Monthly Rainfall Runoff Model Research Articles

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

GIS-based assessment of morphological and hydrological parameters of Wainganga River Basin, Central India

River basin planning is a systematic approach to the specific problems related to availability and access to water for various purposes, and to simultaneously ensure the ecological integrity of a river system. GIS-based assessment of morphological and hydrological process inflows, rainfall–runoff modeling and regression equations was discussed using geographical information systems and remote sensing techniques. The Wainganga River emerges from a spring in the plains of Madhya Pradesh and has a meandering flow. The river is mostly shallow and wide in this region. The depth of the river increases in the backwaters of the Sanjay Sarovar Dam. In observation, the total number of streams of 27 sub-basins is 9472; the diverse geology implies geohydrological heterogeneity at the river basin scale. This heterogeneity could be amenable to comprehensive river-basin planning of recharge, discharge and conservation. The monthly rainfall–runoff models (R–R models) so developed are R = 1.019 * P − 418.20 height in the Bhimkund catchment area, and the lowest is R = 0.532 * P + 194.60 in Bamni catchment. The present study depicts the process of evaluating various morphological and hydrological parameters of the Wainganga River by applying remote sensing, geographical information system and global positioning system techniques. The data can be used for basin management, hydrological, and utilized in restoration and conservation of natural resource studies in the future.

Towards revised physically based parameter estimation methods for the Pitman monthly rainfall-runoff model

This paper presents a preliminary stage in the development of an alternative parameterisation procedure for the Pitman monthly rainfall runoff model which enjoys popular use in water resource assessment in Southern Africa. The estimation procedures are based on the premise that it is possible to use physical basin properties directly in the quantification of the soil moisture accounting, runoff, and recharge and infiltration parameters. The results for selected basins show that the revised parameters are at least as good as current regionalised sets or give satisfactory results in areas where no regionalised parameters exist.

Combined deterministic – stochastic forecasting of monthly river flows for water management

Monthly discharges can be modelled and predicted by the decomposition of the runoff process model into two components – deterministic and stochastic. For such approach the term hybrid was often adopted. In this study a hybrid (deterministic-stochastic) modelling approach for one step ahead forecasting of mean monthly discharges at the gauging stations Banská Bystrica and Brehy on the Hron River in Slovakia was developed. The aim was to join the conceptual monthly rainfall runoff model KVHK and several time series models of the forecasting error time series of the conceptual model into a hybrid framework. Since these rainfall-runoff model error series may exhibit nonstationarity and heteroscedasticity, beside traditional ARMA models, GARCH type nonlinear time series models were also considered.

Regional water resources assessments using an uncertain modelling approach: The example of Swaziland

Study regionThe 5 river basins that flow through or within Swaziland in southern Africa. Study focusA regional water resource assessment using an uncertainty version of the Pitman monthly rainfall-runoff model whose outputs are constrained by six indices of natural hydrological response (mean monthly runoff, mean monthly groundwater recharge, Q10, Q50 and Q90 percentage points of the flow duration curve and% time of zero flows) for each of the 122 sub-basins within the whole of Swaziland. A 2-step approach is adopted where the first step establishes behavioural, but uncertain, model parameter ranges for natural incremental sub-basin hydrological responses, while the second step links all the sub-basin outputs to generate cumulative sub-basin outflows and allows for water use parameters to be included. New hydrological insights for this regionThe analysis of hydrological indices highlights the regional variations in hydrological processes and sub-basin response. The adopted modelling approach provides further insight into all of the uncertainties associated with quantifying the available water resources of Swaziland. The study has provided more insight into the spatial variability of the hydrological response and existing development impacts than was previously available. These new insights should provide an improved basis for future water management in Swaziland.

Assessing the impact of global changes on the surface water resources of southwestern Nigeria

Understanding the relative impact of land use, land cover (LULC) and climate change (CC) on basin runoff is necessary in assessing basin water stress, for which long-term observed rainfall time series and LULC spatial data are required. However, there are challenges with the availability of spatio-temporal data, particularly the limited range of available historical hydro-meteorological measurements. The study used long-term (1961–2007) rainfall data to drive the Pitman monthly rainfall–runoff model to assess changes to the water resources of three basins in Nigeria—Asa, Ogun and Owena. Three CGCMs—CSIRO Mark3.5, MIROC3.2-medres and UKMO-HadCM3—dynamically downscaled to a 60 km by 60 km grid using the Conformal-Cubic Atmospheric Model (C-CAM) are used to simulate impacts of future climate changes on water resources. These three models were found suitable for simulating rainfall–runoff based on the insignificant differences of the modelled mean with mean of observed rainfall and temperature for pre-2010 data compared to other downscaled C-CAM models (GFDL-CM2.0, GFDL-CM2.1 and ECHAM5/MPI-Ocean model). The model results show increases in the runoff coefficient with decreases in forest cover between 1981 and 2007, with average runoff coefficients of 5.3%, 12.0% and 6.4% for Asa, Ogun and Owena basins respectively. Based on annual reduction in rainfall trend projected by CSIRO, MIROC and UKMO, the future scenarios revealed a low runoff coefficient for the three basins—Asa (CSIRO 6.0%, MIROC 6.0% and UKMO 5.9%), Ogun (CSIRO14.6%, MIROC 14.6% and UKMO 14.4%) and Owena (CSIRO 8.5%, MIROC 8.7% and UKMO 8.9%). In all scenarios, Asa basin has a lower runoff coefficient when compared to Ogun and Owena basins, indicating that future water stress in Asa basin would be much greater.Editor Z.W. Kundzewicz; Guest editor D. Hughes

