Climate change has increased the intensity, frequency and duration of heatwaves and droughts in Europe turning water management into an even more complicated issue. Because water is a fundamental resource for agriculture, water management has to be addressed with climate change adaptation. While stakeholders in Lower Saxony are aware of adaptation measures they could implement to dampen the effects of climate change, evidence of the effectiveness of adaptation measures at a local scale is still missing. An analysis of adaptation measures using a new hydrological model was performed to test four adaptation measures suggested by stakeholders. Changing crops has the strongest effect followed by improving irrigation efficiency, humification and, finally, artificial aquifer recharge. If crops are changed, irrigation water demand and energy consumption could be reduced by up to 20.7%, costs could be reduced in 19.1%, the aquifer level could rise up to 284.85 mm and emissions could be reduced by 26.6% by the end of the century. Artificial recharge proved to be an inadequate method for the region as it does not impact the irrigation water demand, and an insufficient amount of water is available to have a substantial effect on the aquifer.

  • Changing crops is the most impactful measure followed by efficiency irrigation and humification.

  • Decarbonizing electricity can substantially reinforce the mitigating effects of climate change adaptation.

  • Climate change adaptation can drastically reduce water demand and promote aquifer recharge.

  • Climate change adaptation makes agriculture more cost efficient by reducing energy costs.

  • In the study region artificially recharging the aquifer has a negligible effect.

Water is a fundamental resource for food production and life. Around 12% of the ice-free global surface is used for agriculture; with 10% covered by rain-fed agriculture and 2% by irrigated agriculture (IPCC 2019). While irrigated agriculture only covers a relatively small fraction of the Earth's crust, it provides 40% of the global food supply (Turral et al. 2011). This makes irrigation essential to ensure food security. Since 1961, irrigation water demand (IWD) has been continuously increasing and the irrigation water volume has now more than doubled (IPCC 2019). Currently, irrigated agriculture is responsible for 70% of the global fresh water extractions (FAO 2014; Chen et al. 2016).

In Europe, cropland covers around 24% of the land (Eurostat 2021). European agriculture accounts for 25% of the European Union's (EU) water extractions (EEA 2019) with the bigger users of irrigation water being Spain and Italy with 16.7 and 11.7 billion m3 per year (Rossi 2019). In some countries, water extractions for irrigation have already reduced the quality of aquifers with Cyprus, Hungary, Spain, Greece, Malta, Italy and France having the most aquifers affected by agricultural water extractions (EEA 2021). It is expected that agricultural water demand will continue to increase due to climate change. Because of the strong relationship between agriculture and water, adequate water resources management is essential to ensure food security for the future.

Moreover, droughts are becoming a new challenge in Central Europe. In Germany, there has been an increase of heatwaves and droughts since the beginning of the 21st century (Tomczyk & Sulikowska 2018) with noticeable events in 2003, 2015, 2018 and 2019 (Zink et al. 2016; Ionita 2020). These drought events have been set in April (Ionita 2020), and some of the most intense events have happened during the vegetation period (Hanel et al. 2018; Rakovec et al. 2022). Climate change is expected to exacerbate heat-related events such as heat waves and droughts in Europe (Roudier et al. 2016; IPCC 2019; Ionita 2020). This means that drought events will set in faster, last longer and be more intense (Samaniego et al. 2018). This is particularly alarming as drought events can lead to severe economic losses (Naumann et al. 2019). The drought of 2003, for example, caused losses of €1.5 billion in the agricultural sector alone (Zink et al. 2016).

Clearly, climate change is turning water management for agriculture into a more complicated issue (Iglesias & Garrote 2015). Therefore, adapting to climate change is especially important for agriculture to ensure food security (EEA 2019). In the case of irrigation-dependent agriculture, a useful method to promote adaptation is through water management. Traditionally, there are two approaches to water management: increasing water supply and managing water demand (Correia de Araujo et al. 2019). Measures used to reduce the amount of water necessary to achieve a goal are known as water demand management (WDM) (Wang et al. 2016).

WDM can trigger changes in agricultural systems; however, some of these changes imply trade-offs. For example, increasing water pricing or imposing water restrictions can force changes in crop rotation (Sapino et al. 2022), but these changes can also cause declines in the region's net benefits from agriculture (Mitter & Schmid 2021) and yield reduction (Sapino et al. 2022). Reducing farmers' income could lead to rebounding maladaptation. Rebounding maladaptation refers to measures that increase the vulnerability of the targeted or implementing actor (Juhola et al. 2016). Because of this, understanding how multiple individuals' decisions interact and aggregate is crucial to determining how water will be allocated between multiple users (Cravens et al. 2021).

Modern irrigation methods consume considerable amounts of energy (Flammini et al. 2014). Consequently, the optimization of irrigation could lead to significant energy savings (European Commission 2015). By reducing energy demand, emissions generated during the electricity production can be avoided. Following the goals of the EU, Germany opted to reach a reduction of 80–95% in the GHG emissions by 2050 (BMU 2016). The target date was later updated to 2045 (BMUV 2021). This commitment is known as the Net-Zero initiative. Agriculture is one of the five sectors bound to reduce its emissions. For this reason, considering energy consumption in the agricultural climate change adaptation process should be a priority. Adaptation measures, which increase energy and water use, should be avoided, as increasing the consumption of resources would result in maladaptation.

Prior to this analysis, local stakeholders were engaged using a participatory modeling approach to assess their perceptions on climate change and identify if adaptation measures were being implemented (Valencia Cotera et al. 2022). Twenty local stakeholders, including farmers and decision-makers, were interviewed and engaged in a group model building (GMB) process (Vennix 1996). Previous studies have shown the advantages of participatory modeling processes for climate change adaptation (Máñez Costa et al. 2017; Gómez Martín et al. 2020; Williams 2020). During the GMB process, a series of adaptation measures mentioned by stakeholders was compiled. The measures were then qualitatively assessed using a leverage point analysis (Egerer et al. 2021). However, most of these measures have not been quantitatively analyzed or implemented yet. This is a knowledge gap in the region's climate change adaptation process, as evidence of the outcome, efficiency and systemic effect of implementing adaptation measures is still missing.

