Significant part of water resources of Tajikistan is reserved in glaciers which occupy about 6% of the country's area. The runoff from rivers is used to produce electricity in the country, and up to 95% of it is generated at 13 hydroelectric power stations. Changes in temperature and precipitation due to climate change, will increase uncertainty in water availability and may lead to a deterioration in the development of the economy and the quality of life in the country. The aim of the study is to assess the characteristics of the flow regime of the Gunt River, a tributary of the Amu River Basin. The Gunt River originates from Eastern Pamirs and drains catchment area of 13,700 km². This river is the main source of water for agriculture and two hydroelectric power stations. The study area is characterized by two types of cold summers and moderately-warm summers. The basin has low humidity. On the plains, precipitation is 140-250 mm/y, and in the mountains up to 800 mm/y.
The “Hydrograph” hydrological model which is successfully used for the simulations of streamflow in similar climate in the region was used. The model was parametrized based on the available datasets on topography, catchment physical characteristics and hydroclimate of the studied area. First, the model was calibrated by using streamflow and water balance datasets for the historical period (1965-1986). Then model was used to simulate runoff hydrographs for the period of 1940-1964 & 1987-2020. Simulated results showed good agreement with observed historical discharge time series. This model will be used to study climate change impacts on water availability based on different climate change scenarios. The results can also be used to better understand hydrological regime of the Gunt River in the future and plan for sustainable water and hydropower resources management.

This paper proposes an Adaptive forecasting through exponential re-weighting based on Structural Similarity Index Measure (AFTER-SSIM) algorithm to evaluate the performance of global climate models, which are from the Coupled Model Intercomparison Project (CMIP5) under different emission scenarios during 2006 to 2018, and try to reduce the uncertainty among them. The SSIM approach is used in loss function to dig out more information in spatial distribution between model outputs and the observed data, where the genetic algorithm (GA) is taken to optimal the parameters in both seasonal cycle and long-term trend of sea ice concentration and sea ice thickness. The re-weighting mechanism of the AFTER-SSIM algorithm guarantees the improving performance in sea ice volume simulation as the new information adding. Finally, the ranked models have been combined to estimate the future Arctic sea ice volume and the possibility of navigability on Arctic Northern Sea Route. The results show that the proposed algorithm reduces the uncertainty among models, sea ice volume will continue to shrink in the future, and the open period for 1A super vessels are likely to reach to 5 months ranging from August to December in 2030.

Knowledge of the hydrological response to climate change is important for planning and management of water resources. We posit that the uncertainty associated with GCM forcings that drive the hydrological models is an important challenge in generating actionable information for hydrological impact assessment studies. For this, we generate and compare the different sources of uncertainty in projection of the hydrological response of a watershed in a semi -arid region of Central India (CI). Downstream uncertainty propagation is analyzed in an Agro-Hydrological model SWAT (The Soil and Water Assessment Tool) based on 27 CMIP6 GCMs, two statistical downscaling techniques, and two global warming scenarios (SSP245 and SSP585). Available CMIP6 GCMs are evaluated over CI using different statistical indicators to first discard the poor performing models. Using further sub-selection criteria, the sub-selected models were first statistically downscaled and then provided as an input to the multi-calibrated and validated hydrological model. Results from the uncertainty analysis of the hydrological response to climate change over CI will be presented.

Environmental and climate changes may alter water quality and quantity dynamics. Its relation to ecosystem services is a key element to evaluate environmental quality. Land Use and Cover Changes (LUCC) processes can affect directly soil cover services as regulating water connectivity and purification that may further affect the quality of underground and surface waters.The aim of this work is to develop a spatial analysis methodology to evaluate the influence of LUCC on the capacity of soils as water ecosystem services regulators. The methodology developed applied Geographical Information Systems, Cellular automata and Markov chain analysis to stablish future LUCC trends to foreseen soils susceptibility to LUCC in its water regulation services.The methodology were applied in two contrasting areas in Spain, the Albufera the Valencia natural park (Mediterranean Lagoon near the city of Valencia) and the basin of the Limia River, whose waters are regulated by the As Conchas Reservoir, in Galicia, to North-west, with a Atlantic temperate rainy climate.Results show that in both areas LUCC changes influence soils water regulation with soils sealing processes being relevant as major agent of soils imperviousness. Intensity, rates of change and future trends are different. Is in the Mediterranean area, due to the expansion of the Valencia Metropolitan Area where changes are more intense.Acknowledgements: This work was supported by the Spanish Fundación Biodiversidad, Ministry for Ecological Transition and the Demographic Challenge (CianoMOD Project, CA_CC_2018).

