Hydro-meteorological droughts across the Baltic Region: The role of the accumulation periods.

Based on the physical and geographical conditions, the Baltic Region is categorised as a humid climate zone. This means that, there is usually more precipitation than evaporation throughout the year, suggesting that droughts should not occur frequently in this region. Despite the humid climate in the region, the study focused on assessing the spatio-temporal patterns of droughts. The drought events were analysed across the Baltic Region, including Sweden, Finland, Lithuania, Latvia, and Estonia. This analysis included two drought indices, the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI), for different accumulation periods. Daily data series of precipitation and river discharge were used. The spatial and temporal analyses of selected drought indices were carried out for the Baltic Region. In addition, the decadal distribution of drought classes was analysed to disclose the temporal changes and spatial extent of drought patterns. The Pearson correlation between SPI and SDI was applied to investigate the relationship between meteorological and hydrological droughts. The analysis showed that stations with more short-duration SPI or SDI cases had fewer long-duration cases and vice versa. The number of SDI cases (SDI ≤ -1) increased in the Western Baltic States and some WGSs in Sweden and Finland from 1991 to 2020 compared to 1961-1990. The SPI showed no such tendencies except in Central Estonia and Southern Finland. The 6-month accumulation period played a crucial role in both the meteorological and hydrological drought analyses, as it revealed prolonged and widespread drought events. Furthermore, the 9- and 12-month accumulation periods showed similar trends in terms of drought duration and spatial extent. The highest number of correlation links between different months was found between SPI12-SDI9 and SPI12-SDI12. The results obtained have deepened our understanding of drought patterns and their potential impacts in the Baltic Region.

Nordic socio-recreational ecosystem services in a hydropeaked river.

Fluctuating energy prices call for short-term river flow regulation at hydropower plants (HPPs), which can lead to hydropeaking - the pulsating water flow downstream from a HPP. Hydropeaking can affect land use areas of regulated rivers and subsequently their socio-recreational ecosystem services (SRESs). These areas often offer a range of services, such as swimming, boating, fishing, hiking, cycling, and berry picking. Such activities hold significant value in Nordic culture and for human wellbeing. We have examined how SRES land use areas are affected by hourly hydropeaking in a reach of the Kemijoki River in Finland. First, we determined the state of hydropeaking in the river by employing two indicators, normalized daily maximum flow difference and sub-daily flow ramping. Next, we looked at the spatiotemporal impacts of peaking hydrology using inundation maps derived from 2D-hydrodynamic modeling and a high-resolution land use map with clearly identified SRES areas. Finally, we examined the hazards to hydraulic safety in the river channel in the context of instream recreation. Our results show that hydropeaking levels in the study area remained consistently high throughout the entire study period, from 2010 to 2021. This was the case in all seasons except for the spring of 2013, 2016 and 2019. We determined that hydropeaking impacts on SRESs are mostly felt in the littoral zone (0.84 km2 i.e., 3.1 % of the study area) during the summer season as 25 % (0.21 km2) of this zone is influenced by hydropeaking. In addition, multiple recreational use areas in this zone, such as beaches, riparian forest, and summer cottages, were found to be affected by hydropeaking. The results show that most of the river channel becomes hydraulically unsafe during high ramping flows. The highest hazard to instream recreation opportunities is likely to occur during summer. Consequently, hydropeaking can threaten the social and recreational services of Nordic rivers.

Spatio-temporal analysis of climate and irrigated vegetation cover changes and their role in lake water level depletion using a pixel-based approach and canonical correlation analysis.

