Water Table Depth Fluctuations Research Articles

Recent hummock establishment in the margin of a subarctic fen, Finnish Lapland

Northern fens, that host unique biota and form a remarkable carbon stock, are sensitive to changes in the moisture balance and, therefore, may be strongly affected by climatic fluctuations. However, long‐term monitoring and palaeoecological studies of fens are relatively rare and, as a result, their responses to past and current climatic fluctuations are poorly known. In this study, we examined the recent vegetation change as well as changes in testate amoeba communities in the mire margin of a subarctic fen in Finnish Lapland with four peat profiles. Testate amoebae were used as indicators of past fluctuations in water table depth. The vegetation showed a drastic shift from sedge‐dominated fen to Sphagnum‐dominated communities during the late 20th and the early 21st centuries. This shift was accompanied by a turnover in the testate amoeba community. Testate amoeba‐based water table reconstructions indicated recent drying. This may be due to the lowering of the water table either from accelerated Sphagnum increment or enhanced evaporation. The observed hummock establishment concurs with the documented hemisphere‐wide expansion trend of hummock communities in fens. This change may strengthen the carbon sink and storage capacity of these peatlands, which could be viewed as a welcome negative feedback process to the ongoing climate warming. However, the change also poses a threat to biodiversity since fens are not only species‐rich habitats but are also endangered ecosystems.

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales.

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17±11days, and lagged air and soil temperature by median values of 8±16 and 5±15days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

Dinamika Tinggi Muka Air Tanah Pada Lahan Gambut Yang Terbakar

Seasonal fluctuations of water table depth are very important regarding the sustainable peat management on tropical peatland. Water level depth can affect the greenhouse gases emissions and as the main indicator for peat fire risk management. Therefore, direct measurement of water level dynamics in the peat burning area is absolutely necessary. The research objective was studied the water level fluctuations on burning peatland in Central Kalimantan, Indonesia. Water level measurement performed manually in the field and ditch, every week from February to December 2014. Water level from February to December 2014 were -86.6 and -51.9 cm, on the ditch and field, respectively. Peat soil water level was strongly correlated to the variations of rainfall.

Water table depth fluctuations during ENSO phenomenon on different tropical peat swamp forest land covers in Katingan, Indonesia

As it is the main role to maintain hydrological function, peatland has been a limelight since drainage construction for agriculture evolved. Drainage construction will decrease water table depth (WTD) and result in CO2 emission release to the atmosphere. Regardless of human intervention, WTD fluctuations can be affected by seasonal climate and climate variability, foremost El Niño Southern Oscillation (ENSO). This study aims to determine the correlation between rainfall in Katingan and ENSO index, analyze the pattern of WTD fluctuation of open area and forest area in 2015 (during very strong El Niño) and 2016 (during weak La Niña), calculate the WTD trendline slope during the dry season, and rainfall and WTD correlation. The result showed that open area has a sharper slope of decreasing or increasing WTD when entering the dry, compared to the forest area. Also, it is found that very strong El Niño in 2015 generated a pattern of more extreme decreasing WTD during the dry season than weak La Niña in 2016.

Modeling water table depth using adaptive Neuro-Fuzzy Inference System

A comparative study on prediction of water table depth using Back Propagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is addressed in this paper. BPNN and ANFIS models are developed using precipitation, temperature, humidity, surface runoff and evapotranspiration loss to simulate water table depth fluctuations at Rengali, Odisha. Multiple linear regressions have been applied to observe the correlation between the dependent variable i.e. water table depth and the independent variables i.e. precipitation, temperature, humidity, surface runoff and evapotranspiration losses. Six different types of membership functions for ANFIS and three different transfer functions in case of BPNN are employed to compare the results of the models to predict water table depth. The model considering the physical parameters- precipitation, average temperature, evapotranspiration losses and humidity shows better performances on ANFIS as compared to BPNN. BPNN model using purelin transfer function produced best result with MAE testing 0.189701, RMSE testing 0.209842, and R2 testing 0.8512. ANFIS model with gauss2 membership function as input mf presented a superior ability to predict water table depth fluctuation over BPNN with MAE testing 0.080684, RMSE testing 0.089129, and R2 testing 0.9671. Taken as a whole, ANFIS is superior to BPNN for predicting water table depth.

