during wet periods, but the latter performed relatively better in dry periods. DSC is found to be dependent on seasonal antecedent moisture conditions on surface topography. The new methodology is beneficial in situations where data on depressional storage is unavailable for calibrating models requiring this input parameter. Copyright © 2012 John Wiley & Sons, Ltd.     

Development of probability distributions for urban hydrologic model parameters and a Monte Carlo analysis of model sensitivity

This paper proposes an approach to estimating the uncertainty related to EPA Storm Water Management Model model parameters, percentage routed (P_R) and saturated hydraulic conductivity (K_sat), which are used to calculate stormwater runoff volumes. The methodology proposed in this paper addresses uncertainty through the development of probability distributions for urban hydrologic parameters through extensive calibration to observed flow data in the Philadelphia collection system. The established probability distributions are then applied to the Philadelphia Southeast district model through a Monte Carlo approach to estimate the uncertainty in prediction of combined sewer overflow volumes as related to hydrologic model parameter estimation. Understanding urban hydrology is critical to defining urban water resource problems. A variety of land use types within Philadelphia coupled with a history of cut and fill have resulted in a patchwork of urban fill and native soils. The complexity of urban hydrology can make model parameter estimation and defining model uncertainty a difficult task. The development of probability distributions for hydrologic parameters applied through Monte Carlo simulations provided a significant improvement in estimating model uncertainty over traditional model sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

A conceptual hydrologic model for a forested Carolina bay depressional wetland on the Coastal Plain of South Carolina,USA

This paper describes how climate influences the hydrology of an ephemeral depressional wetland. Surface water and groundwater elevation data were collected for 7 years in a Coastal Plain watershed in South Carolina USA containing depressional wetlands, known as Carolina bays. Rainfall and temperature data were compared with water‐table well and piezometer data in and around one wetland. Using these data a conceptual model was created that describes the hydrology of the system under wet, dry, and drought conditions. The data suggest this wetland operates as a focal point for groundwater recharge under most climate conditions. During years of below‐normal to normal rainfall the hydraulic gradient indicated the potential for groundwater recharge from the depression, whereas during years of above‐normal rainfall, the hydraulic gradient between the adjacent upland, the wetland margin, and the wetland centre showed the potential for groundwater discharge into the wetland. Using high‐resolution water‐level measurements, this groundwater discharge condition was found to hold true even during individual rainfall events, especially under wet antecedent soil conditions. The dynamic nature of the hydrology in this Carolina bay clearly indicates it is not an isolated system as previously believed, and our groundwater data expand upon previous hydrologic investigations at similar sites which do not account for the role of groundwater in estimating the water budget of such systems. Copyright © 2007 John Wiley & Sons, Ltd.     

Development and application of a remote sensing-based salinity prediction model for a large estuarine lake in the US Gulf of Mexico coast

Salinity in estuaries is highly variable due to river discharge, tidal motion, and winds. Information on the spatial and temporal changes in salinity can provide important ecological indications, but accurate monitoring of the space–time variability for a large estuary is often costly and time-consuming. This study applied remote sensing techniques to develop a salinity prediction model for Lake Pontchartrain, a large estuarine lake located in the Northern Gulf of Mexico, USA. “Ground truth” salinity was measured along two transects across the lake and near the shoreline. Water-leaving reflectance from the measurement locations was extracted from Landsat Thematic Mapper (TM) images pre-processed through “banding” noise reduction and radiometrical correction approaches. Ordinary least square and ridge regression methods were performed to identify model parameters and to determine relationships between salinity and reflectance. Salinity in the lake on eight dates was predicted with the developed model. Difference in salinity level and patterns, and impacts of Hurricanes Katrina and Rita on salinity were assessed with ANOVA and Fuzzy Similarity methods. The results showed that the model achieved a high power in prediction of the lake salinity (R² = 0.89 and RMSE of validation = 0.27). Reflectance from TM bands 1, 2, and 4 was positively correlated to salinity levels and explained 1.9%, 20.3%, and 10.2% variance in salinity levels. Reflectance from bands 3 and 5 was negatively correlated to salinity and explained 34.1% and 31.2% variance. Under normal circumstances without the impacts of hurricanes, the lake salinity presented two patterns with average salinity level of 5.5 ppt. After Katrina’s landfall, the average was significantly increased by 1.1 ppt and the spatial patterns were altered. The pattern on 30 August 2005 was the most dissimilar one as compared to the two normal patterns, and then followed by the patterns on 9 and 25 October, and 7 September 2005. This study demonstrated that satellite remote sensing techniques can be applied to monitor salinity in coastal environments, and that freshwater discharge not only affects salinity levels and patterns under normal conditions but also is crucial for the return of salinity patterns to normal conditions after hurricane disturbance.     

