Small reservoirs are important to flood control and water resource utilization in local areas. This study proposes a WebGIS-based flood control management system to support the flood discharge of small reservoirs during intensive rainfall in the flood season. The agile software development method and a loosely coupled structure are used to combine multidisciplinary knowledge from different experts. A flood level forecasting model for reservoirs in humid regions is established based on rainfall and water level measurements. It aims to provide concise information for reservoir managers to choose an appropriate discharging scheme, so that the capacity is maintained in a safe range on the next day. Using the Hengshan Reservoir in the lower reaches of the Yangtze River as an example, the model verification reveals that it is acceptable for rainfall events whose daily amount is near or above 100 mm (the heavy rainstorm level in China), and the system is verified by a trial operation during the typhoon season. While most existing flood control systems focus on river basins and large reservoirs, this study considers the data availability and practical flood discharging scenario of small reservoirs, and provides a useful tool for flood control management.

INTRODUCTION

To modify the uneven distribution of water resources in both time and space, more than 847,000 reservoirs have been constructed globally in the last 100 years, and approximately 95% of these are small reservoirs (Song et al. 2015). They not only provide water, hydroelectric energy and irrigation, but also stabilize extreme inflows to mitigate floods or droughts. Due to the natural uncertainties, public concern about the small reservoirs and the specific efforts for flood control management have increased in recent years (Chang & Chang 2006; Rodrigues et al. 2012).

Reservoir flood control management is a historic issue regarding the use of structural and non-structural measures to optimize flood discharge and reduce flood risks. It requires simultaneous considerations about the hydrologic, geotechnical, environmental and behavioural aspects. Among them, the flood forecast, including volume, peaks and duration, is one of the key factors. Many flood-prone countries have employed forecasting systems since the 1990s. These include the National Weather Service River Forecasting System (NWSRFS) for 13 main rivers across the USA (Burnash & Singh 1995), the Delft Flood Early Warning System (Delft-FEWS) for several European countries (Werner et al. 2009), the integrated flood control management system for 69 large reservoirs of China (Cheng & Chau 2004), etc. These early examples provide flood control decision support by establishing chart-based user interfaces around the hydrological and hydraulic models used. However, their application and usage requires a good understanding of flood processes, which is feasible for national operational centres (Werner et al. 2013; Cools et al. 2016), but difficult for small reservoir managers with uneven levels of hydrological knowledge.

Recent development in information technology has provided substantial opportunities to enhance early warning and flood control at different spatial scales. There are global systems for upcoming floods in large world river basins (Alfieri et al. 2013; Wu et al. 2014), and continental systems that complement the national systems with medium- and long-range forecasts for transboundary rivers and lakes (Roo et al. 2011; Werner et al. 2013; Thiemig et al. 2015). Flood control systems for local areas have widely incorporated web-based technologies. Demir & Krajewski (2013) provided an integrated online platform with flood forecasts and inundation maps. Horita et al. (2015) combined monitoring data with volunteered geographic information for river flood risk management. The above systems combined with desktop- and web-based geographic information system (GIS) have proved to be integrative and cooperative environments for river basins and urban watersheds, so that researchers, decision-makers and the general public are better involved. However, practical flood control management usually requires targeted and deterministic data to help formulate discharging schemes, especially for the numerous and widely distributed small reservoirs. In order to keep the results concise and effective for reservoir managers, the system has to cover multidisciplinary knowledge within, but present as user-friendly interfaces.

Many rainfall–runoff models have been developed to generalize hydrologic processes and offer flood forecasts. Some are lumped conceptual models, such as the Sacramento model, the Xinanjiang model and the tank model (Zhao 1992; et al. 2013). They are applied in many regions but typically have more than ten parameters that require gradual adjustment to the measured stream-flow data (Chang & Chang 2006). In particular, the Soil Conservation Service Curve Number (SCS-CN) model is designed for small watersheds (Mishra & Singh 2003). Due to the simple structure and clearly stated assumptions, relatively small amounts of data are required when using this model. However, the spatial analysis for watershed characteristics, such as elevation, soil type and land use, is essential to obtain more accurate CN values, rather than depending on the tables developed for US conditions.

