Ensemble Precipitation Research Articles

Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications

Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications

Evaluation of TIGGE Precipitation Forecast and Its Applicability in Streamflow Predictions over a Mountain River Basin, China

The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that: (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1~5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins.

Ensemble Representation of Satellite Precipitation Uncertainty Using a Nonstationary, Anisotropic Autocorrelation Model

AbstractThe usefulness of satellite multi‐sensor precipitation (SMP) and other satellite‐informed precipitation products in water resources modeling can be hindered by substantial errors which vary considerably with spatiotemporal scale. One approach to cope with these errors is to combine SMPs with ensemble generation methods, such that each ensemble member reflects one plausible realization of the true—but unknown—precipitation. This requires replicating the spatiotemporal autocorrelation structure of SMP errors. The climatology of this structure is unknown for most locations due to a lack of ground‐reference observations, while the unique anisotropy and nonstationarity within any particular precipitation system limit the relevance of this climatology to the depiction of individual storm systems. Characterizing and simulating autocorrelation across spatiotemporal scales has thus been called a grand challenge within the precipitation community. We introduce the Space‐Time Rainfall Error and Autocorrelation Model (STREAM), which combines uncalibrated, anisotropic and nonstationary SMP spatiotemporal correlation modeling with a pixel‐scale precipitation error model to stochastically generate ensemble precipitation fields that resemble “ground truth” precipitation. We generate STREAM precipitation ensembles at high resolution (1‐hr, 0.1°) and evaluate these ensembles at multiple scales. STREAM ensembles consistently bracket ground‐truth observations and replicate the autocorrelation structure of ground‐truth precipitation fields. STREAM is compatible with pixel‐scale error/uncertainty formulations beyond those presented here, and could be applied to other precipitation sources such as numerical weather predictions or blended products. Although ground truth is used here to parameterize pixel‐scale uncertainty, if combined with other recent work in SMP uncertainty characterization, STREAM could be used without any ground data.

Short-Term Convection-Allowing Ensemble Precipitation Forecast Sensitivity to Resolution of Initial Condition Perturbations and Central Initial States

Abstract Nine sets of 36-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were produced over the conterminous United States for a 4-week period. These CAEs had identical configurations except for their initial conditions (ICs), which were constructed to isolate CAE forecast sensitivity to resolution of IC perturbations and central initial states about which IC perturbations were centered. The IC perturbations and central initial states were provided by limited-area ensemble Kalman filter (EnKF) analyses with both 15- and 3-km horizontal grid spacings, as well as from NCEP’s Global Forecast System (GFS) and Global Ensemble Forecast System. Given fixed-resolution IC perturbations, reducing horizontal grid spacing of central initial states improved ∼1–12-h precipitation forecasts. Conversely, for constant-resolution central initial states, reducing horizontal grid spacing of IC perturbations led to comparatively smaller short-term forecast improvements or none at all. Overall, all CAEs initially centered on 3-km EnKF mean analyses produced objectively better ∼1–12-h precipitation forecasts than CAEs initially centered on GFS or 15-km EnKF mean analyses regardless of IC perturbation resolution, strongly suggesting it is more important for central initial states to possess fine-scale structures than IC perturbations for short-term CAE forecasting applications, although fine-scale perturbations could potentially be critical for data assimilation purposes. These findings have important implications for future operational CAE forecast systems and suggest CAE IC development efforts focus on producing the best possible high-resolution deterministic analyses that can serve as central initial states for CAEs. Significance Statement Ensembles of weather model forecasts are composed of different “members” that, when combined, can produce probabilities that specific weather events will occur. Ensemble forecasts begin from specified atmospheric states, called initial conditions. For ensembles where initial conditions differ across members, the initial conditions can be viewed as a set of small perturbations added to a central state provided by a single model field. Our study suggests it is more important to increase horizontal resolution of the central state than resolution of the perturbations when initializing ensemble forecasts with 3-km horizontal grid spacing. These findings suggest a potential for computational savings and a streamlined process for improving high-resolution ensemble initial conditions.

Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation

Abstract. In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance.