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Soil moisture is a key hydrological variable in flood forecasting: it largely influences the partition of rain between runoff and infiltration and thus controls the flow at the outlet of a catchment. The methodology developed in this paper aims at improving the commonly used hydrological tools in an operational forecasting context by introducing soil moisture data into streamflow modelling. A sequential assimilation procedure, based on an extended Kalman filter, is developed and coupled with a lumped conceptual rainfall–runoff model. It updates the internal states of the model (soil and routing reservoirs) by assimilating daily soil moisture and streamflow data in order to better fit these external observations. We present in this paper the results obtained on the Serein, a Seine sub-catchment (France), during a period of about 2 years and using Time Domain Reflectivity probe soil moisture measurements from 0–10 to 0–100 cm and stream gauged data. Streamflow prediction is improved by assimilation of both soil moisture and streamflow individually and by coupled assimilation. Assimilation of soil moisture data is particularly effective during flood events while assimilation of streamflow data is more effective for low flows. Combined assimilation is therefore more adequate on the entire forecasting period. Finally, we discuss the adequacy of this methodology coupled with Remote Sensing data.     

A dynamical system approach to soil salinity and sodicity

Soil salinity and sodicity impose severe constrains to agriculture, especially in arid and semi-arid regions, where good-quality water for irrigation is scarce. While detailed models have been proposed in the past to describe the dynamics of salt and sodium in the soil, they typically require cumbersome calculations and are not amenable to theoretical analysis. Here we present an analytical model for the dynamics of salinity and sodicity in the root zone. We determine the dependence of steady-state salinity and sodicity levels on irrigation water quality and derive the trajectories in the phase space. The only stationary solution the equations admit is a stable node. Through numerical integration and analysis of the eigenvalues of the derived two-dimensional system of equations, the slower time scale associated with sodification is quantified with respect to the faster time scale associated to salinization. The role of different cation exchange equations (Gapon and Vanselow conventions) are shown to be practically the same with regard to the phase-space dynamics and the time scales. The results can be applied in controlling for low levels of salinity and sodicity, and in planning remediation strategies that are timely and economical.     

The soil hydrologic response to forest regrowth: a case study from southwestern Amazonia

As a large and dynamic land‐use category, tropical secondary forests may affect climate, soils, and hydrology in a manner different from primary forests or agricultural areas. We investigated the saturated hydraulic conductivity K_sat of a Kandiudult under different land uses in Rondonia, Brazil. We measured K_sat at four depths (12·5, 20, 30 and 50 cm) under (a) primary forest, (b) a former banana–cacao plantation (SF1), and (c) an abandoned pasture (SF2). At 12·5 cm, all three land uses differ significantly (α = 0·1), but not at the 20 and 30 cm depths. At 50 cm, K_sat was significantly greater in the former pasture than in other land uses. Lateral subsurface flow is expected during intense rainfall (about 30 times per year) at 30 cm depth in SF1 and at 50 cm depth in the forest, whereas the relatively low permeability at shallow 12·5 cm in the SF2 may result not only in lateral subsurface flow, but also saturation overland flow. For modelling purposes, recovering systems seem to have K_sat values distinct from primary forest at shallow depths, whereas at deeper layers (>20 cm) they may be considered similar to forests. Copyright © 2002 John Wiley & Sons, Ltd.     

