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The article presents the research results aimed at determining the impact of meteorological droughts on groundwater droughts in the Gwda River catchment (northern Poland). The analysis was based on the Standardised Precipitation Index (SPI) and Standardised Groundwater Index (SGI) in various cumulation periods (1, 6, 12, 18, 24, 30 and 36 months) from 1986-2015. Monthly groundwater levels measured in wells and monthly sums of precipitation from meteorological stations in the vicinity of those wells were used to assess the relationships between droughts. During the study period from 1986-2015, three to 43 meteorological droughts and one to five groundwater droughts were identified. Meteorological droughts were most numerous for the shortest cumulation period (1 month), while droughts for the longer accumulation of 24 to 36 months were less numerous. The SPI and SGI indices were most strongly correlated over the annual cycle in the upper part of the catchment (between the Sępólno Wielkie station and well I-33_1). The correlation coefficient r was highest (0.69) between SPI-18 and SGI-1. Correlations were much lower in the middle of the catchment, where the maximum annual r coefficient was 0.39. There was no correlation between droughts in the lower catchment (r=0.14). The correlations presented for the Gwda catchment indicate that the relationship between droughts is not clear. [ABSTRACT FROM AUTHOR]

The present paper presents the influence of the main factors that generate the maximum flow in the upper basin of the Crisul Negru river. Starting from the three types of flow in the basin (maximum, average and minimum) and selecting among them the most representative from the hydrological point of view - the maximum characteristic leak, data from meteorological and hydrological observations have been used since the 1950s. Characteristic and important, for this type of flow in the basin, are the data collected from the main gauging stations in the catchment area of the upper reception basin of Crişul Negru. Within the paper are presented and analyzed the main elements of the maximum leak as well as the meteorological data recorded in the receiving basin taken into consideration. The characteristic elements of the flow in the riverbeds - flows, levels, hydrological parameters, as well as the determining elements in the formation of the flow - air temperatures and precipitation are taken into account. On the basis of the data processed, analyzed and graphically represented in various forms, the close connection between all these generating elements was emphasized and in some cases they maintain the maximum flow type in the reception area of the upper Crisul Negru basin. [ABSTRACT FROM AUTHOR]

This paper presents the first study to identity, in historical records, regional changes in the mechanisms of extreme water available for runoff (W). We used a quality‐controlled Snowpack Telemetry data set (1979–2017) combined with the nonparametric regional Kendall test to examine changes in annual maximum W under four hydrometeorological conditions (melt only/rain‐on‐snow/all melt/all melt plus rainfall) over the mountainous regions of the western United States. Under a warming climate, our analyses indicated significant declining trends in annual maximum W at regional scale under all four conditions. The annual maximum of all melt plus rainfall decreased significantly by 15% in the southwestern United States, while the frequency of rain‐on‐snow events increased significantly by 32% in the northwestern United States. The annual maximum snowmelt only decreased significantly by 21% across the entire western United States. Our results confirmed that interaction between regional humidity and solar radiation with warming temperature helps drive these changes. Plain Language Summary: In the western United States, mountain snowmelt generates about 50% of the region's streamflow and plays a critical role in sustaining water resources. It is important to understand how snowpack are changing under warming, as risks related to these changes involve trillions of dollars. This paper presents the first study to identify, in historical records, large‐scale changes in the mechanisms of extreme water available for runoff (W). We used a regional trend analysis to examine spatiotemporal changes in rain‐on‐snow (ROS) frequency and the mechanisms of extreme W from 1979 to 2017, based on our newly developed quality‐controlled observed snow and precipitation data set. We found that, under a significant warming trend, annual maximum W from snowmelt only (not including ROS) has been decreasing by 21% across the western United States. The northwestern United States showed significant increasing trends in ROS frequency by 32%, but the rate of annual maximum ROS showed nonsignificant trends (i.e., more frequent but similar in magnitude). The annual maximum W from all conditions (melt plus rain) has been decreasing significantly by 15% in the southwestern United States. Decreasing trends in the mechanisms of extreme W may have significant ramifications for water management relative to flood risk and public safety. Key Points: Annual maximum snowmelt (not including rain‐on‐snow) decreased significantly by 21% averaged across the snowy regions of the western United StatesThe frequency of rain‐on‐snow events increased significantly by 32% averaged in the higher humidity snowy regions of the northwestern United StatesAnnual maximum water available for runoff (melt + rain) decreased significantly by 15% in the snowy regions of the southwestern United States [ABSTRACT FROM AUTHOR]

A novelty spatial and temporal forecast method of water balance variables in Urama basin wetland, Venezuela is presented in this paper. The methodology consists of two stages: 1. Compilation of the information, using meteorological data on precipitation (rainfall) and evaporation from 73 climate stations, Landsat images and a digital elevation model for the study basin. 2. Processing the information: a) modelling the spatial statistical forecast; b) forecast model; and c) forecasting time series of water balance variables. The advantage of the method is to use available data to forecast the water balance variables by means of statistical spatial prediction models, selecting those with a seasonal trend approximating the observed values. Correlation and determination coefficients were greater than 0.99 indicating satisfactory predictions, so that the method, is appropriate to reproduce the spatio-temporal profile of future periods to be used for planning sustainable management of wetlands. [ABSTRACT FROM AUTHOR]

The Nemunas River basin falls within the territories of five different countries - Belarus, Lithuania, Russia, Poland and Latvia. In general, the beginning of spring floods highly depends on rapid rise of air temperature, heavy precipitation and sudden snow melting in the analysed basin. In this paper, the conditions of formation and consequences of two catastrophic floods in 1958 and 1979 in the Nemunas River basin were studied regarding the hydrometeorological parameters (maximum snow water equivalent before the beginning of flood and precipitation amount during the flood) as well as runoff coefficients for each selected catastrophic flood. Differences between the main drivers and evolution of these floods were analysed. Spatial distribution of maximum snow water equivalent and precipitation, as well as runoff coefficient in different parts of the river basin, were identified as having the most significant impact on the formation of the studied catastrophic floods. [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]