To bridge this gap, a hydrological impact model was developed based on the System Dynamics (SD) theory to test the effectiveness of adaptation measures (Valencia Cotera et al. 2023). The impact model was initially developed to perform an impact assessment for Seewinkel, an important agricultural region in East Austria. In this work, the model has been adapted and re-calibrated for the county of Uelzen, Germany, with historical hydrological and climate data. The effect of several adaptation measures on the systems' dynamic behavior was tested under three representative concentration pathway (RCP) scenarios. The impact model runs with future climate projections provided by the World Climate Research program EURO-CORDEX initiative (Jacob et al. 2014) for three RCPs (RCP 2.6, 4.5 and 8.5) bias corrected with the standard deviation method proposed by Bouwer et al. (2004).

Extensive evidence suggests that the willingness of farmers to adapt to climate change is significantly influenced by their climate change perceptions and awareness (Abid et al. 2019; Aidoo et al. 2021; Jha & Gupta 2021). Education and access to weather and climate information significantly influenced their climate change perceptions (Jha & Gupta 2021; Li et al. 2021; Nyang'au et al. 2021). Therefore, providing climate information to farmers and other decision-makers is crucial to shape their climate change perceptions and promote adaptation (Brosch 2021; Maltby et al. 2021). Based on this, farmers and decision-makers in Uelzen could benefit significantly from the results of this impact model analysis as no previous study has done a similar analysis in the region. By promoting science-based decision-making, this study strives to reduce the local risk of implementing measures, which could lead to maladaptation.

Under this framework, this study seeks to support climate change adaptation by providing information regarding the effectiveness of adaptation measures. The goal of this analysis is to model changes in the system's behavior after implementing adaptation measures to anticipate ineffective practices and possible maladaptive effects. The analysis is done under two emissions scenarios: first, the current emission trend, which will reach carbon neutrality around the year 2064; second, the Net-Zero initiative, in which carbon neutrality is reached in 2050. With this, the analysis aims to determine (I) How effective are adaptation measures to reduce water demand and preserve the aquifer? (II) How do adaptation measures reduce energy, costs and emissions? (III) Is there a significant effect of Net-Zero trends on emissions savings compared to the current emissions trend?

This study focuses on the county of Uelzen, an important agricultural district in Lower Saxony, Germany (Figure 1). More specifically, Uelzen is located in northeast Lower Saxony (NELS) and is part of the largest irrigated area in Germany (Egerer et al. 2021). In NELS (10,731 km2), 47% of the land is used for agriculture (Umweltbundesamt 2018). The region is of particular interest, as farmers focus on cultivating essential crops such as potatoes, sugar beets, corn and grains. These crops, in turn, support the local production of sugar, starch and biogas. The latter is subsequently used to produce electricity and district heating.
Figure 1

Location of Uelzen in Germany.

Figure 1

Location of Uelzen in Germany.

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NELS is characterized by its sandy soils. Hence, intensive land use is mainly possible due to field irrigation, as it compensates for the low summer precipitation and the low water storage capacity of the soil (Grocholl et al. 2014). Since the second half of the 20th century, farmers have made a high investment in irrigation systems to secure high yields (LWK Niedersachsen 2012). Farmers chose gun-cart irrigation systems as they require less work than other irrigation methods while being efficient enough. Around 90% of the water used for irrigation is extracted from aquifers (Wittenberg 2015). Historically, water availability and the cost of water were not issues for farmers in Uelzen.

Climate change is expected to affect precipitation patterns in NELS. As to the extent of this change, models show no conclusive trends yet. However, the models show a tendency for precipitation to decrease during the summer and increase during spring, autumn and winter (Rechid et al. 2014; Scheihing 2019). Related to this, an increase in yearly precipitation of 8 mm per decade for the period 1897–2007 has been recorded (Grocholl et al. 2014). That increase in mean annual precipitation can be explained by the intensification of winter precipitation.

However, it should be kept in mind that a change in rain patterns could cause a reduction in groundwater recharge and an increase in the soil water deficit during the summer months (Bender et al. 2017). Because of this, increasing irrigation demand and associated groundwater extraction are expected under climate change conditions (Schulz & Wendland 2014; Bender et al. 2017). In addition, extreme weather events, such as drought and heatwaves, could further increase irrigation demand. Such was the case with the 2018 drought. The lack of precipitation and increased temperatures led to a drastic increase in groundwater demand for irrigation. As a result, more than 30% of the groundwater-monitoring stations in NELS surpassed their historically recorded low points (Wriedt 2019).

System modeling

System thinking is a helpful method to understand behaviors, predict them and subsequently adjust their outcomes (Arnold & Wade 2015). Because of this, implementing systems thinking modeling, like, for example, SD, is useful to design more effective policies (Sterman 2000). In the case of climate change adaptation, impact models based on system thinking can be developed to test adaptation strategies and understand their effect on the entire system. This is particularly beneficial as, at a local scale, evidence of the efficiency of climate change adaptation measures is usually lacking. In addition, a simulation of management strategies must be ideally done before any strategies are implemented (Bala et al. 2017). Systems thinking theory has been used for assessing the effectiveness of nature-based solutions (Gómez Martín et al. 2021), groundwater management (Arasteh & Farjami 2021), evaluation of climate change adaptation strategies for water resource management (Gohari et al. 2017) and drought-oriented water management of rivers (Rubio-Martin et al. 2020). Under this framework, we have decided to implement system modeling to simulate the region's behavior under climate change and test the effect of adaptation measures on the system.