To represent the spatial conditions of underlying surface and meteorological data, GIS has enabled the models to have physically distributed characteristics. The LISFLOOD model is an example that simulates the spatial and temporal pattern of river discharge in large basins. The TOPKAPI model is applied to medium-sized river basins, and transforms rainfall–runoff routing processes into non-linear reservoir differential equations. The HEC-HMS model supports numerous infiltration loss parameterizations to calculate overland flow runoff (Vieux 2001; Ciarapica & Todini 2002; Van Der Knijff et al. 2010). The selection and application of rainfall–runoff models is one of the most important tasks for reservoir flood control management, since the specific flood forecast relies on not only the practical hydrological measurements but also the available time-range to guide flood discharge.

The Yangtze River has been subjected to flooding throughout history, especially for the provinces and municipalities located in its lower reaches, which are of great economic importance for China (Cheng & Chau 2004). Due to the intensive rainfall events and humid climate during the flood season, inflow floods rapidly raise the water levels and cause dam failure risks (Zhao et al. 2014). Although flood control management is available for large reservoirs, it is inadequate for small reservoirs. Instead of using advanced forecasting and decision-support tools, these reservoirs primarily rely on the long-term field experience of the operation personnel who prefer to maintain a higher capacity due to economic benefits, but this approach introduces a significant amount of uncertainties regarding dam safety. Meanwhile, the hydrological measurements are mostly limited to water level logged by days or hours, and rainfall in or near the catchment, which limits the direct usage of existing rainfall–runoff models.

To improve this situation, a WebGIS-based flood control management system has been developed since 2014, and the Hengshan Reservoir in Jiangsu Province selected as the study area for the typical hydrologic and reservoir project conditions in the lower reaches of the Yangtze River. To date, it has completed a trial operation and will be gradually applied to other small reservoirs in the vicinity.

In order to provide useful reference to the flood control and water resource utilization in humid regions, this study focuses on the design and implementation of the WebGIS-based system. It tries to use multidisciplinary knowledge to establish user-friendly tools to bridge the gap between large-scale flood control systems and practical management of small reservoirs. It also investigates the capability to present concise flood forecasts based on limited hydrological measurements to guide the flood discharge during intensive rainfall in the flood season. Typical rainfall events in the Hengshan Reservoir are chosen to estimate the model parameter values, and approximately 100 rain days are used to analyse the forecast accuracy. After describing a real flood discharging example, future improvements and recommendations for the system are discussed.

METHODOLOGY

Study area and materials

The catchment of the Hengshan Reservoir covers an area of 154.8 km2 with an average elevation of 300 m, which is obtained by ArcGIS Hydrology toolset (http://resources.arcgis.com/) and the 30-m-resolution Aster DEM (http://asterweb.jpl.nasa.gov/gdem.asp). There is a hydrological station on the dam and five rain gauges evenly distributed in the catchment (Figure 1). The automatic gauging system records the hourly rainfall and water level. According to the measurements, flood peak inflows approximately 8 hours after the intensive rainfall. The reservoir flood limited water level (FLWL) is 34.0 m, the normal water level (NWL) is 35.0 m and the maximum flood level is 37.0 m. Particularly, the FLWL is the most significant indicator to offer adequate storage for flood prevention during the flood season, and the maximum discharge is about 550 m3/s, which takes 4 hours to lower the stage from the NWL to the FLWL.
Figure 1

Study area of the Hengshan Reservoir in the lower reaches of the Yangtze River.

Figure 1

Study area of the Hengshan Reservoir in the lower reaches of the Yangtze River.

There are two classes of basic materials used in this study. One is necessary for flood level forecasts, as shown in Table 1, and the other is optional to enrich the map browse and water level monitoring, which contains an online geographic base map, 30-m digital elevation model (DEM), dam construction designs, photos taken at key locations and video surveillance.

Table 1

Basic materials for small reservoir flood level forecasts

Data type Content and resolution Source 
Rainfall measurements Hourly, from 2006 to 2015 Automatic gauging system 
Water level measurements Hourly, from 2006 to 2015 Automatic gauging system 
Reservoir basic information Stage–storage curve and average daily water consumption Reservoir administrative division 
Evaporation data Daily, from 2006 to 2012 China National Meteorological Information Centre 
Data type Content and resolution Source 
Rainfall measurements Hourly, from 2006 to 2015 Automatic gauging system 
Water level measurements Hourly, from 2006 to 2015 Automatic gauging system 
Reservoir basic information Stage–storage curve and average daily water consumption Reservoir administrative division 
Evaporation data Daily, from 2006 to 2012 China National Meteorological Information Centre 

Adopting agile methods to involve different participants

Providing accurate and efficient inflow flood forecasts during intensive rainfall to support flood discharge is the main purpose of the system. Meanwhile, some characteristics of small reservoirs have to be considered, such as limited hydrological measurements, fast rainfall–runoff processes and uncertainties of geospatial data composition. Therefore, the system design and implementation requires a combination of multidisciplinary knowledge from different fields, such as reservoir management, flood discharge, hydrological modelling, map development and software integration.