Evaluating the impacts of crop rotations on groundwater storage and recharge in an agricultural watershed

The Mississippi River Valley Alluvial Aquifer, which underlies the Big Sunflower River Watershed (BSRW), is the most heavily used aquifer in Mississippi. Because the aquifer is primarily used for irrigating crops such as corn, cotton, soybean, and rice, the water levels have been declining rapidly over the past few decades. The objectives of this study are to analyze the relationship and interactions between evapotranspiration and groundwater recharge rates in the BSRW, and model the effects of various crop rotation practices on groundwater storage and recharge.The model performed well during the calibration period (R2=0.53–0.68 and NSE=0.49–0.66) and validation period (R2=0.55–0.75 and NSE=0.49–0.72) for daily streamflow, which was achieved by the SUFI-2 auto-calibration algorithm in the SWAT-CUP package. The model also performed well in simulating seasonal water table depth fluctuations during calibration (R2=0.76 and NSE=0.71) and validation (R2=0.86 and NSE=0.79). This study demonstrated a seasonal relationship between evapotranspiration and groundwater storage and recharge in the BSRW SWAT model. In general, groundwater storage decreased during the summer months while ET rates were high, and increased during the winter and spring months when ET rates were low. The crop rotation scenarios that include rice planting resulted in the lowest groundwater storage (down to −10.7%) compared to the baseline crop scenario, which is due to the high irrigation rates of the rice crop. However, the rice crop rotations resulted in the highest increases of groundwater recharge rates (up to +60.1%), likely because of the response to the deficiency of groundwater needed for irrigation as well as the limited water uptake by the rice crop. The crop rotations with corn and cotton resulted in the largest increases in groundwater storage (up to +27.2%), which is the result of the low irrigation rates as well as the short time period for irrigation applications. The results of this study are expected to aid farmers and watershed managers to conserve groundwater resources, but still maintain crop production.

Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations

The ability of a wavelet and neuro-fuzzy conjunction technique for groundwater depth forecasting was investigated in this study. The wavelet-neuro-fuzzy model was improved by combining two methods, the discrete wavelet transform and the neuro-fuzzy model. The conjunction model was applied to different input combinations of daily groundwater depth data of Bondville and Perry wells. Root mean square error (RMSE) and correlation coefficient (R) statistics were used for evaluating the accuracy of wavelet-neuro-fuzzy models. The accuracy of the conjunction models was compared with those of the single neuro-fuzzy models in one-, two- and three-day-ahead groundwater depth forecasting. Comparison of the results revealed that the wavelet-neuro-fuzzy models perform better than the neuro-fuzzy models especially for the two- and three-day-ahead forecasting cases.

Spatio-temporal stochastic resonance induces patterns in wetland vegetation dynamics

Water availability is a major environmental driver affecting riparian and wetland vegetation. The interaction between water table fluctuations and vegetation in a stochastic environment contributes to the complexity of the dynamics of these ecosystems. We investigate the possible emergence of spatial patterns induced by spatio-temporal stochastic resonance in a simple model of groundwater-dependent ecosystems. These spatio-temporal dynamics are driven by the combined effect of three components: (i) an additive white Gaussian noise, accounting for external random disturbances such as fires or fluctuations in rain water availability, (ii) a weak periodic modulation in time, describing hydrological drivers such as seasonal fluctuations of water table depth, and (iii) a spatial coupling term, which takes into account the ability of vegetation to spread and colonize other parts of the landscape. A suitable cooperation between these three terms is able to give rise to ordered structures which show spatial and temporal coherence, and are statistically steady in time.

Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations

This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting. Five different GP and ANFIS models comprising various combinations of water table depth values from two stations, Bondville and Perry, are developed to forecast one-, two- and three-day ahead water table depths. The root mean square errors ( RMSE), scatter index ( SI), Variance account for ( VAF) and coefficient of determination ( R 2) statistics are used for evaluating the accuracy of models. Based on the comparisons, it was found that the GP and ANFIS models could be employed successfully in forecasting water table depth fluctuations. However, GP is superior to ANFIS in giving explicit expressions for the problem.

Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks

Groundwater is a prominent source of drinking and domestic water in the world. In this context a reliable water supply policy, specifically during the dry season necessitates accurately acceptable predictions of water table depth fluctuations. Owing to the difficulties of identifying non-linear model structure and estimating the associated parameters, Back Propagation Neural Network (BPNN) and Radial Basis function network (RBFN) model is taken into account for study. Back propagation neural network model with delta algorithm is calibrated using historical groundwater level records and related hydro-meteorological data to simulate water table fluctuations in the study area. Similarly RBFN network has been used to analyze the water table depth prediction for four different stations. In the present investigation comparative assessment of water table depth for four different stations as well as the sensitivity of above two different models have been identified.