Lake Urmia, located in northwest Iran, was among the world's largest hypersaline lakes but has now experienced a 7 m decrease in water level, from 1278 m to 1271 over 1996 to 2019. There is doubt as to whether the pixel-based analysis (PBA) approach's answer to the lake's drying is a natural process or a result of human intervention. Here, a non-parametric Mann-Kendall trend test was applied to a 21-year record (2000-2020) of satellite data products, i.e., temperature, precipitation, snow cover, and irrigated vegetation cover (IVC). The Google Earth Engine (GEE) cloud-computing platform utilized over 10 sub-basins in three provinces surrounding Lake Urmia to obtain and calculate pixel-based monthly and seasonal scales for the products. Canonical correlation analysis was employed in order to understand the correlation between variables and lake water level (LWL). The trend analysis results show significant increases in temperature (from 1 to 2 °C during 2000-2020) over May-September, i.e., in 87 %-25 % of the basin. However, precipitation has seen an insignificant decrease (from 3 to 9 mm during 2000-2019) in the rainy months (April and May). Snow cover has also decreased and, when compared with precipitation, shows a change in precipitation patterns from snow to rain. IVC has increased significantly in all sub-basins, especially the southern parts of the lake, with the West province making the largest contribution to the development of IVC. According to the PBA, this analysis underpins the very high contribution of IVC to the drying of the lake in more detail, although the contribution of climate change in this matter is also apparent. The development of IVC leads to increased water consumption through evapotranspiration and excess evaporation caused by the storage of water for irrigation. Due to the decreased runoff caused by consumption exceeding the basin's capacity, the lake cannot be fed sufficiently.

Three-decade assessment of dry and wet spells change across Iran, a fingerprint of climate change.

Extended periods of hydro-climate extremes with excessive or scarce rainfall associated with high or low temperatures have resulted in an imbalanced water cycle and inefficient socio-economic systems in several regions of Iran. However, there is a lack of comprehensive investigations on short-term to long-term variations in timing, duration, and temperature of wet/dry spells. This study bridges the current gap through a comprehensive statistical analysis of historical climatic data (1959-2018). Results indicated that the negative tendency of the accumulated rainfall (- 0.16/ - 0.35 mm/year during the past 60/30 years) in 2- to 6-day wet spells had made significant contributions to the ongoing downward trend in annual rainfall (- 0.5/ - 1.5 mm/year during the past 60/30 years) owing to a warmer climate condition. Warmer wet spells are likely responsible for precipitation patterns changes in snow-dominated stations since their wet spells temperature has more than threefold growth with increasing distance to coasts. The most detected trends in climatic patterns have started in the last two decades and become more severe from 2009 to 2018. Our results confirm the alteration of precipitation features across Iran due to anthropogenic climatic change, and suggest expected increase in air temperature would likely result in further dry and warm conditions over the coming decades.

The detection and monitoring of pollution caused by gold mining using a vegetation cover index.

This study explores how a vegetation cover (VC) index can be employed as a pollution warning tool in gold mining areas in the Northwest of Iran. The analysis included the following: (a) the extraction of normalized difference vegetation index (NDVI) maps from Landsat images in three zones, i.e., mining operations, upstream areas without any exploration, and the downstream area of the mining activities, (b) calculation of the zones' VC, (c) investigation of transformation trends in each pixel of VC time series using the Mann-Kendall trend test, (d) determination of the pixels with significant VC reduction and the significant starting points of the trend using the sequential Mann-Kendall test, (e) assessment of the correlation between the zones with significantly reduced VC, and (f) a correlation test between average monthly and annual climate parameters and VC. Our results indicate that although 51 ha of VC has been demolished around the mining activities areas (i.e., zone 1), an overall upward trend in vegetation with no chemical leakage is observed into the downstream area of the basin (i.e., zone 3). This upward trend can be mostly attributed to the increasing precipitation and decreasing temperature in the study period. The fact that the area downstream of the mine shows that the heap leaching method for gold mining in Andaryan mine is currently not damaging the vegetation, this likely means that there is no leakage to the surrounding environment from the mine. Our results further show that using NDVI in a pixel-based scale and statistical methods has a high potential to quantify the effects of human activities on surface biophysical characteristics.

Causes & effects of upstream-downstream flow regime alteration over Catchment-Estuary-Coastal systems.

