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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]

The major tasks of the Baltic Sea Experiment (BALTEX) are to simulate the water and energy cycles of the Baltic Sea catchment and to identify important processes, which are relevant to the climate in the Baltic region. A fully coupled regional climate model system for the Baltic Sea region, called BALTIMOS, was developed in the framework of the German climate research program DEKLIM by linking existing model components for the atmosphere, for the ocean including sea ice, and for hydrology. With this system, it is possible to model the complete water and energy cycles for the Baltic Sea catchment for today's climate, as well as for the future with climate scenarios. The emphasis in this paper is given to the description of the modelling system and on the comparison of the results of the fully coupled BALTIMOS system against the results of the uncoupled atmospheric component REMO for today's climate. Both simulations are driven at the atmospheric lateral boundaries with ECMWF analysis data from the European Centre for Medium Range Weather Forecast (ECMWF) for 5 years each. In the uncoupled run, the sea surface temperature (SST) and ice surface temperature (IST) analysed by ECMWF are prescribed, while in the coupled run, SST and IST are provided by the ocean/sea ice model BSIOM. Several analysed parameters are very similar for the land part of the Baltic Sea catchment, while there are differences especially for surface and near-surface temperatures for the fraction of the Baltic Sea itself that indicate further research efforts. As no corrections for the modelled air-sea fluxes are applied for the coupled model system, it is a good result that no drift of the coupled model system is visible in the 5-year integration. The present paper gives an overview about the development of the coupled model system and shows results from first integrations of the system. [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]

Different sources of uncertainty exist in climate change impacts projection. This study aims to propose a framework to deal with the various sources of uncertainties involved in hydro-climate projections of Zayandeh-Rud River Basin with area of 26,917 km2 in Central Iran. The Bayesian model averaging (BMA) was here used through two distinct approaches for weighting the hydrologic outputs (App. I) as well as the global climate models (GCMs) (App. II) based on their abilities to simulate the baseline period. The results showed that different GCMs have different abilities in estimating the hydro-climatic variables and the application of uncertainty analysis is necessary for climate change studies. Application of the BMA can significantly reduce the errors in historical runoff prediction. Although App. I showed a better performance of generating the stream flow time series during the baseline period, the App. II approach has an acceptable ability in different months. The findings of flow duration curves under both approaches revealed that App. II is more appropriate to deal with uncertainty of hydro-climate projection especially in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Climate change/variability and subsequent exacerbation of extremes are affecting human and ecological health across the globe. This study aims at unpacking hydro-climatic extremes in a snow-fed Marshyangdi watershed, which has a potential for water infrastructure development, located in Central Nepal. Bias-corrected projected future climate for near (2014–2033) and mid-future (2034–2053) under moderate and pessimistic scenarios were developed based on multiple regional climate models. Historical (1983–2013) and future trends of selected climatic extreme indices were calculated using RClimDex and hydrological extremes using Indicators of Hydrologic Alteration tool. Results show that historical trends in precipitation extremes such as number of heavy and very heavy precipitation days and maximum 1-day precipitation are decreasing while the temperature-related extremes have both increasing and decreasing trends (e.g., warm spell duration index, warm days and summer days are increasing whereas cold spell duration index, cool days and warm nights are decreasing). These results indicate drier and hotter conditions over the historical period. The projected future temperature indices (hot nights, warm days) reveal increasing trend for both the scenarios in contrast with decreasing trends in some of the extreme precipitation indices such as consecutive dry and wet days and maximum 5-day precipitation. Furthermore, the watershed has low mean hydrological alterations (27.9%) in the natural flow regime. These results indicate continuation of wetter and hotter future in the Marshyangdi watershed with likely impacts on future water availability and associated conflicts for water allocation, and therefore affect the river health conditions. [ABSTRACT FROM AUTHOR]

