Project no. 505428 (GOCE)
AquaTerra
Integrated Modelling of the river-sediment-soil-groundwater system; advanced tools for the management of catchment areas and river basins in the context of global change
Integrated Project
Thematic Priority: Sustainable development, global change and ecosystems
Deliverable No.: TREND2.12 Title: Draft overview paper on trend analysis in groundwater summarizing the main results of TREND2 in relation to the new Groundwater Directive
Due date of deliverable: 15 September 2007 Actual submission date: 15 September 2008
Start date of project: 01 June 2004 Duration: 60 months
Organisation name and contact of lead contractor and other contributing partners for this deliverable 1 2 2 3 3 3 3 Broers H.P , Visser A. ,(eds.), Bierkens M.F.P. , Dubus I.G , Pinault J.L. , Surdyk N. , Guyonnet D. Batlle Aguilar J.4, Brouyere S.4, Goderniaux P.4, Ph. Orban4, Korcz M6, Bronder J.6, Dlugosz J.6, Odrzywolek M.6 1 Netherlands Organisation for Applied Scientific Research TNO, Division Groundwater and Soil, Princetonlaan 6, P.O. Box 80015, 3508 TA Utrecht, The Netherlands. E-mail: [email protected] (lead partner) 2 Universiteit Utrecht, Faculty of Geographical Sciences, Budapestlaan 4, 3584 CD Utrecht, The Netherlands. 3 BRGM, Water Division, BP 6009, 45060 Orléans Cedex 2, France. 4 HG-ULg, University of Liege, Building B52/3, 4000 Sart Tilman, Belgium. 6 Institute for Ecology of Industrial Areas, ul. Kossutha 6, 40-844 Katowice, Poland Revision: M.H. Gerzabek / J. Barth
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006) Dissemination Level PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services) SUMMARY The current document provides a draft manuscript of a scientific paper to be submitted to an international peer reviewed journal. The paper provides an overview on trend analysis in groundwater summarizing the main results of TREND2 in relation to the new Groundwater Directive.
MILESTONES REACHED
T2.12: Draft overview paper on trend analysis in groundwater summarizing the main results of TREND2 in relation to the new Groundwater Directive.
2 1 Comparison of methods for the detection and extrapolation
2 of trends in groundwater quality
3
4 Abstract
5 Land use changes and the intensification of agriculture since 1950 have resulted in a
6 deterioration of groundwater quality in most EU countries. For the protection of
7 groundwater quality it is necessary to (1) assess the current groundwater quality
8 status, (2) detect changes or trends in groundwater quality, (3) assess the threat of
9 deteriorating groundwater quality and (4) predict future changes in groundwater
10 quality. A variety of tool can be used to detect and extrapolate trends in groundwater
11 quality, ranging from simple linear statistics to distributed 3D groundwater
12 contaminant transport models. For this paper we compared four methods for the
13 detection and extrapolation of trends in groundwater quality: (1) statistical methods,
14 (2) groundwater dating, (3) transfer functions, and (4) deterministic models.
15 The choice of the method for trend detection and extrapolation should firstly
16 be made on the basis of the system under study, and secondly on the available
17 resources and goals. For trend detection in groundwater quality, the most important
18 difference between groundwater bodies is whether the character of the subsurface or
19 the monitoring system causes mixing of groundwater with different travel times. We
20 conclude that there is no single optimal method to detect trends in groundwater
21 quality across widely differing catchments.
22
23 1. Introduction
24 Land use changes and the intensification of agriculture since 1950 have resulted in
25 increased pressures on natural systems. For example, the diffuse pollution of
3 26 groundwater with agricultural contaminants like nitrate and pesticides has resulted in
27 a deterioration of groundwater quality. In general, the pressure of agricultural
28 contaminants on the groundwater has increased since 1950, resulting in an increasing
29 surplus of N applied to agricultural land. Following national legislation, the pressure
30 decreased since the mid-1980s in most EU countries, resulting in a very similar
31 evolution of the contamination history for diffuse pollution by agriculture in European
32 countries and the US (Broers et al., 2004a). The transfer of these contaminants to
33 deeper groundwater and surface water represents a major threat to the long-term
34 sustainability of water resources across the EU and elsewhere.
35
36 [Figure 1]
37
38 For the protection of groundwater quality, legislators will ask the scientific
39 community to (1) assess the current groundwater quality status, (2) detect changes or
40 trends in groundwater quality, (3) assess the threat of deteriorating groundwater
41 quality by relating them to historical changes in land use, (4) predict future changes in
42 groundwater quality, by extrapolating present day trends and possibly predict trend
43 reversal in response to legislation aimed at protecting groundwater quality.
44 So far, the awareness of the threat to groundwater quality has led to the
45 installation of groundwater quality monitoring networks (Almasri and Ghabayen,
46 2008; Almasri and Kaluarachchi, 2004; Broers, 2002; Daughney and Reeves, 2005;
47 Hudak, 2000; Lee et al., 2007; Van Maanen et al., 2001) providing time series of
48 groundwater quality. These time series have been used to detect changes in
49 groundwater quality (Batlle-Aguilar et al., 2007; Broers et al., 2005; Broers and van
4 50 der Grift, 2004; Burow et al., 2007; Daughney and Reeves, 2006; Reynolds-Vargas et
51 al., 2006; Stuart et al., 2007; Xu et al., 2007).
52 In practice it is difficult to detect trends in groundwater quality for a number
53 of reasons. Most often the period of interest is longer than the period of record (Loftis,
54 1996) and available time series are rather short and sparse because of the high cost of
55 sampling and analysis. This limits the available statistical methods to simple linear
56 statistics, rather than more complex time series analysis tools. Other factors
57 complicating trend detection are:
58 variations in the duration and pathways of the transport of
59 contaminants towards monitoring location by groundwater flow
60 variations in application of contaminants at the ground surface, in
61 space and time
62 (partial) degradation of contaminants in the subsurface
63 The long travel times of contaminants through the groundwater system
64 towards the monitoring location further complicates the detection of trends. The travel
65 time of sampled groundwater may be uncertain, in particular because the groundwater
66 sample may represent a range of groundwater travel times. Decreasing the uncertainty
67 of the travel time by relating measured concentrations to the time of recharge often
68 reveals clearer trends (Bohlke et al., 2002; Laier, 2004; MacDonald et al., 2003;
69 Tesoriero et al., 2005; Wassenaar et al., 2006).
70 To assess whether an upward trend in the concentration of a contaminant will
71 continue to threaten groundwater quality, trends in groundwater quality can be related
72 to changing land use pattern (Gardner and Vogel, 2005; Jiang et al., 2006; Lapworth
73 et al., 2006; Ritter et al., 2007), and future trends can be predicted based on land use
74 scenarios (Almasri and Kaluarachchi, 2005; Di et al., 2005). Eventually, trends in
5 75 groundwater quality may be predicted using a distributed 3D groundwater
76 contaminant transport models (Almasri and Kaluarachchi, 2007; Refsgaard et al.,
77 1999; Van der Grift and Griffioen, 2008).
