Madan K. Jha

Integrated remote sensing and GIS-based approach for assessing groundwater potential in West Medinipur district, West Bengal, India

A systematic planning of groundwater exploitation using modern techniques is essential for the proper utilization and management of this precious but shrinking natural resource. With the advent of powerful and high‐speed personal computers, efficient techniques for water management have evolved, of which RS (remote sensing), GIS (geographic information system) and GPS (Global Positioning System) are of great significance. In the present study, an attempt has been made to delineate and classify possible groundwater potential zones in the West Medinipur district of West Bengal, India using integrated remote sensing and GIS techniques. The thematic layers considered in this study are lithology, landform, drainage density, recharge, soil, land slope and surface water body, which were prepared using the IRS‐1D imagery and conventional data. All these themes and their individual features were then assigned weights according to their relative importance in groundwater occurrence and the corresponding normalized weights were obtained based on the Saatys analytical hierarchy process. The thematic layers were finally integrated using ArcInfo GIS software to yield a groundwater potential zone map of the study area. Thus, three different groundwater potential zones were identified, namely ‘good’, ‘moderate’ and ‘poor’. The area having good groundwater potential is about 1400 km2, which is about 15% of the total study area. The eastern portion and some small patches in the central and northern portions of the study area fall under moderate groundwater potential zone, which encompasses an area of 5400 km2 (55%). However, the groundwater potential in the western, south‐western and parts of north‐eastern portions of the study area is poor, encompassing an area of about 3000 km2. Moreover, the average annually exploitable groundwater reserve in the good zone was estimated to be 0.29 MCM/km2, whereas it is 0.25 MCM/km2 for the moderate zone and 0.13 MCM/km2 for the poor zone. Finally, it is concluded that the RS and GIS techniques are very efficient and useful for the identification of groundwater potential zones.

Comparative evaluation of statistical tests for time series analysis : application to hydrological time series

