Inflow Forecasting Research Articles

Revolutionizing JIRA Management with Artificial Intelligence: Streamlining Workflow Efficiency and Enhancing Project Outcomes

IT organizations are investing heavily on artificial intelligence (AI), though the value realization and areas of application of AI are still limited and not explored completely. Artificial intelligence applications are using large set of data to train the system to either generate new content or to predict the outcomes. Predictive analytics is used in agile management projects for budgeting, forecasting and capacity management. The article represents the use of cycle time and cumulative flow diagram metrics in ActionableAgile or JIRA to predict in agile development projects for forecasting of inflow of work and for the improvement of current work processes.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

Modeling runoff in Bhima River catchment, India: A comparison of artificial neural networks and empirical models

ABSTRACT Effective water resource management in gauged catchments relies on accurate runoff prediction. For ungauged catchments, empirical models are used due to limited data availability. This study applied artificial neural networks (ANNs) and empirical models to predict runoff in the Bhima River basin. Among the tested models, the ANN-5 model, which utilized rainfall and one-day delayed rainfall as inputs, demonstrated superior performance with minimal error and high efficiency. Statistical results for the ANN-5 model showed excellent outcomes during both training (R = 0.95, NSE = 0.89, RMSE = 17.39, MAE = 0.12, d = 0.97, MBE = 0.12) and testing (R = 0.94, NSE = 0.88, RMSE = 11.47, MAE = 0.03, d = 0.97, MBE = 0.03). Among empirical models, the Coutagine model was the most accurate, with R = 0.82, MBE = 74.36, NSE = 0.94, d = 0.82, KGE = 0.76, MAE = 70.01, MAPE = 20.6%, NRMSE = 0.22, RMSE = 87.4, and DRV = −9.2. In contrast, Khosla's formula (KF) significantly overestimated runoff. The close correlation between observed and ANN-predicted runoff data underscores the model's utility for decision-makers in inflow forecasting, water resource planning, management, and flood forecasting.

Connecting digital twins to control collections systems and water resource recovery facilities: From siloed to integrated urban (waste)water management

ABSTRACT The use of digital twins is a rapidly emerging field for improved real-time control (RTC) and decision support for the operation of collection systems and water resource recovery facilities (WRRFs). Digital twins for collection systems can help minimize the impacts of flow variation due to extreme weather events, attenuate flows to the WRRF, and reduce sewer overflows and the associated effects. Similarly, digital twins for WRRFs can help improve process, energy, and cost efficiency, fully utilise plant volumes, reduce carbon footprint, and support operator training. The current study provides an overview of two digital twin applications for collection systems (Future City Flow) and WRRFs (TwinPlant) and presents a first example of digital twin integration for proactive collection system-WRRF operation under wet-weather conditions. Current applications of the integrated digital twin are described, including (i) proactive implementation of wet-weather operation mode in WRRF based on inflow forecast and (ii) evaluation of the impacts of RTC in collection systems on WRRF performance. Other potential application examples are described together with the challenges related to the use of this solution. Overall, this new approach has a wide potential to support the cooperation within water utilities towards the adoption of integrated wastewater management.

Long-term inflow forecast using meteorological data based on long short-term memory neural networks

ABSTRACT Long-term inflow forecasting is extremely important for reasonable dispatch schedules of hydropower stations and efficient utilization plans of water resources. In this paper, a novel forecast framework, meteorological data long short-term memory neural network (M-LSTM), which uses the meteorological dataset as input and adopts LSTM, is proposed for monthly inflow forecasting. First, the meteorological dataset, which provides more effective information for runoff prediction, is obtained by inverse distance weighting (IDW). Second, the maximal information coefficient (MIC) can adequately measure the degree of correlation between meteorological data and inflow; therefore, the MIC can distinguish key attributes from massive meteorological data and further reduce the computational burden. Last, LSTM is chosen as the prediction method due to its powerful nonlinear predictive capability, which can couple historical inflow records and meteorological data to forecast inflow. The Xiaowan hydropower station is selected as the case study. To evaluate the effectiveness of the M-LSTM for runoff prediction, several methods including LSTM, meteorological data backpropagation neural network (M-BPNN), meteorological data support vector regression (M-SVR) are employed for comparison with the M-LSTM and six evaluation criteria are used to compare its performance. Results revealed that M-LSTM outperforms other test methods in developing the long-term prediction method.

