洪水聚类有效性分析

在聚类分析中,聚类数是一个非常重要的参数,最佳聚类数的确定问题是聚类分析研究的热点之一。在模糊聚类迭代模型的基础上,首先提出了基于类间相关系数的聚类有效性指标确定最佳聚类数,并给出了最佳聚类数的确定步骤。随后以IRIS和Ruspini数据集作为分析样本验证了所提聚类有效性指标的有效性。以双牌水库的45场典型洪水过程和碧流河水库的13场典型洪水过程为例,重点进行了洪水聚类分析,进一步验证了所提公式的有效性。碧流河水库洪水聚类,遵循了成因分析和聚类分析相结合的原则,其目的是确定各类主要天气系统的典型代表过程,以便进行水库防洪分类预报调度设计。     

Water Quality Sensor Placement: A Multi-Objective and Multi-Criteria Approach

To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.

     

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.     

Sustainable Groundwater Management in the Arid Southwestern US: Coachella Valley,California

Sustainable groundwater management requires approaches to assess the influence of climate and management actions on the evolution of groundwater systems. Traditional approaches that apply continuity to assess groundwater sustainability fail to capture the spatial variability of aquifer responses. To address this gap, our study evaluates groundwater elevation data from the Coachella Valley, California, within a groundwater sustainability framework given the adoption of integrative management strategies in the valley. Our study details an innovative approach employing traditional statistical methods to improve understanding of aquifer responses. In this analysis, we evaluate trends at individual groundwater observation wells and regional groundwater behaviors using field significance. Regional elevation trends identified no significant trends during periods of intense groundwater replenishment, active since 1973, despite spatial variability in individual well trends. Our results illustrate the spatially limited effects of groundwater replenishment occur against a setting of long-term groundwater depletion, raising concerns over the definition of sustainable groundwater management in aquifer systems employing integrative management strategies.

     

Combined Management of Groundwater Resources and Water Supply Systems at Basin Scale Under Climate Change

Water stress conditions associated with population growth, climate change, and groundwater contamination, represent a significant challenge for all stakeholders in the water sector. Increasing the resilience of Water Supply Systems (WSSs) becomes of fundamental importance: along with an adequate level of service, sustainability targets must be ensured. A long-term management strategy is strictly connected to a holistic approach, based on analyses at different scales. To this end, both groundwater modeling tools and water management models, with different spatial and temporal scales, are routinely and independently employed. Here, we propose a coupled approach combining: i) groundwater models (MODFLOW) to investigate different stress scenarios, involving climate change and anthropic activities; ii) water management models (Aquator), to assess the water resources availability and the best long-term management strategy for large-scale WSS. The management models are implemented starting from input and output flows derived by groundwater models: this leads to establish a comprehensive framework usually not defined in management models and including a quantitative characterization of the aquifer. The proposed methodology, general and applicable to any study area, is here implemented to the WSS of Reggio Emilia Province, and its main groundwater resource, the Enza aquifer, considering three different stress scenarios for groundwater models (BAU, ST1, and ST2), and for management strategies (BAU, BAU_RV2, ST2). Among the key results, we observe that coupling the two model types: i) allows evaluating water resources availability in connection with management rules; ii) leads to examining more realistic operation choices; iii) permits planning of infrastructures at basin scale.

     

Agent-Based Socio-Hydrological Hybrid Modeling for Water Resource Management

Hybrid socio-hydrological modeling has become indispensable for managing water resources in an increasingly unstable ecology caused by human activity. Most work on the subject has been focused on either qualitative socio-political recommendations with an unbounded list of vague factors or complex sociological and hydrological models with many assumptions and specialized usability. In this paper, we propose a simple agent-based socio-hydrological decision modeling framework for coupling dynamics associated with social behavior and groundwater contamination. The study shows that using social health risk, instead of contaminant concentration, as an optimization variable improves water management decisions aimed at maximizing social wellbeing. The social models and computational framework are designed with enough flexibility and simplicity to encourage extensions to more general socio-hydrological dynamics without compromising either computability or complexity for better data-/model-driven environmental management.     