Future droughts in northern Italy: high-resolution projections using EURO-CORDEX and MED-CORDEX ensembles

We analyse the expected characteristics of drought events in northern Italy for baseline (1971–2000), near (2021–2050), and far (2071–2100) future conditions, estimating the drought spatial extent and duration, the percentage of affected area, and the frequency of drought episodes. To this end, daily ensembles of precipitation and temperature records from Global Climate Models (GCMs) and Regional Climate Models (RCMs) pairs, extracted from EURO-CORDEX and MED-CORDEX for the RCP 4.5 and 8.5 scenarios, are collected at spatial resolution of 0.11 degrees. Before the analysis, model outputs are validated on daily weather station time series, and scaling factors for possible use in bias correction are identified. Annual temperature and precipitation anomalies for near and far future conditions are investigated; drought events are identified by the standardized precipitation evapotranspiration index and standardized precipitation index at the 12-, 24-, and 36-month timescales. This study highlights the importance of using multiple drought indicators in the detection of drought events, since the comparison reveals that evapotranspiration anomaly is the main triggering factor. For both scenarios, the results indicate an intensification of droughts in northern Italy for the period 2071–2100, with the Alpine chain being especially affected by an increase of drought severity. A North-to-South spatial gradient of drought duration is also observed.

Spatial verification of a regional ensemble precipitation forecasting system at the Hydrometeorological Research Center of the Russian Federation using a free verification package, MET

Ensemble weather forecasting is a necessary tool for assessing the probabilities of weather events, alternative scenarios of their development, and forecast uncertainties. At the Hydrometeorological Research Center of the Russian Federation, a regional high-resolution ensemble system is under development with a grid mesh of 2.2 km for the central region of Russia. Consequently, developing a state-of-the-art ensemble verification system is of high priority. In this work, preliminary results of a high-resolution ensemble verification system are given for precipitation as an example. The system includes both standard probability scores and some novel spatial scores. A method for object-based diagnostic evaluation, MODE, is used as the main object-based approach. The system is based on a free verification package, METplus. Examples of scores are given.

A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts

Abstract An ensemble precipitation forecast post-processing method is proposed by hybridizing the Analog Ensemble (AnEn), Minimum Divergence Schaake Shuffle (MDSS), and Convolutional Neural Network (CNN) methods. This AnEn-CNN hybrid takes the ensemble mean of Global Ensemble Forecast System (GEFS) 3-hourly precipitation forecasts as input and produces bias-corrected, probabilistically calibrated, and physically realistic gridded precipitation forecast sequences out to 7-days. The AnEn-CNN hybrid post-processing is trained on the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and verified against station observations across British Columbia (BC), Canada, from 2017 to 2019. The AnEn-CNN hybrid produces more skillful forecasts than a quantile-mapped GEFS baseline and other conventional AnEn methods, with a roughly 10% increase in Continuous Ranked Probability Skill Score. Further, it outperforms other AnEn methods by 0-60% in terms of Brier Skill Score (BSS) for heavy precipitation periods across disparate hydrological regions. Longer forecast lead times exhibit larger performance gains. Verification against 7-day accumulated precipitation totals for heavy precipitation periods also demonstrates that precipitation sequences are realistically reconstructed. Case studies further show that the AnEn-CNN hybrid scheme produces more realistic spatial precipitation patterns and precipitation intensity spectra. This work pioneers the combination of conventional statistical post-processing and neural networks, and is one of only a few studies pertaining to precipitation ensemble post-processing in BC.

Projected climate change and its impacts on glaciers and water resources in the headwaters of the Tarim River, NW China/Kyrgyzstan

Glacierised river catchments are highly sensitive to climate change, while large populations may depend on their water resources. The irrigation agriculture and the communities along the Tarim River, NW China, strongly depend on the discharge from the glacierised catchments surrounding the Taklamakan Desert. While recent increasing discharge has been beneficial for the agricultural sector, future runoff under climate change is uncertain. We assess three climate change scenarios by forcing two glacio-hydrological models with output of eight general circulation models. The models have different glaciological modelling approaches but were both calibrated to discharge and glacier mass balance observations. Projected changes in climate, glacier cover and river discharge are examined over the twenty-first century and generally point to warmer and wetter conditions. The model ensemble projects median temperature and precipitation increases of + 1.9–5.3 °C and + 9–24%, respectively, until the end of the century compared to the 1971–2000 reference period. Glacier area is projected to shrink by 15–73% (model medians, range over scenarios), depending on the catchment. River discharge is projected to first increase by about 20% in the Aksu River catchments with subsequent decreases of up to 20%. In contrast, discharge in the drier Hotan and Yarkant catchments is projected to increase by 15–60% towards the end of the century. The large uncertainties mainly relate to the climate model ensemble and the limited observations to constrain the glacio-hydrological models. Sustainable water resource management will be key to avert the risks associated with the projected changes and their uncertainties.

