Gökçen Uysal

1 Water resources managers leverage the complexity of natural and human water systems for equitable sharing of water among competing users and beneficiaries. Natural water systems, such as rivers, ...

The Mediterranean Basin is typically classified as a region with decreasing streamflow and as a hotspot for future climate change, with important impacts on agricultural and ecosystem functioning. Here we present an assessment of past trends and future projections of water resources at the scale of the Mediterranean Basin. We evaluated how historical climate change, vegetation greening, and reservoir construction affected Mediterranean discharge trends, based on over a century of discharge trends (1914–2022) obtained from 654 discharge stations across the Mediterranean Basin. The results show that mean discharge trends align mostly with precipitation trends between 1921-2000, distinguishing periods of significant increasing (1941–1971) and decreasing discharge (1961–2000). However, between 1981 and 2020 the relationship between precipitation and mean discharge mostly diminishes. We show through multiple linear regression that precipitation and vegetation greenness have an equally strong but counteracting effect on runoff. Reservoir construction caused a reduction of the mean and maximum discharge. Furthermore, we assessed the impact of climate change on water resources, which is predominantly studied through the application of hydrological models forced by climate model output. Through a systematic review we analysed the results of 262 Mediterranean climate change assessments. We show that runoff is projected to decrease on average with 19%, with a stronger decrease towards the end of the century and with increasing emission scenarios (up to −39%). Similarly, soil moisture (−14%) and aquifer recharge (−21%) are also negatively affected by climate change, while irrigation demand is projected to increase (8%). While past trends were shown to be heavily affected by vegetation development and reservoir construction, most future hydrological assessments focus solely on the impact of climate change, neglecting vegetation development. This urges future studies to incorporate vegetation development and anthropogenic activities into these assessments, to obtain more accurate water resources projections, including possible feedbacks.

Floods exacerbated by climate change significantly increase the risk of dam failure, posing a critical threat to downstream regions. A cost-effective way to analyze the consequences of dam break floods is by using unsteady hydrodynamic models that incorporate St. Venant’s or diffusion wave equations. These models require detailed topographic data, land cover information, and a dam break hydrograph. This study assesses the influence of various remote sensing topographic datasets on 2-dimensional (2D) hydrodynamic flood modeling using HEC-RAS v6. The methodology is applied to İmranlı town in Türkiye, located downstream of an irrigation dam. Under a 500-year return period flood scenario, a breach hydrograph is simulated in HEC-RAS, assuming overtopping when the reservoir is at full capacity. Manning's roughness values are derived from the ESA-WorldCover satellite land use map. Two types of topographic data are tested: Digital Surface Models (DSMs) and Digital Terrain Models (DTMs). Specifically, datasets include field-based Light Detection and Ranging (LiDAR) DSM (0.5 x 0.5 m resolution), Turkish General Directorate of Mapping (HGM)-based DSM (5 x 5 m resolution), Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR)-sourced DTM (12.5 x 12.5 m resolution), and Shuttle Radar Topography Mission (SRTM)-sourced DTM (30 x 30 m resolution).The study also explores the impact of combining high-resolution and low-resolution topographic data by mosaicking LiDAR data, limited to urbanized areas, with other datasets. Results are evaluated using performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), F-index, and correlation coefficient (R²). Additionally, comparisons are drawn using flood-related maps, including flood inundation area, water depth, velocity, duration, and hazard. The study highlights that nearly the entire İmranlı district center and the Doğançal settlement would be inundated in the event of a dam failure, exposing approximately 7,028 individuals to flood risk. The findings suggest that while high-resolution HGM-based data serve as a reliable reference, integrating satellite datasets like ALOS-PALSAR with LiDAR enhances model performance, making them valuable alternatives when high-resolution data are unavailable.

Climate change significantly impacts global water resources, particularly in snow-fed mountainous basins where reservoir operations and hydropower generation are crucial. This study investigates future snowmelt runoff, water resource management, and hydropower production under climate change scenarios for two headwater basins in Türkiye: Peterek on the Çoruh River in the Eastern Black Sea region and Göksu on the Seyhan River in the Mediterranean region. These basins were selected for their similar topographies but distinct climatic conditions, representing regions predicted to experience varying climate change impacts.An ensemble of five Global Climate Models (GCMs) from the CORDEX-Europe database, adjusted for local bias corrections, was employed under Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 scenarios, along with Global Warming Levels (GWL) from the Paris Climate Agreement. The HBV-Light model simulated future reservoir inflows (2020–2099), revealing significant reductions in snow accumulation and inflows due to rising temperatures and altered precipitation patterns. Reservoir operations and hydropower generation projections were conducted using the Water Resources Assessment and Planning (WEAP) model, predicting a 4–9% reduction in hydropower generation for the Çoruh Basin and a 10–35% reduction for the Seyhan Basin over the final decades (2076–2099).To adapt to these changes, four alternative management strategies were evaluated to optimize reservoir operations under climate challenges. This study emphasizes the importance of comprehensive scientific assessments for policymakers in understudied, snow-dominated transboundary river basins, particularly those with significant energy production potential. The findings contribute to improved water and energy management by providing critical insights into climate-driven changes in reservoir storage, flow patterns, and hydropower generation.