EFFECT OF DRAIN‐BLOCKING AND METEOROLOGICAL FACTORS ON GROUND WATER TABLE FLUCTUATIONS IN KAMANOS MIRE/GRIOVIŲ BLOKAVIMO BEI METEOROLOGINIŲ FAKTORIŲ ĮTAKA KAMANŲ PELKĖS GRUNTINIŲ VANDENŲ SLŪGSOJIMO LYGIO SVYRAVIMAMS/ ВЛИЯНИЕ ИЗМЕНЕНИЯ КЛИМАТА И УНИЧТОЖЕНИЯ КАНАВ НА ГИДРОЛОГИЧЕСКИЙ РЕЖИМ БОЛОТА КАМАНОС

The study was carried out in a raised bog located in the Kamanos state reserve in northwestern Lithuania (56°16'N, 22°39'W). The area of the raised bog is 1722 ha. To assess the effect of meteorological factors and damming of drainage ditches on the water regime of the Kamanos mire, we have analysed the water table depth fluctuations over the last 22 years in the northern part of the mire, which was drained out in 1907, and the drainage ditch was dammed in 1999. The control plots were established in the central part of the raised bog without draining activities, where Eriophorum va‐ginatum‐Sphagnum spp. communities prevail in the ground vegetation and small Scots pine trees may also be found. The results showed that, in both natural and damaged‐by‐drainage sites of the raised bog, the air moisture deficiency and the amount of precipitation had a significant effect on the fluctuation of the water table depth during the growth period (R = 0.416 - 0.761 p < 0.05). In comparison with the control plots, in 7 years after establishing dams in a 0.9–1.2 m deep ditch, the groundwater table increased by 9.8 to 12.2 cm in the area up to 980 m apart from the ditch. After damming the ditches in the extensively and intensively drained parts of the raised bog, the levelling out of the water table to a similar depth as in the control plots, occurred within 4 years and is forecasted to occur within 12 years, respectively. The water table of the drained sites of the raised bog was more sensitive to increasing annual temperatures than the water table in the undrained sites of the raised bog. Santrauka Tyrimai buvo atlikti Kamanų valstybinio gamtinio rezervato aukštapelkėje (56º16´N, 22º39´W), kuri yra šiaurės vakarų Lietuvoje. Aukštapelkės plotas – 1722 ha. Tam, kad būtų įvertinta meteorologinių faktorių bei griovių tvenkimo įtaka Kamanų pelkės hidrologijai, buvo išanalizuotos gruntinių vandenų slūgsojimo lygio fluktuacijos per pastaruosius 22 metus – 1907 m. nusausintoje ir 1999 m. patvenktoje šiaurinėje pelkės dalyje. Kontroliniai plotai išskirti centrinėje nesausintoje aukštapelkės dalyje. Čia vyrauja Eriophorum vaginatum-Sphagnum spp. (kupstinio švylio-kiminų) bendrijos su žemaūgėmis pušelėmis. Nustatyta, kad pagrindiniai meteorologiniai veiksniai, veikiantys vegetacijos periodo gruntinių vandenų fluktuacijas tiek natūraliuose, tiek sausinimu pažeistuose Kamanų aukštapelkės plotuose, yra oro drėgmės deficitas ir krituliai (R 2 = 0,416 – 0,761 p < 0,05). Per 7 metų periodą, praėjusį po 0,9–1,2 m gylio griovio, sausinusio aukštapelkę 92 m. blokavimo užtvaromis (1999 m.), gruntinių vandenų lygis 980 m pločio pagriovio zonoje, palyginus su kontrole, pakilo vidutiniškai 9,8–12,2 cm. Gruntinių vandenų atsistatymas iki kontrolinio lygio, blokavus griovius užtvaromis ekstensyviai nusausintose pelkės dalyse, įvyko per 4 metus, o intensyviai nusausintoje aukštapelkės dalyje, kaip rodo prognozės, įvyks apytiksliai per 12 m. Išaiškinta, kad į klimato šiltėjimą, t. y. į metinių temperatūrų didėjimą, nusausintų pelkių gruntiniai vandenys reaguoja labiau negu nenusausintų. Резюме Гидрологические исследования были проведены на верховом болоте государственного заповедника Каманос (56º16´N, 22º39´W), который находится на северо-западе Литвы. Площадь верхового болота – 1722 га. Целью работы было установить влияние метеорологических факторов и уничтожения осушительных канав на гидрологический режим верхового болота. Исходным материалом послужили данные о динамике уровня грунтовых вод за последние 22 года на в 1907 г. осушенных и в 1999 г. вновь затопленных площадях заповедника с болотными почвами. Контрольными площадями послужили нетронутые осушением участки болота, выделенные в центральной части заповедника, где преобладают сообщества пушицы и белого мха (Eriophorum vaginatum – Sphagnum spp.) с низкорослой сосной. Выявлено, что за 7-летний период, прошедший после уничтожения в 1999 г. осушительных канав глубиной 0,9–1,2 м, уровень грунтовых вод на приканавной полосе, равной 980 м, повысился в среднем на 9,8–12,2 см по сравнению с контрольным уровнем. Восстановление уровня грунтовых вод (до контрольного уровня) после уничтожения канав на экстенсивно осушенных площадях происходит спустя 4 года, а на интенсивно осушенных площадях – спустя 12 лет. Установлено, что на потепление климата, т. е. на повышение годовых температур, сильнее реагируют грунтовые воды осушенных болот, чем неосушенных.