Climate projection data

This study used EURO-CORDEX data (Jacob et al. 2014) and considered three RCP scenarios: RCP 2.6, RCP 4.5 and RCP 8.5. First presented in the IPCC's Fifth Assessment Report (AR5), the RCP scenarios represent three possible climate change futures provided by the additional radiative forcing in 2100 (2.6, 4.5 and 8.5 W/m2, respectively). These future projections are dependent on the amount of carbon emissions that will be emitted in the upcoming decades. The three RCP scenarios were selected because RCP 2.6 and RCP 8.5 are considered the lower and upper boundaries and RCP 4.5 is considered an intermediate scenario. The study uses RCP scenarios as the Shared Socioeconomic Pathways (SSPs) are so new that EURO-CORDEX data with SSP scenarios is currently not available.

A multi-model ensemble was used for each RCP scenario (Appendix A). The model was run with each ensemble, and the average of the outputs was taken as a result afterwards. Monthly near-surface temperature (tas) and precipitation (pr) were directly taken from the ensemble members. Potential evapotranspiration (evaspsblpot) was calculated based on daily tas and maximum and minimum near-surface temperature (tasmax and tasmin) using the method by Hargreaves & Samani (1985) included in the python package xclim (Logan et al. 2021). Afterward, spatial averages over Uelzen were calculated using the pyweights function.

Climate model outputs, however, are biased from observations by inaccuracies in conceptualization, discretization, spatial averaging within grid cells (Soriano et al. 2019), limited representation of local features, and incorrect boundary conditions and parametrization (Pastén-Zapata et al. 2020). The driving global climate models (GCM) are the main source of uncertainty (Senatore et al. 2022). Therefore, it is not recommended to use climate model data directly as input for impact models, as regional climate models (RCMs) may still have considerable systematic biases that could produce inaccurate results (Gudmundsson et al. 2012; Mendez et al. 2020; Pastén-Zapata et al. 2020; Olschewski et al. 2023).

In the case of local applications, such as impact models for water resources research, bias correction is necessary before the data can be used (Chen et al. 2013; Fang et al. 2015; Tabari et al. 2021). By applying bias correction methods, large errors are expected to be removed, thus increasing confidence on hydrological models (Tumsa 2022). Hydrological models using bias-corrected data produce results with a reduced simulation spread, thus making them more useful for impact assessments (Teutschbein & Seibert 2012; Pastén-Zapata et al. 2020). Therefore, in this study, the climate data were bias adjusted before feeding the hydrological impact model to avoid producing results with a high spread.

In most climate change impact studies, bias correction methods are used to improve the RCM data's statistical properties to be more comparable to observed ones (Galmarini et al. 2019; Mendez et al. 2020). With bias correction methods, climate model projections are corrected using the model bias calculated against observations (Senatore et al. 2022). All bias correction methods are able to improve the RCM data; however, distribution-based methods are better than statistical transformation methods (Gudmundsson et al. 2012; Teutschbein & Seibert 2012; Chen et al. 2013). This, however, does not automatically render all other bias correction methods invalid as all methods could perform poorly in some instances. This means that the performance of bias correction methods is location specific, and validation should be made for each region (Teutschbein & Seibert 2012; Chen et al. 2013).

In this study, data correction was performed by applying the standard deviation method (Leung et al. 1999; Bouwer et al. 2004; Torralba et al. 2017) (Equation (1)). The advantages of this method are its ability to correct the climate model's mean and extreme values (Bouwer et al. 2004). In addition, the simplicity of this method makes it an advantageous option, as it does not require complicated calculations or high computational power. By applying this method, the climate data are corrected against the observed average and for the observed variance. In this case, the model was calibrated using ERA5 data. The climate data were corrected against ERA5 to maintain consistency with the calibration. The chosen baseline period is 1978–2005.
formula
(1)

In Equation (1), a′cm,j is the corrected climate parameter of a particular month ‘j’. acm,j is the uncorrected simulated climate parameter. ācm,j is the average simulated climate parameter over the baseline period. σcm,j is the standard deviation of the simulated parameter over the baseline period. σobs,j is the standard deviation of the observed climate parameter over the baseline period and āobs,j is the average observed climate parameter over the baseline period.

The impact model

The quantitative model is composed of two sections: a hydrological sub-model and an energy–cost–emissions sub-model. The hydrological sub-model has been previously developed and tested in a comparable agricultural region in Austria (Valencia Cotera et al. 2023). The energy–cost–emissions sub-model was developed exclusively for the Uelzen region, as stakeholders emphasized the strong relationship between irrigation systems and energy consumption. The energy sub-model calculates the energy required to irrigate using aquifer water. Energy use implies that farmers have to cover energy costs and that their energy use attributes the CO2 emissions of energy production to them.

Because of the large influence of irrigation on the regional hydrology, the model includes an irrigation demand equation (Agricultural water in Figure 2). This equation calculates the IWD of the region based on evapotranspiration, precipitation, a crop factor dependent on the planted crops, the efficiency of the irrigation method and the total irrigated area. By adding the irrigation demand to the drinking water and industry water demand, the model calculates the total water extracted from the aquifer on a monthly basis.
Figure 2

The model including the hydrological sub-model on the left and the energy, costs and emissions sub-model on the right. A detailed description of the model's elements can be found in the model documentation in Appendix B.

Figure 2

The model including the hydrological sub-model on the left and the energy, costs and emissions sub-model on the right. A detailed description of the model's elements can be found in the model documentation in Appendix B.

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Description and development of the hydrological sub-model

The hydrological sub-model (left panel in Figure 2) runs using climate data of the region, more specifically, precipitation (Pp) and evapotranspiration (ETP). It consists of two stocks, one representing the water stored in the upper layers of the soil (Soil water in Figure 2) and one representing the water stored in the aquifer (Groundwater in Figure 2). The model also includes an irrigation equation to simulate agricultural water demand for irrigation (Agricultural water in Figure 2).