[Display omitted] • The proposed MSDI can capture the occurrence of drought. • There was a tendency of aridity with a mutation point of 1983. • The drought severity was the heaviest in the DXJB, with an average MSDI of –0.75. • The most severe drought event occurred from September 1988 to October 1990. • ENSO had the largest influence on the drought variation in the XJB. The reasonable and appropriate drought indicator is a crucial instrument for drought investigation and assessment, which can objectively compare the severity, duration, and frequency of drought events, so as to achieve the purpose of accurately monitoring and evaluating drought. However, a univariate drought index is often difficult to precisely describe the complex drought situation, and it is urgent to establish a multivariate comprehensive drought index to identify and reveal the drought characteristics. Based on Copula models, combined with meteorological factors precipitation and hydrological factors runoff, this paper constructed a Multivariate Standardized Drought Index (MSDI), which can characterize meteorological drought and hydrological drought simultaneously and evaluate the spatial–temporal evolution characteristics of drought over the Xijiang River Basin (XJB) in South China. The results indicated that: (1) the proposed MSDI can capture the occurrence of drought as sensitively as the Standardized Precipitation Index (SPI), and can represent the termination of drought as effectively as the Standardized Runoff Index (SRI), which can comprehensively depict the drought situation in the XJB, China; (2) since the 1980 s, there was a tendency of aridity with a mutation point of 1983 in the downstream of Xijiang River (DXJB), and the worst comprehensive drought occurred in 1989; (3) among the tributaries, the comprehensive drought severity was the heaviest in the DXJB, with an average MSDI value of –0.75; (4) based on the run theory and Copula model, the most serious drought event occurred from September 1988 to October 1990, with a drought severity of 33.36, a drought duration of 27 months, and a drought return period exceeding 50 years; and (5) the drought evolution was significantly influenced by abnormal atmospheric circulation factors, while ENSO had the largest influence on the drought variation in the XJB, followed by sunspot. [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]

Abstract Global Satellite Mapping of Precipitation (GSMaP) product is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission, which provides an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment was required before implementation. The objective of this paper is to evaluate and calibrate the errors of GSMaP_Gauge (GG) products, then SWAT model is chosen to evaluate its performance in hydrological simulation. It was found that the GG has a superior ability in detecting moderate rainfall events of 10–20 mm/d, while other magnitude accuracy needs to be strengthened. The Nash-Sutcliffe coefficient of efficiency (NS), coefficient of determination (R2) and the percent bias (PBIAS) were used to evaluate hydrological model performance in the daily scale, supporting the view that GG still allow considerable room for improvement. SWAT model driving by GG performed relatively poorly which is 0.77 ≤ R2 ≤ 0.84, 0.53 ≤ NS ≤ 0.64 and |PBIAS| < 9%, while the performance of corrected GG is relatively better that is 0.85 ≤ R2 ≤ 0.87, 0.70 ≤ NS ≤ 0.75 and |PBIAS| < 15%. Through the error's correction of GG, it brings a better performance of runoff simulation under SWAT modeling, leading to 11.94% and 6.1% maximum increase in NS and R2 respectively. Certain shortcomings are also noted that improvements to the relative runoff depth errors are limited or worse. But in general, GSMaP product has the potential to be an alternative data source for the research on hydrological simulation and water resources management in data-poor or ungauged basins. Highlights • GSMaP_Gauge has a superior ability in detecting moderate rainfall events of 10–20 mm/d. • GSMaP_Gauge-based model has the potential to describe well the runoff. Through correction of its errors, it brings the improvement of runoff simulation. • Certain shortcomings are that there are still large relative runoff depth errors, mainly reflected in the base flow. • The results can provide a deep insight to the GSMaP estimates'error features correction and potential utilization in water resources management. [ABSTRACT FROM AUTHOR]

The sum of design precipitation of a selected repetition period, provided that it is evenly distributed over the river basin area, is a basic input for the calculation of the direct outflow volume by the curve number method. It is necessary to determine the design precipitation for each location using the statistical methods and the longest available data series on daily precipitation sums, or more specifically their annual maximums. This paper deals with the determination of design precipitation from data of eight stations of the Czech Hydrometeorological Institute for the period 1961 - 2013. From a series of annual maximum values of daily precipitation sums, N-year design precipitations were calculated using two methods (Gumbel and generalized extreme value distributions). The conformity of both models with empirical distribution of values was statistically tested to evaluate which of the models gave more accurate results. In these cases, it was more appropriate to use the generalized extreme value distribution. Finally, the newly calculated characteristics were compared with the design values used by Šamaj et al. (1985), where significant differences were found. [ABSTRACT FROM AUTHOR]

This paper investigates the annual and seasonal precipitation variability at 20 stations over upper Indus River basin (UIRB) by using an innovative trend analysis (ITA) method, Mann-Kendall (MK) and Sen's slope estimator tests. Moreover, the annual precipitation time series is investigated for five precipitation intensities i.e. light, low, moderate, high and heavy; and on a seasonal scale for extreme values i.e. light and heavy precipitation intensities. Annual precipitation showed an increasing trend at four stations in the northeast region and a decreasing trend at two stations in the southeast region of UIRB. On a seasonal scale, the results showed that Garidopatta, Peshawar, Astore and Gupis stations are more sensitive to seasonal precipitation variabilities. Changes in heavy precipitation (> 90th percentile) are found more severe in winter and summer seasons in the northern regions suggesting a probable flooding aggravation. However, a reduction in flooding event observed during the spring season in the southern regions of the UIRB indicating a shift in the precipitation regime from south to north. The results of light precipitation (< 10th percentile) intensity showed that winter and spring seasons are more vulnerable to the occurrence of prolonged drought events. Moreover, ITA method has the advantage of detecting the sub-trends in the precipitation series because of its ability to present the results in graphical format. The results of this study could help to understand seasonal and annual precipitation variability over UIRB and will have imperative insinuations for further studies. [ABSTRACT FROM AUTHOR]

