Environmental changes can result in dramatic increases in human migration as households become unable to adapt to such changes. Addressing environmental migration is a complex puzzle that can become a wicked problem. Despite the growing literature on the nexus between environmental change and migration, the inextricable link between nature and society has made it difficult to establish causal relations between the two. To examine the relationship between environmental change and migration, it is necessary to develop a conceptual model that includes environmental changes as potential causes of rural-urban migration (RUM). Such a model should be built on an enhanced understanding of the different factors that stimulate environmentally induced RUM. This paper proposes such a model, focusing on loss of agricultural land, loss of agricultural productivity and the economic repercussions of these losses. The model is based on the model of Perch-Nielsen et al. but extends this model by incorporating additional factors. In our model, the three leading causes of RUM are climate change, human maladaptive activities, and hydro-climatic disasters (the push factors). In addition, there may be pull factors in the cities. RUM may be counteracted or reduced by governmental policy and individuals' characteristics. The model was applied to Iran. The results show that the model can help to bridge the knowledge gap regarding environmentally induced RUM and may inform policymaking on RUM and related issues, such as environmental management and adaptation to climate change.

@en

In arid and semi-arid regions of the world, the occurrence of prolonged drought events (megadroughts) associated with climate change can seriously affect the balance between water supply and demand, thereby severely increasing the susceptibility of such regions to adverse impacts. In this study, a simple framework is introduced to estimate renewable water volumes (RW) to mitigate the challenges of megadrought events by managing the groundwater resources. The framework connects a weighted annual hydrological drought index (wSPEI) to RW, based on the short time-scale precipitation volume. The proposed framework, which was in a proof-of-concept case study applied to the Neishaboor watershed in the semi-arid part of Iran, showed that developing the weighted drought index can be valuable to estimate RW. The results suggested that the wSPEI, aggregating hydrological drought index (HSPEI) with the time scale k = 5 days and the regional coefficient s = 1.3 can be used to estimate RW with reasonable accuracy (R2 = 0.73, RMSE = 11.5 mm year−1). This indicates that in the Neishaboor watershed, the best estimation of RW can be determined by precipitation volumes (or the lack thereof) falling over 5-day aggregation periods rather than by any other time scales. The accuracy of the relationship was then investigated by cross validation (leave-one-out method). According to the results, the proposed framework performed fairly well for the estimation of RW, with R2 = 0.75 and RMSE = 12.2 mm year−1 for k = 5 days. The Overall agreement between the wSPEI, the RW derived from water balance calculations, and the estimated RW by the proposed framework was also assessed for a period of 34 years. It showed that the annual RW followed closely the wSPEI, indicating a reasonable relationship between wSPEI and the annual RW. Accordingly, the proposed framework is capable to estimate the renewable water of a given watershed for different climate change scenarios.@en

Due to the effects of global climate change on duration, frequency and number of drought events, the occurrence of prolonged droughts, referred to as “megadroughts” (lasting for two decades or longer) will become more probable in the future. Thus, it is crucial for countries especially in arid and semi-arid regions of the world to develop appropriate preparedness plans for megadrought risk management. Since groundwater is the key water resource in these regions, it is important to reliably quantify the maximum sustainable extraction to ensure a sufficient groundwater reserve, i.e. the Strategic Groundwater Reserve, for a probable future megadrought event. For this purpose, a new concept of Probable Maximum Drought is proposed in this study, based on the concept of Probable Maximum Flood. As the spillways of large dams are designed based on the Probable Maximum Flood to minimize the probability of failure and the associated casualties and damages, the Probable Maximum Drought concept is proposed to estimate Strategic Groundwater Reserves to limit the consequences of prolonged droughts, including damage and threats to societal stability. This will allow water resources managers and policymakers to develop appropriate strategies to adapt and restrict development plans of a given region based on a sustainable megadrought risk management.

@en

The Budyko framework was firstly developed to estimate actual evaporation as a function of precipitation and the aridity index at steady state conditions. Based on this framework, the water storage change in the watershed is assumed to be negligible at large spatial and temporal scales. However, steady state conditions are not valid for many watersheds worldwide or at finer temporal or spatial scales. Accordingly, the application of the Budyko framework has become challenging for these situations. Therefore, many researchers have tried to extend the Budyko framework for non-steady state conditions. The aim of this study is to provide a review of the extended equations and to discuss using the Budyko framework in a changing world. While the extended equations are more complex than the original ones, they still require little data. Thus, the Budyko framework, either the original or the extended can be a very useful tool for hydrological modeling with lots of applications, especially in data scarce regions.

@en

In arid and semi-arid regions, water scarcity is the crucial issue for crop production. Identifying the spatial and temporal trends in aridity, especially during the crop-growing season, is important for farmers to manage their agricultural practices. This will become especially relevant when considering climate change projections. To reliably determine the actual trends, the influence of short- and long-term memory should be removed from the trend analysis. The objective of this study is to investigate the effect of short- and long-term memory on estimates of trends in two aridity indicators—the inverted De Martonne (ϕ IDM ) and Budyko (ϕ B ) indices. The analysis is done using precipitation and temperature data over Iran for a 50-year period (1966–2015) at three temporal scales: annual, wheat-growing season (October–June), and maize-growing season (May–November). For this purpose, the original and the modified Mann–Kendall tests (i.e., modified by three methods of trend free pre-whitening (TFPT), effective sample size (ESS), and long-term persistence (LTP)) are used to investigate the temporal trends in aridity indices, precipitation, and temperature by taking into account the effect of short- and long-term memory. Precipitation and temperature data were provided by the Islamic Republic of Iran Meteorological Organization (IRIMO). The temporal trend analysis showed that aridity increased from 1966 to 2015 at the annual and wheat-growing season scales, which is due to a decreasing trend in precipitation and an increasing trend in mean temperature at these two timescales. The trend in aridity indices was decreasing in the maize-growing season, since precipitation has an increasing trend for most parts of Iran in that season. The increasing trend in aridity indices is significant in Western Iran, which can be related to the significantly more negative trend in precipitation in the West. This increasing trend in aridity could result in an increasing crop water requirement and a significant reduction in the crop production and water use efficiency. Furthermore, the modified Mann–Kendall tests indicated that unlike temperature series, precipitation, ϕ IDM , and ϕ B series are not affected by short- and long-term memory. Our results can help decision makers and water resource managers to adopt appropriate policy strategies for sustainable development in the field of irrigated agriculture and water resources management.@en