Dust emission from wet and dry playas in the Mojave Desert, USA

AbstractThe interactions between playa hydrology and playa‐surface sediments are important factors that control the type and amount of dust emitted from playas as a result of wind erosion. The production of evaporite minerals during evaporative loss of near‐surface ground water results in both the creation and maintenance of several centimeters or more of loose sediment on and near the surfaces of wet playas. Observations that characterize the texture, mineralogic composition and hardness of playa – surfaces at Franklin Lake, Soda Lake and West Cronese Lake playas in the Mojave Desert (California), along with imaging of dust emission using automated digital photography, indicate that these kinds of surface sediment are highly susceptible to dust emission. The surfaces of wet playas are dynamic surface texture and sediment availability to wind erosion change rapidly, primarily in response to fluctuations in water‐table depth, rainfall and rates of evaporation. In contrast, dry playas are characterized by ground water at depth. Consequently, dry playas commonly have hard surfaces that produce little or no dust if undisturbed except for transient silt and clay deposited on surfaces by wind and water. Although not the dominant type of global dust, salt‐rich dusts from wet playas may be important with respect to radiative properties of dust plumes, atmospheric chemistry, windborne nutrients and human health. Published in 2007 by John Wiley & Sons, Ltd.

Plant species distribution in relation to water-table depth and soil redox potential in montane riparian meadows

The distribution of riparian plant species is largely driven by hydrologic and soil variables, and riparian plant communities frequently occur in relatively distinct zones along streamside elevational and soil textural gradients. In two montane meadows in northeast Oregon, USA, we examined plant species distribution in three riparian plant communities—defined as wet, moist, and dry meadow—along short topographic gradients. We established transects from streamside wet meadow communities to the dry meadow communities located on the floodplain terrace. Within each of the three communities, we sampled plant species composition and cover and monitored water-table depth and soil redox potential (Eh) at 10- and 25-cm depths through three growing seasons (1997, 1998, and 1999). The study objectives were (1) to characterize and compare seasonal patterns of water-table depth and soil redox potential in portions of the floodplain dominated by the three different plant communities; (2) to compare plant species composition, distribution, and diversity among the three communities; and (3) to relate plant species diversity and distribution to water-table depth and redox potential. Strong environmental gradients existed along the transects. Water-table depth followed the seasonal patterns of stream stage and discharge and was consistently highest in the wet meadow communities (ranging from +26 cm above the soil surface to −37 cm below the surface), lowest in the dry meadow communities (−8 cm to − 115 cm), and intermediate in the moist meadow communities (+17 cm to −73 cm). Dynamics of redox potential were associated with the seasonal fluctuations in water-table depth and differed among the plant communities. In the wet meadow communities, anaerobic soil conditions (Eh ≤ 300 mV) occurred from March through July at 10-cm depth and throughout the year at 25-cm depth. In the moist meadow communities, soils were anaerobic during spring high flows and aerobic in summer and fall during low flows. In the dry meadow communities, soil conditions were predominantly aerobic throughout the year at both depths. Wet meadow communities were dominated by sedges (Carex spp.) and had the lowest species richness and diversity, whereas dry meadow communities were composed of a mixture of grasses and forbs and had the greatest number of species. Species richness and total plant cover were negatively correlated with mean water-table depth and positively correlated with mean redox potential at 10-cm and 25-cm depths (P < 0.01). Distribution of the 18 most abundant plant species in relation to water-table depth and soil redox potential showed that certain species, such as the obligate wetland sedges, occurred within a fairly restricted range of water-table depth, whereas other graminoids occurred over wide ranges. These results suggest that the biological diversity often observed in montane riparian meadows is strongly related to steep environmental gradients in hydrology and soil redox status.