78 This shows that a variety of tool can be used to detect and extrapolate trends in
79 groundwater quality, ranging from simple linear statistics to distributed 3D
80 groundwater contaminant transport models. The efficiency of these tools depends on
81 several factors like the availability of groundwater quality data, the character of the
82 groundwater flow system, and the available resources for trend assessment. One
83 important factor for the success of trend detection in groundwater quality is whether
84 the character of the groundwater flow system causes mixing of groundwater, for
85 example in dual porosity systems, and whether the groundwater sample contains a
86 mixture of groundwater, for example from springs or production wells.
87 The aim of this comparison study was to assess the capabilities and efficiency
88 of various tools to detect and extrapolate of trends in groundwater quality in a variety
89 of different groundwater systems, ranging from unconsolidated unconfined aquifers to
90 fissured dual porosity systems. For this paper we compared four sorts of methods for
91 the detection and extrapolation of trends in groundwater quality: (1) statistical
92 methods to detect and possibly extrapolate linear trends in the measured
93 concentrations, (2) the use of groundwater dating to analyze observed concentrations
94 in relation to the recharge time of sampled groundwater, (3) transfer functions to
95 detect and extrapolate trends in non-linearly behaving dual-porosity groundwater
96 systems, (4) deterministic models simulating the transport of contaminants through
97 the groundwater system to predict future groundwater quality. This paper is based on
98 research carried out within the framework of the FP6 program AquaTerra, for which
6 99 these methods were applied to various hydrogeologically different sites with varying
100 types of data and knowledge of the hydrological system available.
101 In the next section the test sites are described. In the following sections each of
102 the four methods is described in detail, and the results for each method are presented.
103 Finally, all methods are compared and discussed in terms of data requirement,
104 additional monitoring costs, applicability in different geohydrological systems, and
105 their power to extrapolate.
106
107 2. Test sites
108 Groundwater bodies were selected at four locations to test these methods for
109 detecting trends in groundwater quality: the Dutch part of the Meuse basin, the
110 Walloon part of the Meuse basin, the Brévilles catchment in France and the German
111 Bille-Krückau watershed in the Elbe basin. The characteristics of each of the test sites
112 (Figure 2) are summarized in Table 1 and described in detail in the following sections.
113 The test sites vary strongly in geohydrological characteristics, but are more similar
114 with respect to climate and agricultural land use history.
115
116 [Table 1]
117
118 [Figure 2]
119
120 2.1 Dutch Meuse basin
121 The Dutch part of the Meuse basin almost entirely belongs to the groundwater
122 body Sand Meuse, which covers most of the province of Noord-Brabant and part of
123 Limburg (5000 km2 in total) (Visser et al., 2004). The groundwater body consists of
7 124 fluvial unconsolidated Pleistocene sands, covered by fluvio-periglacial and aeolian
125 deposits of fine sands and loam 2-30 m thick. The history of intensive livestock
126 farming on 62% of the area has produced a large surplus of manure contributing to
127 widespread agricultural pollution (Broers et al., 2004b). The relatively flat area (0-30
128 m above mean sea level) is drained by a natural system of brooks, extended in the 20th
129 century with drains and ditches to allow agricultural practices in the poorly drained
130 areas. Groundwater tables are 1-5 m below surface as a result (Broers, 2002). Net
131 groundwater recharge is around 300 mm/y resulting in a downward groundwater flow
132 velocity of about 1 m/y in recharge areas (Broers, 2004).
133 Time series of major cations, anions and trace metals are available since 1992
134 from the dedicated national and provincial monitoring network sampled annually
135 from 2 m long screens in multilevel wells at depths of 8 and 25 meters below the
136 surface (Broers and van der Grift, 2004). 3H/3He groundwater ages were obtained
137 from 34 screens of 14 wells in agricultural recharge areas (Visser et al., 2007a).
138 Thanks to the dedicated monitoring wells with short screens and the character of the
139 aquifer, little mixing occurs between recharge and sampling and a groundwater
140 sample contains a mixture of water recharged within a period of less than 5 years.
141
142 2.2 Walloon Meuse basin
143 Four groundwater bodies were selected as test cases in the Walloon part of the
144 Meuse basin (Batlle-Aguilar and Brouyère, 2004), which represent various
145 hydrogeological settings: the cretaceous chalk of Hesbaye, the Cretaceous chalk of
146 Pays de Herve, the Néblon basin in the carboniferous limestone of the Dinant
147 synclinorium, and the alluvial plain of the Meuse river.
8 148 The Cretaceous chalk groundwater body of Hesbaye covers an area of 440
149 km2 located north-west from Liège (Dassargues and Monjoie, 1993). The
150 groundwater body is drained by the Geer, a tributary of the Meuse River, and is also
151 referred to as the Geer basin. 25 million m3 of groundwater is pumped annually from
152 the fissured dual porosity chalk aquifer to supply the city of Liège and surrounds. 85%
153 of the area of the Hesbaye groundwater body is covered by agriculture, mostly
154 meadowland. Time series of nitrate are available from 32 monitoring points in the
155 groundwater body, varying from dedicated monitoring wells to pumping wells,
156 traditional wells, springs and galleries.
157 The chalk groundwater body of Pays de Herve covers an area of 285 km2 of
158 which about 80% is covered by meadowland. Groundwater is pumped at a rate of 12
159 million m3/year from the chalk aquifer. High concentrations of nitrate are observed in
160 the 59 monitoring points distributed throughout the groundwater body.
161 The Néblon basin covers an area of 65 km2 in the “Entre Sambre et Meuse”
162 groundwater body, built of 500 thick folded and karstified Carboniferous limestone
163 and sandstone. Nitrate concentrations have been monitored since 1979 at two of the
164 six monitoring locations in the basin. Meadows cover most of the area: 50%
165 permanently and 25% seasonally.
166 The alluvial plain of the Meuse groundwater body (125 km2 along 80 km of
167 the Meuse) consists of gravel bodies embedded in old meandering channels filled with
168 clay, silt and sandy sediments. Land use is 40 % residential or industrial, and 60%
169 natural land. Groundwater quality data is available from 47 monitoring points.