Agile software development methods are currently adopted in many cross-regional and cross-domain practices to cope with crises, such as rising complexity and extended cycles (Brhel et al. 2015). Compared with traditional planning-based methods, they value a progressive and iterative approach to achieve effective results. Following the principles of agile methods, this study uses scenario description, visual design and rapid prototyping to keep the system available during the entire process of design, implementation and application. Thus, different participants are initiatively involved, and make suggestions to improve the system based on their respective expertise.

Specifically, two process assistant tools are used from the initial design phases. One is an online collaborative tool named Mingle (https://www.thoughtworks.com/mingle/) to help resolve the overall objective into several detailed sub-tasks in each iterative development cycle (Figure 2(a)). The other is a prototyping tool named Mockups (https://balsamiq.com/) to help visualize the abstract concepts and embody their interactions in webpage wireframes (Figure 2(b)), so that reservoir managers and hydrological experts can work in a what-you-see-is-what-you-get (WYSIWYG) environment.
Figure 2

Adopting agile methods during the entire development process. Experts from different fields fully participate to resolve the objectives based on function requirement cards (a), while the corresponding prototypes are used to keep the designs available and shareable (b).

Figure 2

Adopting agile methods during the entire development process. Experts from different fields fully participate to resolve the objectives based on function requirement cards (a), while the corresponding prototypes are used to keep the designs available and shareable (b).

System architecture design

The system consists of three parts: the data tier, the service tier and the decision-support tier (Figure 3). It is integrated using .NetFramework in VS2012 and adopts a loosely coupled development approach of different tools and languages to enable collaborative implementation by the experts from different fields. In the data tier, several types of basic data are collected from stable and mobile devices, which include rainfall and water level measurements, as well as video streaming and multimedia files, such as dam construction designs and photos. These data are respectively stored in rational tables of Oracle 11 and file system of Windows Server 2008. In addition, external map tiles are used by invoking static Google Maps APIs (https://developers.google.com/maps/). In the service tier, the flood level forecasting model and its integration with flood discharging schemes are developed using the FORTRAN language. The records over the past days, as well as the current hourly data, are provided by the water level and rainfall data service. In addition, a similar rainfall process query retrieves the 5 consecutive days in history whose cumulative rainfall is approximate to the current amount, so that the reservoir manager has a real discharging instance for reference in the flood season. The decision-support tier is in a WebGIS-based application integrated by Ext JS (https://www.sencha.com/), JQuery (http://jquery.com/) and TeeChart (http://www.steema.com/). This tier provides a routine overview of the reservoir project, as well as water level visualization and flood discharging schemes' comparison.
Figure 3

Architecture of the system.

Figure 3

Architecture of the system.

Development of the flood level forecasting model

Overall structure and procedure

The forecasting model aims to evaluate the accumulated inflow flood volume and flood level in the future based on occurred rainfall so that different discharging schemes can be compared to optimize the final capacity. The lead time has to cover inflow flood peak to ensure the validity of the results. Thus, a daily forecast range is used to adapt practical flood control management and other similar small reservoirs that possess only daily hydrological measurements. Following the schematic diagram of the Xinanjiang model (Zhao 1992; et al. 2013), it is established by generalizing the rainfall–runoff process into four parts. They are rainfall and evaporation, a double-layered soil model, surface and subsurface runoff, as well as flood discharge and water consumption (Figure 4(a)).
Figure 4

Overall structure and procedure of the flood level forecasting model. The rainfall–runoff generalization and four primary parts are shown in (a) and the five main calculation steps are shown in (b).

Figure 4

Overall structure and procedure of the flood level forecasting model. The rainfall–runoff generalization and four primary parts are shown in (a) and the five main calculation steps are shown in (b).

