Search

This paper uses hindcasts (1981–2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mainly of noise which enhances degradation of the skill. [ABSTRACT FROM AUTHOR]

The objective of this paper is to identify better performing Coupled Model Intercomparison Project phase 3 (CMIP3) global climate models (GCMs) that reproduce grid-scale climatological statistics of observed precipitation and temperature for input to hydrologic simulation over global land regions. Current assessments are aimed mainly at examining the performance of GCMs from a climatology perspective and not from a hydrology standpoint. The performance of each GCM in reproducing the precipitation and temperature statistics was ranked and better performing GCMs identified for later analyses. Observed global land surface precipitation and temperature data were drawn from the Climatic Research Unit (CRU) 3.10 gridded data set and re-sampled to the resolution of each GCM for comparison. Observed and GCM-based estimates of mean and standard deviation of annual precipitation, mean annual temperature, mean monthly precipitation and temperature and Köppen-Geiger climate type were compared. The main metrics for assessing GCM performance were the Nash-Sutcliffe efficiency (NSE) index and root mean square error (RMSE) between modelled and observed long-term statistics. This information combined with a literature review of the performance of the CMIP3 models identified the following better performing GCMs from a hydrologic perspective: HadCM3 (Hadley Centre for Climate Prediction and Research), MIROCm (Model for Interdisciplinary Research on Climate) (Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change), MIUB (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), MPI (Max Planck Institute for Meteorology) and MRI (Japan Meteorological Research Institute). The future response of these GCMs was found to be representative of the 44 GCM ensemble members which confirms that the selected GCMs are reasonably representative of the range of future GCM projections. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic assessment of the performance of an analytical modeling framework for streamflow probability distributions for a set of 25 Swiss catchments. These catchments show a wide range of hydroclimatic regimes, including namely snow-influenced streamflows. The model parameters are calculated from a spatially averaged gridded daily precipitation data set and from observed daily discharge time series, both in a forward estimation mode (direct parameter calculation from observed data) and in an inverse estimation mode (maximum likelihood estimation). The performance of the linear and the nonlinear model versions is assessed in terms of reproducing observed flow duration curves and their natural variability. Overall, the nonlinear model version outperforms the linear model for all regimes, but the linear model shows a notable performance increase with catchment elevation. More importantly, the obtained results demonstrate that the analytical model performs well for summer discharge for all analyzed streamflow regimes, ranging from rainfall-driven regimes with summer low flow to snow and glacier regimes with summer high flow. These results suggest that the model's encoding of dischargegenerating events based on stochastic soil moisture dynamics is more flexible than previously thought. As shown in this paper, the presence of snowmelt or ice melt is accommodated by a relative increase in the discharge-generating frequency, a key parameter of the model. Explicit quantification of this frequency increase as a function of mean catchment meteorological conditions is left for future research. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zeroprecipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area. [ABSTRACT FROM AUTHOR]

The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important. [ABSTRACT FROM AUTHOR]

This paper evaluates whether snow-covered area and streamflow measurements can help assess altitudinal gradients of temperature and precipitation in data-scarce mountainous areas more efficiently than using the usual interpolation procedures. A dataset covering 20 Alpine catchments is used to investigate this issue. Elevation dependency in the meteorological fields is accounted for using two approaches: (i) by estimating the local and time-varying altitudinal gradients from the available gauge network based on deterministic and geostatistical interpolation methods with an external drift; and (ii) by calibrating the local gradients using an inverse snow-hydrological modelling framework. For the second approach, a simple two-parameter model is proposed to target the temperature/precipitation–elevation relationship and to regionalize air temperature and precipitation from the sparse meteorological network. The coherence of the two approaches is evaluated by benchmarking several hydrological variables (snow-covered area, streamflow) computed with snow-hydrological models fed with the interpolated datasets and checked against available measurements. Results show that accounting for elevation dependency from scattered observations when interpolating air temperature and precipitation cannot provide sufficiently accurate inputs for models. The lack of high-elevation stations seriously limits correct estimation of lapse rates of temperature and precipitation, which, in turn, affects the performance of the snow-hydrological simulations due to imprecise estimates of temperature and precipitation volumes. Instead, retrieving the local altitudinal gradients using an inverse approach enables increased accuracy in the simulation of snow cover and discharge dynamics while limiting problems of over-calibration and equifinality. [ABSTRACT FROM AUTHOR]

The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and generated with methods giving it multiple advantages over the previous release, the ERA-Interim reanalysis product. Notably, it has a finer spatial resolution, is archived at the hourly time step, uses a more advanced assimilation system and includes more sources of data. This paper aims to evaluate the ERA5 reanalysis as a potential reference dataset for hydrological modelling by considering the ERA5 precipitation and temperatures as proxies for observations in the hydrological modelling process, using two lumped hydrological models over 3138 North American catchments. This study shows that ERA5-based hydrological modelling performance is equivalent to using observations over most of North America, with the exception of the eastern half of the US, where observations lead to consistently better performance. ERA5 temperature and precipitation biases are consistently reduced compared to ERA-Interim and systematically more accurate for hydrological modelling. Differences between ERA5, ERA-Interim and observation datasets are mostly linked to precipitation, as temperature only marginally influences the hydrological simulation outcomes. [ABSTRACT FROM AUTHOR]

The accuracy of quantitative precipitation estimation (QPE) over a given region and period is of vital importance across multiple domains and disciplines. However, due to the intricate temporospatial variability and the intermittent nature of precipitation, it is challenging to obtain QPE with adequate accuracy. This paper aims to simulate rainfall fields while honoring both the local constraints imposed by the point-wise rain gauge observations and the global constraints imposed by the field measurements obtained from weather radar. The conditional simulation method employed in this study is modified phase annealing (PA), which is practically an evolution from the traditional simulated annealing (SA). Yet unlike SA, which implements perturbations in the spatial field, PA implements perturbations in Fourier space, making it superior to SA in many respects. PA is developed in two ways. First, taking advantage of the global characteristic of PA, the method is only used to deal with global constraints, and the local ones are handed over to residual kriging. Second, except for the system temperature, the number of perturbed phases is also annealed during the simulation process, making the influence of the perturbation more global at initial phases and decreasing the global impact of the perturbation gradually as the number of perturbed phases decreases. The proposed method is used to simulate the rainfall field for a 30 min event using different scenarios: with and without integrating the uncertainty of the radar-indicated rainfall pattern and with different objective functions. [ABSTRACT FROM AUTHOR]

Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors. [ABSTRACT FROM AUTHOR]

