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Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future. [ABSTRACT FROM AUTHOR]

This paper presents a validation of outputs from some GCMs of the CMIP6 project when used to assess climate projection and hydrological flows at a catchment scale for the case study area of the Lombardy region (Northern Italy). The modeling chain consists of (i) a choice of climatic scenarios from 10 GCMs of the CMIP6, (ii) the application of a stochastic downscaling procedure to make projections usable at the local scale, and (iii) the use of a semi-distributed physically based hydrological model Poli-Hydro for the generation of hydrological scenarios. Data on observed precipitation and temperature were collected from automatic weather stations, and the hydrological budget of four target catchments within the study area was assessed using Poli-Hydro. An ex-post (back-casting) analysis was performed upon the control data series from the GCMs by comparing statistics of relevant climate variables and model-simulated discharges against observed counterparts during the historical period 2002–2014. Then, during 2015–2021, the goodness of projections was assessed using confidence intervals. Our results show that the accuracy of GCMs in representing regional climate is not always reflected in a credible evaluation of local hydrology. The validation of climate patterns provides somewhat poor results; thus, the interaction among climate and hydrology needs to be explored carefully to warrant the credibility of hydrological scenarios. Overall, the spatial and temporal consistency of GCM projections, as explored here climatically and hydrologically, provides a clue about their dependability for basin scale management. [ABSTRACT FROM AUTHOR]

In the midsummer of 2022, a rare regional high-temperature event occurred in the Yangtze River Basin. Chongqing, as the center of this event, also experienced its strongest high temperature since 1961. In this paper, the characteristics of this event in Chongqing are reviewed in detail and compared with those of 2006. We have confirmed the following conclusions with detailed data: (1) In the midsummer of 2022, the number of days that the daily maximum temperature is exceeding 35 ℃ in Chongqing was the most since 1961, with the number of days that the daily maximum temperature above 40 ℃ increasing significantly compared with 2006. (2) The impact range of this event in Chongqing was quite extensive, especially the range with the daily maximum temperature exceeding 40 ℃ was the widest since 1961. (3) The high-temperature event was more extreme in 2022 than that in 2006. The maximum of daily maximum temperature was 45 ℃, and this is the first time that China has recorded a high temperature of 45 ℃ outside Turpan. There were 5 stations with daily maximum temperature exceeding 44 ℃, accounting for 1/3 of the number of stations with such temperature (15 stations) in China. (4) In the midsummer of 2022, Chongqing was dominated by the compound high-temperature day. Compared with 2006, the daytime high-temperature day decreased significantly, while the compound one increased obviously. [ABSTRACT FROM AUTHOR]

The utilization of regional climate methods (RCMs) to predict future climate is an important study under the changing environment. The primary objective of the paper is to correct the temperature and precipitation simulations for the period of 1980–2005 and 2026–2098 in the Wei River Basin (WRB), to evaluate the performance of RCMs for the period of 1980–2005, and further, to analyze the future changes of projected temperature and precipitation during 2026–2098. In this paper, the linear scaling method was used to correct the temperature simulations. Quantile mapping, local intensity scaling method, and hybrid method were used to correct the precipitation simulations. The future changes of projected temperature and precipitation for the near term (2026–2050), mid-term (2051–2075), and far term (2076–2098), relative to the period of 1980–2005, were investigated under RCP 2.6 and RCP 8.5. Results indicate that (1) the temperature biases were either warm or cold in the spatial scale, and the precipitation wet biases were detected. After correction, HadGEM2-ES driven by RegCM4-4 had the best temperature reproducibility, and NCC-NorESM1-M driven by RegCM4-4 had the best precipitation reproducibility. (2) Under RCP 2.6, the projected annual, winter, and spring temperature showed decreasing trends. The temperature was higher than that for the period of 1980–2005 except for the spring temperature decreases in the Beiluo River Basin. Under RCP 8.5, the temperature showed significantly increasing trends. The temperature for the near term was similar to that of the period of 1980–2005, while the temperature increased significantly for the mid-term and far term. (3) Under RCP 2.6, the precipitation had decreasing trends. Under RCP 8.5, precipitation trends were also spatially distributed. The relative deviation of winter precipitation was the largest. Relative to the period of 1980–2005, the light- and moderate-rain days showed little change for the period of 2026–2098, while the extreme-rain days showed significantly increasing trends. [ABSTRACT FROM AUTHOR]

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]

