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Most northern peatlands are severely degraded by land use and drainage. Peatland restoration is an effective way to return the natural functions of peatlands in the catchment hydrology, discontinue the peat degradation and re-establish the long-term carbon sinks. The main aim of the rewetting is to direct the water flows back to the pristine routes and to increase the water-table levels. Conventional monitoring methods such as stand-pipe wells are typically limited to sparse locations and cannot give a spatially representative overview.We introduced a novel high-resolution approach to spatially evaluate the surface flow path and wetness changes after restoration. We applied a UAS SfM (Unmanned Aerial System Structure-from-Motion) method supported by ubiquitous LiDAR (Light Detection and Ranging) data to produce digital elevation models, flow accumulation maps and SWI (SAGA Wetness Index) models for two boreal, minerotrophic restoration sites and their pristine control sites. The pristine sites were to represent natural changes and technology-related uncertainty.According to our results, the hydrological restoration succeeded at the sites showing that the wetness increased by 2.9–6.9% and its deviation decreased by 13–15% 1–10 months after the restoration. Absolute changes derived with data from simultaneous control flights at the pristine sites were 0.4–2.4% for wetness and 3.1–3.6% for the deviation. Also, restoration increased the total length of the main flow routes by 25–37% while the controlling absolute change was 3.1–8.1%.The validity of the topography-derived wetness was tested with field-gathered soil moisture samples which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites. We conclude the water accumulation modelling based on topographical data potential for assessing the changed surface flows in peatland restoration monitoring. However, the uncertainties related to the heterogenic soil properties and complex groundwater interactions require further method development.

Climate change and anthropogenic activities have always affected the hydrological condition of watersheds. The uniqueness of Nordic watersheds characteristics (systems of lakes and rivers dominant by cold climate) and land cover (drained and pristine forests and peatlands) results in different river regimes in these regions compared to the other parts of the world. Long extreme cold winters usually freeze the river and lakes deeply to some depth, while, during short Nordic summers, the river flows can be influenced by forest and forestry activities, especially drainage systems. In addition, the changing climate is another driver that impacts river flows, especially extreme hydrological events (floods and droughts). This study investigates the long-term flood frequency alteration in two snowmelt and rainfall-dominant seasons for several headwaters in Finland as a Nordic region. The long-term daily discharge, rainfall, snow depth, and temperature data for selected watersheds were analyzed. The monthly and annual changes in mean, maximum, and minimum of discharge and rainfall and their trends were assessed to detect the rain and snowmelt-dominated seasons. Then the flood frequencies are estimated using EV (Extreme Value) method for both seasons in different periods. Investigating such changes provides a broad view of the current and long-term situation of the river systems, which can help for long-term water resources planning and hydrosystem developments.

The Nordic Bioeconomy Pathways (NBPs), conceptualized subsets of Shared Socioeconomic Pathways varying from environmentally friendly to open-market competition scenarios, can lead to plausible stressors in future for using bioresources. This study analysed the impacts of NBPs on hydrology and water quality based on two different land system management attributes: management strategy and a combination of reduced stand management and biomass removal at a catchment-scale projection. To understand the potential impacts of NBPs, the Simojoki catchment in northern Finland was chosen, as the catchment mainly covered peatland forestry. The analysis integrated a stakeholder-driven questionnaire, the Finnish Forest dynamics model, and Soil and Water Assessment Tool to build NBP scenarios, including Greenhouse gas emission pathways, for multiple management attributes to simulate flows, nutrients, and suspended solids (SS). For the catchment management strategy, an annual decrease in nutrients was observed for sustainability and business-as-usual scenarios. Reduced stand management and biomass removal also led to decreased export of nutrients and SS for the same scenarios, whereas, in other NBPs, the export of nutrients and SS increased with decreased evapotranspiration. Although the study was investigated at a local scale, based on the current political and socioeconomic situation, the approach used in this study can be outscaled to assess the use of forest and other bioresources in similar catchments.

In the present study, we review the methods and approaches used for uncertainty handling in hydrological forecasting of streamflow, floods, and snow. This review has six thematic sections: (1) general trends in accounting uncertainties in hydrological forecasting, (2) sources of uncertainties in hydrological forecasting, (3) methods used in the studies to address uncertainty, (4) multi-criteria approach for reducing uncertainty in hydrological forecasting and its applications (5) role of remote sensing data sources for hydrological forecasting and uncertainty handling, (6) selection of hydrological models for hydrological forecasting. Especially, a synthesis of the literature showed that approaches such as multi-data usage, multi-model development, multi-objective functions, and pre-/post-processing are widely used in recent studies to improve forecasting capabilities. This study reviews the current state-of-the-art and explores the constraints and advantages of using these approaches to reduce uncertainty. The comparative summary provided in this study offers insights into various methods of uncertainty reduction, highlighting the associated advantages and challenges for readers, scientists, hydrological modelers, and practitioners in improving the forecast task. A set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

Trends in river flow at national scale in Iran remain largely unclear, despite good coverage of river flow at multiple monitoring stations. To address this gap, this study explores the changes in Iranian rivers’ discharge using regression and analysis of variance methods to historically rich data measured at hydrometric stations. Our assessment is performed for 139 selected hydrometric stations located in Iranian data-rich basins that cover around 97% of the country’s rivers with more than 30 years of observations. Our findings show that most of Iran’s rivers (>56%) have undergone a downward trend (p–value

Snow conditions in the northern hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale, and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the results and their accuracy compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the Structure from Motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall RMSEs varying between 13.0 to 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

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.