The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments. [ABSTRACT FROM AUTHOR]

Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively. [ABSTRACT FROM AUTHOR]

An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP – Australian Water Availability Project), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, and modified Kling–Gupta efficiency), categorical metrics, and other statistics (wet-day frequency, transition probabilities, and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both the point and grid scale of 5 km. BARRA also more closely reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias, and modified Kling–Gupta efficiency (KGE ′) indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms ERA-Interim in terms of the KGE ′ score and the components of the KGE ′ score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions. [ABSTRACT FROM AUTHOR]

Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making a distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by a few outliers even in large datasets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multivariate data and to quantify their impact on correlation structures. [ABSTRACT FROM AUTHOR]

The main sources of uncertainty in hydrological modelling can be summarized as structural errors, parameter errors, and data errors. Operational modellers are generally more concerned with predictive ability than model errors, and this paper presents a new, simple method to improve predictive ability. The method is called parameter-state ensemble thinning (P-SET). P-SET takes a large ensemble of continuous model runs and applies screening criteria to reduce the size of the ensemble. The goal is to find the most promising parameter-state combinations for analysis during the prediction period. Each prediction period begins with the same large ensemble, but the screening criteria are free to select a different sub-set of simulations for each separate prediction period. The case study is from June to October 2014 for a small (1324 km 2) watershed just north of Lake Superior in Ontario, Canada, using a Canadian semi-distributed hydrologic land-surface scheme. The study examines how well the approach works given various levels of certainty in the data, beginning with certainty in the streamflow and precipitation, followed by uncertainty in the streamflow and certainty in the precipitation, and finally uncertainty in both the streamflow and precipitation. The approach is found to work in this case when streamflow and precipitation are fairly certain, while being more challenging to implement in a forecasting scenario where future streamflow and precipitation are much less certain. The main challenge is determined to be related to parametric uncertainty and ideas for overcoming this challenge are discussed. The approach also highlights model structural errors, which are also discussed. [ABSTRACT FROM AUTHOR]

Various fields of application, such as risk assessments of the insurance industry or the design of flood protection systems, require reliable precipitation statistics in high spatial resolution, including estimates for events with high return periods. Observations from point stations, however, lack of spatial representativeness, especially over complex terrain. Current numerical weather models are not capable of running simulations over thousands of years. This paper presents a new method for the stochastic simulation of widespread precipitation based on a linear theory describing orographic precipitation and additional functions that consider synoptically driven rainfall and embedded convection in a simplified way. The model is initialized by various statistical distribution functions describing prevailing atmospheric conditions such as wind vector, moisture content, or stability, estimated from radiosonde observations for a limited sample of observed heavy rainfall events. The model is applied for the stochastic simulation of heavy rainfall over the complex terrain of southwestern Germany. It is shown that the model provides reliable precipitation fields despite its simplicity. The differences between observed and simulated rainfall statistics are small, being of the order of only ±10 % for return periods of up to 1000 years. [ABSTRACT FROM AUTHOR]

This paper proposes a protocol to assess the space–time consistency of 12 satellite-based precipitation products (SPPs) according to various indicators, including (i) direct comparison of SPPs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPPs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPPs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the space–time variability of the SPPs and to a large dataset composed of 12 SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TMPA–RT v.7, TMPA–Adj v.7 and SM2Rain–CCI v.2), an unprecedented comparison. The aim of using different space scales and timescales and indicators was to evaluate whether the efficiency of SPPs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPPs. Compared to precipitation gauge observations, some SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, GSMaP v.6, PERSIANN, and TMPA–RT v.7) are unable to estimate regional precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH–BLD v.1, MSWEP v.2.1, PERSIANN–CDR, and TMPA–Adj v.7) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In 9 out of 10 of the cases studied, streamflow was more realistically simulated when SPPs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks, whereas the SPP's ability to reproduce the duration of MODIS-based snow cover resulted in poorer simulations than simulation using available precipitation gauges. Interestingly, the potential of the SPPs varied significantly when they were used to reproduce gauge precipitation estimates, streamflow observations or snow cover duration and depending on the time window considered. SPPs thus produce space–time errors that cannot be assessed when a single indicator and/or time window is used, underlining the importance of carefully considering their space–time consistency before using them for hydro-climatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the highest space–time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration. [ABSTRACT FROM AUTHOR]

Global climate models project widespread decreases in soil moisture over large parts of Europe. This paper investigates the impact of model resolution on the magnitude and seasonality of future soil drying in central-western Europe. We use the general circulation model EC-Earth to study two 30-year periods representative of the start and end of the 21st century under low-to-moderate greenhouse gas forcing (RCP4.5). In our study area, central-western Europe, at high spatial resolution (∼25 km) soil drying is more severe and starts earlier in the season than at standard resolution (∼112 km). Here, changes in the large-scale atmospheric circulation and local soil moisture feedbacks lead to enhanced evapotranspiration in spring and reduced precipitation in summer. A more realistic position of the storm track at high model resolution leads to reduced biases in precipitation and temperature in the present-day climatology, which act to amplify future changes in evapotranspiration in spring. Furthermore, in the high-resolution model a stronger anticyclonic anomaly over the British Isles extends over central-western Europe and supports soil drying. The resulting drier future land induces stronger soil moisture feedbacks that amplify drying conditions in summer. In addition, soil-moisture-limited evapotranspiration in summer promotes sensible heating of the boundary layer, which leads to a lower relative humidity with less cloudy conditions, an increase in dry summer days, and more incoming solar radiation. As a result a series of consecutive hot and dry summers appears in the future high-resolution climate. The enhanced drying at high spatial resolution suggests that future projections of central-western European soil drying by CMIP5 models have been potentially underestimated. Whether these results are robust has to be tested with other global climate models with similar high spatial resolutions. [ABSTRACT FROM AUTHOR]