Drought is a natural, global and recurring phenomenon caused by climatic anomalies and inevitable meteorological changes. Lake Urmia in northwestern Iran has been drying up in recent years mainly because of its management and operations. On the other hand, with decreasing rainfall, the process of the lake drying up has intensified and affected different aspects of people's lives, especially farmers. In this paper, regional drought was studied in the Urmia Lake Basin from an agricultural perspective in the period of 2001 to 2021 using different vegetation indices such as the Enhanced Vegetation Index (EVI), the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Temperature Vegetation Dryness Index (TVDI) as well as by applying the temperature product and vegetation cover product of the MODIS sensor of the Terra satellite. The efficiency of the methods was evaluated. The results of the linear correlation between drought indices and cumulative precipitation values during 1 month, 2 months, 3 months, 5 months and 7 months before the acquisition of the image showed that the correlation of indices, especially of the TVDINDVI index with the cumulative rainfall of 4 months, showed that the precipitation is more frequent than with other time scales due to the delayed response of vegetation to rainfall. From 2001 to 2020, it was observed that the severity of annual drought decreased according to TVDI values. The area with heavy drought decreased from 4.45 to 1.8% and medium drought decreased from 43.49 to 33%. Also, the correlation coefficient between other vegetation indices and temperature vegetation drought indices (TVDINDVI) showed that TCI and TVDI values were negatively (r = −0.73) correlated. Generally, by observing the calculated TVDI maps, it was seen that this basin has not encountered heavy drought so far. The results showed that the Urmia Lake Basin experienced long-term light and medium drought conditions. On the other hand, by calculating the NDWI index, changes in the area of Lake Urmia were monitored and it was found that the lake surface started to decrease beginning in 2005 and reached its lowest level in 2015, which corresponds to the other droughts that occurred. Finally, by conducting a trend analysis of TVDI, it was observed that there is no specific trend in the drought time series so it can be seen that drought may be one of the causes of a sharp decline in the lake's water level, but not the only factor. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) governs the exchange of energy and water between the atmosphere and land surface. In situ measurements of SM are uneven in Iran. This knowledge gap can be filled using satellite- and model-based products. This study assessed the performance of SM products, including Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR2), and Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) against in situ observations considering the influence of soil texture, climate, and land cover over Lake Urmia Basin, which is the largest salt lake in Iran and the Middle East. In situ SM was measured over Lake Urmia Basin in the morning and afternoon using the time domain reflectometry (TDR) and oven drying and weighing techniques. Five statistical indicators, including correlation (R), absolute correlation (R(abs)), bias, root mean square error (RMSE), and unbiased root mean square error (ubRMSE), were applied. C-band AMSR2 products showed the best performance in grassland and croplands with the highest absolute correlation (0.63) and lowest average bias (−0.01). Among soil textures, SM products performed better in clay soils with the highest absolute correlation between C-band AMSR2 products and in situ observations (0.64) and low average bias and RMSE. Analyzing data based on climate, AMSR2 C1, and GLDAS products with the lowest average RMSE (0.08 m3m−3) and bias (0.01) and AMSR2 C2 with the absolute correlation of 0.6 showed the best performance in both temperate (Csa) and cold (Dsa) climate classes. For all classifications (land cover, soil texture, climate divisions), SMAP products reported the lowest average value of ubRMSE (0.03 m3m−3). The major contribution of the paper is finding the best SM products that can fill the gap in SM measurements data in Lake Urmia. In this analysis, the impacts of land cover, climate, and soil texture on the performance of products were considered. [ABSTRACT FROM AUTHOR]

Estimates of probable maximum precipitation (PMP) and corresponding probable maximum flood (PMF) are necessary for planning, design, and risk assessment of flood control structures whose failure could have catastrophic consequences. For PMP estimation, multifractal approach (MA) is deemed to be better than conventional approaches, which are based either on statistical concepts or physical aspects. The MA yields physically meaningful PMP estimates by attempting to capture scale-invariant multiplicative cascade mechanism inherent in rainfall. This paper attempts to gain insights into the performance of MA by comparing PMP estimates obtained using the approach with those resulting from the use of two widely used empirical approaches (storm maximization approach (SMA) and Hershfield method (HM)) on two flood-prone river basins (Mahanadi and Godavari) in India. The results indicate that rainfall data of the two river basins exhibit multifractal properties, and the use of MA has an advantage over HM and SMA in estimating PMP corresponding to longer durations (>3 days). PMP estimates obtained using HM are generally lower (higher) than those obtained using SMA for 1-day (higher) duration. PMP maps are prepared for the two Indian river basins corresponding to 1-day to 5-day durations. Further, PMP estimates obtained based on the PMP maps are provided for 18 catchments in the Mahanadi basin and 53 catchments in the Godavari river basin. [ABSTRACT FROM AUTHOR]

Research has shown that the climate in northwest China has turned to warm-wet in recent decades. Does this mean that the entire northwest of China has become wet in the last few decades? In the context of the climate transition in northwest China, this paper investigates whether the Bosten Lake Basin (BLB) located in southern Xinjiang has been dry or wet in the last 30 years. We used the Standardized Precipitation Evapotranspiration Index (SPEI) and Temperature Vegetation Drought Index (TVDI) to investigate these changes. The results show that although the dry-wet changes indicated by the SPEI and TVDI exhibited slight spatiotemporal differences, the entire BLB reflected a tendency to be dry. That is, the BLB has not become wet during the climate transition in northwest China, but is developing a trend toward aridification. The main conclusions of this study are as follows: (1) The SPEI indicated a drying trend in seasonal and interannual variations in plains; in mountains, except in spring and summer it has a drying trend, too. (2) The TVDI showed that although the drying trend in summer and winter was weaker than that in spring and autumn in the BLB, where some regions represented a significant tendency toward wetness owing to seasonal or annual changes, the entire study area has still become more dry. [ABSTRACT FROM AUTHOR]

