While evapotranspiration (ET) is normally measured as one hydrologic component, evaporation (E) and transpiration (T) result from different physical-biological processes. In the two-source model, a trapezoidal framework has been widely applied in recent years. The key to applying the trapezoidal framework is the determination of the dry/wet boundaries of the land surface temperature-vegetation coverage trapezoid (LST-fc). Although algorithms have been developed to characterize the two boundaries, , there still, however, exists a significant uncertainty near the wet boundary which scatters in a discrete and uneven manner. It thus is difficult to precisely locate the wet boundary. To tackle the problem, a Wet Boundary Algorithm (WBA) was developed in this study and the algorithm was applied in the region of Huang-Huai-Hai plain of China by using Pixel Component Arranging and Comparing Algorithm (PCACA) to retrieve ET from MODIS Data. The latent heat flux (LE) observed by eddy covariance (EC) measurements from China FLUXNET was used to verify the modified model where the coefficient of determination (R2) was found to 0.81 and the root-mean-square-error (RMSE) was 22.8 W/m2. The ratio of transpiration to evapotranspiration (T/ET) varied between 0.5-0.75 over the region of Huang-Huai-Hai plain. The spatial and temporal distribution of ET revealed that agriculture practices had a great influence on the hydrological cycle.

Based on historical records and crop harvest scores extracted from historical documents, this study reconstructed the spatial-temporal distribution and severities of floods in the Yangtze-Huai River valley in 1823 and 1849. We also summarized the effects of the floods on society and identified government measures taken to cope with the floods in the context of the economic recession in the period of 1801--1850. The 1823 flood, which was caused by the heavy precipitation of the Meiyu period and typhoons, severely affected areas in the lower reaches of the Yangtze River. Meanwhile, the 1849 flood, triggered by long-term, high-intensity Meiyu precipitation in the middle and lower reaches of the Yangtze River, mainly affected areas along the Yangtze River. The 1849 disaster was more serious than the one in 1823. In the lower reaches of the Yangtze River, the 1849 flood caused the worst agricultural failure of the period 1730--1852. To deal with the disasters, the Qing government took relief measures, such as exempting taxes in the affected areas, distributing grain stored in warehouses, and transferring grain to severely afflicted areas. These relief measures were supplemented by auxiliary measures, such as exempting commodity taxes on grain shipped to disaster areas and punishing officials who failed to provide adequate disaster relief. The flood disasters disrupted the water system of the Grand Canal and forced the Qing government to transport Cao rice by sea beginning in 1826. This laid the groundwork for the rise of coastal shipping in modern China. With the economic recession of the 19th century, Chinese society was not as resilient to floods as it was in the 18th century. Compared to droughts, floods are more difficult to deal with and pose greater threats to infrastructure and to normal life and work in the cities.

While 1992 marked the first major dam – Manwan – on the main stem of the Mekong River, the post-2010 era has seen the construction and operationalisation of mega dams such as Xiaowan (started operations in 2010) and Nuozhadu (started operations in 2014) that were much larger than any dams built before. The scale of these projects implies that their operations will likely have significant ecological and hydrological impacts from the Upper Mekong Basin to the Vietnamese Delta and beyond. Historical water level and water discharge data from 1960 to 2020 were analysed to examine the changes to streamflow conditions across three time periods: 1960-1991 (pre-dam), 1992-2009 (growth) and 2010-2020 (mega-dam). At Chiang Saen, the nearest station to the China border, monthly water discharge in the mega-dam period has increased by up to 98% during the dry season and decreased up as much as -35% during the wet season when compared to pre-dam records. Similarly, monthly water levels also rose by up to +1.16m during the dry season and dropped by up to -1.55m during the wet season. This pattern of hydrological alterations is observed further downstream to at least Stung Treng (Cambodia) in our study, showing that Mekong streamflow characteristics have shifted substantially in the post-2010 era. In light of such changes, the 2019-2020 drought – the most severe one in the recent history in the Lower Mekong Basin – was a consequent of constructed dams reducing the amount of water during the wet season. This reduction of water was exacerbated by the decreased monsoon precipitation in 2019. Concurrently, the untimely operationalisation of the newly opened Xayaburi dam in Laos coincided with the peak of the 2019-2020 drought and could have aggravated the dry conditions downstream. Thus, the mega-dam era (post-2010) may signal the start of a new normal of wet-season droughts.