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from El Niño to Yangtze River basin rainfall meant that the strong El Niño in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements. [ABSTRACT FROM AUTHOR]

Urbanization is a global process that has taken billions of people from the rural countryside to concentrated urban centers, adding pressure to existing water resources. Many cities are specifically reliant on renewable freshwater regularly refilled by precipitation, rather than fossil groundwater or desalination. A precipitationshed can be considered the “watershed of the sky” and identifies the origin of precipitation falling in a given region. In this paper, we use this concept to determine the sources of precipitation that supply renewable water in the watersheds of the largest cities of the world. We quantify the sources of precipitation for 29 megacities and analyze their differences between dry and wet years. Our results reveal that 19 of 29 megacities depend for more than a third of their water supply on evaporation from land. We also show that for many of the megacities, the terrestrial dependence is higher in dry years. This high dependence on terrestrial evaporation for their precipitation exposes these cities to potential land-use change that could reduce the evaporation that generates precipitation. Combining indicators of water stress, moisture recycling exposure, economic capacity, vegetation-regulated evaporation, land-use change, and dry-season moisture recycling sensitivity reveals four highly vulnerable megacities (Karachi, Shanghai, Wuhan, and Chongqing). A further six megacities were found to have medium vulnerability with regard to their water supply. We conclude that understanding how upwind landscapes affect downwind municipal water resources could be a key component for understanding the complexity of urban water security. [ABSTRACT FROM AUTHOR]

In coastal catchments, rainfall events primarily drive bacterial inputs to the sea by causing land runoff, surface leaching and sewer overflow. Under semi-arid climate, extensive dry periods are interspersed with extreme precipitation. This paper aims to assess the impact of intense summer rainstorms events on Fecal Indicator Bacteria loadings to Mediterranean seawaters. Firstly, explanatory relationships were derived between an Antecedent Precipitation Index and the loads of thermo-tolerant coliforms and intestinal enterococci measured at three catchment outlets in the Gulf of Aigues-Mortes (southern France). Secondly, fecal bacterial loadings were simulated during summer season from 2006–2016, with a confidence interval arising from measurements uncertainties. On average, more than two rainstorms per summer season elevate bacterial loads at least by one order of magnitude, potentially leading to the degradation of bathing and fishing water quality observed in regulatory monitoring data. The results highlight the crucial importance of considering hydrological conditions in coastal water quality management. [ABSTRACT FROM AUTHOR]

It is now common knowledge that many water resources stresses relate to access to water within a basin. Yi River Basin, a typical river basin characterized by intensive agricultural processes, significant population growth, and water management, has been undergoing grave water problems. In this paper, the long-term trend of precipitation and streamflow in Yi River Basin, from 1964 to 2010, was investigated via Mann-Kendall test. The change point occurred in the year 1965 dividing the long-term series into two periods. Climate elasticity method and linear regression method were implemented to quantify the impact of precipitation and human activities on runoff and presented basically consistent results of the percentage change in an annual runoff for the postchange period. The results reveal that the decline of annual runoff in postchange period is mainly attributed to precipitation variability of 53.66–58.25% and human activities of 46.34–41.74%, as estimated by climate elasticity method and linear regression method, respectively. This study detected the changes in the precipitation-streamflow relationship and investigated the possible causes in the Yi River, which will be helpful for providing a reference for the management of regional water resources. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the hydrological risk parameters registered in the Voroneţ river basin. The studies and researches took place on the Voroneţ River after the June 2016 floods. The research has taken into account the hydrological risk parameters of the last 20 years recorded in the Siret River Basin. The river basin of the Siret River has been affected by multiple floods in recent years. The floods have morphologically modified the minor and the major river Voroneţ, a situation that influenced the floodplain areas in the urban environment and outside. The research analyzed the precipitation, the liquid flows (minimum, average and maximum), the way of formation and evolution of the floods, the volume of the damage produced, etc. The investigation of the precipitation volume indicated a number of factors that have prevailed over the last 20 years. The precipitation value was 71.6 l/m² in two days. The flow recorded in the downstream section of the river was 118.12 m³/s (the probability of calculation is 1%). The processing of liquid flow data revealed more flood flows in the same year. The effects of the floods have brought about the excessive degradation of shore defense works (about 2600 m) on the Voroneţ River located in the town of Gura Humorului, Voronet neighborhood and outside the city. The county road DJ 177D was degraded on 1600 m and two bridges were destroyed. Floods have resulted in the destruction of some economic and social objectives in the coastal area. The parameters of hydroclimatic risk highlighted by this research require special conditions for the design of works in the riverbed and the riparian area. [ABSTRACT FROM AUTHOR]

Precipitation data from 30 stations in the Huaihe River basin (HRB), a climatic transitional zone in east China, were used to investigate the spatiotemporal variability and trends of extreme precipitation on multitimescales for the period 1961–2010. Results indicated that (1) the spatial pattern of the annual precipitation, rainy days, extreme precipitation, and maximum daily precipitations shows a clear transitional change from the south (high) to the north (low) in the HR; it confirmed the conclusion that the HRB is located in the transitional zone of the 800 mm precipitation contour in China, where the 800 mm precipitation contour is considered as the geographical boundary of the south and the north. (2) Higher value of the extreme precipitation intensity mainly occurs in the middle of the east and the central part of the basin; it reveals a relatively distinct west-east spatial disparity, and this is not in line with the spatial pattern of the extreme precipitation total, the sum of the precipitation in 95th precipitation days. (3) Annual precipitation of 22 stations exhibits increasing trend, and these 22 stations are located from the central to the northern part. There is no significant trend detected for the seasonal precipitation. The summer precipitation exhibits a larger change range; this might cause the variation of the flood and drought in the HBR. However, the increasing trend in winter precipitation may be beneficial to the relief of winter agricultural drought. Rainy days in 12 stations, mostly located in and around the central northeastern part, experienced significant decreasing trend. Extreme precipitation days and precipitation intensity have increasing trends, but no station with significant change trend is detected for the maximum precipitation of the basin. (4) The spatiotemporal variability in the HRB is mainly caused by the geographic differences and is largely influenced by the interdecadal variations of East Asian Summer Monsoon in eastern China. The output of this paper could provide references for the practical decision making and integrated basin management of Huaihe River basin. [ABSTRACT FROM AUTHOR]