Postprocessing Ensemble Weather Forecasts for Introducing Multisite and Multivariable Correlations Using Rank Shuffle and Copula Theory

Abstract Statistical methods have been widely used to postprocess ensemble weather forecasts for hydrological predictions. However, most of the statistical postprocessing methods apply to a single weather variable at a single location, thus neglecting the intersite and intervariable dependence structures of forecast variables. This study synthesized a multisite and multivariate (MSMV) postprocessing framework that extends the univariate method to the MSMV version by directly rearranging the postprocessed ensemble members (post-reordering strategy) or by rearranging the latent variables used in the univariate method (pre-reordering strategy). Based on the univariate generator-based postprocessing (GPP) method, the two reordering strategies and three dependence reconstruction methods [rank shuffle (RS), Gaussian copula (GC), and empirical copula (EC)] totaling six MSMV methods (RS-Pre, GC-Pre, EC-Pre, RS-Post, GC-Post, and EC-Post) were evaluated in postprocessing ensemble precipitation and temperature forecasts for the Xiangjiang basin in China using the 11-member ensemble forecasts from the Global Ensemble Forecasting System (GEFS). The results showed that raw GEFS forecasts tend to be biased for both the forecast ensembles and the intersite and intervariable dependencies. The univariate method can improve the univariate performance of ensemble mean and spread but misrepresent the intersite and intervariable dependence among the forecast variables. The MSMV framework can well utilize the advantages of the univariate method and also reconstruct the intersite and intervariable dependencies. Among the six methods, RS-Pre, RS-Post, GC-Post, and EC-Post perform better than the others with respect to reproducing the univariate statistics and multivariable dependences. The post-reordering strategy is recommended to combine the univariate method (i.e., GPP) and reconstruction methods.

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

AbstractPredictive performance is one of the most important issues for practical applications of ensemble hydroclimatic forecasts. While different forecasting studies tend to use different combinations of verification metrics and diagnostic plots, this paper presents a literature survey of popular verification metrics, diagnostic plots and their corresponding forecast attributes. Twenty metrics to quantify predictive performance of ensemble, deterministic and categorical forecasts are detailed; six types of diagnostic plots to visualize the relationship between ensemble forecasts and observations are presented; and the correspondence relationships of verification metrics and diagnostic plots with ten forecast attributes are illustrated. Numerical experiments are devised for raw ensemble precipitation forecasts generated by the Climate Forecast System version 2 (CFSv2) to illustrate the verification metrics, diagnostic plots and forecast attributes. The results suggest that verification metrics that pay attention to some similar attributes can be related to one another. Meanwhile, the metrics are not necessarily in full accordance as they tend to reflect different attributes. Time series plots visualize forecasts over a period of interest and graphically illustrate forecast bias, reliability and skill. Quantile range plots provide an overall diagnosis for the association of ensemble forecasts with observations. Overall, verification metrics can be illustrated by diagnostic plots for individual grid cells and also be screened by spatial plots at regional and global scales. The survey presented in this article can serve as a roadmap for the selection of verification metrics, diagnostic plots and forecast attributes for verifying ensemble hydroclimatic forecasts.This article is categorized under: Science of Water > Hydrological Processes

A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data

Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the standardized precipitation index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981–2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23%, 8%, 30%, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5–12 months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.