Value of long-term inflow forecast for hydropower operation: A case study in a low forecast precision region

Many studies have evaluated the value of long-term inflow forecast for hydropower operation in high-precision watersheds and explored the impact of forecast uncertainty on hydropower benefits. However, few of them focuses on the low-accuracy regions and the specific impact of forecast errors on hydropower benefits. Taking Hunjiang cascaded system in China as an example, this study investigated the value of low accuracy long-term inflow forecast for hydropower operation, revealed the influence mechanism of forecast errors on hydropower benefits, and determined the critical threshold for accuracy of beneficial forecast. Results show that low accuracy forecast but higher than critical threshold can increase hydropower generation. Hunjiang has a low accuracy long-term forecast with qualified rate being 30–40 %, but annual hydropower generation can be increased by 14.86–29.58 million kWh. The influence mechanism of forecast errors on hydropower operation presents three typical scenarios, two of which are harmless. One is that the error tolerance of operation rules can avoid some decision-making errors; the other one is that when current misreporting is consistent with the inflow in a longer lead time, the misreporting may instead be beneficial. We conclude that low accuracy long-term forecast is valuable but higher accuracy brings higher benefits.

Stochastic multi-attribute group decision-making for long-term comprehensive operation of cascade reservoirs under multiple uncertainties

Study regionThe Three Gorges cascade (TGC) reservoirs, China. Study focusSince uncertainties inherent in inflow forecasting, elevation-storage, discharge capacity, flood routing, and the preferences of decision makers (DMs) persist in the attribute measurements (AMs) and weights (AWs), problems where multiple DMs select the best compromise scheme for the long-term comprehensive operation of cascade reservoirs considering numerous attributes become a stochastic multi-attribute group decision-making (MAGDM) process. To represent uncertain AMs and AWs and consider the bounded rationality of DMs, we propose a stochastic MAGDM with integrated stochastic multicriteria acceptability analysis (SMAA) and gray cumulative prospect theory (GCPT), namely, SMAA-GCPT. After quantifying the uncertainties, SMAA-GCPT is used to model uncertain AMs and imperfect AWs with calculable probability distributions and map the sampled AMs and AWs to utility values to reflect the overall performance assuming bounded rationality. New hydrological insights for the regionExperiments on the TGC indicate that SMAA-GCPT can effectively differentiate alternative schemes and select the best compromise scheme to harmonize the conflicting benefits and risks of the TGC, with higher reliability than SMAA-2 and SMAA-GRA. The influence of uncertain AMs on the reliability of SMAA-GCPT is more obvious than that of uncertain AWs. The decision-making error risk is primarily sensitive to the inflow forecasting uncertainty, followed by the flood routing uncertainty, and is less affected by the uncertainties in elevation-storage and discharge capacity curves.

Applicability of seasonal forecasts from dynamical models for reservoir management practices

Despite the availability of reliable seasonal forecasts of Indian Summer Monsoon Rainfall (ISMR), the use of dynamical models driven by these forecasts for reservoir level management is limited. Reservoir water management can specially be useful if it can be done several months in advance, in view of an impending drought/flood scenario. The applicability of seasonal forecasts from the Monsoon Mission (MM) seasonal forecast model for seasonal and monthly inflow forecasts for tropical Indian reservoirs (Mula and Kangsabati) is studied using the Soil and Water Assessment Tool (SWAT) hydrological model, at a lead time of 3 months. Long-term observed inflow datasets are used for calibration and validation of SWAT-Calibration and Uncertainty Procedure (CUP) with Sequential Uncertainty Fitting (SUFI)-2 algorithm using insitumeteorological data. Observed inflows and inflow simulations are compared with simulated inflow using SWAT with same calibrated parameters, but with forcing derived from reforecasts from the MM model. The SWAT-CUP calibrated well with reasonable Nash Sutcliffe Efficiency (NSE) (Mula = 0.75, Kangsabati = 0.79) and Percentage Bias (PBIAS) (Mula = -28%, Kangsabati = 17%) for both reservoirs. The skill scores for streamflow predictions vary from 0.6-0.70 during the monsoon season, indicating reasonable accuracy for these predictions. The SWAT-MM model has a reasonable skill with 0.52-0.53 NSE and 26%-40% PBIAS. Therefore, SWAT-MM-based model has a good potential to forecast monthly and seasonal reservoir inflow for various agro-climatic zones of India. These forecasts when used in real-time, can serve as a guideline for managing the reservoir storage and release, and hence proving to be of great socio-economic importance.