Closing the water balance of mountainous catchments may sometimes become tricky, even before applying a hydrological model. In this paper, we focus on mountainous, snow-affected catchments and try to understand the reasons for the “unrealistic” hydrological behaviours that they sometimes show: when annual runoff is greater than the annual areal precipitation estimate, something is obviously wrong, but finding the appropriate means to adjust the water balance is far from a trivial matter. This paper aims to improve our knowledge of the water balance of mountainous and snow-affected catchments in two different countries: Sweden and France. We use a simple non-dimensional framework to detect outliers and then propose an regionalization of precipitation and air temperature in order to better estimate inputs over high-altitude catchments. Since we are interested in catchment water balance, we evaluate our regionalized input estimates by comparing them to streamflow measurements. The results are mixed: in Sweden, our approach is successful because it can make most outlier catchments become regular ones; but in France it is disappointing because it does not solve most of the water balance problems identified. However, for both countries, the regionalization approach significantly improves the performance of a rainfall-runoff model at a daily time step. Citation Valery, A., Andreassian, V. & Perrin, C. (2010) Regionalization of precipitation and air temperature over high-altitude catchments - learning from outliers. Hydrol. Sci. J. 55(6), 928-940. [ABSTRACT FROM AUTHOR]

This study analyzes the variation trends of temperature and precipitation in the Dadu River Basin of China based on observed records from fourteen meteorological stations. The magnitude of trends was estimated using Sen’s linear method while its statistical significance was evaluated using Mann-Kendall’s test. The results of analysis depict increase change from northwest to southeast of annual temperature and precipitation in space. In temporal scale, the annual temperature showed significant increase trend and the annual precipitation showed increase trend. For extreme indices, the trends for temperature are more consistent in the region compared to precipitation. This paper has practical meanings for an effective management of climate risk and provides a foundation for further study of hydrological situation in this river basin. [ABSTRACT FROM AUTHOR]

This paper presents some improvements of a probabilistic quantitative precipitation forecasting method based on analogues, formerly developed on small basins located in South-Eastern France. The scope is extended to large scale basins mainly influenced by frontal systems, considering a case study area related to the Saône river, a large basin in eastern France. For a given target situation, this method consists in searching for the most similar situations observed in a historical meteorological archive. Precipitation amounts observed during analogous situations are then collected to derive an empirical predictive distribution function, i.e. the probabilistic estimation of the precipitation amount expected for the target day. The former version of this forecasting method (Bontron, 2004) has been improved by introducing two innovative variables: temperature, that allows taking seasonal effects into account and vertical velocity, which enables a better characterization of the vertical atmospheric motion. The new algorithm is first applied in a perfect prognosis context (target situations come from a meteorological reanalysis) and then in an operational forecasting context (target situations come from weather forecasts) for a three years period. Results show that this approach yields useful forecasts, with a lower false alarm rate and improved performances from the present day D to day D + 2. [ABSTRACT FROM AUTHOR]

Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons. [ABSTRACT FROM AUTHOR]

This paper draws attention to highlight the spatial and temporal variability in precipitation lapse rate (PLR) and precipitation extreme indices (PEIs) through the mesoscale characterization of Teesta river catchment, which corresponds to north Sikkim eastern Himalayas. A PLR rate is an important variable for the snowmelt runoff models. In a mountainous region, the PLR could be varied from lower elevation parts to high elevation parts. In this study, a PLR was computed by accounting elevation differences, which varies from around 1500 m to 7000 m. A precipitation variability and extremity were analysed using multiple mathematical functions viz. quantile regression, spatial mean, spatial standard deviation, Mann–Kendall test and Sen's estimation. For this reason, a daily precipitation, in the historical (years 1980–2005) as measured/observed gridded points and projected experiments for the 21st century (years 2006–2100) simulated by CMIP5 ESM-2 M model (Coupled Model Intercomparison Project Phase 5 Earth System Model 2) employing three different radiative forcing scenarios (Representative Concentration Pathways), utilized for the research work. The outcomes of this study suggest that a PLR is significantly varied from lower elevation to high elevation parts. The PEI based analysis showed that the extreme high intensity events have been increased significantly, especially after 2040s. The PEI based observations also showed that the numbers of wet days are increased for all the RCPs. The quantile regression plots showed significant increments in the upper and lower quantiles of the various extreme indices. The Mann–Kendall test and Sen's estimation tests clearly indicated significant changing patterns in the frequency and intensity of the precipitation indices across all the sub-basins and RCP scenario in an intra-decadal time series domain. The RCP8.5 showed extremity of the projected outcomes. [ABSTRACT FROM AUTHOR]

Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1) modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2) parameterizing hydrological models in ungauged catchments, (3) improving hydrological model structure, and (4) using new remote sensing precipitation data. [ABSTRACT FROM AUTHOR]