The detailed concepts are as follows:

  1. The Thiessen polygons of the rainfall gauges are established to obtain total rainfall amounts in the catchment.

  2. The double-layered soil moisture model is used to estimate the rainfall–runoff. The upper layer receives rainfall and quickly gathers surface runoff, while the lower layer receives infiltration from the above layer and gradually forms subsurface runoff.

  3. The evaporation assessment depends on the rain conditions. On a rainy day, it is ignored due to the humid conditions. On a non-rainy day, it assumes that there is a constant consumption in the upper soil layer.

  4. Flood inflow contains the surface and subsurface runoff. The reservoir water consumption, such as drinking, irrigation and leakage, is generalized as flood discharge. Thus, there is a minimum daily discharge amount.

The model contains eight parameters, which are shown in the first three columns of Table 2, and the overall forecasting procedure is presented in Figure 4(b) with detailed descriptions as follows:

  1. Retrieve the evaporation constant and daily rainfall amounts during N days before the forecasting day.

  2. Estimate the soil moisture of the two layers from N day before to 1 day before.

  3. Calculate the accumulated inflow flood volume based on the surface and subsurface runoff. 
    formula
    1
    where IF is the accumulated inflow flood volume (m3) and Rl is the total runoff volume (mm).
  4. If the forecasted flood level based on reservoir stage–storage curve is higher than the FLWL during the flood season, flood discharge is required immediately. The total outflow volume can be calculated as follows: 
    formula
    2
    where OF is the total outflow volume (m3), vi is the flood discharge during a particular period (m3/s) and ti is the corresponding duration (s).
  5. Finally, the discharged water level is obtained by the actual change of storage capacity.

Table 2

Model parameters and their values for the Hengshan Reservoir

Parameter Description Unit Value 
Wm Maximum antecedent soil moisture mm 90.4 
WUm Maximum antecedent moisture of the upper soil layer mm 31.8 
WLm Maximum antecedent moisture of the lower soil layer mm 58.6 
N Number of days before for soil moisture estimation  14 
Rb Daily subsurface runoff volume mm 5.3 
E Evaporation consumption on a non-rainy day mm 4.2 
A Catchment area m2 1.548 × 108 
OFmin Minimum daily discharge amount m3 2.5 × 105 
Parameter Description Unit Value 
Wm Maximum antecedent soil moisture mm 90.4 
WUm Maximum antecedent moisture of the upper soil layer mm 31.8 
WLm Maximum antecedent moisture of the lower soil layer mm 58.6 
N Number of days before for soil moisture estimation  14 
Rb Daily subsurface runoff volume mm 5.3 
E Evaporation consumption on a non-rainy day mm 4.2 
A Catchment area m2 1.548 × 108 
OFmin Minimum daily discharge amount m3 2.5 × 105 

Antecedent soil moisture estimation

Based on the saturation–excess runoff theories in humid regions (Vieux 2001), a double-layered soil moisture model is designed for the rainfall–runoff estimation. It uses a concept model of two boxes, with one placed inside the other (Figure 5). The small box directly receives rainfall and evaporates on a non-rainy day. If it is filled, rainfall overflows into the big box. If both of them are filled, then extra rainfall spills over. Thus, the soil moisture estimation of the two layers is presented below:
  1. On a non-rainy day, 
    formula
    3
    where WU2 and WL2 are the soil moisture values (mm) of the two layers, while WU1 and WL1 are the same parameters 1 day before.
  2. On a rainy day, the evaporation is ignored; thus, 
    formula
    4
    where P is the rainfall amount (mm).
Figure 5

Sketch map of the antecedent soil moisture estimation.

Figure 5

Sketch map of the antecedent soil moisture estimation.

Rainfall–runoff calculation

Analogous to a series of tanks in a vertical array to express water storage, infiltration and runoff in the tank model, the rainfall–runoff calculation uses several laterally connected soil moisture models to express the hydrological process (Figure 6). The initial soil moisture is set to zero on the N-th day before to start the calculation process. During the N days, surface and subsurface runoff gradually inflows and presents as water level measurements, while the remaining soil moisture is used to calculate the runoff volume on the forecasting day. The detailed procedures are as follows:
Figure 6

Sketch map of the rainfall–runoff calculation.

Figure 6

Sketch map of the rainfall–runoff calculation.