Evaluating Streamflow Forecasts in Hydro‐Dominated Power Systems—When and Why They Matter

AbstractThe value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large‐scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled‐water energy model that is subject to reservoir inflow forecasts with different levels of predictive performance. We implement the modeling framework on a real‐world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro‐dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

Assessing Hydrological Response in the Timah-Tasoh Reservoir Sub-Catchments: Calibration and Validation using the HEC-HMS Model

Hydrological modelling is a tool that is frequently used for assessing the hydrological response of a basin as a result of precipitation. It is also a vital component as water resources and environmental planning management. The study deals with calibrating and validating the hydrological response in the sub-catchments of the Timah-Tasoh reservoir using the hydrological model named Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS). This study uses the SCS Curve Number, the SCS Unit Hydrograph, the constant monthly baseflow, and lag routing for the model development. The model was simulated for ten (10) years for calibration and nine (9) years for validation. The model calibration and validation efficiency were assessed using the coefficient of correlation (R). The findings show that the HEC-HMS model performs satisfactorily in simulating the observed daily inflow series, with the R-value of 0.4902-0.5139 during calibration and 0.5047-0.5559 during validation process. Thus, the result obtained from this study can be used as a preliminary development of hydrological modelling of the catchment of the Timah-Tasoh reservoir and can be used for extend application such as water inflow forecasting, impact of land use to the reservoir and others.

Numerical forecast of groundwater inflow to the mines of the Legnica-Głogów Copper District with a particular emphasis on the “Polkowice-Sieroszowice” mine

The paper presents the results of work on the numerical hydrogeological model of the mines of theLegnica-Głogów Copper District (LGCD) in Poland. Due to the extensive impact on the rock mass and the multi-layer depression cone caused by drainage, the model covers an area of 3,049 km2 . The complicated, mutual hydraulic connections of the multi-aquifer hydrogeological system required the model to cover the area beyond the range of mining areas, and to separate 17 numerical layers. The research was carried out in the GMS software environment using Modflow computing modules. The model was updated successively, since its structure, parameters, and boundary conditions are subject to change. It was also additionally calibrated based on new data from measurements in piezometers and changes recorded in inflows to the excavations. The simulations made it possible to determine the exact water balance, with a particular emphasis on the Sieroszowice mining area, where sudden unexpected inflows were noted. Subsequently, two prognostic simulations were performed to estimate dynamic water inflows to mine workings in the years 2020 and 2025.

Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model

Accurate and reliable inflow forecasting is essential for efficient reservoir operation, which serves various purposes such as flood control, hydropower generation, water supply and irrigation. Although data-driven models have been vastly used to improve forecasting, the lack of interpretability and physical insights, particularly for neural network models, limit their trustworthiness and usability. In this study, we introduce an interpretable hybrid model to decompose inflow into precipitation-runoff and routing processes. This approach not only improves model performance but also facilitates the extraction of valuable hydrological insights. When applied to the Missouri River Basin cascade reservoir systems, the hybrid model demonstrates strong performance when compared to the state-of-the-art hydrologic model HEC-HMS and the widely used Long Short-Term Memory (LSTM) model in predicting reservoir inflow, especially for lower and smaller reservoirs in the cascade reservoir systems. For the upper and larger reservoirs, it achieves substantial improvement in Nash-Sutcliffe efficiency, ranging from 0.05 to 0.08 and 0.28 to 0.32, except for Fort Peck reservoir, when compared with the LSTM and HEC-HMS models. The decomposition of physical processes enables the hybrid model to represent each component more effectively, thereby leading to improved results in simulation and forecasting. Furthermore, the hybrid model shows how meteorological information updates the memory state of the LSTM unit and routing parameters, which in turn influences inflow dynamics, particularly in situations involving snowmelt periods. The proposed approach also allows the extraction of valuable hydrological insights, including the assessment of the contributions of precipitation-runoff processes versus routing to inflow dynamics, dynamic water travel time, and routing coefficients. The case studies have demonstrated the promising capabilities of this interpretable hybrid model in improving reservoir inflow forecasting and providing meaningful hydrological insights into reservoir inflow dynamics.