Permafrost thaw has been observed in recent decades in the Northern Hemisphere and is expected to accelerate with continued global warming. Predicting the future of permafrost requires proper representation of the interrelated surface/subsurface thermal and hydrologic regimes. Land surface models (LSMs) are well suited for such predictions, as they couple heat and water interactions across soil-vegetation-atmosphere interfaces and can be applied over large scales. LSMs, however, are challenged by the long-term thermal and hydraulic memories of permafrost and the paucity of historical records to represent permafrost dynamics under transient climate conditions. In this study, we address the challenge of model initialization by characterizing the impact of initial climate conditions and initial soil frozen and liquid water contents on the simulation length required to reach equilibrium. Further, we quantify how the uncertainty in model initialization propagates to simulated permafrost dynamics. Modelling experiments are conducted with the Modélisation Environmentale Communautaire – Surface and Hydrology (MESH) framework and its embedded Canadian Land Surface Scheme (CLASS). The study area is in the Liard River basin in the Northwest Territories of Canada with sporadic and discontinuous regions. Results show that uncertainty in model initialization controls various attributes of simulated permafrost, especially the active layer thickness, which could change by 0.5-1.5m depending on the initial condition chosen. The least number of spin-up cycles is achieved with near field capacity condition, but the number of cycles varies depending on the spin-up year climate. We advise an extended spin-up of 200-1000 cycles to ensure proper model initialization under different climatic conditions and initial soil moisture contents.

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources, but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper reports scientific developments over the past decade of MESH, the Canadian community hydrological land surface scheme. New cold region process representation includes improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi-stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km2). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. This imposes extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

This study compares the U.S. National Water Model (NWM) reanalysis snow outputs to observed snow water equivalent (SWE) and snow-covered area fraction (SCAF) at SNOTEL sites across the Western U.S. SWE was obtained from SNOTEL sites, while SCAF was obtained from MODIS observations at a nominal 500 m grid scale. Retrospective NWM results were at a 1000 m grid scale. We compared results for SNOTEL sites to gridded NWM and MODIS outputs for the grid cells encompassing each SNOTEL site. Differences between modeled and observed SWE were attributed to both model errors, as well as errors in inputs, notably precipitation and temperature. The NWM generally under-predicted SWE, partly due to precipitation input differences. There was also a slight general bias for model input temperature to be cooler than observed, counter to the direction expected to lead to under-modeling of SWE. There was also under-modeling of SWE for a subset of sites where precipitation inputs were good. Furthermore, the NWM generally tends to melt snow early. There was considerable variability between modeled and observed SCAF as well as the binary comparison of snow cover presence that hampered useful interpretation of SCAF comparisons. This is in part due to the shortcomings associated with both model SCAF parameterization and MODIS observations, particularly in vegetated regions. However, when SCAF was aggregated across all sites and years, modeled SCAF tended to be more than observed using MODIS. These differences are regional with generally better SWE and SCAF results in the Central Basin and Range and differences tending to become larger the further away regions are from this region. These findings identify areas where predictions from the NWM involving snow may be better or worse, and suggest opportunities for research directed towards model improvements.

Smart drainage management to limit summer drought damage in Nordic agriculture under the circular economy conceptSyed Md Touhidul Mustafa 1, *, Kedar Ghag1, Anandharuban Panchanathan2, Bishal Dahal 1, Amirhossein Ahrari1, Toni Liedes 3, Hannu Marttila1, Tamara Avellán1, Mourad Oussalah2, Björn Klöve 1, & Ali Torabi Haghighi11Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN90014, Oulu, Finland2Center for Machine Vision and Signal Analysis, University of Oulu, Finland3Intelligent Machines and Systems, University of Oulu, PO Box 4200, 90014 Oulu, Finland

Recent revisions to the definition of the “waters of the United States” (WOTUS) have considerably altered how wetlands are federally regulated under the Clean Water Act. The two most recent modifications to WOTUS, the Clean Water Rule (CWR) and the Navigable Waters Protection Rule (NWPR), represent two opposing approaches to the federal wetland policy. Despite their implementation, the impacts of these rules on the regulation of wetlands have as of yet been poorly characterized at broad spatial scales. Using New York State (NYS) as a case study, we evaluated the jurisdictional statuses of more than 373,000 wetlands under the CWR and the NWPR to assess the landscape-scale effects of WOTUS re-definitions. We found that statewide and within each of NYS’s hydrologic regions, the NWPR protects fewer wetlands and less total wetland area than the CWR. The efficacy of the two regulations varied considerably in space across NYS, highlighting the need for comprehensive, nationwide assessments of wetland policy outcomes. We also observed that both rules produced non-uniform patterns in jurisdiction across a range of landscape positions and wetland sizes, preferentially protecting large wetlands close to the stream network. This effect was particularly pronounced under the NWPR, which excludes all geographically isolated wetlands from protection. Our findings in NYS emphasize the existence of unique patterns in protected wetlands across spatial scales, highlighting the value in applying geospatial analyses to evaluate environmental policy.