Ensemble Prediction Systems (EPSs) are increasingly applied for rainfall forecasts and flooding warning systems. In this paper, these forecasts and their skills are evaluated through relevant criteria, particularly by considering forecast performances for different lead times. Furthermore, to enhance their performance, we propose to preprocess the EPS forecasts' output using bias correction methods. For this aim, forecasts for different ranges of precipitation as well as various climatic conditions are evaluated, which is particularly important for extreme events that can lead to flooding. The Karun River basin in Iran is used as case study, a large area including various climate conditions. The results showed that the performance of European Center for Medium-Range Weather Forecasts (ECMWF) forecasts vary with sub-basin properties, e.g., area and skewness of daily precipitation, and vary across dry to humid regions, between flooding and non-flooding seasons, and different lead times and show the effect of different methods for bias correction on the forecast skill. The forecast skill is decreasing from wet regions towards dry regions and the bias correction was more effective in the flooding season, for which the skill was increased by 40% based on continuous ranked probability skill score (CRPSS). When the precipitation thresholds were increased towards extreme values, the forecast performance of ECMWF became better. [ABSTRACT FROM AUTHOR]

Characterizing the response of temperature variables to agricultural irrigation is expected to be an important challenge for understanding the full impact of water management on regional climate change. In this paper, the trend analysis and abrupt change test were applied to detect the global warming effect. Then, the quantitative irrigation-induced cooling effects on temperature variables between April and August from 1970 to 2010 in the Lhasa River basin were estimated using historical time series of gridded meteorological records and a map of the area equipped for irrigation. Trends in the maximum temperature (Tmax) were statistically positive, and a significant increasing trend for the minimum temperature (Tmin) was detected at the 0.01 and 0.05 confidence levels. No abrupt changing point of warming was detected in the time series for Tmax. The abrupt changes in Tmin in the irrigation concentration period took place in 1995, 5 years later than the corresponding change in April. Affected by global warming, the increase in temperature was the largest in July and August, when the irrigation-induced cooling effect was also the most significant. The irrigation-induced cooling effect for Tmax and Tmin in April–August (except for June) ranged from − 0.017 to − 0.009 °C/decade and from − 0.011 to − 0.001 °C/decade, respectively, and the cooling effect for diurnal temperature range (DTR) ranged from − 0.011 to 0 °C/decade. The cooling effect on temperature reached above 0.01 °C in July and August, but for the growing seasons, the effect was weak, only 0.001 °C. The Tmax and Tmin trends during the whole growing seasons decreased by both 0.002 °C/decade, respectively, with a 10% increase in irrigation land proportion. Even in July and August, the trends were expected to decrease by about 0.005 °C/decade with a 10% increase in irrigation land proportion. The irrigation-induced cooling effect could partially slow global warming. [ABSTRACT FROM AUTHOR]

Floods are considered as natural hazards. The parameter estimation of hydrologic methods is time-consuming in the flood routing of multiple inflows river systems. This paper presents the application of gene expression programming (GEP) and artificial neural network (ANN) as alternative approaches to predict the outflow hydrograph in downstream of multiple inflows systems. GEP and ANN models were compared with the equivalent Muskingum inflow model. The Gharesoo River Basin as a multiple inflows river system was applied for the calibration and verification phases of these models. The GEP obtained the formula as a function of the inflow branches based on the fitting data for simulating the outflow hydrograph of the multiple inflows system. GEP and ANN models investigated inflow hydrographs at different time steps. The obtained outflow hydrograph by the GEP model indicated an excellent performance compared with ANN and equivalent Muskingum inflow models in the case involving multiple inflows system. [ABSTRACT FROM AUTHOR]

Global analyses show that climate change has already increased the frequency and strength of extreme weather events. The observed and remote-sensed precipitation productions have been deeply studied considering the stationary and non-stationary characteristics of extreme series. However, as these studies largely addressed the evolution of precipitation extremes at a single station, the consistency risk of extremes at multiple stations within close days has been little researched. In this paper, a multivariate frequency analysis method is proposed to reveal the exceeding and coincidence probabilities of the most dependent extreme precipitation series. First, a generalized extreme distribution, which has proved better at describing the tail behavior of extreme series, is developed to simulate the marginal distribution of rainfall extremes. Then, a mixed von Mises distribution is introduced to fit the occurrence time interval of the extreme events. Taking advantage of its flexibility in choosing arbitrary marginals, a copula is constructed to implement a joint probability analysis of extreme precipitation events at multiple stations. The upper reaches of the Yangtze River, where frequent heavy rains are the main source of the regional floods, were selected as a typical research area. Five hundred twenty-eight copula models were finally constructed by arbitrarily choosing two precipitation extreme series among the total 33 meteorological stations. The results revealed the risk of extreme rainfall occurrence once in 2, 5, 10, 20, 50, or 100 years, within a time interval of 1–90 days. The research has great significance for understanding complicated extreme events and is helpful for the disaster emergency response and management. [ABSTRACT FROM AUTHOR]