Rice paddy accounts for about 58% of all farmlands in South Korea, and it is closely related to the water environment. Climate change is expected to affect agricultural productivity and the water circulation and the environment. In this study, APEX-Paddy model which reflect rice paddy environment by modifying APEX (Agricultural Policy and Environmental eXtender) model was used. Bias corrected climate change scenarios were applied to the APEX-Paddy model to evaluate the impact of climate change. Using the AIMS (APCC Integrated Modeling Solution) offered by the APEC Climate Center, bias correction was conducted for 9 GCMs using non-parametric quantile mapping. The changes in irrigation water requirement and mineral nitrogen load were evaluated using multi-model ensemble. Paddy irrigation water requirement showed a change of -1.3 to 2.7% compared the 2085s (2071 to 2100) against the base period (1976 to 2005), which was not significant. The mineral nitrogen load was found to be increased from 16.9% to 76.0%. The result of this study seems to be used as a basis for mid- and long-term policies for water resources and environment considering climate change. Financial Support: This work was carried out by the support of Cooperative Research Program for Agriculture Science & Technology Development (PJ014932), Rural Development Administration, Republic of Korea.

Projections of rainfall provide critical information relevant to future energy security, due to the direct impact of water availability on hydropower generation. In this study, climate model output from the SEACLID/CORDEX Southeast Asia project have been used to evaluate future changes in rainfall in the Philippines and its potential impact on hydropower in the country. Water availability is gauged by means of the flow duration curve (FDC), which allows water managers to ascertain streamflow values lying at or above a particular level in a river. The FDC is a key factor in calculating the output of run-of-river hydropower plants. A process-based approach is taken to obtain an analytical expression for the FDC which links the distribution of streamflow values to rainfall frequency as well other landscape-related factors. Its performance is subsequently measured by comparison with empirical FDCs for gauged rivers in the Philippines. Lastly, we utilize the downscaled rainfall output to generate hydropower projections for the country based on the dependence of the probability distribution of streamflow values on rainfall frequency. These projections are carried out for two future periods: 2016-2035 and 2045-2065, under the Representative Concentration Pathway (RCP) scenarios, RCP4.5 and RCP8.5.

Many studies were conducted for the impact of climate change on rainfall intensity-duration-frequency (IDF) curves. These studies have been performed using simulated climate model data to reduce or quantify the uncertainty caused by climate change. And, these quantifications of the potential effects and preparations for adapting to climate change could lead to meaningful results to reduce vulnerability. This study focused on the rainfall data among the various variables from the climate model. The extreme rainfall quantiles were estimated by using at-site frequency analysis (GEV and L-moments). The HadGEM3-RA regional climate model (RCM) was selected for this study. The rainfall data was extracted from 60 observation sites in South Korea. Furthermore, the bias correction was conducted using the simple quantile mapping (SQM) method to consider the existing bias between the observed and simulated data. The projection data were divided into 4 periods, which are the historical (1981-2010), near-future (2011-2040), mid-future (2041-2070), and far-future (2071-2100) periods. Finally, the IDF curves were derived from rainfall quantiles and compared for each future period. In addition, the IDF curve impacts from climate change were assessed quantitatively in this study.