First, beginning on the N-th day before, the following can be expressed: 
formula
5
Second, the soil moisture is estimated day by day until the forecasting day is reached. 
formula
6
Finally, the resulting flood inflow is composed of the surface and subsurface runoff. 
formula
7
where Rd is the surface runoff volume (mm).

Development of reservoir maps

Due to the large amounts and remote distribution of small reservoirs, fast location identification for the reservoir area and the spatial layout of important hydraulic structures are useful for a flood control management system. Therefore, three types of online maps are integrated, so that reservoir managers can easily acquire the background information without geospatial expertise. These include a geographic base map, a 2.5D map and panoramic views. These maps actually form a three-angle perspective of vertical downward, obliquely downward and horizontal directions for the entire reservoir area. The development method avoids using a bundled WebGIS software platform, such as ArcServer; thus, the map browsing functions are more flexible according to the availability of spatial materials of a particular reservoir. A technical roadmap is shown in Figure 7, and the details are as follows:
  1. The 3D terrain model is generated by overlaying the image of the reservoir area onto the DEM data, while the 3D dam model is designed and textured from dam construction CAD drawings in 3D Studio Max (http://www.autodesk.com/). The matching and blending of their relative positions establish the reservoir 3D scene. After that, the 2.5D map tiles are acquired by rendering at a 45-degree angle downward via the 3D Studio Max slicing tool.

  2. Based on the photos taken in the six directions of forward, backward, left, right, above and below at key locations in the reservoir area, local panoramic photos are stitched and generated. After setting their positions in the 3D scene, the panoramic views are created using Pano2VR (http://ggnome.com/pano2vr/) and exported to a single flash file to embed into web pages.

  3. The geographic base map is a direct reference of external map services from Google Maps. It is used as the very basic material to explore the reservoir area when the above multimedia files are unavailable.

Figure 7

Technical roadmap of the reservoir map development.

Figure 7

Technical roadmap of the reservoir map development.

RESULTS AND DISCUSSION

Model parameter acquisition and verification

According to the theories of the SCS-CN model, the antecedent soil moisture can be approximated as zero if intensive rainfall occurs over the entire watershed after several days of drought (Shi et al. 2009). Thus, the value of the Hengshan Reservoir catchment is analysed by choosing typical rainfall events based on three empirical principles: (a) there are 10–20 non-rainy days before the rainfall; (b) the rainfall lasts more than 1 day with a total amount exceeding 50 mm; (c) the surface runoff is almost zero during and after the rainfall process, which is estimated by the change of measured water level and the stage–storage curve.

The resulting rainfall events are shown in Table 3 with an average total amount of 90.4 mm, indicating that the conditions are optimal for the antecedent soil moisture without surface runoff following non-rainy days (Wm). It is also found that the water level rises at an average rate of 5.3 mm per day during 6 days after the rainfall events. Therefore, based on the speculated subsurface runoff volume from previous soil moisture, Rb is 5.3 mm, WLm is 31.8 mm (5.3 × 6) and WUm is 58.6 mm (90.4 − 31.8). After choosing the closest meteorological stations in the China Daily Ground Climate Dataset (http://data.cma.cn/), E is regarded as 4.2 mm by averaging the evaporation consumption on non-rainy days from June to September between 2006 and 2012. Thus, regardless of how much it rains, the remaining soil moisture in the upper layer gradually evaporates over the next 14 days (58.6/4.2), and N is finally obtained. Combined with the basic information of the Hengshan Reservoir, the values of the model parameters are shown in the last column of Table 2.

Table 3

Typical rainfall events for model parameter acquisition

Date of rainfall process Total rainfall amount (mm) Estimated surface runoff volume (mm) Number of non-rainy days before Number of non-rainy days after 
2006.08.08–2006.08.11 105.2 1.3 11 10 
2006.09.09–2006.09.15 102.1 2.1 12 27 
2007.07.07–2007.07.16 93.2 3.3 17 
2009.07.05–2009.07.09 91.2 0.0 13 
2011.07.10 81.1 1.0 18 15 
2011.09.05–2011.09.09 101.3 1.6 14 10 
2012.03.18–2012.03.22 81.0 0.2 13 26 
2013.07.4 74.4 1.4 11 
2014.08.26–2014.08.28 83.7 1.7 15 13 
Date of rainfall process Total rainfall amount (mm) Estimated surface runoff volume (mm) Number of non-rainy days before Number of non-rainy days after 
2006.08.08–2006.08.11 105.2 1.3 11 10 
2006.09.09–2006.09.15 102.1 2.1 12 27 
2007.07.07–2007.07.16 93.2 3.3 17 
2009.07.05–2009.07.09 91.2 0.0 13 
2011.07.10 81.1 1.0 18 15 
2011.09.05–2011.09.09 101.3 1.6 14 10 
2012.03.18–2012.03.22 81.0 0.2 13 26 
2013.07.4 74.4 1.4 11 
2014.08.26–2014.08.28 83.7 1.7 15 13 