The soil water has an inflow and two outflows. Precipitation water is stored in the soil and leaves through evapotranspiration. When the maximum water retention capacity of the soil is reached, water moves from the soil into the aquifer as recharge water (Recharge in Figure 2). The soil characteristics and the amount of runoff produced after precipitation are calculated using the curve number (CN) method (Bos et al. 2009).

The aquifer has a water inflow (Recharge in Figure 2) and three outflows (Extraction, Underground runoff and Base flow in Figure 2). The amount of extracted water is determined by the total water demand of agriculture, drinking water and industrial use. Water also moves out of the aquifer through underground runoff and base flow. Underground runoff feeds local rivers, like the Ilmenau. The base flow simulates the water leaving the aquifer in other directions than the river. This flow is calibrated using the recession flow parameter.

Irrigation equation

To consider the IWD, an adaptation of the irrigation equation presented by Wang et al. (2016) (Equation (2)) was used. This equation approximates IWD based on evapotranspiration and usable precipitation. The irrigation equation is the second driver of the model after the climate data (Pp and ETP). By adding such an equation, the model seeks to approximate and simulate the future IWD based on climate conditions.
formula
(2)
where IN is the irrigation water (mm), ETc is the reference crop evapotranspiration (mm), Pe is the effective precipitation (mm), ΔW is the soil water storage capacity (mm) and G is the ground water recharge during the period (mm). To simplify the process, IN was taken as the difference between ETc and Pe (Equation (3)).
formula
(3)
ETc is defined as the multiplication of a crop factor (Kc) and the crop evapotranspiration (ETP). In this case, the model uses the potential evapotranspiration as an input, and that value was chosen instead of a reference value (Equation (4)).
formula
(4)
Substituting Equation (4) in Equation (3) yields
formula
(5)
where Pe is estimated as suggested by Brouwer & Heibloem (1986):
formula
(6)
formula

IN values were only used if they were equal to or greater than zero. Negative values refer to months in which the precipitation was greater than the evapotranspiration; therefore, they were discarded and substituted by a zero.

As mentioned earlier, the region is specialized in growing three crops: potatoes, sugar beets and corn. However, data are not available to determine how many hectares of each crop are planted each season. Because of this, the lack of available information forced us to approximate Kc and the days per growing stage to a single value that represents the combined behavior of the three crops. The calculation of these factors is based on the information presented by Brouwer & Heibloem (1986) (Appendix C).

Finally, the total irrigation water requirement (IWR) is calculated based on Shen et al. (2013) by taking all the previous factors into account, plus the total agricultural area and the irrigation efficiency (Equation (7)). S is the total agricultural area (km2), IN is the previously calculated irrigation water (mm) and n is the efficiency of the irrigation method implemented (%).
formula
(7)
Equation (7) was validated by comparing it with the irrigation data of Uelzen (Appendix D). In this case, the formula required a correction factor (cf), which resulted in the final formula (Equation (8)) to be as follows:
formula
(8)

Description and development of the energy–cost–emissions sub-model

In Germany, emissions produced by electricity generation have been declining since 1990 (Umweltbundesamt 2014). If this trend continues emissions from electricity production will reach zero around 2064. However, if measures are implemented to achieve the goals of the German Climate Action Plan 2050, emissions should reach zero in 2050. These two emissions trends were considered in the energy–cost–emissions sub-model (BAU and NettoNull in Figure 2) to calculate the emissions per kWh consumed.

The energy–cost–emissions sub-model (right panel in Figure 2) used historical data to calculate the approximate energy needed per cubic meter of irrigation water. This energy expended was then translated into costs (in Euros) and emissions (kg of CO2 per kWh). The costs for electricity were based on a fixed price of 0.3 €/kWh. The emissions per kWh were calculated based on the average emissions for the current national electricity mix trend in Germany and the Net-Zero scenario.

The sub-model also includes other factors such as diesel consumption, work cost of irrigation and irrigation equipment repairs to calculate the costs and emissions per km2. These values were based on historical averages. The model does not calculate the emissions produced directly by agricultural activities, such as fertilizer use, as the goal of the study is to simulate energy use under climate change.

Model calibration

The model was calibrated using normalized observational data for the aquifer and ERA5 data (Hersbach et al. 2020) for Pp and ETP. The aquifer data include information from more than 55 groundwater-monitoring stations in the county of Uelzen. The data were provided by the Lower Saxony Department for Water, Coastal and Nature Conservation (NLWKN). A long reference period (1978–2018) was used to reduce uncertainty.

The model has five parameters that needed calibration. The five parameters are CN, irrigation efficiency (n), porosity (p), recession time (α) and underground runoff coefficient (β) (Figure 2). The optimization function of Vensim was used to calibrate these parameters. The built-in optimization function of Vensim allows the user to select the variables to optimize the model. The software then runs the model several times and compares the model output to the observed data. After this, the software suggests values for the selected variables.

A goodness-of-fit analysis was performed after the calibration. In hydrology, the coefficient of determination (R2) is the standard metric to test the goodness-of-fit between observations and simulations. R2 indicates correlation but does not quantify the model bias, and it can be low for an accurate model and high for an inaccurate model (Onyutha 2022). Because of this, two additional coefficients were considered: the Nash–Sutcliffe efficiency (NSE) (Nash & Sutcliffe 1970) and the Kling–Gupta efficiency (KGE) (Gupta et al. 2009).