Water use efficiency (WUE) exhibits a decisive role in agricultural water-saving implementation in arid areas facing the water shortage and warming climate. Better understanding of crop water consumption and water-carbon fluxes coupling of agroecosystems in arid areas is still needed to explore the variation of WUE. Three-years field experiments (maize transpiration observation by Flow32 System in 2016-2017, water-carbon fluxes observation by eddy covariance in 2017-2018) were conducted in irrigated maize field in Hetao Irrigation District in northwest China. Maize transpiration with shallower groundwater table depth WTD (average 1.6m) in 2016 was larger than that with the deeper WTD (average 2.0m). Interestingly, the transpiration difference between them mainly occurred during midday periods. Given similar yields in 2016 and 2017, the maize field with shallower WTD was estimated to have lower WUE_yield (Yield/Transpiration). Thus the diurnal variation of WUE was further explored considering the hourly coupling of evapotranspiration (ET) and net ecosystem CO₂ exchange (NEE) over irrigated maize fields. The average diurnal dynamics indicated that when ET continued to increase at midday time, the increasing rate of NEE decreased or even ceased. Therefore, NEE reached the plateau ahead of ET and WUEc (defined as -NEE/ET) decreased during midday periods. This nonlinear relationship between hourly NEE and ET resulting in the decreased WUEc during midday periods was well explained by the contrasting effect of vapor pressure deficit (VPD) on the correlations of ET-PAR (photosynthetically active radiation) and NEE-PAR. With the increase of VPD (from <1 kPa to >3 kPa), the relationship of ET-PAR was always strengthened, while NEE-PAR was weakened. This study sheds light on the best use of irrigated water in arid areas with shallow groundwater. Full irrigation while high VPD (drought meteorological condition) in arid areas would decrease WUE given the current maize physiological characteristics.

Freshwater resources in coastal regions are under enormous stress due to population growth, pollution, climate change and political conflicts, and many coastal cities have already suffered extreme water shortages. Offshore freshened groundwater (OFG) - groundwater with a total dissolved solid concentration below that of seawater, which is stored in sediments and rocks in the sub-seafloor - has been proposed as an alternative source of freshwater to relieve water scarcity, provide a buffer to increased demand during periods of intense drought, and mitigate the adverse effects of groundwater depletion in coastal regions. First reported in the 1960s, OFG has now been documented in most continental margins around the world. The estimated global volume of OFG is in the order of 10⁵-10⁶ km³. OFG can be emplaced by meteoric recharge (either at present or during sea-level lowstands, sub-glacial and pro-glacial injection, diagenesis, or gas hydrate dissociation. The large majority of OFG records are meteoric in origin and located in siliciclastic, passive, non-glaciated margins, within 50 km of the coast, down to a water depth of 100 m, and a sub-seafloor depth of <200 m. OFG bodies have not been directly or intentionally exploited so far. The main reasons for this are: (i) lack of appropriate data, (ii) limited availability of approaches to constrain OFG systems, (iii) the technological and economic feasibility of OFG utilisation is unknown; (iv) the environmental impact of OFG utilisation is poorly constrained, and (v) the legal implications of OFG use are unclear.  In this study we will address the following research questions:  (1) Where is OFG found? (2) What are the most appropriate approaches to detect and characterise OFG systems? (3) What is the most cost-effective strategy to utilise OFG in coastal regions? (4) What are the environmental and legal challenges to sustainable OFG use?

In order to accurately predict the impact of high intensity human activities such as coal mining and groundwater extraction on regional groundwater inflow, the mine water drainage of Hetaoyu Mine was taken as case study in Longdong area, China. Based on the numerical simulation and theories of groundwater movement, a three-dimensional unsteady flow numerical model of groundwater was established by generalizing the hydrogeology model of the study area. According to the production schedule of mine, it was quantitatively analyzed the groundwater reserves in the Quaternary phreatic aquifer, Cretaceous Huanhe Group confined aquifers and Luohe Group confined aquifers. The results showed a drop funnel with an area of about 2.3 km² was formed in the mine water drainage area during the mining construction of Hetaoyu Mine, which changed the local groundwater inflow characteristics. The mine water drainage had the greatest impact on the Cretaceous aquifer, in which the aquifer reserves of the Luohe Formation decreased by 30861.8 m³ on average every month, accounting for about 92% of the total changes in local groundwater reserves. With the continuation of mining activities, the mine water drainage will reach 19364m³/d.