A conceptual model of wind erosion of soil surfaces by saltating particles

A conceptual model is described for the prediction of wind erosion rates dependent on the distribution of impact energy delivered to the surface by saltating grains, P[Ei], and the distribution of local surface strength, P[Es]. Methods are presented for the measurement of both distributions and consequent loss of material from the bed. It is concluded that saltating sand grains can rupture weak crusts under even moderate wind conditions, and that the rate of erosion will depend on the shape of the distribution tails. Copyright © 1999 John Wiley & Sons, Ltd.     

Introduction of the soil/vegetation/atmosphere continuum in a conceptual rainfall/runoff model

Abstract

This paper presents the inclusion of the soil-vegetation-atmosphere interactions in a proven conceptual model. This new scheme simulates the daily streamflows over small catchments by taking into account the average characteristics of the surface (soil and vegetation) for the calculation of actual evaporation and evapotranspiration. The model also simulates the daily evolution of soil moisture in two layers: the surface layer representing the first ten centimetres of the soil and the bulk layer representing the root zone. The results of the model calibration on a test site, and the results of the model validation on 36 watersheds, show its good capability to simulate streamflows and soil moisture in the surface layer and in the bulk soil layer. These first results are very encouraging and open the possibility of using these quantities for hydrological applications.     

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.     

Experimental and conceptual evidence about the limitations of shear wave velocity to predict liquefaction

Based on the liquefaction performance of sites with seismic activity, the normalized shear wave velocity, V_s1, has been proposed as a field parameter for liquefaction prediction. Because shear wave velocity, V_s, can be measured in the field with less effort and difficulty than other field tests, its use by practitioners is highly attractive. However, considering that its measurement is associated with small strain levels, of the order of 10⁻⁴–10⁻³%, V_s reflects the elastic stiffness of a granular material, hence, it is mainly affected by soil type, confining pressure and soil density, but it is insensitive to factors such as overconsolidation and pre-shaking, which have a strong influence on the liquefaction resistance. Therefore, without taking account of the important factors mentioned above, the correlation between shear wave velocity and liquefaction resistance is weak.In this paper, laboratory test results are presented in order to demonstrate the significant way in which OCR (overconsolidation ratio) affects both shear wave velocity and liquefaction resistance. While V_s is insensitive to OCR, the liquefaction resistance increases significantly with OCR. In addition, the experimental results also confirm that V_s correlates linearly with void ratio, regardless of the maximum and minimum void ratios, which means that V_s is unable to give information about the relative density. Therefore, if shear wave velocity is used to predict liquefaction potential, it is recommended that the limitations presented in this paper be taken into account.     

Effect of spatial variability and seasonality in soil moisture on drainage thresholds and fluxes in a conceptual hydrological model

This paper uses soil moisture data from 17 recording sensors within the 50 km² Mahurangi catchment in New Zealand to determine how measured variability in soil moisture affects simulations of drainage in a typical lumped conceptual model. The data show that variability smoothes the simulated field capacity threshold such that a proportion of the catchment contributes to drainage even when mean soil moisture content is well below field capacity. Spatial variability in soil moisture controls by extension the catchment drainage behaviour: the resulting smoothed shape of the catchment‐scale drainage function is demonstrated and is also determined theoretically under simplifying assumptions. The smoothing effect increases the total simulated discharge by 130%. The analysis explains previous findings that different drainage equations are required at point scale versus catchment scale in the Mahurangi. The spatial variability and hence the emergent drainage behaviour are found to vary with season, suggesting that time‐varying parameters would be warranted to simulate drainage. Copyright © 2012 John Wiley & Sons, Ltd.