Previsões de vazões sub sazonais para barragens hidrelétricas no Sistema Interligado Elétrico Brasileiro

ABSTRACT Inflow prediction on sub-seasonal timescale have the potential for important contributions to the management of water resources in hydroelectric dam operations. These forecasts challenge the limitations of the medium-term and extend it, bridging a long-standing technical-scientific gap in the forecasting field. In Brazil, the use of sub-seasonal hydrological predictions can boost the hydroelectric production of the National Interconnected System (SIN), since inflow forecast in reservoirs of up to 2 weeks are routinely used using a rain-flow model. This study aimed at the statistical evaluation of hydrological forecasts of up to 6 weeks using a hydrological-hydrodynamic model on a continental scale associated with ensemble precipitation forecasts generated by an atmospheric model, producing future streamflow in the continent basins, and consequently at the SIN’s hydroelectric dams. The statistical evaluation was based on deterministic scores typically used by the SIN operating agent, and additionally we assessed the skill of forecasts based on atmospheric models in relation to simpler forecasts based on the climatology of observed inflows. The performance of the forecasts varies according to the season and geographic location, that is, depending on different hydrological regimes. The best performances were obtained in dams located in the southwest and central-west regions, which have well-defined seasonality, while dams in the south showed greater sensitivity in metrics according to the season. The study presented serves as a technical-scientific contribution for agents and decision makers who seek to improve water resource management by incorporating extended forecasts into the operational chain.

Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method

Study RegionUpper Colorado River Basin and Great Basin in the United StatesStudy FocusAccurate subseasonal reservoir inflow forecasts and understanding the influence of hydrometeorological forcings on these forecasts are crucial for improving water resources management. Machine learning (ML) techniques, such as long short-term memory (LSTM) networks, perform well for short-term inflow forecasts but have deficiencies in subseasonal forecasts and lack interpretability. To address these limitations, we propose an explainable ML method that integrates an encoder–decoder LSTM (ED-LSTM) network to improve long-term reservoir inflow forecasts and a gradient-based explanation method to quantify the importance of individual hydrometeorological forcings and their interactions on inflow forecasts.New Hydrological Insights for the RegionThe ED-LSTM model outperforms the standard LSTM in the 30-day inflow forecasts at all 30 reservoirs. At the 1-day lead time, ED-LSTM produces NSEs exceeding 0.75 at 29 reservoirs; at the 15-day lead time, about half of reservoirs maintain this high-accurate performance, and when forecasting 30 days ahead, ED-LSTM achieves NSEs exceeding 0.5 at most reservoirs. The variable importance identifies past inflow and temperature as crucial drivers for predicting inflow dynamics. When considering interactions between hydrometeorological forcings, precipitation contributes significantly to inflow forecasting through its interaction with temperature and historical inflow. The proposed method enhances subseasonal reservoir inflow forecasts and the understanding of the impact of hydrometeorological factors, which supports decision-making in reservoir operations.

Reservoir operations under uncertainty with moving-horizon approach and ensemble forecast optimization

Inflow forecasts are a basic pre-requisite to support decisions for anticipating reservoir operations. The fact that hydrological forecasts are uncertain, noting that reservoirs often fulfill multiple purposes, bears the risk of non-ideal reservoir operation. Optimization techniques can help to identify the best operational scheme under given inflow. To address the forecast uncertainty, operators can repeat optimization (moving horizon approach) and optimize for inflow forecast ensembles. This article aims to contribute to a better understanding of how the different methods work, how to interpret the results and what the effect of user choices on the optimization results is. The moving-horizon approach and three ensemble optimization methods were applied on a hydropower reservoir in Norway under flood conditions. The functional principle of each method is explained, and advantages and drawbacks of the different methods are discussed with the help of performance indicators.