Assessing surface water–groundwater interactions in a complex river‐floodplain wetland‐isolated lake system

Floodplain systems are most often hydrologically complex settings characterized by highly variable surface water–groundwater interactions that are subjected to wide‐ranging wetting and drying over seasonal timeframes. This study used field methods, statistical analysis, and the Darcy's law approach to explore surface water–groundwater dynamics, interactions, and fluxes in a geographically complex river‐floodplain wetland‐isolated lake system (Poyang Lake, China). The floodplain system of Poyang Lake is affected by strongly seasonal shifts between dry and wet processes that cause marked changes in surface water and groundwater flow regimes. Results indicate that wetland groundwater is more sensitive to variations in river levels than the seasonal isolated lakes. In general, groundwater levels are lower than those of the isolated lakes but slightly higher than river levels. Statistical analysis indicates that the river hydrology plays a more significant role than the isolated lakes in controlling floodplain groundwater dynamics. Overall, the river shows gaining conditions and occasionally losing conditions with highly variable Darcy fluxes of up to +0.4 and ?0.2 m/day, respectively, whereas the isolated lakes are more likely to show slightly losing conditions (less than ?0.1 m/day). Although seasonal flux rates range from 7.5 to 48.2 m/day for surface water–groundwater interactions in the floodplain, the flux rates for river–groundwater interactions were around four to seven times higher than that of the isolated lake–groundwater interactions. The outcomes of this study have important implications for improving the understanding of the water resources, water quality, and ecosystem functioning for both the river and the lake.     

Drought Detection of Regional Nonparametric Standardized Groundwater Index

Groundwater drought index characterizes hydrological drought, aquifer characteristics and human disturbance in the hydrological system. For drought management, the values of standardized groundwater index (SGI) at local and regional scales are usually determined in a specific site and regional area. The SGI in the studied area is influenced mainly by precipitation, hydrogeology, and human disturbance occurring in the high-usage pumping area. The underlying signals of SGI at local and regional scales can therefore be identified using data clustering and decomposition analysis e.g. empirical orthogonal functions (EOFs). Using cluster analysis, the three primary SGI clusters of the investigated aquifer are identified to be situated at the proximal fan, mid-fan, and distal fan areas. With EOF, the meteorological drought pattern and the trend of long-term pumping in the aquifer are also identified. Specifically, the meteorological drought pattern is mainly from the proximal fan, while the over-pumping signal is from the coastal area of the distal fan. The regional SGI integrated with EOF is a useful and direct way for detecting and quantifying groundwater drought. The proposed method for identifying drought signals and sustainable zone for water supply is a substantial step toward an effective regional groundwater resource planning.     

Elements concentration analysis in groundwater from the North Serra Geral aquifer in Santa Helena-Brazil using SR-TXRF spectrometer

In this work the analysis of elements concentration in groundwater was performed using the synchrotron radiation total-reflection X-ray fluorescence (SR-TXRF) technique. A set of nine tube-wells with serious risk of contamination was chosen to monitor the mean concentration of elements in groundwater from the North Serra Geral aquifer in Santa Helena, Brazil, during 1 year. Element concentrations were determined applying a SR-TXRF methodology. The accuracy of SR-TXRF technique was validated by analysis of a certified reference material. As the groundwater composition in the North Serra Geral aquifer showed heterogeneity in the spatial distribution of eight major elements, a hierarchical clustering to the data was performed. By a similarity in their compositions, two of the nine wells were grouped in a first cluster, while the other seven were grouped in a second cluster. Calcium was the major element in all wells, with higher Ca concentration in the second cluster than in the first cluster. However, concentrations of Ti, V, Cr in the first cluster are slightly higher than those in the second cluster. The findings of this study within a monitoring program of tube-wells could provide a useful assessment of controls over groundwater composition and support management at regional level.     