Abstract Statistical analyses of hydrological time series play a vital role in water resources studies. Twenty-nine statistical tests for detecting time series characteristics were evaluated by applying them to analyse 46 years of annual rainfall, 47 years of 1-day maximum rainfall and consecutive 2-, 3-, 4-, 5- and 6-day maximum rainfalls at Kharagpur, West Bengal, India. The performance of all the tests was evaluated. No severe outliers were found, and both the annual and maximum rainfall series were found to be normally distributed. Based on the known physical parameters affecting the homogeneity, the cumulative deviations and the Bayesian tests were found to be superior to the classical von Neumann test. Similarly, the Tukey test proved excellent among all the multiple comparison tests. These tests indicated that all the seven rainfall series are homogeneous. Two parametric t tests and the non-parametric Mann-Whitney test indicated stationarity in all the rainfall series. Of 12 trend detection tests, nine tests indicated no trends in the rainfall series. The Kendalls Rank Correlation test and the Mann-Kendall test were found equally powerful. Moreover, the Fourier series analysis revealed no apparent periodicities in all the seven rainfall series. The annual rainfall series was found persistent with a time lag of nine years. All the rainfall series were subjected to stochastic analysis by fitting 35 autoregressive moving-average (ARMA) models of different orders. The best-fit models for the original annual rainfall and 1-, 2- and 3-day maximum rainfall series were found to be ARMA(0,4), ARMA(0,2), ARMA(0,2) and ARMA(3,0), respectively. The best-fit model for the logarithmically transformed 4-day maximum rainfall was found to be ARMA(0,2). However, for the inversely transformed 4-, 5- and 6-day maximum rainfall series, ARMA(0,1) was obtained as the best-fit model. It is concluded that proper selection of time series tests and use of several tests is indispensable for making useful and reliable decisions.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.RésuméLes potentialités des techniques de régression linéaire multiple (RLM) et de réseau neuronal artificiel (RNA), en matière de prédiction des niveaux d’eau transitoires dans un bassin souterrain, sont comparées. Les modélisations RLM et RNA ont été mises en œuvre dans 17 sites au Japon, en prenant en compte toutes les données importantes : précipitations, température ambiante, état de la rivière, 11 variables muettes saisonnières et le déphasage des précipitations, de la température ambiante, de l’état de la rivière et du niveau de l’eau souterraine. Dix sept modèles de RNA spécifiques à chacun des sites ont été développés, en utilisant des réseaux neuronaux directs multicouches formés par les algorithmes de rétro-propagation de Levenberg-Marquardt. La performance des modèles a été évaluée en recourant aux indicateurs statistiques et graphiques La comparaison des statistiques sur la qualité d’ajustement des modèles RLM et des modèles RNA indique qu’il y a, pour tous les sites, un meilleur calage entre les niveaux d’eau souterraine prédits par RNA et les niveaux observés. Les résultats sont fondés sur les indicateurs graphiques et l’analyse résiduelle. Ainsi, la conclusion est que la technique RNA est supérieure à la technique RLM pour la prédiction de la distribution spatio-temporelle des niveaux d’eau souterraine dans un bassin. Cependant, en prenant en compte ses avantages pratiques, la technique RLM est recommandée, en tant qu’outil alternatif rentable, pour la modélisation des eaux souterraines.ResumenSe compararon el potencial de las técnicas de regresión linear múltiple (MLR) y de redes neuronales artificiales (ANN) para predecir los niveles transitorios de agua en una cuenca de agua subterránea. El modelado de MLR y ANN fue llevado a cabo en 17 sitios en Japón, considerando todas las entradas significativas: precipitación, temperatura ambiente, estados de los ríos, 11 variables estacionales mudas, y la influencia de los retardos de la precipitación, temperatura ambiente, estado del río y nivel de agua subterránea. Se desarrollaron diecisiete modelos ANN en sitios específicos, usando redes neuronales multicapas de alimentación progresiva entrenadas con algoritmos de retropropagación de Levenberg-Marquardt. Se evaluó el rendimiento de los modelos usando indicadores estadísticos y gráficos. La comparación de la bondad de ajuste estadístico de los modelos MLR con aquellos de los modelos ANN indicó que existe un mejor acuerdo entre los niveles de agua subterránea predichos por ANN y los niveles observados de agua subterránea en todos los sitios, comparados con los MLR. Este hallazgo fue apoyado por los indicadores gráficos y los análisis de residuos. Así, se concluyó que la técnica ANN es superior a la técnica MLR para la predicción espacio – temporal de los niveles de agua subterránea en una cuenca. Sin embargo, considerando las ventajas prácticas de la técnica MLR, se recomienda como una herramienta y una alternativa de modelado de agua subterránea a costo razonable.摘要多元线性回归(MLR)和人工神经网络技术(ANN)在预测地下水盆地中的瞬时水位的精确度进行了对比分析。考虑到所有重要的输入因子:雨量充沛,环境温度,河流水位,11个季节雨量的虚拟变量,以及降雨的影响力的滞后和地下水位,在日本的17个地点进行了MLR和ANN模拟。采用多层前馈神经网络的Levenberg—Marquardt反向传播算法对17个特定站点进行了ANN模拟并利用统计和图形标志对模型的性能进行了评估。MLR模型和ANN模型的拟合优度统计结果显示,与MLR模型相比,ANN模型预测的所有地点的地下水位值与实际观测到的地下水位值的吻合性更好。图形指标和残差分析也证实了这一点。因此,在预测一个盆地的地下水位时空分布时,ANN技术要优于MLR技术。但是,考虑到MLR技术的优势,可以将它作为一个具有替代性和经济效益的地下水建模工具。ResumoFoi feita a comparação do potencial das técnicas de regressão linear múltipla (RLM) e de redes neuronais artificiais (RNA) na predição de níveis piezométricos transitórios numa bacia de água subterrânea. Foi aplicada a RLM e a modelação de RNA em 17 sítios no Japão tendo em conta todos os dados de entrada significativos: precipitação, temperatura ambiente, níveis hidrométricos do rio, 11 variáveis sazonais assumidas e episódios de chuva influente, temperatura ambiente, níveis hidrométricos do rio e níveis piezométricos. Foram desenvolvidos dezassete modelos de RNA específicos de cada local, usando redes neuronais de alimentação progressiva multi-camada treinadas com algoritmos de retropropagação Levenberg-Marquardt. O desempenho dos modelos foi testado usando indicadores estatísticos e gráficos. A comparação da estatística do ajuste da simulação dos modelos de RLM com os modelos de RNA indica que existe uma maior concordância entre os níveis piezométricos previstos pelas RNA e os níveis piezométricos observados em todos os locais, comparativamente com os resultados obtidos pela RLM. Esta constatação é corroborada pelos indicadores gráficos e a análise residual. Assim, conclui-se que a técnica de RNA é superior à de RLM na predição espácio-temporal da distribuição de níveis de água subterrânea numa bacia. No entanto, tendo em conta as vantagens práticas da técnica de RLM, esta é recomendada como uma ferramenta de modelação de água subterrânea alternativa e rentável.