170
171
9 172 2.3 Brévilles catchment
173 The Brévilles catchment (2.8 km2) 75 km northwest of Paris, France, is built
174 up out of a thick unsaturated zone (0-35 m) of fissured dual porosity chalk, overlying
175 the Cuise sands, 8-20 m thick and outcropping in the west of the catchment (Dubus et
176 al., 2004; Mouvet et al., 2004). There is no superficial drainage and the catchment is
177 drained by the Brévilles spring in the outcrop of the Cuise sands. Land use is mostly
178 agricultural, with predominantly peas, wheat and corn. Corn is particularly interesting
179 because atrazine, an herbicide detected at the Brévilles spring causing the disuse of
180 spring water for drinking purposes, is applied exclusively on corn (Baran et al., 2004).
181 Monthly time series of concentrations of atrazine and its decomposition product DEA
182 are available from seven piezometers in the catchment since 2001.
183
184 2.4 Elbe basin
185 The groundwater bodies in the Bille-Krückau watershed (1300 km2), located
186 in Schleswig-Holstein, northern Germany, consist of unconsolidated glacial deposits
187 of sand and gravel (Korcz et al., 2004). The sediments were deposited during the last
188 and previous glaciations and subsequently denudated to a plateau-like landscape
189 approximately 40 m above mean sea level. The area is drained by a dense network of
190 natural streams, of which the Bille River is the largest draining 335 km2. Groundwater
191 is abstracted for drinking water purposes from the sandy and gravely deposits.
192
193 Two groundwater quality monitoring networks are in place, aimed at
194 describing the natural conditions (baseline) and detecting trends in groundwater
195 quality (trend). From these networks composed of 27 observation screens in total we
196 selected 19 time series, sampled bi-annually from 8 shallow and 11 deep monitoring
10 197 wells. The time series contain the concentrations of major cations and anions, from
198 which we selected K, NO3, Al and Cl, and constructed OXC and SUMCAT, for trend
199 analysis
200
201 3. Methods
202
203 3.1 Statistical trend detection and estimation
204 The success of a statistical trend analysis depends on choosing the right
205 statistical tools (Harris et al., 1987), considering whether the data have a normal
206 distribution, contain seasonality (Hirsch et al., 1982), whether the trend is monotonic
207 or abrupt (Hirsch et al., 1991), whether trends are expected to be univariate or
208 multivariate (Loftis et al., 1991). A clear definition of “trend” should be adopted
209 before analyzing the data (Loftis, 1996). Here we define a temporal trend as a
210 significant change in groundwater quality over a specific period of time, over a given
211 region, which is related to land use or water quality management.
212 The aim of the statistical methods discussed here was to detect and estimate
213 statistically significant changes in the concentrations of contaminants over time. The
214 methods had to be robust and applicable to typical groundwater quality time series,
215 with a limited amount of data, a rather short observation period with possibly missing
216 data, often non-normally distributed either annually sampled or containing seasonal
217 trends. To meet these requirements, a three-step procedure was adopted (Batlle-
218 Aguilar et al., 2005; Batlle-Aguilar et al., 2007) following Hirsch et al. (Hirsch et al.,
219 1991). First, time series were tested for normality; second, the presence a trend was
220 assessed; and third, the slope of the trend was estimated. The procedure (Figure 3)
221 was applied to various time series from different study sites.
11 222 [Figure 3]
223
224 To test the data for normality, the Shapiro-Wilks test (Shapiro and Wilk, 1965)
225 was used for data sets with less than 50 records, or the Shapiro-Francia test (Shapiro
226 and Francia, 1972) for data sets with 50 or more records. The type of trend detection
227 and estimation depended on the normality of the time series. On time series with a
228 normal distribution, a linear regression was performed. The correlation coefficient
229 was used as the robustness of the trend (Carr, 1995). On time series with a non-normal
230 distribution, the non-parametric Mann-Kendall test (Kendall, 1948; Mann, 1945) was
231 performed, which is commonly used in hydrological sciences since its appearance in
232 the paper by Hirsch et al (Hirsch et al., 1982). It is rather insensitive to outliers (Helsel
233 and Hirsch, 1995). This test has recently been proven as powerful as the Spearman’s
234 rho test (Yue et al., 2002). If a significant trend was detected, the slope of the trend
235 was determined as the slope of the linear regression equation for normally distributed
236 time series, or using Sen’s slope (Hirsch et al., 1991). To aggregate the trend analysis
237 over the entire groundwater body, the number of significant trends was expressed as a
238 percentage. Further analysis could include determining the median trend, or the spatial
239 distribution of trends.
240
241 3.2 Groundwater dating
242 The aim of groundwater dating was to remove the travel time of groundwater
243 as a complicating factor for trend analysis, by relating measured concentrations to the
244 time of recharge (Figure 4)(Visser et al., 2007b).
245 [Figure 4]
246
12 247 Trends detected in this way could directly be related to changes in land use or
248 contamination history. Groundwater dating also provided a new way of aggregating
249 time series from an entire groundwater body into a single trend analysis, such as
250 required by the new EU Groundwater Directive (EU, 2006). The aggregated data were
251 analyzed using a LOWESS smooth (Helsel and Hirsch, 1995) to indicate the general
252 pattern of change and compare that to contamination history. Trends in the aggregated
253 data were detected using simple linear regression.
254 Groundwater dating as a method for trend detection requires the possibility to
255 accurately sample for groundwater age tracers, preferably 3H/3He (Schlosser et al.,
256 1988), or CFCs (Busenberg and Plummer, 1992) and/or SF6 (Busenberg and
257 Plummer, 2000). If these gaseous tracers are impractical, a qualitative approach based
258 on 3H measurements alone can be applied to distinguish between old (recharged prior
259 to 1950) and young (recharged after 1950) groundwater (Orban and Brouyère, 2007).
260
261 3.3 Transfer functions to predict future trends
262 The aim of the transfer function approach was to detect and extrapolate trends
263 in the concentrations of agricultural contaminants in macro-porous or dual-porosity
264 systems where concentrations are strongly correlated to other hydrological
265 parameters, such as precipitation or stream flow. Transfer functions were modeled
266 using the TEMPO tool (Pinault, 2001) which is capable of modeling time series
267 through iterative calibrations of combinations of transfer functions (Pinault et al.,
268 2005).
269 Hydraulic heads were modeled as a function of effective rainfall using
270 combined convolution functions for transport and dispersion. Effective rainfall is in
271 turn modeled as a function of the actual rainfall and of a threshold value representing
13 272 the water storage in the soil. The threshold value for soil water storage is related to the
273 rainfall and potential evapotranspiration with trapezoid impulse response functions
274 with four degrees of freedom. Concentrations of contaminants were modeled in a
275 similar fashion, using the effective flux of the contaminant from the unsaturated soil
276 instead of the effective rainfall, to predict the flux or concentrations in the Brévilles
277 spring. To predict spring fluxes and concentrations, the impulse response functions
278 were extended to include the contribution of various pathways of contaminants to the
279 spring. Future concentrations were calculated based on 5-year long generated
280 meteorological time series based on the median annual precipitation and the 5, 10 and
281 20 year extreme wet and dry years (Pinault and Dubus, 2008).
