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We present a statistical method to quantify the contribution of urbanization to precipitation changes during 1958–2017 across the greater Beijing–Tianjin–Hebei metropolitan region in northern China. We find distinct trends in precipitation in the past six decades: decreasing in annual and summer while increasing in other seasons. The spatial patterns of precipitation show discernible terrain-induced characteristics with high values in the buffer zones of plain and mountain areas and low values in the northwestern mountainous regions. Our results indicate that although urbanization has limited impacts on the trends and spatial patterns of precipitation, it has a positive contribution to the changes in precipitation for about 80% of the comparisons conducted, especially in autumn (100%), with the negative contribution being dominant in summer (66.67%). In addition, these results are sensitive to the classifications of urban and rural stations, suggesting that how to classify urban/rural areas is a crucial step to estimate the potential contribution of urbanization to precipitation changes. These findings also support that urbanization can diversify and enhance the variations in precipitation, with urban areas becoming a secondary center along with more increasing or less decreasing trends in precipitation.

Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method.•The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas.•The representative locations selected following the SRR method have sufficient flood data for developing emulation models.•Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%., Graphical abstract Image, graphical abstract

Moisture recycling by terrestrial evaporation and transpiration has recently been confirmed as an important source of precipitation, but little is known of this contribution in inland river basins of China. This study determines the fractions contributed by terrestrial evaporation and transpiration to precipitation in the Shiyang river basin, located in Gansu province of northwestern China. The basin has an area of 4.16 × 104 km2 and mean annual precipitation of 300 mm/yr. Hundreds of samples of precipitation, surface water, plant stem water and soil water were collected and analyzed for their isotopic compositions. The Craig-Gordon model and a three-end-member mixing model were used to partition precipitation into water sourced from evaporation, transpiration and advection. On average, evaporation, transpiration and advection were responsible for 9%, 14% and 77%, respectively, of precipitation, and the contribution from terrestrial evaporation and transpiration also increased with elavation; they also varied with season, being highest in August. The significant contribution from transpiration highlights the importance of vegetation conservation in this ecologically fragile basin.

Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0–14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to −15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.

We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modelling is accounted for with a 3-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12-months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12-month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers. This article is protected by copyright. All rights reserved.

The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP’s capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982–2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

Under a warming climate, the cryosphere shrinking is accelerating and the evopotranspiration is strengthening, which have caused the spatial and temporal changes of water resources and water cycle in inland river basins. With a vast area of 1.0 × 104 km2 and an annual average evopotranspiration of 515 mm, the influence from recycling moisture to precipitation and the contribution from cryosphere meltwater to runoff have been quantified in source region of Heihe river basin at the central Qilian Mountains, where 365 glaciers locate within an area of 77.22 km2, whiles frozen soil accounts for 80% of the region. Results indicated that frozen soil meltwater and glacier snow meltwater have contributed by 28% and 7%, on average, to the outlet river water in the basin, respectively. It was founded that evaporation and transpiration moisture were responsible for 10% and 17%, on average, of local precipitation, respectively. These findings provide new progresses on isotopic hydrology of cold basin, which will strengthen further understanding on the role of frozen soil meltwater and local moisture recycling in the water cycle for inland river basins.

Assessment of forecast precipitation is required before it can be used as input to hydrological models. Using radar observations in southeastern Australia, forecast rainfall from the Australian Community Climate Earth-System Simulator (ACCESS) was evaluated for 2010 and 2011. Radar rain intensities were first calibrated to gauge rainfall data from four research rainfall stations at hourly time steps. It is shown that the Australian ACCESS model (ACCESS-A) overestimated rainfall in low precipitation areas and underestimated elevated accumulations in high rainfall areas. The forecast errors were found to be dependent on the rainfall magnitude. Since the cumulative rainfall observations varied across the area and through the year, the relative error (RE) in the forecasts varied considerably with space and time, such that there was no consistent bias across the study area. Moreover, further analysis indicated that both location and magnitude errors were the main sources of forecast uncertainties on ho...