Changes in summer and diurnal precipitation in the upper reaches of the Yangtze River Basin (UYRB) in the middle of the twenty-first century were estimated by RegCM4 with a resolution of 50 km under two representative concentration pathway scenarios. ERA-Interim, CSIRO-MK3.6.0 and MPI-ESM-MR were used as the initial and lateral boundary conditions, and two observation data sets (CN05.1 and CRU) were used to evaluate the precipitation performance. RegCM4 captured the spatial characteristics of summer precipitation in the UYRB during the reference period. Compared with the two observational data sets, the three groups of downscaling results underestimated the precipitation in the eastern part of the basin by 20% and overestimated that in the west by more than 80%. In the middle of the twenty-first century, the total precipitation (TPR) in the UYRB varied significantly from east to west. The multiyear average TPR in the eastern plains was projected to significantly decrease, while it significantly increased in the western mountains. As a major contributor to the TPR in the UYRB, convective precipitation (CPR) differed between the eastern and western regions, especially at night. The TPR changes in the east (decrease) and west (increase) were projected to be strongest in the afternoon. The probability of high-intensity precipitation in the central and western areas will increase, implying a potential increase in the risk of flooding in these areas. The diametric changes in the TPR between the east and west may further exacerbate the spatial heterogeneity of water resources, resulting in large impacts to surface hydrological processes. The spatial variations in precipitation were mainly due to the variation in precipitation mechanisms between the western mountainous area and the eastern basin, while the changes in water vapour transport and atmospheric stability also played a role. [ABSTRACT FROM AUTHOR]

Mahanadi River Basin (MRB) is one of the largest tropical pluvial river basin systems in India contributing the major source of freshwater to more than 71 million people in east-central India. Being located in the monsoon "core" region (18–28° N latitude and 73–82° E longitude) and its proximity to Bay of Bengal, Mahanadi River Basin (MRB) system is highly vulnerable to tropical depression-induced severe storms and extreme precipitation-induced fluvial floods during southwest monsoon as reflected in several successive and catastrophic flood episodes in recent years (2001, 2003, 2006, 2008, 2011, 2013, 2014, 2016, 2019). While previous studies so far focused on analyzing either flood trends or frequency and show the role of precipitation in flood generating mechanism over MRB using both instrumental records and climate model simulations, this study for the first time examines space-time coherence in floods and the role catchment wetness in flood response (i.e., magnitude and the timing of floods) over the basin. We examine the incidence of flooding in three different time windows: 1970–2016 (whole 47 years), 1970–2006, and 2007–2016 (post-2007s) using monsoonal maxima peak discharge (MMPD) and peak over threshold (POT) series at 24 stream gauges spatially distributed over the basin. Our analysis reveals the mean dates of floods for most of the gauges are temporally clustered during the month of August irrespective of the type of flood series and the choice of time frames. Further, we observe sensitiveness of runoff responses (flood magnitude, FM and the flood timing, FT) to lagged d-day mean catchment wetness (CW), suggesting a physical association between them. We also note FT is more strongly correlated (as manifested by statistically significant correlations) to CW rather than FM. Overall, we observe, the correlation of CW versus FT is negative, where the flood timing is relatively irregular. The outcomes of the study help to improve the predictability of floods, which can, in turn, enhance existing flood warning techniques. [ABSTRACT FROM AUTHOR]

The Tizinafu watershed is characterized by extremely scarce precipitation and low temperature, and snow and glacier melting which dominate the main water resource of this area. Thus, assessing the snowmelt water resource has great significance to local residents and ecological systems. Based on snowmelt runoff model (SRM) and Markov chain Monte Carlo (MCMC) simulation, four combinations of meteorological data consisting of two daily temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the variable infiltration capacity (VIC) model and two daily precipitation data from Tropical Rainfall Measuring Mission (TRMM) and VIC, respectively, are evaluated for this ungauged basin in Bayesian uncertainty analysis. By comparing the performances of the four meteorological data combinations based on SRM (SRMMODIS + TRMM, SRMMODIS + VIC, SRMVIC + TRMM and SRMVIC + VIC, the former and latter subscripts denote temperature and precipitation data, respectively), results show that the four SRMs are both capable of reproducing the runoff process of the Tizinafu watershed on daily runoff simulation, and SRMVIC + VIC has the best performance. In addition, the impact of climate change on the water resources of the Tizinafu watershed is also evaluated in Bayesian uncertainty analysis, and four climate change scenarios under the condition of Representative Concentration Pathway 8.5 of CMIP5 projection are considered. The results demonstrate that the mean annual runoff prediction of the 2090s increased by 17.2%, 20.2%, 24.1%, and 16.2% compared to that of base year for the four scenarios, respectively. In addition, with the increase of the cryosphere area, snow cover area will have an increasing impact on the mean annual runoff of the study area in the 2090s. The runoff components of snowpack melt and new snow are both sensitive to the cryosphere area, while the runoff component of rainfall is not sensitive to the cryosphere area. [ABSTRACT FROM AUTHOR]

