Quantification Of Water Balance Components Research Articles

A multi‐method approach for the identification and quantification of water balance components of two lentic small water bodies

AbstractThe protection of the globally widespread lentic small water bodies (LSWB) must be based on detailed knowledge about their hydrological connectivity and water balance. The study aimed to identify and quantify water balance components as well as surface‐groundwater interaction of two LSWB in a characteristic lowland region with a combination of different methods. This includes the collection of hydrological data and the use of bromide and water stable isotopes (δ2H and δ18O) as tracers. With their help, mixing models were established, and daily water balances were assessed. The results show a strong bidirectional interaction of both LSWB systems with shallow groundwater. Bromide and stable isotope tracers allowed for the identification of the most relevant in‐ and outflow sources and pathways. Thereby, isotope data revealed isotopic enrichment typical for open‐water bodies and only minor precipitation inputs mainly relevant at the end of the dry season. Water balance calculations suggested accentuated seasonal dynamics that were strongly influenced by shallow groundwater, which represented large inputs into both LSWB. By that, different phases could be identified, with high inflow rates in winter and spring and decreasing fluxes in summer. In one LSWB, a drainage system was found to have a major impact next to the shallow groundwater interaction. The findings of this research provide detailed insights into the influence and importance of shallow groundwater for LSWB in lowland regions. This impacts the diffuse input of agricultural pollutants into these ecologically important landscape features.

Quantitative analysis of water balance components in Lake Urmia, Iran using remote sensing technology

Accurate quantification of water balance components in large lakes is of great importance in assessing the changes in hydrological components, and subsequent variations in lakes water balance. The water balance of Urmia Lake in northwestern Iran was quantitatively analyzed in the year 2005 using remotely-sensed data (e.g. evapotranspiration and precipitation) and meteorological data. The analysis was based on GIS operations. For this purpose, the estimation of pixel-scaled monthly evapotranspiration (ET) was conducted via the Surface Energy Balance Algorithm for Land (SEBAL) using time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images and meteorological data; precipitation was derived from the monthly Tropical Rainfall Measuring Mission (TRMM) satellite observations; surface runoff was estimated by the empirical formula; and finally, the computations of recharge were carried out using the water balance approach for the winter months where estimation of ET and surface runoff were subtracted from precipitation. Water balance components were quantified for the lake and the lake basin separately. Results showed that the water balance components calculated by remote sensing were in conformity with the reported figures in the literature. According to the results of the water balance method, Urmia Lake was suffering from a negative water balance of 3443 MCM yr−1. Findings of this study demonstrated the application of the derived satellite data in estimating lake's water balance components in order to improve the accuracy of the lake water budget calculation.

Quantification of water resources uncertainties in the Luvuvhu sub-basin of the Limpopo river basin

In the absence of historical observed data, models are generally used to describe the different hydrological processes and generate data and information that will inform management and policy decision making. Ideally, any hydrological model should be based on a sound conceptual understanding of the processes in the basin and be backed by quantitative information for the parameterization of the model. However, these data are often inadequate in many sub-basins, necessitating the incorporation of the uncertainty related to the estimation process. This paper reports on the impact of the uncertainty related to the parameterization of the Pitman monthly model and water use data on the estimates of the water resources of the Luvuvhu, a sub-basin of the Limpopo river basin. The study reviews existing information sources associated with the quantification of water balance components and gives an update of water resources of the sub-basin. The flows generated by the model at the outlet of the basin were between 44.03 Mm3 and 45.48 Mm3 per month when incorporating +\\-20% uncertainty to the main physical runoff generating parameters. The total predictive uncertainty of the model increased when water use data such as small farm and large reservoirs and irrigation were included. The dam capacity data was considered at an average of 62% uncertainty mainly as a result of the large differences between the available information in the national water resources database and that digitised from satellite imagery. Water used by irrigated crops was estimated with an average of about 50% uncertainty. The mean simulated monthly flows were between 38.57 Mm3 and 54.83 Mm3 after the water use uncertainty was added. However, it is expected that the uncertainty could be reduced by using higher resolution remote sensing imagery.