磴口县地下水埋深时空变化特征

选取荒漠绿洲区磴口县1988年-2013年17个观测站逐月水位埋深数据,运用kernel K-means及经验模态分解(EMD)方法,探索26年来研究区地下水埋深时空变化特征。结果表明:17个测站分为三个聚类中心,第一聚类中心包括6个测站,地下水平均埋深最大。第二聚类中心包括4个测站,地下水平均埋深次之。第三聚类中心包括7个测站,地下水平均埋深最小;26年来第一和第二聚类中心地下水埋深呈增大趋势,增大幅度分别为0.014 m、0.26m。第三聚类中心地下水埋深呈减小趋势,减小幅度为0.08m;三个聚类中心地下水埋深年内变化趋势基本相同。     

National classification of surface–groundwater interaction using random forest machine learning technique

Characterization of surface–groundwater interaction is an increasingly useful skill for riverine ecologists and water resource managers interested in the dynamics of water, nutrient, and micro‐organism exchange at the reach scale, as it can be used to better represent point‐scale processes within larger catchment‐scale models. This study describes a method for predicting the nature of reach‐scale surface–groundwater interaction, using the random forest (RF) machine learning technique with national‐scale geology, hydrology, and land use data. Observed stream flow depletion and accretion surveys from riparian areas and spring‐line flow accretion surveys were also used. The RF model allowed prediction of observed losing and gaining reaches with a high prediction accuracy (“out‐of‐bag” error < 10%). The performance of the model, however, was found to be dependent on the geographic administrative region. The model was also found to be sensitive to slope, distance to headwater, distance to coast, and underlying geological characteristics. When applied in New Zealand, this approach yielded a realistic conceptual representation of national surface–groundwater dynamics that are subsequently being used to inform a national‐scale hydrological model.     

Floodplains and Connectivity Zones: Enhancing the Provision of Ecosystem Services

Floodplains cover only 6% of the Earth’s surface. Connectivity occurs in multidirectional patterns in riverbeds, throughout both the drainage paths of the tributaries and the areas contiguous to the riverbed. This study discusses the variations in the levels of the water table as a component of a spatiotemporal representation for evaluating the importance of floodplains as hydric regulatory elements, and the application of this knowledge for developing techniques for the renaturalization of water functions to produce ecosystem services. We measured the variation in the water table level in a floodplain of the Paraiba do Sul River (southeastern Brazil), whose 77 floodplains occupy only 3.87% of the basin and present a high potential for the renaturalization of their water functions. The litho-structural control point (LSCP) is situated in the river channel and mark the end of the floodplain. Areas near the LSCP remain continually saturated, storing water and contributing to dry-season hydrographs. Areas up to 4400 m away from the LSCP perform hydric regulation, storing water from floods of the riverbed and reducing its flow downstream. Areas up to 7500 m away from the LSCP have an increased absorption potential. These three areas operate differently and integrally in absorbing floodwaters and recharging the water table, influencing the increase in minimum flows in the riverbed. The understanding of the functions of these sectors enables the design of objective measures that safeguard and increase the likelihood of the renaturalization of the hydrological functions of floodplains.

     

Geographic Information Systems (GIS) for the Determination of Inundation Maps of Lake Mogan,Turkey

Abstract

The use of geographic information systems (GIS) allows for a very efficient analysis of spatial hydrologic data for water resources projects especially for water control studies. In this respect, flood hydrographs for Mogan basin, Turkey for different durations and return periods are determined by using Soil Conservation Service (SCS) method. The basic parameter of the SCS method, curve number (CN), is evaluated by using GIS employing digital land use and hydrologic soil group data derived from analog maps. A Digital Elevation Model (DEM) of Mogan basin is constructed by making use of topographic maps with a scale of 1:25,000. Flood hydrographs for these three productive subbasins of Lake Mogan are attached and superimposed to obtain the total flood hydrographs. Total flood hydrographs entering into Lake Mogan are routed by Puls Method to determine the outflow hydrographs from the lake; then, associated lake levels are computed. Furthermore, inundation maps of Lake Mogan are obtained; these results are presented by GIS techniques.     