Aimed at intensive rainfall in the flood season, the forecasting model is to extend information for reservoir flood discharge and ensure the dam safety. Meanwhile, according to the precipitation magnitude provided by the State Flood Control and Drought Relief Headquarters, the 24-hour total precipitation is classified as heavy rain (25–50 mm), rainstorm (50–100 mm), heavy rainstorm (100–200 mm), etc. (Zhang & Li 1992). Therefore, based on the actual flood discharging logs from 2006 to 2014, 106 days whose total rainfall amount exceeds 25 mm are chosen to verify the model's accuracy. These include 75 days of heavy rain, 27 days of rainstorm and 4 days of heavy rainstorm. Since the sample days are discrete, the verification is performed by comparing the measured water levels on the next day with forecasted water levels. The latter are based on actual changes of storage capacity and stage–storage curve of the Hengshan Reservoir. The analysis contains the following three folds:
  1. Based on the scatter diagram of forecasted and measured water levels on the above samples, a linear regression model is established (Figure 8). According to the result values of slope and R2, the two water levels generally coincide with each other, which indicates that forecasted values are close to the measurements.

  2. The forecast errors are analysed by dividing the samples into three classes based on the precipitation magnitude (Figure 9). As the average errors and error distributions show in the figure, the forecasted values are more likely to be higher than measurements in heavy rain class, while in the rainstorm and heavy rainstorm class, the results are the opposite.

  3. Due to the slight water level variations in the Hengshan Reservoir, the absolute errors are used and analysed by logarithmic, exponential and power regression with total rainfall amount of the above samples (Figure 10(a)10(c)). The resulting R2 values are approximately 0.4, which implies that the amount can explain the forecast errors to some degree but not completely. The regression coefficients are significantly negative and pass the t-test at 99% confidence level in all the three models; thus, the forecast errors gradually decrease as total rainfall amount increases, as shown by the curves. Meanwhile, their F-values are significantly positive at 99% confidence level, indicating that the three models have also passed the F-test and have equal variances. Thus, the three regressions are similar in nature, and the above conclusion is robust and reliable regardless of the model type.

Figure 8

Comparison between forecasted and measured water levels.

Figure 8

Comparison between forecasted and measured water levels.

Figure 9

Analysis of the forecast error according to the precipitation magnitude.

Figure 9

Analysis of the forecast error according to the precipitation magnitude.

Figure 10

Analysis of the water level absolute error and daily rainfall amount. Three types of regression models are created in (a), (b) and (c), while the verification result of the forecasting model is shown in (d).

Figure 10

Analysis of the water level absolute error and daily rainfall amount. Three types of regression models are created in (a), (b) and (c), while the verification result of the forecasting model is shown in (d).

According to the national standard of hydrological forecasting (MWR 2008), the accepted range of water level forecast errors is within 20% of measured variations, and takes 0.1 m as a minimum value. Thus, 0.1 m is regarded as the maximum absolute error in this study. Based on the above results, although there is some error for the heavy rain class, the forecast is acceptable for rainfall events whose daily amount is above or close to the heavy rainstorm level (Figure 10(d)).

Application to flood control management

Following the previously described methodology, the WebGIS-based system is implemented and applied in the Hengshan Reservoir. The system functions are presented from the perspective of a reservoir manager, and a real example, when Typhoon Chan-hom hit southeastern China, is used to verify the decision-support for flood discharge during intensive rainfall in the flood season.

