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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.

Catchment storage sustains ecologically important low flows in headwater systems. Understanding the factors controlling storage is essential in analysis of catchment vulnerability to global change. We calculated catchment storage and storage sensitivity of streamflow for 61 boreal headwater catchments in Finland. We also explored the connection between computed storage indices and low flow conditions. The relationships between selected climate, snow, and catchment characteristics and calculated storage properties and low flows were investigated, in order to assess the importance of different factors that render catchments vulnerable to climate and environmental change. We found that the most sensitive areas to climate change were located in the southern boreal coastal zone, with fine‐grained soils and agricultural areas. In contrast, catchments in the middle and northern boreal zone, with till and peatland soils and higher snow water equivalent values, were less sensitive under current conditions. In addition, we found a threshold at a snow to precipitation ratio of 0.35. Above that threshold, summer low flows were generally sensitive to changes in snow conditions, whereas below that threshold catchment characteristics gained importance and the sensitivity was more directly related to changes in temperature and timing of rainfall. These findings suggest that a warming climate will have pronounced impacts on hydrology and catchment sensitivity related to snow quantity and snow cover duration in certain snow to precipitation ratio zones. Moreover, land use activities had an impact on storage properties in agricultural and drained peatland areas, resulting in a negative effect on low flows.

In separate analyses of the impacts of land use change and climate change, a scenario-based approach using remote sensing and hydro-climatological data was developed to assess changes in hydrological indices. The data comprised three Landsat TM images (1988, 1998, 2008) and meteorological and hydrological data (1983–2012) for the Aligudarz and Doroud stations in the Marboreh watershed, Iran. The QUAC module and supervised classification maximum likelihood (ML) algorithm in ENVI 5.1 were used for remote sensing, the SWAT model for hydrological modelling and the Mann-Kendall and t test methods for statistical analysis. To create scenarios, the study period was divided into three decades (1983–1992, 1993–2002, 2003–2012) with clearly different land use/land cover (LULC). After hydrological modelling, 10 hydrological indices related to high and low flow indices (HDI and LDI) were analysed for seven scenarios developed by combining pre-defined climate periods and LULC maps. The major changes in land use were degradation of natural rangeland (−18.49%) and increasing raid-fed farm area (+16.70%) and residential area (+0.80%). The Mann-Kendall test results showed a statistically significant (p

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.

With the development of shipping in the Caspian Sea, the need for high-quality data on wave characteristics has increased. In the present work, the analysis of maximum wave height at coastal stations in Kazakhstan was carried out using wave and wind data and numerical modeling with the SWAN model. The influence of stationary wind and its direction on the wave height was determined. The results show that the changes in the parameters of the wind wave depend on the wind speed and direction. At wind speeds below 10 m/s, the predicted wave height can reach 3 m in the Middle Caspian basin. In the Northern Caspian basin, where the depths are shallow, the waves do not exceed 2 m. The study output produced the first wind zoning map for the Kazakh part of the Caspian Sea.

Significance Iran is facing a state of water bankruptcy that threatens its socioeconomic development and natural environments. Using an exceptionally rich measured groundwater dataset, we illustrate the extent and severity of Iran’s groundwater depletion and salinization problems during the 2002 to 2015 period, when the number of groundwater extraction points nearly doubled. Iran’s nonrenewable groundwater withdrawal was about 66 million m3 in 1965, which cumulatively grew to approximately 133 × 103 million m3 in 2019. This increase is about 3.4 times the capacity of the famous Three Gorges Dam in China. Groundwater decline due to extensive overexploitation of nonrenewable groundwater and rising salinity levels are documented in almost all subbasins, pointing to dire, worsening water security risks across the country., Global groundwater assessments rank Iran among countries with the highest groundwater depletion rate using coarse spatial scales that hinder detection of regional imbalances between renewable groundwater supply and human withdrawals. Herein, we use in situ data from 12,230 piezometers, 14,856 observation wells, and groundwater extraction points to provide ground-based evidence about Iran’s widespread groundwater depletion and salinity problems. While the number of groundwater extraction points increased by 84.9% from 546,000 in 2002 to over a million in 2015, the annual groundwater withdrawal decreased by 18% (from 74.6 to 61.3 km3/y) primarily due to physical limits to fresh groundwater resources (i.e., depletion and/or salinization). On average, withdrawing 5.4 km3/y of nonrenewable water caused groundwater tables to decline 10 to 100 cm/y in different regions, averaging 49 cm/y across the country. This caused elevated annual average electrical conductivity (EC) of groundwater in vast arid/semiarid areas of central and eastern Iran (16 out of 30 subbasins), indicating “very high salinity hazard” for irrigation water. The annual average EC values were generally lower in the wetter northern and western regions, where groundwater EC improvements were detected in rare cases. Our results based on high-resolution groundwater measurements reveal alarming water security threats associated with declining fresh groundwater quantity and quality due to many years of unsustainable use. Our analysis offers insights into the environmental implications and limitations of water-intensive development plans that other water-scarce countries might adopt.

