Spatial assessments of historical climate change provide information that can be used by scientists to analyze climate variation over time and evaluate, for example, its effects on biodiversity, in ...order to focus their research and conservation efforts. Despite the fact that there are global climatic databases available at high spatial resolution, they represent a short temporal window that impedes evaluating historical changes of climate and their impacts on biodiversity. To fill this gap, we developed climate gridded surfaces for Mexico for three periods that cover most of the 20.sup.th and early 21.sup.st centuries: t.sub.1 -1940 (1910-1949), t.sub.2 -1970 (1950-1979) and t.sub.3 -2000 (1980-2009), and used these interpolated surfaces to describe how climate has changed over time, both countrywide and in its 19 biogeographic provinces. Results from our characterization of climate change indicate that the mean annual temperature has increased by nearly 0.2°C on average across the whole country from t.sub.2 -1970 to t.sub.3 -2000. However, changes have not been spatially uniform: Nearctic provinces in the north have suffered higher temperature increases than southern tropical regions. Central and southern provinces cooled at the beginning of the 20.sup.th century but warmed consistently since the 1970s. Precipitation increased between t.sub.1 -1940 and t.sub.2 -1970 across the country, more notably in the northern provinces, and it decreased between t.sub.2 -1970 and t.sub.3 -2000 in most of the country. Results on the historical climate conditions in Mexico may be useful for climate change analyses for both environmental and social sciences. Nonetheless, our climatology was based on information from climate stations for which 9.4-36.2% presented inhomogeneities over time probably owing to non-climatic factors, and climate station density changed over time. Therefore, the estimated changes observed in our analysis need to be interpreted cautiously.
ABSTRACT
Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple ...applications in research planning, experimental design, and technology transfer. Although high‐resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high‐resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910–2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin‐plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26–1.12 °C and 24.67% for monthly precipitation, and a RTMSE of 0.48–0.56 °C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high‐resolution climatology for the country.
Delineating spring protection zones is key to managing groundwater. This work presents a proposal for delineating spring protection zones (SPZs) that uses hydrogeological, topographical, land use, ...and climate characteristics as a basis to protect springs located in fractured volcanic media from potential contamination processes. This was accomplished through five stages: (1) identification of hydrogeological characteristics of the environments in which springs are located and physicochemical properties of water, (2) delineation of spring potential catchment zones, (3) estimation of spring recharge zones in the potential catchment zones, (4) SPZ proposal based on annual recharge analysis for each spring, and (5) projection of future land use and climate change scenarios. The result was a proposal of three typically established zones for protecting springs: SPZ1 was defined by a 50-m radius around springs, SPZ2 was delineated based on spring annual recharge zone estimate, and SPZ3 was considered the remainder of potential catchment zone. By delineating these zones, more suitable protection measures can be identified based on trends in land use and climate changes, measures which would thereby aid in sustainable use of these types of springs.
Key message
An increase in winter-spring precipitation appears to have a positive effect on radial growth of
P. hartwegii
a treeline species in Mexican volcanoes.
To address the degree to which ...high-elevation trees reflect climatic variability, a ring-width chronology of 308 years (1705–2012) was developed from a pure stand of
Pinus hartwegii
forest at the upper treeline of Monte Tlaloc in the Trans-Mexican Volcanic System. A series of variables were used to verify the climatic correlation of the chronology, including climate data, historical archives dealing with agricultural crises and climate anomalies in central Mexico, regional dendroclimatic reconstructions of precipitation, values of the Southern Oscillation Index (SOI), and Tropical Rainfall Index, which are estimators of El Niño/Southern Oscillation phenomena (ENSO). Correlations with local and regional climate data suggested that wet conditions in the winter–spring season had a positive effect on radial growth. Reduced growth was associated with extreme droughts in central Mexico, with some exceptions, such as in 1997, when, due to the massive warming of sea surface temperatures and low precipitation, growth did not begin until May or June and peaked during the cool season. The ring-width chronology indicated an increase in radial growth when SOI values were negative and precipitation was above-mean during the growing season. The lowest recorded growth measurements are associated with extreme droughts and strong warm ENSO events. Given the significant relationship between climate and radial growth of
P. hartwegii
, this study highlights the potential vulnerability of this high-elevation species to climate change.
Multidisciplinary information such as geophysical, hydraulic, geological, and hydrogeological information combined with GIS can be a powerful tool to better understand aquifers. The aim of this paper ...is to assess groundwater pollution risk through regional hydrogeological and local hydrogeophysical models built from multidisciplinary information and geophysical well logs. Once the regional model of the Toluca Valley Aquifer (Mexico) was built, the critical area (CA) of the aquifer was identified through features such as fractures and faults, pollution sources, subsidence, groundwater table depletion, and well density, which were transformed into GIS with multicriteria analysis. With the CA a local hydrogeophysical model was set to extract lithological information and compute the intrinsic vulnerability (IV) through the Dar Zarrouk parameters according to the AVI-Fr method which considers the influence of fractures and faults as preferential paths for pollutants. On the other hand, the potential harmful load (PHL) of the different pollution sources was estimated with the POSH method. Finally, with the relation between the IV and PHL, risk pollution map could be made. The results of this study show that different areas are exposed to pollution risk due to their proximity to fractures, oil and gas subsurface infrastructure and high pumping ratios and their relationships. The available geophysical data and multidisciplinary information were powerful tools for identifying the critical area of the TVA and proposing actions to mitigate or control excessive withdrawals for sustainable management.
This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main ...input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R2). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.
The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results ...still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins.
Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed ...independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R2 ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies.
This work proposes a methodology whereby the selection of hydrologic and land-use cover change (LUCC) models allows an assessment of the proportional variation in potential groundwater recharge (PGR) ...due to both land-use cover change (LUCC) and some climate change scenarios for 2050. The simulation of PGR was made through a distributed model, based on empirical methods and the forecasting of LUCC stemming from a supervised classification with remote sensing techniques, both inside a Geographic Information System. Once the supervised classification was made, a Markov-based model was developed to predict LUCC to 2050. The method was applied in Acapulco, an important tourism center for Mexico. From 1986 to 2017, the urban area increased 5%, and by 2050 was predicted to cover 16%. In this period, a loss of 7 million m3 of PGR was assumed to be caused by the estimated LUCC. From 2017 to 2050, this loss is expected to increase between 73 and 273 million m3 depending on the considered climate change scenario, which is the equivalent amount necessary for satisfying the water needs of 6 million inhabitants. Therefore, modeling the variation in groundwater recharge can be an important tool for identifying water vulnerability, through both climate and land-use change.
The current study presents a method for automating the Köppen–Garcia climate classification using a GIS module. This method was then applied in a case study of the Lerma-Chapala-Santiago watershed to ...compare time series data on climate from 1960 to 1989, 1981 to 2010, and 1960 to 2010. The kappa statistic indicated that the climate classifications of the generated model had a perfect degree of agreement with those of a prior nonautomated study. The climate data from the period 1960 to 2010 were used to create a climate map for the watershed. Overall, the dominant climates were dry, semiarid, temperate, and semiwarm temperate with a summer rainfall pattern. A comparative analysis of climate behavior between 1960 and 1989 and between 1981 and 2010 showed changes in temperature and extreme temperatures over 13.6% and 9.9%, respectively, of the watershed; the presence or absence of mid-summer drought also changed over 0.8% of the watershed. The module developed herein can be used to classify climates across all of Mexico, and data of varying spatial resolution and coverage can be inputted to the module. Finally, this module can be used to automate the creation of climate maps or to update climate maps at diverse spatial-temporal scales.
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