On the Estimation of Phreatic Aquifer Parameters by the Tidal Response Technique

This paper focuses on the effects of tidal fluctuations on groundwater in the Konan groundwater basin of Japan and the methodology for estimating aquifer parameters by the tidal response technique. The field investigation revealed that the twowells (H-5 and I-2) near the coastline are significantly affectedby seawater intrusion, and the water quality is not suitable for most beneficial uses. The tidal cycle further aggravates the groundwater contamination by seawater intrusion into the basin. Using the tidal response model, the aquifer hydraulic conductivity(K) at these two sites is estimated to be 4.5 × 10-3 and 5.1 × 10-3 m s-1, respectively. It was also indicated by the inverse modeling that the tidal fluctuations affect the study area up to about 1 km from the coastline. Further, the tidal efficiency was determined in the range of 20 to 21% at Site I-2 and 38 to 41% at Site H-5. The estimates of the storage coefficient (S) based on the time lag equation were not found reliable for the phreatic aquifer. However, the tidal efficiency-factor equation yielded reliable S estimates in this study. Finally, it is concluded that the tidal response techniqueis effective and reliable for estimating aquifer parameters in the coastal region, and that the Konan basin must be managed judiciously to ensure sustainable utilization of its vital groundwater resources.

Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS

Continuous depletion of groundwater levels from deliberate and uncontrolled exploitation of groundwater resources lead to the severe problems in arid and semi-arid hard-rock regions of the world. Geostatistics and geographic information system (GIS) have been proved as successful tools for efficient planning and management of the groundwater resources. The present study demonstrated applicability of geostatistics and GIS to understand spatial and temporal behavior of groundwater levels in a semi-arid hard-rock aquifer of Western India. Monthly groundwater levels of 50 sites in the study area for 36-month period (May 2006 to June 2009; excluding 3 months) were analyzed to find spatial autocorrelation and variances in the groundwater levels. Experimental variogram of the observed groundwater levels was computed at 750-m lag distance interval and the four most-widely used geostatistical models were fitted to the experimental variogram. The best-fit geostatistical model was selected by using two goodness-of-fit criteria, i.e., root mean square error (RMSE) and correlation coefficient (r). Then spatial maps of the groundwater levels were prepared through kriging technique by means of the best-fit geostatistical model. Results of two spatial statistics (Geary’s C and Moran’s I) indicated a strong positive autocorrelation in the groundwater levels within 3-km lag distance. It is emphasized that the spatial statistics are promising tools for geostatistical modeling, which help choose appropriate values of model parameters. Nugget-sill ratio (<0.25) revealed that the groundwater levels have strong spatial dependence in the area. The statistical indicators (RMSE and r) suggested that any of the three geostatistical models, i.e., spherical, circular, and exponential, can be selected as the best-fit model for reliable and accurate spatial interpolation. However, exponential model is used as the best-fit model in the present study. Selection of the exponential model as the best-fit was further supported by very high values of coefficient of determination (r2 ranging from 0.927 to 0.994). Spatial distribution maps of groundwater levels indicated that the groundwater levels are strongly affected by surface topography and the presence of surface water bodies in the study area. Temporal pattern of the groundwater levels is mainly controlled by the rainy-season recharge and amount of groundwater extraction. Furthermore, it was found that the kriging technique is helpful in identifying critical locations over the study area where water saving and groundwater augmentation techniques need to be implemented to protect depleting groundwater resources.

Methods for Time Series Analysis

Natural time series, including hydrologic, climatic and environmental time series, which satisfy the assumptions of homogeneity, randomness, non- periodic, non-persistence and stationarity, seem to be the exception rather than the rule (Rao et al., 2003). In fact, for all water resources studies involving the use of hydrologic time series data, preliminary statistical analyses must always be carried out to confirm whether the hydrologic time series possess all the required assumptions/characteristics (Adeloye and Montaseri, 2002). Nevertheless, most time series analysis is performed using standard methods after relaxing the required conditions one way or another in the hope that the departure from these assumptions is not large enough to affect the analysis results (Rao et al., 2003). A comprehensive survey of the past studies on the hydrologic time series analysis (Machiwal and Jha, 2006) revealed that no studies considered all the aspects of time series analysis. Major work is reported dealing with only linear trend analysis, and the homogeneity, stationarity, periodicity, and persistence, which are equally important characteristics of the hydrologic time series, have been ignored. In most past studies on time series analysis, only regression and/or Kendalls rank correlation tests are applied for trend detection. Esterby (1996) and Hess et al. (2001) presented an overview of selected trend tests. Thus, very limited studies are reported to date concerning a detailed analysis of homogeneity, stationarity, periodicity and persistence in the hydrologic time series.

Natural and Anthropogenic Disasters

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