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

Dam System and Reservoir Operational Safety: A Meta-Research

Dams are critical infrastructure necessary for water security, agriculture, flood risk management, river navigation, and clean energy generation. However, these multiple, and often conflicting, objectives introduce complexity in managing dam operations. In addition, dam infrastructure has been evolving as complex systems-of-systems with multiple interacting components and subsystems, all susceptible to a wide range of uncertainties. Such complexities and uncertainties have triggered extensive research initiatives focused on dam systems and reservoir operational safety. Focusing on the latter, this paper meta-researches (conducts research-on-research) previously published studies to identify the critical research gaps and propose future research directions. In this respect, this paper first performs a quantitative analysis of the pertinent literature, using text mining and subsequent topic modeling, to identify and classify major and uncover latent topics in the field. Subsequently, qualitative analysis is conducted to critically review the identified topics, exploring the concepts, definitions, modeling tools, and major research trends. Specifically, the study identified seven topics: optimization models; climate change; flood risk; inflow forecasting; hydropower generation; water supply management; and risk-based assessment and management. The study also presents three main research gaps associated with the limitations in modeling concepts, modeling tools capabilities, and the lack of resilience-guided management of dam operational safety. Overall, this study presents a road map of the currently available dam and reservoir operational safety research and associated knowledge gaps, as well as potential future research directions to ensure the resilience of such critically important infrastructure, especially in the age of climate change.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Seasonal forecast-informed reservoir operation. Potential benefits for a water-stressed Mediterranean basin

The increased seasonal demand for water puts pressure on Mediterranean water resources, which are often exploited in a non-renewable way. Besides, climate change can alter hydroclimatic patterns and exaggerate freshwater stress. Flexible operation of existing water reservoirs is one of the most cost-effective ways to mitigate water-related stress by storing water when it is abundant and releasing it when droughts persist. In this context, hydroclimatic forecasts can be central in properly informing reservoir operation. Nevertheless, the link between forecast skill and forecast value is neither easily predictable nor necessarily positive. Each system requires specific forecasts according to its characteristics, and the skill of existing forecast systems does not necessarily translate into a significant gain in system performance. In this work, we develop downscaled seasonal forecasts of reservoir inflow for the Faneromeni irrigation dam on Crete island. We then quantify the value of these seasonal forecasts in informing the reservoir operations. While the current operation of this reservoir is based on the available storage at the beginning of the irrigation season, we investigate alternatives by using the Evolutionary Multi Objectives Direct Policy Search method, which allows the design of flexible rules to cope with the variability of the hydrologic conditions as well as to include forecast information for conditioning operational decisions. Under historical climate conditions, results demonstrate a notable enhancement in performance solely by implementing more flexible operating policies. Incorporating perfect forecasts results in an additional improvement of 4% on average throughout the period from 1993 to 2019. However, when using actual forecasts, this gain diminishes to 1%. These outcomes support the exploration of potential trade-off solutions that effectively balance the competing demands within the region.

Use of Climate Information in Water Allocation: A Case of Study in a Semiarid Region

The value of climate information has been explored by various scholars and in various sectors, but its operational use, particularly in water resources management, in countries like Brazil remains limited. This article describes climate and inflow forecast systems used in the process of water allocation in the state of Ceará (Brazil) and evaluates their performance at three key reservoirs in the state for forecasts issued in January for the period from January to May when most of the annual rainfall and inflows occur. To illustrate the value of forecasting in the water-allocation process, a simple experiment based on the use of a decision support system (DSS) is carried out. The use of the DSS SIGA with inflows estimated from observations and forecasts demonstrated the value of forecasts in the process of water allocation, as the forecasts allowed for better identification of end-of-the-year reservoir volumes. The use of the forecast system successfully described the variability of the percentage of demands met and the demands identified using estimated inflows, in particular for the Banabuiú and Castanhão Reservoirs. Overall, the results of this study highlight the importance of climatic-hydrological forecasting in the process of water allocation.