In wetlands, the water budget is traditionally quantified by measuring the hydrologic components including precipitation, evapotranspiration and surface water-groundwater inflows and outflows. However, the reliability of measurements is often questioned due to the difficulty of rigorously monitoring all components of the water budget. Quantifying the rainfall event to water table response ratio is an alternative approach with minimal need for data commonly collected in peatland studies. However, the method has been used only in a limited number of biophysical settings including different microforms, hydroclimatic and hydrogeological settings. The objectives of this study are to quantify the reactivity of the water table to precipitation for different pristine peatlands located in different hydroclimatic conditions and to provide quantitative assessments of water storage of as many peatlands as possible. To do so, site-specific hourly water table and precipitation measurements was collected from northern peatlands worldwide. In total, data from more than 30 sites were retrieved from 8 research groups. For all peatlands, water-table depths varied between 80 cm below the peat surface and 10 cm above the peat surface. The results highlight that the hydrology of all peatlands is characterized by a shift from an environment that can store water to an environment that contributes to rapid outflow, and this is a uniform feature across sites. However, for peatlands with the lowest water storage capacities, this shift occurs during relatively moderate rainfall events (40 mm) or successive small rainfall events. Blanket peat bog best embodied this type of hydrological response. For peatlands with the highest water storage capacity, this shift occurs following multiple moderate to large precipitation events (40 mm – 80 mm) and it is strongly enhanced by the shift from high to low evaporative periods. The peatlands with the highest storage capacity are raised bogs with deep water-table. These conditions are best observed in peatlands located in geographical settings with high evaporation rates. Among all the peatlands, maximum water storage capacity for given rainfall events was equal to ≈150 mm. These analyses also confirm that the water table rise caused by precipitation events contain sufficient information to constrain water storage variations around monitored wells peatlands for a wide array of biophysical settings.

Spatiotemporal information on historical peatland drainage is needed to relate past land use to observed changes in catchment hydrology. Comprehensive knowledge of historical development of peatland management is largely unknown at the catchment scale. Aerial photos and light detection and ranging (LIDAR) data enlarge the possibilities for identifying past peatland drainage patterns. Here, our objectives are (1) to develop techniques for semiautomatically mapping the location of ditch networks in peat-dominated catchments using aerial photos and LIDAR data, and (2) to generate time series of drainage networks. Our approaches provide open-access techniques to systematically map ditches in peat-dominated catchments through time. We focused on the algorithm in such a way that we can identify the ditch networks from raw aerial images and LIDAR data based on the modification of multiple filters and number of threshold values. Such data are needed to relate spatiotemporal drainage patterns to observed changes in many northern rivers. We demonstrate our approach using data from the Simojoki River catchment (3,160 km²) in northern Finland. The catchment is dominated by forests and peatlands that were almost all drained after 1960. For two representative locations in cultivated peatland (downstream) and peatland forest (upstream) areas of the catchment; we found total ditch length density (km/km²), estimated from aerial images and LIDAR data based on our proposed algorithm, to have varied from 2% to 50% compared with the monitored ditch length available from the National Land survey of Finland (NLSF) in 2018. A different pattern of source variation in ditch network density was observed for whole-catchment estimates and for the available drained-peatland database from Natural Resources Institute Finland (LUKE). Despite such differences, no significant differences were found using the nonparametric Mann-Whitney U test with a 0.05 significance level based on the samples of pixel-identified ditches between (1) aerial images and NLSF vector files and (2) LIDAR data and NLSF vector files.

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.

Due to the rise in global temperature, changes in precipitation patterns are predicted particularly in Arctic regions. Such changes in patterns and modifications in typical snow to precipitation ratios will affect the snowpack thickness and the timing of snow accumulation and snow melting. Stable water isotopes (δ2H, δ18O) are one of the latest tools in exploring and tracing such changes, however, snow isotope and particularly snowmelt isotope datasets are rarely available which hamper the high-resolution isotope based hydrological investigations in Arctic regions. In this study, we perform an investigation for evaluating spatiotemporal variations in stable isotopes of snow and snowmelt water. Our Pallas research catchment is located in a subarctic setting in northern Finland. The measurements were made at 11 locations along a 2 km snow survey, which is established on the transect of the catchment, comprising of different landscape features (i) forested hillslope, (ii) mixed forest and (iii) open mires. We sampled depth-integrated bulk snowpack and fixed 5 cm incremental snow stratigraphy profile in snowpits. For snowmelt sampling, we used a system of snowmelt lysimeter, deployed at 11 locations. The bulk snowpack samples were collected biweekly, fixed 5 cm incremental stratigraphic snowpit samples during the period of maximum snowcover thickness and during the start of peak melting, during the peak melting and after the peak melting. Snowmelt samples were collected daily during the spring season until the complete disappearance of snow with complementary measurements of snowmelt flux, snow density and snow water equivalent. Our results indicate the higher mean values of snowmelt isotopes relative to the bulk snowpack and surface snow isotopes. The snow isotope profiles in snowpack reveal that the isotopes at the snow-air and snow-ground interfaces are enriched in heavier isotopes as compared to the middle of the snowpack. The snowmelt isotopes show that the isotopes are initially depleted in heavier isotopes but with the progress of melting, they start to become enriched. A well defined depleted to enriched pattern is observed at different locations in the forested hillslope area, while a relatively dispersed depleted to enriched pattern is observed at different locations in the mixed forested area. Our unique high-resolution dataset of snow and snowmelt isotopes will be useful in many applications; such as for evaluating post-depositional isotope modification in the seasonal snowpack, developing tracer-aided mass and energy based snow models. The establishment of snowmelt isotope dataset, showing spatiotemporal variability of snowmelt isotopes, is an important step forward in isotope based plant-water uptake studies and hydrological analyses in snow-influenced catchments.