The gap between optimization models and implementable real-world policies in reservoir operation problems has been denoted since the late 20^th century. Despite the excessive efforts to advance theoretical optimization algorithms used in reservoir operation problems, only a few of the developments have been adopted during real-world decision making. This study contributes to the linking of the optimization models and the real-world zone-based hedging policy to expand the practicality of the solutions. Multiple alternative models, using dynamic programming as the main optimization tool and evolutionary multi-objective direct policy search, were developed and adopted to derive optimal release rules in a reservoir with conflicting objectives. Then, a selected alternative was coupled with the actual zone-based hedging rule during the simulation process. The results were then evaluated in terms of proximity to the ideal and three popular categories: reliability, resiliency, and vulnerability. As a result, the performance of the optimization models depended more on the shape of the approximating functions. On the other hand, coupling the real-world policy to the optimally derived solutions improved in all indices from the shoes of the suppliers; whereas the demand side had to experience a trade-off between the indices. The results from this study prove that the coupling of real-world policy into the solutions from optimization models enhances the range of solutions decision makers can select upon.

Inter-basin water transfers (IBWTs) provide a way to manage water scarcity in the presence of considerable regional differences in water supply and demands between two basins. We investigate the role of groundwater development in reducing reliance on inter-basin water transfers for monsoon-dominated basins in Southern India. We evaluate an IBWT scheme in Southern India named the Inchampalli-Nagarjuna Sagar (INS) link for transferring water from the Godavari river basin (Inchampalli reservoir) to the Krishna river basin (Nagarjuna Sagar reservoir). We formulate a multi-objective decision problem to evaluate the impact of the INS-IBWT megaproject with and without conjunctive use of groundwater. Groundwater development in the command area of the recipient Nagarjuna Sagar reservoir is simulated using artificial recharge scenarios considering the hydro-geology of the region. The objectives used to evaluate the generated alternative water transfer strategies are reliability, resilience, and vulnerability of demand satisfaction, reliability of high flow exceedance, and minimum environmental flows. Two variants of the decision problem are used to understand the effect of artificial groundwater recharge on resultant transfer strategies. The first variant includes an artificial recharge module coupled to the IBWT surface water balance model (Recharge formulation), while the other does not (no-recharge formulation). Results show that for the same range of water transferred, the range of demand deficits averaged across both donor and recipient basins across all the Pareto optimal strategies are reduced by 50% in the recharge formulation compared to the no-recharge formulation. Regional sensitivity analysis is performed to identify the months in which transfer volume is highly sensitive to performance metrics. It is identified that the transfer volumes in the post-monsoon months are most sensitive to the system-level performance. Our analysis highlights the contradictory role played by groundwater development in reducing the reliance on inter-basin water transfers for monsoon-dominated basins.

Affected by the external environmental factors such as population growth, economic development and climate change, the challenges of water resources utilization and management are increasing seriously, and water security related issues are becoming more and more prominent. Therefore, in the context of the complex changing environment, how to improve the level of water resources management effectively is crucial and it is also a hot topic worldwide. As located in the Northwest part of China, Qingyang city have serious water shortage during past year. With the rapid development of energy-chemical industries of city, the contradiction between water supply and demand is more sharp, so we proposed a scientific water resource allocation scheme which comprehensive allocated unconventional water and conventional water for different users including industrial production, agriculture and domestic users of the city, and then we give suggestions for efficient use of reclaimed water, rainwater, mine water, brackish water and other unconventional water sources, the proposed strategy of unconventional water utilization will mitigate the contradiction between water supply and demand so as to ensure sustainable development of the economy in water-deficient areas of Northwest China.