Hydrological functions of river basins are summarized as collection, storage and discharge, which can be characterized by the dynamics of hydrological variables including precipitation, evaporation, storage and runoff. The temporal patterns of each variable can be indicators of the functionality of a basin. In this paper we introduce a measure to quantify the degree of similarity in intra-annual variations at monthly scale at different years for the four main variables. We introduce this measure under the term of recurrence and define it as the degree to which a monthly hydrological variable returns to the same state in subsequent years. The degree of recurrence in runoff is important not only for the management of water resources but also for the understanding of hydrologic processes, especially in terms of how the other three variables determine the recurrence in runoff. The main objective of this paper is to propose a simple hydrologic classification framework applicable to large basins at global scale based on the combinations of recurrence in the four variables using a monthly scale time series. We evaluate it with lagged autocorrelation (AC), fast Fourier transforms (FFT) and Colwell's indices of variables obtained from the EU-WATCH data set, which is composed of eight global hydrologic model (GHM) and land surface model (LSM) outputs. By setting a threshold to define high or low recurrence in the four variables, we classify each river basin into 16 possible classes. The overview of recurrence patterns at global scale suggested that precipitation is recurrent mainly in the humid tropics, Asian monsoon area and part of higher latitudes with an oceanic influence. Recurrence in evaporation was mainly dependent on the seasonality of energy availability, typically high in the tropics, temperate and sub-arctic regions. Recurrence in storage at higher latitudes depends on energy/water balances and snow, while that in runoff is mostly affected by the different combinations of these three variables. According to the river basin classification, 10 out of the 16 possible classes were present in the 35 largest river basins in the world. In the humid tropic region, the basins belong to a class with high recurrence in all the variables, while in the subtropical region many of the river basins have low recurrence. In the temperate region, the energy limited or water limited in summer characterizes the recurrence in storage, but runoff exhibits generally low recurrence due to the low recurrence in precipitation. In the sub-arctic and arctic regions, the amount of snow also influences the classes; more snow yields higher recurrence in storage and runoff. Our proposed framework follows a simple methodology that can aid in grouping river basins with similar characteristics of water, energy and storage cycles. The framework is applicable at different scales with different data sets to provide useful insights into the understanding of hydrologic regimes based on the classification. [ABSTRACT FROM AUTHOR]

Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces. [ABSTRACT FROM AUTHOR]

Lake Victoria is the largest lake in Africa and one of the two major sources of the Nile river. The water level of Lake Victoria is determined by its water balance, consisting of precipitation on the lake, evaporation from the lake, inflow from tributary rivers and lake outflow, controlled by two hydropower dams. Due to a scarcity of in situ observations, previous estimates of individual water balance terms are characterized by substantial uncertainties, which means that the water balance is often not closed independently. In this first part of a two-paper series, we present a water balance model for Lake Victoria, using state-of-the-art remote sensing observations, high-resolution reanalysis downscaling and outflow values recorded at the dam. The uncalibrated computation of the individual water balance terms yields lake level fluctuations that closely match the levels retrieved from satellite altimetry. Precipitation is the main cause of seasonal and interannual lake level fluctuations, and on average causes the lake level to rise from May to July and to fall from August to December. Finally, our results indicate that the 2004-2005 drop in lake level can be about half attributed to a drought in the Lake Victoria Basin and about half to an enhanced outflow, highlighting the sensitivity of the lake level to human operations at the outflow dam. [ABSTRACT FROM AUTHOR]

Lake Victoria, the second largest freshwater lake in the world, is one of the major sources of the Nile river. The outlet to the Nile is controlled by two hydropower dams of which the allowed discharge is dictated by the Agreed Curve, an equation relating outflow to lake level. Some regional climate models project a decrease in precipitation and an increase in evaporation over Lake Victoria, with potential important implications for its water balance and resulting level. Yet, little is known about the potential consequences of climate change for the water balance of Lake Victoria. In this second part of a two-paper series, we feed a new water balance model for Lake Victoria presented in the first part with climate simulations available through the COordinated Regional Climate Downscaling Experiment (CORDEX) Africa framework. Our results reveal that most regional climate models are not capable of giving a realistic representation of the water balance of Lake Victoria and therefore require bias correction. For two emission scenarios (RCPs 4.5 and 8.5), the decrease in precipitation over the lake and an increase in evaporation are compensated by an increase in basin precipitation leading to more inflow. The future lake level projections show that the dam management scenario and not the emission scenario is the main controlling factor of the future water level evolution. Moreover, inter-model uncertainties are larger than emission scenario uncertainties. The comparison of four idealized future management scenarios pursuing certain policy objectives (electricity generation, navigation reliability and environmental conservation) uncovers that the only sustainable management scenario is mimicking natural lake level fluctuations by regulating outflow according to the Agreed Curve. The associated outflow encompasses, however, ranges from 14m³ day-1 (-85%) to 200m³ day-1 (C100%) within this ensemble, highlighting that future hydropower generation and downstream water availability may strongly change in the next decades even if dam management adheres to he Agreed Curve. Our results overall underline that managing the dam according to the Agreed Curve is a key prerequisite for sustainable future lake levels, but that under this management scenario, climate change might potentially induce profound changes in lake level and outflow volume. [ABSTRACT FROM AUTHOR]

Regional evaporation has increased in many parts of the world in the last decades, but the drivers of these increases are widely debated. Part of the difficulty lies in the scarcity of high-quality long-term data on evaporation. In this paper, we analyze changes in catchment evaporation estimated from the water balances of 156 catchments in Austria over the period 1977-2014 and attribute them to changes in atmospheric demand and available energy, vegetation, and precipitation as possible drivers. Trend analyses suggest that evaporation has significantly increased in 60% of the catchments (p ≥ 0:05) with an average increase of 29±14mmyr-1 decade-1 (±standard deviation) or 4:9±2:3%decade-1. Pan evaporation based on 24 stations has, on average, increased by 29±5mmyr-1 decade-1 or 6:0±1:0%decade-1. Reference evaporation over the 156 catchments estimated by the Penman-Monteith equation has increased by 18±5mmyr-1 decade-1 or 2:8± 0:7%decade-1. Of these, 2.1% are due to increased global radiation and 0.5% due to increased air temperature according to the Penman-Monteith equation. A satellite-based vegetation index (NDVI) has increased by 0:02±0:01 decade-1 or 3:1±1:1%decade-1. Estimates of reference evaporation accounting for changes in stomata resistance due to changes in the NDVI indicate that the increase in vegetation activity has led to a similar increase in reference evaporation as changes in the climate parameters. A regression between trends in evaporation and precipitation yields a sensitivity of a 0:22±0:05mmyr-2 increase in evaporation to a 1mmyr-2 increase in precipitation. A synthesis of the data analyses suggests that 43±15%of the observed increase in catchment evaporation may be directly attributed to increased atmospheric demand and available energy, 34±14% to increased vegetation activity, and 24±5% to increases in precipitation. [ABSTRACT FROM AUTHOR]