This paper studied hydrological drought identified by the Streamflow Drought Index (SDI) at eight river-gauging stations in the Eastern Mediterranean part of Turkey for a roughly 43-year period covering from 1972 to 2014 (4 stations), from 1973 to 2015 (2 stations), and from 1969 to 2011 (2 stations). Results of SDI analyses indicate that the number of drought years was highest during the 3-month October–December period. A considerable part of the drought years was determined to be mild drought. Although moderate drought conditions were observed in all stations, moderate drought conditions were determined especially at Göksu-H in the Seyhan River Basin and Söğütlü and Savrun in the Ceyhan River Basin. Short-term fluctuations of dry and wet conditions are the dominant pattern for most of the stations; however, long-term persistent drought conditions are also prominent, especially at Tacin and Körkün in Seyhan Basin and Göksun in Ceyhan Basin. The starting point for the negative trend in the 12-month series was the years 1996, 1999, and 2000. [ABSTRACT FROM AUTHOR]

Investigation of precipitation characteristics on daily, monthly, and annual time scales can contribute to gaining important information related to temporal and spatial distribution of precipitation or even flow rate challenges (e.g., hydrological droughts). The low levels of long-term precipitation and high variability in different time scales are considered the main inherent characteristics of climate in Iran. Due to the direct effects of precipitation on water resources, especially on the river flow rate, it is necessary to assess the efficient indices to visualize the variations in the components of water resources. One of the main indices is the precipitation concentration index (PCI) which is known as a strong indicator of the precipitation distribution generally used on annual and seasonal scales. In this study, drought analysis in the Lake Urmia Basin (LUB) located in northwest of Iran was performed with the daily river flow rate and monthly precipitation values within the period of 1984–2013. The results of changes in precipitation indicated that the irregularity of precipitation distribution had grown in spring months. Also, due to the diminishing precipitation trend on the annual time scale, PCI index also increased. It is concluded that LUB detected a significant descending trend on the annual, spring, and winter time scales in the last 30 years. The PCI values were proved high irregularity in summer with PCI amount of 20.1 and most regularity in winter with PCI amount of 10.4. This paper also aims to assess the effects of PCI on the river flow rate along with the flow shortness volume values using hydrometric and rain gauge stations within LUB. The results obtained from the changes in river flow rate and flow shortness volume revealed that the river flow rate has mostly a falling trend. Finally, it was observed that the time when the river flow rate data changed happened after beginning of changes in the precipitation data. A decrease in inflow from 900 million cubic meters up to 14 billion cubic meters with high flow shortness volume may happen in worst conditions. These results highlighted the importance of applying water resources management in LUB. [ABSTRACT FROM AUTHOR]

The focus of this paper was to investigate the spatial and temporal variability of dry and wet events using the standard precipitation and evapotranspiration index (SPEI) in the Tana River Basin (TRB) in Kenya. The SPEI is a new drought index which incorporates the effect of evapotranspiration on drought analysis thus making it possible to identify changes in water demand in the context of global warming. The SPEI was computed at 6- and 12-month timescales using a 54-year long monthly rainfall data from the Global Precipitation and Climate Center (GPCC) and temperature data from the Climate Research Unit (CRU) both recorded between 1960 and 2013. Both datasets have a spatial resolution of 0.5° by 0.5° and were extracted for every grid point in the basin. The SPEI was used to assess the temporal and spatial evolution of dry and wet events as well as determine their duration, severity, and intensity. The evolution of significant historical dry and wet events and the frequency of occurrence were clearly identified. The index showed that the period between 1960 and 1980 was dominated by dry events while wet events were dominant in the period between 1990 and 2000. The SPEI6 had the longest duration of dry events of 30 months and severity of 44.67 which was observed at grid 5while the highest intensity was 2.18 observed at grid 31. Grid 19 had the longest duration (52 months) and highest severity (88.08) of dry events for SPEI12 and the intensity was highest (1.94) in grid 31. The longest duration (23) and highest severity (40.03) of wet events for SPEI6 were recorded in grid 39. The highest intensity of wet events for SPEI6 was 1.91 at grid 23 and 1.81 at grid 37 for SPEI12. The principal component analysis (PCA) was applied to the SPEI time series in order to assess the spatial pattern of variability of the dry and wet events in the basin. The PCA showed that there were two leading components which explained over 80% of the spatial variation of dry and wet events in the basin. Further, the continuous wavelet transform (CWT) was applied to the PCA scores in order to capture the time-frequency dynamics. The wavelet transform of the SPEI6 and SPEI12 identified significant periodicities of 1 to 2 years across the spectrum. [ABSTRACT FROM AUTHOR]