There are a number of challenges that must be overcome if GCM forecasts are to be widely adopted in climate-sensitive industries such as agriculture and water management. GCM outputs are frequently biased relative to observations and their ensembles are unreliable in conveying uncertainty through appropriate spread. The calibration, bridging, and merging (CBaM) method has been shown to be an effective tool for postprocessing GCM rainfall forecasts to improve ensemble forecast attributes. In this study, CBaM is modified and extended to postprocess seasonal minimum and maximum temperature forecasts from the POAMA GCM in Australia. Calibration is postprocessing GCM forecasts using a statistical model. Bridging is producing additional forecasts using statistical models that have other GCM output variables (e.g., SST) as predictors. It is demonstrated that merging calibration and bridging forecasts through CBaM effectively improves the skill of POAMA seasonal minimum and maximum temperature forecasts for Australia. It is demonstrated that CBaM produces bias-corrected forecasts that are reliable in ensemble spread and reduces forecasts to climatology when there is no evidence of forecasting skill. This work will help enable the adoption of GCM forecasts by climate-sensitive industries for quantitative modeling and decision-making.

The absence of long sub-daily rainfall records can hamper development of continuous streamflow forecasting systems run at sub-daily time steps. We test the hypothesis that simple disaggregation of daily rainfall data to hourly data, combined with hourly streamflow data, can be used to establish efficient hourly rainfall-runoff models. The approach is tested on four rainfall-runoff models and a range of meso-scale catchments (150-3500?km2). We also compare our disaggregation approach to a method of parameter scaling that attains an hourly parameter-set from daily data.Simple disaggregation of daily rainfall produces hourly streamflow models that perform almost as well as those developed from hourly rainfall data. Rainfall disaggregation performs at least as well as parameter scaling, and often better. For the catchments and models we test, simple disaggregation is a very straightforward and effective way to establish hydrological models for continuous sub-daily streamflow forecasting systems when sub-daily rainfall data are unavailable. Daily rainfall is disaggregated to hourly to calibrate hourly hydrological models.Models perform almost as well as models calibrated with observed hourly rainfall.Disaggregation performed at least as well as parameter scaling.A way to develop hourly river forecast systems with daily rainfall.

Cryosphere meltwater has been recognized as an important source of local water resources. However, there are few assessments on the contribution from frozen soil meltwater. In this study, we quantify the fraction from frozen soil meltwater and glacier snow meltwater to runoff in Shiyang River, an in-land river basin of northwestern China, where glaciers were disappearing and frozen soil was in degradation. A large number of samples for precipitation, surface water, groundwater, frozen soil meltwater and glacier snow meltwater have been collected and analyzed for their isotopic compositions. Results indicated that runoff was mainly generated from the cryosphere belt, and it was found that frozen soil meltwater was responsible for 20%, on average, of the outlet river water during flood season in the basin. The contribution rates from frozen soil meltwater to the outlet river runoff changed among the seven sub-basins. The results confirmed that frozen soil meltwater has played an important role in runoff of in-land river basins, and evaluating its influence on the hydrological process under a climate warming scenario is of great significance.

The Schaake shuffle is a simple and effective method for re-ordering calibrated ensemble forecasts. It is widely used in forecast calibration methods where realistic spatial and temporal sequences are important. We illustrate a previously unidentified problem with the application of the Schaake shuffle. When the autocorrelation of uncalibrated forecasts is markedly different from observations, the Schaake shuffle cannot guarantee that the calibrated ensemble is reliable when ensemble members are accumulated through time. Accumulations in time and space are particularly important for applications that integrate rainfall over these dimensions, notably hydrological modelling. We demonstrate that ensemble means of uncalibrated forecasts tend to be more autocorrelated than observations. This can cause poor reliability if variables are accumulated across lead times under certain conditions, even if forecasts are perfectly reliable at individual lead times. Specifically, if the following conditions occur, ensemble predictions of accumulated variables tend to be too wide: 1) Forecasts are more autocorrelated than observations. 2) Forecasts are skillful; i.e., cross-correlations between forecasts and observations are high. We show with a real-world case study that these conditions can readily occur. We discuss potential solutions to this issue.

