Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2310.09311

Assessment of Climate Change Impacts on the Precipitation and Temperature: A Case Study on Krishna River Basin

Nandikanti Siva Sai Syam·Akshay Sunil·Subbarao Pichuka·Anirban Mandal
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this study, the statistical downscaling model (SDSM) is employed for downscaling the precipitation (PREC), maximum temperature (T max ) and minimum temperature (T min ) over Krishna River Basin (KRB). The Canadian Earth System Model, version 2 (CanESM2) General Circulation Model (GCM) outputs were considered as predictor variables. First, the SDSM is calibrated using 30-years (1961-1990) of data and subsequently validated for 15-years (1991-2005). Upon perceiving the satisfactory performance, the SDSM is further used for projecting the predictand variables (PRECP, T max and T min ) for the 21 st century considering three Representative Concentration Pathway (RCP) scenarios viz. RCP2.6, RCP4.5 and RCP8.5. The future period is divided into three 30-year time slices named epoch-1 (2011-2040), epoch-2 (2041-2070) and epoch-3 (2071-2100) respectively. Further, 1976-2005 is considered as baseline period and all the future results are compared with this data. The results were analysed at various temporal scales, i.e., monthly, seasonal and annual. The study reveals that the KRB is going to become wetter during all the seasons. The results are discussed for the worst-case scenario i.e., RCP8.5 epoch-3. The average annual maximum and minimum temperature is expected to increase. The extreme event analysis is also carried out considering the 90 th and 95 th percentile values. It is noticed that the extreme (90 th and 95 th percentiles) are going to increase. There are events more than extreme values. The outcome of this study can be used in flood modelling for the KRB and also for the modelling of future irrigation demands along with the planning of optimal irrigation in the KRB culturable command area.

Explore related subjects

physics.ao-phcs.NAmath.NA

Keep this discovery

BibTeXRIS

Nandikanti Siva Sai Syam, Akshay Sunil, Subbarao Pichuka, Anirban Mandal. 2023-10-12. Assessment of Climate Change Impacts on the Precipitation and Temperature: A Case Study on Krishna River Basin. https://arxiv.org/abs/2310.09311

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