Artificial neural network modeling of water table depth fluctuations

Three types of functionally different artificial neural network (ANN) models are calibrated using a relatively short length of groundwater level records and related hydrometeorological data to simulate water table fluctuations in the Gondo aquifer, Burkina Faso. Input delay neural network (IDNN) with static memory structure and globally recurrent neural network (RNN) with inherent dynamical memory are proposed for monthly water table fluctuations modeling. The simulation performance of the IDNN and the RNN models is compared with results obtained from two variants of radial basis function (RBF) networks, namely, a generalized RBF model (GRBF) and a probabilistic neural network (PNN). Overall, simulation results suggest that the RNN is the most efficient of the ANN models tested for a calibration period as short as 7 years. The results of the IDNN and the PNN are almost equivalent despite their basically different learning procedures. The GRBF performs very poorly as compared to the other models. Furthermore, the study shows that RNN may offer a robust framework for improving water supply planning in semiarid areas where aquifer information is not available. This study has significant implications for groundwater management in areas with inadequate groundwater monitoring network.

Bioremediation of nitrate-contaminated shallow soils and waters via water table management techniques: evolution and release of nitrous oxide

Nitrate (NO 3–N) commonly accumulates in soils because of fertilizer additions or when crop demand is much less than the rate of NO 3–N production. Water table management (WTM) has been proposed to stimulate denitrifying bacteria, thus removing the accumulated NO 3–N by converting it to N 2O (a greenhouse gas) and N 2. We studied the emission of N 2O and N 2 as affected by water table depth. Undisturbed soil columns (30 cm dia by 90 cm long) from three soil series (Blount, somewhat poorly drained Aeric Ochraqualf; Clermont, poorly drained Typic Glossaqualf; and Huntington, well drained Fluventic Hapludoll) were treated with 2.11 g N (as KNO 3) applied as a band 10 cm below the surface. Two different WTM schemes were studied: static (WTM1) and dynamic (WTM2). We repeated WTM2 using 15N and this treatment, applied to the Huntington soil only, was designated WTM3. In general, N 2O concentrations in a soil column responded to fluctuations in water table depth. Concentrations of N 2O were usually higher in soils immediately below, as compared to above, the water table. The Clermont columns departed from this general trend. Maintaining the water table at 50 cm depth resulted in N 2O emission rates (1.8–44 mg N 2O–N m −2 d −1) comparable to those reported for cultivated fields. A water table only 10 cm below the surface caused N 2O emission rates to increase considerably (60–560 mg N 2O–N m −2 d −1). Four days after imposition of a water table 10 cm below the soil surface, N 2O comprised 95% of the N gas emitted (i.e. N 2O mole fraction was 0.95). One week later, however, the N 2O mole fraction was 0.35 which was significantly ( P≤0.05) lower than the mole fraction (0.68) measured prior to raising the water table. These results suggest that when using WTM practices, the best option to maintain high NO 3–N removal rates and to reduce the proportion of N 2O in the emitted gases is to maintain a high water table for a prolonged period in the most biologically-active portion of the soil profile.

STATISTICAL METHOD FOR EVALUATION OF A WATER TABLE MANAGEMENT MODEL

Evaluations of Hydrologic/Water Quality (H/WQ) models have been largely based on the comparison of modelresults with observed data for a particular site-specific location. However, this procedure is usually subject to extensiveparameter estimation within the simulation model to best fit the model results to the observed data. In direct contrast to thisapproach, methods developed by Haan et al. (1995) utilize input probability distributions to transform parameteruncertainty into prediction uncertainty using probability distribution functions. In this study, a procedure is introduced forapplying the statistical approach defined by Haan et al. (1995) to water table management models such as DRAINMOD, aH/WQ model used to simulate lateral and deep seepage through the soil profile. In the evaluation procedure, probabilitydistribution functions were developed for the most sensitive input parameters, output probability distribution functions weredeveloped using Monte Carlo simulation, and the output probability distribution functions were used to assess the model.The model was evaluated based on its capability to predict average annual runoff volume, subsurface drainage volume, anddaily fluctuations of water table depth. DRAINMOD performed successfully in the evaluative procedure in predicting therunoff and subsurface drainage for the research site. Furthermore, water table depth fluctuations, which were expected tobe most susceptible to input uncertainty, were also predicted accurately by the model.