Water scarcity is a serious socio-environmental challenge for sustainable development which is recognized as a potential cause of social conflict within and between countries. It is expected to intensify due to increasing water demands from increasing populations, rapid urbanization, industrialization, and climate changes. With predictions of dire global water scarcity, attention is turning to Unconventional Water Resources (UWRs) which are considered as supplementary water resources that need specialized processes to be used as water supply. The literature encompasses a vast number of studies on various UWRs and their usefulness in certain environmental and/or socio-economic contexts. Considering the increasing importance of UWRs in the global push for water security, the current study intends to offer a nuanced understanding of the existing research on UWRs by summarizing the key concepts in the literature. The number of articles published on UWRs have increased significantly over time and most publications were authored from researchers based in the USA or China, India, Iran, and Spain. Here, twelve general types of UWRs including fog, dew, rainwater harvesting, and cloud seeding as Atmospheric Unconventional Water (AUW); artificial recharge, fossil water as Unconventional Ground Water (UGW); iceberg water and virtual water as Transferred Unconventional Water (TUW), and wastewater, desalinated water, and agricultural drainage water as Processed Unconventional Water (PUW), were used to assess their global distribution, showing that climatic conditions are the main driver for the application of certain UWRs. Overall, the literature review demonstrated that, even though UWRs provide promising possibilities for overcoming water scarcity, current knowledge is patchy and points towards UWRs being, for the most part, limited in scope and applicability due to geographic, climatic, economic, and political constraints. Future studies focusing on improved quantitative documentation and demonstration of the physical and socio-economic potential of various UWRs could help in strengthening the case for some, if not all, UWRs as avenues for the sustainable provision of water.Keywords: Water scarcity; UWRs; distribution maps; literature review

The majority of lake temperature studies have investigated climate-induced changes occurring at the lake surface, primarily by analyzing detailed satellite images of surface water temperature. Whilst essential to observe long-term change, satellite images do not provide information on the thermal environment at depth, thus limiting our understanding of lake thermal responses to a warming world. Long-term in situ observational data can fill some of the information gap, with depth-resolved field measurements providing a detailed view of thermal change throughout the water column. However, many previous studies that have investigated multi-decadal changes in lake temperature, both at the surface and at depth, have typically focused on north temperate lakes. Relatively few studies have investigated temperature variations in lakes situated north of the Arctic Circle, which is one of the most rapidly warming regions on Earth. Here, using a 60-year (1961–2020) observational data set of summer water temperature (July–September) from Lake Inari (Finland), we investigate changes in the thermal environment of this pristine lake. Our analysis suggests a statistically significant summer warming trend at the lake surface (+0.25°C decade⁻¹, p-value

This study examined changes in Iran’s river flows by applying regression and analysis of variance methods to long-term ground-truth data. Evaluations were performed for the country’s data-rich rivers, covering almost 97% of all rivers and including more than 35 years of measurements. The results showed that about 56% of Iran’s rivers have experienced a negative trend in mean annual flow that is approximately 2.5 times greater than that reported for world’s rivers, leading to a shift from perennial to intermittent for about 20% of rivers in Iran’s sub-basins. This reflects surface freshwater shortages in Iran caused by natural and, more importantly, anthropogenic disturbances. It may even indicate the development of new hydrological regimes which can have significant implications for future surface water storage in Iran. This research improves understanding of changes in Iran’s river flows and provides beneficial information for sustainable water resources management in the country.

Key Points: • Land use such as agriculture and peat extraction alter the physical and hydraulic properties of the peat more strongly than other land uses • The top 30 cm peat depth was most affected by agriculture and peat extraction, as indicated by the bulk density, specific yield, and porosity values • The van Genuchten-Mualem soil water retention model was applied successfully to different layers of peat under different land use Undisturbed peatlands are effective carbon sinks and provide a variety of ecosystem services. However, anthropogenic disturbances, especially land drainage, strongly alter peat soil properties and jeopardize the benefits of peatlands. The effects of disturbances should therefore be assessed and predicted. To support accurate modeling, this study determined the physical and hydraulic properties of intact and disturbed peat samples collected from 59 sites (in total 3,073 samples) in Finland and Norway. The bulk density (BD), porosity, and specific yield (Sy) values obtained indicated that the top layer (0–30 cm depth) at agricultural and peat extraction sites was most affected by land use change. The BD in the top layer at agricultural, peat extraction, and forestry sites was 441%, 140%, and 92% higher, respectively, than that of intact peatlands. Porosity decreased with increased BD, but not linearly. Agricultural and peat extraction sites had the lowest saturated hydraulic conductivity, Sy, and porosity, and the highest BD of the land use options studied. The van Genuchten-Mualem (vGM) soil water retention curve (SWRC) and hydraulic conductivity (K) models proved to be applicable for the peat soils tested, providing values of SWRC, K, and vGM-parameters (α and n) for peat layers (top, middle and bottom) under different land uses. A decrease in peat soil water content of ≥10% reduced the unsaturated K values by two orders of magnitude. This unique data set can be used to improve hydrological modeling in peat-dominated catchments and for fuller integration of peat soils into large-scale hydrological models.

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.

For accurate forecasting of extreme events in rivers, streamflow time series with sub‐daily temporal resolution (1–6 hour) are preferable, but discharge time series for long rivers are usually available at daily or monthly resolution. In this study, the scaling properties of hourly and daily streamflow time series were measured. As an innovation, the effects of extreme values on the multifractal behavior of these series were evaluated. Interestingly, both hourly and daily discharge records led to nearly identical scaling trends and identical crossover times. Daily and hourly discharge time series appeared to be non‐stationary when the timescale ranged from 75 to 366 days. Otherwise, the signals may be considered stationary time series. In addition, the results indicated that the extreme values strongly contribute to the multifractality of the series. The width of singularity spectra decreased considerably when the extreme events were removed from both hourly and daily discharge records.

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.

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.

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.

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

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.

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.