282
283
284 3.4 Physical-deterministic modeling
285 The aim of using physical-deterministic models was to predict future trends in
286 groundwater quality under complex circumstances, such as non-conservative transport
287 of contaminants. 3D groundwater flow and transport models were built and used to
288 predict and extrapolate trends in concentrations of contaminants. Due to the
289 differences between the study sites these models were developed separately and
290 specifically for each site. These models consisted of either a contaminant transport
291 model driven by a separate groundwater flow model or an integrated groundwater
292 flow and contaminant transport model. The unsaturated zone was either modeled
293 separately one-dimensionally, or as part of the fully integrated 3D flow and transport
294 model. Transport models included advective transport, hydrodynamic dispersion and,
295 where necessary, dual-porosity effects, sorption and degradation of contaminants.
14 296 Predictions of future concentrations were based on scenarios of land use and
297 agricultural application of fertilizer and pesticides, and climate scenarios. Each of
298 these models is described in more detail further below.
299 The physical-deterministic groundwater flow and transport model for the
300 Dutch part of the Meuse basin was a steady-state MODFLOW (Harbaugh et al., 2000)
301 model for groundwater flow, and MT3DMS (Zheng and Wang, 1999) for solute
302 transport. The modeled area was 34.5x24km, only part of the Dutch Sand Meuse
303 groundwater body, known as the Kempen area (Visser et al., 2005c). Historical
304 concentrations of contaminants at the land surface were reconstructed based on
305 statistical records of atmospheric depositions and manure applications (Van der Grift
306 and Van Beek, 1996). Leaching of heavy metals from the unsaturated zone, sensitive
307 to sorption and fluctuating water tables, was modeled with Hydrus-1D (Van der Grift
308 and Griffioen, 2008). The coupled transport model was used to predict concentrations
309 of nitrate, potassium and heavy metals in groundwater at the monitoring locations
310 within the model area (Visser et al., 2006).
311 A physical-deterministic model was constructed for the Geer basin in the
312 Walloon part of the Meuse basin (Orban et al., 2005) using the SUFT3D code
313 (Carabin and Dassargues, 1999). This model combines a new approach to solute
314 transport (Hybrid Finite Element Mixing Cell) with a conventional finite element
315 model for groundwater flow based on Darcy’s law. The model was calibrated on
316 groundwater levels, as well as measured tritium concentrations. The model was used
317 to reproduce and to extrapolate observed nitrate concentrations in the Geer basin at
318 the monitoring points.
319 The physical deterministic model constructed for the Brévilles catchment
320 consisted of a series of a 1D unsaturated zone models to simulate water flow and
15 321 contaminant transport through the fissured dual porosity chalk, and a 2D groundwater
322 flow and transport model for the Cuise sands (Dubus et al., 2005). The 1D model
323 MACRO is dedicated to simulate transport through the macro pores of the fissures
324 and the micro pores of the chalk, and transfer of water and solutes between the two.
325 The combined model was used to reproduce observed groundwater levels, as well as
326 nitrate, atrazine and DEA concentrations. 13 regional climate model scenarios were
327 used for predicting future trends in concentrations, because of the sensitivity of
328 atrazine transport to climate conditions.
329
330 4. Results
331 4.1 Statistical trend detection and estimation
332 Statistical trend analysis was applied to the data set of 34 time series of NO3,
333 K, OXC and SUMCAT concentrations from the Dutch part of the Meuse basin. The
334 time series from shallow (8 m below surface) and deep (25 m below surface) were
335 analyzed separately. Non-parametric statistical trend analysis demonstrated significant
336 trends for OXC and SUMCAT concentrations: increasing in deep screens and
337 decreasing in shallow screens. No significant trends for NO3 were detected (Visser et
338 al., 2005a).
339 Statistical trend analysis was applied to 97 nitrate time series from the
340 Walloon part of the Meuse basin (Table 2). Significant trends were detected in 60% of
341 the time series (Batlle-Aguilar et al., 2005). Most of the detected trends were
342 increasing, except for the Meuse alluvial plain, where both increasing and downward
343 trends were detected. For 36 time series in the Geer basin, the estimated slope was
344 used to predict the year in which the concentration of nitrate would exceed the
345 drinking water limit (50 mg/l). For most of the points, the drinking water limit will be
16 346 exceeded within 10-70 years (Batlle-Aguilar et al., 2007). This estimate is the worst-
347 case scenario, assuming no changes in land use take place to protect groundwater
348 quality.
349 [Table 2]
350 Statistical trend analysis was applied to the time series of NO3, K, Al, OXC,
351 Cl and SUMCAT concentrations from the Bille-Krückau watershed in the Elbe basin
352 (Table 3). Time series from shallow and deep screens were analyzed separately. For
353 conservative indicators (for OXC, Cl and SUMCAT) significant upward trends were
354 detected in time series from deep monitoring screens, whereas significant decreasing
355 concentrations were detected in time series from the shallow screens (Korcz et al.,
356 2007).
357
358 [Table 3]
359 A further analysis of spatially weighted means indicated significant downward
360 trend of potassium in shallow screens and significant upward trends of chlorides and
361 sum of negative ions. The significant trends in deep screens were not detected.
362
363 4.2 Groundwater dating
364 Samples from 34 monitoring screens in the Dutch part of the Meuse basin
3 3 365 were analyzed for H/ He, CFCs and SF6, to determine groundwater travel times. CFC
366 samples showed irregularities attributed to degassing caused by denitrification (Visser
367 et al., 2007a) and contamination (Visser et al., 2005a). 3H/3He ages were considered
368 more reliable thanks to the internal checks on degassing or contamination. 3H/3He
369 ages were used to interpret the time series of concentrations, by relating
370 concentrations to the estimated time of recharge and aggregating all data available for
17 371 the entire groundwater body (Visser et al., 2005b). The aggregated data were analyzed
372 using linear regression to detect trends in concentrations in groundwater recharged
373 between 1960 and 1980, or between 1990 and 2000 (Figure 5). Significant upward
374 trends were found in the concentrations of NO3, K, OXC and SUMCAT in old
375 (recharged between 1960 and 1980), but also significant downward trends in the
376 concentrations of NO3, OXC and SUMCAT in young groundwater (recharged
377 between 1990 and 2000). With these results, trend reversal in groundwater quality
378 was demonstrated (Visser et al., 2007b) on the relevant scale of a groundwater body,
379 as required by the EU Groundwater Directive (EU, 2006).
