On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony, Germany
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Atmospheric and Climate Sciences, 2013, 3, 552-561 http://dx.doi.org/10.4236/acs.2013.34058 Published Online October 2013 (http://www.scirp.org/journal/acs) On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony, Germany Rico Kronenberg1,2*, Klemens Barfus1, Johannes Franke1, Christian Bernhofer1 1Technische Universität Dresden, Dresden, Germany 2Bauman Moscow State Technical University, Moscow, Russia Email: *[email protected] Received July 31, 2013; revised August 28, 2013; accepted September 5, 2013 Copyright © 2013 Rico Kronenberg et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT In this study, recurrent networks to downscale meteorological fields of the ERA-40 re-analysis dataset with focus on the meso-scale water balance were investigated. Therefore two types of recurrent neural networks were used. The first ap- proach is a coupling between a recurrent neural network and a distributed watershed model and the second a nonlinear autoregressive with exogenous inputs (NARX) network, which directly predicted the component of the water balance. The approaches were deployed for a meso-scale catchment area in the Free State of Saxony, Germany. The results show that the coupled approach did not perform as well as the NARX network. But the meteorological output of the coupled approach already reaches an adequate quality. However the coupled model generates as input for the watershed model insufficient daily precipitation sums and not enough wet days were predicted. Hence the long-term annual cycle of the water balance could not be preserved with acceptable quality in contrary to the NARX approach. The residual storage change term indicates physical restrictions of the plausibility of the neural networks, whereas the physically based cor- relations among the components of the water balance were preserved more accurately by the coupled approach. Keywords: Downscaling; Recurrent Neural Networks; NARX; WaSim-ETH; Water Balance; ERA-40 Re-Analysis 1. Introduction ent methods for single sites. They divided the approaches into three categories: 1) Regression methods; 2) Weather Information density and calculation speed of global cir- pattern based methods; 3) Stochastic generators. Gener- culation models (GCM) are highly dependent on the con- ally they concluded that pattern based approaches per- sidered spatial and temporal resolution. The resulting form best on daily basis but were restricted to the quality contradiction could be solved by different downscaling of GCM data. This restriction refers to a strict depend- approaches. Here two major approaches can be distin- guished, complex (dynamic) and conceptual (statistic) ency of GCM derived weather patterns and downscaled methods [1]. Dynamic approaches solve the primitive climate variables. Reference [7] noted different categori- equations with higher temporal and at higher spatial re- zation of stastistic methods. They divided empirical solution with higher resolved and better parameterized downscaling into linear and non-linear regression, artifi- processes and use the GCM output as initial and bound- cial neural networks (ANN), canonical correlation and ary conditions. Often more than one nesting step is used, principle component analysis. In their study they com- like in REMO [2,3], CLM [4] or WRF [5], where the pre- pared a recurrent neural network (i.e. temporal neural vious nesting step delivered the initial and boundary condi- network) with a regression approach for the downscaling tions for the current nesting step of the considered domain. of minimum, maximum temperature and precipitation Instead stastistic approaches are less computational and found that the ANN results reproduced the observed demanding. Reference [6] compared in one of the first extremes in adequate quality, which is not necessarily the summaries of stastistic downscaling approaches differ- case for the unobserved future values. Reference [8] used an artificial neural network to downscale hourly 27 km *Corresponding author. spaced precipitation fields of mesoscale model 5 (MM5) Copyright © 2013 SciRes. ACS R. KRONENBERG ET AL. 553 output to hourly 3 km spaced fields. They obtained their best results under consideration of elevation, the relations to neighbouring grid cells and time components as pre- dictors. Predictors derived from GCM output and the considered domain play an important role, which [9] deeply investigated. They developed a scheme to opti- mize, by simulated annealing, the necessary number of predictors and their 3-dimensional domain for single sites to downscale precipitation by means of an ANN ap- proach. Their results showed a strong dependency of climate variable and predictor domain, which can differ from climate variable to climate variable. But