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A study of climate change scenarios is presented in this paper by projecting the model of recorded precipitation/temperature data into three future periods by statistical downscaling methods through LARS-WG with data from 7 synoptic stations. The study area largely spans the closed basin of Lake Urmia with overlaps to some of the surrounding basins. The model is at two stages (the downscaling stage comprises the following: (i) it uses large-scale GCM models to provide climate variables (predictors), and (ii) the outcomes are downscaled to the local climatic variables for correlating with the observed time series (e.g. rainfall) for the period of T0, 1961–2001—40 years; the projecting stage comprises the derived precipitation/temperature values at T0 to T1: 2011–2030; T2: 2046–2065; and T3: 2080–2099 periods at synoptic stations using three standard scenarios of A1B, A2, and B1). The outputs are used to map the climate zones, which show the following: (i) climates at T1 are still similar to T0, and if there is any declining trend in precipitation during both periods, they are small to the extent that the shrinkage of Lake Urmia is unlikely to be driven by climate change; (ii) changes are likely at T2 and T3 periods including climatic regimes may turn peakier at the northern and drier at central regions; and (iii) precipitation is likely to decrease in some of the zones at T2 and T3 periods. This underpins the need for more responsive policymaking, and should this not be realised in the near future, the prospect seems bleak in terms of serious damages to Lake Urmia, depleting aquifers and subsequent immigration from the region, but can be averted by responsive planning. [ABSTRACT FROM AUTHOR]

A large body of scientific research has demonstrated a changing climate, which affects river flow regimes and extreme flood frequencies and magnitudes. The magnitude and frequency of extreme events are of critical importance in the evaluation of river systems to inform flood risk reduction under current and future conditions. The global climate projections from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) datasets were used by the Variable Infiltration Capacity (VIC) land surface model to produce a runoff dataset, implementing a Bias-Correction Spatial Disaggregation (BCSD) approach. The resulting runoff was then used as input to the Routing Application for Parallel computatIon of Discharge (RAPID) river routing model to simulate daily flows within all 1.2 million Mississippi River Basin river reaches for years 1950 through 2099. This research effort analyzed the performance of the models for the historical time period, comparing with the observations at 64 gage locations for 16 different climate models. A recurrence interval analysis was performed to determine the 2-, 5-, 10-, 50-, 100-, 500-, and 1000-year events within both the historical and projected time periods, highlighting the relative changes predicted into the future. Anticipated seasonal changes are demonstrated by comparing monthly average streamflows for three different time periods (1951–2005, 2006–2049, and 2050–2099). Results indicate that the hydrologic conditions of the Lower Mississippi River are not stationary. Based on all 16 models considered in this study, the median of the model projections shows an 8% increase in the 100-year return period discharge at Vicksburg, Mississippi, into the future time period, although the full range of 16 models varies widely from − 11 to + 85% change in the 100-year discharge in the future. [ABSTRACT FROM AUTHOR]

Data scarcity globally has impeded our understanding of hydrological processes. This study was aimed at evaluating skills of models in reproducing past climate in the Shire River Basin (SRB) in Malawi for future climate impact assessments. The study used observed and simulated data by Global Climate Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). A total of 52 models were considered comprising a mixture of models in the representative concentration pathways of RCP4.5 and RCP6.0. The mean annual bias, correlation, extreme precipitation indices obtained from the RClimdex package of R software program, and frequency distributions were used to quantify the accuracy of the GCM simulations. On the precipitation indices, emphasis was placed on the frequency indices (number of heavy precipitation days (RR ≥ 10 mm), R10mm; number of very heavy precipitation days (RR ≥ 20 mm), R20mm; number of extremely heavy precipitation days (RR ≥ 25 mm), R25mm; consecutive dry days (RR < 1 mm), CDD; and consecutive wet days (RR ≥ 1 mm), CWD) and on the intensity indices (daily maximum precipitation, RX1day; 5-day maximum precipitation, RX5days; annual total wet-day precipitation, PRCPTOT; and very wet days, (R95P)). Study results have revealed that there is variation in the performances of individual models and that the overall performance of the models over the SRB is generally low. Some individual models perform better than the multi-model ensemble. Results have also shown the better performance of the following models: ACCESS1-3_rcp45_r1i1p1, BNU-ESM_rcp45_r1i1p1, CSIRO-Mk3-6-0_rcp45_r3i1p1, CSIRO-Mk3-6-0_rcp45_r8i1p1, and GFDL-ESM2G_rcp45_r1i1p1 of medium–low emission pathway, RCP4.5, in replicating the historical extreme precipitation for Shire River Basin. [ABSTRACT FROM AUTHOR]

The influence of climate change on the catchment-scale hydrologic processes can have a profound impact on river flow and the availability of fresh water. Here, we develop an integrated modeling framework to assess the near- and long-term hydrological response to climate change in a mountain river basin. The framework samples climate model outputs under different representative concentration pathways to force the calibrated hydrologic model and generate daily streamflow projections. We implement a framework in the Modi River basin, with an elevation ranging from 750 m to over 8000 m above sea level. Under the highest warming scenario, the mean annual precipitation and temperature are projected to vary to be as high as 4531 mm and 25.7 °C, respectively. The study results show that the future streamflow of the Modi River basin will increase during the latter time windows, i.e., far future (2075–2099) > > mid future (2050–2074) > > near future (2025–2049). Exploring how climate change can alter different hydrological processes can help improve the fundamental understanding of water balance and hydrologic controls, which are critical in ensuring the functionality of the natural ecosystems. [ABSTRACT FROM AUTHOR]