Located in a transition zone between the northern and southern climates in China, the Huai River Basin is prone to extreme events such as drought and flood. Based on the daily precipitation data at 134 stations between 1961 and 2013, this paper analyzed the spatial and temporal patterns of the dry and wet conditions in the Huai River Basin through the statistical analysis of the rainfall stations' annual and seasonal standard precipitation index (SPI) series. Annual SPI series exhibited a decreasing trend at 86 stations and an increasing trend at the remaining stations. None of the increasing trend was significant, while the decreasing trend was significant at two stations at 5% significance level (α = 0.05) and one station at 10% level. Seasonal-wise, there has been a prevailing trend of drying in spring and autumn, and wetting in summer and winter. The trends in the spring and summer SPI series have been mostly insignificant, while those in autumn and winter, significant (α = 0.10) at over 30 stations. The Pettitt test results indicated that the significant transitions (α = 0.10) in the autumn and winter SPI series mostly occurred in the middle to late 1980s. Comparison of the average number of dry and wet years between the two sub-periods of 1961–1984 and 1990–2013 suggested a significant increase (α = 0.05) in the average number of severely wet years across much of the basin. Overall, significant changes have already occurred in the dry and wet conditions of the Huai River Basin, which could have profound impacts on the food and water safety situation of the region. [ABSTRACT FROM AUTHOR]

A significant decrease in mean river flow as well as shifts in flood regimes have been reported at several locations along the River Niger. These changes are the combined effect of persistent droughts, damming and increased consumption of water. Moreover, it is believed that climate change will impact on the hydrological regime of the river in the next decades and exacerbate existing problems. While decision makers and stakeholders are aware of these issues, it is hard for them to figure out what actions should be taken without a quantitative estimate of future changes. In this paper, a Soil and Water Assessment Tool (SWAT) model of the Niger River watershed at Koulikoro was successfully calibrated, then forced with the climate time series of variable length generated by nine regional climate models (RCMs) from the AMMA-ENSEMBLES experiment. The RCMs were run under the SRES A1B emissions scenario. A combination of quantile-quantile transformation and nearest-neighbour search was used to correct biases in the distributions of RCM outputs. Streamflow time series were generated for the 2026–2050 period (all nine RCMs), and for the 2051–2075 and 2076–2100 periods (three out of nine RCMs) based on the availability of RCM simulations. It was found that the quantile-quantile transformation improved the simulation of both precipitation extremes and ratio of monthly dry days/wet days. All RCMs predicted an increase in temperature and solar radiation, and a decrease in average annual relative humidity in all three future periods relative to the 1981–1989 period, but there was no consensus among them about the direction of change of annual average wind speed, precipitation and streamflow. When all model projections were averaged, mean annual precipitation was projected to decrease, while the total precipitation in the flood season (August, September, October) increased, driving the mean annual flow up by 6.9% (2026–2050), 0.9% (2051–2075) and 5.6% (2076–2100). At-test showed that changes in multi-model annual mean flow and annual maximum monthly flow between all four periods were not statistically significant at the 95% confidence level. [ABSTRACT FROM PUBLISHER]

Zero-order drainage basins, and their constituent hillslopes, are the fundamental geomorphic unit comprising much of Earth's uplands. The convergent topography of these landscapes generates spatially variable substrate and moisture content, facilitating biological diversity and influencing how the landscape filters precipitation and sequesters atmospheric carbon dioxide. In light of these significant ecosystem services, refining our understanding of how these functions are affected by landscape evolution, weather variability, and long-term climate change is imperative. In this paper we introduce the Landscape Evolution Observatory (LEO): a large-scale controllable infrastructure consisting of three replicated artificial landscapes (each 330 m 2 surface area) within the climate-controlled Biosphere 2 facility in Arizona, USA. At LEO, experimental manipulation of rainfall, air temperature, relative humidity, and wind speed are possible at unprecedented scale. The Landscape Evolution Observatory was designed as a community resource to advance understanding of how topography, physical and chemical properties of soil, and biological communities coevolve, and how this coevolution affects water, carbon, and energy cycles at multiple spatial scales. With well-defined boundary conditions and an extensive network of sensors and samplers, LEO enables an iterative scientific approach that includes numerical model development and virtual experimentation, physical experimentation, data analysis, and model refinement. We plan to engage the broader scientific community through public dissemination of data from LEO, collaborative experimental design, and community-based model development. [ABSTRACT FROM AUTHOR]

The South Asian monsoon is critically important for agricultural production in the region that includes the vast, fertile Indus and Ganges basins. However, the behavior of the South Asian monsoon is not well understood because of its complex nature, and existing instrumental climate records are insufficient for investigating the risks of the low-frequency but high-impact megadroughts that have historically occurred. This paper develops an inverse Palmer drought severity index (PDSI) model to retrieve paleoprecipitation for the region during the time period of 1300-1899, using available data of the water-holding capacity of soil, temperature, and reconstructed PDSI based on the tree-ring analysis of Cook et al.. Temperature data are reconstructed by a regression analysis utilizing an existing temperature reconstruction in an adjacent region and the Pacific decadal oscillation. Based on the retrieved paleoprecipitation, several megadroughts are identified during the reconstruction period. The drought frequency in the Indus basin is higher than that in the Ganges basin. The intensity, frequency, and spatial extent of severe droughts increased from 1300-1899 to 1900-2010. As a signal of climate change, increasing intensity and frequency of severe drought in the Indus and Ganges River basins needs adaptation strategies and drought preparedness measures to secure the food production in this area. [ABSTRACT FROM AUTHOR]

This paper is focused on the probability modeling with a range of distribution models over two inland river basins in China, together with the estimations of return levels on various return periods. Both annual and seasonal maximum precipitations (MP) are investigated based on daily precipitation data at 13 stations from 1960 to 2010 in Heihe River and Shiyang River basins. Results show that GEV, Burr, and Weibull distributions provide the best fit to both annual and seasonal MP. Exponential and Pareto 2 distributions show the worst fit. The estimated return levels for spring MP show decreasing trends from the upper to the middle and then to the lower reaches totally speaking. Summer MP approximates to annual MP both in the quantity and in the spatial distributions. Autumn MP shows a little higher value in the estimated return levels than Spring MP, while keeping consistent with spring MP in the spatial distribution. It is also found that the estimated return levels for annual MP derived from various distributions differ by 22%, 36%, and 53% on average at 20-year, 50-year, and 100-year return periods, respectively. [ABSTRACT FROM AUTHOR]