The statistical characteristics of precipitation play an extremely important role in the risk assessment of drought and flood disasters and water resource management. In this paper, the precipitation concentration degree (PCD), precipitation concentration period (PCP) and precipitation concentration index (PCI) were used to analyse the spatiotemporal variation in precipitation concentration characteristics in China from 1960 to 2015. In addition, the cross-wavelet transform was used to analyse the possible dependencies and spatial characteristics between these three precipitation concentration indices (PCD, PCP and PCI) and monsoon indices (East Asian summer monsoon index, EASMI; South Asian summer monsoon index, SASMI; and South China Sea summer monsoon index, SCSSMI) of nine river basins in China. The results show that the spatial distribution of precipitation concentration indices in China has obvious north-south and east-west variability. China's precipitation concentration indices are generally declining, with weak volatility. Among these indices, the PCD (P < 0.05) and PCI (P < 0.01) have decreased significantly at rates of − 0.005/10 year and − 0.006/10 year, respectively, while there has been no obvious decrease in the PCP. Compared with the PCD before 1978, the PCD in the rainy season after 1978 has decreased. In most areas, the rainy season is concentrated in July every year; however, the rainy season has been delayed in the middle and lower reaches of the Yangtze River and has advanced mainly in the other basins. The PCI has declined since 1978, indicating a decreasing contribution of heavy precipitation to the total annual precipitation, but this change in the PCI has occurred mainly at high-value stations (PCI > 0.7), and the PCI at low-value stations has increased. Most of China's precipitation concentration indices and monsoon indices, as well as the NWRB, have small-scale (2–4 years) or medium-scale (12–15 years) oscillation periods. The oscillation periods between the precipitation concentration indices and monsoon indices in the Yangtze and Pearl River basins are more significant than those in other basins. The results of this study can help to understand the differences among the precipitation concentration characteristics in different basins in China and the intrinsic relationship between these characteristics and the summer monsoon and provide a reference for further research. [ABSTRACT FROM AUTHOR]

This paper studies the Upper Tigris River (UTR) drainage basin in Turkey for climate change impacted runoff estimations. Statistical downscaling method (SDM) is used by taking into consideration spatial dependence function (SDF) for the scenario precipitation projections at a set of available meteorology stations. Temporal adjustment between the climate scenarios and precipitation record time series is achieved by the white Markov (WM) stochastic process. Although various climate research center scenario data are considered, herein, only the general circulation model (GCM) A2 scenario data are adapted from the Hadley Center, England. The precipitation and runoff results are presented in decadal groups starting from 2001 to 2050 as cumulative monthly precipitation (CMP) and cumulative monthly runoff (CMR) graphs. It is observed that after 2021, precipitation decreases at about 12.5% and after 2030, it is 26%. Runoff projections indicate that they may decrease at about 30% especial after 2040. [ABSTRACT FROM AUTHOR]

This study presents the precipitation changes of the Heihe River Basin (HRB), Northwest China, using a long-term (1980–2014) and highly resolved modeling (up to 3 km by 3 km) with the Regional Integrated Environment Modeling System (RIEMS) forced by ERA-Interim reanalysis data. Firstly, the added value of high-resolution RIEMS simulation nested with ERA-Interim reanalysis was presented by comparisons among the RIEMS simulation results, site-based ground measurements, satellite-retrieved precipitation from TRMM 3B43 (V7), and the EAR-interim reanalysis data. The bias of the ERA-Interim reanalysis was largely corrected by the RIEMS simulation, although a little bias existed in the RIEMS simulation. RIEMS simulation showed that there was an increasing trend in the precipitation of the HRB from 1980 to 2014 with a rate of increase of approximately 3%/year for summer and 5%/year for autumn in the southeast part of the HRB. The increased precipitation mainly resulted from more precipitation days and the strengthening of daily precipitation. Upward motion from strengthened convergence over the eastern part of the HRB was the common reason for increased precipitation in summer and autumn, which was partly offset by decreased water vapor supply in summer while it was strengthened by increased water vapor supply in autumn. This paper presents precipitation changes for the HRB associated with global warming since 1980 and provides a higher-resolution climatological data set useful for reference climate impact studies. [ABSTRACT FROM AUTHOR]

In this study, region-of-influence (ROI) approach was used for modeling extreme values of 24-h maximum rainfall using seven groups of defined attributes including climatic, geographical, and statistical attributes and their hybrids. According to the degree of importance and the role of each defined attribute, different weighting scenarios were defined for each of the attributes relating to the corresponding group. The goal of this paper is to select the best weighting scenario for attributes in the ROI model for estimating more accurate and reliable quantiles of 24-h maximum rainfall over Urmia Lake Basin. The investigation of 140 pooling groups built with the ROI approach showed that each of the seven groups of defined attributes has a different performance in the estimation of quantiles. Moreover, results showed that, in most cases, performance of the models with weighted attributes was better than that of the non-weighted ones. Among the seven categories, the statistical attributes and their combination with climatic and geographical attributes had the best estimation of quantiles in terms of low relative error. In addition, results showed that skewness plays an effective role in the estimation of quantiles. In general, it can be concluded that by applying appropriate weights for available attributes in regions without data, the acceptable and reliable results of extreme values can be earned at ungauged stations. Furthermore, results showed that the use of median of metric distance as one of the threshold options can obtain better results in the estimation of quantiles. [ABSTRACT FROM AUTHOR]

