The effective management of national hydrometric data: experiences from the United Kingdom

Abstract

Access to hydrometric information underpins many areas of effective water management. This paper explores the operational practices of one national hydrological information service, the UK National River Flow Archive, in collating, managing and providing access to river flow data. An information lifecycle approach to hydrometric data management is advocated, with the paper detailing current UK procedures in the areas of: monitoring network design and development; data sensing and recording; validation and archival; synthesis and analysis; and data dissemination. The methods and policies outlined herein are widely transferable to other hydrological data archives around the world.

Editor D. Koutsoyiannis

Citation Dixon, H., Hannaford, J., and Fry, M.J., 2013. The effective management of national hydrometric data: experiences from the United Kingdom. Hydrological Sciences Journal, 58 (7), 1383–1399.     

Effects of managed burning in comparison with vegetation cutting on dissolved organic carbon concentrations in peat soils

Given the continuing concern about rising concentrations of dissolved organic carbon (DOC) in stream water leaving peat‐covered catchments, this study has considered the impact of managed burning or cutting of Calluna vulgaris, a dominant vegetation cover in many UK peatlands. Pristine mature Calluna stands were compared with those that had been subject to cutting and or managed burning up to 5 years after intervention. The study measured the DOC concentration of both soil and surface runoff water over a period of 12 months in comparison with water table depth, conductivity, and pH. The results show the following:

相似文献   

Laboratory testing of trace metals removal from mine drainage and arsenic removal from groundwater in the Black Hills of South Dakota

The Gilt Edge Superfund Site is a former heap-leach gold mine that currently is being remediated in the Black Hills of South Dakota. Mine runoff water is treated before release from the site. The field pH, before treatment, is about 3; the water contains arsenic at low levels and some trace metals at elevated levels, in addition to total dissolved solids concentrations of more than 1,900 mg/L. In the Keystone area of the Black Hills, naturally occurring arsenic has been detected at elevated concentrations in groundwater samples from wells. The City of Keystone’s Roy Street Well, which is not used currently, showed arsenic concentrations of 36 parts per billion and total dissolved solids of 320 mg/L. With field samples of water from the Gilt Edge site, a limestone-based method was successful in reducing trace metals concentrations to about 0.001 mg/L or less; at the Keystone site, the limestone method reduced arsenic levels to about 0.006 mg/L. The results are significant because previous research with the limestone-based method mainly had involved samples prepared with distilled water in the laboratory, in which interference of other ions such as sulfate did not occur. The research indicates the potential for broader applications of the limestone-based removal method, including scale-up work at field sites for water treatment.     

Hypothesis testing of buoyant plume migration using a highly parameterized variable-density groundwater model at a site in Florida, USA

A highly parameterized variable-density groundwater flow and solute transport model was developed to test multiple hypotheses for upward movement of treated wastewater (effluent) injected into a saline coastal aquifer in southeastern Florida, USA. The model was designed to assess risk to a drinking-water aquifer above the zone of injection, where monitoring wells have detected effluent. The model-based analysis accommodated geological and data complexity, including the observed presence of effluent in upper monitoring wells, but not in lower monitoring wells, thereby giving the appearance of the effluent having bypassed geological layers. The modeling approach included the application of multiple methodologies to reduce model run times during parameter estimation while providing detailed calibrated model(s) that can be used to assess the potential capacity for different mechanisms of effluent migration. The methods included use of a semi-analytical equation to quickly calculate initial concentrations, parallelization of model runs over multiple processors when calibrating, and utilization of the concepts of singular value decomposition and Tikhonov regularization to accommodate a high level of parameterization complexity. The results reveal that vertical effluent migration could occur as diffuse flow through heterogeneous confining units; however, flow through a channelized pathway caused by well construction appears to be more likely.     

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract