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The PODMT3DMS-Tool, which consists of proper orthogonal decomposition (POD) linked to the Modular Transport 3-Dimensional Multi Species (MT3DMS) code for nitrate simulation in groundwater, is introduced. POD, as a statistical technique, reduces a large amount of information produced by the MT3DMS model to provide the main components of the PODMT3DMS-Tool, i.e., space- and time-dependent terms of nitrate. The low-dimensional components represent time- and space-dependent factors in the aquifer response such as hydraulic, hydrogeological and water quality variables represented in the simulation using the MT3DMS model. The PODMT3DMS-Tool is thus a combined statistical and conceptual model with a simple structure and comparable accuracy to MT3DMS. Practical application of the PODMT3DMS-Tool to the Karaj Aquifer in Iran for a period of 6 years revealed agreement between nitrate concentrations simulated by the PODMT3DMS-Tool and MT3DMS, with a mean absolute error of less than 0.5 mg/L in most parts of the aquifer. Moreover, the PODMT3DMS-Tool needed only about 10% of the calculation time required by MT3DMS. The PODMT3DMS-Tool can be used to predict nitrate concentration in the Karaj Aquifer, while its simplicity also makes it potentially useful for other water resources problems.

Subarctic ecohydrological processes are changing rapidly, but detailed and integrated ecohydrological investigations are not as widespread as necessary. We introduce an integrated research catchment site (Pallas) for atmosphere, ecosystems, and ecohydrology studies in subarctic conditions in Finland that can be used for a new set of comparative catchment investigations. The Pallas site provides unique observational data and high-intensity field measurement datasets over long periods. The infrastructure for atmosphere- to landscape-scale research in ecosystem processes in a subarctic landscape has recently been complemented with detailed ecohydrological measurements. We identify three dominant processes in subarctic ecohydrology: (a) strong seasonality drives ecohydrological regimes, (b) limited dynamic storage causes rapid stream response to water inputs (snowmelt and intensive storms), and (c) hydrological state of the system regulates catchment-scale dissolved carbon dynamics and greenhouse (GHG) fluxes. Surface water and groundwater interactions play an important role in regulating catchment-scale carbon balances and ecosystem respiration within subarctic peatlands, particularly their spatial variability in the landscape. Based on our observations from Pallas, we highlight key research gaps in subarctic ecohydrology and propose several ways forward. We also demonstrate that the Pallas catchment meets the need for sustaining and pushing the boundaries of critical long-term integrated ecohydrological research in high-latitude environments.

This study examined the effect of different attributes on regionalization of potential evapotranspiration (ETp) in Urmia Lake Basin (ULB), Iran, using the region of influence (RoI) framework. Data for the period 1997–2016 from 30 weather stations were selected for the analysis. To achieve similarity between stations, climate, geographical, and statistical attributes were selected. To determine the effect of each attribute, the Shannon entropy weighting method was used. The results showed that attribute weighting had a significant impact on ETp clustering. Among the groups studied, the most significant effect of weighting was observed in the statistical attributes category. Among all attributes, skewness coefficient (CS) was the most useful in determining similarity between stations. Based on the results, ULB can be divided into three homogeneous regions. Proximity of weather stations did not always indicate similarity between them, but by weighting the stations in addition to weighting the attributes, more accurate estimates of ETp in the basin were obtained. Overall, the results demonstrate potential for application of the RoI approach in regionalization of ETp, by assigning a weight to weather stations and to influencing attributes.

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.

Hydrological connections between aquifers and boreal mires need to be better understood for protection of this type of wetland. Three-dimensional (3D) models have so far been sparsely used for such systems. This study investigated the effect of parameterisation with global sensitivity analysis on groundwater-surface water (GW-SW) interactions in a boreal esker-aapa mire system. Sensitivity analysis by the elementary effect (Morris) method was applied to a 3D steady-state hydrological model built with the fully-integrated HydroGeoSphere code. Parameter sensitivity with respect to various model outputs was explored, providing comprehensive insights into the most hydrologically relevant parameters. The results indicated that depending on model outputs the most influential model parameters varied. They also revealed existence of feedback in terms of interdependence of parameters between the esker aquifer and surrounding aapa mires. The properties of the mire (or peatland) landscape affected groundwater levels in the unconfined aquifer and, conversely, esker-mire interactions depended on esker hydraulic characteristics. This implies that accurate representation of both systems is required and that reliable determination of GW-SW interactions may be impeded by parameter interactions. In this study, the van Genuchten functions were used to represent hydraulic properties of unsaturated flow domain and the results revealed that formal sensitivity analysis methods based on random sampling might not be appropriate for evaluating parametric sensitivity. If the van Genuchten parameters ranges are large, randomly sampled values may not always produce physically realistic water retention characteristics. Overall, our results demonstrate that the computationally efficient elementary effect method is a suitable tool for investigating the parametric sensitivity of integrated models, enhancing modelling of boreal groundwater-dependent ecosystems.

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.

Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.

Although dimensional analysis suggests sound functional forms (FFs) to calculate longitudinal dispersion coefficient (Kx), no attempt has been made to quantify both reliability of the estimated Kx value and its sensitivity to variation of the FFs' parameters. This paper introduces a new index named bandwidths similarity factor (bws–factor) to quantify the reliability of FFs based on a rigorous analysis of distinct calibration datasets to tune the FFs. We modified the bootstrap approach to ensure that each resampled calibration dataset is representative of available datapoints in a rich, global database of tracer studies. The dimensionless Kx values were calculated by 200 FFs tuned with the generalized reduced gradient algorithm. Correlation coefficients for the tuned FFs varied from 0.60 to 0.98. The bws–factor ranged from 0.11 to 1.00, indicating poor reliability of FFs for Kx calculation, mainly due to different sources of error in the Kx calculation process. The calculated exponent of the river's aspect ratio varied over a wider range (i.e., −0.76 to 1.50) compared to that computed for the river's friction term (i.e., −0.56 to 0.87). Since Kx is used in combination with one-dimensional numerical models in water quality studies, poor reliability in its estimation can result in unrealistic concentrations being simulated by the models downstream of pollutant release into rivers.

Cycling of water quality constituents in lakes is affected by thermal stratification and homo-thermal conditions and other factors such as oligotrophication, eutrophication, and microbial activities. In addition, hydrological variability can cause greater differences in water residence time and cycling of constituents in man-made lakes (reservoirs) than in natural lakes. Thus, investigations are needed on vertical mixing of constituents in new impounded reservoirs, especially those constructed to supply domestic water. In this study, sampling campaigns were conducted in the Sabalan reservoir, Iran, to investigate vertical changes in constituent concentrations during the year in periods with thermal stratification and homo-thermal conditions. The results revealed incomplete mixing of constituents, even during cold months when the reservoir was homo-thermal. These conditions interacted to create a bottom-up regulated reservoir with sediment that released settled pollutants, impairing water quality in the Sabalan reservoir during both thermal stratification and homo-thermal conditions. Analysis of total nitrogen and total phosphorus concentrations revealed that the reservoir was eutrophic. External pollution loads, internal cycling of pollutants diffusing out from bottom sediments, reductions in inflow to the reservoir, and reservoir operations regulated vertical mixing and concentrations of constituents in the Sabalan reservoir throughout the year.

Highlights • Nutrient and SS estimations were predicted by comparing different approaches. • Peatland drainage strongly affected TN, TP, and SS loads and concentrations. • Uncertainty in estimates captured 29–90% of measured TN, TP, and SS values. • The uncertainty in export coefficients decreased with catchment size. Maintaining and improving surface water quality requires knowledge of nutrient and sediment loads due to past and future land-use practices, but historical data on land cover and its changes are often lacking. In this study, we tested whether land-use-specific export coefficients can be used together with satellite images (Landsat) and/or regional land-use statistics to estimate riverine nutrient loads and concentrations of total nitrogen (TN), total phosphorus (TP), and suspended solids (SS). The study area, Simojoki (3160 km2) in northern Finland, has been intensively drained for peatland forestry since the 1960s. We used different approaches at multiple sub-catchment scales to simulate TN, TP, and SS export in the Simojoki catchment. The uncertainty in estimates based on specific export coefficients was quantified based on historical land-use changes (derived from Landsat data), and an uncertainty boundary was established for each land-use. The uncertainty boundary captured at least 60% of measured values of TN, TP, and SS loads or concentrations. However, the uncertainty in estimates compared with measured values ranged from 7% to 20% for TN, 0% to 18% for TP, and 13% to 43% for SS for different catchments. Some discrepancy between predicted and measured loads and concentrations was expected, as the method did not account for inter-annual variability in hydrological conditions or river processes. However, combining historical land-use change estimates with simple export coefficients can be a practical approach for evaluating the influence on water quality of historical land-use changes such as peatland drainage for forest establishment.

Nordic water bodies face multiple stressors due to human activities, generating diffuse loading and climate change. The ‘green shift’ towards a bio-based economy poses new demands and increased pressure on the environment. Bioeconomy-related pressures consist primarily of more intensive land management to maximise production of biomass. These activities can add considerable nutrient and sediment loads to receiving waters, posing a threat to ecosystem services and good ecological status of surface waters. The potential threats of climate change and the ‘green shift’ highlight the need for improved understanding of catchment-scale water and element fluxes. Here, we assess possible bioeconomy-induced pressures on Nordic catchments and associated impacts on water quality. We suggest measures to protect water quality under the ‘green shift’ and propose ‘road maps’ towards sustainable catchment management. We also identify knowledge gaps and highlight the importance of long-term monitoring data and good models to evaluate changes in water quality, improve understanding of bioeconomy-related impacts, support mitigation measures and maintain ecosystem services.

The anthropogenic impacts of development and frequent droughts have limited Iran's water availability. This has major implications for Iran's agricultural sector which is responsible for about 90% of water consumption at the national scale. This study investigates if declining water availability impacted agriculture in Iran. Using the Mann‐Kendall and Sen's slope estimator methods, we explored the changes in Iran's agricultural production and area during the 1981‐2013 period. Despite decreasing water availability during this period, irrigated agricultural production and area continuously increased. This unsustainable agricultural development, which would have been impossible without the over‐abstraction of surface and ground water resources, has major long‐term water, food, environmental, and human security implications for Iran. Given the heavy reliance of the agricultural sector on water availability, it is important to examine if Iran's agriculture has been impacted by water availability changes in recent decades. The investigation of the long‐term impacts of natural water availability changes on agricultural activities in the country during the 1981–2013 period revealed that the agricultural sector in Iran continued to expand regardless of decreasing water availability in the country. This expansion was facilitated by the excessive use of nonrenewable water resources which has significant environmental and socioeconomic implications. Validerad;2020;Nivå 2;2020-10-22 (alebob)

In cold (i.e. boreal, subarctic, snowy) climate zones, dynamic groundwater storage is greatly affected by the timing and amount of snowmelt. With global warming, cold climates in the northern hemisphere will transition to temperate. As temperatures rise, the dominant type of precipitation will change from snow to rain in winter. Further, the growing season is prolonged. This has a direct impact on the aquifer recharge pattern. However, little is known about the effect of changing annual recharge regimes on groundwater storage. The present work deduces the impact of shifting climate zones on groundwater storage by evaluating the effect of climate seasonality on intra-annual hydraulic head fluctuations. The work compares intra-annual hydraulic head fluctuations in a temperate-cold climate transition zone (Fennoscandia) from two different periods (1980–1989, 2001–2010). This is done by associating rising vs. declining hydraulic heads with hydrometeorology. Due to the northwards migration of the temperate climate zone, there is a shift in seasonality between the two periods. This has a negative impact on groundwater levels, which are significantly lower in 2001–2010, particularly near the climate transition zone. The results demonstrate that increasing temperatures in cold climate regions may change the seasonality of groundwater recharge, by altering the main recharge period from being snowmelt-dominated (spring) to rain-dominated (winter). Additionally, this is connected to the duration of the growing season, which impedes groundwater recharge. The coupled effect of this on groundwater in the study area has led to a significant decrease in groundwater storage.

Optimal nutrient pollution monitoring and management in catchments requires an in-depth understanding of spatial and temporal factors controlling nutrient dynamics. Such an understanding can potentially be obtained by analysing stream concentration&ndash, discharge (C-Q) relationships for hysteresis behaviours and export regimes. Here, a classification scheme including nine different C-Q types was applied to a total of 87 Nordic streams draining mini-catchments (0.1&ndash, 65 km2). The classification applied is based on a combination of stream export behaviour (dilution, constant, enrichment) and hysteresis rotational pattern (clock-wise, no rotation, anti-clockwise). The scheme has been applied to an 8-year data series (2010&ndash, 2017) from small streams in Denmark, Sweden, and Finland on daily discharge and discrete nutrient concentrations, including nitrate (NO3&minus, ), total organic N (TON), dissolved reactive phosphorus (DRP), and particulate phosphorus (PP). The dominant nutrient export regimes were enrichment for NO3&minus, and constant for TON, DRP, and PP. Nutrient hysteresis patterns were primarily clockwise or no hysteresis. Similarities in types of C-Q relationships were investigated using Principal Component Analysis (PCA) considering effects of catchment size, land use, climate, and dominant soil type. The PCA analysis revealed that land use and air temperature were the dominant factors controlling nutrient C-Q types. Therefore, the nutrient export behaviour in streams draining Nordic mini-catchments seems to be dominantly controlled by their land use characteristics and, to a lesser extent, their climate.

Impacts of mining on water quality are a great concern in the Arctic region. This study evaluated the impact of pre-treated mine effluent on river water quality. The study was conducted along the Seurujoki River in sub-Arctic Finland, which is impacted by Kittila gold mine. The study analyzed water quality and hydrological data upstream and downstream of the mining area over an eight-year period, including a tailing dam leakage event in 2015. The analysis focused on water quality determinants such as electrical conductivity (EC), sulfate, antimony, manganese, and total nitrogen (Ntotal). Descriptive statistics on river water at four stations along the river corridor showed negative impacts of mining activities on the recipient water body. In order to find an indicator for water quality, correlation analysis between the water quality determinants was carried out. It identified EC as a good indicator for continuous water quality monitoring, especially to detect mining accidents such as partial failure of a tailings dam. The results showed increasing contaminant concentrations due to mining as more mine effluent was generated over time. A linear mixed model was developed to predict the coefficient of different elements affecting EC at river water monitoring stations impacted by mining effluents. The results provide new information on how to assess mining water impacts and plan future water quality monitoring.

Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

The Arvandroud river (also known as Shatt-al-Arab) and its estuary have been degraded due to the changing river flow regime in the Tigris and Euphrates. This study assessed changes in flow from the major rivers and the impacts on the estuary. To assess the river flow changes, three major flow regime attributes were computed: timing (TIF), magnitude (MIF), and variability (VIF). By combining these indices, the total flow regime impact factor (IF) was scaled between 0 and 1, and classified into five groups: Low (0.80

Bed siltation can drastically alter the physical conditions of headwater streams and is therefore a stressor for stream ecosystems. We studied 32 headwater streams that represented near‐natural (reference; N = 11), sediment‐impacted (N = 12), or wood‐ (N = 4) or stone‐restored (N = 5) streams to quantify how extensive siltation and restoration with either large woody debris (LWD) or boulder structures influence transient storage conditions. We carried out repeated stream tracer experiments, field measurements of habitat characteristics, and numerical simulations to determine the effects of siltation and restoration on total transient storage. Compared with reference streams, impacted streams had a smaller storage zone cross‐sectional area (Aₛ/A) ratio and fraction of median travel time due to transient storage (F₂₀₀), whereas restored streams had transient storage conditions similar to near‐natural conditions. Both of the two restoration methods had positive but differing impacts on bed sediment and transient storage properties. The LWD restoration created diverse total transient storage conditions, whereas boulder restoration decreased fine sediment cover. Addition of both LWD and boulders could thus aid the recovery of headwater streams from excessive sediment input and increase transient storage and in‐stream habitat complexity.

Fifteen temperature indices recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices) were applied to evaluate spatiotemporal variability and trends in annual intensity, frequency, and duration of extreme temperature statistics in Finland during 1961–2011. Statistically significant relationships between these high-resolution (10 km) temperature indices and seven influential Northern Hemisphere teleconnection patterns (NHTPs) for the interannual climate variability were also identified. During the study period (1961–2011), warming trends in extreme temperatures were generally manifested by statistically significant increases in cold temperature extremes rather than in the warm temperature extremes. As expected, warm days and nights became more frequent, while fewer cold days and nights occurred. The frequency of frost and icing days also decreased. Finland experienced more (less) frequent warm (cold) temperature extremes over the past few decades. Interestingly, significant lengthening in cold spells was observed over the upper part of northern Finland, while no clear changes are found in warm spells. Interannual variations in the temperature indices were significantly associated with a number of NHTPs. In general, warm temperature extremes show significant correlations with the East Atlantic and the Scandinavia patterns and cold temperature extremes with the Arctic Oscillation and the North Atlantic Oscillation patterns.

In cold climate regions, ice roads are engineered as temporary winter transportation routes on frozen lakes, rivers and seas. The ice road season start, end and duration principally depend upon ice thickness, which is controlled by surface air temperature (SAT) in terms of freezing and thawing degree-days (FDD and TDD, respectively). Both FDD and TDD are indicators of climate variability and change, and are naturally influenced by large-scale atmospheric circulation patterns (ACPs). This study examined the role of ACPs in interannual variations in the operating season of the Oulu-Hailuoto ice road in the Bay of Bothnia, northern Baltic Sea, during 1974–2009. Significant (p

Continuous data on spatial and temporal patterns of snowmelt rates are essential for hydrological studies, but are commonly not available, especially in the subarctic, mainly due to high monitoring costs. In this study, temperature loggers were used to measure local and microscale variations in snowpack temperature, in order to understand snowmelt processes and rates in subarctic northern Finland. The loggers were deployed on six test plots along a hillslope with varying topography (elevation and aspect) and vegetation (forest, transitional zone and mires, i.e. treeless peatlands) during two consecutive winters (2014 and 2015). At each test plot, the sensors were installed in five locations, at two heights in a snow profile. Algorithms were developed to analyse the snowmelt rates from high-resolution snowpack temperature data. The validity of the results was evaluated using snow depth and soil moisture data from adjacent reference sensors and the results were tested using an empirical degree-day snow model calibrated for each test plot. Snowmelt rates were relatively similar in mires (median 2.3 mm d−1 °C−1) and forests (median 2.6 mm d−1 °C−1) with apparent inter-annual variation. The observed melt rates were highest in the highest elevation plots, in transition zone in 2014 (median 4.6 mm d−1 °C−1) and southwest-facing forest line in 2015 (median 3.2 mm d−1 °C−1). The timing of the modelled meltwater outflow and snowpack ablation showed good agreement with the snowpack temperature-derived estimates and the soil moisture and snow depth measurements. The simple approach used represents a novel and cost-effective method to improve the spatial accuracy of in situ snow cover ablation measurements and melt rates and the precision of snowmelt models in the subarctic. An open-access R-based model is provided with this paper for analysis of high-frequency snow temperature data.

Saline lakes have diminished considerably due to large-scale irrigation projects throughout the world. Environmental flow (EF) release from upstream reservoirs could help conserve and restore these lakes. However, experiences from regions lacking environmental legislation or with insufficient water resources management show that, despite EF allocation, farmers tend to use all available water for agriculture. In this study, we employed a new method for designing environmental flow release strategies to restore desiccated terminal lakes in arid and semi-arid regions with intensive cultivation within the catchment. The novelty of the method is that it takes into account farmers’ water use behavior and the natural flow regime in upstream systems to design an optimum monthly EF release strategy for reservoirs. We applied the method to the water resource system of Lake Urmia, once the largest saline lake in the Middle East and now one of the most endangered saline lakes in the world. The analysis showed that the EF released is exploited by lowland farmers before reaching Lake Urmia and that inflow to the lake from some rivers has decreased by up to 80%. We propose a new EF release strategy that requires a considerable change in practice whereby water is released in the shortest possible time (according to reservoir outlet capacity) during the period of lowest irrigation demand in winter. Restoring the lake to minimum ecological level would require 2.4–3.4 km³ EF allocation by different methods of release based on the recent condition (2002–2011) of the lake.

Nutrient export from drained peatland has significant impacts on aquatic environments in Nordic catchments. Spatial information on variations in nutrient concentrations across different landscapes and land uses is needed to design measures for achieving the good ecological status of the EU Water Framework Directive. In this study we determined background concentrations in natural peatland-dominated streams and examined effects of peatland use practices on water quality in headwater catchments. We quantified sources for different elements by joint analysis of water chemistry, isotopes, and hydrology for 62 small catchments in North Ostrobothnia, Finland. Concentrations of nutrients and suspended solids were relatively high in catchments containing drained peatland. In particular, dissolved nitrogen and phosphorus concentrations were elevated during baseflow conditions when flow likely originated from deeper soil layers. Total concentrations of nutrients, suspended solids, and loss on ignition also showed elevated values, and changes in the ratio of dissolved and particulate forms, especially the C/N ratio, were observed. Past drainage had a stronger effect on organic matter and nutrients concentrations than local hydroclimate conditions. These results strongly indicate that current water protection methods are not sufficient to capture all seasonal variations in nutrient and suspended solid loads from drained peatland. Thus, more effort and actions are needed for water protection in such areas.

Drainage is known to affect peatland natural hydrology and water quality, but peatland restoration is considered to ameliorate peatland degradation. Using a replicated BACIPS (Before-After-Control-Impact Paired Series) design, we investigated 24 peatlands, all drained for forestry and subsequently restored, and 19 pristine control boreal peatlands with high temporal and spatial resolution data on hydroclimate and pore water quality. In drained conditions, total nitrogen (Ntot), total phosphorus (Ptot), and dissolved organic carbon (DOC) in pore water were several-fold higher than observed at pristine control sites, highlighting the impacts of long-term drainage on pore water quality. In general, pore water DOC and Ntot decreased after restoration measures but still remained significantly higher than at pristine control sites, indicating long time lags in restoration effects. Different peatland classes and trophic levels (vegetation gradient) responded differently to restoration, primarily due to altered hydrology and varying acidity levels. Sites that were hydrologically overrestored (inundated) showed higher Ptot, Ntot, and DOC than well-restored or insufficiently restored sites, indicating the need to optimize natural-like hydrological regimes when restoring peatlands drained for forestry. Rich fens (median pH 6.2–6.6) showed lower pore water Ptot, Ntot, and DOC than intermediate and poor peats (pH 4.0–4.6) both before and after restoration. Nutrients and DOC in pore water increased in the first year postrestoration but decreased thereafter. The most important variables related to pore water quality were trophic level, peatland class, water table level, and soil and air temperature.

Organic matter and nutrient export from drained peatlands is affected by complex hydrological and biogeochemical interactions. Here, partial least squares regression (PLSR) was used to relate various soil and catchment characteristics to variations in chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) concentrations in runoff. Peat core samples and water quality data were collected from 15 peat extraction sites in Finland. PLSR models constructed by cross-validation and variable selection routines predicted 92, 88, and 95% of the variation in mean COD, TN, and TP concentration in runoff, respectively. The results showed that variations in COD were mainly related to net production (temperature and water-extractable dissolved organic carbon (DOC)), hydrology (topographical relief), and solubility of dissolved organic matter (peat sulfur (S) and calcium (Ca) concentrations). Negative correlations for peat S and runoff COD indicated that acidity from oxidation of organic S stored in peat may be an important mechanism suppressing organic matter leaching. Moreover, runoff COD was associated with peat aluminum (Al), P, and sodium (Na) concentrations. Hydrological controls on TN and COD were similar (i.e., related to topography), whereas degree of humification, bulk density, and water-extractable COD and Al provided additional explanations for TN concentration. Variations in runoff TP concentration were attributed to erosion of particulate P, as indicated by a positive correlation with suspended sediment concentration (SSC), and factors associated with metal-humic complexation and P adsorption (peat Al, water-extractable P, and water-extractable iron (Fe)).

Ten precipitation indices recommended by the Expert Team on Climate Change Detection and Indices were used to analyse spatial distribution and temporal changes in annual amount, intensity, frequency and duration of precipitation statistics based on daily data with a focus on extremes in Finland during 1961–2011. Also, the connections between these high resolution (10 km) precipitation indices and six influential atmospheric circulation patterns (ACPs) for climate variability in Finland were studied. On the country scale, the results suggest significant increases in the amount and intensity of precipitation extremes, more precipitation days, and no clear changes in wet and dry spells. Both intensity and frequency of precipitation extremes over Finland decrease from south and southwest to north. The only exception is the distribution of precipitations days which is generally more frequent over eastern and upper areas of northern Finland. Wet (dry) spells were typically longer in the western (eastern) part of Finland. Significant increases in annual total wet-day precipitation, very wet days precipitation and simple daily intensity index were found over most parts of Finland during 1961–2011. Increasing trends in both extremely wet days and maximum 1-day precipitation were mostly observed in western, eastern, central and northern Finland. Such spatial pattern was also identified for significant increases in the frequency of heavy and very heavy precipitation days. The precipitation days increased over small parts of western, eastern and northern Finland. Wet spells showed lengthening trends in several grids over southwestern Finland, while dry spells lengthened in some areas over the entire country except southern parts. These variations in precipitation statistics were found to be significantly correlated with the East Atlantic/West Russia, East Atlantic, Scandinavia and Polar patterns.

Lake Urmia in northwest Iran, once one of the largest hypersaline lakes in the world, has shrunk by almost 90% in area and 80% in volume during the last four decades. To improve the understanding of regional differences in water availability throughout the region and to refine the existing information on precipitation variability, this study investigated the spatial pattern of precipitation for the Lake Urmia basin. Daily rainfall time series from 122 precipitation stations with different record lengths were used to extract 15 statistical descriptors comprising 25th percentile, 75th percentile, and coefficient of variation for annual and seasonal total precipitation. Principal component analysis in association with cluster analysis identified three main homogeneous precipitation groups in the lake basin. The first sub-region (group 1) includes stations located in the center and southeast; the second sub-region (group 2) covers mostly northern and northeastern part of the basin, and the third sub-region (group 3) covers the western and southern edges of the basin. Results of principal component (PC) and clustering analyses showed that seasonal precipitation variation is the most important feature controlling the spatial pattern of precipitation in the lake basin. The 25th and 75th percentiles of winter and autumn are the most important variables controlling the spatial pattern of the first rotated principal component explaining about 32% of the total variance. Summer and spring precipitation variations are the most important variables in the second and third rotated principal components, respectively. Seasonal variation in precipitation amount and seasonality are explained by topography and influenced by the lake and westerly winds that are related to the strength of the North Atlantic Oscillation. Despite using incomplete time series with different lengths, the identified sub-regions are physically meaningful.

The unique biodiversity and plant composition of peatlands rely on a mix of different water sources: precipitation, runoff and groundwater (GW). Methods used to delineate areas of ecosystem groundwater dependence, such as vegetation mapping and solute tracer studies, are indirect and lack the potential to assess temporal changes in hydrology, information needed in GW management. This paper outlines a new methodology for mapping groundwater-dependent areas (GDAs) in peatlands using a 2H and 18O isotope mass balance method. The approach reconstructs the initial isotopic composition of the peat pore water in the uppermost peat layer before its modification by evaporation. It was assumed that pore water in this layer subject to evaporation is a two-component mixture consisting of GW and precipitation input from the month preceding the sampling period. A Bayesian Monte Carlo isotope mixing model was applied to calculate the proportions of GW and rainwater in the sampled pore water and to assess uncertainties. The approach revealed large spatial variability in the contribution of GW to the pore water present in the top layer of peatland, covering the range from approximately 0 to 100%. Results show that the current GW protection zones determined by Finnish legislation do not cover the GDAs in peatlands and highlight a need for better classification of groundwater-dependent ecosystems and conceptualisation of aquifer-ecosystem interactions. Our approach offers an efficient tool for mapping GDAs and quantifying the contribution of GW to peatland pore water. However, more studies are needed to test the method for different peatland types. This article is protected by copyright. All rights reserved.

In arid regions, the construction of dams has led to an increase in irrigated agriculture, resulting in the desiccation of vulnerable lakes and wetlands. In many arid mountainous regions, such as in the Middle East, upstream dams typically feed rivers that flow into lowland terminal (closed) lakes or wetlands. The release of water for environmental purposes is a widely recognised option for reducing such impacts. The present study used monthly hydrological data from the Kor river in southern Iran, its main reservoirs and data above and below the Korbal irrigation system. The Kor river is a major source for feeding the Bakhtegan and Tashk lakes, which have recently started to disappear. An analysis of the water resource system before the dam construction (before 1973) showed that the monthly lake inflow depended on available water in river above the irrigation system (for eight months) and, during the irrigation season, water consumed for irrigation as well (for four months). However, in the post-development period (after 1997), the flow rate to the lake depended almost entirely on the Korbal irrigation system, except during some winter months when little irrigation was needed. Environment flow release has not been effective as it has led to greater water availability in the river, which results in more water being consumed for irrigation, as demonstrated here. To overcome this management mismatch, a new environmental flow release strategy (regime) was designed in which water is released from the upstream reservoirs during periods of low irrigation demand (e.g. winter months).

Management of peat soils is regionally important as they cover large land areas and have important but conflicting ecosystems services. A recent management trend for drained peatlands is the control of greenhouse gases (GHG) by changes in agricultural practices, peatland restoration or paludiculture. Due to complex antagonistic controls of moisture, water table management can be difficult to use as a method for controlling GHG emissions. Past studies show that there is no obvious relationship between GHG emission rates and crop type, tillage intensity or fertilization rates. For drained peat soils, the best use options can vary from rewetting with reduced emission to efficient short term use to maximize the profit per amount of greenhouse gas emitted. The GHG accounting should consider the entire life cycle of the peatland and the socio-economic benefits peatlands provide locally. Cultivating energy crops is a viable option especially for wet peat soils with poor drainage, but harvesting remains a challenge due to tractability of wet soils. Paludiculture in lowland floodplains can be a tool to mitigate regional flooding allowing water to be stored on these lands without much harm to crops. This can also increase regional biodiversity providing important habitats for birds and moisture tolerant plant species. However, on many peatlands rewetting is not possible due to their position in the landscape and the associated difficulty to maintain a high stable water table. While the goal of rewetting often is to encourage the return of peat forming plants and the ecosystem services they provide such as carbon sequestration, it is not well known if these plants will grow on peat soils that have been altered by the process of drainage and management. Therefore, it is important to consider peat quality and hydrology when choosing management options. Mapping of sites is recommended as a management tool to guide actions. The environmental status and socio-economic importance of the sites should be assessed both for continued cultivation but also for other ecosystem services such as restoration and hydrological functions (flood control). Farmers need advice, tools and training to find the best after-use option. Biofuels might provide a cost-efficient after use option for some sites. Peat extraction followed by rewetting might provide a sustainable option as rewetting is often easier if the peat is removed, starting the peat accumulation from scratch. Also this provides a way to finance the after-use. As impacts of land use are uncertain, new policies should consider multiple benefits and decisions should be based on scientific evidence and field scale observations. The need to further understand the key processes and long term effects of field scale land use manipulations is evident. The recommended actions for peatlands should be based on local condition and socio-economic needs to outline intermediate and long term plans.

Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment and monitoring. Traditionally, in situ measurements and airborne Light Detection and Ranging (LiDAR) sensors are among the commonly employed methods for vegetation height estimation. However, such methods are known for their high incurred labor, time, and infrastructure cost. The emergence of wearable technology offers a promising alternative, especially in rural environments and underdeveloped countries. A method for a locally designed data acquisition ubiquitous wearable platform has been put forward and implemented. Next, a regression model to learn vegetation height on the basis of attributes associated with a pressure sensor has been developed and tested. The proposed method has been tested in Oulu region. The results have proven particularly effective in a region where the land has a forestry structure. The linear regression model yields (r2 = 0.81 and RSME = 16.73 cm), while the use of a multi-regression model yields (r2 = 0.82 and RSME = 15.73 cm). The developed approach indicates a promising alternative in vegetation height estimation where in situ measurement, LiDAR data, or wireless sensor network is either not available or not affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.

The Bakhtegan catchment, an important agricultural region in south-western Iran, has suffered groundwater depletion in recent years. As groundwater is considered the main source of fresh water in the catchment, especially for agriculture, monitoring groundwater responses to irrigation is important. Gravity Recovery and Climate Experiment (GRACE) satellite data can help determine water mass changes in catchments and assess water volume changes. In this study, we compared GRACE-derived water mass data against groundwater volume variations measured in situ. We also assessed the efficiency of GRACE-derived data in catchments smaller than the 200,000 km2 recommended area when using GRACE. For the study period (January 2002 through December 2011), the GRACE data showed a 7.6 mm annual decline in groundwater level, with a total volume loss of 2.6 km3 during the period. The in situ monthly measurements of groundwater level showed an average depletion of 10 m in catchment aquifers during the study period. This depletion rate was supported by the recorded decrease in precipitation volume, especially in the post-drought period after 2007. These results demonstrate that GRACE can be useful tool for monitoring groundwater depletion in arid catchments.

Managed aquifer recharge (MAR) structure is widely used to expand groundwater resources. In arid regions with flash flooding, MAR can also be used as a flood control structure to decrease peak discharge of rivers. In this paper, we present a method for quantifying the role of MAR in head water systems and assess its impact on the total water balance in a river basin. The method is based on rainfall-runoff modeling, reservoir flood routing, recharge analysis and river flow analysis. For the case selected, Kamal Abad MAR in Lake Maharlou basin in southern Iran, we analyzed changes in the downstream river regime using two scenarios (with MAR and without MAR) with different return periods. The results revealed a significant impact of MAR on river flow in terms of changes in flow timing, magnitude and variability. With MAR, the ephemeral river studied became disconnected from the main stream, albeit, whereas the case without MAR, floods with return period higher than 10 years would be connected to the downstream. Even though, MAR structures are useful in arid and semi-arid climates for irrigation water supply, their placing and designing need more attention. The developed method can be used to assess the impacts of MAR on river flow and find the best location for it to make the connection of the ephemeral river and downstream river, an issue which has not received much attention in hydrological research.

Cultivated peatlands under drainage practices contribute significant carbon losses from agricultural sector in the Nordic countries. In this research, we developed the BASGRA-BGC model coupled with hydrological, soil carbon decomposition and methane modules to simulate the dynamic of water table level (WTL), carbon dioxide (CO2) and methane (CH4) emissions for cultivated peatlands. The field measurements from four experimental sites in Finland, Denmark and Norway were used to validate the predictive skills of this novel model under different WTL management practices, climatic conditions and soil properties. Compared with daily observations, the model performed well in terms of RMSE (Root Mean Square Error; 0.06–0.11 m, 1.22–2.43 gC/m2/day, and 0.002–0.330 kgC/ha/day for WTL, CO2 and CH4, respectively), NRMSE (Normalized Root Mean Square Error; 10.3–18.3%, 13.0–18.6%, 15.3–21.9%) and Pearson's r (Pearson correlation coefficient; 0.60–0.91, 0.76–0.88, 0.33–0.80). The daily/seasonal variabilities were therefore captured and the aggregated results corresponded well with annual estimations. We further provided an example on the model's potential use in improving the WTL management to mitigate CO2 and CH4 emissions while maintaining grass production. At all study sites, the simulated WTLs and carbon decomposition rates showed a significant negative correlation. Therefore, controlling WTL could effectively reduce carbon losses. However, given the highly diverse carbon decomposition rates within individual WTLs, adding indicators (e.g. soil moisture and peat quality) would improve our capacity to assess the effectiveness of specific mitigation practices such as WTL control and rewetting.

This study evaluates interannual variations and trends in growing season daily temperature sum and daily precipitation sum in Finland during 1961–2011, and their connections to well known atmospheric circulation patterns. Changes in summer (June–August) climate partially explain changes in growing season daily temperature sum and daily precipitation sum over Finland, which naturally decreased from south to north. On a national scale, growing season warmed and became wetter during 1961–2011, as growing season daily temperature sum and daily precipitation sum significantly (p < 0.05) increased by 5.01 ± 3.17°C year–1 and 1.39 ± 0.91 mm year–1, respectively. The East Atlantic pattern was the most influential atmospheric circulation pattern for variations in growing season daily temperature sum (rho = 0.40) across Finland and the East Atlantic/West Russia pattern was most influential for growing season daily precipitation sum variability (rho = –0.54). There were significant (p < 0.05) increasing tren...

Pristine peat soils are characterized by large porosity, low density and large water and organic matter contents. Drainage and management practices change peat properties by oxidation, compaction and mineral matter additions. This study examined differences in physical properties (hydraulic conductivity, water retention curve, bulk density, porosity, von Post degree of decomposition) in soil profiles of two peatland forests, a cultivated peatland, a peat extraction area and two pristine mires originally within the same peatland area. Soil hydraulic conductivity of the drained sites (median hydraulic conductivities: 3.3 × 10−5 m/s, 2.9 × 10−8 m/s and 8.5 × 10−8 m/s for the forests, the cultivated site and the peat extraction area, respectively) was predicted better by land use option than by soil physical parameters. Detailed physical measurements were accompanied by monitoring of the water levels between drains. The model ‘DRAINMOD’ was used to assess the hydrology and the rapid fluctuations seen in groundwater depths. Hydraulic conductivity values needed to match the simulation of observed depth to groundwater data were an order of magnitude greater than those determined in field measurements, suggesting that macropore flow was an important pathway at the study sites. The rapid response of depth to groundwater during rainfall events indicated a small effective porosity and this was supported by the small measured values of drainable porosity. This study highlighted the potential role of land use and macropore flow in controlling water table fluctuation and related processes in peat soils.

A before-after-control approach was used to analyze the impact of peatland restoration on hydrology, based on high temporal resolution water-table (WT) data from 43 boreal peatlands representative of a south-boreal to north-boreal climate gradient. During the study, 24 forestry drained sites were restored and 19 pristine peatlands used as control sites. Different approaches were developed and used to analyze WT changes (mean WT position, WT fluctuation, WT hydrograph, recession, and storage characteristics). Restoration increased WT in most cases but particularly in spruce mires, followed by pine mires and fens. Before restoration, the WT fluctuation (WTF) was large, indicating peat temporary storage gain (SG). After restoration, the WT hydrograph recession limb slopes and SG coefficients (Rc) declined significantly. Drainage or restoration did not significantly affect mean diurnal WT fluctuations, used here as a proxy for evapotranspiration. Overall, the changes in WT characteristics following restoration indicated creation of favorable hydrological conditions for recovery of functional peatland ecosystems in previously degraded peatland sites. This was supported by calculation of bryophyte species abundance thresholds for WT. These results can be used to optimize restoration efforts in different peatland systems and as a qualitative conceptual basis for future restoration operations.

Long-term variations and trends in a wide range of statistics for daily precipitation characteristics in terms of intensity, frequency and duration in Finland were analysed using precipitation records during 1908–2008 from 3 meteorological stations in the south (Kaisaniemi), centre (Kajaani) and north (Sodankylä). Although precipitation days in northern part were more frequent than in central and southern parts, daily precipitation intensity in the south was generally higher than those in the centre and north of the country. Annual sum of very light precipitation (0 mm < daily precipitation ≤ long-term 50th percentile of daily precipitation more than 0 mm) significantly (p < 0.05) decreased over time, with the highest rate in northern Finland. These decreasing trends might be the result of significant increases in frequency of days with very light precipitation at all the stations, with the highest and lowest rates in northern and southern Finland, respectively. Ratio of annual total precipitation to number of precipitation days also declined in Finland over 1908–2008, with a decreasing north to south gradient. However, annual duration indices of daily precipitation revealed no statistically significant trends at any station. Daily precipitation characteristics showed significant relationships with various well-known atmospheric circulation patterns (ACPs). In particular, the East Atlantic/West Russia (EA/WR) pattern in summer was the most influential ACP negatively associated with different daily precipitation intensity, frequency and duration indices at all three stations studied.

Assessment and monitoring of river morphology own an important role in river engineering; since, changes in river morphology including erosion and sedimentation affect river cross-sections and flow processes. An approach for River Morphodynamics Analysis based on Remote Sensing (RiMARS) was developed and tested on the case of Mollasadra dam construction on the Kor River, Iran. Landsat multispectral images obtained from the open USGS dataset are used to extract river morphology dynamics by the Modified Normalized Difference Water Index (MNDWI). RiMARS comes with a river extraction module which is independent of threshold segmentation methods to produce binary-level images. In addition, RiMARS is equipped with developed indices for assessing the morphological alterations. Five characteristics of river morphology (spatiotemporal Sinuosity Index (SI), Absolute Centerline Migration (ACM), Rate of Centerline Migration (RCM), River Linear Pattern (RLP), and Meander Migration Index (MMI)), are applied to quantify river morphology changes. The results indicated that the Kor River centerline underwent average annual migration of 40 cm to the southwest during 1993–2003 (pre-construction impact), 20 cm to the northeast during 2003–2011, and 40 cm to the south-west during 2011–2017 (post-construction impact). Spatially, as the Kor River runs towards the Doroudzan dam, changes in river morphology have increased from upstream to downstream; particularly evident where the river flows in a plain instead of the valley. Based on SI values, there was a 5% change in the straight sinuosity class in the pre-construction period, but an 18% decrease in the straight class during the post-construction period. Here we demonstrate the application of RiMARS in assessing the impact of dam construction on morphometric processes in Kor River, but it can be used to assess other riverine changes, including tracking the unauthorized water consumption using diverted canals. RiMARS can be applied on multispectral images.

Fog is an important component of the water cycle in northern coastal regions of Iran. Having accurate tools for mapping the precise spatial distribution of fog is vital for water harvesting within integrated water resources management in this semi-humid region. In this study, environmental variables were considered in prediction mapping of areas with high concentrations of fog in the Vazroud watershed, Iran. Fog probability maps were derived from four artificial intelligence algorithms (Generalized Linear Model, Generalized Additive Model, Generalized Boosted Model, and Generalized Dissimilarity Model). Models accuracy were assessed using Receiver Operating characteristic Curve (ROC). Three social variables were also selected according to their relevance for fog suitability mapping. Finally, Fog-water harvesting Capability Index (FCI) maps were produced by multiplying fog probability by fog suitability maps. The results showed high accuracy in fog probability mapping for the study area, with all models proving capable of identifying areas with high fog concentrations in the south and southeast. For all models, the highest values of importance were obtained for sky view factor and the lowest for slope curvature. Analytic Hierarchy Process results showed the relative importance of social conditioning factors in fog suitability mapping, with the highest weight given to distance to residential area, followed by distance to livestock buildings and distance to road. Based on the fog suitability map, southeast and southern parts of the study area are most suitable for fog water harvesting. The fog spatial distribution maps obtained can increase fog water harvesting efficiency. They also indicate areas for future study with regions where fog is a critical component in the water cycle.

A stable isotope study of 67 kettle lakes and ponds situated on an esker aquifer (90 km2) in northern Finland was carried out in the summer of 2013 to determine the role of groundwater inflow in groundwater-dependent lakes. Distinct seasonal fluctuations in the δ18O and δ2H values of lakes are the result of seasonal ice cover prohibiting evaporation during the winter. An isotope mass balance approach was used to calculate the inflow-to-evaporation ratios (ITOT/E) of all 67 lakes during the summer of 2013 when the isotopic compositions of the lakes were approaching a steady-state. The normalised relative humidity needed in this approach came from assuming a terminal lake situation for one of the lakes showing the highest isotope enrichment. Since evaporation rates were derived independently of any mass balance considerations, it was possible to determine the total inflow (ITOT) and mean turnover time (MTT) of the lakes. Furthermore, the groundwater seepage rates of those lakes revealing no visible surface inflow were calculated. Here, a quantitative measure was introduced for the dependence of a lake on groundwater (G index) that is defined as the percentage contribution of groundwater inflow to the total inflow of water to the given lake. The G index values of the lakes studied ranged from 27.8–95.0%, revealing large differences in groundwater dependency among the lakes. This study shows the effectiveness of applying an isotope mass balance approach to quantify the groundwater reliance of lakes situated in a relatively small area with similar climatic conditions.