The manager first retrieves the basic information about the target reservoir, including current water level and storage capacity, highest and lowest water level in history, and recent discharging logs (Figure 11(a)). In the reservoir map controls, a geographic base map, a 2.5D map and panoramic views are provided to help explore the entire reservoir area. The manager can retrieve the daily and hourly data in statistical charts, and compare the water levels in a sketch map. During the continuous rainfall in the flood season, the manager switches the system into flood discharging mode (Figure 11(b)). The forecasting model is executed by integrating the 24-hour total rainfall amount, the current water level and a particular discharging scheme. When the measured water level is acquired on the next day, the manager can analyse the accuracy and optimize the model parameters online.
Figure 11

Web interfaces of the flood control management system, which include reservoir routine management in (a) and decision-support for flood discharge in (b).

Figure 11

Web interfaces of the flood control management system, which include reservoir routine management in (a) and decision-support for flood discharge in (b).

When Typhoon Chan-hom arrived on July 11th, 2015, the water level was 33.86 m. Based on the 24-hour total rainfall amount of 117.2 mm and the antecedent soil moisture estimation, the forecasted inflow flood volume would be 126.5 million m3, and the water level would rise to 35.42 m until the next day (Figure 12). Since it was around the middle of the flood season, water level could not exceed the FLWL, so flood discharging decisions had to be made immediately. Based on the concise and deterministic data, the manager loaded and compared three discharging schemes, which would respectively reduce the water level to 33.84 m, 33.90 m and 33.94 m in the next 24, 12 and 20 hours. Due to the minimal effect on the downstream channels and the reserved time for post-discharging modifications, the last scheme with a total outflow discharge of 150 m3/s was selected. The measured water level on the next day was 33.98 m with an absolute error of 0.04 m, which was within the above-mentioned error threshold; thus, the system effectively helps the reservoir manager to formulate appropriate discharging schemes, so that the water level and capacity is maintained in a safe range during intensive rainfall in the flood season.
Figure 12

A real example of flood level forecasting and discharging schemes' comparison during heavy rainstorm.

Figure 12

A real example of flood level forecasting and discharging schemes' comparison during heavy rainstorm.

Discussion

The numerous and widely distributed small reservoirs are characterized by the uneven levels of management and limited hydrological measurements. Their storage capacity is normally maximized for multiple purposes, but discharged for flood control and conservation during the flood season (Rodrigues et al. 2012; Song et al. 2015). In such circumstances, this study presents the WebGIS-based system as an effective tool to guide the flood control management particularly for reservoir managers. It is different from the existing flood early warning systems for large river basins and reservoirs (Roo et al. 2011; Wu et al. 2014; Thiemig et al. 2015), which focus on flood routing simulation for different lead times, and also from the flood assessment systems for local areas (Shivakoti et al. 2011; Demir & Krajewski 2013; Horita et al. 2015), which currently adopt web-based technologies to involve different communities. Since several institutional and social conditions have to be fulfilled when applying sophisticated flood control systems (Cools et al. 2016), a targeted and adaptable tool could play a more useful role in practical flood discharge of small reservoirs.

In order to make the result system concise and practical, this study introduces the agile development methods and tools during the entire system development process (Figure 2). They help to embody multidisciplinary knowledge from different fields, such as reservoir management, flood discharge, hydrological modelling, map development and software integration. Since different experts usually prefer popular methods within their respective domains, a loosely coupled structure of different tools and languages is used (Figure 3). The reservoir map browse is composed of three independent widgets, considering the uncertainties of spatial materials possessed by different reservoirs.

As the core of the system, the forecasting model is mainly based on rainfall in the catchment and water level in front of the dam. Aiming at concise information for flood discharge, it is a simplified transformation of lumped conceptual hydrological models, and adopts a horizontal water level to generalize the fluctuating water surface caused by flood routing (Zhao 1992; Shi et al. 2009; et al. 2013). Meanwhile, the model currently uses daily lead time, considering the actual formulation mode of flood discharging scheme in the study area, and the applicability to other similar reservoirs that possess only daily hydrological measurements. A shorter forecast range, e.g., hourly, is also available using this method, but the corresponding flood discharge may have to include several results to make an overall decision.

According to the verification based on historical rainfall events, the forecast errors gradually decrease as the total rainfall amount increases (Figure 10(d)). In addition, the forecasted water levels are more likely to be greater than measurements in lighter rainfall, but smaller during heavier rainfall (Figure 9). The explanation and the analysis are as follows:

  1. When acquiring the parameter values of the study area, typical rainfall events are chosen based on the empirical principles that surface runoff does not form during and after the rainfall process, but the water level gradually rises in the following days. Then, the total rainfall amount of each typical event is averaged to estimate the value of Wm; however, the evaporation consumption is ignored during the calculation process. Therefore, the resulting soil moisture value is slightly over-estimated, which is presumably the reason why the forecasted inflow flood volume tends to be smaller during heavier rainfall.