The NSE measures the relative magnitude between the residual and measured data variance (Moriasi et al. 2015). NSE = 1 indicates that the model output perfectly matches the observations, NSE < 0 indicates that the model is a worse predictor than the mean of observations and NSE = 0 is usually used as the threshold to distinguish between a satisfactory and an unsatisfactory model (Knoben et al. 2019). More specifically, NSE values <0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8 and >0.8 are classified as insufficient, sufficient, good, very good and excellent, respectively (Okiria et al. 2022). Others set the limit for a satisfactory model at NSE >0.5 (Moriasi et al. 2007).

The KGE is based on an equal weighting of bias, correlation and variability measures, and when used for model calibration, it considerably improves the bias and the variability and only slightly decreases the correlation (Gupta et al. 2009). The KGE has been increasingly used for model evaluation and calibration as it was developed to address some shortcomings in the NSE (Knoben et al. 2019). For KGE, values below zero are considered unsatisfactory, while 0.5 > KGE > 0 is considered a poor performing model (Knoben et al. 2019). It is important to emphasize that KGE and NSE cannot be compared directly, and they should not be considered equivalents (Knoben et al. 2019).

After calibration, the model achieved an R2 = 0.48, an NSE = 0.48 and a KGE = 0.78 when comparing the historical data of the aquifer to the simulations (Figure 3). While this performance can be deemed acceptable, there are several possible shortcomings in the model and in the data that do not allow for a higher model fit. First, the model is a lumped model without spatial distribution; therefore, it has reduced accuracy. Second, there are strong variations in several of the measuring stations, with considerable drops in the water level during the growing season. A possible reason for this is well drawdown caused by water extractions too close to the measuring point. Third, the area of the county of Uelzen might be too large (1,454 km2) as the model was originally developed for a 500 km2 region. However, because this is an impact model, this study is more focused on understanding trends and behaviors rather than presenting high-precision hydrological modeling information.
Figure 3

Comparison between observation data for the aquifer depth (in red) and the model (in blue). The reference period of 492 months starts in January 1978 and ends on December 2018.

Figure 3

Comparison between observation data for the aquifer depth (in red) and the model (in blue). The reference period of 492 months starts in January 1978 and ends on December 2018.

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Simulating adaptation measures

The adaptation scenarios are based on adaptation measures suggested by stakeholders and recorded after a previously implemented GMB process (Valencia Cotera et al. 2022). That study identified some of the most mentioned adaptation measures such as (I) Reusing and storing water; (II) Humification to increase the soil quality; (III) Digitalization and precision agriculture and (IV) New crop rotation. Based on this, five adaptation scenarios were developed and simulated. These scenarios are as follows: first, a scenario where no adaptation measures are implemented (BAU); second, a scenario where a fraction of the gray water is used to recharge the aquifer (ARC). This gray water is produced after urban water (drinking and industry water) is treated; third, a scenario where humification is promoted at a large scale (HUM); fourth, a scenario where precision agriculture increases the efficiency of irrigation (PIR); fifth, a change to a new crop rotation with less water-demanding crops (CRP).

Parameters in the model were changed to simulate the large-scale implementation of these adaptation measures. In the BAU scenario, the model ran without changes to simulate the current conditions. In the ARC scenario, 50% of the water extracted for drinking and for industry was used to artificially recharge the aquifer. This was preferred over building storage ponds, because they require high initial investments and large areas. To counteract these disadvantages, the aquifer can be used as a natural reservoir. In the HUM scenario, CN was reduced by 1.04 × 103 per month to simulate a gradual increase in humus content resulting in a reduction of one in the CN by the end of the century. Because humus retains humidity, it decreases the need for irrigation. Therefore, cf was also adjusted accordingly. In the PIR scenario, irrigation efficiency was increased to 85% to simulate a change from gun-cart irrigation systems to lateral movement irrigation. Finally, in the CRP scenario, the Kc was changed to simulate a large-scale implementation of a crop rotation with less water-demanding crops (Appendix C).

Because adaptation is not an immediate process, the model considers the time required to implement the adaptation measures. Humification, for example, is a process that requires decades to complete. Therefore, the HUM scenario begins in 2023 and gradually changes CN and cf until 2100. Improving the irrigation method is a costly process that not all farmers can immediately implement. To simulate a slow transition from the current irrigation methods to more efficient ones, in the PIR scenario, n begins to increase in 2023 and ends in 2035. Similarly, changing the crop rotation is a slow and complex process. In the CRP scenario, Kc starts to change in 2023 and it ends in 2035 to simulate a slow transition to a new crop rotation. The ARC scenario, however, begins in 2023 as an artificial recharge project could be implemented in less than a year.

Some modeling software, like Vensim, limits the input data to one dataset. This means that the software can perform only one simulation at a time. This is a severe limitation for impact models using climate data, as usually simulations are performed using climate data ensembles. In order to overcome this limitation, the Python library PySD was used to run and feed the model. The PySD library was developed specifically to facilitate the integration of data science and models developed using Vensim (Houghton & Siegel 2015).

This section presents the dynamic behavior of the system under three climate change scenarios. By focusing on the behavior of the system, it is possible to determine the systemic effect of implementing the suggested adaptation measures. The adaptation measures are compared to the BAU scenario, which is used as a baseline. To compare and rank the effectiveness and viability of the different adaptation measures, this study focuses on the long-term trends of the irrigation demand, the aquifer, and the energy consumption, costs and emissions. The following sections include a detailed description of the results summarized in Table 1.

Table 1

The average effect of the four adaptation measures on different parameters across all RCPs.