Errors from hydrological simulations have substantial influence on hydrological applications. There are increasing interests to incorporate statistical models of the errors (error models) into hydrological applications to improve simulations. However, conventional error models are usually temporally unvaried and may be inadequate to handle model simulation errors when the underlying error statistics are strongly seasonal. To overcome this problem, the use of temporally varied error models in both prediction and forecasting applications are investigated. We analyze different levels of temporal granularity to identify the optimal temporally varied error model to best improve simulations and to quantify uncertainty. The Error Reduction and Representation In Stages (ERRIS) model is adapted in this study to improve streamflow simulations produced by the Variable Infiltration Capacity model for the Yarlung Tsangbo River basin. Well-marked seasonal and sub-seasonal variations in error statistics are found. Accordingly, three temporally varied ERRIS models are constructed at semi-annual (ERRIS-H), seasonal (ERRIS-S) and monthly (ERRIS-M) temporal granularity and compared with a benchmark model, the temporally unvaried model (ERRIS-A). Results show that the temporally varied ERRIS models are considerably more effective than the temporally unvaried one, with 34% reduction in continuous ranked probability score (CRPS) and 23% increase in Nash-Sutcliffe Efficiency (NSE) for prediction applications. With respect to forecasting applications, improvements of about 7% in CRPS are achieved by the temporally varied models. The performance of different temporally varied error models roughly follows the same order as the level of temporal granularity. Generally, ERRIS-S and ERRIS-M are similarly effective, with ERRIS-M providing additional improvement by more than 15% in CRPS for the spring season and by at least 30% for the autumn season. It is concluded that the consistency of temporal granularity between error models and variations of error statistics is the key to effective error reduction and uncertainty quantification. When complicated hydrological processes exist, hydrological model tends to produce seasonally and even sub-seasonally varied error statistics. Consequently, temporally finer error models are expected to lead to higher accuracy and reliability.

Available high quality precipitation datasets generally cover only recent periods. To extend these datasets back in time is challenging due to historically lower rain gauge network density or poorer remote sensing. In this study, a Bayesian joint probability method is presented to extend the temporal coverage of high quality precipitation datasets by calibrating reanalysis estimates. Relationships between precipitation estimates from a high quality dataset and precipitation estimates from a reanalysis dataset are established by using data from the overlapping period of the two datasets. The relationships are then used to calibrate reanalysis estimates for the period when only reanalysis data is available. Rain gauge observations are also used to enhance the calibration. The method brings the reanalysis dataset to the same spatial resolution as the high quality dataset, corrects bias, and makes the reanalysis data more consistent with the high quality dataset. The calibrated estimates generated are presented in the form of ensembles to represent uncertainty. The method is applied to the Han River basin in China, using the CMPA high quality dataset and the ERA-interim reanalysis dataset. The method is shown to be highly effective in improving the quality of ERA-Interim.

Hydrodynamic models are commonly used to understand flood risk and inform flood management decisions. However, their high computational cost can impose practical limits on real-time flood forecasting and uncertainty analysis which require fast modelling response or many model runs. Emulation models have the potential to reduce simulation times while still maintaining acceptable accuracy of the estimates. In this study, we propose an artificial neural networks (ANNs) based emulation modelling framework for flood inundation modelling. We investigate the suitability of ANNs as flood inundation models using a river segment in Queensland, Australia. Our results show that ANNs can model the time series behaviour of flood inundation and significantly reduce the simulation times required, which facilitates their use in applications requiring fast model response or a large number of model runs. Based the model development process and results, the major challenges and future research directions are discussed.

Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

SLEUTH Model (slope, landuse, exclusion, urban extent, transportation and hillshade) is an important tool for landuse planning and land policy. To evaluate the performance of SLEUTH model, implementation of Sensitivity Analysis (SA) is essential. The main limitation of SA in SLEUTH application is a lack of insight into model input self-modification parameters (SMPs) variation, namely, uncertainty involved in the model transfer metrics and model presumptions, which often misled the decision makers and model users. To address this issue, this study divided the forward process into two stages. Firstly, during the transfer process ①, the contribution scores of five SMPs were drawn, and parameters highly sensitive to model output were given. Apart from that, the recommended initial value for SMPs of 0.11, 0.2, 0.87, 1.13, 15, 1.01, 0.49 were found to be subordinated to such a heterogeneous urban area simulation. Secondly, during the transfer process ②, SMP caused imagery metrics indicated the disparity between parameters with Fixed Reference and with Successive Reference. Reversely, it derives reasonable threshold for the best fit values of five prediction coefficients’ initialization by comparing the real image with the predicted one. The framework of SLEUTH model transfer mechanism not only could distinguish highly sensitive SMPs with higher contribution scores, but also could give parametric analysis for simulation imagery based on metrics. The study was found to be a practical tool for quantization response of model input variables for modelling complex urban systems. So, this insight can help geographic information scientists decide how to find out the inner forward transfer mechanism of SLEUTH model for further make good use of it and improve the model.

Linking atmospheric and hydrological models is challenging because of a mismatch of spatial and temporal resolutions in which the models operate: dynamic hydrological models need input at relatively fine temporal (daily) scale, but the outputs from general circulation models are usually not realistic at the same scale, even though fine scale outputs are available. Temporal dimension downscaling methods called disaggregation are designed to produce finer temporal-scale data from reliable larger temporal-scale data. Here, we investigate a hybrid stochastic weather-generation method to simulate a high-frequency (daily) precipitation sequence based on lower frequency (monthly) amounts. To deal with many small precipitation amounts and capture large amounts, we divide the precipitation amounts on rainy days (with non-zero precipitation amounts) into two states (named moist and wet states, respectively) by a pre-defined threshold and propose a multi-state Markov chain model for the occurrences of different states (also including non-rain days called dry state). The truncated Gamma and censored extended Burr XII distributions are then employed to model the precipitation amounts in the moist and wet states, respectively. This approach avoids the need to deal with discontinuity in the distribution, and ensures that the states (dry, moist and wet) and corresponding amounts in rainy days are well matched. The method also considers seasonality by constructing individual models for different months, and monthly variation by incorporating the low-frequency amounts as a model predictor. The proposed method is compared with existing models using typical catchment data in Australia with different climate conditions (non-seasonal rainfall, summer rainfall and winter rainfall patterns) and demonstrates better performances under several evaluation criteria which are important in hydrological studies.

Summary Uncertainty is inherent in streamflow forecasts and is an important determinant of the utility of forecasts for water resources management. However, predictions by deterministic models provide only single values without uncertainty attached. This study presents a method for using a Bayesian joint probability (BJP) model to post-process deterministic streamflow forecasts by quantifying predictive uncertainty. The BJP model is comprised of a log–sinh transformation that normalises hydrological data, and a bi-variate Gaussian distribution that characterises the dependence relationship. The parameters of the transformation and the distribution are estimated through Bayesian inference with a Monte Carlo Markov chain (MCMC) algorithm. The BJP model produces, from a raw deterministic forecast, an ensemble of values to represent forecast uncertainty. The model is applied to raw deterministic forecasts of inflows to the Three Gorges Reservoir in China as a case study. The heteroscedasticity and non-Gaussianity of forecast uncertainty are effectively addressed. The ensemble spread accounts for the forecast uncertainty and leads to considerable improvement in terms of the continuous ranked probability score. The forecasts become less accurate as lead time increases, and the ensemble spread provides reliable information on the forecast uncertainty. We conclude that the BJP model is a useful tool to quantify predictive uncertainty in post-processing deterministic streamflow forecasts.