  2. Runoff usually forms quickly after heavy rainfall, owing to the saturated soil moisture in the flood season. However, during the non-flood season, the rainfall infiltration from the upper to the lower soil layer requires further refinement, and the actual evaporation consumption should be greater than the flood season (Vieux 2001; et al. 2013). Thus, the forecasted runoff volume tends to be greater than the actual value. This explains why the forecasted water level is more likely to be greater after lighter rainfall.

Therefore, detailed evaporation analysis is recommended for further improvement of the model. When estimating the antecedent soil moisture, the evaporation consumption in the upper layer should be classified by months and weather conditions, and deducted from the value of Wm. In order to acquire more accurate forecasts during the non-flood season and lighter rainfall, slightly more complex hydrological models are worth trying when distributed characteristics with relatively high resolution of the catchment are available. For example, the applicable CN values of the SCS-CN model could be calibrated combined with water level variations, so that the resulting runoff volumes are comparable (Mishra & Singh 2003; Shi et al. 2009). In addition, the usage of the WFlow model (http://wflow.readthedocs.io/) with rainfall interception and kinematic wave modelling could give the runoff process for each stream channel.

Due to the relatively sufficient flood discharging capacity and water storage requirements of the study area, the system currently relies on occurred rainfall, rather than numerical weather predictions, such as the Weather Research and Forecasting model (Bartholmes & Todini 2005; Skamarock et al. 2005), and satellite- and radar-based imagery data (Park & Hur 2012; Wu et al. 2014). However, meteorological forecasts are important to provide additional lead time to flood control preparedness, especially for the small reservoirs affected by flash floods. In addition, user interfaces of water level comparison are used to help formulate discharging schemes. This method is concise, but still requires empirical judgement by reservoir managers. Based on the above runoff process simulation, stage-wise flood control operation rules and optimization algorithms currently used in cascade reservoirs and river–reservoir systems (Che & Mays 2015; Chou & Wu 2015) have good reference value to balance flood prevention and water storage, as well as to flood discharge of small reservoirs.

CONCLUSIONS

Small reservoirs and their current status of flood management are garnering increased attention from both researchers and decision-makers. In this study, a WebGIS-based flood control management system with a single targeted forecasting model is outlined to provide decision support for flood discharge during intensive rainfall in the flood season. The features and contributions are summarized as follows:

  1. In order to make the result concise and effective for reservoir managers, agile development methods are adopted during the entire implementation process, which helps to combine multidisciplinary knowledge from different fields. A loosely coupled structure of different tools and languages is used to integrate reservoir map browse, flood level forecasts and discharging schemes comparison, which enables the experts to use their popular methods from different domains.

  2. Based on the hydrological measurements of rainfall in the catchment and water level in front of the dam, a flood level forecasting model with daily lead time is established by estimating the antecedent soil moisture and accumulated inflow flood volume. The forecast result is acceptable for the rainfall amount above or close to the heavy rainstorm level, according to the national standard of hydrological forecasting of China.

Currently applied in the Hengshan Reservoir in the lower reaches of the Yangtze River, the system is validated by historical rainfall events and a trial operation during the typhoon season. It is characterized by the usage of limited hydrological data that a small reservoir possesses, and the user-friendly interfaces for routine management and flood discharge for reservoir managers, which make it adaptable to other small reservoirs in humid regions. Since the optimization for flood control management is a complex and multifaceted issue, further improvements to the flood level forecasting model and study on the stage-wise flood control operation is recommended to better support the flood discharge of small reservoirs.

ACKNOWLEDGEMENTS

This study was sponsored by the National Natural Science Foundation of China (No. 41471460 and 41130750), Science and Technology Service Network Initiative (No. KFJ-SW-STS-174), ‘One Hundred Talents Program’ of the Chinese Academy of Sciences, and Postdoctoral Research Funding Programs of Jiangsu Province (No. 1601038B). We appreciate the editor and three anonymous reviewers for their valuable suggestions and comments.

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