380
381 [Figure 5]
382
383 Tritium samples were taken from 33 monitoring points in the Geer basin. The
384 distribution of tritium concentrations shows a qualitative distribution of groundwater
385 travel times (Figure 6), because travel times cannot be estimated accurately and
386 univocally based on the tritium concentration only. High concentrations of tritium
387 were observed in a large southwestern portion of the basin, where recharge is assumed
388 to take place. Towards the downstream end of the basin, tritium concentrations
389 decrease, indicating mixing of younger and older groundwater. No tritium is found in
390 the northern confined part of the basin, indicating old (<1950) groundwater (Orban
391 and Brouyère, 2007). The presence of old groundwater explains the absence of nitrate
392 here.
393
394 [Figure 6]
395
18 396 The interpretation of groundwater age tracers (3H and CFCs) is not
397 straightforward in hydrogeological complex systems like the Brévilles catchment. An
398 experimental sampling campaign was performed to assess whether an extensive data
399 set of groundwater age tracers would provide additional knowledge on the functioning
400 of the system. Tritium and CFC samples were taken from 8 piezometers and the
401 Brévilles spring. The estimated ages showed a high variability within the small
402 catchment with both old (<1960) and young (>1980) water in close proximity. The
403 individual CFC ages (CFC-11, CFC-12, CFC-113) were generally in good agreement,
404 but some samples showed signs of degradation or contamination. Qualitative tritium
405 groundwater age estimates were generally younger than the CFC age due to the dual
406 porosity system. The tracers confirmed the complex hydrogeology of the system, but
407 could not be used for trend interpretation because of the doubts over the potential of
408 CFC due to mixing in the thick unsaturated zone (Gourcy et al., 2005).
409
410 Instead of dating groundwater from analyzed wells, an empirical exponential
411 relationship between depth and groundwater age was assumed. Such an exponential
412 increase of groundwater age with depth may be expected in unconsolidated
413 unconfined aquifers, according to Vogel (1967). Using the empirical relationship, the
414 groundwater quality time series were related to the approximate time of recharge, and
415 analyzed again for trends using LOWESS smooth (Figure 7). The LOWESS smooth
416 shows that the overall pattern in the measured concentration - recharge time
417 relationship is similar to the historical surplus of N applied at the surface. Similar
418 results were found in the Dutch part of the Meuse basin, probably due to the
419 similarities in land use history and hydrogeology.
420
19 421 [Figure 7]
422
423 4.3 Transfer functions to predict future trends
424 The transfer function approach was applied to time series of head, flux, and
425 nitrate, atrazine and DEA concentrations from the piezometers and spring in the
426 Brévilles catchment using the TEMPO tool. The transfer function model was capable
427 of reproducing the general trends in the time series, both in the monitoring wells and
428 in the spring. The good fit is remarkable given the short monitoring period and the
429 long travel times in the groundwater system, as indicated by impulse response
430 functions of over 10 years long. Because of these long transfer times, it was possible
431 to reconstruct the concentrations of the contaminants in the vadose zone (Pinault et
432 al., 2005). The reconstructed inputs were in agreement with the historical application
433 of atrazine in the catchment.
434 Future concentrations of atrazine and DEA at the Brévilles spring were
435 predicted using the transfer function model and rainfall data generated by the TEMPO
436 tool (Figure 8). The generated rainfall series contained either only wet or dry years,
437 with historical recurrence intervals of 5, 10 or 20 years, to illustrate the response of
438 atrazine concentrations to different future climates. Atrazine release occurs more
439 during wet years, because of the sorption of atrazine to the unsaturated zone in dry
440 years. Nevertheless, atrazine concentrations in the spring will decrease dramatically
441 over the next 5 years, thanks to the ban on atrazine and the degradation in the
442 unsaturated zone to DEA. DEA concentrations on the other hand will remain constant,
443 because the main source of DEA in the spring is the stock of DEA accumulated in the
444 soils (Pinault and Dubus, 2008).
445
20 446 [Figure 8]
447
448 4.4 Physical-deterministic modeling
449
450 The 3D model built for the Kempen area in the Dutch part of the Meuse basin
451 predicted significant trends in the concentrations of nitrate and OXC for the period
452 1995-2005: upward in deep groundwater, downward in shallow groundwater (Visser
453 et al., 2008). Due to variations in groundwater travel times and the constant recharge
454 concentrations from 2005 onward, few significant trends are predicted for the future,
455 except a decrease in OXC between 2010 and 2020. Between 2010 and 2020, the
456 model also predicts a significant upward trend in the concentration of zinc in shallow
457 groundwater. This trend is caused by slow release of zinc accumulated in the
458 unsaturated zone and the retarded transport of zinc through the groundwater system
459 due to cation exchange (Broers and van der Grift, 2004; Van der Grift and Griffioen,
460 2008).
461 The physical deterministic model of the Geer basin was capable of
462 reproducing both groundwater levels and the distribution of tritium in the aquifer. The
463 model also accurately reproduced the upward trends in nitrate concentrations in the
464 Geer basin. Due to the long transfer times in the unsaturated zone, if no new nitrate
465 would leach into the soil from present day forward, it would take 7-24 years to show
466 as a trend reversal in the monitoring points (Orban et al., 2008).
467 The physical deterministic model of the Brévilles catchment accurately
468 reproduced the observed groundwater levels at the piezometers and also the discharge
469 from the Brévilles spring (Amraoui et al., 2008). Modeled Atrazine concentrations at
470 the piezometers were in the same order of magnitude as the measurements, but
21 471 underestimated the concentrations in the spring, probably due to the lack of accurate
472 data on the application of atrazine at the individual fields in the catchment. Future
473 modeled concentrations of atrazine decrease exponentially over the next 15 years in
474 the piezometers, similar to the transfer function predictions, but the concentration in
475 the spring decreases more slowly.
476
477
478
479 5. Discussion and comparison of methods
480 In this section we compared and discussed the methods one by one in terms of
481 data requirement, additional monitoring costs, applicability in different
482 geohydrological systems, and their power to extrapolate. The prerequisites, costs, and
483 potential of all methods are summarized in Table 4.
484
485
486 5.1 Statistical trend detection and estimation
487 The 3-step approach to detect trends in groundwater quality was applied at 3
488 test sites and proved to be a robust technique for trend detection. Statistical time series
489 analysis is based on the available data set and requires no additional costs for
490 sampling. The method provides an objective detection of trends and is applicable to
491 existing time series of contaminant concentrations, having a normally distribution or
492 not. However, it requires series that span over several years to detect a significant
493 trend. Gaps in the time series pose no serious problem.