Data scarcity is the biggest problem for scientific research related to hydrology and climate studies in the Great Himalayas region. High-quality precipitation data are difficult to obtain due to a sparse network, cold climate and high heterogeneity in topography. In this paper, we examine four datasets in northern India of the Western Himalayas: interpolated gridded data based on gauge observations (IMD, 1° × 1°, and APHRODITE, 0:25° × 0:25°), reanalysis data (ERA-Interim, 0:75°×0:75°) and high-resolution simulation by a regional climate model (WRF, 0:15° ×0:15°). The four datasets show a similar spatial pattern and temporal variation during the period 1981-2007, though the absolute values vary significantly (497-819 mm year-1). The differences are particularly large in July and August at the windward slopes and high-elevation areas. Overall, the datasets show that the summer is getting wetter and the winter is getting drier, though most of the trends in monthly precipitation are not significant. Trend analysis of summer and winter precipitation at every grids confirms the changes. Wetter summers will result in more and bigger floods in the downstream areas. Warmer and drier winters will result in less glacier accumulation. All the datasets show consistency in the period 1981-2007 and can give a spatial overview of the precipitation in the region. Comparing with the Bhuntar gauge data, the WRF dataset gives the best estimates of extreme precipitation. To conclude, we recommend the APHRODITE dataset and the WRF dataset for hydrological studies for their improved spatial variation which match the scale of hydrological processes as well as accuracy in extreme precipitation for flood simulation. [ABSTRACT FROM AUTHOR]

This paper compares state-of-the-art atmospheric moisture tracking models. Such models are typically used to study the water component of coupled land and atmosphere models, in particular quantifying moisture recycling and the source-sink relations between evaporation and precipitation. There are several atmospheric moisture tracking methods in use. However, depending on the level of aggregation, the assumptions made and the level of detail, the performance of these methods may differ substantially. In this paper, we compare three methods. The RCM-tag method uses highly accurate 3-D water tracking (including phase transitions) directly within a regional climate model (online), while the other two methods (WAM and 3D-T) use a posteriori (offline) water vapour tracking. The original version of WAM is a single-layer model, while 3D-T is a multi-layer model, but both make use the "well-mixed" assumption for evaporation and precipitation. The a posteriori models are faster and more flexible, but less accurate than online moisture tracking with RCM-tag. In order to evaluate the accuracy of the a posteriori models, we tagged evaporated water from Lake Volta in West Africa and traced it to where it precipitates. It is found that the strong wind shear in West Africa is the main cause of errors in the a posteriori models. The number of vertical layers and the initial release height of tagged water in the model are found to have the most significant influences on the results. With this knowledge small improvements have been made to the a posteriori models. It appeared that expanding WAM to a 2-layer model, or a lower release height in 3D-T, led to significantly better results. Finally, we introduced a simple metric to assess wind shear globally and give recommendations about when to use which model. The "best" method, however, very much depends on the research question, the spatial extent under investigation, as well as the available computational power. [ABSTRACT FROM AUTHOR]

We analyse the control of hydroclimatic factors on suspended sediment concentration (SSC) in Alpine catchments by differentiating among the potential contributions of erosion and suspended sediment transport driven by erosive rainfall, defined as liquid precipitation over snow-free surfaces, ice melt from glacierized areas, and snowmelt on hillslopes. We account for the potential impact of hydropower by intercepting sediment fluxes originated in areas diverted to hydropower reservoirs, and by considering the contribution of hydropower releases to SSC. We obtain the hydroclimatic variables from daily gridded datasets of precipitation and temperature, implementing a degree-day model to simulate spatially distributed snow accumulation and snow--ice melt. We estimate hydropower releases by a conceptual approach with a unique virtual reservoir regulated on the basis of a target-volume function, representing normal reservoir operating conditions throughout a hydrological year. An Iterative Input Selection algorithm is used to identify the variables with the highest predictive power for SSC, their explained variance, and characteristic time lags. On this basis, we develop a hydroclimatic multivariate rating curve (HMRC) which accounts for the contributions of the most relevant hydroclimatic input variables mentioned above. We calibrate the HMRC with a gradient-based nonlinear optimization method and we compare its performance with a traditional discharge-based rating curve.We apply the approach in the upper Rhône Basin, a large Swiss Alpine catchment heavily regulated by hydropower. Our results show that the three hydroclimatic processes -- erosive rainfall, ice melt, and snowmelt -- are significant predictors of mean daily SSC, while hydropower release does not have a significant explanatory power for SSC. The characteristic time lags of the hydroclimatic variables correspond to the typical flow concentration times of the basin. Despite not including discharge, the HMRC performs better than the traditional rating curve in reproducing SSC seasonality, especially during validation at the daily scale. While erosive rainfall determines the daily variability of SSC and extremes, ice melt generates the highest SSC per unit of runoff and represents the largest contribution to total suspended sediment yield. Finally, we show that the HMRC is capable of simulating climate-driven changes in fine sediment dynamics in Alpine catchments. In fact, HMRC can reproduce the changes in SSC in the past 40 years in the Rhône Basin connected to air temperature rise, even though the simulated changes are more gradual than those observed. The approach presented in this paper, based on the analysis of the hydroclimatic control of suspended sediment concentration, allows the exploration of climate-driven changes in fine sediment dynamics in Alpine catchments. The approach can be applied to any Alpine catchment with a pluvio-glacio-nival hydrological regime and adequate hydroclimatic datasets. [ABSTRACT FROM AUTHOR]

A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis. [ABSTRACT FROM AUTHOR]

Many multi-site stochastic models have been proposed for the generation of daily precipitation, but they generally focus on the reproduction of low to high precipitation amounts at the stations concerned. This paper proposes significant extensions to the multi-site daily precipitation model introduced by Wilks, with the aim of reproducing the statistical features of extremely rare events (in terms of frequency and magnitude) at different temporal and spatial scales. In particular, the first extended version integrates heavy-tailed distributions, spatial tail dependence, and temporal dependence in order to obtain a robust and appropriate representation of the most extreme precipitation fields. A second version enhances the first version using a disaggregation method. The performance of these models is compared at different temporal and spatial scales on a large region covering approximately half of Switzerland. While daily extremes are adequately reproduced at the stations by all models, including the benchmark Wilks version, extreme precipitation amounts at larger temporal scales (e.g., 3-day amounts) are clearly underestimated when temporal dependence is ignored. [ABSTRACT FROM AUTHOR]

