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

Analysis of Longitudinal Cracks in Crest of Doroodzan Dam

Doroodzan earth dam is located in 85 km north western of Shiraz. Because of the unusual seepage flow in the left abutment, in 1992 an impermeable vane was grouted there. Soon after that, obvious changes in water Table profile occurred and simultaneously some incremental number of cracks in left abutment crest was appeared. In present study seepage through left abutment has been analyzed by considering water Table changes. Different phreatic surface line was carried out from recent 20 years in order to find the most vulnerable one. In addition, Seismic loading used to get proper perception of seismic stability. First, by gathering data from piezometric head through the left abutment, most critical phreatic line in left abutment section of dam was observed. Then by using present phreatic surface in numerical modeling of critical section in the left abutment of dam, long term stability of downstream in different situation were calculated. The conditions were changed by increasing the saturation zone and the time which saturation zone stay through the downstream body

Optimization of Water-Energy-Food Nexus considering CO2 emissions from cropland: A case study in northwest Iran

Water-Energy-Food (WEF) Nexus and CO2 emissions for a farm in northwest Iran were analyzed to provide data support for decision-makers formulating national strategies in response to climate change. In the analysis, input–output energy in the production of seven crop species (alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat) was determined using six indicators, water, and energy consumption, mass productivity, and economic productivity. WEF Nexus index (WEFNI), calculated based on these indicators, showed the highest (best) value for silage corn and the lowest for potato. Nitrogen fertilizer and diesel fuel with an average of 36.8% and 30.6% of total input energy were the greatest contributors to energy demand. Because of the direct relationship between energy consumption and CO2 emissions, potato cropping, with the highest energy consumption, had the highest CO2 emissions with a value of 5166 kg CO2eq ha−1. A comparison of energy inputs and CO2 emissions revealed a direct relationship between input energy and global warming potential. A 1 MJ increase in input energy increased CO2 emissions by 0.047, 0.049, 0.047, 0.054, 0.046, 0.046, and 0.047 kg ha−1 for alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat, respectively. Optimization assessments to identify the optimal cultivation pattern, with emphasis on maximized WEFNI and minimized CO2 emissions, showed that barley, rapeseed, silage corn, and wheat performed best under the conditions studied.publishedVersio

Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs

Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations

Strong Warming Rates in the Surface and Bottom Layers of a Boreal Lake: Results From Approximately Six Decades of Measurements (1964–2020)

High-latitude lakes are warming faster than the global average with deep implications for life on Earth. Using an approximately six-decade long in situ data set, we explored the changes in lake surface-water temperature (LST), lake deep-water temperature (LDT), lake depth-weighted mean water temperature (LDMT), and ice-free days in Lake Kallavesi, a boreal lake in central Finland, when the lake was stratified (June–August). Our results suggest that the LST is warming faster than the local air temperature (AT). As for the LST, fast warming was also observed in the LDT and LDMT, but at rates slower than those in the LST. The number of ice-free days also shows an upward trend, with a rate of about 7 days per decade during the study period. The corresponding local AT is the main driver of the LST, followed by the ice-free days and annual mean AT. Air temperature and ice-free days also mainly contribute to the changes in the LDMT. The LDT is affected more by the North Atlantic Oscillation signals in this freshwater lake. The AT in the prior months does not affect the LDT in Lake Kallavesi although the AT during the prior season, that is, spring, is the main driver of summer LDT. This highlights the local AT impact on the LDT at time scales longer than a month. The warming rates in the lake water are at a minimum in June because the lake is not yet strongly stratified in this month when compared to July and August. These findings improve our knowledge of long-term changes in the lake water temperature in a high-latitude lake, a region with severe environmental consequences due to fast changes in the AT and lake ice phenology

The impact of irrigation return flow on seasonal groundwater recharge in northwestern Bangladesh

Peer reviewedPublisher PD

Hydraulic and physical properties of managed and intact peatlands : application of the van Genuchten-Mualem models to peat soils

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 useUndisturbed 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

A power market-based operation support model for sub-daily hydropower regulation practices

Highlights • Investigate the impact of instant energy demand on sub-daily river regime. • Introducing power market impact index. • Introducing system efficiency ratio index. • Provides an efficient tool for sustainable river management. • Assess the interaction of power market and regulation practices.With increasing power production from renewable energy sources, sub-daily variations in energy demand need to be balanced. Today, hydropower is commonly used as balancing power. In this study, we quantified the impact of capacity constraints, in terms of reservoir volume and hydropower capacity, on the potential to comply with instant energy demand. To evaluate the impact, we developed two new metrics, power market impact and system efficiency ratio, which are based on two threshold flow regimes derived from natural flow as lower threshold release and regulated flow (based on hourly energy prices) as upper threshold release. The operation support model comprises 96 different regulation scenarios based on varying combinations of hydropower and reservoir capacities. For each scenario, an hourly water balance was simulated, to obtain the highest complying with upper threshold release based on actual energy demand. We tested the framework on the Kemijoki river with defined thresholds based on the natural flow regime (tributary river Ounasjoki) and the hourly energy price in Finland in 2017, and estimated the impact of regulation on hourly flow regime at the Taivalkoski hydropower station. The annual flow regime impact in 2013, 2014 and 2015 was estimated to be 74%, 84% and 61%, respectively, while the monthly impact varied from 27% to 100%. Our framework for evaluating interactions between the power market and sub-daily regulation practices is a useful novel tool for sustainable river management and can be easily applied to different rivers and regions and evaluated for different timescales

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application's objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models' principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems

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

Highlights • Water balance algorithms were used to simulate semi-arid lake water levels. • Scenarios were formed by combining in-situ and remote sensing data sources. • The proposed combinations can reproduce lake water level even without in-situ data. • Using in-situ data as initial water level matched best to simulate lake water level. • 9 out of 19 scenarios did not vary significantly with in-situ water level.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