Research on ecohydrological separation and plant water use have been increasing in the last few years, and various studies indicate that trees can use winter precipitation as a dominant water source during the growing season. Such studies are of great importance to northern regions, where soil water recharge timing is predicted to be significantly altered due to climate change. In order to assess plant water use in sub-arctic environment, it is necessary to understand how soil water pools under different land covers evolve throughout the year and how cryogenic processes alter the isotope input signal. This field study was conducted from May 2019 to June 2020 in Pallas catchment, located in sub-arctic conditions in Finnish Lapland. Soil cores up to 1 meter depth with 5 cm increments and xylem water of dominant tree species were collected in 4 locations, ranging from forest to shrubland/forest transitional area, and to forested peatland. All locations are positioned on a snow survey, in the vicinity of previously installed groundwater wells and snow lysimeters, and within 2 kilometers of rain gauge. Additional spatial samples of topsoil and xylem water were collected throughout the catchment during 2019 growing season. Relative proportions of tree source water were calculated by Bayesian mixing model MixSIAR. We produce new data set that displays plot and catchment scale soil water heterogeneities in a snow dominated environment, and examine: i) How soil properties affect isotopic composition of soil water?; ii) What is the effect of rising groundwater level on soil water isotope composition?; and iii) How snowpack thickness and melt timing modify soil water isotope patterns? We analyze if these varying pools of water are reflected in tree xylem water. Soil water isotope dynamics under deep snowpack, during and after snowmelt reveal how snow accumulation and melt timing and magnitude influence plant available water for growing season.

Globally peatlands are degrading due to drainage and intensified land use e.g. for forestry, agriculture and peat extraction. Peatland restoration can recover biodiversity of the threatened habitats, reestablish the natural hydrological role of the peatland as retaining water and nutrients and diminish greenhouse gas emissions.Restoration monitoring for peatlands is urgent in order to reveal the peatland hydrological recovery and ecological succession after restoration, needs for corrective actions and to enable further method development. Restoration monitoring with conventional approaches is laborious, time-consuming and does not cover large areas. Visual evaluation is biased, and the traditional systematic methods give only focused information while conditions for most of the site remain hidden.Unmanned Aircraft Systems (UAS) imaging produces large coverage information on restoration success in high spatial resolution. Aerial perspective with superior resolution alone extends the monitoring aspect together with the photogrammetric high-precision digital elevation models (DEMs) allowed by the Structure from Motion (SfM) technology.Additionally, external instruments such as thermal cameras attached in the drone allow revealing temperature anomalies and moisture patterns. We used thermal infrared (TIR) imaging to monitor changes at a boreal rewetted peatland site. The uncalibrated thermal data alone turned out to be useful showing near-surface flow routes recovered in restoration. We further applied a variety of processing methods for the data to explore their applicability on boreal peatlands. The results show the thermal UAS imaging to have great potential in monitoring the hydrological changes due to peatland restoration in high spatial resolution.

The development, the alternative pathways for use of bioresources, can lead to plausible stressors in the future on forestry dominated catchments. It is needed to analyse the impact of regional future projections on different land system management (LSM) attributes. The catchment scale projections are downscaled from Nordic Bioeconomic Pathways (NBPs), the subsets of Shared Socioeconomic Pathways (SSPs). As a case study, the Simojoki catchment (3160 km2) in northern Finland has been considered where drained peatlands and forests dominate (53%) in the catchment. We integrated stakeholder-driven input, Finnish forest inventory model pathways (MELA) and hydrological catchment model (SWAT) to explore the future consequences of forest management practices for different NBP scenarios. We calibrated and validated water quality parameters in SWAT for the Simojoki catchment. Then, based on the output of MELA model of LSM attributes including stand management, catchment management strategy and fertilizer use, we used NBP scenario projections in SWAT model. We also included stakeholders’ evaluations of biomass removal at the time of harvesting at the Simojoki catchment. Additionally, climate imposing emission scenarios have been integrated into SWAT model to analyse longer perception of climate change (CC). The final outcomes of the proposed scenarios (NBP and/or CC) will portray the probable impacts on each LSM attribute in the Simojoki catchment, to adapt to the future forest management consequences.

Snowmelt spring floods dominate the annual carbon flux in Arctic streams. However, climate change is altering their timing and magnitude due to changes in snow conditions, further altering the processes controlling the carbon cycle at the catchment scale. Current knowledge is limited by a lack of high-resolution data from Arctic areas. In this study we combine high-resolution biogeochemical-hydro-climatological variables with spectral wavelet analysis for new insights into carbon processes.This study was conducted during the snowmelt spring flood period in a sub-arctic headwater catchment in Pallas-Ylläs national park, Finland (68°02′N, 24°16′W). We collected in-stream dissolved organic carbon (DOC), carbon dioxide (C02), and terrestrial C02 flux alongside a suite of hydro-climatological variables measured at 30-minute intervals. Continuous wavelet transformations and wavelet coherence were produced to assess the relationship between hydro-climatological variables and carbon variables at different periodicities.Wavelet transforms indicated that the onset of snowmelt caused the development of significant diel periodicity for in-stream DOC, CO2 and terrestrial CO2 flux, while substantial periods of significant periodicity were observed at multiple day periodicities. Wavelet coherence analysis identified that DOC was consistently lead by flow and conductivity across daily and multiple daily scales suggesting that transport of carbon from the surface and shallow sub-surface pathways to the stream were the predominant processes controlling in-stream DOC. Interestingly for in-stream CO2, groundwater level showed periodic rather than consistent spectral coherence suggesting it is not a consistent control on CO2 in the spring flood. The strongest coherence for in-stream CO2 was with in-stream O2, which may suggest the importance of in-stream metabolism as a control on in-stream CO2 dynamics. Terrestrial CO2 fluxwas controlled by notably different processes than in-stream Carbon and linked strongest to climatological variables. Photosynthetically active radiation (PAR) showed the strongest relationship with CO2 terrestrial flux dynamics. Our study highlights the unique processes controlling different parts of the carbon cycle in a headwater arctic catchment during the snowmelt spring flood. We highlight in-stream DOC as particularly vulnerable to changes in spring flood magnitude and timing given the importance of snowmelt dominated transport processes to DOC flux. To identify future changes in the Arctic carbon cycle, wavelet analysis shows potential as tool to analyse changes in processes in high-resolution datasets.

