ALBERT

Description: This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 yr of daily precipitation, potential evapotranspiration (PET), and streamflow data. Nash-Sutcliffe efficiency (NSE), modified index of agreement (d1) and water balance error (WBE) were used as performance criteria. We used a differential split-sample test to split up the data into 120 sub-periods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation should be used to quantify uncertainties due to parameter instability and non-uniqueness.

Description: Most of the forested headwater catchments are an important source of water supply in many parts of the world. A prime example is southeast Australia where forests supply major river systems and towns and cities with water. It is critical for an informed and adaptive water resource management to understand changes in streamflow caused by vegetation changes in these headwater forest catchments. Natural disturbances such as bushfires and anthropogenic activities like forestation, deforestation, or logging alter vegetation, evapotranspiration and soil water status, and may affect water supplies. Although catchment water yield is mainly controlled by climatic conditions, but it is also strongly influenced by land cover changes because of natural disturbances and anthropogenic activities. It is necessary to accurately estimate streamflow in water supply catchments subjected to dramatic land surface changes. This paper summarises the methods commonly used to investigate the impacts of land cover change on water resources, and provides some examples of impacts of afforestation/deforestation and bushfire on water resources in two southeast Australian catchments.

Description: This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 years of daily precipitation, potential evapotranspiration (PET), and streamflow data. Nash-Sutcliffe efficiency (NSE) and absolute percentage water balance error (WBE) were used as performance criteria. We used a differential split-sample test to split up the data into 120 sub-periods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation can reduce uncertainties due to parameter instability and non-uniqueness.

Description: The response of soil respiration (Rs) to soil temperature and moisture have been well documented in forests, but data and information from desert shrub ecosystems are limited. Soil CO2 efflux from a desert shrub ecosystem was measured continuously with automated chambers in Ningxia, northwest China, from June to October 2012. The responses of Rs to Ts was strongly affected diurnally by soil moisture, with the diel variation in Rs being strongly related to 10 cm soil temperature (Ts) at moderate and high soil volumetric water content (VWC), but less related to Ts at low VWC. Ts typically lagged Rs by 3–4 h, however, the lag time varied in relation to VWC, with increased lag times at low VWC. Over the seasonal cycle, daily mean Rs was positively correlated with Ts when VWC exceeded 0.08 m3 m−3, but became decoupled from Ts when VWC dropped below this threshold. The annual temperature sensitivity of Rs (Q10) was 1.5. The short-term sensitivity of Rs to Ts, computed using three-day windows, varied significantly over the seasonal cycle; the short-term Q10 was negatively correlated with Ts and positively correlated with VWC. These results suggest the potential for a negative feedback to climate warming in desert ecosystems, related to the impact of low soil moisture on Rs. The results highlight the biological causes of diel hysteresis between Rs and Ts and the need for carbon cycle models to account for the interacting effects of Ts and VWC as joint determinants of Rs in desert ecosystem.

Description: The carbon (C) cycling in semiarid and arid areas remains largely unexplored, despite the wide distribution of drylands globally. Rehabilitation practices have been carried out in many desertified areas, but information on the C sequestration potential of recovering vegetation is still largely lacking. Using the eddy-covariance technique, we measured the net ecosystem CO2 exchange (NEE) over a recovering shrub ecosystem in northwest China throughout 2012 in order to (1) quantify NEE and its components, (2) examine the dependence of C fluxes on biophysical factors at multiple timescales. The annual budget showed a gross ecosystem productivity (GEP) of 456 ± 8 g C m−2 yr−1 and an ecosystem respiration (Re) of 379 ± 3 g C m−2 yr−1, resulting in a net C sink of 77 ± 7 g C m−2 yr−1. The maximum daily NEE, GEP and Re were −4.7, 6.8 and 3.3 g C m−2 day−1, respectively. Both the maximum C assimilation rate (i.e., at optimum light intensity) and the quantum yield varied strongly over the growing season, being higher in summer and lower in spring and autumn. At the half-hourly scale, water stress exerted a major control over daytime NEE, and interacted with heat stress and photoinhibition in constraining C fixation by the vegetation. Low soil moisture also reduced the temperature sensitivity of Re (Q10). At the synoptic scale, rain events triggered immediate pulses of C release from the ecosystem, followed by peaks of CO2 uptake 1–2 days later. Over the entire growing season, leaf area index accounted for 45 and 65% of the seasonal variation in NEE and GEP, respectively. There was a linear dependence of daily Re on GEP, with a slope of 0.34. These results highlight the role of abiotic stresses and their alleviation in regulating C cycling in the face of an increasing frequency and intensity of extreme climatic events.