Flood vulnerability estimation is vital in assessing possible losses resulting from flood hazard. Estimating flood vulnerability needs a large amount of physical and socioeconomic data, which is often difficult to collect. A study has been conducted to estimate the major flood vulnerability factors of Peninsular Malaysia, population density, household income, economy of the region, and forest cover using satellite nighttime light (NTL) data. Visible Infrared Imaging Radiometer Suite (VIIRS) NTL data was correlated and regressed with district-level data of flood vulnerability factors for this purpose. Nonlinear models were developed where the model parameters were optimized using machine learning algorithms for better fitting. The results revealed a positive exponential relationship of NTL with population density, household income, economy of the region, while a negative exponential relationship of NTL with forest cover. Nonlinear regression models' performance revealed the NTL data's ability to estimate different flood vulnerability factors in terms of different statistical metrics. The spatial distribution of flood vulnerability factors estimated using the models matched well with the observed data, which indicates the potential of the models to monitor the change of various socioeconomic and physical flood vulnerability factors from satellite data.

Drought in South Korea significantly affects agricultural production and recreational activities in this fast-changing economy. This paper describes a long-term (1980-2020) gridded-based (0.01^o) surface hydrologic dataset in South Korea at an hourly time step based on simulations from the Korean Land Data Assimilation System (KLDAS). The Modern-Era Retrospective analysis for Research and Application, version 2 (MERRA-2) and the recently fully established Automated Synoptic Observing System (ASOS) of Korean Meteorological Administration (KMA) weather forcing datasets were used to drive the land model. The model was validated against observed soil moisture (10 sites) and evapotranspiration (6 sites) over four major river basins in S. Korea. The error statistics indicated that the ASOS-based hydrologic outputs have been improved compared to those from the baseline MERRA2 simulations. Historical drought events were reconstructed and analyzed using various drought indices such as meteorological, agricultural, and hydrological drought perspectives. In particular, the spatially distributed severity and duration of major droughts in S. Korea were compared to identify new characteristics, which might be induced by the recent climate change. The historical drought stood out as the most catastrophic event considering three factors (severity, duration, and areal extent) at specific years. This dataset is expected to serve as a climatic and hydrologic indicator which contributes to water resources management, and to mitigating agricultural drought, especially in the context of the long-term understanding of drought impacts on crop yield in South Korea. An analysis of agricultural reservoir and dam storage levels based on precipitation deficits is expected to understand the propagation of different types of droughts.

A recent innovative approach of forecasting inundation extents was developed and demonstrated over the Tonle Sap Lake floodplains in Cambodia (Chang et al., RSE, 2020), utilizing regression analysis between temporal patterns extracted from a time series stack of historical Sentinel-1 Synthetic Aperture Radar (SAR) images using Rotated Empirical Orthogonal Function (REOF) analysis and historical streamflow or water level data (Forecasting Inundation Extents using REOF analysis – FIER). Forecasted streamflow obtained from a rainfall-runoff model, along with the corresponding temporal patterns obtained from the regression model is integrated with REOF-extracted spatial patterns of SAR images to generate SAR-like intensity images from which forecasted inundation extents can be produced using typical water classification techniques. Here, we present a study of pseudo-forecasting inundation extents for the case of 2020 Korea floods over different landscapes in South Korea utilizing C-band Sentinel-1 imagery coupled with national streamflow/water level monitoring/forecasting systems. The pseudo-forecasted inundation extents are validated with historical inundation extents derived from satellite sensors and ancillary information. Finally, we also present a new water classification technique based on an iterative threshold calibration approach using historical streamflow or water levels. By overcoming difficulties in traditional inundation forecasting approaches, it is expected that FIER can be easily scalable to other flood-prone regions in the world, especially with the rise of cloud-based satellite data providers. Acknowledgments: This study was funded by the Korea Ministry of Environment under the Demand Responsive Water Supply Service Program (Grant number 2019002650004).