One of the key changes of the global climate system is the loss of Arctic sea ice, particularly through its impact on ocean-atmosphere interactions. Enhanced evaporation under open-water conditions is widespread from places and periods previously precluded by perennial sea ice cover, leading to an increase in vapor uptake across the Arctic. However, the response of ocean-atmosphere system to sea ice loss varies significantly over time and space. To quantify these variations, the Arctic Water Isotope Network (AWIN) has been established to make continuous water vapor isotope measurements (δD, δ18O, and d-excess) at seven land-based stations from Barrow, Alaska to Ny Alesund, Svalbard. This network has been supplemented by continuous mobile isotope data from the CiASOM project on the Polarstern ice-breaker throughout the MOSAiC “Arctic-drift” expedition. With this network, we comprehensively track water vapor from its source to sink, thereby demonstrating how it varies simultaneously across the entire Arctic Basin.Here, we utilize AWIN measurements to specifically quantify how variations in sea ice extent and distribution affect moisture content, water vapor isotope traits, and transport along several critical storm tracks. By monitoring vapor isotopic changes in air masses advected from one site to another, we are able to track how much moisture is added along a given trajectory. We investigate several primary vapor transport pathways into the Arctic, including the North Atlantic/Greenland Sea, Baffin Bay, and the Bering Strait, and track the geochemical signature of this vapor as it transits along these well-established storm pathways into and within the Arctic. By quantifying isotopic changes between our sites we: 1) identify the distinct isotopic fingerprint of moisture sourced by evaporation from Arctic seas that is critically dependent on variable sea ice conditions, 2) detect moisture addition into critical storm tracks as they transit across the Arctic, and 3) determine the spatial variability of this enhanced Arctic-sourced evaporation and moisture. We find that for every major storm track observed, the Arctic Ocean and surrounding seas are significant sources of enhanced moisture uptake, acting within an amplified water cycle.

Recirculating aquaculture systems (RAS) discharge management limits the development of the aquaculture sector, because RAS do not automatically result in low nutrient emissions. Research has helped develop discharge management systems such as wetlands and woodchip bioreactors that have been adopted by Danish commercial model trout farms. To further develop the Danish concept, we have modelled and built a novel “zero-discharge” recirculating aquaculture system with an annual capacity of approximately 14 tonnes. The aim of this paper is to describe the entire concept and present the results from the start-up phase of the whole system. The concept includes the treatment of RAS effluent (overflow and sludge supernatant) using a hybrid solution of a woodchip bioreactor, constructed vertical wetland, and sand infiltration. Using this three-step process, the nitrate, phosphorus, and organic matter effluent are decreased to acceptable levels to reuse the water in the RAS process reducing the need for new raw water. In the first nine months of operation, a water treatment field was used as an end-of-pipe treatment to ensure the water was safe to recirculate for fish. During the winter, the water temperature dropped to 2.7 degrees in the sand filter, but the frost did not reach the water levels in any of the treatment processes. It therefore appears that a hybrid solution can operate sufficiently even in winter conditions. In the first year of operation, a woodchip bioreactor can remove 97 % of the nitrate, although the slow start-up of the RAS caused the bioreactor to be N-limited. On average, 79 % and 92 % of the inflow phosphate concentration was removed in the woodchip bioreactor and the entire hybrid treatment field respectively. The wetland and sand filter removed organic matter sufficiently (35 %), but because of the longer than designed actual water residence, it leached from the bioreactor more than was expected. Further experimentation is needed to identify the financial applicability and performance during higher feeding rates.

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.

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 (CO

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.

Fully integrated physically based hydrological modeling is an essential method for increasing hydrological understanding of groundwater‐surface water (GW‐SW) interactions in peatlands and for predicting anthropogenic impacts on these unique ecosystems. Modeling studies represent peat soil in a simplistic manner, as a homogeneous layer of uniform thickness, but field measurements consistently show pronounced spatial variability in peatlands. This study evaluated uncertainty in groundwater levels and exfiltration fluxes associated with the simplified representation of the peat soil layer. For transferability of the results, impacts of selected topographical and hydrogeological conceptual models on GW‐SW exchange fluxes were simulated in a hypothetical hillslope representing a typical aquifer‐mire transect. The results showed that peat soil layer geometry defined the simulated spatial GW‐SW exchange patterns and groundwater flow paths, whereas total groundwater exfiltration flux to the hillslope and groundwater level in the peatland were only subtly altered by different conceptual peat soil geometry models. GW‐SW interactions were further explored using different scenarios and dimensionless parameters for peat hydraulic conductivity and hillslope‐peatland system slope. The results indicated that accurate representation of physical peat soil properties and landscape topography is important when the main objective is to model spatial GW‐SW exchange. Groundwater level in virtual peatland was not greatly affected by groundwater drawdown in an adjacent aquifer, but the magnitude and spatial distribution of GW‐SW interactions was significantly altered. This means that commonly used groundwater depth observations near peat‐mineral soil interfaces and within peatlands may not be a suitable indicator for monitoring the hydrological state of groundwater‐dependent peatland ecosystems.