Groundwater plays a crucial role in sustaining global food security but is being over-exploited in many basins of the world. Despite its importance and finite availability, local-scale monitoring of groundwater withdrawals required for sustainable water management practices is not carried out in most countries, including the United States. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. Here we include evapotranspiration, precipitation, crop coefficients, land use, well density, and watershed stress metrics for our predictions. We employ random forests to predict groundwater withdrawals from 2002-2020 at a 2 km spatial resolution using in-situ groundwater withdrawal data available for Arizona Active Management Areas (AMA) and Irrigation Non-Expansion Areas (INA) from 2002-2009 for training and 2010-2020 for validating the model respectively. The results show high training (R2≈ 0.86) and good testing (R2≈ 0.69) scores with normalized mean absolute error (NMAE) ≈ 0.64 and normalized root mean square error (NRMSE) ≈ 2.36 for the AMA/INA region. Using this method, we spatially extrapolate the existing groundwater withdrawal estimates to the entire state and observe the co-occurrence of both groundwater withdrawals and land subsidence in South-Central and Southern Arizona. Our model predicts groundwater withdrawals in regions where production wells are present on agricultural lands and subsidence is observed from Interferometric Synthetic Aperture Radar (InSAR), but withdrawals are not monitored. By performing a comparative analysis over these regions using the predicted groundwater withdrawals and InSAR-based land subsidence estimates, we observe a varying degree of subsidence for similar volumes of withdrawals in different basins. The performance of our model on validation datasets and its favorable comparison with independent water use proxies such as InSAR demonstrate the effectiveness and extensibility of our combined remote sensing and machine learning-based approach.

Groundwater age is often used to estimate groundwater recharge through a simplified analytical approach. This estimated recharge is thought to be representative of the mean recharge between the point of entry and the sampling point. However, given the complexity in actual recharge, whether the mean recharge is reasonable is still unclear. This study examined the validity of the method to estimate long-term average groundwater recharge and the possibility of obtaining reasonable spatial recharge pattern. We first validated our model in producing reasonable age distributions using a constant flux boundary condition. We then generated different flow fields and age patterns by using various spatially-varying flux boundary conditions with different magnitudes and wavelengths. Groundwater recharge was estimated and analyzed afterwards using the method at the spatial scale. We illustrated the main findings with a field example in the end. Our results suggest that we can estimate long-term average groundwater recharge with 10% error in many parts of an aquifer. The size of these areas decreases with the increase in both the amplitude and the wavelength. The chance of obtaining a reasonable groundwater recharge is higher if an age sample is collected from the middle of an aquifer and at downstream areas. Our study also indicates that the method can also be used to estimate local groundwater recharge if age samples are collected close to the water table. However, care must be taken to determine groundwater age regardless of conditions.

Mine reclamation in the Athabasca oil sands region Canada, is required by law where companies must reconstruct disturbed landscapes into functioning ecosystems such as forests, wetlands and lakes that existed in the Boreal landscape prior to mining. Winter is a major hydrological factor in this region as snow covers the landscape for 5 to 6 months and is ~25% of the annual precipitation, yet few studies have explored the influence of winter processes on the hydrology of constructed watersheds. One year (2017-2018) of intensive snow hydrology measurements are supplemented with six years (2013-2018) of meteorological measurements from the constructed Sandhill Fen Watershed to: 1) understand snow accumulation and redistribution, snowmelt timing, rate and partitioning, 2) apply a physically-based model for simulating winter processes on hillslopes and 3) evaluate the impact of soil prescriptions and climate change projections on winter processes in reclaimed systems. The 2017-2018 snow season was between November and April and SWE ranged between 40-140 mm. Snow distribution was primarily influenced by topography with little influence of snow trapping from developing vegetation. Snow accumulation was most variable on hillslopes and redistribution was driven by slope position, with SWE greatest at the base of slopes and decreased towards crests. Snowmelt on hillslopes was controlled by slope aspect, as snow declined rapidly on west and south-facing slopes, compared to east and north-facing slopes. Unlike results previously reported on constructed uplands, snowmelt runoff from uplands was much less (~30%), highlighting the influence of different construction materials. Model simulations indicate that antecedent soil moisture and soil temperature have a large influence on partitioning snowmelt over a range of observed conditions. Under a warmer and wetter climate, average annual peak SWE and snow season duration could decline up to 52 % and up to 61 days, respectively while snowmelt runoff ceases completely under the warmest scenarios. Results suggest considerable future variability in snowmelt runoff from hillslopes, yet soil properties can be used to enhance vertical or lateral flows.