This paper evaluates a postprocessing method for deterministic quantitative precipitation forecasts (raw QPFs) from a numerical weather prediction model. The postprocessing aims to produce calibrated QPF ensembles that are bias free, more accurate than raw QPFs, and reliable for use in streamflow forecasting applications. The method combines a simplified version of the Bayesian joint probability (BJP) modeling approach and the Schaake shuffle. The BJP modeling approach relates raw QPFs and observed precipitation by modeling their joint distribution. It corrects biases in the raw QPFs and generates ensemble forecasts that reflect the uncertainty in the raw QPFs. The BJP modeling approach is applied to each lead time and each forecast location separately. The Schaake shuffle is then employed to produce calibrated QPFs with appropriate space–time correlations by linking ensemble members generated by the BJP modeling approach. Calibrated QPFs are produced for 10 Australian catchments that cover a wide range of climatic conditions and hydrological characteristics. The calibrated QPFs are bias free, contain smaller forecast errors than that of the raw QPFs, reliably quantify the forecast uncertainty at a range of lead times, and successfully discriminate common and rare events of precipitation occurrences at shorter lead times. The postprocessing method is able to instill realistic within-catchment spatial variability in the QPFs, which is crucial for accurate and reliable streamflow forecasting.

Seasonal rainfall forecasts in Australia are issued based on concurrent sea surface temperature anomalies (SSTAs) using a Bayesian model averaging (BMA) approach. The SSTA fields are derived from the Predictive Ocean-Atmosphere Model for Australia (POAMA) initialized in the preceding season. This study investigates the merits of the rainfall forecasted using POAMA SSTAs in contrast to that forecasted using a multimodel combination of SSTAs derived using five existing models. In addition, seasonal rainfall forecasts derived from multimodel and POAMA SSTA fields are subsequently combined to obtain a single weighted forecast over Australia. These three forecasts are compared against “idealized” forecasts where observed SSTAs are used instead of those predicted. The results indicate that while seasonal rainfall forecasts derived using multimodel-based SSTA indices offer improvements in selected seasons over a majority of grid cells in comparison to the case where a single SSTA model is used in two seasons, these improvements are not as significant as the improvements in the SSTA field that drive the rainfall forecasting model. The forecasts derived from the combination of multimodel and POAMA SSTA indices forecasts are found to offer greater improvements over the multimodel or the POAMA forecasts for a majority of grid cells in all seasons. It is also observed that these combined forecasts are touching the upper limits of forecastability, which are reached when observed SSTAs are used to forecast the rainfall. This suggests that further improvements in rainfall forecasting are only possible through the use of an improved forecasting algorithm, and not the driver (SSTA) information used in the current study.

Real-time discharge observations can be assimilated into flood models to improve forecast accuracy; however, the presence of time lags in the routing process and a lack of methods to quantitatively represent different sources of uncertainties challenge the implementation of data assimilation techniques for operational flood forecasting. To address these issues, an integrated error parameter estimation and lag-aware data assimilation (IEELA) scheme was recently developed for a lumped model. The scheme combines an ensemble-based maximum a posteriori (MAP) error estimation approach with a lag-aware ensemble Kalman smoother (EnKS). In this study, the IEELA scheme is extended to a semidistributed model to provide for more general application in flood forecasting by including spatial and temporal correlations in model uncertainties between subcatchments. The result reveals that using a semidistributed model leads to more accurate forecasts than a lumped model in an open-loop scenario. The IEELA scheme improves the forecast accuracy significantly in both lumped and semidistributed models, and the superiority of the semidistributed model remains in the data assimilation scenario. However, the improvements resulting from IEELA are confined to the outlet of the catchment where the discharge observations are assimilated. Forecasts at “ungauged” internal locations are not improved, and in some instances, even become less accurate.