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.

The Zayanderud Basin is an important agricultural area in central Iran. In the Basin, irrigation consumes more than 90 percent of the water used, which threatens both the downstream historical city of Isfahan and the Gavkhuni Wetland reserve&mdash, the final recipient of the river water. To analyze impacts of land use changes and the occurrence of metrological and hydrological drought, we used groundwater data from 30 wells, the standardized precipitation index (SPI) and the streamflow drought index (SDI). Changes in the wetland were analyzed using normalized difference water index (NDWI) values and water mass depletion in the Basin was also assessed with gravity recovery and climate experiment (GRACE)-derived data. The results show that in 45 out of studied 50 years, the climate can be considered as normal in respect to mean precipitation amount, but hydrological droughts exist in more than half of the recorded years. The hydrological drought occurrence increased after the 1970s when large irrigation schemes were introduced. In recent decades, the flow rate reached zero in the downstream part of the Zayanderud River. NDWI values confirmed the severe drying of the Gavkhuni Wetland on several occasions, when compared to in situ data. The water mass depletion rate in the Basin is estimated to be 30 (&plusmn, 5) mm annually, groundwater exploitation has reached an average of 365 Mm3 annually, with a constant annual drop of 1 to 2.5 meters in the groundwater level annually. The results demonstrate the connection between groundwater and surface water resources management and highlight that groundwater depletion and the repeated occurrence of the Zayanderud River hydrological drought are directly related to human activities. The results can be used to assess sustainability of water management in the Basin.

Seasonal snow accumulation and melt dominates the hydrology in high latitude areas, providing water storages for both ecological and human needs. However, until recent years there has been a lack of cost-efficient way to measure the spatiotemporal variability of the snow depth and cover in high resolution. Unmanned aircraft systems (UAS) can offer spatial resolutions up to few centimeters, depending on the weather and light conditions, camera quality and drone specification. We used multiple different quadcopters and a fixed wing UAS to determine and analyze the spatiotemporal variability of snow depth and cover in three test plots with different land-cover types (forested slope, open peatland, and peatland-forest) in subarctic northern Finland, where weather and light conditions are challenging. Five measurement campaigns were conducted during winter 2018/2019 and a snow-free bare ground survey after snowmelt. Snow depth maps were constructed using Structure from Motion (SfM) photogrammetry technique and by differentiating the acquired models from snow-covered and snow-free surveys. Due to poor sub-canopy penetration with UAS-SfM method, tree masks were utilized to remove canopy effects prior to analysis. The snow depth maps produced with different UAS were compared to in situ snow course and an automatic ultrasonic measurement data. We highlight the difficulties of working in subarctic winter conditions and discuss the accuracy of UAS-derived snow depth maps. We show that the UAS-derived snow depth measurements agree well with manual snow survey measurements and UAS are suitable method for extending the spatial snow data coverage, whereas a continuous point snow depth measurement is unable to accurately present sub-catchment scale snow depth variability. Furthermore, the spatiotemporal variability of snow accumulation and melt between and within different land cover types is presented.

Arctic rivers’ flow regime has changed under climate change and its consequences on melting glaciers, thawing permafrost, and precipitation patterns. Reservoirs, hydro-power sites, and water diversions have also changed flow regimes in the Arctic. The flow regime alteration in the Arctic rivers has a strong influence on the conservation and sustainability of the native biodiversity of the riverine ecosystem. The main objective of this paper is to evaluate changes in the (1) magnitude of monthly stream flows, (2) magnitude and duration of annual maxima and minima flows, (3) timing of annual maxima and minima, (4) frequency and duration of high and low pulses, and (5) rate and frequency of daily flows in seven major Arctic Rivers. The analyses provide an important basis to characterize and understand the influence of climate change and anthropogenic activities on the flow regimes in the Arctic. Streamflow observations were obtained from the outlet of the Lena, Yenisei, Kolyma, Ob (Russia), Yukon (USA and Canada), Mackenzie (Canada), and Tana (Norway and Finland) rivers in this study. These rivers are main freshwater suppliers for Arctic Ocean. Of these, five have been regulated and two are considered pristine rivers. In addition, the impact of 16 reservoirs on flow regime in the headwaters and tributaries of Lena, Yenisei, Mackenzie, and Kolyma were evaluated. The annual flow showed an increasing trend in all rivers and with a statistically significant level in Yenisei, Lena, and Mackenzie. Our results also indicated that changes in the observed flow regimes at the outlet stations vary from low to incipient level. Out of 16 reservoirs that were analyzed for flow regimes changes, construction of Krasnoyarsk and Shushenskaya dams on the Yenisei River showed the highest impact on flow regime and flow regime alteration was classified as severe in this river.