The variability and uncertainty of water resources associated with climate change are critical issues in arid and semi-arid regions. In this study, we used the soil and water assessment tool (SWAT) to evaluate the impact of climate change on the spatial and temporal variability of water resources in the Bazoft watershed, Iran. The analysis was based on changes of blue water flow, green water flow, and green water storage for a future period (2010–2099) compared to a historical period (1992–2008). The r-factor, p-factor, R2, and Nash–Sutcliff coefficients for discharge were 1.02, 0.89, 0.80, and 0.80 for the calibration period and 1.03, 0.76, 0.57, and 0.59 for the validation period, respectively. General circulation models (GCMs) under 18 emission scenarios from the IPCC's Fourth (AR4) and Fifth (AR5) Assessment Reports were fed into the SWAT model. At the sub-basin level, blue water tended to decrease, while green water flow tended to increase in the future scenario, and green water storage was predicted to continue its historical trend into the future. At the monthly time scale, the 95% prediction uncertainty bands (95PPUs) of blue and green water flows varied widely in the watershed. A large number (18) of climate change scenarios fell within the estimated uncertainty band of the historical period. The large differences among scenarios indicated high levels of uncertainty in the watershed. Our results reveal that the spatial patterns of water resource components and their uncertainties in the context of climate change are notably different between IPCC AR4 and AR5 in the Bazoft watershed. This study provides a strong basis for water supply-demand analyses, and the general analytical framework can be applied to other study areas with similar challenges. [ABSTRACT FROM AUTHOR]

Prior to hydrological assessment of climate change at catchment scale, an applied methodology is necessary to evaluate the performance of climate models available for a given catchment. This study presents a grid-based performance evaluation approach as well as an intercomparison framework to evaluate the uncertainty of climate models for rainfall reproduction. For this purpose, we used outputs of two general circulation models (GCMs), namely ECHAM5 and CCSM3, downscaled by a regional climate model (RCM), namely RegCM3, over ten small to mid-size catchments in Rize Province, Turkey. To this end, five rainfall-borne climatic statistics are computed from the outputs of ECHAM5-RegCM3 and CCSM3-RegCM3 combinations in order to compare with those of observations in the province for the reference period 1961-1990. Performance of each combination is tested by means of scatter diagram, bias, mean absolute bias, root mean squared error, and model performance index (MPI) measures. Our results indicated that ECHAM5-RegCM3 overestimates the total monthly rainfall observations whereas CCSM3-RegCM3 tends to underestimate. In terms of maximum monthly and annual maximum rainfall reproduction, ECHAM5-RegCM3 shows higher performance than CCSM3-RegCM3, particularly in the coastland areas. In contrast, CCSM3-RegCM3 outperforms ECHAM5-RegCM3 in reproducing the number of rainy days, especially in the inland areas. The results also revealed that if a GCM-RCM combination performs well for a portion (statistic) of a catchment, it is not necessarily appropriate for the other portions (statistics). Moreover, the MPI measure demonstrated the superiority of ECHAM5-RegCM3 to CCSM3-RegCM3 up to 33 % excelling for annual rainfall reproduction in Rize Province. [ABSTRACT FROM AUTHOR]

In this study, meteorological time series from five meteorological stations in and around a watershed in Turkey were used in the statistical downscaling of global climate model results to be used for future projections. Two general circulation models (GCMs), Canadian Climate Center (CGCM3.1(T63)) and Met Office Hadley Centre (2012) (HadCM3) models, were used with three Special Report Emission Scenarios, A1B, A2, and B2. The statistical downscaling model SDSM was used for the downscaling. The downscaled ensembles were put to validation with GCM predictors against observations using nonparametric statistical tests. The two most important meteorological variables, temperature and precipitation, passed validation statistics, and partial validation was achieved with other time series relevant in hydrological studies, namely, cloudiness, relative humidity, and wind velocity. Heat waves, number of dry days, length of dry and wet spells, and maximum precipitation were derived from the primary time series as annual series. The change in monthly predictor sets used in constructing the multiple regression equations for downscaling was examined over the watershed and over the months in a year. Projections between 1962 and 2100 showed that temperatures and dryness indicators show increasing trends while precipitation, relative humidity, and cloudiness tend to decrease. The spatial changes over the watershed and monthly temporal changes revealed that the western parts of the watershed where water is produced for subsequent downstream use will get drier than the rest and the precipitation distribution over the year will shift. Temperatures showed increasing trends over the whole watershed unparalleled with another period in history. The results emphasize the necessity of mitigation efforts to combat climate change on local and global scales and the introduction of adaptation strategies for the region under study which was shown to be vulnerable to climate change. [ABSTRACT FROM AUTHOR]