The climate of the Eurasia inland basin (EIB) is characterized by limited precipitation and high potential evapotranspiration; as such, water storage in the EIB is vulnerable to global warming and human activities. There is increasing evidence pointing to varying trends in water storage across different regions; however, a consistent conclusion on the main attributes of these trends is lacking. Based on the hydrological budget in a closed inland basin, the main attributes of changes in actual evapotranspiration (AET) and terrestrial water storage (TWS) were identified for the EIB and each closed basin. In the EIB and most of its closed basins, the TWS and AET showed significantly decreasing and non-significantly increasing trends, respectively. The primary cause underpinning the significantly decreasing TWS in the EIB was increasing AET. Approximately 70% of the increase in AET has been supplied by increased irrigation diversions and glacial melt runoff. At the basin scale, similar to the EIB, changes in AET were the predominant factor driving changes in TWS in most basins; the exception to this was the Balkhash Lake basin (BLB), Iran inland river basin (IIRB), Qaidam basin (QB), and Turgay River basin (TuRB). In these basins, changes in precipitation largely contributed to the TWS changes. The AET consumption of other water resources was the main factor contributing to AET changes in seven of 16 basins, including the Aral Sea, Caspian Sea, Junggar, Monglia Plateau, Qiangtang Plateau, and Tarim River basins. The increase in precipitation contributed more than 60% of increasing AET in four of 16 basins, particularly in the Helmand River basin and QB (>90%). Changes in precipitation and consumption by other water supply sources contributed to approximately half of the AET changes in the other five basins, including the Inner Mongolia Plateau, Issyk-Kul Sarysu, BLB, IIRB, and TuRB basins.

The Mississippi River channel from New Orleans to the Gulf of Mexico (GOM) is a key deep draft navigation channel and an active deltaic lobe. Natural and engineered lateral exits from this reach into adjacent receiving basins historically has provided mineral sediment for wetland accretion in the face of rising relative sea level and supported estuarine-coastal food webs. However, our analysis indicates water losses from the channel have increased by 25% since 2004 due to (1) bank failures during large floods since 2012 that have created several large exit channels downriver of the flood protection levee, and (2) the opening of an engineered diversion at West Bay in 2004. This has resulted in a 60-80% loss in stream power in the lowermost navigation channel that is accompanied by net shoaling between 2012 and 2022 and an increased dredging need. Our 2022 survey in the GOM exit passes indicates that only 20% of the freshwater, 5% of the total suspended sediment (2% of the sand) at New Orleans now reaches the GOM: this supports previous research indicating the delta front is retreating after centuries of progradation. Together these results indicate that (1) river containment and the sustainability of the navigation channel is threatened, (2) sediment load reaching the seaward end of the delta may be insufficient to avoid major degradation, and (3) the increased freshwater flux into adjacent shallow coastal water bodies has unknown implications for coastal hypoxia and food webs, including commercial species (e.g., oysters) and marine mammals. Future acceleration in sea level rise rates and tropical storm frequency/intensity likely will worsen these trends.

Environmental dating tracers (3H, 3He, 4He, CFC-12, CFC-11, SF6) and the natural response of spring (hydrochemistry, water temperature, and hydrodynamics) were jointly used to asses mixing processes and to characterize groundwater flow in a relatively small carbonate aquifer with complex geology in South Spain. Results evidence a marked karst behavior of some temporary outlets, while some perennial springs show buffer and delayed responses to recharge events. There is also a general geochemical evolution pattern, from higher to lower altitudes, in which mineralization and the relation Mg/Ca rises, evidencing longer water-rock interaction. The large SF6 concentrations in groundwater suggest terrigenic production, while CFC-11 values are affected by sorption or degradation. The groundwater age in the perennial springs deduced from CFC-12 and 3H/3He point out to mean residence times of several decades, although the difference between both methods and the large amount of radiogenic 4He in the samples indicates a contribution of old groundwater (free of 3H and CFC-12). Lumped Parameter Models and Shape-Free Models were created based on 3H, tritiogenic 3He, CFC-12, and radiogenic 4He data in order to interpret the age distribution of the samples. The resulting groundwater-age distributions evidence the existence of two mixing components, with an old fraction ranging between 160 and 220 years. Some dating parameters derived from the mixing models and their correlation to physicochemical parameters permits to explain the hydrogeochemical processes occurring within the system. All these results prove that large time residence times are possible in small alpine systems with a clear karst behavior when the geological setting is complex, and they highlight the importance of applying different approaches, including groundwater dating techniques, to completely understand the groundwater flow regime within this type of media.