Seasonally frozen ground (SFG) occurs on ~25% of the Northern Hemisphere’s land surface, and the influence of SFG on water, energy, and solute fluxes is important in cold climate regions. The hydrological role of permafrost is now being actively researched, but the influence of SFG has been receiving less attention. Intuitively, water movement in frozen ground is blocked by ice forming in soil pores that were open to water flow prior to freezing. However, it has been shown that the hydrological influence of SFG is insignificant in some cases, with soil remaining permeable to water even when frozen. There is a clear knowledge gap concerning (1) how intensively and (2) under what physiographical and climate conditions SFG influences hydrological fluxes. We conducted a systematic literature review examining the hydrological importance of SFG we found reported in 143 publications. We found a clear hydrological influence of frozen ground in small-scale laboratory measurements, but a more ambiguous effect when the spatial scale under study increased to hillslopes, catchments, or watersheds. We also found that SFG may be hydrologically less important in forested areas or in regions with deep snow cover. Our systematic review suggests that hydrological influence of SFG may become more important in a future warmer climate with less snow and intensified land use in high-latitude areas.

Due to changes in the snowmelt timing and the potential shift towards less snowfall and more rainfall, infiltration patterns into the soil will increasingly be altered in a warming climate. Mixing and transport processes of water in the unsaturated topsoil layer regulate the subsurface transport and retention of solutes and contaminants, as well as the distribution of plant available water. Recent advances in soil isotope ecohydrology indicate that in some ecosystems, water in macropores largely bypasses soil matrix and rapidly percolates into the groundwater. Here we combine tracer experiments and geophysical surveys to explore soil water mixing in non-stratified till soil in the Pallas catchment located in sub-arctic conditions in Finnish Lapland. A 5x20 m plot at the Kenttärova hilltop was sprinkled with deuterated water (d2H 88‰) for two days (totally 200 mm at average intensity of 6.7 mm/h), until surface water ponding was observed on substantial share of the plot. Soil moisture dynamic were monitored by a network of soil moisture sensors and manual soil probe measurements. Soil water was sampled hourly with suction cup lysimeters at three (5 cm, 30 cm, 60 cm) depths and pan lysimeter at 35 cm depth in two soil profiles on the irrigation plot. Groundwater was sampled hourly, while xylem samples from spruce and birch trees in the plot were collected on each day of the experiment and on a weekly basis during the following month. Ground penetrating radar (GPR) survey and soil coring with window sampler down to 1 m depth were completed four times over the course of the experiment, and additional set of soil cores were taken two weeks after the experiment to inspect how natural precipitation events have infiltrated into the deuterium enriched zone. We investigate the mechanisms of soil matrix water replenishment by answering the following questions: i) Can all soil matrix water be displaced during high volume events and when does newly introduced soil matrix water become available to the plants?; ii) What is the extent of soil water mixing at different depths?; and iii) What is the effect of increased moisture content and groundwater table rise on soil water mixing? Due to paucity of field data sets and inability of most hydrological models to accurately simulate soil freezing and thawing effects, ecohydrologic partitioning has been barely studied in Northern regions with seasonal snow cover. We present a novel field data set that focuses on soil matrix water replenishment in glaciated till soil at sub-arctic conditions. Results support our understanding of ecohydrological processes in northern environments where hydrological cycle is dominated by intense infiltration events as it occurs during snowmelt.

Erosion and sedimentation play a significant role in river morphology and are among the most important issues in river engineering. Riverbank protection is one of the common efforts in river engineering to stop or reduce the rate of side erosion in rivers. Riprap is one of the simplest and most economical river protection methods due to construction material availability, operation simplicity, flexibility, easiness to construct and repair. Anthropogenic disturbances could have several side effects in rivers and subsequently induce a change in river morphology. Hence, morphological analysis is needed to trace the history of channel formation and forecast future changes. Riprap is widely used in the Southern parts of Iran to save the rural and agricultural areas located along the river. The Qareaqaj River is one of the major rivers in the South of Iran that is affected by side erosion in many places due to its high meandering morphology. Hence, a riprap structure was constructed in 2006 to protect the Qasr Ahmad village located in the right bank of the Qareaqaj River. The objective of this study is to evaluate how the river training has affected the channel morphology for 18 years in a 10 Km stretch (5 km above and 5 km below the riprap structure). Five Landsat multispectral images captured in 1995, 1999, 2003, 2010, and 2013 were used as input in the RiMARS (River Morphodynamics Analysis method based on Remote Sensing data) for morphological analysis. The Sinuosity Index (SI) has been estimated for meanders for 18 years and the results indicated that most meanders along the stretch are classified as twisty (about 36%), meandering (22%) and winding (18%). Furthermore, the river is divided into ten sections along the flow path and temporal migration of each section is separately analyzed. The river in its halfway (where the riprap was constructed) has migrated on average by 12.5 m, 2.2 m, 5.5 m, and 9 m in 1999, 2003, 2010, 2013, respectively, when compared to the year 1995. The maximum rate of river migration was observed (6.5 m per year) during 2010-2013 at the 7th decile of the stretch, which is about 2000 m below the protected area. The results clearly indicated that the migration rates increased in the downstream of the riprap protected area after the construction date.