Groundwater Drought in the Northwestern Districts of Bangladesh

Prolonged absence of groundwater within the operating range of shallow tube-wells during dry season is a common problem in the northwestern districts of Bangladesh in the recent years. In this paper, groundwater scarcity and drought in three northwestern districts of Bangladesh have been investigated. The Cumulative Deficit approach from a threshold groundwater level has been used for the computation of severity of groundwater droughts. Monthly groundwater fluctuation data collected from 85 sites is used for the study. The study shows that groundwater scarcity in 42% area is an every year phenomenon in the region. Analysis of groundwater hydrographs and rainfall time-series reveals that ever increasing groundwater extraction for irrigation in the dry season and recurrent droughts are the causes of groundwater level drop in the region.     

Lagrangian simulation of multi-step and rate-limited chemical reactions in multi-dimensional porous media

Management of groundwater resources and remediation of groundwater pollution require reliable quantification of contaminant dynamics in natural aquifers, which can involve complex chemical dynamics and challenge traditional modeling approaches. The kinetics of chemical reactions in groundwater are well known to be controlled by medium heterogeneity and reactant mixing, motivating the development of particle-based Lagrangian approaches. Previous Lagrangian solvers have been limited to fundamental bimolecular reactions in typically one-dimensional porous media. In contrast to other existing studies, this study developed a fully Lagrangian framework, which was used to simulate diffusion-controlled, multi-step reactions in one-, two-, and three-dimensional porous media. The interaction radius of a reactant molecule, which controls the probability of reaction, was derived by the agent-based approach for both irreversible and reversible reactions. A flexible particle tracking scheme was then developed to build trajectories for particles undergoing mixing-limited, multi-step reactions. The simulated particle dynamics were checked against the kinetics for diffusion-controlled reactions and thermodynamic well-mixed reactions in one- and two-dimensional domains. Applicability of the novel simulator was further tested by (1) simulating precipitation of calcium carbonate minerals in a two-dimensional medium, and (2) quantifying multi-step chemical reactions observed in the laboratory. The flexibility of the Lagrangian simulator allows further refinement to capture complex transport affecting chemical mixing and hence reactions.     

Integrated simulation of runoff and groundwater in forest wetland watersheds

A Distributed Forest Wetland Hydrologic Model （DFWHM） was constructed and used to examine water dynamics in the different climates of three different watersheds （a cold region, a sub-tropic region, and a large-scale watershed）. A phenological index was used to represent the seasonal and species changes of the tree canopy while processes of snow packing, soil freezing, and snow and ice thawing were also included in the simulation. In the cold region, the simulated fall of the groundwater level in winter due to soil f~eezing and rise in spring due to snow and ice melting compare well with the observed data. Because the evapotranspiration and interaction of surface water and groundwater are included in the model, the modeled seasonal trend of the groundwater level in the sub-tropic region is in agreement with observations. The comparison between modeled and observed hydrographs indicates that the simulations in the large-scale watershed managed to capture the water dynamics in unsaturated and saturatedzones.     

Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling

Reliable and precise forecasts of future groundwater level fluctuations are crucial constituents of sustainable management of scarce water resources and design of remediation plans. Groundwater simulations and predictions are often performed by employing physically based models, which are not applicable in a majority of water scarce areas around the globe, particularly in the developing countries like Bangladesh due to data limitations. On the other hand, data-driven statistical forecast models have demonstrated their suitability to model nonlinear and complex hydrogeological processes to forecast short- and long-term groundwater level fluctuations. The purpose of this effort is to propose a non-physical based approach by utilizing a discrete Space-State model as a prediction tool to forecast future scenarios of groundwater level fluctuations. The present study utilizes the prediction focused approach of the system identification process in which the overall objective is to develop a pragmatic dynamic system model. The performance of the proposed approach is evaluated for groundwater level data at three observation wells of Tanore upazilla in Rajshahi district, Bangladesh. Historical weekly time series data of groundwater level fluctuations from the three observation wells for 39 (1980–2018) years is used to develop the time series model, which is used for future groundwater level predictions for a period of next 22 years (up to 2040). The findings demonstrate the conceivable applicability of the proposed discrete Space-State modelling approach in forecasting future scenarios of groundwater level fluctuations in the selected observation wells.