ParameterCRPPIRHUMARC
IWD −20.7% −15.2% −7.5% N.E. 
Groundwater (mm) +240.85 +177.05 +118.03 +3.15 
Energy (BAU 48.9 kWh/ha) 38.8 (−20.7%) 41.5 (−15.2%) 45.2 (−7.5%) N.E. 
Emissions (current trend) −15.3% −3.8% −1% N.E. 
Emissions (Net-Zero) −26.7% −16.1% −14.6% N.E. 
Costs (BAU 23.3 €/ha) 18.8 €/ha (−19.1%) 20.6 €/ha (−11.4%) 21.9 €/ha (−5.7%) N.E. 
ParameterCRPPIRHUMARC
IWD −20.7% −15.2% −7.5% N.E. 
Groundwater (mm) +240.85 +177.05 +118.03 +3.15 
Energy (BAU 48.9 kWh/ha) 38.8 (−20.7%) 41.5 (−15.2%) 45.2 (−7.5%) N.E. 
Emissions (current trend) −15.3% −3.8% −1% N.E. 
Emissions (Net-Zero) −26.7% −16.1% −14.6% N.E. 
Costs (BAU 23.3 €/ha) 18.8 €/ha (−19.1%) 20.6 €/ha (−11.4%) 21.9 €/ha (−5.7%) N.E. 

Note: N.E. stands for ‘no effect’.

IWD under climate change and adaptation scenarios

For all three RCPs, the CRP adaptation scenario was the most efficient in reducing IWD followed by PIR and HUM (Figure 4). The ARC scenario did not affect the IWD as it only affects the aquifer but influences neither the decisions taken by farmers nor the crops' water requirements. The average IWD for the BAU scenario across all RCPs was 8.15 mm/month. This value was reduced to 6.46 mm/month in the CRP scenario, to 6.91 mm/month in the PIR scenario and to 7.54 mm/month in the HUM scenario. This means that, when compared to BAU across all RCPs, the CRP scenario reduces IWD by an average of 20.7%, PIR reduces it by 15.2% and HUM reduces it by 7.5%.
Figure 4

The effect of the four adaptation measures on the IWD in Uelzen compared to the BAU baseline for three climate change scenarios.

Figure 4

The effect of the four adaptation measures on the IWD in Uelzen compared to the BAU baseline for three climate change scenarios.

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Aquifer dynamics under climate change and adaptation scenarios

The CRP adaptation scenario was again the most effective in increasing the groundwater level for all RCPs followed by PIR, HUM and lastly ARC (Figure 5). When compared to BAU across all RCPs, the groundwater level increased by an average of 240.85 mm in the CRP scenario, by 177.05 mm in the PIR scenario, by 118.03 mm in the HUM scenario and by only 3.15 mm in the ARC scenario. The highest increase in groundwater level happened in RCP 8.5, and the lowest happened in the RCP 2.6 scenario.
Figure 5

The effect of the four adaptation measures on the groundwater in Uelzen compared to the BAU baseline for three climate change scenarios.

Figure 5

The effect of the four adaptation measures on the groundwater in Uelzen compared to the BAU baseline for three climate change scenarios.

Close modal

Energy, costs and emissions under climate change and adaptation scenarios

Regarding the irrigation energy demand in Uelzen during the 21st century, the adaptation scenarios had again the same order of effect as with the IWD (Figure 6). The average monthly energy consumption for the BAU scenario across all RCPs was 48.9 kWh/ha. The CRP scenario reduced the energy demand to 38.8 kWh/ha, PIR to 41.5 kWh/ha, and HUM to 45.2 kWh/ha. ARC did not have any effect as it only influenced the volume of available water but did not affect IWD. This means that CRP reduced the energy demand by 20.7%, PIR by 15.2% and HUM by 7.5%.
Figure 6

The effect of the four adaptation measures on the energy demand of agriculture in Uelzen compared to the BAU baseline for three climate change scenarios.

Figure 6

The effect of the four adaptation measures on the energy demand of agriculture in Uelzen compared to the BAU baseline for three climate change scenarios.

Close modal

The reduction in energy consumption corresponds to a similar reduction in GHG emissions. Under the current trend and when compared to BAU, CRP produced 15.3% less emissions, PIR 3.8% less and HUM almost 1% less by the end of the century. However, if actions are implemented to reach the Net-Zero scenario in 2050, CRP could avoid 26.7% emissions, while PIR and HUM could avoid up to 16.1% and 14.6%, respectively.

Energy and costs are closely linked, as energy is the highest cost of irrigation. Therefore, by decreasing irrigation, farmers save energy and reduce operational costs (Figure 7). The average monthly energy costs for the BAU scenario across all RCPs was 23.3 €/ha. The CRP scenario reduced the energy costs to 18.8 €/ha by the end of the century. PIR reduced the energy costs to 20.6 €/ha and HUM to 21.9 €/ha. ARC had no effect on the IWD, and it also did not affect the costs. They represent reductions in cost of 19.1, 11.4 and 5.7%, accordingly.
Figure 7

The effect of the four adaptation measures on the costs to agriculture in Uelzen compared to the BAU baseline for three climate change scenarios.

Figure 7

The effect of the four adaptation measures on the costs to agriculture in Uelzen compared to the BAU baseline for three climate change scenarios.

Close modal

Effectiveness, benefits and trade-offs of the adaptation measures

The results of this study showed that, under all RCPs, the most efficient adaptation measure to reduce IWD and, therefore, energy, costs and emissions was a change to less water-demanding crops. The CRP scenario was followed by an investment in precision agriculture to increase the irrigation efficiency and, finally, a shift in agricultural practices to promote humification. The same order of effectiveness applied to the aquifer. However, in this case, artificially recharging the aquifer with treated water did have an effect. The effect was negligible in comparison to the other measures, as the water volume extracted for drinking water and the industry was considerably smaller in comparison to the water volume extracted by agriculture.

Furthermore, the findings indicate that both the RCP 4.5 and RCP 8.5 scenarios are wetter in comparison to RCP 2.6. Although the effectiveness of adaptation measures remains consistent across the RCPs, their impact on IWD and aquifer levels is more pronounced in RCP 4.5 and RCP 8.5. This suggests that factors beyond the adaptation measures contribute to the decline in IWD and the enhancement of aquifer recharge. Since the model relies solely on evapotranspiration and precipitation data, it is reasonable to assert that these factors amplify the effects of adaptation measures. Consequently, it can be inferred that the region could become wetter under the RCP 4.5 and RCP 8.5 scenarios compared to RCP 2.6.

