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In order to assess the effects of climate change and land use change on Rawal Dam, a major supply of water for Rawalpindi and Islamabad, this study uses hydrological modeling at the watershed scale. The HEC-HMS model was used to simulate the hydrological response in the Rawal Dam catchment to historical precipitation. The calibrated model was then used to determine how changes in land use and climate had an impact on reservoir inflows. The model divided the Rawal Dam watershed into six sub-basins, each with unique features, and covered the entire reservoir’s catchment area using data from three climatic stations (Murree, Islamabad Zero Point and Rawal Dam). For the time spans of 2003–2005 and 2006–2007, the model was calibrated and verified, respectively. An excellent fit between the observed and predicted flows was provided by the model. The GCM (MPI-ESM1-2-HR) produced estimates of temperature and precipitation under two Shared Socioeconomic Pathways (SSP2 and SSP5) after statistical downscaling with the CMhyd model. To evaluate potential effects of climate change and land use change on Rawal Dam, these projections, along with future circumstances for land use and land cover, were fed to the calibrated model. The analysis was carried out on a seasonal basis over the baseline period (1990–2015) and over future time horizon (2016–2100), which covers the present century. The findings point to a rise in precipitation for both SSPs, which is anticipated to result in an increase in inflows throughout the year. SSP2 projected a 15% increase in precipitation across the Rawal Dam catchment region until the end of the twenty-first century, while SSP5 forecasted a 17% increase. It was determined that higher flows are to be anticipated in the future. The calibrated model can also be utilized successfully for future hydrological impact assessments on the reservoir, it was discovered.

This research investigates the long-term impact of climate change on groundwater recharge (seepage) within the Talomo-Lipadas Watershed, Davao City, Philippines, over the next eighty-nine (89) years. Employing the Statistical Downscaling Method (SDSM), stationscale climate scenarios were generated for three future time slices centered on 2020 (2011-20140), 2050 (2041-2070), and 2080 (2071-2100). These scenarios, indicating a projected increase in temperature within the watershed, were then used as input for the BROOK90 hydrological model to simulate groundwater recharge. The modelling results project a decline in groundwater supply from 109.01 million cubic meters (MCM) in 2020 to 103.53 MCM in 2050 and further down to 99.81 MCM by 2080. This projected decrease in groundwater recharge has significant implications beyond just water availability. Reduced groundwater flow can impact baseflow in rivers, affecting aquatic ecosystems and potentially exacerbating water scarcity during dry periods. Decreased recharge also has implications for other water-related sectors, including agriculture (irrigation), industry (water supply), and domestic water use, potentially leading to increased competition for dwindling resources. These findings underscore the urgent need for adaptation strategies to mitigate the effects of climate change on groundwater recharge within the Talomo-Lipadas Watershed. Further research employing diverse hydrological models is recommended to validate these findings and provide a more robust basis for developing sustainable water management plans.

To estimate the impacts of future climate change on streamflow in the Tangwang River basin (TRB) in northeastern China, 2 hydrological models, the Soil and Water Assessment Tool and the Hydro-Informatic Modeling System, were used. These models are driven by future (2021−2050) local rainfall and temperature scenarios downscaled from global climate model (GCM) simulations from the fifth phase of the Coupled Model Intercomparison Project under 2 emission scenarios (Representative Concentration Pathway [RCP] 4.5 and RCP8.5). The downscaling of rainfall is done with the help of a multisite stochastic rainfall generator (MSRG), which extends the 'Richardson type' rainfall generator to a multisite approach using a modified series-independent and spatial-correlated random numbers method by linking its 4 parameters to large-scale circulations using least-squares regressions. An independent validation of the MSRG shows that it successfully preserves the major daily rainfall characteristics for wet and dry seasons. Relative to the reference period (1971−2000), the annual and wet season (April to October) streamflow during the future period (2021−2050) would decrease overall, which indicates that water resources and the potential flood risk would decline in the TRB. The slightly increased dry season (November to March) streamflow would, to some extent, contribute to the 'spring drought' over this region. Although rainfall is projected to remain unchanged in the wet season and the whole year, the increased total evapotranspiration due to the increase in temperature would lead to a decline in total streamflow for this basin. The projected streamflow changes from multiple GCMs in this paper could provide a glimpse into a very plausible future for the water resource management community, and would hence provide valuable references for the sustainable management of water and forest ecosystems under a changing climate.

We test a strategy for downscaling seasonal mean temperature for many locations within a region, based on principal component analysis (PCA), and assess potential benefits of this strategy which include an enhancement of the signal-to-noise ratio, more efficient computations, and reduced sensitivity to the choice of predictor domain. These conditions are tested in some case studies for parts of Europe (northern and central) and northern China. Results show that the downscaled results were not highly sensitive to whether a PCA-basis or a more traditional strategy was used. However, the results based on a PCA were associated with marginally and systematically higher correlation scores as well as lower root-mean-squared errors. The results were also consistent with the notion that PCA emphasises the large-scale dependency in the station data and an enhancement of the signal-to-noise ratio. Furthermore, the computations were more efficient when the predictands were represented in terms of principal components.

Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space‐time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small‐scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

Reliable projections of extremes at finer spatial scales are important in assessing the potential impacts of climate change on societal and natural systems, particularly for elevated and cold regions in the Tibetan Plateau. This paper presents future projections of extremes of daily precipitation and temperature, under different future scenarios in the headwater catchment of Yellow River basin over the 21st century, using the statistical downscaling model (SDSM). The results indicate that: (1) although the mean temperature was simulated perfectly, followed by monthly pan evaporation, the skill scores in simulating extreme indices of precipitation are inadequate; (2) The inter-annual variabilities for most extreme indices were underestimated, although the model could reproduce the extreme temperatures well. In fact, the simulation of extreme indices for precipitation and evaporation were not satisfactory in many cases. (3) In future period from 2011 to 2100, increases in the temperature and evaporation indices are projected under a range of climate scenarios, although decreasing mean and maximum precipitation are found in summer during 2020s. The findings of this work will contribute toward a better understanding of future climate changes for this unique region.

Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). In the longer term, the final aim of this tool is to enlarge the high-resolution RCM simulation ensembles at low cost to explore better the various sources of projection uncertainty at local scale. Using a neural network, we build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12 km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM, particularly how the RCM refines the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a substantial computational benefit of running the emulator rather than the RCM, since training the emulator takes about 2 h on GPU, and the prediction takes less than a minute. However, further work is needed to improve the reproduction of some temperature extremes, the climate change intensity and extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

General circulation models (GCMs) allow the analysis of potential changes in the climate system under different emissions scenarios. However, their spatial resolution is too coarse to produce useful climate information for impact/adaptation assessments. This is especially relevant for regions with complex orography and coastlines, such as in Chile. Downscaling techniques attempt to reduce the gap between global and regional/local scales; for instance, statistical downscaling methods establish empirical relationships between large-scale predictors and local predictands. Here, statistical downscaling was employed to generate climate change projections of daily maximum/minimum temperatures and precipitation in more than 400 locations in Chile using the analog method, which identifies the most similar or analog day based on similarities of large-scale patterns from a pool of historical records. A cross-validation framework was applied using different sets of potential predictors from the NCEP/NCAR reanalysis following the perfect prognosis approach. The best-performing set was used to downscale six different CMIP5 GCMs (forced by three representative concentration pathways, RCPs). As a result, minimum and maximum temperatures are projected to increase in the entire Chilean territory throughout all seasons. Specifically, the minimum (maximum) temperature is projected to increase by more than 2 °C (6 °C) under the RCP8.5 scenario in the austral winter by the end of the twenty-first century. Precipitation changes exhibit a larger spatial variability. By the end of the twenty-first century, a winter precipitation decrease exceeding 40% is projected under RCP8.5 in the central-southern zone, while an increase of over 60% is projected in the northern Andes.

We present a non-homogeneous hidden Markov model for the spatiotemporal analysis of rainfall data, within a subjective Bayesian framework. In this model, daily rainfall patterns are driven by a small number of unobserved states, interpreted as states of the weather, that evolve in time according to a first-order non-homogeneous Markov chain, with transition probabilities dependent on time varying atmospheric data. The weather states alone do not account for all the space–time structure in the data and so we introduce latent multivariate normal random variables in a flexible model for the probability of rain and the distribution of non-zero rainfall amounts. In the resulting hierarchical non-homogeneous hidden Markov model, rainfall occurrences and non-zero rainfall amounts are spatially dependent and conditionally Markov in time, given the weather state. We build a prior distribution that conveys genuine initial beliefs and apply the model and inferential procedures to data from a network of 12 sites located throughout the UK.

Assessing local climate change impacts often requires downscaling coarse global climate model (GCM) output to finer resolution. Two main approaches exist: dynamical downscaling using high‐resolution regional climate models, and statistical downscaling based on historical relationships between large‐scale and local variables. In a recent analysis of five dynamically downscaled simulations over the western United States, Koszuta et al. (2024, https://doi.org/10.1029/2023gl107298) found that warming weakens orographic influence on winter precipitation, damping increases on windward slopes and amplifying them in rain‐shadowed regions. Here we show that this effect is robust across seasons and multiple dynamically downscaled ensembles, and is more pronounced at higher model resolutions. However, it is absent in projections from a widely used statistical model (LOCA2), even when trained on high‐resolution future simulations (LOCA2‐Hybrid). This highlights a key limitation of many statistical downscaling methods: their preservation of parent GCM trends, which usually fail to capture emergent changes in orographic precipitation patterns.