494 Statistical trends analysis was applied to data from dedicated monitoring wells
495 in simple unconsolidated unconfined aquifers, as well as from springs and galleries in
22 496 more complex geohydrological settings. In the simple groundwater systems, such as
497 the Walloon Meuse alluvial plane, the Dutch Meuse groundwater body and the Bille-
498 Krückau watershed, groundwater samples represented a distinct time of recharge and
499 downward trends were detected in young groundwater from shallow parts of the
500 aquifers. Samples from the complex groundwater systems, such as the Walloon part
501 of the Meuse basin, mostly represented a mixture of young and old groundwater.
502 Because of the long travel times involved here, as well as mixing of young and old
503 groundwater, mostly upward trends were detected and trend reversal was not yet
504 demonstrated.
505 Statistical trend analysis may be of limited operational use because no link to
506 the driving forces is incorporated in the analysis. Therefore the trends that are found
507 in individual time series may be extrapolated over short periods of time only.
508 Statistical trends cannot sensibly be extrapolated over longer periods of time, because
509 they are incapable of dealing with changes in land use and are therefore not capable of
510 predicting trend reversal, which is a major disadvantage. In conclusion, Statistical
511 trend analysis provides a sound initial survey of possible changes in groundwater
512 quality as required by the EU Groundwater Directive (EU, 2006), but is less suitable
513 for analyzing whether these changes will pose a threat for future groundwater quality.
514
515 5.2 Groundwater dating
516 Groundwater dating can be used to reinterpret groundwater quality time series
517 and demonstrate trend reversal in groundwater quality. Qualitative groundwater
518 dating using tritium can be applied to detect the presence of “old” groundwater, for
519 example explaining the absence of nitrate due to old age rather than denitrification. In
520 single porosity aquifers with short screened monitoring wells, groundwater dating
23 521 greatly enhances the interpretation of groundwater quality data by eliminating
522 variations in groundwater age as a complicating factor. Monitoring wells with short
523 screens are a benefit, because groundwater is expected to have a distinct age, rather
524 than to be a mixture of older and younger water. Knowledge about the travel times in
525 the groundwater system may also explain the slow improvement of groundwater
526 quality. In hydrogeologically complex aquifers, groundwater dating may only confirm
527 the complexity of the system. Groundwater age tracers are difficult to interpret in dual
528 porosity aquifers, or under a variably or thick unsaturated zone. Groundwater dating
529 requires a substantial financial investment for sampling and sample analysis, even if a
530 proper monitoring network is in place. The benefit is that the existing groundwater
531 quality data becomes more valuable as the re-analysis of this data may reveal trends
532 which could not be demonstrated without knowledge of the recharge times of the
533 groundwater samples.
534
535 5.3 Transfer functions to predict future trends
536 The transfer function approach is intermediate between statistical and
537 deterministic models because it requires the calibration of a transfer function, which
538 expresses the delay in transfers of water and pollutants in the systems considered.
539 The main advantages of transfer functions are that they require little information
540 about the physical functioning of the system, but rather rely on the available data
541 which makes them suitable for application in a wide variety of systems. Transfer
542 functions provide a good agreement with measured time series in the complex aquifer
543 of the Brévilles catchment showing that they are capable of reproducing the non-
544 linear behavior of dual porosity systems, where other approaches fail.
545
24 546 5.4 Physical-deterministic modeling
547 Because of the geohydrological diversity in the test sites, site-specific
548 physical-deterministic models had to be built. For example, leaching of heavy metals
549 from the unsaturated zone, sensitive to sorption and fluctuating water tables in the
550 Dutch Meuse basin, or contaminant transport through the fissured dual porosity chalk
551 of the Brévilles catchments required the use of dedicated 1D unsaturated zone models
552 combined with 2D or 3D models for the saturated zone, while the transport of
553 contaminants through the Geer basin could be modeled with an integrated 3D
554 groundwater flow and transport model. Large-scale 3D models are generally not
555 suitable for predicting short-term variation, due to the uncertainty in input and
556 transport behavior, but could be used for long-term trends. The quality of predicted
557 future trends relies on the certainty of the land use and future contamination scenarios
558 and the fit of the model to the existing data.
559 The main advantage of physically-deterministic models is their capability to
560 predict trends in the future that are not yet observed in the monitoring data, for
561 example due to the slow release of zinc from the unsaturated zone. They can provide
562 estimates of the time scales at which trend reversal should be expected as a result of
563 protective legislation, which may be several decades because of the long travel times
564 of groundwater. Physical-deterministic models may also be used for scenario analysis
565 to aid policy makers decide on the effectiveness of proposed regulations. Physical-
566 deterministic models are very useful to gain scientific knowledge about the
567 functioning of the system, if that is one of the objectives. The very large financial,
568 human resources and time investments associated with the collection of data and their
569 integration into an overarching modeling exercise means that the deployment of
570 deterministic models for operational analysis of trends across the EU is beyond reach.
25 571 Such modeling activities should concentrate on areas of high ecological, sustainability
572 or economical importance within the context of the Water Framework Directive.
573
574 [Table 4]
575
576 6. Conclusions
577 The trends we aim to detect are a change in groundwater quality over a
578 specific period of time, over a given region, which is related to land use or water
579 quality management. The driving changes in land use practices or water quality
580 management are applied at the surface, whereas the changes in groundwater quality
581 are observed at some depth in the groundwater body or at the outlet of a groundwater
582 system. Therefore, it is essential to know the time or timeframe when sampled
583 groundwater recharged and contaminants were introduced into the system. Only then
584 can trends in groundwater quality be linked to contamination history.
585 For trend detection in groundwater quality, the most important difference
586 between groundwater bodies is whether the character of the subsurface or the
587 monitoring system causes mixing of groundwater with different travel times. In single
588 porous systems, groundwater at a specific location typically has a distinct
589 groundwater age. In practice, the possibility of sampling groundwater with a distinct
590 age also requires a monitoring network with short (< 5 m) monitoring screens or the
591 use of packers in long screened wells to prevent mixing during sampling. On the
592 contrary, in dual porosity systems, a groundwater sample may be composed of a
593 young fast component and an old slow component. In such cases the contributions of
594 either component should be separated to properly analyze the trends in groundwater
595 quality.
26 596 As a consequence, there is no unique solution to detect trends in groundwater
597 quality across widely differing catchments. The choice of the method for trend
598 detection and extrapolation should firstly be made on the basis of the system under
599 study, and secondly on the available resources and goals (Table 5). A classical
600 statistical approach may serve for an initial survey to detect changes in groundwater
601 quality. In simple single-porosity groundwater bodies with access to monitoring wells
602 with short screens groundwater dating is an excellent tool for the demonstration of
603 trend reversal. In complex dual-porosity systems, a transfer function approach is
604 better suited for preliminary trend detection. Transfer functions may be used for trend
605 extrapolation, but only with great care to ensure that the predicted trends are within
606 the range of the observations. In these systems, groundwater dating may serve to
607 confirm the hydrological functioning and transfer times of the system. Deterministic
608 groundwater modeling should be applied in areas with high ecological, economical or
609 sustainability importance.