Initial evaluation of a simple coupled surface and ground water hydrological model to assess sustainable ground water abstractions at the regional scale

Additional surface–ground water interaction routines were recently added to the Pitman monthly rainfall–runoff model, widely used in South Africa for quantifying water resources in ungauged catchments. Some evaluations of the model have demonstrated that it can realistically simulate interactions between surface and ground water at catchment scales of approximately 100 to 5,000 km2. The model allows ground water abstractions to be simulated, but no reported evaluations of this component are available. This study uses the model to estimate sustainable abstraction volumes in a semi-arid catchment and includes an assessment of model parameter uncertainties. In recognition of potential spatial scale issues related to the model structure an alternative model configuration, based on splitting the total catchment into recharge and abstraction sub-catchments, was also tested. While the results appear to be conceptually appropriate, there is insufficient available information to quantitatively confirm the model parameters and results. The same would apply regardless of the type of model being applied in such a data-deficient area. Additional geo-hydrological information is required to resolve the model uncertainties and improve the parameter estimation process. This pilot study has highlighted the type of information required, but further work is needed to identify how best to obtain that information.

Monthly rainfall-runoff models applied to arid and semiarid catchments for water resource estimation purposes

Abstract Problems associated with the application of monthly rainfall-runoff models to arid and semiarid areas for water resource estimation purposes are discussed with respect to the representation of hydrological processes and the model rainfall input. The problems are illustrated using a specific monthly model applied to catchments within the semiarid to arid parts of southern Africa. Some model improvements are suggested and briefly evaluated and the application of a daily model to one of the catchments is compared. The overall conclusion is that while a detailed examination of the available rainfall data can account for many of the inadequate simulation results on an individual month basis, it is difficult to make allowances for the lack of resolution in the normally available information. Other problems related to the structure of individual models can be surmounted by limited model developments, but very little can be done to bypass the rainfall quantification problem.

Parsimonious monthly rainfall-runoff models for humid basins with different input requirements

The availability of data is a major problem for the widespread application of rainfall-runoff models. In order to assist the practicing hydrologists in the selection of suitable models, four levels of monthly water balance models are developed requiring different sets of input data. Essentially four types of models are considered according to the inputs necessary, which are respectively: precipitation and potential evapotranspiration; precipitation, temperature and humidity; precipitation and temperature; precipitation alone. The outputs from the models are monthly river flow and other water balance components. The models are examined on 91 small and medium sized basins, of which 85 are situated in Belgium and 6 in southern China, and they are capable of reproducing both the magnitude and timing of monthly and seasonal runoff, as well as changes in soil moisture conditions. Results are satisfactory and there are barely any differences between the model types, although the amount of information used is very different. Calibration is by automatic optimization without any subjective element. Application in forecasting and Monte Carlo simulation are exemplified.

Sensitivity of monthly rainfall-runoff models to input errors and data length

Two problems are addressed which arise when using monthly water balance models as an aid to making decisions in water resources engineering: what is the influence of data errors on model performance, and what is the data length required in order to obtain reliable models? Two previously defined types of models are used: in PE type models the input series are precipitation and potential evapotranspiration; in P type models the only input is precipitation. The main conclusions are: (1) random errors in precipitation data, when great enough, affect model performance significantly; (2) systematic errors in precipitation data are less important for the estimation of river flow; and (3) a data length of 10 years is necessary and sufficient for a reliable calibration of monthly water balance models of humid basins.

Methodology and comparative study of monthly water balance models in Belgium, China and Burma

A set of new monthly rainfall runoff models (water balance models) is defined, for use in river catchments smaller than about 4000 km 2, without appreciable frost or natural or artificial lakes. The input series are areal precipitation and potential evapotranspiration. The output is riverflow. The number of parameters, used in the description of the hydrological phenomena in the catchment, is in most cases three, sometimes four. The statistical methodology use for calibrating the models of given catchment is described; it reduces essentially to regression analysis, including residual analysis, sensitivity to calibration period and extrapolation test. In particular, automatic calibration is used, excluding subjective elements. The models are applied to 79 river basins in Belgium, China and Burma. The results are compared with four similar models taken from the literature. The results of applying the new models are satisfactory from a statistical point of view and are much better than those quoted in the literature; a greater part of the observed runoff is explained and there is no residual seasonality. This results from the different mathematical structure of the models, and especially from the use, in the published models, of several storages with maximum ‘capacities’, with no distinction between slow and fast runoff corresponding to baseflow and direct runoff, respectively.