Changes in hydrological cycles and water resources will certainly be a direct consequence of climate change, making the forecast of hydrological components essential for water resource assessment and management. This research was thus carried out to estimate water balance components and water yield under current and future climate change scenarios and trends in the Guder Catchment of the Upper Blue Nile, Ethiopia, using the soil and water assessment tool (SWAT). Hydrological modeling was efficaciously calibrated and validated using the SUFI-2 algorithm of the SWAT model. The results showed that water yield varied from 926 mm to 1340 mm per year (1986–2016). Regional climate model (RCM) data showed, under representative concentration pathways (RCP 8.5), that the precipitation will decrease by up to 14.4% relative to the baseline (1986–2016) precipitation of 1228 mm/year, while the air temperature will rise under RCP 8.5 by +4.4 °C in the period from 2057 to 2086, possibly reducing the future basin water yield output, suggesting that the RCP 8.5 prediction will be warmer than RCP 4.5. Under RCP 8.5, the total water yield from 2024 to 2086 may be reduced by 3.2 mm per year, and a significant trend was observed. Local government agencies can arrange projects to solve community water-related issues based on these findings.

Against the backdrop of ongoing climate change, the Qinghai–Tibet Plateau, a region highly sensitive to climatic variation, exhibits intricate spatiotemporal patterns in reference crop evapotranspiration (ET_O), with significant implications for regional water-resource planning. This study selected four agro-climatic zones across the plateau region (TSA, TSH, TAZ, and WCH). Long-term daily observations from 28 meteorological stations were used to estimate ET_O via the FAO 56 Penman–Monteith equation. This extensive dataset enabled robust trend analysis using the Mann–Kendall test, alongside a cloud-model framework, and analyses of sensitivity and contributions to evaluate ET_O’s spatiotemporal evolution, its distributional uncertainty, and the underlying drivers. Results reveal pronounced regional heterogeneity in the interannual variability of ET_O. Annual ET_O declined in TSH and TSA (trend rates of −1.12 and −6.58 mm·10a⁻¹, respectively) and increased in TAZ and WCH (15.76 and 10.74 mm·10a⁻¹, respectively). At monthly and seasonal timescales, ET_O exhibited an unimodal pattern, with the greatest stability in winter and spring and lower stability in summer and autumn. The cloud-model parameter He indicates that ET_O stability is greatest in TSH and weakest in WCH, with He values of 7.15 and 12.29 mm, respectively. Contribution-rate analyses identify T_max and T_mean as the principal determinants of rising ET_O across all study zones, reflecting the largest individual contributions. Temperature-related factors together account for the majority of ET_O variability across the regions, with their absolute contributions ranging from 5.61% to 8.63%, well above those of aerodynamic factors (0.62–1.78%). Stability assessments indicate that ET_O is generally more unstable than its meteorological drivers, with substantial regional disparities, implying that ET_O evolution cannot be explained by a single controlling factor. Overall, the study characterizes the uncertainty in ET_O variations under complex terrain, highlights the value of the cloud model for refined hydrological assessments, and provides a scientific basis for adaptive agricultural water-resource management in the region.

Understanding spatiotemporal variation in soil CO₂ flux (FCO₂) along elevational gradients is essential for predicting carbon–climate feedback in alpine ecosystems. However, how temperature- and moisture-related factors jointly regulate daily-scale FCO₂ and how their contributions vary with elevation remain unclear, particularly in the Sejila Mountains (Southeastern Tibetan Plateau). We conducted continuous in situ measurements of daily-scale FCO₂, air temperature (Ta), relative humidity (RH), soil temperature (ST, 0–10 cm), and volumetric soil water content (SW) across five elevational bands (3000–4200 m) in 2024–2025. Across both years, FCO₂ showed a unimodal seasonal cycle and a robust nonmonotonic spatial pattern, with the highest efflux at 3000 and 4200 m and peak rates exceeding 5.0 µmol CO₂ m⁻² s⁻¹. Cumulative carbon loss at 4200 m (909.90 g C m⁻²) exceeded that at mid-elevation sites. Linear mixed-effects models identified Ta as the most consistent positive predictor; the ST × SW interaction was not significant, indicating that temperature and moisture effects are largely additive at the daily scale. Piecewise regression revealed nonlinear SW thresholds (θ) in the FCO₂ response, with θ varying nonmonotonically with elevation. Multiple linear regression further showed that thermal predictors (Ta, ST) explained substantially more variance than moisture predictors (RH, SW), and the relative importance of thermal drivers increased with elevation. These results challenge the common expectation of a monotonic decline in soil respiration with elevation and suggest that, when SW remains above critical thresholds, warming may amplify soil carbon losses at high elevations on the Tibetan Plateau.

There are many methods for calculating unit hydrograph, such as analysis method, trial algorithm and least squares method. But these methods have certain requirements for flood datas and the unit hydrograph may not be optimal. Based on the theory of composition, a hydrological system was viewed as a generalized collection in this study and Gamma functions were used to simulate the basin convergence process. At the same time, the Gamma function is parameterized and the parameters of Gamma function are optimized by genetic algorithm, which is based on the minimum error between the calculation of confluence process and the measurement process, before deriving the unit hydrograph. The Collins iteration method was used to compute the unit hydrograph. The results of actual calculated examples showe that this method is more precise than other methods, while it can also illustrate the law of runoff.