Soil Moisture Accounting And Routing Research Articles

Investigation of the propagation of meteorological to hydrological drought and water required to recover from drought over Ethiopian basins

Abstract This study investigates drought propagation from meteorological to hydrological and streamflow required to recover from drought in four sub-basins: Genale, Tekeze, Awash, and Baro basins of Ethiopia. Due to limited observed streamflow data, the soil moisture accounting and routing (SMAR) model was used to extend the streamflow data for each sub-basin from 1985 to 2017. Drought characteristics in terms of duration, severity, and onset/offset of drought and propagation time at different time scales are investigated using run theory and Pearson correlation, respectively. Two Archimedean copulas (Clayton and Gumbel) are used to identify the joint return period between drought duration and severity and the amount of streamflow required to recover from hydrological drought for each sub-basin. Our results revealed that drought frequency has increased over most sub-basins over the last two decades. The propagation time from meteorological drought to hydrological drought is shorter over the Tekeze sub-basin (1–3 months); however, Genale and Awash sub-basin show 3- to 6-month propagation time. The more extended propagation time is seen over the Baro sub-basin (6–9 months). The required amount of water for drought recovery estimation shows a linear relationship between the duration of the drought and the amount required.

A combined rotated general regression neural network method for river flow forecasting

ABSTRACTThis study focused on the performance of the rotated general regression neural network (RGRNN), as an enhancement of the general regression neural network (GRNN), in monthly-mean river flow forecasting. The study of forecasting of monthly mean river flows in Heihe River, China, was divided into two steps: first, the performance of the RGRNN model was compared with the GRNN model, the feed-forward error back-propagation (FFBP) model and the soil moisture accounting and routing (SMAR) model in their initial model forms; then, by incorporating the corresponding outputs of the SMAR model as an extra input, the combined RGRNN model was compared with the combined FFBP and combined GRNN models. In terms of model efficiency index, R2, and normalized root mean squared error, NRMSE, the performances of all three combined models were generally better than those of the four initial models, and the RGRNN model performed better than the GRNN model in both steps, while the FFBP and the SMAR were consistently the worst two models. The results indicate that the combined RGRNN model could be a useful river flow forecasting tool for the chosen arid and semi-arid region in China.Editor D. Koutsoyiannis; Associate editor not assigned

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

AbstractThis paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using gene expression programming (GEP) to perform symbolic regression. The GEP multimodel combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments. The four selected models for the multimodel combinations are the linear perturbation model (LPM), the linearly varying gain factor model (LVGFM), the soil moisture accounting and routing (SMAR) model, and the probability-distributed interacting storage capacity (PDISC) model. The first two of these models are black-box models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical locations and hydrological and climatic conditions are used to test the perfor...

A “monster” that made the SMAR conceptual model “right for the wrong reasons”

In earlier studies involving simulation of the Fergus River flows in Ireland, the conceptual Soil Moisture Accounting and Routing (SMAR) model had been found to consistently outperform a number of black-box models. Subsequently, in investigating any loss of flow through this catchment's subsurface karstic features, it was verified from the overall long-term water balance that such losses were substantial. This raised the awkward question of why the volume-conservative SMAR model had performed so well on this considerably non-conservative catchment. Further analyses revealed that, to compensate for the excess volume of total runoff generated by the model's conservative water balance component, the memory length of the surface runoff response function had been unrealistically curtailed in the optimization process, effectively truncating that function and thereby violating the conservation property of the routing process. This embarrassing revelation called for reconsideration of the model structure to account more sensibly for actual losses, while still achieving high model efficiency. This paper highlights not only the discovery of the karstic Fergus catchment as a “hydrological monster”, in the context of the SMAR model, but also why conservative models perform poorly in such cases. In an attempt to “tame the monster”, better simulation of the observed flows was achieved by conceptually adapting the SMAR model, in a pragmatic empirical manner, by simply modifying its water balance component. Citation Goswami, M. & O'Connor, K. M. (2010) A “monster” that made the SMAR conceptual model “right for the wrong reasons”. Hydrol. Sci. J. 55(6), 913–927.

Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model

In this application-based study, six automated strategies of parameter optimization are used for calibration of the conceptual soil moisture accounting and routing (SMAR) model for rainfall—runoff simulation in two catchments, one small and the other large. The methods used are: the genetic algorithm, particle swarm optimization, Rosenbrock's technique, shuffled complex evolution of the University of Arizona, simplex search, and simulated annealing. A comparative assessment is made using the Nash-Sutcliffe model efficiency index and the mean relative error (MRE) to evaluate the performance of each optimization method. It is found that the degree of variation of the values of the water balance parameters is generally less for the small catchment than for the large one. In the case of both catchments, the probabilistic global population-based optimization method of simulated annealing is considered best in terms of having the least variability of parameter values in successive tests, thereby alleviating the phenomenon of equifinality in parameter optimization, and also in producing the lowest MRE in verification.