until now ANNs have not been applied in the modelling of climate datasets for the study region of Saxony. The major ques- tion was how do ANNs perform in the context of a water balance model (i.e. the driving input of the water balance model is the ANN output) or to downscale the water balance of a meso-scale catchment area directly. This question arises, precisely because the generated local meteorological information is often used as further input for another model. This study aims to answer the follow- Figure 1. Catchment area of the Zschopau River with rain ing questions: gauges and climate stations; the catchment lies in the east- How large are the quantitative differences between ern part of Germany, in the south of the federal state of modelled and observed water balance in a meso-scale Saxony and covers a large part of the Ore mountains. catchment area? 2 How consistent are the resulting dataset in terms of discharge for a 1757 km large area. The gauging station physically based correlations among the elements of Kriebstein is located downstream behind the dam Krieb- the water balance? stein and is operated by the State Reservoir Administra- Do ANNs prevent the long-term annual cycle of the tion of Saxony. Thus more or less meaningful influence water balance? can be observed at the gauging station through manipu- To answer these questions two approaches using dif- lated discharge and flood behaviour of the river. ferent kinds of recurrent neural networks are presented. 2.2. Meteorological Data 2. Data The daily observed meteorological variables for the study 2.1. Catchment Area and Hydrological Data region were measured by the monitoring network of the German weather service (DWD) and the Czech hydro- The study region is the catchment area of the Zschopau logical-meteorological service (CHMI). The data were River, which wells in the Ore Mountains and flows into processed by the Saxonian climate database CLISAX the Freiberger Mulde River. The study region is depicted [12-15], where also the quality assessment of the series in Figure 1. The catchment is characterized by a large was done. The deployed elements are summarized in percentage of mountains that is the reason why here the Table 1, which are the minimum input variables for the coldest average temperature (1961-1990) of Saxony can distributed water balance model WaSim-ETH [16]. be found. The average temperature ranges from <4˚C in Hence, the choice of stations which were appropriate for the ridges of the mountains to 8˚C in the valleys of the the water balance simulations are limited by the catch- catchment area. Furthermore in the catchment can be ment area of the Zschopau River. observed a comparable large variability in the annual Besides all stations within the catchment also time se- precipitation amount from 750 mm in the valleys to ries of neighbouring stations were used (cp. Figure 1) >1200 mm in the mountain ranges (i.e. mean annual pre- aiming as much dens information as possible for the spa- cipitation sums from 1961 to 1990) [10]. The whole catch- tial interpolation of the meteorological fields. Overall 68 ment reaches from the middle Ore Mountains and the daily precipitation gauges and 19 climate stations of the eastern Ore Mountains to the Mulde River and Ore DWD and CHMI were available for the downscaling and Mountain valleys [11]. The gauging station for the water balance modelling within the period from 1975 to catchment area is Kriebstein, which is located 14.4 km 2000. The meteorological fields are summarized in Ta- downstream of the Zschopau River and measures the ble 1, which were deployed as predictor variables. The Copyright © 2013 SciRes. ACS 554 R. KRONENBERG ET AL. Table 1. Used daily predictor variables derived from the ERA-40 re-analysis data set. Predictor Abbreviation Time Altitude Mean air temperature air 1.1.1980 - 31.12.2000 0.995 sigma level Relative humidity rhum 1.1.1980 - 31.12.2000 0.995 sigma level Specific humidity shum 1.1.1980 - 31.12.2000 0.995 sigma level V-component wind speed vwnd 1.1.1980 - 31.12.2000 0.995 sigma level U-component wind speed uwnd 1.1.1980 - 31.12.2000 0.995 sigma level Gopotential height hgt 1.1.1980 - 31.12.2000 0.995 sigma level Vertical velocity omega 1.1.1980 - 31.12.2000 0.995 sigma level ERA-40 re-analysis data set was described in [15]. Fields Reference [20] stated that the NSE is the best perform- were extracted with the bounding box of the shown ter- ance index to evaluate hydrological models. It is defined ritory in Figure 1, which resulted into four grid cells in Equation (1). with a spatial resolution of about 125 km (i.e. 2.5˚). n 2 obs sim YYii 3. Methods NSE 1 i1 (1) n 2 obs obs, mean 3.1. Water Balance Modelling with WaSim-ETH YYi i1 For the modelling of the water balance the distributed The RMSE was deployed to calculate the mean daily model WaSim-ETH was applied.