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.

A new flow for Canadian young hydrologists: Key scientific challenges addressed by research cultural shiftsCaroline Aubry-Wake1, Lauren D. Somers2,3, Hayley Alcock4, Aspen M. Anderson5, Amin Azarkhish6, Samuel Bansah7, Nicole M. Bell8, Kelly Biagi9, Mariana Castaneda-Gonzalez10, Olivier Champagne9, Anna Chesnokova10, Devin Coone6, Tasha-Leigh J. Gauthier11, Uttam Ghimire6, Nathan Glas6, Dylan M. Hrach11, Oi Yin Lai14, Pierrick Lamontagne-Halle3, Nicolas R. Leroux1, Laura Lyon3, Sohom Mandal12, Bouchra R. Nasri13, Nataša Popović11, Tracy. E. Rankin14, Kabir Rasouli15, Alexis Robinson16, Palash Sanyal17, Nadine J. Shatilla9, 18, Brandon Van Huizen11, Sophie Wilkinson9, Jessica Williamson11, Majid Zaremehrjardy191 Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada2 Civil and Environmental Engineering, Massachusetts Institute of Technology, MA, USA3 Department of Earth and Planetary Sciences, McGill University, Montreal QC4 Department of Natural Resource Science, McGill University, Montreal, QC, Canada5 Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada6 School of Engineering, University of Guelph, Ontario, ON, Canada7 Department of Geological Sciences, University of Manitoba, Winnipeg, Canada8 Centre for Water Resources Studies, Department of Civil & Resource Engineering, Dalhousie University, Halifax, NS, Canada9 School of Geography and Earth Sciences, McMaster University, Hamilton, ON, Canada.10 Department of Construction Engineering, École de technologie supérieure, Montreal, QC, Canada11 Department of Geography & Environmental Management, University of Waterloo, Waterloo, ON, Canada12 Department of Geography and Environmental Studies, Ryerson University, Toronto, ON, Canada13 Department of Mathematics and Statistics, McGill University, Montréal, Qc, Canada14 Geography Department, McGill University, Montreal, QC, Canada15 Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada16 Department of Geography and Planning, University of Toronto, Toronto, ON17 Global Institute for Water Security, University of Saskatchewan.18 Lorax Environmental Services Ltd, Vancouver, BC, Canada.19 Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB, Canada

Using data from five long-term field sites measuring soil moisture, we show the limitations of using soil moisture observations alone to constrain modelled hydrological fluxes. We test a land surface model, MESH/CLASS, with two configurations: one where the soil hydraulic properties are determined using a pedotransfer function (the texture-based calibration) and one where they are assigned directly (the hydraulic properties-based calibration). The hydraulic properties-based calibration outperforms the texture-based calibration in terms of reproducing changes in soil moisture storage within a 1.6 m deep profile at each site, but both perform reasonably well, especially in the summer months. When the models are constrained using observations of changes in soil moisture, the predicted hydrological fluxes are subject to very large uncertainties associated with equifinality. The uncertainty is larger for the hydraulic properties-based calibration, even though the performance was better. We argue that since the pedotransfer functions constrain the model parameters in the texture-based calibrations in an unrealistic way, the texture-based calibration underestimates the uncertainty in the fluxes. We recommend that reproducing observed cumulative changes in soil moisture storage should be considered a necessary but insufficient criterion of model success. Additional sources of information are needed to reduce uncertainties, and these could include improved estimation of the soil hydraulic properties and direct observations of fluxes, particularly evapotranspiration.

Monitoring of the fluctuations of groundwater storage is particularly important in arid and semi-arid regions where water scarcity brings about various challenges. Remote sensing data and techniques play a preponderant role in developing solutions to environmental problems. Emergence of GRACE satellites have eased the remote monitoring and evaluation of groundwater resources with an unprecedented precision over large scales. Within the scope of the current study, the latest release of GRACE Mascon’s dataset as well as GLDAS models of Noah and CLSM were integrated to extract groundwater storage anomalies (GWSA) over Turkey. The temporal interactions of the estimated GWSA with the climatic variables of precipitation and temperature (derived from the reanalysis datasets of CHELSA and FLDAS, respectively) were investigated statistically. The results suggest the there is a descending trend for TWSA and GWSA over Turkey with a total loss of 11 and 6 cm respectively. The statistical analysis results also indicate that the monthly variations of GWS over Turkey are highly correlated with precipitation and temperature at 2-month lag. The analysis of the climatology (long-term) values of monthly GWSA, precipitation and temperature also revealed high agreement between the variables.