For several hydrological modelling tasks, precipitation time series with a high (i.e. sub-daily) resolution are indispensable. The data are, however, not always available, and thus model simulations are used to compensate. A canonical class of stochastic models for sub-daily precipitation are Poisson cluster processes, with the original Bartlett-Lewis (OBL) model as a prominent representative. The OBL model has been shown to well reproduce certain characteristics found in observations. Our focus is on intensity-duration-frequency (IDF) relationships, which are of particular interest in risk assessment. Based on a high-resolution precipitation time series (5 min) from Berlin-Dahlem, OBL model parameters are estimated and IDF curves are obtained on the one hand directly from the observations and on the other hand from OBL model simulations. Comparing the resulting IDF curves suggests that the OBL model is able to reproduce the main features of IDF statistics across several durations but cannot capture rare events (here an event with a return period larger than 1000 years on the hourly timescale). In this paper, IDF curves are estimated based on a parametric model for the duration dependence of the scale parameter in the generalized extreme value distribution; this allows us to obtain a consistent set of curves over all durations. We use the OBL model to investigate the validity of this approach based on simulated long time series. [ABSTRACT FROM AUTHOR]

Peninsular India is a unique region with major mountain ranges that govern regional atmospheric circulation and precipitation variability, the monsoons, and regional geology at range of timescales and process scales. However, the landscape and climatic feature controls on streamflow variability at a regional scale using flow duration curves (FDCs) – compact descriptions of streamflow variability that offer a window into the multiple, interacting processes that contribute to streamflow variability – have received little attention. This study examines the suitability of the partitioning of (1) an annual streamflow FDC into seasonal FDCs and (2) a total streamflow FDC into fast- and slow-flow FDCs to unravel the process controls on FDCs at a regional scale, with application to low-gradient rivers flowing east from the Western Ghats in Peninsular India. The results indicate that bimodal rainfall seasonality and subsurface gradients explain the higher contribution of slow flow to total flow across the north–south gradient of the region. Shapes of fast and slow FDCs are controlled by recession parameters, revealing the role of climate seasonality and geological profiles, respectively. Systematic spatial variation across the north–south gradient is observed, highlighting the importance of the coherent functioning of landscape–hydroclimate settings in imparting a distinct signature of streamflow variability. The framework is useful to discover the role of time and process controls on streamflow variability in a region with seasonal hydro-climatology and hydro-geological gradients. [ABSTRACT FROM AUTHOR]

Soil-atmosphere feedback is a key for understanding the hydrological cycle and the direction of potential system changes. This paper presents an analytical framework to study the interplay between soil and atmospheric moisture, using as input only the boundary conditions at the upstream end of trajectory, assuming advective moisture transport with average wind speed along this trajectory and vertical moisture exchange with the soil compartment of uniform vertical properties. Precipitation, evaporation from interception and runoff are assumed to depend through simple functional relationships on the soil moisture or the atmospheric moisture. Evaporation from soil moisture (including transpiration) depends on both state variables, which introduces a nonlinear relationship between the two compartments. This nonlinear relationship can explain some apparently paradoxical phenomena such as a local decrease of precipitation accompanied by a runoff increase. The solutions of the resulting water balance equations correspond to two different spatial moisture regimes showing either an increasing or a decreasing atmospheric moisture content along a trajectory starting at the coast, depending on boundary conditions and parameters. The paper discusses how different model parameters (e.g. time scales of precipitation, evaporation or runoff) influence these regimes and how they can create regime switches. Such an analysis has potential to anticipate the range of possible land use and climate changes or to interpret the results of complex land-atmosphere interaction models. Based on derived analytical expressions for the Horton index, the Budyko curve and a precipitation recycling ratio, the analytical framework opens new perspectives for the classification of hydrological systems. [ABSTRACT FROM AUTHOR]

Here we use the skewness parameter, and the procedure developed in the companion paper (Ignaccolo and De Michele, 2012), to investigate the variability of instantaneous renormalized spectra of rain drop diameter in presence of orographic precipitation. Disdrometer data, available at Bodega Bay and Cazadero, California, are analyzed either as a whole, or as divided (using the bright band echo) in precipitation intervals weakly and strongly influenced by orography, and compared to results obtained at Darwin, Australia. We find that also at Bodega Bay and Cazadero exists a most common distribution of the skewness values of instantaneous spectra of drop diameter, but peaked at values greater than 0.64, found at Darwin. No appreciable differences are found in the skewness distributions of precipitation weakly and strongly influenced by orography. However the renormalized drop diameter spectra of precipitation with strong orographic component have fatter right tail than precipitation with a weaker orographic component. The differences between orographic and non-orographic precipitation are investigated within the parametric space represented by number of drops, mean value and standard deviation of drop diameter. A filter is developed which is able to identify 1 min time intervals during which precipitation is mostly of orographic origin. [ABSTRACT FROM AUTHOR]

In this paper we investigate the climatic and landscape controls on the flow duration curve (FDC) with the use of a physically-based rainfall-runoff model. The FDC is a stochastic representation of the variability of runoff, which arises from the transformation, by the catchment, of within-year variability of precipitation that can itself be characterized by a corresponding duration curve for precipitation (PDC). Numerical simulations are carried out with the rainfall-runoff model under a variety of combinations of climatic inputs (i.e. precipitation, potential evaporation, including their within-year variability) and landscape properties (i.e. soil type and depth). The simulations indicated that the FDC can be disaggregated into two components, with sharply differing characteristics and origins: the FDC for surface (fast) runoff (SFDC) and the FDC for subsurface (slow) runoff (SSFDC), which included base flow in our analysis. SFDC closely tracked PDC and can be approximated with the use of a simple, nonlinear (threshold) filter model. On the other hand, SSFDC tracked the FDC that is constructed from the regime curve (i.e. mean monthly runoff), which can be closely approximated by a linear filter model. Sensitivity analyses were carried out to understand the climate and landscape controls on each component, gaining useful physical insights into their respective shapes. In particular the results suggested that evaporation from dynamic saturated areas, especially in the dry season, can contribute to a sharp dip at the lower tail of the FDCs. Based on these results, we develop a conceptual framework for the reconstruction of FDCs in ungauged basins. This framework partitions the FDC into: (1) a fast flow component, governed by a filtered version of PDC, (2) a slow flow component governed by the regime curve, and (3) a correction to SSFDC to capture the effects of high evapotranspiration (ET) at low flows. [ABSTRACT FROM AUTHOR]

This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations. [ABSTRACT FROM AUTHOR]