Changing and diversifying crops was the most effective measure as, on average, it helped reduce the IWD by 20.7% by the end of the century. This had positive effects on the aquifer as, on average, it increased its level by 240.85 mm by the end of the century compared to the BAU. As irrigation is linked to energy use and emissions, decreasing irrigation through CRP caused a reduction in costs of 23.3 €/ha (BAU) to 18.8 €/ha, a reduction of energy use of 20.7% and considerable cuts in emissions. In the current emissions trend, CRP could avoid 15.3% emissions by the end of the century and 26.7% under the Net-Zero trend. The drastic cuts in emissions and IWD could be explained by the lower Kc of the new crop rotation but also because this crop rotation is shorter in comparison to the current crop rotation, thus it requires fewer inputs, less water for example. These results are in line with other studies, which have also confirmed that changing crops can reduce energy demands and thus CO2 emissions (Mohammadi et al. 2014; Canaj & Mehmeti 2022; Rathore et al. 2022). While changing and diversifying crops to create a shift into less water-demanding crops might have a considerable effect on the IWD of the region, achieving this in Uelzen might be extremely difficult in reality. The agriculture industry in Uelzen is entirely based on the cultivation of these crops because of their high economic value; this in turn creates a reinforcing feedback loop (Egerer et al. 2021). This can cause a high resistance to change due to the chances of economic losses caused by a shift to a new crop rotation (Valencia Cotera et al. 2022). However, opting for a new crop rotation as an adaptation strategy can have additional benefits such as improving yields (Farina et al. 2008; Teixeira et al. 2018; Huynh et al. 2019), increasing the soil biota (Zhang et al. 2021; Torppa & Taylor 2022), increasing soil organic carbon and nitrogen content (Zhang et al. 2021), increasing the plant nitrogen uptake and intrinsic water use efficiency (Bowles et al. 2022) and increasing yields even under drought conditions (Bowles et al. 2020). Therefore, designing a new crop rotation for Uelzen would not only be beneficial for the local water resources, and help cut down emissions, but could also promote that farms shift to more favorable practices.

Irrigation is one of the most critical factors affecting yields. In dry years, irrigation can almost double the yields in comparison to rain-fed agriculture (Huynh et al. 2019). In Uelzen, aquifer water is mainly used for irrigation. Around 81% of the extractions are used for irrigation, while 19% are for industry and drinking water. Because irrigation has such a strong effect on the local groundwater, the results showed that increasing irrigation efficiency would bring many benefits such as the protection of local water resources, reduced emissions and a reduction in operational costs. By implementing PIR, the IWD could be reduced by an average of 15.2% and the aquifer level could increase by an average of 177.05 mm by the end of the century for all RCP scenarios compared to the BAU. This means that farmers could still rely on irrigation to secure their yields while promoting an increase in the groundwater level at the same time. However, the shift to more efficient irrigation methods requires a high initial investment, which farmers are generally not ready to make unless incentives are granted (Pilarova et al. 2022). Therefore, subsidizing more efficient irrigation systems can promote their adoption and implementation (Heumesser et al. 2012; Issaka et al. 2018). The results also showed that a reluctance to change the irrigation method would increase the costs per hectare, as in the PIR scenario the costs per hectare were, on average, 11.4% lower for all RCPs and when compared to the BAU scenario. This could be crucial to keep farms operational and financially sustainable during drought periods. A similar benefit was observed for the emissions as in the PIR scenario, 3.8% less CO2 could be emitted under the current trend and 16.1% less under the Net-Zero trend. The results were in line with other studies, which have already shown that irrigation methods should be improved to promote energy efficiency and reduce carbon emissions (Khoshnevisan et al. 2013; Zhao et al. 2018; Jamali et al. 2021).

The results also showed that changing agricultural practices to promote humification can positively influence reducing IWD and preserving the aquifer. This is because humus conservation and improvement have beneficial effects on soil structure, water-holding capacity and plant nutrient supply (Piccolo 1996). More specifically, higher organic matter contents result in higher soil water contents (De Jong et al. 1983; Bordoloi et al. 2019). Because of this, by promoting humification, the IWD could be reduced by an average of 7.51% for all RCPs. This in turn would imply an average reduction in costs of 23.3–21.9 €/ha, an average reduction of energy use of 7.51% and considerable cuts in emissions. In the current emissions trend, HUM could avoid the emission of 1% less CO2 by the end of the century and 14.6% less CO2 under the Net-Zero trend when compared to the BAU. Moreover, soil organic matter promotes good soil structure, increases water infiltration and reduces soil erosion (Bot & Benites 2005). This was confirmed by the results as humification also had positive effects on the aquifer. On average, humification promoted an increase of 118.03 mm in the aquifer levels by the end of the century compared to the BAU. Increasing the soil organic content has additional benefits from which farmers could profit. Supplementing soils with humus-rich compost can increase nutrient content in the soil (Lord & Sakrabani 2019) and promote shoot and root growth, nutrient uptake and mycorrhizal colonization (Solaiman et al. 2019). Usually, no-till farming is suggested to promote humification as intensive tillage may damage soil properties and degrade soil. However, it has been proved that no tillage can negatively affect yields, at least during the first years (Camarotto et al. 2018; Huynh et al. 2019). Finally, it should be taken into account that humification is a slow process happening over several decades and might imply changes to the crop rotation.