Quantifying short-term changes in river flow is important in understanding the environmental impacts of hydropower generation. Energy markets can change rapidly and energy demand fluctuates at sub-daily scales, which may cause corresponding changes in regulated river flow (hydropeaking). Due to increasing use of renewable energy, in future hydropower will play a greater role as a load balancing power source. This may increase current hydropeaking levels in Nordic river systems, creating challenges in maintaining a healthy ecological status. This study examined driving forces for hydropeaking in Nordic rivers using extensive datasets from 150 sites with hourly time step river discharge data. It also investigated the influence of increased wind power production on hydropeaking. The data revealed that hydropeaking is at high levels in the Nordic rivers and have seen an increase over the last decade and especially over the past few years. These results indicate that increased building for renewable energy may increase hydropeaking in Nordic rivers. © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Vast regions of the northern hemisphere are exposed to snowfall and seasonal frost. This has large effects on spatiotemporal distribution of infiltration and groundwater recharge processes as well ...

Numerous studies have reported significant declines in snow resources in Finland and elsewhere during the 20th century. To identify the main climate factors controlling these declines in Finland, this study evaluated long-term variations and trends in wintertime climate, snowpack hydrological processes (SHPs) and continuous snow cover duration (CSCD), and their links to atmospheric circulation patterns (ACPs). Analyses were conducted using observed daily climatological time series and simulated SHPs at three stations in southern (Kaisaniemi), central (Kajaani) and northern (Sodankyla) Finland with about 100 years of data. The Mann-Kendall nonparametric test was used to detect significant trends, the Pearson's coefficient (r) to identify relationships within snow-related variables, and Spearman's coefficient (ρ) to measure correlations of these variables with ACPs. Sensitivity of the snow-related variables with projected changes in temperature and precipitation was assessed. The results showed increases in wintertime temperature only at Kaisaniemi, but decreases in wintertime precipitation, snowfall and snow water equivalent (SWE) and shorter CSCD at all stations. In general, variations in wintertime temperature were positively associated with the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). However, wintertime precipitation showed significant relationships with the East Atlantic/West Russia (EA/WR), AO and West Pacific (WP) patterns in southern, central and northern Finland, respectively. SHPs and CSCD in southern Finland were associated with the same ACP influencing wintertime temperature (AO), and those in central and northern areas with the patterns influencing wintertime precipitation (EA, EA/WR and AO). Thus, declines in snow resources in Finland are mainly the result of reductions in snowfall owing to both wintertime warming and decreased precipitation at Kaisaniemi, while only to decreases in wintertime precipitation at Kajaani and Sodankyla stations. However, increase in precipitation (up to 30%) plays an important role in offsetting effects of temperature warming (up to 4 °C) on snow resource decline in Finland.

A new precipitation index-based model, which includes a snow accumulation and melt component, has been developed to simulate hydrology in high latitude catchments. The model couples a point snowmelt model with a current precipitation index (CPI) formulation to simulate continuous daily runoff from catchments with seasonal snow cover. A new runoff conversion factor: CT and Lf, threshold flow factor ThQ and runoff transformation function Maxbas were introduced into the CPI equation, which converts and transforms the routed daily CPI into daily runoff and maintains the daily base flow in the catchment. The model was developed using twelve sub-arctic boreal catchments located above and below the Arctic Circle in northern Finland, representing a region with considerable seasonal snow cover. The results showed that the model can adequately simulate and produce the dynamics of daily runoff from catchments where the underlying physical conditions are not known. An open-access Excel-based model is provided with this paper for daily runoff simulations. The model can be used to estimate runoff in sub-arctic regions where little data is typically available but significant changes in climate are expected, with considerable shifts in the amount and timing of snowmelt and runoff.

Construction of dams affects river flow regimes and changes downstream ecosystems in many ways. The impacts of dams depend on local climate, dam purpose and operation policy. This study analysed the impacts of dam construction on river flow regimes and developed a new method to evaluate the effects on river flow. By using three main flow characteristics of river regime (magnitude, timing and intra-annual alteration), a new combined river impact (RI) index to assess construction impacts based on monthly flow data was developed. A preliminary evaluation of the method was made using the river Ebro as a case. A classification was developed where the impact was assessed using a scale from low to drastic. The method was further tested to quantify the effect of dams in 15 rivers located in different regions. The method showed good potential to classify different dam types of variable sizes constructed for different purposes. The RI index method also proved suitable for assessing the environmental impacts of dam construction and could be extended by adding other characteristics of flow regime to evaluate the effects of climate change or changes in land use on river flow regimes.

Climate change’s effect on sea surface temperature (SST) at the regional scale vary due to driving forces that include potential changes in ocean circulation and internal climate variability, ice cover, thermal stability, and ocean mixing layer depth. For a better understanding of future effects, it is important to analyze historical changes in SST at regional scales and test prediction techniques. In this study, the variation in SST across the Persian Gulf and Gulf of Oman (PG&GO) during the past four decades was analyzed and predicted to the end of 21st century using a proper orthogonal decomposition (POD) model. As input, daily optimum interpolation SST anomaly (DOISSTA) data, available from the National Oceanic and Atmospheric Administration of the United States, were used. Descriptive analyses and POD results demonstrated a gradually increasing trend in DOISSTA in the PG&GO over the past four decades. The spatial distribution of DOISSTA indicated: (1) that shallow parts of the Persian Gulf have experienced minimum and maximum values of DOISSTA and (2) high variability in DOISSTA in shallow parts of the Persian Gulf, including some parts of southern and northwestern coasts. Prediction of future SST using the POD model revealed the highest warming during summer in the entire PG&GO by 2100 and the lowest warming during fall and winter in the Persian Gulf and Gulf of Oman, respectively. The model indicated that monthly SST in the Persian Gulf may increase by up to 4.3 °C in August by the turn of the century. Similarly, mean annual changes in SST across the PG&GO may increase by about 2.2 °C by 2100.