Water, energy, and food security in today's world have been hampered by high population and economic growth, pressures on limited resources, and climate change. Accordingly, balancing the various critical components of biomass in the form of a water-energy-food (WEF) Nexus approach is one of the essential pillars of water resources management, which will enhance the long-term sustainability of water resources by promoting sustainable development. Assessing the WEF Nexus based on CO2 emissions leads to quantify the role of each component of WEF. This work aims to quantify WEF Nexus in a pilot study in the North West of Iran based on analyzing the CO2 emission of the involved sectors. Gathering all require data that are involved in different activities in water, energy, and food sectors is the main challenge in this regard. Sahand Agro-Industry CO2, established in 1996 and expanded in an area about 200 ha to produce alfalfa, maize, potato, rapeseed, sugar beet, and wheat. The area with an average annual temperature of 10.1 °C and bout 356 mm precipitation is located in a warm, dry-summer continental climate (Dsb climate, according to köppen climate classification). A detailed dataset including labor, machinery, diesel oil, fertilizer (nitrogen, potassium, and phosphorus), biocide (pesticide, fungicide, and herbicide), irrigation water (groundwater and surface water), and output per unit area per product has been collected for 2008-2017. We evaluated the WEF Nexus by estimating CO2 emission based on the water and energy equivalent and food production per unit area of crop production systems. In this regard, we applied several indices, including the WEF Nexus, water, and energy consumption, mass, and economic productivity, to estimate the CO2 emitted during a ten-year time period, besides the effect of changing the cropping pattern on the amount of CO2 emission. Furthermore, we developed an approach to achieve optimal cropping pattern to minimize water and energy consumption and maximize productivity. Because of the detail calculation of mentioned indices and existing operational limitations, first, two margin scenarios were developed: 1- crop pattern with the lowest CO2 emission and 2- Crop pattern with the maximum net benefit. For each pattern, we calculated the area for different crops. Then by combining these two marginal patterns and using dynamic programming, we developed 128 different patterns between the two mentioned margins. The results showed that as the differentiation in the amount of CO2 equivalent for each crop, different cultivation patterns would have a different effect on the carbon dioxide emission. Water withdrawal (extraction, displacement, and distribution of water in the field) requires energy consumption, which varies depending on the source used for irrigation. Also, water productivity per kcal per m3 will vary depending on the type of crop, cropping system, and agricultural management. Finally, we clustered scenarios based on CO2 emission and net benefit and suggested the optimum condition.Keywords: CO2 emission, economic productivity, optimization, sustainable development, water-energy-food Nexus

Peatlands are globally threatened by the increasing exploitation. Majority of peatlands in Finland are severely degraded by land use and drainage activities. Peatland restoration is an effective way to increase biodiversity, return natural function of peatlands in catchment hydrology and reduce negative impacts of drainage.Restoration activities recover the wet and open habitats crucial for many valuable species and peatlands ability to store water and nutrients. Restoration activates peat forming processes, and thus reduces greenhouse gas (GHG) emissions and returns peatlands to act as carbon sinks.Restored sites are monitored to determine whether the restoration has succeeded and to gather the experiences to further develop restoration methods. The traditional restoration monitoring demands intensive field work with high labor costs and special ecological expertise. Evaluation is mainly based on visual assessment at present. In addition, monitoring typically cannot cover the entire restored site.There is strong need to develop unbiased indicators and new cost-effective methods producing spatially representative high-quality information on restoration success. We will study new technical possibilities for evaluation of peatland restoration success with unmanned aerial systems (UAS).The latest image processing techniques and their use in mapping and analyzing peatland areas are to be studied. UAS provides prospects not only to ease the demanding restoration field work but also to transform the discrete nature of conventional single data points into a spatial continuum over the whole restored peatland.

Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and monitoring LD progression in areas under human‐induced stresses has become an essential task. In this study, we developed an approach for evaluating and mapping potential LD risks associated with human-induced and biophysical driving variables. We employed 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. The performance of different algorithms was assessed using receiver operating characteristic (ROC), Kappa index, and Taylor diagram. An urbanized watershed, Pole-doab in central Iran, was selected as the case study. The performance data indicated that DA (an novel optimized algorithm) was most accurate in LD risk mapping. In LD zone 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. In all cases, the LD risk maps indicated that land in the southern part of the Pole-doab watershed is most exposed to degradation of different types.

MOSAiC is a one of a kind, year-long study of the Arctic Basin’s behavior focused in large part on interactions between sea ice, atmospheric processes, ecosystem dynamics and oceanography, as well as connections between the Arctic and the mid-latitudes. Our MOSAiC project is focused on how the Arctic Basin’s water cycle behaves throughout the year, especially now that sea ice loss allows for a new source of moisture to the atmosphere during times when this basin was formerly frozen over. These massive changes in open water and corresponding fluxes in conjunction with significant shifts in atmospheric circulation, are altering how moisture is transported into, within, and out of the Arctic Basin. In order to help quantify these Arctic hydrologic cycle variations, we have established the AWIN (Arctic Water Isotope Network) that uses continuous water vapor isotope measurements (δD, δ18O, and deuterium excess) at eight land-based stations from Barrow in Alaska to Ny Alesund in Svalbard, as well as on board the Polarstern.With a network of sites rather than a single station, we gain the significant advantage of being able to track water vapor and how it varies from site to site, allowing us to identify the sources of moisture, and how and where that moisture is transported into, within, and out of the Arctic. For this analysis, we focus on the first months of the expedition (October-December 2019) to closely examine cases of critical events including a major low-pressure system in mid-November that impacted much of the Arctic Ocean basin and three key repeating transport regimes – 1) transport into the Arctic from the North Atlantic via the Greenland Sea, 2) transport into the Arctic via Baffin Bay, and 3) transport out of the Arctic via the Greenland Sea, as well as transport within the Arctic during each of these regimes. For example, in the scenario of transport into the Arctic via Baffin Bay, at our site in Thule, Greenland, we see significant reductions in deuterium excess each time the southerly flow initiates, suggesting significant moisture evaporating from nearby in Baffin Bay. We then can track that moisture to another site to observe how much of that locally-sourced vapor is transported to a given downwind location, allowing us to quantify vapor fluxes and isotopic fractionation processes across the Arctic. By examining these scenarios under varying sea ice conditions and large-scale atmospheric circulation patterns, this circum-Arctic network of water isotope measurements is transforming our understanding of the Arctic hydrologic cycle during MOSAiC.