Finally, the least effective adaptation measure was the reuse of drinking and industrial water. This adaptation measure has three main downsides. First, as mentioned above, the amount of water extracted for drinking and industrial use is too small in comparison to the water extracted for irrigation. Therefore, even when 50% of that water is treated and returned to the aquifer, the effect is almost negligible. If implemented, the aquifer would only increase its level by an average of 3.15 mm for all RCPs by the end of the century. Second, because this measure only affects the aquifer, it does not have any effect on the IWD and, therefore, also does not reduce energy, costs and emissions caused by the reliance on agriculture. Third, artificial recharge has potential risks to aquifer water quality as recharge water disinfection could lead to the generation of disinfection by-products (Chai et al. 2022), untreated water could introduce pathogens (Page et al. 2010) and polluted water could introduce chemical pollutants (Yu et al. 2022). In addition, current wastewater treatments fail to completely remove drugs, micro- and nano-plastics, hormones, antibiotic resistance germs and bacteria, contaminants of emerging concern and personal care products among others (Valhondo et al. 2020). Therefore, recharge with treated water could pose additional risks, as it could be a vector for other pollutants. However, solutions like treatment barriers (Page et al. 2010), reactive barriers (Valhondo et al. 2020) and disinfection and colloid supplements (Chai et al. 2022) have been implemented to deal with pollutants in aquifer recharge water. Because of the ineffectiveness of water reuse to reduce IWD and increase the aquifer level, building additional water storage ponds in Uelzen could be equally futile.

Importance of Net-Zero

This study showed the relevance and importance of implementing actions to reach the goals of the German Climate Action Plan 2050. While decarbonizing electricity is challenging, a 100% renewable-based system for electricity is feasible in Germany (Maruf 2021). Solar and wind energy are currently the main pillars of renewable energy in Germany, and it is expected that they will become the primary source of energy in the next decades (Dotzauer et al. 2022). Decarbonizing electricity by 2050 would assist many sectors, including agriculture, in reducing their indirect emissions through energy consumption. The results show that in the case of Uelzen, the Net-Zero trend has a substantial effect in avoiding emissions when compared to the current trend. On average, 26.7% less CO2 could be emitted in the CRP scenario, 16.1% less CO2 in the PIR scenario and 14.6% less CO2 in the HUM scenario compared to BAU in the current emissions trend. This means that no matter which adaptation measure would be implemented, the Net-Zero trend would always have substantial emission savings over the current trend. In addition, these savings could be even more significant if two or more adaptation measures were implemented at the same time, as their combined effects on the system would induce higher energy savings.

Limitations and further research

While this study gave a general overview of the systemic effects that adaptation could have in Uelzen, certain limitations need to be addressed. First, this study does not consider yearly spikes caused by drought events. This means that while the adaptation measures might be useful in the long run, this study did not show how effectively they buffer the effects of climate extremes. Second, because the model lacked an agricultural sub-model, it did not model the yields. Therefore, it did not calculate the profits generated. With the profits and the costs available, an assessment could be performed to determine if farms would become unprofitable under climate change.

To bridge the limitations of this study, we propose that further research could focus on climate extremes and their effects on the region. By focusing on climate extremes and doing a similar analysis to the one presented here, it should be possible to determine which adaptation measures could help mitigate the effects of seasonal droughts. Further research could develop an agricultural sub-model to simulate the effect that climate adaptation could have on yields. By doing this, it should be possible to better understand the positive economic effects that adaptation could have.

This study implemented an impact assessment of climate change adaptation measures in Uelzen using an impact model under three RCP scenarios. The results showed that changing crops is the most efficient way to adapt to climate change, followed by increasing irrigation efficiency, promotion of humus formation and, finally, artificial recharge of the aquifer. Implementing a new crop rotation, increasing irrigation efficiency and promoting humus formations would significantly reduce IWD and reduce costs and emissions. Artificially recharging the aquifer has almost unperceivable effects on the aquifer level, and it affects neither the IWD nor the costs and emissions.

The results have also highlighted the importance of achieving the objectives of the German Climate Action Plan 2050. While adaptation measures should be implemented at a farm level, decision-makers should also promote and accelerate the shift to clean electricity. If a shift to renewable energy is achieved by 2050, adaptation of agriculture could also have a mitigating effect. The results have shown that if proper measures are implemented to decarbonize electricity, a portion of the emissions caused by energy demand in agriculture could be avoided. This is another example of the indirect benefits of climate change adaptation at a systemic level.

Finally, future research may include an economic analysis for promoting climate change adaptation in Uelzen. This further research is proposed to tackle a common challenge: as long as current practices remain profitable, farmers might be hesitant to implement adaptation measures due to the required initial investments. Therefore, future research could identify optimal financial approaches to support and motivate stakeholders in their climate change adaptation process. A second research opportunity is to develop and suggest a new crop rotation to substitute the current one. The results of the current analysis showed that changing crops is the best adaptation measure to reduce IWD and increase the local water resources. However, changing the crop rotation is a difficult issue as it could lead to substantial shifts in the local economy. Hence, the ideal approach would be to develop a crop rotation that benefits all stakeholders, offering additional advantages like promoting humification and reducing IWD.

The authors would like to thank Dr Laurens Bouwer and Dr Thea Wübbelmann for their support.

Methodology: R.V.C..; investigation: R.V.C. and S.E.; formal analysis: R.V.C.; software: R.V.C. and L.M.; writing-original draft preparation: R.V.C.; writing-review and editing: S.E., C.N., L.L., L.M. and M.M.C.; supervision: M.M.C.; funding acquisition: M.M.C. All authors have read and agreed to the published version of the manuscript.

This work was conducted and financed within the framework of the Helmholtz Institute for Climate Service Science (HICSS), a cooperation between Climate Service Center Germany (GERICS) and the Universität Hamburg, Germany.

All relevant data are available from an online repository or repositories: https://zenodo.org/doi/10.5281/zenodo.10647730 and https://github.com/MoorsTech/Climate-Change-Adaptation-Lower-Saxony.

The authors declare there is no conflict.

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