Root growth, water potential, and yield of irrigated rice

Root length density (LV), mid-day leaf water potential (Ψleaf) and yield of wheat were studied in 1983 – 1984 and 1984 – 1985 on a Phoolbagh clay loam (Typic Haplaquoll) and on a Beni silty clay loam (Aquic Hapludoll) in the Tarai region of Uttar Pradesh under naturally fluctuating shallow (0.4 – 0.9 m, SWT) and medium-depth (0.8 – 1.3 m, MWT) water table conditions with six water regimes: rainfed (I0); irrigation at cown root initiation (I1); at crown root initiation and milk (I2); at crown root initiation, maximum tillering and milk (I3); at crown root initiation, maximum tillering, flowering and milk (I4); and at crown root initiation, maximum tillering, flowering, milk and dough (I5). Maximum rooting depth (0.8 m under SWT and 1.05 m under MWT conditions) was attained at the dough stage (115 days after sowing, DAS) and was more strongly influenced by fluctuations in water table depth than by the water regime. For wet regimes (I2– I5), roots were concentrated at and above the water table interface and had greater horizontal development, whereas in dry regimens (I0 and I1), due to deficient moisture conditions in the upper soil layer (0.45 m) they invaded lower horizons and had a greater vertical distribution Ψleaf was not significantly affected by water regime (I1– I5) up to 94 DAS during a wet year (1983 – 1984) and up to 74 DAS during a dry year (1984 – 1985), but was significantly affected thereafter. Grain yields with water regimens I1– I5 during a wet year and for the I2– I5 treatments during a dry year at either water table depth were not significantly different, but there was a (non-significant) trend to lower yield with increasing soil water deficit. Under SWT in I2, the average grain yield wsa 5130 kg ha–1 and under the I3 regime, 5200 kg ha–1. Likewise, under MWT in I3, it was 5188 kg ha–1 and under the I4 regime, 5218 kg ha–1. The results indicate that application of irrigation of more than 120 and 180 mm under SWT and MWT conditions, respectively, did not raise yield. Irrigation given as per schedule I2 under SWT and I3 under MWT conditions in the Tarai situation, appears to be more effective than a very wet regime (I5).

Regressive modelling of soil water table fluctuations

Environmental and agricultural studies require spatial information on the soil water table level. Data collection based on dip-well measurements requires frequent measurements and a high number of dip-wells as the soil water table depth is varying strongly within short distances and time intervals. This paper presents a model based method, requiring a limited number of dip-wells and related mapped soil information, to describe water table depth fluctuations in time and space. A dip-well specific, regressive model was developed with meteorological data as input. This model described the fluctuations in time at the dip-well locations successfully. Based on the relationship between model parameters and mapped soil data, spatially extended models were created.

A methodology to evaluate the reliability of the Belgian soil map for predicting the actual water table characteristics

Most data on Belgian soils were gathered during the National Soil Survey (1947–1971), which resulted in the Belgian soil map (1:20,000). Due to the impact of man on the soil some of this information may be outdated. This study presents a methodology for evaluating soil maps with respect to actual water table characteristics. Ninety dip-wells were located on permanent grassland of two contrasting areas. The phreatic water table depth of these dip-wells was recorded fortnightly during a period of 2 to 9 years (1984–1992). A dip-well specific, regressive model was developed to describe these water table depth fluctuations and to detect changes in water management. This was found to be the case in 10 dip-wells, making them unsuitable for further analyses. Of the remaining 80 dip-wells the mean highest water table depth and mean lowest water table depth were calculated as characterising parameters of the water table depth fluctuations. In the Polder Area we found several indications of changes in the fluctuations of the water table depth due to human interventions during recent decades. Recorded depth to the gley mottles was found to be no longer related to the actual water table depth characteristics ( R 2 = 0.01). Accordingly, if information on the actual water table depth fluctuations is needed, these pedogenetic features are no longer workable. The Plateau of Izenberge was hardly influenced by water management practices. Therefore, the depth to the gley mottles could still be related to the mean highest water table depth ( R 2 = 0.68). Accordingly, it remains possible to quantify the mean highest water table depth on the basis of traditional soil survey observations like those used during the National Soil Survey. As a consequence, the soil map data of this area can still be used to predict the actual water table characteristics.