This paper evaluates changes in precipitation in the upper the Euphrates–Tigris basin enclosing the whole area of Southeastern Anatolian Project (GAP), which is the largest investment for regional development in the history of Turkey and the fourth largest irrigation project in the world. For this purpose, the analyses are based on new data sequences formed by summing up monthly precipitation of 19 stations in the study area at seven different time scales. The Mann–Kendal (MK) and Theil–Sen Slope estimator (TSE) statistics to detect the direction and magnitude of an available monotonic trend in any given data, respectively, were used for the statistical analysis of the new data sequences. Besides, the sequential version of Mann–Kendal rank test (SVMK) to detect the approximate starting point of a trend was also applied the data set of the stations with significant monotonic trend. The monotonic trend was detected at 63% of the stations (12 of 19 stations) for the different time scales at 5% significance level. The stations with positive and negative or insignificant precipitation trends were scattered in the different parts of the basin. The significant trends were found in six stations with respect to the critical value (± 2.33) from the standard normal distribution for one-sided hypothesis at 1% significance level. The TSE results for 12 stations with monotonic trend at different time scales varied from − 2.36 mm to 2.51 mm per year. Of the six stations, the maximum decrease (− 72.5%) and increase (166.9%) in the percent change (PC) were determined in Agri and Bitlis stations, respectively. The approximate starting points of upward and downward trend for Kilis, Sirnak, Agri, Elazig, Bitlis and Siirt stations were at about 1980, 1979, 1987, 1986, 1984 and 1965 with respect to the SVMK test, respectively. According to the results obtained from the previous studies, the river basin is affected by the North Atlantic Oscillation (NAO) and global warming. Therefore, it is very likely that the NAO and global warming have been contributing to changes in precipitation of the river basin. [ABSTRACT FROM AUTHOR]

This paper summarises environmental monitoring activities and small plot research in agriculture as practiced in the Heilongjiang province located northeast in China and in Norway. For both countries, there is a need to recommend efficient mitigation measures to farmers and monitor the environmental effect of agricultural systems and management practices. Climate change scenarios predict changes in precipitation which potentially can influence agricultural practices as well as lead to changes in run-off and related to land use as well as the environment losses. As such, cooperation in the further development of monitoring programmes and related techniques will be useful in documenting both present and future, climate change-induced changes, related land use as well as the environmental. [ABSTRACT FROM AUTHOR]

Data analysis and characterization of precipitation in the Haihe River Basin (HRB) of China are required for management practices for the purpose of flood water control and utilization. In the companion paper, we presented precipitation data in the HRB during 1951-2010 and reported its basic statistics such as temporal trend and spatial variability. In this study, spatiotemporal variability on the precipitation was further investigated comprehensively for the underlying physics and the implication to water resource management. During the summer flood season of the study area, basin-wide precipitation was negatively correlated to average NINO3.4 index. Spatially, summer precipitation was correlated with gridded sea surface temperature (SST) observed in the eastern tropic Pacific Ocean and the western tropic Indian Ocean. SST in two representative areas was identified as potential predictors for precipitation in the HRB. No spatial or temporal correlations were confirmed between precipitation and soil moisture as annual averages in the study area. Copula analysis suggested about 40% possibility in a year with a potential for cross-watershed water diversion within HRB. [ABSTRACT FROM AUTHOR]

Assessing climate change risk to municipal water supplies is often conducted by hydrologic modeling specific to local watersheds and infrastructure to ensure that outputs are compatible with existing planning frameworks and processes. This study leverages the modeling capacity of an operational National Weather Service River Forecast Center to explore the potential impacts of future climate-driven hydrologic changes on factors important to planning at the Salt Lake City Department of Public Utilities (SLC). Hydrologic modeling results for the study area align with prior research in showing that temperature changes alone will lead to earlier runoff and reduced runoff volume. The sensitivity of average annual flow to temperature varies significantly between watersheds, averaging -3.8% °F-1 and ranging from 21.8% to 26.5% flow reduction per degree Fahrenheit of warming. The largest flow reductions occur during the high water demand months of May-September. Precipitation drives hydrologic response more strongly than temperature, with each 1% precipitation change producing an average 1.9% runoff change of the same sign. This paper explores the consequences of climate change for the reliability of SLC's water supply system using scenarios that include hydrologic changes in average conditions, severe drought scenarios, and future water demand test cases. The most significant water management impacts will be earlier and reduced runoff volume, which threaten the system's ability to maintain adequate streamflow and storage to meet late-summer water demands. [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]

In the paper the evaluation of influence of changes in water catchment area land use on the size of peak outflow was conducted. The investigations were conducted in Winna Góra catchment area located in Mściwojów, Lower Silesian voivodeship. The catchment area is 11.78 hectares. At present, the catchment is used as arable land, forest and meadows. In the future the area of sealed surfaces such as: roofs, roads and car parks will increase. This can contribute to the change of water balance components. Analyses has shown, that changes in the use of a catchment area lead to reduction of surface flow time from the catchment (less resistance to motion) -- in effect it causes increase of the outflow volume at about 28%. The increase of the water outflow volume may have significant influence on the Winna Góra development. To counteract the results of adverse changes caused by the catchment sealing -- it is recommended for the investigated area to apply a balanced approach. This would consist of retaining precipitation water in its place of origin. [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]