Description: Soil respiration (Rs) and its biophysical controls were measured over a fixed sand dune in a desert-shrub ecosystem in northwest China in 2012 to explore the mechanisms controlling the spatial heterogeneity in Rs and to understand the plant effects on the spatial variation in Rs in different phenophases. The measurements were carried out on four slope orientations (i.e., windward, leeward, north- and south-face) and three height positions on each slope (i.e., lower, upper, and top) across the phenophases of the dominant shrub species (Artemisia ordosica). Coefficient of variation (i.e., standard deviation/mean) of Rs across the 11 microsites over our measurement period was 23.5 %. Soil respiration was highest on the leeward slope, but lowest on the windward slope. Over the measurement period, plant-related factors, rather than micro-hydrometeorological factors, affected the topographic variation in Rs. During the flowering-bearing phase, root biomass affected Rs most, explaining 72 % of the total variation. During the leaf coloration-defoliation phase, soil nitrogen content affected Rs the most, explaining 56 % of the total variation. Our findings highlight that spatial pattern in Rs was dependent on plant distribution over a desert sand dune, and plant-related factors largely regulated topographic variation in Rs, and such regulations varied with plant phenology.

Description: The current understanding of the responses of soil respiration (Rs) to soil temperature (Ts) and soil moisture is limited for desert ecosystems. Soil CO2 efflux from a desert shrub ecosystem was measured continuously with automated chambers in Ningxia, northwest China, from June to October 2012. The diurnal responses of Rs to Ts were affected by soil moisture. The diel variation in Rs was strongly related to Ts at 10 cm depth under moderate and high volumetric soil water content (VWC), unlike under low VWC. Ts typically lagged Rs by 3–4 h. However, the lag time varied in relation to VWC, showing increased lag times under low VWC. Over the seasonal cycle, daily mean Rs was correlated positively with Ts, if VWC was higher than 0.08 m3 m−3. Under lower VWC, it became decoupled from Ts. The annual temperature sensitivity of Rs (Q10) was 1.5. The short-term sensitivity of Rs to Ts varied significantly over the seasonal cycle, and correlated negatively with Ts and positively with VWC. Our results highlight the biological causes of diel hysteresis between Rs and Ts, and that the response of Rs to soil moisture may result in negative feedback to climate warming in desert ecosystems. Thus, global carbon cycle models should account the interactive effects of Ts and VWC on Rs in desert ecosystems.

Description: The carbon (C) cycling in semiarid and arid areas remains largely unexplored, despite the wide distribution of drylands globally. Rehabilitation practices have been carried out in many desertified areas, but information on the C sequestration capacity of recovering vegetation is still largely lacking. Using the eddy-covariance technique, we measured the net ecosystem CO2 exchange (NEE) over a recovering shrub ecosystem in northwest China throughout 2012 in order to (1) quantify NEE and its components and to (2) examine the dependence of C fluxes on biophysical factors at multiple timescales. The annual budget showed a gross ecosystem productivity (GEP) of 456 g C m−2 yr−1 (with a 90% prediction interval of 449–463 g C m−2 yr−1) and an ecosystem respiration (Re) of 379 g C m−2 yr−1 (with a 90% prediction interval of 370–389 g C m−2 yr−1), resulting in a net C sink of 77 g C m−2 yr−1 (with a 90% prediction interval of 68–87 g C m−2 yr−1). The maximum daily NEE, GEP and Re were −4.7, 6.8 and 3.3 g C m−2 day−1, respectively. Both the maximum C assimilation rate (i.e., at the optimum light intensity) and the quantum yield varied over the growing season, being higher in summer and lower in spring and autumn. At the half-hourly scale, water deficit exerted a major control over daytime NEE, and interacted with other stresses (e.g., heat and photoinhibition) in constraining C fixation by the vegetation. Low soil moisture also reduced the temperature sensitivity of Re (Q10). At the synoptic scale, rain events triggered immediate pulses of C release from the ecosystem, followed by peaks of CO2 uptake 1–2 days later. Over the entire growing season, leaf area index accounted for 45 and 65% of the seasonal variation in NEE and GEP, respectively. There was a linear dependence of daily Re on GEP, with a slope of 0.34. These results highlight the role of abiotic stresses and their alleviation in regulating C cycling in the face of an increasing frequency and intensity of extreme climatic events.