This paper describes the evaluation of the KNMI Cloud Physical Properties -- Precipitation Properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) retrievals from visible, near-infrared and thermal infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites to estimate rain occurrence frequency and rain rate. For the 2005 and 2006 monsoon seasons, it is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence frequency and rain rate over West Africa with sufficient accuracy, using Tropical Monsoon Measurement Mission Precipitation Radar (TRMM-PR) as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring the seasonal and daytime evolution of rainfall during the West African monsoon (WAM), using Climate Prediction Center Morphing Technique (CMORPH) rainfall observations. The SEVIRI-detected rainfall area agrees well with TRMM-PR, with the areal extent of rainfall by SEVIRI being ∼10% larger than from TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. Examination of the TRMM-PR and CPP-PP cumulative frequency distributions revealed that differences between CPP-PP and TRMM-PR are generally within +/-10%. Relative to the AMMA rain gauge observations, CPP-PP shows very good agreement up to 5 mm h-1. However, at higher rain rates (5-16 mm h-1) CPP-PP overestimates compared to the rain gauges. With respect to the second goal of this paper, it was shown that both the accumulated precipitation and the seasonal progression of rainfall throughout the WAM is in good agreement with CMORPH, although CPP-PP retrieves higher amounts in the coastal region of West Africa. Using latitudinal Hovmüller diagrams, a fair correspondence between CPP-PP and CMORPH was found, which is reflected by high correlation coefficients (∼0.7) for both rain rate and rain occurrence frequency. The daytime cycle of rainfall from CPP-PP shows distinctly different patterns for three different regions in West Africa throughout the WAM, with a decrease in dynamical range of rainfall near the Inter Tropical Convergence Zone (ITCZ). The dynamical range as retrieved from CPP-PP is larger than that from CMORPH. It is suggested that this results from both the better spatio-temporal resolution of SEVIRI, as well as from thermal infrared radiances being partly used by CMORPH, which likely smoothes the daytime precipitation signal, especially in case of cold anvils from convective systems. The promising results show that the CPP-PP algorithm, taking advantage of the high spatio-temporal resolution of SEVIRI, is of added value for monitoring daytime precipitation patterns in tropical areas. [ABSTRACT FROM AUTHOR]

In this paper, we implement the region-of-influence (ROI) approach for modelling probabilities of heavy 1-day and 5-day precipitation amounts in the Czech Republic. The pooling groups are constructed according to (i) the regional homogeneity criterion (assessed by a built-in regional homogeneity test), which requires that in a pooling group the distributions of extremes are identical after scaling by the at-site mean; and (ii) the 5T rule, which sets the minimum number of stations to be included in a pooling group for estimation of a quantile corresponding to return period T . The similarity of sites is evaluated in terms of climatological and geographical site characteristics. We carry out a series of sensitivity analyses by means of Monte Carlo simulations in order to explore the importance of the individual site attributes, including hybrid pooling schemes that combine both types of the site attributes with different relative weights. We conclude that in a dense network of precipitation stations in the Czech Republic (on average 1 station in a square of about 20×20 km), the actual distance between the sites plays the most important role in determining the similarity of probability distributions of heavy precipitation. There are, however, differences between the optimum pooling schemes depending on the duration of the precipitation events. While in the case of 1-day precipitation amounts the pooling scheme based on the geographical proximity of sites outperforms all hybrid schemes, for multi-day amounts the inclusion of climatological site characteristics (although with much lower weights compared to the geographical distance) enhances the performance of the pooling schemes. This finding is in agreement with the climatological expectation since multi-day heavy precipitation events are more closely linked to some typical precipitation patterns over central Europe (related e.g. to the varied roles of Atlantic and Mediterranean influences) while the dependence of 1-day extremes on climatological characteristics such as mean annual precipitation is much weaker. The findings of the paper show a promising perspective for an application of the ROI methodology in evaluating outputs of regional climate models with high resolution: the pooling schemes might serve for defining weighting functions, and the large spatial variability in the grid-box estimates of high quantiles of precipitation amounts may efficiently be reduced. [ABSTRACT FROM AUTHOR]

In this paper catchments are conceptualised as linear space-time filters. Catchment area A is interpreted as the spatial support and the catchment response time T is interpreted as the temporal support of the runoff measurements. These two supports are related by T ∼ Ak which embodies the space-time connections of the rainfall-runoff process from a geostatistical perspective. To test the framework, spatiotemporal variograms are estimated from about 30 years of quarter hourly precipitation and runoff data from about 500 catchments in Austria. In a first step, spatio-temporal variogram models are fitted to the sample variograms for three catchment size classes independently. In a second step, variograms are fitted to all three catchment size classes jointly by estimating the parameters of a point/instantaneous spatiotemporal variogram model and aggregating (regularising) it to the spatial and temporal scales of the catchments. The exponential, Cressie-Huang and product-sum variogram models give good fits to the sample variograms of runoff with dimensionless errors ranging from 0.02 to 0.03, and the model parameters are plausible. This indicates that the first order effects of the spatio-temporal variability of runoff are indeed captured by conceptualising catchments as linear space-time filters. The scaling exponent κ is found to vary between 0.3 and 0.4 for different variogram models. [ABSTRACT FROM AUTHOR]

This paper presents the result of the regional coupled climatic and hydrologic model of the Nile Basin. For the first time the interaction between the climatic processes and the hydrological processes on the land surface have been fully coupled. The hydrological model is driven by the rainfall and the energy available for evaporation generated in the climate model, and the runoff generated in the catchment is again routed over the wetlands of the Nile to supply moisture for atmospheric feedback. The results obtained are quite satisfactory given the extremely low runoff coefficients in the catchment. The paper presents the validation results over the sub-basins: Blue Nile, White Nile, Atbara river, the Sudd swamps, and the Main Nile for the period 1995 to 2000. Observational datasets were used to evaluate the model results including radiation, precipitation, runoff and evaporation data. The evaporation data were derived from satellite images over a major part of the Upper Nile. Limitations in both the observational data and the model are discussed. It is concluded that the model provides a sound representation of the regional water cycle over the Nile. The sources of atmospheric moisture to the basin, and location of convergence/divergence fields could be accurately illustrated. The model is used to describe the regional water cycle in the Nile basin in terms of atmospheric fluxes, land surface fluxes and land surface-climate feedbacks. The monthly moisture recycling ratio (i.e. locally generated/total precipitation) over the Nile varies between 8 and 14%, with an annual mean of 11%, which implies that 89% of the Nile water resources originates from outside the basin physical boundaries. The monthly precipitation efficiency varies between 12 and 53%, and the annual mean is 28%. The mean annual result of the Nile regional water cycle is compared to that of the Amazon and the Mississippi basins. [ABSTRACT FROM AUTHOR]