A distributed monthly water balance model: formulation and application on Black Volta Basin

Recharge assessment is of critical importance for groundwater resources evaluation in arid/semiarid areas, as these have typically limited surface water resources. There are several models for water balance evaluation. One of them is WetSpass, which has the ability to simulate spatially distributed recharge, surface runoff, and evapotranspiration for seasonally averaged conditions. This paper presents a modified methodology and model, WetSpass-M, in which the seasonal resolution is downscaled to a monthly scale. A generalized runoff coefficient was introduced, enabling runoff estimation for different land-use classes. WetSpass-M has been calibrated and validated with observed streamflow records from Black Volta. Base-flow from simulated recharge was compared with base-flow derived via a digital filter applied to the observed streamflow and has shown to be in agreement. Previous studies have concluded that for this basin, small changes in rainfall could cause a large change in surface runoff, and here a similar behavior is observed for recharge rates. An advantage of the new model is that it is applicable to medium- and large-sized catchments. It is useful as an assessment tool for evaluating the response of hydrological processes to the changes in associated hydrological variables. Since monthly data for streamflow and climatic variables are widely available, this new model has the potential to be used in regions where data availability at high temporal resolution is an issue. The spatial–temporal characteristics of the model allow distributed quantification of water balance components by taking advantage of remote sensing data.

Reducing uncertainty in hydrological modelling in a data sparse region

One subproject within the project IWAS aims at the identification of effective and sustainable measures to improve the water quality of the river basin Western Bug in the Ukraine in preparation of changes of climate, land use and socio-economy. An important part of the system analysis is the quantification of water balance components. Rare and uncertain data impede hydrological modelling in the region. Approaches to reduce the modelling uncertainty are needed. In this work, we focus on the reduction of uncertainty that results from precipitation observations and parameter estimation during the calibration process. The main aim is to set up a well performing model for water balance simulations. The semi-physically based model Soil and Water Assessment Tool (SWAT) was applied. A calibration and uncertainty reduction strategy was set up that consists of time- and spatial-scale dependencies as well as alternative precipitation inputs. The single models that were set up and calibrated with alternative precipitation inputs are treated as an ensemble and were averaged with different methods. Nine parameters were chosen from the completed sensitivity analysis for all calibration approaches. The calibration strategy revealed the benefit of applying a complex bottom-up calibration that starts with sub-basins and ends with the entire basin. Not only performance improved significantly, but also water balance gaps were identified and henceforth reduced. The usage of daily runoff data in the calibration procedure did not enhance simulations in comparison to monthly data. Simulations improved and parameter uncertainty was reduced applying the SWAT model variants, where precipitation was included in calibration and by the ensemble averaging of the different models. Nevertheless, chosen uncertainty measures are not optimal and indicate that parameter uncertainty is still high. This is attributed to the low density of precipitation stations in the region, their low representativity, and scarce data situation in general. Differences between modelled and observed runoff are large in some occasions and can not be balanced completely by the applied methods. Although this approach reached its limit, it was demonstrated that simulations can be improved and modelling uncertainty can be reduced with an appropriate calibration strategy, with extended precipitation information and the application of an ensemble approach.

Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany)

Abstract. The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine subbasins. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the hydrological model. This is achieved by an artificial neural network approach (ANN), where four input variables are taken to calculate the unknown subsurface storage conditions. This was first developed for the high Alpine subbasin Königsseer Ache to improve the monthly water balance. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the hydrological model as boundary flux, and considered during the consecutive model process. We tested several ANN setups in different time increments to investigate ANN performance and to examine resulting runoff dynamics of the hydrological model. The ANN with 5-day time increment showed best results in reproducing the observed water storage data (r2 = 0.6). The influx of the 20-day ANN showed best results in the hydrological model correction. The boundary influx in the subbasin improved the hydrological model, as performance increased from NSE = 0.48 to NSE = 0.57 for subbasin Königsseetal, from NSE = 0.22 to NSE = 0.49 for subbasin Berchtesgadener Ache, and from NSE = 0.56 to NSE = 0.66 for the whole catchment within the test period. This combined approach allows distributed quantification of water balance components including subsurface water transfer.