Global Circulation Models (GCMs) contributed to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and are widely used in global change research. This paper assesses the performance of the AR4 GCMs in simulating precipitation and temperature in China from 1960 to 1999 by comparison with observed data, using system bias (B), root-mean-square error (RMSE), Pearson correlation coefficient (R) and Nash-Sutcliffe model efficiency (E) metrics. Probability density functions (PDFs) are also fitted to the outputs of each model. It is shown that the performance of each GCM varies to different degrees across China. Based on the skill score derived from the four metrics, it is suggested that GCM 15 (ipsl_cm4) and GCM 3 (cccma&lowbar;cgcm&lowbar;t63) provide the best representations of temperature and precipitation, respectively, in terms of spatial distribution and trend over 10 years. The results also indicate that users should apply carefully the results of annual precipitation and annual temperature generated by AR4 GCMs in China due to poor performance. At a finer scale, the four metrics are also used to obtain best fit scores for ten river basins covering mainland China. Further research is proposed to improve the simulation accuracy of the AR4 GCMs regarding China. [ABSTRACT FROM AUTHOR]

Rainfall anomaly and the Standard Precipitation Index (SPI) are the most common meteorological drought indices. The most used and simplest index in the meteorological literature is the SPI. Agriculture is usually the first economic sector to be affected by drought. Agricultural drought can be monitored by focusing on precipitation shortages, differences between ETa and ET0, soil water deficits and reduced ground water and/or reservoir levels. Hydrological drought is associated with the effect of periods of precipitation shortfalls on surface or subsurface water supply. The frequency and severity of hydrological drought is often defined on a watershed basin scale. Although climate is a primary contributor to hydrological drought, other factors such as changes in land use, land degradation, and the construction of dams all affect the hydrological characteristics of a basin. The climate change scenario for Albania indicates a decrease in the amount of precipitation by about 12.5% up to the year 2100. Consequently, severe and extremely dry conditions will have a tendency to increase. The aim of this paper is to present an overview of the agricultural drought indices in current use in Albania, considering the meteorological, climatological, hydrological and agrometeorological elements provided by the IGEWE (a partner in the regional project DMCSEE). Drought is a frequent and common climate phenomenon that happens in almost all climate zones, with impacts in socio-economical areas such as agriculture, energy, etc. [ABSTRACT FROM AUTHOR]

Abstract: Following construction of the Three Gorges Dam, many farmers resettled in surrounding mountain areas and cultivated marginal lands, which are mostly on steep slopes with soil of poor structure. In general, high soil loss rates occur during intense storms. Therefore, soil erosion is a major environmental problem in the Three Gorges area (TGA) of China. Understanding and quantifying sediment load is important for sustainable agriculture and the environment in this region. This paper analyzes the relationships between rainfall, runoff, and sediment transport in the Wangjiaqiao watershed. Strong seasonal and monthly variability in sediment load was found. Sediment was strongly transported during summer months, a period when frequent flood events of high magnitude and intensity occurred. Analysis of the relationships between precipitation, discharge and sediment transport at an individual event scale showed significant correlations between total precipitation, peak discharge, total water yield, maximum 30min rainfall intensity, and sediment-related variables. Stepwise multiple regression analysis revealed that rainfall amount is the major cause of runoff, while events producing a large discharge in a short time play an important role in inducing severe soil erosion. During 40 flood events, three different types of hysteretic loops were observed: clockwise (28 events, 70%), figure-eight (5 events, 12.5%), and complex (7 events, 17.5%). The results of this study confirm the complex and heterogeneous nature of sediment response in the Wangjiaqiao watershed. [Copyright &y& Elsevier]

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)--Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10%% of months during the cold half of the year (October--March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations. [ABSTRACT FROM AUTHOR]

This paper presents a calibration framework for a precipitation-runoff model for flood prediction in a mesoscale Alpine basin with discharges strongly influenced by hydraulic works. The developed methodology addresses two classical hydrological calibration challenges: computational limitations to run optimization algorithms for distributed hourly models and the absence of concomitant meteorological and natural discharge time series. The presented processes-oriented, multi-signal approach is based on hydrological data from a variety of sources and for different periods, corresponding to various spatial scales. The model parameters are calibrated by sequentially minimizing differences between observed and simulated values for different hydrological signals and signatures such as: (a) the phase of precipitations, (b) the time evolution of point-scale snow heights, (c) the mean inter-annual cycle of daily discharges, and (d) timing of snowmelt-induced spring runoff. We compare the model performance to a benchmark model obtained by simply using the globally optimal parameter values from the nearest gauged and non perturbed catchment. For prediction of flow seasonality and also extreme events, the calibration methodology outperforms the benchmark. Citation Hingray, B., Schaefli, B., Mezghani, A. & Hamdi, Y. (2010) Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments. Hydrol. Sci. J. 55(6), 1002-1016. [ABSTRACT FROM AUTHOR]

In earlier studies involving simulation of the Fergus River flows in Ireland, the conceptual Soil Moisture Accounting and Routing (SMAR) model had been found to consistently outperform a number of black-box models. Subsequently, in investigating any loss of flow through this catchment's subsurface karstic features, it was verified from the overall long-term water balance that such losses were substantial. This raised the awkward question of why the volume-conservative SMAR model had performed so well on this considerably non-conservative catchment. Further analyses revealed that, to compensate for the excess volume of total runoff generated by the model's conservative water balance component, the memory length of the surface runoff response function had been unrealistically curtailed in the optimization process, effectively truncating that function and thereby violating the conservation property of the routing process. This embarrassing revelation called for reconsideration of the model structure to account more sensibly for actual losses, while still achieving high model efficiency. This paper highlights not only the discovery of the karstic Fergus catchment as a “hydrological monster”, in the context of the SMAR model, but also why conservative models perform poorly in such cases. In an attempt to “tame the monster”, better simulation of the observed flows was achieved by conceptually adapting the SMAR model, in a pragmatic empirical manner, by simply modifying its water balance component. Citation Goswami, M. & O'Connor, K. M. (2010) A “monster” that made the SMAR conceptual model “right for the wrong reasons”. Hydrol. Sci. J. 55(6), 913-927. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes estimation of stream channel geometry with multiple regression analysis of GIS-derived watershed characteristics including drainage area, catchment-averaged precipitation, mean watershed slope, elevation, forest cover, percent area with slopes greater than 30 percent, and percent area with north-facing slopes greater than 30 percent. Results from this multivariate predictor method were compared to results from the traditional single-variable (drainage area) relationship for a sample of 98 unregulated and undiverted streams in Idaho. Root-mean-squared error (RMSE) was calculated for both multiple- and single-variable predictions for 100 independent, random subsamples of the dataset at each of four different subsample levels. The multiple-variable technique produced significantly lower RMSE for prediction of both stream width and depth when compared to the drainage area-only technique. In the best predictive equation, stream width depended positively on drainage area and mean watershed precipitation, and negatively on fraction of watershed consisting of north-facing slopes greater than 30%. Stream depth depended positively on drainage area and precipitation, and negatively on mean watershed elevation. Our results suggest that within a given physiographic province, multivariate analysis of readily available GIS-derived watershed variables can significantly improve estimates of stream width and depth for use in flow-routing software models. [Copyright &y& Elsevier]