In this paper, we sought to investigate the spatial and temporal variability of the global sea surface temperature in the 1884–2014 period, through wavelet analysis. Results for tropical, subtropical, and extratropical areas indicate important oscillations in the 1–12-month and 1–2-, 2–4-, 4–8-, and 8–12-year spectral ranges, as obtained in other studies which used regional mean SST values instead of global gridpoint data with 2.5o resolution, as used in this study. The intraseasonal (1–12 months) and interannual (1–2 years) oscillations are much more evident in the tropical latitudes. On the other hand, lower frequency oscillations (2–4, 4–8, and 8–12 years) are more dominant away from tropical regions. The faster oscillation scale (1–12-month and 1–2- and 2–4-year spectral ranges) SST variance presents negative trends throughout the entire period, while the values associated with the slower oscillation scales (4–8- and 8–12-year spectral ranges) present increasing trends. The reversal of trends for distinct SST spectral ranges over the whole period suggests energy exchanges between distinct oscillatory phenomena. In addition, the energy increase of slower frequencies suggests the prolonged settlement of low-frequency climatic patterns in the future, providing more inertial climatic patterns. The strongest diminishing values of SST variance associated with the higher frequency oscillations were observed in the Atlantic oceanic basins. The strongest increasing values for the lower oscillations, mainly related to the 2–4- and 4–8-year spectral ranges, were observed in the Pacific basins. In general, the Pacific basins are closely correlated with the global mean (0.55 ≤ r ≤ 0.92), compared with the Atlantic (0.31 ≤ r ≤ 0.75) and Indian (0.12 ≤ r ≤ 0.56) basins. [ABSTRACT FROM AUTHOR]

The Arabian Sea basin represents a minor component of global total cyclones annually and has not featured so prominently in cyclone research compared with other basins where greater numbers of cyclones are registered each year. This paper presents the results of exploratory analysis of various features of cyclone tracks in the Arabian Sea, with a particular focus on examining their temporal and spatial patterns. Track morphometry also reveals further information on track shape. The study indicates how cyclones spawned during May in the early pre-monsoon period (often strong events) have a tendency to follow more sinuous tracks, whereas cyclones occurring in October in the post-monsoon period tend to follow straighter tracks. Track sinuosity is significantly related to other attributes, including cyclone longevity and intensity. Comparisons are also drawn between the general characteristics of cyclone tracks in the Arabian Sea and other ocean basins, suggesting how the size and geography of the Arabian Sea basin exert influences on these characteristics. [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]

Climate variability and human activities are two main contributing attributions for runoff changes in the Yellow River, China. In the loess hilly-gully regions of the middle Yellow River, water shortage has been a serious problem, and this results in large-scale constructions of soil and water conservation (SWC) measures in the past decades in order to retain water for agricultural irrigation and industrial production. This disturbed the natural runoff characteristics. In this paper, we focused on a typical loess hilly-gully region (Wudinghe and Luhe River basins) and investigated the effects of SWC measures and climate variability on runoff during the period of 1961-2013, while the SWC measures were the main representative of human activities in this region. The nonparametric Mann-Kendall test was used to analyze the changes of annual precipitation, air temperature, potential evapotranspiration (PET), and runoff. The analysis revealed the decrease in precipitation, significant rise in temperature, and remarkable runoff reduction with a rate of more than 0.4 mm per year. It was found that runoff capacity in this region also decreased. Using the change point detection methods, the abrupt change point of annual runoff series was found at 1970, and thus, the study period was divided into the baseline period (1961-1970) and changed period (1971-2013). A conceptual framework based on four statistical runoff methods was used for attribution analysis of runoff decline in the Wudinghe and Luhe River basins (−37.3 and −56.4%, respectively). Results showed that runoff reduction can be explained by 85.2-90.3% (83.3-85.7%) with the SWC measures in the Wudinghe (Luhe) River basin while the remaining proportions were caused by climate variability. The findings suggested that the large-scale SWC measures demonstrated a dominant influence on runoff decline, and the change of precipitation extreme was also a promoting factor of the upward trending of SWC measures' contribution to runoff decline. This study enhances our understanding of runoff changes caused by SWC measures and climate variability in the typical semiarid region of Loess Plateau, China. [ABSTRACT FROM AUTHOR]

B Correction to: Theoretical and Applied Climatology b https://doi.org/10.1007/s00704-022-04168-4 The affiliation of one of the authors (Pranaya Diwate) has been wrongly mentioned in the paper. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

Haors are large, round-shaped floodplain depressions located in the North-Eastern region of Bangladesh. Extreme events such as heavy rainfall routinely affect the haor basin with flash floods. These haors are predicted to experience severe stress because of changes in rainfall and temperature patterns. The biotic community of the wetlands may not have enough time to adjust itself in such varying temperature and rainfall extremes. This paper evaluates various aspects of the future projections of rainfall and temperature extremes, including magnitudes and frequencies thereof. The impacts of extreme events are examined using Hadley Centre's high-resolution regional climate model known as PRECIS (Providing REgional Climates for Impact Studies). Daily temperature and rainfall simulations of the 17-member ensembles are generated through Hadley Centre Coupled Model (HadCM3). These simulations are used in Rclimdex-a software specially designed for this study. A total of 12 core climate indices are computed, analyzed, and statistically examined (Mann-Whitney U test) over the space of three time slices-(1) short (2020s, i.e., 2011-2040), (2) medium (2050s, i.e., 2041-2070), and (3) long (2080s, i.e., 2071-2098). Here, the 1980s (1971-2000) are considered as the baseline period. The study has found that the highest significant variability in both rainfalls and temperatures was during the pre-monsoon season when flash floods normally occur. Also, rainy days are projected to be less frequent albeit more intense where the deeply flooded haors are located. Though the annual total rainfall does not show any difference in spatial distribution (except for in magnitude), the seasonal patterns of most extreme events show that the probable affected areas have shifted from North-east to further North. In addition, a significant increase in both RX1 (1-day maximum rainfall) and RX5 (5-day maximum rainfall) are projected during the 2080's pre-monsoon season near Sunamganj. This projection also indicates the possible frequent occurrence of flash floods with high volumes. Probability distribution frequencies (PDF) show a rightward shift in time indicating an increase in the amount of total rainfall in the future. Exceptions are, however, found in case of PDFs for consecutive dry days (CDD) and consecutive wet days (CWD). The decrease in CWD is found to be more pronounced than that of CDD. All these projections made in this study are expected to contribute further in the advancements of the Master Planning of the haor area that was done by the government of Bangladesh in 2012. [ABSTRACT FROM AUTHOR]