Water is the most important resource for sustainable agriculture in arid and semi-arid regions, where agriculture is the mainstay for rural societies. By relating the water usage to renewable water resources, we define three stages from sustainable to unsustainable water resources: (1) sustainable, where water use is matched by renewable water capacity, ensuring sustainable water resources, (2) transitional, where water use occasionally exceeds renewable water capacity, and (3) unsustainable, with lack of water resources for agriculture, society, and the environment. Using available drought indicators (standardized precipitation index (SPI) and streamflow drought index (SDI)) and two new indices for agricultural drought (overall agricultural drought index (OADI) and agricultural drought index (ADI)), we evaluated these stages using the example of Fars province in southern Iran in the period 1977&ndash, 2016. A hyper-arid climate prevailed for an average of 32% of the province&rsquo, s spatio-temporal coverage during the study period. The area increased significantly from 30.6% in the first decade (1977&ndash, 1986) to 44.4% in the last (2006&ndash, 2015). The spatiotemporal distribution of meteorological drought showed no significant negative trends in annual precipitation during 1977&ndash, 2016, but the occurrence of hydrological droughts increased significantly in the period 1997&ndash, 2016. The expansion of irrigated area, with more than 60% of rainfed agriculture replaced by irrigated agriculture (especially between 1997 and 2006), exerted substantial pressure on surface water and groundwater resources. Together, climate change, reduced river flow, and significant declines in groundwater level in major aquifers led to unsustainable use of water resources, a considerable reduction in irrigated area, and unsustainability in agricultural production in the period 2006&ndash, 2015. Analysis of causes and effects of meteorological, hydrological, and agricultural drought in the area identified three clear stages: before 1997 being sustainable, 1997&ndash, 2006 being transitional, and after 2006 being unsustainable.

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.

In semi-arid regions, reduced river flows present is a major challenge in water resources management. We present a new standardized contribution of rainfall to runoff index (SCRI) for evaluating changes in rainfall contribution to river flow. We employ the standardized precipitation index (SPI), standardized discharge index (SDI) and SCRI to characterize meteorological drought, hydrological drought and land-use change impacts on river flow, respectively. These indices are applied to the Mond River Basin (Iran), which is regulated by the Salman Farsi and Tangab dams since 2006. A new concept called ‘mirage water’ is proposed that represents the reduced water delivery to downstream areas due to new developments and water withdrawals in headwater tributaries. In particular, mirage water accounts for changes in upstream water consumption between the planning phase and construction/operation life of dams. We recommend that this concept be used for communication with decision makers and managers to clarify the need for revising dimensions of planned dams.

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.

Peatland-based treatment wetlands that purify incoming water by means of natural physical, chemical and biological processes belonging to the peatland ecosystem are widely used at Finnish peat extraction sites. They can comprise either undrained or drained overland flow areas (OFAs or DOFAs), with the OFAs representing the best available technology (BAT) for peat extraction. We analyse here the long-term treatment performance of these OFAs and DOFAs and factors affecting this performance. Data on 14 OFAs and DOFAs in different parts of Finland were taken from the extensive long-term environmental pollution control databases. Nearly half of these wetlands had been monitored for at least 4 years and seven for 8–23 years. The results indicated that peatland-based treatment wetlands purify drainage water as efficiently as other natural treatment wetlands on soils in general, the common challenge being phosphorus retention. Iron was also efficiently retained. The average reductions were highest in OFAs with good hydraulic function, and these also showed long-term water protection performance. An important factor affecting purification efficiency was the hydraulic loading rate. Important system design elements in this regard were the size of the wetland in relation to its catchment area, its gradient, the length of the water flow route within the wetland, and the efficient flow area of the wetland. The results regarding the latter two design elements strongly indicate that not all the areas potentially suitable in DOFAs for water purification are yet being used efficiently.

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.

Suspended sediment load (SSL) modelling is an important issue in integrated environmental and water resources management, as sediment affects water quality and aquatic habitats. Although classification and regression tree (CART) algorithms have been applied successfully to ecological and geomorphological modelling, their applicability to SSL estimation in rivers has not yet been investigated. In this study, we evaluated use of a CART model to estimate SSL based on hydro-meteorological data. We also compared the accuracy of the CART model with that of the four most commonly used models for time series modelling of SSL, i.e. adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) neural network and two kernels of support vector machines (RBF-SVM and P-SVM). The models were calibrated using river discharge, stage, rainfall and monthly SSL data for the Kareh-Sang River gauging station in the Haraz watershed in northern Iran, where sediment transport is a considerable issue. In addition, different combinations of input data with various time lags were explored to estimate SSL. The best input combination was identified through trial and error, percent bias (PBIAS), Taylor diagrams and violin plots for each model. For evaluating the capability of the models, different statistics such as Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and percent bias (PBIAS) were used. The results showed that the CART model performed best in predicting SSL (NSE=0.77, KGE=0.8, PBIAS