The Australian Bureau of Meteorology produces statistical and dynamic seasonal streamflow forecasts. The statistical and dynamic forecasts are similarly reliable in ensemble spread; however, skill varies by catchment and season. Therefore, it may be possible to optimize forecasting skill by weighting and merging statistical and dynamic forecasts. Two model averaging methods are evaluated for merging forecasts for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. The second method, quantile model averaging (QMA), applies averaging to forecast variable values (quantiles) for a given cumulative probability (quantile fraction). BMA and QMA are found to perform similarly in terms of overall skill scores and reliability in ensemble spread. Both methods improve forecast skill across catchments and seasons. However, when both the statistical and dynamical forecasting approaches are skillful but produce, on special occasions, very different event forecasts, the BMA merged forecasts for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged forecasts for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the forecast users. Such special occasions are found to be rare. However, every forecast counts in an operational service, and therefore the occasional contrast in merged forecasts between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.

Dairy farmers in the southern Murray–Darling (M–DB) of Australia are considering subsurface drip irrigation (SDI) for pasture production in response to reduced water supply. Use of SDI in pasture production is a significant departure from previous SDI knowledge because in pasture irrigation (1) uniform moisture, nutrients and pasture growth is sought across the full paddock width, (2) the pasture is grazed by dairy cattle, and (3) is grown on duplex soils with distinctive hydraulic behaviour. A field experiment was conducted on two dairy farms with contrasting subsoil hydraulic characteristics to investigate the impact of SDI design (tape spacing) and operation (irrigation frequency) on pasture productivity and hydrology under grazed conditions. Treatments comprising of tape spacing (0.6, 1.0 and 1.4 m) and irrigation frequency (D or 4D) were imposed. SDI successfully supported contiguous perennial pasture under grazing during a period of drought. The impact of SDI design and operation on pasture accumulation and its spatial variability differed strongly between sites, reflecting soil hydraulic characteristics. Pasture composition and irrigation efficiency were insensitive to SDI design and operation at both sites. Water balance analysis indicated that deep percolation was the main pathway for water loss in SDI pasture production, though the dominance of this over surface runoff will be influenced by soil hydraulic properties. SDI can support grazed pasture production on the duplex soils of the southern M–DB; however, its performance may be insufficient to offset the high economic cost of SDI technology.

For streamflow forecasting, rainfall–runoff models are often augmented with updating procedures that correct forecasts based on the latest available streamflow observations of streamflow. A popular approach for updating forecasts is autoregressive (AR) models, which exploit the "memory" in hydrological model simulation errors. AR models may be applied to raw errors directly or to normalised errors. In this study, we demonstrate that AR models applied in either way can sometimes cause over-correction of forecasts. In using an AR model applied to raw errors, the over-correction usually occurs when streamflow is rapidly receding. In applying an AR model to normalised errors, the over-correction usually occurs when streamflow is rapidly rising. In addition, when parameters of a hydrological model and an AR model are estimated jointly, the AR model applied to normalised errors sometimes degrades the stand-alone performance of the base hydrological model. This is not desirable for forecasting applications, as forecasts should rely as much as possible on the base hydrological model, with updating only used to correct minor errors. To overcome the adverse effects of the conventional AR models, a restricted AR model applied to normalised errors is introduced. We show that the new model reduces over-correction and improves the performance of the base hydrological model considerably.

Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April-September) streamflow forecasts for inflow to Pakistan's two largest Upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESP) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts show that the BJP approach using two predictors (March flow and an ENSO climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April–September), early Kharif (April–June) and late Kharif (July–September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal time scales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. However, for hydrological applications, GCM forecasts are often biased and unreliable in uncertainty spread, and therefore calibration is required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S) based on the Bayesian joint probability approach for calibrating daily forecasts and the Schaake Shuffle approach for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

International audience; GCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called ‘‘coherence.’’ This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw forecasts are not significantly positively correlated with observations, QM tends to produce negatively skillful forecasts. Even when there is significant positive correlation, QM cannot ensure reliability and coherence for postprocessed forecasts. Therefore, QM is not a fully satisfactory method for post- processing forecasts where the issues of bias, reliability, and coherence pre-exist. Alternative post- processing methods based on ensemble model output statistics (EMOS) are available that achieve not only unbiased but also reliable and coherent forecasts. This is shown with one such alternative, the Bayesian joint probability modeling approach