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

To test the accuracy of statistically downscaled precipitation estimates from numerical weather prediction models, a set of experiments to study what space and time scales are appropriate to obtain downscaled precipitation forecasts with maximum skill have been carried out. Fourteen-day forecasts from the 1998 version of the National Centers for Environmental Prediction (NCEP) Medium-Range Forecast (MRF) model were used in this study. It has been previously found that downscaled temperature fields have significant skill even up to 5 days of forecast lead time, but there is practically no valuable skill in the downscaled precipitation forecasts. Low skill in precipitation forecasts revolves around two main issues. First, the (intermittent) precipitation variability on daily and subdaily time scales could be too noisy to derive meaningful relationships with atmospheric predictors. Second, the model parameterizations and the coarse spatial resolution of the current generation of global-scale forecast models might be unable to resolve the local-scale variability in precipitation. Both of these issues may be addressed by spatial and temporal averaging. In this paper the authors present a diagnostic study using a set of numerical experiments to understand how spatial and temporal aggregations affect the skill of downscaled precipitation forecasts in the upper Colorado River basin. The question addressed is, if the same set of predictor variables from numerical weather prediction models is used, what space (e.g., station versus regional average) and time (e.g., subdaily versus daily) scales optimize regression-based downscaling models so as to maximize forecast skill for precipitation? Results in general show that spatial and temporal averaging increased the skill of downscaled precipitation estimates. At subdaily (6 hourly) and daily time scales, the skill of downscaled estimates at spatial scales greater than 50 km was generally higher than the skill of downscaled estimates at individual stations. For the 6-hourly time scale both for stations and for mean areal precipitation estimates the maximum forecast skill was found to be approximately half that of the daily time scale. At forecast lead times of 5 days, when there is very little skill at daily and subdaily time scales, useful skill emerged when station data are aggregated to 3- and 5-day averages. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the projected regional responses of univariate and bivariate behaviors of extreme precipitation to climate change over the upper-middle Huaihe River Basin. Based on twelve GCM outputs under historical, RCP4.5 and the observations at 32 rainfall stations, the equidistant cumulative distribution function matching method (EDCDFm) was utilized to bias correct daily precipitation during the historical (1961–2005) and future (2021–2080) periods. Four precipitation indices combinations were introduced based on eight precipitation indices to characterize the regional-scale changes of precipitation events, which designate the duration, intensity and amount of heavy and weak precipitation in a year. Their dependence structures were captured by Copulas. Kendall return period (KRP) were applied to discuss hazard scenarios and we quantified the spatial variability of KRPs under different marginal values. The results indicated that projected precipitation characteristics including the average intensity, the amount of annual precipitation, the intensity and amount of extreme precipitation together with annual extremes displayed increasing trends, while the changes of consecutive wet and dry days did not present pronounced trends. Decreased KRPs in the vast majority of the territory manifested that the frequency of simultaneous floods and droughts in a year as well as that of extreme heavy precipitation events would augment. Obvious spatial heterogeneity of the changes of KRP was partly attributed to the topography difference, especially the coastal areas along the main stream of the Huaihe River. Consequently, there will be a higher risk of water resources-related issues in this region for upcoming decades. • Significant decreases will occur in the intensity and amount of heavy precipitation for upcoming decades. • Change rates of precipitation indices combinations illustrate obvious spatial heterogeneity. • The frequency of simultaneous floods and droughts in a year and that of extreme heavy precipitation events will augment. [ABSTRACT FROM AUTHOR]

The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore, the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of 1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (|PBIAS|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009–2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS. • Four precipitation products (TRMM, CDR, CMADS and CFSR) were selected for evaluation and corrected against OBS. • The hydrological simulation results show that satellite-based precipitation performs better than reanalysis precipitation. • TRMM performs best during the wet season, while CDR performes best during the dry season. [ABSTRACT FROM AUTHOR]

Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. This paper establishes a statistical downscaling model to downscale the monthly precipitation in the inland river basin of Northwest China with the Tarim River Basin (TRB) as a typical representation. This method combines polynomial regression and machine learning, and it uses the batch gradient descent (BGD) algorithm to train the regression model. We downscale the monthly precipitation and obtain a dataset from January 2001 to December 2017 with a spatial resolution of 1 km × 1 km. The results show that the downscaling model presents a good performance in precipitation simulation with a high resolution, and it is more effective than ordinary polynomial regression. We also investigate the temporal and spatial variations of precipitation in the TRB based on the downscaling dataset. Analyses illustrate that the annual precipitation in the southern foothills of the Tianshan Mountains and the North Kunlun Mountains showed a significant upward trend during the study periods, while the annual precipitation in the central plains presented a significant downward trend. [ABSTRACT FROM AUTHOR]