     

Challenges and prospects of sustainable groundwater management in an agricultural plain along the Silk Road Economic Belt,north-west China

Abstract

As a major challenge in building a new and sustainable Silk Road Economic Belt, threats induced by poor groundwater management have raised stress on the groundwater resources in the Yinchuan Plain, north-west China. In the present article, an overview of groundwater development in the plain, along with the associated negative effects, is provided. A fragmented management framework is found responsible for the poor groundwater management. Efficient and effective groundwater management will require proper attention of the local authorities to the inherent interaction among various water systems. Only with enhanced cooperation, an integrated monitoring network, strengthened scientific support and active public participation can the sustainability of groundwater management of the plain be achieved.     

Hydrologic and Hydrogeologic Characterization of a Deltaic Aquifer System in Orissa, Eastern India

The present study focuses on the in-depth hydrologic and hydrogeologic analyses of Kathajodi-Surua Inter-basin within the Mahanadi deltaic system of Orissa, eastern India to explore the possibility of enhanced and sustainable groundwater supply. The results of 6 years (2001–2006) streamflow analysis indicated that the river flow is highly seasonal and it reduces to almost no flow during summer seasons. Land use map of the study area for the monsoon (Kharif) and post-monsoon (Rabi) seasons was developed by remote sensing technique and runoff estimation was done by curve number method. The runoff estimated for the 20-year period (1990–2009) varied from a minimum of 10.2% of the total monsoon rainfall in 1995 to a maximum of 43.3% in 2003. The stratigraphy analysis indicated that a leaky confined aquifer comprising medium to coarse sand exists at depths of 15 to 50 m and has a thickness of 20 to 55 m. The analysis of pumping test data at 9 sites by Aquifer-Test software indicated that the aquifer hydraulic conductivity ranges from 11.3 to 96.8 m/day, suggesting significant aquifer heterogeneity. Overall groundwater flow is from north-west to south-east direction. There is a 5 to 6 m temporal variation and 3 to 4 m spatial variation of groundwater levels over the basin. The rainfall-groundwater dynamics and stream-aquifer interaction in the river basin were studied by correlation analysis of groundwater level with weekly rainfall and river stage. The correlation between the weekly rainfall and weekly groundwater level was found to vary from ‘poor’ to ‘fair’ (r = 0.333 to 0.659). In contrast, the weekly groundwater level was found to be strongly correlated with the weekly river stage (r = 0.686 to 0.891). The groundwater quality was found suitable for both irrigation and drinking purposes. It is recommended that a simulation-cum-optimization modeling following an integrated approach is essential for efficient utilization of groundwater resources in the study area.     

Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

In semi-arid regions, the deterioration in groundwater quality and drop in water level upshots the importance of water resource management for drinking and irrigation. Therefore geospatial techniques could be integrated with mathematical models for accurate spatiotemporal mapping of groundwater risk areas at the village level. In the present study, changes in water level, quality patterns, and future trends were analyzed using eight years (2012–2019) groundwater data for 171 villages of the Phagi tehsil, Jaipur district. Kriging interpolation method was used to draw spatial maps for the pre-monsoon season. These datasets were integrated with three different time series forecasting models (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) and Artificial Neural Network models for accurate prediction of groundwater level and quality parameters. Results reveal that the ANN model can describe groundwater level and quality parameters more accurately than the time series forecasting models. The change in groundwater level was observed with more than 4.0 m rise in 81 villages during 2012–2013, whereas ANN predicted results of 2023–2024 predict no rise in water level?>?4.0 m. However, based on predicted results of 2024, the water level will drop by more than 6.0 m in 16 villages of Phagi. Assessment of water quality index reveals unfit groundwater in 74% villages for human consumption in 2024. This time series and projected groundwater level and quality at the micro-level can assist decision-makers in sustainable groundwater management.