610 Regardless of the complexity of the model used, being transfer functions or
611 deterministic models, trend detection and extrapolation is always associated with
612 uncertainty. This means that groundwater quality monitoring should remain a priority.
613 Additional data will improve the detection of trends and increase the knowledge of the
614 functioning of the groundwater system. Better understanding of the system, possibly
615 derived from deterministic modeling, can in turn provide feedback for the
616 optimization of the groundwater quality monitoring networks.
617
618 [Table 5]
619
27 620 7. Acknowledgements
621 This work was supported by the European Union FP6 Integrated Project
622 AquaTerra (Project no. GOCE 505428) under the thematic priority “sustainable
623 development, global change and ecosystems”.
624
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856 2001. Pesticides and nitrate in groundwater and rainwater in The Province of
857 Limburg in the Netherlands. Environmental Monitoring and Assessment,
858 72(1): 95-114.
859 Visser, A., Broers, H.P. and Bierkens, M.F.P., 2007a. Dating degassed groundwater
860 with 3H/3He. Water Resources Research, 43(10): WR10434.
861 Visser, A., Broers, H.P. and Van der Grift, B., 2004. Spatial dataset for Meuse NL
862 (Chpt. 2). In: H.P. Broers and A. Visser (Editors), Documented spatial data set
863 containing the subdivision of the basins into groundwater systems and
864 subsystems, the selected locations per subsystem and a description of these
865 sites, available data and projected additional measurements and equipment
866 (Deliverable T2.1), Utrecht, The Netherlands.
37 867 Visser, A., Broers, H.P. and Van der Grift, B., 2005a. Concentration-depth,
868 concentration–time and groundwater age-depth profiles in groundwater in dry
869 agricultural areas of the lower Meuse basin NL (Chpt. 2). In: H.P. Broers and
870 A. Visser (Editors), Report on concentration-depth, concentration-time and
871 time-depth profiles in the Meuse basin and the Brévilles catchment
872 (Deliverable T2.3), Utrecht, The Netherlands.
873 Visser, A., Broers, H.P. and Van der Grift, B., 2005b. Demonstrating trend reversal
874 using tritium-helium age scaling: results for the Dutch Meuse subcatchment
875 (Chpt. 2). In: H.P. Broers and A. Visser (Editors), Report on extrapolated time
876 trends at test sites (Deliverable T2.4), Utrecht, The Netherlands.
877 Visser, A., Broers, H.P., Van der Grift, B. and Bierkens, M.F.P., 2007b.
878 Demonstrating Trend Reversal of Groundwater Quality in Relation to Time of
879 Recharge determined by 3H/3He. Environmental Pollution, 148 (3): 797-807.
880 Visser, A., Van der Grift, B. and Broers, H.P., 2005c. Subsoil input data prepared for
881 groundwater and reactive transport modelling in Dutch part of the Meuse basin
882 (Chpt. 2). In: H.P. Broers and A. Visser (Editors), Subsoil input data prepared
883 for groundwater and reactive transport modelling in Dutch part of the Meuse
884 basin (Deliverable T2.5), Utrecht, The Netherlands.
885 Visser, A., Van der Grift, B. and Broers, H.P., 2006. Subsoil input data prepared for
886 groundwater and reactive transport modelling in Dutch part of the Meuse basin
887 (Chpt. 2). In: H.P. Broers and A. Visser (Editors), Input data sets and short
888 report describing the subsoil input data for groundwater and reactive transport
889 modelling at test locations in Dutch part of the Meuse basin, the Brévilles'
890 catchment and the Geer catchment (Deliverable T2.5), Utrecht, The
891 Netherlands.
38 892 Visser, A., Van der Grift, B., Heerdink, R. and Broers, H.P., 2008. Physically-
893 deterministic determination and extrapolation of time trends at selected test
894 locations in Dutch part of the Meuse basin (Chpt. 2). In: H.P. Broers and A.
895 Visser (Editors), Report which describes the physically-deterministic
896 determination and extrapolation of time trends at selected test locations in
897 Dutch part of the Meuse basin, the Brévilles catchment and the Geer
898 catchment (Deliverable T2.10), Utrecht, The Netherlands.