Evolving Threshold of Flood-Leading Precipitation in a User-Oriented Forecast System Based on the TIGGE Dataset

Specific users play a key role in interactive forecast systems through user-oriented information (UOI). For hydrological users, a key component of the user-oriented forecast system (UOFS) is to determine the threshold of flood-leading precipitation (TFLP) as a target of the forecast by considering the decision-making information at the user end. This study demonstrates a novel way of simulating TFLP via the inverse simulation of a hydrological model, combined with the flood hazard assessment in the upper reaches of the Huai River Basin controlled by the Wang Jiaba (WJB) hydrological station. The flood hazard, defined as the probability of precipitation beyond the daily evolving TFLP for the next day, was evaluated by using the THORPEX Interactive Global Grand Ensemble (TIGGE) datasets, including 162 members retrieved from 5 TIGGE archive centers. Having integrated the real-time monitored water level (as the UOI) into the UOFS, we applied it to the flood season of 2008 as a case study to evaluate the flood hazard generated by the UOFS for the WJB sub-basin. The simulated TFLP corresponded well with the gap between the monitored and warning water level. The predicted flood hazard probability showed good agreement with the first two flood peaks at 100% accuracy, while exceeding 60% accuracy for the third flood event in that season. Thus, the flood hazard could be better quantified via integration of the forecasted flood-leading precipitation. Overall, this study highlights the usefulness of a UOFS coupled with interactive UOI of real-time water level to determine the dynamical TFLP for flood hazard evaluation with ensemble precipitation forecast. The early flood warning which resulted from such integrated UOFS is directly applicable to operational flood prevention and mitigation.

Postprocessing of hydrometeorological ensemble forecasts based on multisource precipitation in Ganjiang River basin, China

Flood forecasting is a challenging task with major concerns that include forecast uncertainty and lead time. This paper aims to comprehensively address the data correction issue for hydrological models by developing an integrated postprocessing framework for hydrometeorological ensemble forecasts based on three types of ensemble precipitation forecasts (ECMWF, GEFS, and CFSv2). This framework mainly comprises the Ensemble Preprocessor (EPP) and the Bayesian model averaging (BMA) scheme: (1) the EPP eliminates various deviations from the ensemble precipitation forecasts, with the canonical event model extracting useful predictive information and extending the lead time by linking the forecasts seamlessly, and (2) the BMA scheme develops more skillful and reliable probabilistic hydrological forecasts from ensemble streamflow forecasts generated by three hydrological models (XAJ, VIC and DTVGM) driven by the postprocessed ensemble precipitation forecasts. The Ganjiang River Basin was selected as a case study to examine the capacity and efficiency of our developed framework. We found that the performance of the integrated postprocessing of hydrometeorological ensemble forecasts benefitted from a reasonable design of canonical events, especially when the observed precipitation was factored into the canonical events. Streamflow forecasts with increased lead times and accuracy were obtained by employing postprocessed precipitation forecasts instead of raw forecasts as the input for the hydrological models. We also found that the BMA scheme further improved the forecast effect by generating more skillful and reliable forecasts than a single hydrological model, which was verified by a series of statisticalindices and uncertainty analysis. Our results underscore the importance of considering both input and output for hydrological models in hydrological forecasting.

Extending a joint probability modelling approach for post-processing ensemble precipitation forecasts from numerical weather prediction models

Statistical post-processing has been widely employed to correct bias and dispersion errors in raw ensemble precipitation forecasts from numerical weather prediction models. One prominent post-processing scheme is to establish a joint probability model by fitting a bivariate distribution of raw forecasts and corresponding observations. However, current joint probability models only incorporate ensemble mean as the predictor, and ensemble spread is not considered. This is a major disadvantage of joint probability models as ensemble spread can be informative for forecast uncertainty. In this paper, we propose a two-step calibration approach to combine the strengths of joint probability models and the useful information included in the ensemble spread. In the first step, we take the seasonally coherent calibration (SCC) model as an example of joint probability models to calibrate the ensemble mean. As SCC for precipitation forecasts involves transformations for data normalization and special treatments of zero values, we explore three different ways to estimate ensemble mean values when establishing the SCC model. In the second step, we re-calibrate the ensemble forecasts produced in the first step to incorporate ensemble spread information from the raw forecasts. The performance of this two-step calibration is evaluated using ensemble precipitation forecasts from the Australian Bureau of Meteorology. We find that forecasts calibrated using the two-step calibration have better skills than SCC calibrated forecasts, especially for heavy precipitation events. Strengths of joint probability models and raw ensemble spread information are well utilized in the proposed two-step calibration approach.

Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts

Raw forecasts from numerical weather prediction models suffer from systematic bias and cannot be directly used in applications such as hydrological forecasting. Statistical post-processing methods can be used to remove the bias and achieve reliable ensemble forecasts. However, traditional post-processing methods generally use local raw forecasts as the only predictor, which limits their ability to extract spatial information from raw forecasts. In this paper, we develop a convolutional neural network (CNN)-based post-processing method for precipitation forecasts to make use of spatial information and atmospheric circulation variables as auxiliary predictors. The results show that the proposed CNN-based post-processing model outperforms traditional methods in forecast accuracy and reliability, especially for heavy rain. The improvements of the CNN-based model relative to a state-of-the-art joint probability model can reach 5% and 8% in terms of Brier skill score for heavy rain and rainstorms at the lead time of 1 day. The improvements can be attributed to the use of auxiliary predictors such as total column water forecasts in the proposed model. Moreover, the CNN-based model transcends a fully connected network (FCN)-based post-processing model, which illustrates the benefits of using CNNs to extract spatial information. The results illustrate the proposed CNN-based post-processing model is able to utilize spatial information and various auxiliary predictors to improve forecast skill and quantify forecast uncertainty, which is important for further applications such as hydrological forecasting.

Satellite-based rainfall estimates evaluation using a parsimonious hydrological model in the complex climate and topography of the Nile River Catchments

Satellite-based rainfall products with high spatial and temporal resolutions can provide an excellent data source for regions where rain-gauge networks are missing or unevenly and sparsely distributed. The objective of this study was to examine the suitability of six high-resolution satellite-based rainfall products, Climate Hazards Group InfraRed Precipitation with Stations Version 8 (CHIRPSv8), African Rainfall Estimation Algorithm Version 2 (RFEv2), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis version 7 (TMPA3B42v7), Integrated Multi-SatelliteE Retrievals for Global Precipitation Measurement version 6 (IMERGv6), Multi-Source Weighted Ensemble Precipitation version 2.8 (MSWEPv2.8) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), for the complex topography and climate of the Nile headwater catchments. The reliability of these products was assessed using rainfall estimates as an input to force a parsimonious and high-resolution hydrological model. A spatially distributed and parsimonious hydrological model was developed in Wflow-PCRaster/Python modelling framework to simulate streamflow using rain-gauge and satellite-based rainfall inputs. The model was calibrated and validated at three locations for each rainfall product and ground rainfall measurement. The accuracy of satellite-based rainfall products as input to the hydrological model for daily streamflow forecasting was dependent upon the product type. CHIRPSv8, IMERGv6 and RFEv2 demonstrated a reliable capability to simulate the streamflow at different spatial scales. In contrast, the TMPA3B42v7 and PERSIANN products failed to capture the ground streamflow measurements. All products performed inconsistently when simulating large floods and low flows. While TMPA3B42v7 and MSWEPv2.8 underestimated the streamflow with a large magnitude of variation in peak flow, the remaining products underestimated it throughout the study period. CHIRPSv8 and IMERGv6 product outperformed all other products consistently across all periods and locations followed by RFEv2. PERSIANN failed to capture the observed streamflow with lower performance indices. A better agreement of simulated streamflow with ground streamflow measurements was attained from CHIRPSv8 and IMERGv6 compared to the gauge-driven rainfall inputs. Performance of streamflow simulations improved when catchment area was increased from 481 to 46,000 km2. Overall, this study demonstrated the potential of CHIRPSv8 and IMERGv6 rainfall products as an alternate source of rainfall data for hydrological applications in the Nile basin catchments and the potential for its use in other regions which lack direct rainfall measurement capacity.