In recent decades, the Sahel has witnessed a paradoxical increase in surface water despite a general precipitation decline. This phenomenon, commonly referred to as "the Sahelian paradox", is not completely understood yet. The role of cropland expansion due to the increasing food demand by a growing population has been often put forward to explain this situation for the cultivated Sahel. However, this hypothesis does not hold in pastoral areas where the same phenomenon is observed. Several other processes, such as the degradation of natural vegetation following the major droughts of the 1970s and the 1980s, the development of crusted topsoils, the intensification of the rainfall regime and the development of the drainage network, have been suggested to account for this situation. In this paper, a modeling approach is proposed to explore, quantify and rank different processes that could be at play in pastoral Sahel. The kinematic runoff and erosion model (KINEROS-2) is applied to the Agoufou watershed (245km2), in the Gourma region in Mali, which underwent a significant increase of surface runoff during the last 60 years. Two periods are simulated, the "past" case (1960-1975) preceding the Sahelian drought and the "present" case (2000-2015). Surface hydrology and land cover characteristics for these two periods are derived by the analysis of aerial photographs, available in 1956, and high-resolution remote sensing images in 2011. The major changes identified are (1) a partial crusting of isolated dunes, (2) an increase of drainage network density, (3) a marked decrease in vegetation with the nonrecovery of tiger bush and vegetation growing on shallow sandy soils, and (4) important changes in soil properties with the apparition of impervious soils instead of shallow sandy soil. The KINEROS-2 model was parameterized to simulate these changes in combination or independently. The results obtained by this model display a significant increase in annual discharge between the "past" and the "present" case (p value<0.001), which is consistent with observations, despite a slight overestimation of the past discharge. Mean annual discharges are estimated at 0.5 ×106m³ (2. mmyr-1) and 3.29×106m³ (13.4mmyr-1) for past and present, respectively. Changes in soil properties and vegetation cover (tiger bush thickets and grassland on shallow sandy soil) are found to be the main factors causing this increase of simulated runoff, with the drainage network development contributing to a lesser extent but with a positive feedback. These results shed a new light on the Sahelian paradox phenomenon in the absence of land use change and call for further tests in other areas and/or with other models. The synergetic processes highlighted here could play a role in other Sahelian watersheds where runoff increase has been also observed. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) plays a major role in regional and global climate. The understanding of latent heat (LE) flux can help to better describe the complex mechanisms and interactions between land and atmosphere. Despite its importance, accurate estimation of evapotranspiration (ET) over the TP remains challenging. Satellite observations allow for ET estimation at high temporal and spatial scales. The purpose of this paper is to provide a detailed cross-comparison of existing ET products over the TP. Six available ET products based on different approaches are included for comparison. Results show that all products capture the seasonal variability well with minimum ET in the winter and maximum ET in the summer. Regarding the spatial pattern, the High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) ET demonstrator dataset is very similar to the LandFlux-EVAL dataset (a benchmark ET product from the Global Energy and Water Cycle Experiment), with decreasing ET from the south-east to northwest over the TP. Further comparison against the LandFlux-EVAL over different sub-regions that are decided by different intervals of normalised difference vegetation index (NDVI), precipitation, and elevation reveals that HOLAPS agrees best with LandFlux-EVAL having the highest correlation coefficient (R) and the lowest root mean square difference (RMSD). These results indicate the potential for the application of the HOLAPS demonstrator dataset in understanding the land--atmosphere--biosphere interactions over the TP. In order to provide more accurate ET over the TP, model calibration, high accuracy forcing dataset, appropriate in situ measurements as well as other hydrological data such as runoff measurements are still needed. [ABSTRACT FROM AUTHOR]

Drought monitoring and early warning (M&EW) systems are a crucial component of drought preparedness. M&EW systems typically make use of drought indicators such as the Standardised Precipitation Index (SPI), but such indicators are not widely used in the UK. More generally, such tools have not been well developed for hydrological (i.e. streamflow) drought. To fill these research gaps, this paper characterises meteorological and hydrological droughts, and the propagation from one to the other, using the SPI and the related Standardised Streamflow Index (SSI), with the objective of improving understanding of the drought hazard in the UK. SPI and SSI time series were calculated for 121 near-natural catchments in the UK for accumulation periods of 1-24 months. From these time series, drought events were identified and for each event, the duration and severity were calculated. The relationship between meteorological and hydrological drought was examined by cross-correlating the 1-month SSI with various SPI accumulation periods. Finally, the influence of climate and catchment properties on the hydrological drought characteristics and propagation was investigated. Results showed that at short accumulation periods meteorological drought characteristics showed little spatial variability, whilst hydrological drought characteristics showed fewer but longer and more severe droughts in the south and east than in the north and west of the UK. Propagation characteristics showed a similar spatial pattern with catchments underlain by productive aquifers, mostly in the south and east, having longer SPI accumulation periods strongly correlated with the 1-month SSI. For catchments in the north and west of the UK, which typically have little catchment storage, standard-period average annual rainfall was strongly correlated with hydrological drought and propagation characteristics. However, in the south and east, catchment properties describing storage (such as base flow index, the percentage of highly productive fractured rock and typical soil wetness) were more influential on hydrological drought characteristics. This knowledge forms a basis for more informed application of standardised indicators in the UK in the future, which could aid in the development of improved M&EW systems. Given the lack of studies applying standardised indicators to hydrological droughts, and the diversity of catchment types encompassed here, the findings could prove valuable for enhancing the hydrological aspects of drought M&EW systems in both the UK and elsewhere. [ABSTRACT FROM AUTHOR]

This paper presents a regional extreme rainfall analysis based on 10 years of radar data for the 159 regions adopted for official natural hazard warnings in Switzerland. Moreover, a nowcasting tool aimed at issuing heavy precipitation regional alerts is introduced. The two topics are closely related, since the extreme rainfall analysis provides the thresholds used by the nowcasting system for the alerts. Warm and cold seasons' monthly maxima of several statistical quantities describing regional rainfall are fitted to a generalized extreme value distribution in order to derive the precipitation amounts corresponding to sub-annual return periods for durations of 1, 3, 6, 12, 24 and 48 h. It is shown that regional return levels exhibit a large spatial variability in Switzerland, and that their spatial distribution strongly depends on the duration of the aggregation period: for accumulations of 3 h and shorter, the largest return levels are found over the northerly alpine slopes, whereas for longer durations the southern Alps exhibit the largest values. The inner alpine chain shows the lowest values, in agreement with previous rainfall climatologies. The nowcasting system presented here is aimed to issue heavy rainfall alerts for a large variety of end users, who are interested in different precipitation characteristics and regions, such as, for example, small urban areas, remote alpine catchments or administrative districts. The alerts are issued not only if the rainfall measured in the immediate past or forecast in the near future exceeds some predefined thresholds but also as soon as the sum of past and forecast precipitation is larger than threshold values. This precipitation total, in fact, has primary importance in applications for which antecedent rainfall is as important as predicted one, such as urban floods early warning systems. The rainfall fields, the statistical quantity representing regional rainfall and the frequency of alerts issued in case of continuous threshold exceedance are some of the configurable parameters of the tool. The analysis of the urban flood which occurred in the city of Schaffhausen in May 2013 suggests that this alert tool might have complementary skill with respect to radar-based thunderstorm nowcasting systems for storms which do not show a clear convective signature. [ABSTRACT FROM AUTHOR]