The construction of large dams along rivers has significantly changed the natural flow regime, reducing the inflow into many lakes and terminal wetlands. However, the question of the impact of dam operation on downstream estuarine wetlands has less been taken into account. Spatio-temporal flow regime alteration in the Mond River shows the complexity of drivers affecting the estuary-coastal system named the Mond-Protected Area in southern Iran. To this end, we applied river impact (RI) and Indicator of hydrological alteration (IHA) methods on monthly and daily river flow data across the basin. Based on the river impact method, a "drastic" impact below two in-operation (Tangab and Salman Farsi) dams, with RI values of 0.02 and 0.08, diminish to a 'severe' impact with RI value of 0.35 at the last gauge (Ghantareh) on the main corridor of the Mond river due to the addition of flow from a large mid-basin (about 20,254 km2). Furthermore, the degree of hydrological alteration (daily flow analysis) at mid-stream (e.g., Dehram gauges) was similar to the unregulated upstream tributaries (e.g., Hanifaghan gauges). The remote sensing analysis in the Mond Protected Area showed the prevailing impact of sea-level rise in the Persian Gulf with the inundation of the coastal area and a shift of vegetation in a landward direction which complied with standardized precipitation index (SPI) values as a meteorological drought indicator. Thus, the consequence of climate change (e.g., sea-level rise, draught) has a higher impact on the protected area than the upstream river regulation and land-use change in the Mond basin. The holistic approach and the catchment-level study allowed us to see the complexity of the drivers influencing the estuary-coastal system.

The impact of land use and land cover change on groundwater recharge in northwestern Bangladesh.

Groundwater recharge is affected by various anthropogenic activities, land use and land cover (LULC) change among these. The long-term temporal and seasonal changes in LULC have a substantial influence on groundwater flow dynamics. Therefore, assessment of the impacts of LULC changes on recharge is necessary for the sustainable management of groundwater resources. The objective of this study is to examine the effects of LULC changes on groundwater recharge in the northwestern part of Bangladesh. Spatially distributed monthly groundwater recharge was simulated using a semi-physically based water balance model. Long-term temporal LULC change analysis was conducted using LULC maps from 2006 to 2016, while wet and dry LULC maps were used to examine seasonal changes. The results show that the impervious built-up area has increased by 80.3%, whereas vegetated land cover has decreased by 16.4% over the study period. As a result, groundwater recharge in 2016 has decreased compared to the level seen in 2006. However, the decrease in recharge due to long-term temporal LULC changes is very small at the basin scale (2.6 mm/year), although the impact on regional level is larger (17.1 mm/year) due to urbanization. Seasonal LULC variations also affect recharge due to the higher potential for dry seasonal LULC compared to the wet seasonal LULC, a substantial difference (20.6 mm/year). The results reveal important information about the groundwater system and its response to land cover changes in northwestern Bangladesh.

Unconventional water resources: Global opportunities and challenges.

Water is of central importance for reaching the Sustainable Development Goals (SDGs) of the United Nations. With predictions of dire global water scarcity, attention is turning to resources that are considered to be unconventional, and hence called Unconventional Water Resources (UWRs). These 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. However, a recent, all-encompassing article that brings the collective knowledge on UWRs together is missing. 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, particularly in the past ten years. And while most publications were authored from researchers based in the USA or China, other countries such as India, Iran, Australia, and Spain have also featured prominently. Here, twelve general types of UWRs were used to assess their global distribution, showing that climatic conditions are the main driver for the application of certain UWRs. For example, the use of iceberg water obviously necessitates access to icebergs, which are taken largely from arctic regions. 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 documentation and demonstration of the quantitative 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.

Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region.

Lake water level fluctuation is a function of hydro-meteorological components, namely input, and output to the system. The combination of these components from in-situ and remote sensing sources has been used in this study to define multiple scenarios, which are the major explanatory pathways to assess lake water levels. The goal is to analyze each scenario through the application of the water balance equation to simulate lake water levels. The largest lake in Iran, Lake Urmia, has been selected in this study as it needs a great deal of attention in terms of water management issues. We ran a monthly water balance simulation of nineteen scenarios for Lake Urmia from 2003 to 2007 by applying different combinations of data, including observed and remotely sensed water level, flow, evaporation, and rainfall. We used readily available water level data from Hydrosat, Hydroweb, and DAHITI platforms; evapotranspiration from MODIS and rainfall from TRMM. The analysis suggests that the consideration of field data in the algorithm as the initial water level can reproduce the fluctuation of Lake Urmia water level in the best way. The scenario that combines in-situ meteorological components is the closest match to the observed water level of Lake Urmia. Almost all scenarios showed good dynamics with the field water level, but we found that nine out of nineteen scenarios did not vary significantly in terms of dynamics. The results also reveal that, even without any field data, the proposed scenario, which consists entirely of remote sensing components, is capable of estimating water level fluctuation in a lake. The analysis also explains the necessity of using proper data sources to act on water regulations and managerial decisions to understand the temporal phenomenon not only for Lake Urmia but also for other lakes in semi-arid regions.