This study aims to analyze future snowmelt runoff and snow extent for the Upper Euphrates Basin (41 109 km²) which is the most upstream basin of the water tower Mesopotamia and located in the Eastern mountainous part of Türkiye. The future characterization of snowmelt runoff and snow water equivalent is investigated taking the regional climate model projections into account through hydrological model applications. The basin is divided into two major sub-basins Karasu Basin and Murat Basin. The conceptual hydrological models, HBV and HEC-HMS have been utilized to establish a rainfall-runoff relationship. The models are calibrated and validated with observed daily total precipitation and average temperature data for the 1980–2011 period. Future projections are selected for the 2025–2100 periods using EU-CORDEX data with various global circulation models. MPI-ESM-LR and CNRM-CM5 data sets and RCP4.5 and RCP8.5 emission scenarios are used for future projections. The results indicate a decrease in snow cover extent and an increase in snowmelt runoff. The overall assessment will help us to understand how these changes will affect the operations of water resources systems (downstream reservoirs) in terms of flood control, energy production, irrigation, water supply, etc.

Mountainous basins are frequently called “natural water towers” because they supply essential water to downstream regions for irrigation, industrial–municipal use, and hydropower generation. The possible implications of climate change on water supplies have gained prominence in recent years, particularly in snow-dominated mountainous basins. The Euphrates River, a snow-fed transboundary river that originates from the Eastern part of Türkiye with several large dam reservoirs downstream, was chosen within this scope. The study reveals the impact of climate change on two snow-dominated headwaters, namely Karasu and Murat, which have a basin area of 41,109 km². The impact of climate change is assessed across runoff regimes and snow dynamics for future periods (2024–2099). Global Climate Model (GCM) data sets (CNRM-CM5, IPSL-CM5A, EC-EARTH, MPI-ESM-LR, NorESM1-M, HadGEM2-ES) were downscaled by Regional Circulation Models (RCMs), provided from CMIP5 EURO-CORDEX domain for climate projections under RCP4.5 and RCP8.5 scenarios. Future projections of runoff and snow variables are predicted by two conceptual hydrological models, HBV and HEC-HMS. The results indicate a dramatic shrink in snow cover extents (>65%) and snow duration (25%), a decrease in snow water equivalent (>50%), and a timely shift (up to a month) in peak runoff through early spring in the runoff hydrograph for the last future period (2075–2099). The overall assessment shows that operations of downstream water systems should be reconsidered for future changes.

The operation of upstream reservoirs in mountainous regions fed by snowmelt is highly challenging. This is partly due to scarce information given harsh topographic conditions and a lack of monitoring stations. In this sense, snow observations from remote sensing provide additional and relevant information about the current conditions of the basin. This information can be used to improve the model states of a forecast using data assimilation techniques, therefore enhancing the operation of reservoirs. Typical data assimilation techniques can effectively reduce the uncertainty of forecast initialization by merging simulations and observations. However, they do not take into account model, structural, or parametric uncertainty. The uncertainty intrinsic to the model simulations introduces complexity to the forecast and restricts the daily work of operators. The novel Multi-Parametric Variational Data Assimilation (MP-VarDA) uses different parameter sets to create a pool of models that quantify the uncertainty arising from model parametrization. This study focuses on the sensitivity of the parametric reduction techniques of MP-VarDA coupled in the HBV hydrological model to create model pools and the impact of the number of parameter sets on the performance of streamflow and Snow Cover Area (SCA) forecasts. The model pool is created using Monte Carlo simulation, combined with an Aggregated Distance (AD) Method, to create different model pool instances. The tests are conducted in the Karasu Basin, located at the uppermost part of the Euphrates River in Türkiye, where snowmelt is a significant portion of the yearly runoff. The analyses were conducted for different thresholds based on the observation exceedance probabilities. According to the results in comparison with deterministic VarDA, probabilistic MP-VarDA improves the m-CRPS gains of the streamflow forecasts from 57% to 67% and BSS forecast skill gains from 52% to 68% when streamflow and SCA are assimilated. This improvement rapidly increases for the first additional model parameter sets but reaches a maximum benefit after 5 parameter sets in the model pool. The improvement is notable for both methods in SCA forecasts, but the best m-CRPS gain is obtained for VarDA (31%), while the best forecast skill is detected in MP-VarDA (12%).