A parsimonious post-processor for uncertainty evaluation of ensemble precipitation forecasts: an application to quantitative precipitation forecasts for civil protection purposes

Abstract In this study, we present a post-processing technique designed to assess conditional predictive uncertainty for quantitative precipitation forecasts (QPFs) that extends to the multivariate predictor case. The theoretical foundation of the mixed binary-continuous precipitation process representation for a single predictor is coupled with the univariate version of the model conditional processor (MCP) that allows considering ensemble forecasts while maintaining a parsimonious approach. The experiment set-up is based on a weather vigilance zone (WVZ) of the national warning system in southern Italy and the relative QPFs issued by the Italian Civil Protection Department. Various aspects of the quality of the probabilistic forecast from the uncertainty processor were evaluated. The results indicate that post-processed forecasts can provide improved performances in terms of accuracy and reliability, tend to correct bias and are generally less under-dispersive than raw forecasts for the investigated area. Furthermore, we explored the possibility of issuing warnings employing the full predictive distribution and moving to the use of probabilistic thresholds here identified through a receiver operating characteristic (ROC) analysis. Considering the probability of exceeding a critical rainfall value allowed successful discrimination between events and non-events for critical precipitation occurrences and proved to be a valuable approach to decision-makers and information providers.

Real-time optimization of a large-scale reservoir operation in Thailand using adaptive inflow prediction with medium-range ensemble precipitation forecasts

Study regionThe Sirikit Dam in the Nan River Basin is located on a main tributary of the Chao Phraya River in Thailand. Study focusThis study investigates forecasting river flows and real-time optimization of dam release using a distributed hydrological model with ensemble weather forecasting for reservoir operations which provide hydropower and irrigation facilities in Thailand during a case study of the 2019 drought event. Medium-range ensemble precipitation forecasts were employed using a hydrological model to predict the real-time reservoir inflow. Real-time optimization of the water release strategy determined a week in advance with an effective initial condition for hydropower generation and irrigation was conducted with different scenarios using dynamic programming considering inflow predictions. New hydrological insights for the regionThe medium-range ensemble precipitation forecast conducted by the European Centre for Medium Range Weather Forecasts was used to quantify precipitation for the study basin. The ensemble precipitation forecast with the hydrological model was employed for inflow prediction of the study basin which was located in a tropical climate with a distinct wet and dry season. The initial conditions of the hydrological model largely influenced the real-time inflow forecast. To determine the initial conditions of the model, the empirical data assimilation considering a drainage area factor was utilized and observed precipitation data were used for model input forcing data during the initial analysis period. This method improved the reservoir inflow prediction and real-time reservoir optimization using dynamic programming with considering ensemble forecasts provided more efficient operating decisions than employing historical data. The resulting information will be useful for water resource management, which may be adapted to other basins in the study region.

Blue-green roofs with forecast-based operation to reduce the impact of weather extremes

Conventional green roofs have often been criticised for their limited water buffer capacity during extreme rainfall events and for their susceptibility to droughts when additional irrigation is unavailable. One solution to these challenges is to create an extra blue water retention layer underneath the green layer. Blue-green roofs allow more stormwater to be stored, and the reservoir can act as a water source for the green layer throughout capillary rises. An automated valve regulates the water level of the system. It can be opened to drain water when extreme precipitation is expected. Therefore, the water buffer capacity of the system during extreme rainfall events can be maximised by integrating precipitation forecasts as triggers for the operation of the valve. However, the added value of this forecast-based operation is yet unknown. Accordingly, in this study, we design and evaluate a hydrological blue-green roof model that utilises precipitation forecasts. We test its performance to capture (extreme) precipitation and to increase evapotranspiration and evaporative cooling under a variety of precipitation forecast-based decision rules. We show that blue-green roofs can capture between 70 % and 97 % of extreme precipitation (>20 mm/h) when set to anticipate ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). This capture ratio is considerably higher than that of a conventional green roof without extra water retention (12 %) or that of a blue-green roof that does not use forecast information (i.e., valve always closed; 59 %). Moreover, blue-green roofs allow for high evapotranspiration rates relative to potential evapotranspiration on hot summer days (around 70 %), which is higher than from conventional green roofs (30 %). This serves to underscore the higher capacity of blue-green roofs to reduce heat stress. Using the city of Amsterdam as a case study, we show the high upscaling potential of the concept: on average, potentially suitable flat roofs cover 13.3 % of the total area of the catchments that are susceptible to pluvial flood risk. If the 90th percentile of the ECMWF forecast is used, an 84 % rainfall capture ratio can translate into capturing 11 % of rainfall in flood-prone urban catchments in Amsterdam.