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σ_error) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σ_error ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

River regulation that meet fundamental food, water and energy (FWE) needs could exacerbate local vulnerability to climate change but also help adapt to climate as flow regimes can be controlled. To better estimate regulated streamflow for effective water resource planning, it is essential to integrate dams for model-based impact analysis of hydrological forecasts across space and time. However, only a small fraction of catchments in any part of the world possess a stream gauge whereas monitoring networks are declining significantly and access to available data in transboundary river basin are remarkably limited due to national security concerns. Accordingly, this study proposed a new calibration scheme to better simulate reservoir dynamics inside catchment model by integrating our sub-continental multi-basin model (GM_HYPE) with our developed satellite based reservoir monitoring tool (SaRes) at the poorly gauged and highly regulated Greater Mekong region. This scheme can help improve reliability of streamflow prediction in poorly gauged and highly regulated watersheds and later other reservoir related impacts such as reservoir sedimentation rates and sediment transports if the model has integrated soil erosion process. Since our approach is based on an open source model with inclusion of most of hydrological processes, human intervention activities and spaceborne radar with global coverage over landmasses, it could be highly applicable around the world. It is expected that the results of this study could help inform inclusion of reservoir operation dynamics at poorly gauged or ungauged sites to improve model simulation of terrestrial water storage and flow and provide more reliable future projections.Acknowledgments: This study was funded by NASA Applied Science Program (Grant number 80NSSC18K0423), the National Foundation for Science and Technology of Vietnam (NAFOSTED) and the Korea Ministry of Environment under the Demand Responsive Water Supply Service Program (Grant number 2019002650004).

Soil moisture estimates from satellite retrievals and models are known to have large biases and uncertainties among different products. The satellite and model data have complementary advantages and disadvantages and they can be merged to produce an optimally merged soil moisture product. First, the Korea Land Data Assimilation System (KLDAS) has been established for agricultural drought (i.e. soil moisture deficit) monitoring in South Korea, running the Noah-MP land surface model within the NASA Land Information System (LIS) framework with the added value of local precipitation forcing dataset and soil texture maps. This study compares KLDAS products with two benchmark LDAS products and one remote sensing product (ESA CCI) and examines the performance of KLDAS agricultural drought area percentages in the four major river. Second, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on surface soil moisture (SSM) simulations for the years, 2002–2017. This study analyzes the impact of GRACE DA on modeled SSM and presents the qualitative comparisons of seasonality and annual trend of these SSM estimates in West Africa.

This study is to estimate forest soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Artificial Neural Network (ANN) for 40×50 km² area located in the Geum river basin of South Korea. Both the Sentinel-1A and -1B images (8 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The image processing was performed using the SNAP (SentiNel Application Platform) and converted to backscattering coefficient (BSC) of VV and VH polarization. The in-situ SM data measured at 6 locations (1 sandy loam, 2 loams, 2 clay loams, and 1 clay) using TDR (time domain reflectometry) were used to validate the estimated SM. For the SM modeling, both VV and VH dual-polarization BSC data were used as input of ANN. The 5 days previous daily rainfall data were also used as input data to overcome the estimation difficulty for forest area that microwave not reaching to the ground. The ANN modeling was performed for the 4 soil textures with yearly and seasonal data set. The ANN test results for yearly data set showed correlation coefficient (R) of 0.36, 0.25, 0.39, and 0.29 for 4 soil textures respectively. The R was the highest for 2 clay loam soil. For the result of seasonal ANN, the test R was 0.38, 0.53, 0.35, and 0.22 for spring, summer, autumn, and winter respectively. The summer R showed the highest and the winter R showed the lowest value by the snow cover interruption. This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment(MOE) (2020003050001) and also, this study was funded by the Korea Ministry of Environment (MOE) as “Demand Responsive Water Supply Service Program (2016002650003)”