Drought is a complex natural hazard that is poorly understood and difficult to assess. This paper describes a VIC-PDSI model approach to understanding drought in which the Variable Infiltration Capacity (VIC) Model was combined with the Palmer Drought Severity Index (PDSI). Simulated results obtained using the VIC model were used to replace the output of the more conventional two-layer bucket-type model for hydrological accounting, and a two-class-based procedure for calibrating the characteristic climate coefficient ( K) was introduced to allow for a more reliable computation of the PDSI. The VIC-PDSI model was used in conjunction with GIS technology to create a new drought assessment index (DAI) that provides a comprehensive overview of drought duration, intensity, frequency, and spatial extent. This new index was applied to drought hazard assessment across six subregions of the whole Loess Plateau. The results show that the DAI over the whole Loess Plateau ranged between 11 and 26 (the greater value of the DAI means the more severe of the drought hazard level). The drought hazards in the upper reaches of Yellow River were more severe than that in the middle reaches. The drought prone regions over the study area were mainly concentrated in Inner Mongolian small rivers, Zuli and Qingshui Rivers basin, while the drought hazards in the drainage area between Hekouzhen-Longmen and Weihe River basin were relatively mild during 1971-2010. The most serious drought vulnerabilities were associated with the area around Lanzhou, Zhongning, and Yinchuan, where the development of water-saving irrigation is the most direct and effective way to defend against and reduce losses from drought. For the relatively humid regions, it will be necessary to establish the rainwater harvesting systems, which could help to relieve the risk of water shortage and guarantee regional food security. Due to the DAI considers the multiple characteristic of drought duration, intensity, frequency, and spatial extent, and because it is based on the VIC-PDSI model and GIS technologies, the DAI could provide some new way on directly comparing the drought hazards over different regions during a long-term period. The result of this study may be useful to decision makers when formulating drought management policies to alleviate the risk of water shortages and guarantee regional food security. [ABSTRACT FROM AUTHOR]

Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km. The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes. [ABSTRACT FROM AUTHOR]

Alpine and Mediterranean areas are undergoing a profound change in the typology and distribution of rainfall. In particular, there has been an increase in consecutive non-rainy days, and an escalation of extreme rainy events. The climatic characteristic of extreme precipitations over short-term intervals is an object of study in the watershed of Lake Maggiore, the second largest freshwater basin in Italy (located in the north-west of the country) and an important resource for tourism, fishing and commercial flower growing. The historical extreme rainfall series with high-resolution from 5 to 45 min and above: 1, 2, 3, 6, 12 and 24 h collected at different gauges located at representative sites in the watershed of Lake Maggiore, have been computed to perform regional frequency analysis of annual maxima precipitation based on the L-moments approach, and to produce growth curves for different return-period rainfall events. Because of different rainfall-generating mechanisms in the watershed of Lake Maggiore such as elevation, no single parent distribution could be found for the entire study area. This paper concerns an investigation designed to give a first view of the temporal change and evolution of annual maxima precipitation, focusing particularly on both heavy and extreme events recorded at time intervals ranging from few minutes to 24 h and also to create and develop an extreme storm precipitation database, starting from historical sub-daily precipitation series distributed over the territory. There have been two-part changes in extreme rainfall events occurrence in the last 23 years from 1987 to 2009. Little change is observed in 720 min and 24-h precipitations, but the change seen in 5, 10, 15, 20, 30, 45, 60, 120, 180 and 360 min events is significant. In fact, during the 2000s, growth curves have flattened and annual maxima have decreased. [ABSTRACT FROM AUTHOR]

