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Restoration and water table control on peatlands to limit fire risk are national priorities in Indonesia. The present study was initiated at Padang Island, Sumatra, to increase understanding on peatland hydrology in the tropic. At the pilot site, water table and precipitation were monitored at different stations. The results show variation in water table depths (WTDs) over time and space due to spatial and temporal variability in rain intensity and drainage networks. In part of the island, large-scale drainage for plantations led to deep WTD (−1.8 m) and high WTD recession rates (up to 3.5 cm/day). Around villages, farm-scale drainages had a smaller impact with a lower recession rate (up to 1.8 cm/day) and shallow WTD, typically below −0.4 m, the threshold for sustainable peatland management in Indonesia. The recession rates levelled off at 1.0 cm/day near the drained forest/plantation and at 0.5 cm/day near the farm. Deeper layers had much lower specific yield (Sy), 0.1 at −1.5 m depth, compared with top peat soils with Sy up to 0.3. Proximity to drainages extended discharge flow to deeper layers. The results highlighted the severity of peatland drainage impact on most coastal zones of Padang Island, which have intensive drainage networks. HIGHLIGHTS High spatial and temporal variability of water table was observed in Padang Island.; The variability was partially driven by variation in land use and farm drainages.; Recession rate near pulp plantations remained high (0.01 m/day) at −1.5 m depth.

In this study, metal concentrations in the water column and surface sediment of the Sabalan Dam Reservoir (SDR) were determined. Moreover, heavy metal pollution index (HPI), contamination index (CI), heavy metal evaluation index (HEI), enrichment factor (EF), geo-accumulation index (Igeo), sediment quality guidelines (SQGs), consensus-based SQGs (C-BSQGs), and mean probable effect concentration quotients (mPECQs) were evaluated for water and sediments of SDR. It was observed that metal concentrations in river entry sediment were lower, but those in river entry water were higher than corresponding values in the vicinity of the dam structure. The HPI values of water samples taken from 10 m depth in the center of SDR exceeded the critical limit, due to high concentrations of arsenic. However, according to CI, the reservoir water was not contaminated. The HEI values indicated contamination of SDR water with metals at 10 m depth. A comparison of water quality indices revealed that HEI was the most reliable index in water quality assessment, while CI and HPI were not sufficiently accurate. For SQGs, As and Cu concentrations in sediments were high, but mPECQ, Igeo, and EF revealed some degree of sediment pollution in SDR. The calculated EF values suggested minor anthropogenic enrichment of sediment with Fe, Co, V, and Ni; moderate anthropogenic enrichment with As and Mn; and moderate to severe anthropogenic enrichment with Cu. A comparison of SQG values revealed that the threshold effect and probable effect levels were the most reliable metrics in the assessment of sediment toxicity. Statistical analysis indicated similarities between metal concentrations in the center of the reservoir and near to the dam structure, as a result of similar sediment deposition behavior at these points, while higher flow velocity at the river entry point limited deposition of fine particles and associated metals.

In an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve (ROC-AUC) assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city.

Momentum exchange in the mixing region between the floodplain and the main channel is an essential hydraulic process, particularly for the estimation of discharge. The current study investigated various data mining models to estimate apparent shear stress in a symmetric compound channel with smooth and rough floodplains. The applied predictive models include random forest (RF), random tree (RT), reduced error pruning tree (REPT), M5P, and the distinguished hybrid bagging-M5P model. The models are constructed based on several correlated physical channel characteristic variables to predict the apparent shear stress. A sensitivity analysis is applied to select the best function tuning parameters for each model. Results showed that input with six variables exhibited the best prediction results for RF model while input with four variables produced the best performance for other models. Based on the optimised input variables for each model, the efficiency of five predictive models discussed here was evaluated. It was found that the M5P and hybrid bagging-M5P models with the coefficient of determination (R2) equal to 0.905 and 0.92, respectively, in the testing stage are superior in estimating apparent shear stress in compound channels than other RF, RT and REPT models.

Groundwater dependent ecosystems (GDEs) include many terrestrial and aquatic systems with high biodiversity and important ecosystem services. The need for protection of these systems has recently received increasing recognition in many regions, including the European Union (EU), as pressures on groundwater are increasing due to increased consumption in agriculture and intensive land use. A key issue is to provide legislative frameworks that safeguard the ecosystem services these systems provide. This paper reviews European legislation and present methods for theoretical frameworks, and hydrological and ecological observations of GDEs. Insights into the current state of research are provided and gaps in scientific knowledge identified. Different restoration and protection measures, such as buffer zones, are presented and evaluated. Recommendations are given for the future protection of GDEs. Future research should focus on nationally important GDE sites to establish conceptual models describing the individual and interactive impacts of multiple stressors on the hydrological and ecological functioning of GDEs. Proactive management is required to protect GDEs from contamination, for example by using extended buffer zones and careful land use planning in the groundwater capture zone.

Laboratory incubations with varying O2 and NO3 concentrations were performed with a range of filter materials used in constructed wetlands (CWs). The study included material sampled from functioning CWs as well as raw materials subjected to laboratory pre-incubation. 15N-tracer techniques were used to assess the rates of denitrification versus dissimilatory nitrate reduction to ammonium (DNRA), and the relative role of nitrification versus denitrification in producing N2O. The N2O/(N2+N2O) product ratio was assessed for the different materials. Sand, shell sand, and peat sustained high rates of denitrification. Raw light-weight aggregates (LWA) had a very low rate, while in LWA sampled from a functioning CW, the rate was similar to the one found in the other materials. The N2O/(N2+N2O) ratio was very low for sand, shell sand and LWA from functioning CWs, but very high for raw LWA. The ratio was intermediate but variable for peat. The N2O produced by nitrification accounted for a significant percentage of the N2O accumulated during the incubation, but was dependent on the initial oxygen concentration. DNRA was significant only for shell sand taken from a functioning CW, suggesting that the establishment of active DNRA is a slower process than the establishment of a denitrifying flora.

Horizontal subsurface flow constructed wetlands have proven their efficiency in treating wastewater and removing the pollutants of concern. Treatment efficiency depends on the wastewater residence time, which is a function of the hydraulic loading and the physical conditions of the constructed filter system, which can be described with effective parameters such as: hydraulic conductivity, porosity, dispersivity etc. Because spatial variability is often scale dependent, these effective parameters may be affected by the scale of the system being studied. In this paper the results of tracer experiments in constructed filters using saturated horizontal flow at three scales (small and medium lab scales and full-scale system) using the same filter media is reported. Light-weight aggregate (filter media termed Filtralite-PTM) was used at all scales. Increasing the scale was associated with increasing dispersivity, meanwhile hydraulic conductivity experienced dramatic reduction and variation by increasing the examined scale. Observed changes in the hydraulic parameters indicate that heterogeneity at different scales should be taken into account when the performance of LWA filters are evaluated from small-scale experiments.