Integration of hard and soft supervised machine learning for flood susceptibility mapping.

Flooding is a destructive natural phenomenon that causes many casualties and property losses in different parts of the world every year. Efficient flood susceptibility mapping (FSM) can reduce the risk of this hazard, and has become the main approach in flood risk management. In this study, we evaluated the prediction ability of artificial neural network (ANN) algorithms for hard and soft supervised machine learning classification in FSM by using three ANN algorithms (multi-layer perceptron (MLP), fuzzy adaptive resonance theory (FART), self-organizing map (SOM)) with different activation functions (sigmoidal (-S), linear (-L), commitment (-C), typicality (-T)). We used integration of these models for predicting the spatial expansion probability of flood events in the Ajichay river basin, northwest Iran. Inputs to the ANN were spatial data on 10 flood influencing factors (elevation, slope, aspect, curvature, stream power index, topographic wetness index, lithology, land use, rainfall, and distance to the river). The FSMs obtained as model outputs were trained and tested using flood inventory datasets earned based on previous records of flood damage in the region for the Ajichay river basin. Sensitivity analysis using one factor-at-a-time (OFAT) and all factors-at-a-time (AFAT) demonstrated that all influencing factors had a positive impact on modeling to generate FSM, with altitude having the greatest impact and curvature the least. Model validation was carried out using total operating characteristic (TOC) with an area under the curve (AUC). The highest success rate was found for MLP-S (92.1%) and the lowest for FART-T (75.8%). The projection rate in the validation of FSMs produced by MLP-S, MLP-L, FART-C, FART-T, SOM-C, and SOM-T was found to be 90.1%, 89.6%, 71.7%, 70.8%, 83.8%, and 81.1%, respectively. While integration of hard and soft supervised machine learning classification with activation functions of MLP-S and MLP-L showed a strong flood prediction capability for proper planning and management of flood hazards, MLP-S is a promising method for predicting the spatial expansion probability of flood events.

Battling Water Limits to Growth: Lessons from Water Trends in the Central Plateau of Iran.

The Zayandeh-Rud River Basin in the central plateau of Iran continues to grapple with water shortages due to a water-intensive development path made possible by a primarily supply-oriented water management approach to battle the water limits to growth. Despite inter-basin water transfers and increasing groundwater supply, recurring water shortages and associated tensions among stakeholders underscore key weaknesses in the current water management paradigm. There was an alarming trend of groundwater depletion in the basin's four main aquifers in the 1993-2016 period as indicated by the results of the Mann-Kendall3 (MK3) test and Innovative Trend Analysis (ITA) of groundwater volume. The basin's water resources declined by more than 6 BCM in 2016 compared to 2005 based on the equivalent water height (EWH) derived from monthly data (2002-2016) from the GRACE. The extensive groundwater depletion is an unequivocal evidence of reduced water availability in the face of growing basin-wide demand, necessitating water saving in all water use sectors. Implementing an integrated water resources management plan that accounts for evolving water supply priorities, growing demand and scarcity, and institutional changes is an urgent step to alleviate the growing tensions and preempt future water insecurity problems that are bound to occur if demand management approaches are delayed.

Vegetation height estimation using ubiquitous foot-based wearable platform.

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

Development of novel hybridized models for urban flood susceptibility mapping.

Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.

RiMARS: An automated river morphodynamics analysis method based on remote sensing multispectral datasets.

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

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.

Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the mostdevastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. Results of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy = 90-92%, Kappa = 79-84%, Success ratio = 94-96%, Threat score = 80-84%, and Heidke skill score = 79-84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions.

Changes in short term river flow regulation and hydropeaking in Nordic rivers.

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