This paper tried to reconstruct the time series (TS) of monthly average temperature (MAT), monthly accumulated precipitation (MAP), and monthly accumulated runoff (MAR) during 1901-1960 in the Kaidu River Basin using the Delta method and the three-layered feed forward neural network with backpropagation algorithm (TLBP-FFNN) model. Uncertainties in the reconstruction of hydrometeorological parameters were also discussed. Available monthly observed hydrometeorological data covering the period 1961-2000 from the Kaidu River Basin, the monthly observed meteorological data from three stations in Central Asia, monthly grid climatic data from the Climatic Research Unit (CRU), and Coupled Model Intercomparison Project Phase 3 (CMIP3) dataset covering the period 1901-2000 were used for the reconstruction. It was found that the Delta method performed very well for calibrated and verified MAT in the Kaidu River Basin based on the monthly observed meteorological data from Central Asia, the monthly grid climatic data from CRU, and the CMIP3 dataset from 1961 to 2000. Although calibration and verification of MAP did not perform as well as MAT, MAP at Bayinbuluke station, an alpine meteorological station, showed a satisfactory result based on the data from CRU and CMIP3, indicating that the Delta method can be applied to reconstruct MAT in the Kaidu River Basin on the basis of the selected three data sources and MAP in the mountain area based on CRU and CMIP3. MAR at Dashankou station, a hydrological gauge station on the verge of the Tianshan Mountains, from 1961 to 2000 was well calibrated and verified using the TLBP-FFNN model with structure (8,1,1) by taking MAT and MAP of four meteorological stations from observation; CRU and CMIP3 data, respectively, as inputs; and the model was expanded to reconstruct TS during 1901-1960. While the characteristics of annual periodicity were depicted well by the TS of MAT, MAP, and MAR reconstructed over the target stations during the period 1901-1960, different high frequency signals were captured also. The annual average temperature (AAT) show a significant increasing trend during the 20th century, but annual accumulated precipitation (AAP) and annual accumulated runoff (AAR) do not. Although some uncertainties exist in the hydrometeorological reconstruction, this work should provide a viable reference for studying long-term change of climate and water resources as well as risk assessment of flood and drought in the Kaidu River Basin, a region of fast economic development. [ABSTRACT FROM AUTHOR]

Using statistical methods and contingency table method, this paper evaluates the accuracy of 12 years (1998-2009) Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) daily-accumulated precipitation products within a year, the dry season, and rain season for each of the five subbasins and for each grid point (0.25 × 0.25°) in the Lancang River basin by comparing the results with data from the 35 rain gauges. The results indicate that TMPA daily precipitation estimates tend to show an underestimation comparing to the rain gauge daily precipitations under any scenarios, especially for the middle stream in the dry season. The accuracy of TMPA-averaged precipitation deteriorates with the increase of elevation at both basin and grid scale, with upstream and downstream having the worst and best accuracy, respectively. A fair capability was shown when using daily TMPA accumulations to detect rain events at drizzle rain and this capability improves with the increase of elevation. However, the capability deteriorates when it is used to detect moderate rain and heavy rain events. The accuracy of TMPA precipitation estimate products is better in the rain season than in the dry season at all scenarios. Time difference and elevation are the main factors that have impact on the accuracy of TMPA daily-accumulated precipitation products. [ABSTRACT FROM AUTHOR]

Hydrological processes depend directly on climate conditions [e.g., precipitation, potential evapotranspiration ( PE)] based on the water balance. This paper examines streamflow datasets at four hydrological stations and meteorological observations at 79 weather stations to reveal the streamflow changes and underlying drivers in four typical watersheds (Meigang, Saitang, Gaosha, and Xiashan) within Poyang Lake Basin from 1961 to 2000. Most of the less than 90th percentile of daily streamflow in each watershed increases significantly at different rates. As an important indicator of the seasonal changes in the streamflow, CT (the timing of the mass center of the streamflow) in each watershed shows a negligible change. The annual streamflow in each watershed increases at different rates, with a statistically significant trend (at the 5 % level) of 9.87 and 7.72 mm year, respectively, in Meigang and Gaosha watersheds. Given the existence of interactions between precipitation and PE, the original climate elasticity of streamflow can not reflect the relationship of streamflow with precipitation and PE effectively. We modify this method and find the modified climate elasticity to be more accurate and reasonable using the correlation analysis. The analyses from the modified climate elasticity in the four watersheds show that a 10 % increase (decrease) in precipitation will increase (decrease) the annual streamflow by 14.1-16.3 %, while a 10 % increase (decrease) in PE will decrease (increase) the annual streamflow by −10.2 to −2.1 %. In addition, the modified climate elasticity is applied to estimate the contribution of annual precipitation and PE to the increasing annual streamflow in each watershed over the past 40 years. Our result suggests that the percentage attribution of the increasing precipitation is more than 59 % and the decreasing in PE is less than 41 %, indicating that the increasing precipitation is the major driving factor for the annual streamflow increase for each watershed. [ABSTRACT FROM AUTHOR]

Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046-2065 and 2081-2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T and T for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081-2100. [ABSTRACT FROM AUTHOR]

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures ( T and T) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948-2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001-2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T and T for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method. [ABSTRACT FROM AUTHOR]

The Inner Mongolia grassland of China is representative of semi-arid grasslands in temperate zones. Studying land surface processes in this region will improve the understanding of regional climate formation and the feedback with global climate change. Satellite remote sensing provides an excellent opportunity to study land-atmosphere interactions at the regional scale. It is necessary to develop feasible and reasonable remote sensing-based methods to map surface energy fluxes for a specific study area. In this paper, previously published algorithms and empirical formulae were tested with Landsat 7 ETM+ data to derive the regional distributions of land surface reflectance, surface temperature, NDVI and land surface energy fluxes (net radiation, soil heat flux, sensible heat flux and latent heat flux) over the upper Xilin River watershed in Inner Mongolia, China. A new land use/land cover classification was developed and applied for regionalization analysis. Validation of remote sensing derived surface reflectance, surface temperature, net radiation and sensible heat flux with field measurements shows differences of about 13, 4, 1, and 28%, respectively. This study provides valuable guidance for further investigation of the whole watershed. [ABSTRACT FROM AUTHOR]