899 Vogel, J.C., 1967. Investigation of groundwater flow with radiocarbon, IAEA
900 Symposium on Isotopes in Hydrology. IAEA, Vienna, pp. 355-369.
901 Wassenaar, L.I., Hendry, M.J. and Harrington, N., 2006. Decadal geochemical and
902 isotopic trends for nitrate in a transboundary aquifer and implications for
903 agricultural beneficial management practices. Environmental Science and
904 Technology, 40(15): 4626-4632.
905 Xu, Y., Baker, L.A. and Johnson, P.C., 2007. Trends in Ground Water Nitrate
906 Contamination in the Phoenix, Arizona Region. Ground Water Monitoring &
907 Remediation, 27(2): 49-56.
908 Yue, S., Pilon, P. and Cavadias, G., 2002. Power of the Mann-Kendall and
909 Spearman's rho tests for detecting monotonic trends in hydrological series.
910 Journal of Hydrology, 259(1-4): 254-271.
911 Zheng, C. and Wang, P.P., 1999. MT3DMS: A modular three-dimensional
912 multispecies transport model for simulation of advection, dispersion, and
913 chemical reactions of contaminants in groundwater systems. Documentation
914 and user's guide, Department of Geological Sciences, University of Alabama,
915 Alabama.
916
39 917
918
40 919 Tables
920
Sub-basin Hydrogeological characteristics Spatial scale Contaminants Methods used Dutch part of Unconsolidated Plesitocence 5000/500 km2 Nitrate, sulfate, Statistical, Meuse basin deposits; fine to medium coarse Ni, Cu, Zn, Cd groundwater (Brabant/Kemp sands, loam dating and en) deterministic modelling Walloon part of Meuse basin Wallony- Cretaceous chalk, fissured, dual 440 km2 Nitrate Statistical, Hesbaye porosity aquifer groundwater dating and deterministic modelling Wallony-Pays Cretaceous chalk and sands, 285 km Nitrate Statistical de Herve fissured Wallony- Carboniferous limestone, folded 65 km Nitrate Statistical Néblon karstified Wallony-Meuse Unconsolidated deposits; gravels, 125 km2 Nitrate Statistical alluvial plain sands and clays Brévilles Lutecian limestone over Cuise 2.5 km2 Pesticides Transfer sands, limestone fissured (Atrazine and functions and DEA) deterministic modelling Elbe basin unconsolidated glacial deposits of 1300 km2 Nitrate Statistical and sand and gravel groundwater dating 921 Table 1: Characteristics of the test sites 922 923 Groundwater Number of nitrate Number of Number of Percent of body points downward trends upward trends significant trends Geer basin 26 0 15 57.7% Pays of Herve 12 2 6 66.6% Néblon basin 6 1 4 83.3% Alluvial plain 38 15 11 68.4% 924 Table 2. Summary of trend tests results for each groundwater body in the Walloon part of 925 the Meuse basin 926 NO3 K Al OXC CL SUMCAT shallow - 40% 0% 20% 20% 20% deep - 11% - 33% , 44% 11% 11% 927 Table 3: Percentage of significant trends detected in Bille-Krückau data set. 928 929 Purely Transfer Age dating Deterministic Deterministic statistical function modelling modelling approaches approaches with poor fit with good fit to the data to the data Prerequisite Collection of Collection of Collection of Collection of Collection of monitoring monitoring monitoring monitoring monitoring data in the data in the data in the data in the data in the field field field + field + field +
41 + Collection information on Heavy effort Heavy effort of information the evolution in collection in collection on the input of the input of additional of additional flux (rainfall, function + information information and either analysis of (other (other inputs or land tracers in piezometers, piezometers, use) samples pumping and pumping and tracer tests, tracer tests, geophysics, geophysics, soil mapping) soil mapping) Associated None 1 (surveys if 10 100 100 cost magnitude not already (geophysics, (geophysics, (on top of the available - additional additional data collection purchasing of piezometers, piezometers, effort) met data) soil mapping) soil mapping) Understanding No Functional Functional Detailed data Potential of the system? understanding understanding understanding on the system, detailed of the system of the system of the system but lack of understanding (identification overall of the system of the key understanding under study factors and of the understanding functioning of of their the system influence) (exemplified by the lack of fit of the deterministic model) Extrapolation Poor Good Good Poor Good potential Potential Potentially Potentially Limited, only Potentially Potentially universality to applies to all systems homogeneous systems. Potential for - ++ + - + operational use Knowledge - ++ + ++ +++ about the functioning of the system 930 Table 4: Summary table comparing the strengths and weaknesses of each of the trends analysis 931 methodologies 932 Groundwater system
simple complex
Trend preliminary Statistics Statistics
detection elaborate Groundwater Transfer
dating functions
Trend preliminary Statistical Transfer
extrapolation methods for short term functions for short term
extrapolation extrapolation
42 elaborate deterministic deterministic
model model
933 Table 5: Recommended preliminary and elaborate methods for trend detection and extrapolation in 934 simple and complex groundwater systems. 935
936
43 937 Figures
938 939 kg/( ha a) N N surpluses Wielom. (N-surpluses)
Schleswig-Holstein, Korcz, IETU
Source : CEA
Wallony Belgium, Brouyere, ULg Visser et al, EnvPol, 2007
940 Figure 1: History of N-fertilizer application in NL, Belgium, Germany 941 !. !. ELLINGSTEDT OSTER Strande/Neubulk OLDERSBEK Treia !. .! !. BREKENDORF .! Kl. Bennebek !. Felm !. !. .! F !. Kropp Garding Beo Schneidershoop !. ALT BENNEBEK !. KLAMP/WENTORF FARGEM MARIENWARDER !. .! NORDERHEISTEDT !. Langwedel .! !. !. .! BREIHOLZ-OST !. .! !. Riepsdorf TIEBENSEE Westerborstel KROGASPE Barmissen
!.
Krumstedt Kesdorf NINDORF .! .! Wapelfeld !. Goennebek !..! NIENBUTTEL !. !. !. Nienjahn !. Trappenkamp !. BLUNK !. St. Michaelisdon !. ROHLSTORF HOHENFIERT .! !. HAGEN WAHLSTEDT .! Nordbuttel !. Bockhorn !. !. !. .! Brokreihe !. Schlutup .! Lentforden Leezen-Nord HORST !. !.!. Heidmoor !. BARMSTEDT SIEBENBSUMEN Tangstedter Forst !. Tornesch.!!. HEIDGRABEN Ellerhoop .! !. .! .! Labenz !.!..! !. !. Hasloh Kittlitz Hin AHRENLOHE Legend Seestermuhe .! .! !. KITTLITZ !. RELLINGEN !. MP Trend Net .! Grosshansdorf HOLM .! !. MP Basic Net !. WITZHAVE NEUHORST WEDEL !. Elbe Basin in Shleswig Holstein !. Hamburg Sachsenwald Bille-Kruckau GW bodies FITZEN !. !. GWB CODE .! Langenlehsten Wohltorf Heide El 15 GULZOW !. El 16 !.
El 17 Lutau
El-b
County boundaries 05 000 10 000 20 000 30 000 40 000 Meters
Figure 4: Location of selected
groundwater bodies
942 Figure 2: Location of test sites within Europe and detailed map of test sites
44 943 DATASET
Shapiro-Wilks test (<50 values) Shapiro-Francia test (>50 values)
Normality Non-Normality
Linear regression Mann-Kendall test
Trend No trend Trend No trend
Slope regression Kendall's Slope
944 945 Figure 3: A three step procedure is adopted for trend analysis of nitrate concentrations in the selected 946 groundwater bodies: 1) normal/non-normal distribution data; 2) trend detection; 3) trend estimation. 947
948 949 Figure 4: The concentrations of a conservative chemical indicator (OXC) sampled from the shallow 950 (8m) and deep (24m) screens of observation wells 108 and 122 (right) plotted at the recharge year of 951 the sampled groundwater (left). The result is the concentration - recharge year relationship, from 952 which a clear trend can be observed that was not visible in the individual time series.
45 953 954 Figure 5: Linear trends through concentration - recharge year data show significant trend reversal 955 between 1980 and 1990 for nitrate (a), oxidation capacity (c) and sum of cations (d). 956
957 958 Figure 6: Spatial distribution of tritium (a) and trends in nitrate concentrations (b) in the Geer basin. 959
960 961 Figure 7: Linear trends and LOWESS smooth lines through concentrations of Cl, OXC, and SUMCAT 962 in relation to time of recharge from the Elbe Basin. 963 µg/L µg/L 0.4 1.0 20 y (w et) 5 y (w et) tail 0.9 tail 10 y (w et) 0.8 0.3 observations 0.7 0.6 0.2 forecast 0.5 median 5 y (dry) 0.4 median observations 10 y (dry) 0.3 20 y (dry) 0.1 20 y (w et) calibration 0.2 Initialization Initialization 0.1 calibration forecast 20 y (dry) 0.0 0.0 964 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 965 Figure 8: Predictions of the TEMPO software for concentrations of atrazine and the atrazine metabolite 966 DEA (De-Ethyl Atrazine) at the Brévilles spring. 967 968
46