This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and subdaily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km50 km with an intergauge distance of 6-12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1=4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70% of total variance in the SWM and only ~30% in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ~30 km in both monsoon seasons. Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insignificant diurnal cycle in the NEM. [ABSTRACT FROM AUTHOR]

This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydrometeorological situations. The comparison was conducted over a common 20-year period (1986-2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled data sets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function-transform approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSLCM5A- MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods. [ABSTRACT FROM AUTHOR]

This paper studies a 3-D state space representation of Budyko's framework designed to capture the mutual interdependence among long-term mean actual evapotranspiration (E), potential evapotranspiration (Ep) and precipitatioψn (P). For this purpose we use three dimensionless and dependent quantities: DE=P , ΦDEp=P and ΩDE=Ep. This 3-D space and its 2-D projections provide an interesting setting to test the physical soundness of Budyko's hypothesis. We demonstrate analytically that Budyko-type equations are unable to capture the physical limit of the relation between Ω and Φ in humid environments, owing to the unfeasibility of Ep=P D0 when E=Ep→1. Using data from 146 sub-catchments in the Amazon River basin we overcome this inconsistency by proposing a physically consistent power law: Ψ =k #934; e, with k =0.66, and e=0.83 (R2 =0.93). This power law is compared with two other Budyko-type equations. Taking into account the goodness of fits and the ability to comply with the physical limits of the 3-D space, our results show that the power law is better suited to model the coupled water and energy balances within the Amazon River basin. Moreover, k is found to be related to the partitioning of energy via evapotranspiration in terms of Ω. This suggests that our power law implicitly incorporates the complementary relationship of evapotranspiration into the Budyko curve, which is a consequence of the dependent nature of the studied variables within our 3-D space. This scaling approach is also consistent with the asymmetrical nature of the complementary relationship of evapotranspiration. Looking for a physical explanation for the parameters k and e, the interannual variability of individual catchments is studied. Evidence of space-time symmetry in Amazonia emerges, since both between-catchment and between-year variability follow the same Budyko curves. Finally, signs of co-evolution of catchments are explored by linking spatial patterns of the power law parameters with fundamental characteristics of the Amazon River basin. In general, k and e are found to be related to vegetation, topography and water in soils. [ABSTRACT FROM AUTHOR]

This paper presents three indices for evaluation of hydrometeorological extremes, considering them as areal precipitation events and trans-basin floods. In contrast to common precipitation indices, the weather extremity index (WEI) reflects not only the highest precipitation amounts at individual gauges but also the rarity of the amounts, the size of the affected area, and the duration of the event. Furthermore, the aspect of precipitation seasonality was considered when defining the weather abnormality index (WAI), which enables the detection of precipitation extremes throughout the year. The precipitation indices are complemented with the flood extremity index (FEI) employing peak discharge data. A unified design of the three indices, based on return periods of station data, enables one to compare easily interannual and seasonal distributions of precipitation extremes and large floods. The indices were employed in evaluation of 50 hydrometeorological extremes of each type (extreme precipitation events, seasonally abnormal precipitation events, and large floods) during the period 1961-2010 in the Czech Republic. A preliminary study of discrepancies among historic values of the indices indicated that variations in the frequency and/or magnitude of floods can generally be due not only to variations in the magnitude of precipitation events but also to variations in their seasonal distribution and other factors, primarily the antecedent saturation. [ABSTRACT FROM AUTHOR]

The rainfall in southern Africa has a large interannual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December-February (DJF) with lead times from 0 to 4 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data, which in turn were corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile mapping as well as post-processed using a precipitation threshold to define a dry day. The results indicate that the forecasts show skill in predicting dry spells in comparison with a climatological ensemble based on previous years. Quantile mapping in combination with a precipitation threshold improved the skill of the forecast. The skill in prediction of dry spells was largest over the most droughtsensitive region. Seasonal forecasts have the potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties. [ABSTRACT FROM AUTHOR]

This paper quantitatively analyzed the evolution of human-water relationships in the Heihe River basin of northern China over the past 2000 years by reconstructing the catchment water balance by partitioning precipitation into evapotranspiration and runoff. The results provided the basis for investigating the impacts of societies on hydrological systems. Based on transition theory and the rates of changes of the population, human water consumption and the area of natural oases, the evolution of human-water relationships can be divided into four stages: predevelopment (206 BC-AD 1368), take-off (AD 1368-1949), acceleration (AD 1949-2000), and the start of a rebalancing between human and ecological needs (post AD2000). Our analysis of the evolutionary process revealed that there were large differences in the rate and scale of changes and the period over which they occurred. The transition of the human-water relationship had no fixed pattern. This understanding of the dynamics of the human-water relationship will assist policy makers in identifying management practices that require improvement by understanding how today's problems were created in the past, which may lead to more sustainable catchment management in the future. [ABSTRACT FROM AUTHOR]

As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction - SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 x 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data. [ABSTRACT FROM AUTHOR]

The summer flood of 2013 set a new record for large-scale floods in Germany for at least the last 60 years. In this paper we analyse the key hydro-meteorological factors using extreme value statistics as well as aggregated severity indices. For the long-term classification of the recent flood we draw comparisons to a set of past large-scale flood events in Germany, notably the high-impact summer floods from August 2002 and July 1954. Our analysis shows that the combination of extreme initial wetness at the national scale-caused by a pronounced precipitation anomaly in the month of May 2013-and strong, but not extraordinary event precipitation were the key drivers for this exceptional flood event. This provides additional insights into the importance of catchment wetness for high return period floods on a large scale. The database compiled and the methodological developments provide a consistent framework for the rapid evaluation of future floods. [ABSTRACT FROM AUTHOR]

This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions. [ABSTRACT FROM AUTHOR]