This study is to estimate the low flows via a grid-based continuous hydrological model considering dam and weir releases in a watershed. The model is based on soil moisture routing with single flow path algorithm and it simulates kinematic wave and Darcy flows of surface, subsurface and groundwater simulation with hydrologic components of interception, Penman-Monteith evapotranspiration (ET), infiltration, and groundwater recharge. For inflow and release at dam and weir, a cell-based flux function was implemented. The model was applied to Geum River basin (9,645.5 km²) including 2 dams and 3 weirs. The model runs at 10-minute interval with grid size of 1000 m×1000 m to satisfy cell to cell water balance. The model was calibrated using the observed daily streamflows (Q) and flux tower ET, time domain reflectometry soil moisture content (SMC), and groundwater level (GWL) from 2013 to 2020 (8 years). For Penman-Monteith ET, the hourly input data were collected from 12 meteorological stations. For the low flow impact estimation, the daily groundwater use data with administration unit were collected and applied the extraction as the areal average value. The modeling results of Q and 1/Q at the dam and weir locations were 0.53 ~ 0.65 and 0.46 ~ 0.69 for coefficient of determination (R²), and 0.46 ~ 0.55 and 0.31 ~ 0.65 for Nash-Sutcliffe efficiency (NSE) respectively. The spatial calibration results of ET, SMC, GWL will be analysed. The low flows with stream order classes, land use and soil impacts will be analysed using flow duration curve and Q355.Acknowledgements This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment(MOE) (2020003050001) and also, this subject is supported by the Korea Ministry of Environment(MOE) as “Water Management Research Program(79617)”.

This study used Soil and Water Assessment Tool (SWAT) to investigate the impacts of groundwater abstraction and forest growth on watershed hydrology of Geum river basin (9,645.5 km²) in South Korea. The spatial groundwater use (GU) and forest growth (FG) information from 1976 to 2015 (1980s; 1976~1985, 1990s; 1986~1995, 2000s; 1996~2005, 2010s; 2006~2015) were prepared with 10-year interval values as SWAT input data respectively. The SWAT was calibrated using 10-year (2006~2015) daily observation streamflow data at two multipurpose dams and three multifunction weirs. The SWAT was run at each decade using the corresponding GU and FG information under the same weather condition of 2010s to evaluate the factors impact on hydrologic cycle. From the results, the streamflow at the watershed outlet by both GU and FG impacts showed decrease of 5.6 % (34.3 mm/year) with 617.3 mm/year in 1980s and 583.0 mm/year in 2010s. The surface runoff, lateral flow, groundwater flow, infiltration, and soil moisture showed decreasing trend while evapotranspiration and groundwater recharge (GWR) showed increasing trend. The reason of GWR increase was from the steady lowering of groundwater level by increase of GU and the formation of preferential flow by forest root growth. Thus, we can infer that GWR could be increased by the fast movement of soil water above field capacity during rain event. With flow duration curve analysis, we confirmed the gradual decrease of Q355 temporally at certain watershed location and the decrease propagation of Q355 spatially from upstream to downstream.This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment(MOE) (2020003050001). This subject is supported by the Korea Ministry of Environment(MOE) as “Water Management Research Program(79617)”.

Singapore's V2 project shows climate projections to the year 2100. It has demonstrated large agreements among models of a statistically significant upward trend in heavy rainfall events (>56 mm/h) contributing to the overall annual rainfall totals. These climate related factors combined with the rapid expansion of the city's population will test the resilience for current infrastructures to cope with impacts from precipitation extremes. Thus, there is a need to identify high precipitating storms early on and to accurately nowcast them. It has been shown that strong positive vorticity – associated with vortex tube tilting within supercells – is well correlated with a strong updraft and can be used as an early indicator for heavy rainfall. To study if the latter mentioned holds true for the Singapore climate, high spatiotemporal Doppler Weather Radar observations are needed.There are two major concerns regarding the Singapore radar network when it comes to spatial sampling of the atmosphere, namely the cone-of-silence and a operational requirement to scan for windshear related product at 1-minute intervals for the aviation sector. These limitations will have an impact on the vertical resolution of thunderstorms, especially when within Singapore’s borders.  Thus, this study will attempt to determine the extent of coverage and scale of features that can be resolved in 3-dimensional space with the current operational scan strategy. The impact of vertical elevation scans can be studied through calculating the averages distance from the 10 closest polar coordinate points, as measured by the radar, to a defined point in cartesian space. This allows for the realisation of a CDF that can be analysed and compared to other operational scan strategies and various idealised scan strategies. The impact on the cone-of-silence is also studied by merging two or more weather radars through various scenarios. More results will be presented.