Development of regionalisation procedures using a multi-model approach for flow simulation in an ungauged catchment

Summary Flow simulation in ungauged catchments is presently regarded as one of the most challenging tasks in surface water hydrology. Many of the ungauged catchments are located in the headwaters of rivers in mountainous regions of the world having enormous potential for sustainable water resource development. However, due to inaccessibility, rugged and inhospitable terrain, and historical lack of foresight concerning the need to have these headwaters adequately gauged, their potential is not readily realizable. Many downstream sites also suffer from non-availability of site-specific data as even in countries having extensive networks of gauged stations data may not be available at sites where these are most needed. As predictive tools for water resources, water quality, natural hazard mitigation and water availability assessment are generally data-driven, the lack of adequate hydrometric records poses difficult problems for planners, engineers, managers, and stake-holders alike. In this study, a methodology is developed for flow simulation in ungauged catchments using a regionalisation and multi-model approach involving a suite of rainfall–runoff models and combination techniques. Daily observed hydrometeorological data for 12 French catchments are used for illustrating the procedures. Following a preliminary investigation of the regional homogeneity of that group of catchments, three regional flow simulation techniques are applied. Although all 12 catchments are gauged, initially each catchment is successively considered as being ungauged for the purpose of flow simulation in that catchment, their actual discharges being subsequently used for evaluating the performance of the flow estimation procedures for the catchment. The Nash-Sutcliffe efficiency index ( R 2 ) is used for assessing and ranking the relative performances of the regionalisation–model couples to identify the most appropriate couple for the region. The final step of applying that couple to a truly ungauged (13th) catchment in the region is described. Results are presented and conclusions drawn on the efficacy of the regional-multi-model approach. Of the couples considered, the pooling method of regionalisation coupled with the conceptual soil moisture accounting and routing (SMAR) model is deemed to be the best for simulating flow in an ungauged catchment in the region.

Assessing the performance of eight real-time updating models and procedures for the Brosna River

Abstract. The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

Estimating groundwater recharge using the SMAR conceptual model calibrated by genetic algorithm

Proper groundwater management of a contaminated aquifer requires the accurate estimation of groundwater recharge, which carries the contaminated load. There are many direct and indirect methods and sophisticated models for estimating recharge. However, most of them require many field data or model parameters, which limit their actual field application. To overcome this limitation, the Soil Moisture Accounting and Routing (SMAR), a conceptual rainfall-runoff model, is employed. The SMAR model has the potential for estimating recharge using only rainfall, evaporation and groundwater level data. However, for an aquifer having prominent horizontal groundwater flow, this model cannot be used directly. For this reason a horizontal flow component is added to this model. Model parameters are calibrated by the Genetic Algorithm (GA) optimization technique. Sensitivity of calibrated parameters to model efficiency and estimated recharge, and parameter interdependence are investigated. This model is applied to 11 observation locations in four catchment areas of Miyakojima Island, Japan, where groundwater nitrate contamination is a threat. The effectiveness of the model is evaluated using the model efficiency ( R 2), the mean of the sum of square errors (MSE), plots of observed versus estimated groundwater levels, scatter plots of observed versus estimated groundwater levels, measure of timing of the peaks, and the correlation between monthly rainfall and monthly estimated recharge. All show that this technique is very efficient for estimation of recharge. Model efficiency ( R 2) up to 92%, minimum MSE 0.32 m 2/day, average relative error of timing of the peaks 4.13%, and coefficient of determination ( r 2) up to 0.92 are obtained for the study area. The estimated recharge is 45% of the mean annual rainfall and agrees with other finding. It is thus concluded that the SMAR model could be a viable alternative since it can estimate dependable recharge with a minimum of input data.

Comparison of four updating models for real-time river flow forecasting

Four different error-forecast updating models are investigated in terms of their capability of providing real-time river flow forecast accuracy superior to that of rainfall-runoff models applied in the simulation (nonupdating) mode. The first and most widely used is the single autoregressive (AR) model, the second being an elaboration of that model, namely the autoregressive-threshold (AR-TS) updating model. A fuzzy autoregressive-threshold (FU-AR-TS) updating model is proposed as the third form of model, the fourth and final error-forecast updating model applied being the artificial neural network (ANN) model. In the application of these four updating models, the lumped soil moisture accounting and routing (SMAR) conceptual model has been selected to simulate the observed discharge series on 11 selected test catchments. As expected, it is found that all of these four updating models are very successful in improving the flow forecast accuracy, when operating in real-time forecasting mode. A less expected, but nonetheless welcome, result is that the three updating models having the most parameters, i.e. AR-TS, FU-AR-TS, and ANN, do not show any considerable advantages in improving the real-time flow forecast efficiency over that of the simple standard AR model. Thus it is recommended that, in the context of real-time river flow forecasting based on error-forecast updating, modellers should continue to use the AR model.

A non-linear neural network technique for updating of river flow forecasts

Abstract. A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments having different climatic conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure. The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure. Keywords: Auto-Regressive Exogenous-input model, neural network, output-updating procedure, soil moisture accounting and routing (SMAR) model

Application of an empirical infiltration equation in the SMAR conceptual model

An empirical equation, which relates the infiltration rate to the actual soil moisture content, is proposed and incorporated in the Soil Moisture Accounting and Routing (SMAR) model to demonstrate one application of that equation. A modified SMAR model (SMARY) is developed and tested on four catchments with different climatic conditions. The results show that the SMARY model performs better than the SMAR model in terms of R 2, mean square error and relative error. The modified model provides a more rational interpretation of the physical process of infiltration and also provides a tool for determining the actual infiltration rate under specific soil moisture conditions.

River flow forecasting. Part 5. Applications of a conceptual model

A rainfall-runoff model of the conceptual type, known as the Soil Moisture Accounting and Routing (SMAR) Model, is described and applied to five of the 14 catchments described in Part 3. The model comprises a water balance component in which the rainfall and evaporation interact to produce runoff and a linear routing component which transforms the generated runoff into discharge. The structure of the model emphasizes the distinct roles of the non-linear water balance component and the linear routing component. A calibration procedure is suggested which maintains this distinction. Good results seem to be obtained particularly on semiarid catchments on which models of the systems analysis type do not perform well.